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Project Description

Early Warning Systems (EWS) are effective in mitigating disaster risk. Receiving a timely warning gives affected populations a chance to take appropriate protective actions. Recently, global meteorological communities have moved towards an evolving framework called ‘Impact based Forecast and Warning Services (IbFW)’. This framework emphasizes that early warning services, should be designed and developed from the perspective of the end users. It changes the focus from information availability to information utility. The importance of this comes from increasing risk from hydro-meteorological hazards in a changing climate and a realization that warning services require to be holistic and integrated. India Meteorological Department (IMD) and the Bureau of Meteorology (BoM), Australia, are fine-tuning IbFW in their respective countries using a multi-hazard approach. This project contributes to this goal focusing on the following aspects a) understanding public expectations from weather warning services, and b) effective communication of weather warnings. It will combine the two aspects together, synthesizing them through the theoretical underpinning and in data analysis. India and parts of Australia have similar climate vulnerability yet also have important differences such as cultural norms, infrastructural conditions, population size and concentration, and economic conditions. A study exploring evolution of IbFW’s structure, respective operational focus and the means through which user group linkages are being established will be useful in understanding how context shapes the success of IbFW in communicating weather and risk information. Methodology: This work will begin with a desktop study that compares the approaches taken to IbFW in the two countries, including an examination of the end products (i.e., the warnings and weather information) and the process used to produce and disseminate the warnings. Then a mixed-methods (interviews, focus groups and participant observation in the field), user-centred design approach will help to understand how context and user characteristics affect warning communication efficacy. The communication efficacy and the effects of the warning on intentions to take protective actions will be examined with a quantitative survey. A cross-cultural comparison provides the opportunity for each country to learn from the other about what works well, introducing new ideas to each country about how to craft effective warnings.

BITS Supervisor

Biswanath Dash

RMIT Supervisor

Amy Griffin

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Summary: Textile industries are the 3rd largest user of freshwater resources in the world, employing water in processes such as bleaching, scouring, dyeing, and finishing. The textile industries in Rajasthan, India, significantly contribute to social and economic empowerment. However, the majority of these industries discharge their effluents directly into the environment without any treatment. The important pollutants present in these effluents are chemical dyes and toxic heavy metals. Prof. Gupta’s research group at BITS Pilani has reported the synthesis and potential application of various eco-friendly nano-adsorbents, including functionalized Cu-based metal oxide nanoparticles, metal oxide frameworks (Ca-MOF, Fe/Al MOF and their GO composites) and composites (silver-yttrium oxide nanocomposites), for the removal of heavy metals and dyes from synthetic wastewater. Prof. Panwar’s research group at BITS Pilani has identified fungal isolates (Aspergillus terreus SJP02 and Ectophoma multirostrata SJP03) for the efficient removal of heavy metals and dyes from synthetic wastewater. Dr. Pramanik’s research group at RMIT, Australia, specializes in the development of separation-based technology for the removal and recovery of heavy metals from wastewater. Given the complexity of pollutants and the substantial volume of effluents, our proposed research aims to develop a nano-myco-membrane-based integrated prototype specifically designed for treating real-time textile effluents. This innovative approach not only seeks to address the removal of pollutants but also focuses on the recovery of heavy metals, offering an eco-friendly and cost-effective solution to a pressing environmental challenge. Aim: The proposed research work aims to develop a nano-myco-membrane based integrated prototype for the treatment of real-time textile effluents and recovery of heavy metals. Methodology: • Collection of wastewater samples from various textile industries of Rajasthan, India and their compositional characterization. • Fabrication of a range of nano-composite membranes with tuneable pore size and high water permeance for the removal of dyes and heavy metals and elucidate the associated removal mechanisms i.e., sieving, electrostatic interaction and/or Donnan effect, thus allowing removal optimization. • Investigate the effect of various physiochemical characteristics of the nanocomposite membrane and selected pollutants to investigate the factors that govern perform.

BITS Supervisor

Prof Suresh Gupta

RMIT Supervisor

Dr Biplob Pramanik

Other Supervisor BITS

Jitendra Panwar

Other Supervisor RMIT

Prof Jega Jegatheesan

Required discipline background of candidate

Project Description

This project aims to address the critical need for sophisticated access control mechanisms in AI-enabled Internet of Things (IoT) systems. With the exponential growth of IoT devices and the increasing integration of AI technologies, securing access to IoT systems has become more complex and essential. This project expects to develop new security access control framework by using an innovative approach that is interdisciplinary, combining insights from cybersecurity, artificial intelligence, and IoT technology. It aims to utilize new techniques in zero knowledge proofs, policy enforcement, and device authentication to develop a dynamic access control framework that adapts to changing contexts and threats. The successful development and implementation of this Access Control Framework should provide significant benefits, including strengthened security and privacy protections in IoT systems, enhanced user trust in IoT technologies, and the promotion of safer and more reliable IoT applications across various sectors. By addressing the complex challenges of access control in the context of AI and IoT, the project will contribute to the resilient and sustainable growth of the Internet of Things as a critical component of our digital future.

BITS Supervisor

Dr. Ashutosh Bhatia

RMIT Supervisor

Abebe Diro

Other Supervisor BITS

Prof. Kamlesh Tiwari

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Due to emergence of clean, green and digital electric mobility, there is huge demand of electro-chemical devices such as battery driven electric vehicle or proton exchange membrane (PEM) fuel cell driven electric vehicles. However, challenges such as high cost, declined electro-chemical performance and poor durability, are hindering the uptake of these technologies. PEM fuel cell driven electric vehicle has several advantages over battery driven electric vehicle such as cost, efficiency, operability and most importantly energy densities. However, the durability of components of PEM fuel cell such as membrane, electro-catalyst, bipolar plates are on of major challenges in PEM fuel cell. Therefore, understanding the degradation behavior and its mechanism in advanced functional materials such as proton conducting membrane followed by its mitigation is a crucial step to enhance the stability of PEM fuel cells. The detailed investigations were carried out to identify the electro-chemical, physical and process parameters causing membrane degradation under real time operation of PEMFC. The membrane thinning, pin-hole formation, cracks formation, polymer backbone detachment and peroxide radical attacks are some of factors causing membrane degradation and affecting PEMFC performance. The proposed project focuses on mitigating PEM degradation through in depth exploration of key degradation factors. This approach involves implication of advanced mitigation strategies such as PEM material enhancements, optimization of operating parameters and implementation of innovative methodologies. The aim of this study is not only to identify but actively address chemical, mechanical and thermal degradation factors for PEM. The essence lies in translating these mitigation efforts in to perceptible improvements, enhancing the overall stability and lifespan of PEM fuel cells performing under typical loads profiles of heavy duty vehicles. This, in turn, will significantly contribute to viability of PEM fuel cells within the realm of clean and green electric mobility solutions. Keywords: Hydrogen mobility, PEM Fuel Cell, Advanced materials, PEM fuel cell durability, Membrane degradation

BITS Supervisor

Prof. Jay Pandey

RMIT Supervisor

Bahman Shabani, PhD

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Every year, 1.38 million cases of breast cancer are diagnosed in India. Of these, 0.45 million people succumb to the disease, giving a mortality rate of about 33%. This means about 1 in 3 die due to breast cancer in India which is an unacceptably high number. Breast Cancer is also the most common cancer in the United Arab Emirates (UAE), with a majority of cases characteristically occurring in women younger than 50 years. Further, Breast Cancer is the most commonly diagnosed cancer for females in Australia. It is estimated there will be around 20,500 breast cancer cases diagnosed in females in 2023. This is around 28% of the estimated cancers diagnosed in females. Breast Cancer is normally diagnosed using physical examination followed by mammogram, Ultrasound and MRI Imaging. While Mammogram involves radiation and MRI is not generally favored by the patients, Ultrasound is simple, elegant and causes no discomfort to the patients. In this work, teams from Australia, UAE and India will work on applying Deep Learning techniques to Ultrasound Images to detect the presence and spread of tumors that characterize the breast cancer. Where available, Mammograms and MRI images will also be used to support the inferences drawn. While a majority of work reported in this domain focused on popular Convolution Neural Networks, literature is scant when it comes to applying Vision Transformers (VTs). In this work, we will focus on applying Vision Transformers for breast tumor detection. As is well known, VTs are quickly becoming the de-facto architecture for Computer Vision but very little is understood about how they work and what they learn. Further, while existing studies visually analyze the mechanisms of CNNs, the same is not widely reported for the VTs. In this work, the team will focus on both understanding and visualizing the functioning of VTs as applied to breast tumor detection. It will work closely with a Breast Cancer Specialist Doctor based in Muscat, Oman whose experience will be built into the VT models as a prior knowledge during training which might help in its early detection.

BITS Supervisor

M B Srinivas, Professor

RMIT Supervisor

Shaun Cloherty

Other Supervisor BITS

SWARNALATHA RAJAGURU

Other Supervisor RMIT

Dr Priya Rani

Required discipline background of candidate

Project Description

Aim: To create a VR based digital twin of Robots that can be used for various industrial applications. The localization of autonomous robots has been an active research area since the advent of Industry 4.0. Several applications in the industry include warehouse management, advanced manufacturing and elderly care. Localization in indoor environments becomes challenging, especially in the absence of the Global Navigation Satellite System and obstacles. The existing systems rely on various low-cost, miniaturized sensors like IMU, camera, LiDAR and UWB devices to perform precise indoor localization. Recent works have focused on using machine learning approaches for sensor fusion; improvements are still needed in terms of data quality, overfitting or underfitting, scalability and reliability. In this proposal, we propose to develop approaches for intelligent scene understanding using computer vision and perform multi-modal sensor data fusion using deep learning approaches, such as transformers, particle-based, and conventional methods. The focus of the work will be two folds: real-time operation and robustness for industry applications. We also propose to explore the application of inexpensive LiDAR line scanners for obstacle avoidance. The system will be tested in indoor environments like the construction industry or shopping malls, and experimental validation will be performed using mobile robots and mobile manipulators. The project will contribute to advancing scientific knowledge in sensor fusion and autonomous robot navigation, in addition to creating a prototype for deployment in the industry. Virtual reality and Digital twins will be used for simulation and for human-machine collaboration.

BITS Supervisor

Dr.V.Kalaichelvi, Professor

RMIT Supervisor

Dr Ehsan Asadi

Other Supervisor BITS

Prof.R. Karthikeyan

Other Supervisor RMIT

Dr Debaditya Acharya

Required discipline background of candidate

Project Description

Bacterial antimicrobial resistance (AMR), is an urgent global public health threat, killing at least 1.27 million people worldwide and associated with nearly 5 million deaths. The six leading pathogens for deaths associated with resistance are Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. There are very few effective drugs to treat multidrug-resistant infections caused due to these pathogens, which represent the main threat at present. Mangrove plants thrive under extreme environment which lead to the development of special metabolic pathways to produce unique compounds that enable them to tolerate stressful environmental conditions. Mangrove species have various applications in folk medicine. Mangrove plants are a rich source of steroids, triterpenes, saponins, flavonoids, alkaloids and tannins. Extracts from different mangrove plants are reported to possess diverse medicinal properties such as antibacterial, antifungal, antihelminthic etc. Due to side effects of existing chemotherapeutic agent and the resistance developed, plant derived compounds have received much attention as a potential source of the future drug. Goa state is located in western coast of India and mangrove vegetation occupies 500 hectares of its area. Goa represents 17 mangrove species that include 14 true and 3 associated species. Australian mangrove forests comprise 45 species across 18 families, which is more than half of the world's total mangrove species. It has been already reported that among them several mangrove species produce bioactive compounds that may control microbial growth. Therefore, an attempt would be made towards isolation of antimicrobial compounds from the bestowed mangrove flora of Goa and Australia. Leaves of mangrove species will be tested against AMR pathogenic bacterial strains, and novel compounds from the identified mangrove species will be isolated, identified and functionally characterized. Aims: 1. Identification of potential mangrove species for their antimicrobial properties against AMR pathogens 2. Isolation and identification of novel antimicrobial compounds from mangrove species 3. Structural and functional characterization of identified molecules isolated from mangrove plant species Methodology: • Collection and extraction of plant material: Leaves of mangrove species of Goa will be collected from the estuarine complex of Mandovi and Zuari river. Man

BITS Supervisor

Kundan Kumar

RMIT Supervisor

Nitin Mantri

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Electromagnetic waves of below 280 nm wavelength are considered solar-blind or UV-C region (200-280 nm) and it has plenty of applications in military, secured space communications, and safety monitoring. Ultra-wideband gap semiconductors such as Ga2O3 emerged as the most natural choice for solar-blind photodetectors (SBPD) due to its high bandgap. The high radiation hardness of Ga2O3 makes it suitable in harsh environments such as space applications. On the other hand, 2D materials are always a preferred choice for those applications where large and highly reactive surface area is required. For current generation SBPD, there exists a trade-off between two key parameters-responsivity and response time. For successful commercialization of SBPD, desired response time should be smaller than 1 µs whereas responsivity should be greater than 1000 AW-1, simultaneously. This project will aim to achieve the above-mentioned responsivity and response speed by realizing the heterojunction of 2D materials with Ga2O3. To ease the fabrication and minimize the experimental optimization load, firstly we will do materials modeling using DFT and the anticipated results would be a comprehensive understanding of the electronic structure, density of states, defect generation, propagation, and relaxation mechanism in 2D materials, Ga2O3, and their heterostructures. Thereafter, optimized materials will be simulated by a multi-physics model to optimize the heterojunction to demonstrate fast response and high responsivity as required for the commercialization of this technology. Subsequently, the optimized heterojunction will be fabricated to demonstrate excellent responsivity and a response time as required for the commercialization of this technology.

BITS Supervisor

Prof. Satyendra Kumar Mourya

RMIT Supervisor

A/Prof. Enrico Della Gaspera

Other Supervisor BITS

Professor RAHUL KUMAR

Other Supervisor RMIT

Professor Sumeet Walia

Required discipline background of candidate

Project Description

This project aims to develop anomaly detection in space systems by utilizing and advancing transfer learning techniques. By adapting sophisticated algorithms developed in other domains to the unique challenges of space exploration, the study seeks to significantly improve the accuracy and speed of anomaly detection in space missions. The project expects to generate new algorithms in the area of space systems engineering and artificial intelligence, using an innovative approach that is interdisciplinary, utilizing new techniques from machine learning, data science, and aerospace engineering. The integration of transfer learning into space systems represents a pioneering effort to cross-pollinate between disciplines, enhancing the capability to predict and mitigate potential anomalies in complex space missions. his project should provide significant benefits, such as increasing the safety and reliability of space missions, reducing the cost and time associated with diagnosing and mitigating system anomalies, and fostering a deeper understanding of the operational dynamics of space systems. Moreover, by accelerating the development of adaptable, intelligent systems for anomaly detection, the project is expected to contribute to the broader goals of space exploration, including the expansion of human presence in the solar system and the enhancement of our ability to monitor and study space environments.

BITS Supervisor

Dr. Ashutosh Bhatia

RMIT Supervisor

Abebe Diro

Other Supervisor BITS

Prof. Kamlesh Tiwari

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Sharing of health data for secondary uses such as research and public policy development has many benefits, but also risks if information about an individual's health record can be inferred. Ensuring healthcare data privacy preservation technology is designed to work as per requirements is essential. The requirements for correct functioning of these techniques includes training of both researchers and the individuals. Data is now gathered and possibly used to train AI models from various sources including clinical trials, participatory health-enabling technologies and other health/medical-related records. These are in addition linked, for research, population disease surveillance, risk prediction etc. The increasing use of participatory health enabling technologies and integration of person-generated data with formal healthcare provider data, adds another layer of complexity. Thus, the design of healthcare privacy preserving techniques needs to be considered from all perspectives. The aims of this project are: Design of a process for testing of techniques used for privacy preservation of Healthcare systems. Design process for verification of functionality with ML based systems. Design and implement customized ML scenarios which providing protection with bounds on information leakage. The project will explore various mechanisms to evaluate suitability of the existing techniques and their vulnerabilities given a set of constraints.

BITS Supervisor

Prof. Shubhangi Gawali

RMIT Supervisor

Prof. Asha Rao

Other Supervisor BITS

Neena Goveas

Other Supervisor RMIT

Amy Corman

Required discipline background of candidate

Project Description

Background: From its origin in the 18th century till now, ordinary Portland cement (OPC) remained one of the most widely used construction materials. Its outstanding properties like high strength and durability also provide the advantage of getting tailored as per user requirements. Such characteristics make OPC fall under materials with extensive adoption and a threat to Earth's ecosystem. Currently, the CO2 emissions of OPC are estimated to be around 8% per year, which is expected to rise due to growing infrastructural needs. As such, alternatives are required to be developed for the sustenance of sustainability along with growth. The utilization of supplementary cementitious materials (fly ash (FA), blast furnace slag (BFS), metakaolin (MK), etc.) can be one of the options, but their incorporation is somewhat limited. Furthermore, their composition varies depending on the manufacturing method adopted. Besides, industrial wastes such as stone, demolition, and other related waste streams possess enough crystallinity, restricting their usage to fillers. Thus, novel techniques must be developed to cover the existing gaps. Geopolymerization, a sub-set of alkali-activation, can be of immense use in the present scenarios due to its wide acceptability of transforming waste streams into ingenious products. It fundamentally relies on activating materials, preferably with a high amount of silicon (Si) and aluminum (Al), to obtain the desired engineering properties. The early transcripts of this technique can be traced back to the 19th century when Prudon introduced it as alkali-activation, later called geopolymerization by Prof. Joseph Davidovits in the late 1970s. High early strength, extensive resistance against fire, effluents, and climatic conditions, and low greenhouse gas emissions compared to OPCs are some of its most desirable properties. The report proposes valorizing construction and demolition wastes (CDW) through geopolymer technology. Every year, around 615 million tons [1,2] of such waste is generated, which is illegally dumped without subjecting them to prior treatments, making it a threat to flora, fauna, and humankind. However, reports from the study of Robayo-Salazar et al. [3] have shown that CDW elemental topology is rich in Si and Al-containing minerals, making it suitable for use as a precursor, viz., geopolymerization. It can also be corroborated by the x-ray fluorescence spectroscopy (XRF, Epsilon 1, Malvern Panalytical, USA) conducted o

BITS Supervisor

Dipendu Bhunia and Professor

RMIT Supervisor

Prof. Guomin (Kevin) Zhang

Other Supervisor BITS

Other Supervisor RMIT

Dr Chamila Gunasekara

Required discipline background of candidate

Project Description

Clinical notes obtained from various sources including electronic health records (EHRs), physician documentation, and medical literature contain valuable medical history and evidence associated with individuals. Leveraging this information can lead to more precise diagnoses, prognoses, and treatment plans. Explainable Clinical Natural Language Processing (ClinicalNLP) entails applying NLP techniques in healthcare with an emphasis on transparency and interpretability. NLP is utilized in healthcare settings to analyze and extract information from these clinical documents. However, employing neural network techniques to analyze such data may face challenges in terms of explainability. In ClinicalNLP, the concept of "explainable" emphasizes the significance of comprehending and interpreting the decisions generated by NLP models. This is especially critical in healthcare, where decisions directly influence patient care and outcomes. The goal of Explainable ClinicalNLP is to render the process and outcomes of NLP models transparent and comprehensible to healthcare professionals, patients, and other stakeholders. The objectives and proposed methodology of the project are as follows: (1) To develop a comprehensive quality control and pre-processing pipeline for clinical text: This involves creating a systematic and robust framework to ensure the accuracy, reliability, and usability of clinical text data. This pipeline will serve as a crucial step in the data preprocessing phase, where raw clinical text is transformed into a format suitable for further analysis and interpretation. (2) To Develop Contextualised, Interpretable Models: Using machine learning models that produce interpretable outputs. Further, analyzing the importance of different features or words in the NLP model's decision-making process,will provide insights into which factors influence the model's predictions. We will consider the context of clinical texts and consider domain-specific knowledge to improve the accuracy and relevance of NLP analyses. (3) To Develop Intuitive Visualization:This objective focuses on presenting the results of NLP analyses in a visually intuitive manner, to help understand the patterns and relationships in the data. Setting up interactive visualisations will reduce barriers for clinicians to directly interact with the data to make informed decisions. (4) Validation:We will closely work with healthcare professionals to set up validation pipelines.

BITS Supervisor

Satnik Mitra, Assistant Professor

RMIT Supervisor

Sonika Tyagi, Associate Professor

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Preamble: Conventional biomethanation of organic wastes, including food waste, leads to the accumulation of volatile fatty acids due to the slow rate of methanogenesis, long retention times, and process instability. Electrobiomethanation (EBM) is a novel approach developed by the Principal Investigator, Prof. P. Sankar Ganesh, to address the shortcomings of biomethanation by facilitating faster conversion of organic matter to methane through enhanced electron transfer, enabling co-digestion of complex substrates, offering precise control of critical process parameters, and potentially improving digestate quality. However, further research is needed to optimize and scale up EBM technology. Aim: The project aims to use electrobiomethanation to treat organic waste and increase the production of methane and other value-added products. Objectives: • To enhance conventional biomethanation by incorporating electrobiomethanation, thereby increasing methane content and improving process stability and resource recovery during organic waste treatment. • To identify the optimum operational parameters of electrobiomethanation, such as applied potential, pH, electrode material, electrode geometry, and temperature for maximum methane generation. • To apply electrobiomethanation for food and other organic waste treatment as sole substrates or in combination. Methodology: Electrobiomethanation reactors (EMBR) will be designed, fabricated, and operated to treat organic waste and optimize process stability and methane production. Setting up EMBR will require environmental biotechnology, chemical engineering, and electrical and electronic engineering expertise. Our team will optimize substrate ratios, implement effective pretreatment methods, and manage operational parameters. However, selecting appropriate electrodes and fine-tuning the optimum power supply are pivotal elements that demand specialized knowledge. This interdisciplinary approach ensures a comprehensive and successful project execution by leveraging the diverse skills and insights of each discipline. Specific focus will be given to the operational parameters of EBMR, such as applied potential, pH, and electrode characteristics. These parameters will be tested at varying configurations to fix the best combination for maximizing methane production. Further, the investigation will focus on the long-term stability, economic feasibility, scalability, and environmental impact of EMBR.

BITS Supervisor

P. Sankar Ganesh and Professor

RMIT Supervisor

Nicky Eshtiaghi and Professor

Other Supervisor BITS

Dr. Ankur Bhattacharjee

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Objectives The study has four-fold objectives, touching upon the demand for and the supply of the EV market. 1. To estimate the potential market of EVs for different groups of customers in India. 2. To examine consumer behaviour by analyzing various socio-economic factors influencing the adoption rate of EVs in India. 3. To evaluate the performance of the supply-chain system of EVs and select the appropriate supply chain configuration for EVs markets. 4. To find optimal locations of EV charging stations in Rajasthan India. . Introduction The automobile sector plays a vital role in economic development by generating employment opportunities (Kang and Lee 2019). According to the Society of Indian Automobile Manufacturers (SIAM), the Indian automobile sector, which is one of the fastest-growing sectors in India, accounts for about 6 per cent of the Gross Domestic Product (hereafter GDP). More importantly, the sector contributes about 50 per cent of India's manufacturing output by providing employment opportunities to more than 35 million people. As per the data released by the SIAM, 4 million passenger vehicles were produced in India during 2018-19, and the country's automotive sector has been ranked fourth largest in the world. The overall automobile production, which includes passenger, commercial, three-wheelers, two-wheelers, and quadricycle, reported an annual growth rate of 6.25 per cent, while the automobile exports grew by 14.50 per cent during 2018-19. Not surprisingly, despite the recent upheavals in the sector, the gross turnover of the automobile manufacturers in India stood at $67 billion in 2016-17, and the industry experts forecasted that the gross turnover would reach about $250 billion by 2030. An analysis of the composition of the automobile industry reveals that the two-wheelers account for almost four-fifths of the domestic market share, followed by passenger vehicles, which consists of 13 per cent of the domestic market share. Interestingly, commercial vehicle and three-wheelers constitute the remaining part of the domestic market share. Less impressive is that ICE vehicles are widely perceived as a major contributory cause of Greenhouse gas emissions (Ajanovic 2014; Jochem et al. 2016). In economics parlance, the transport sector, particularly the automobile sector, is the major source of a negative externality. According to Singh et al. (2019), in spite of the low car density, the transport sector, which accounts for about two-

BITS Supervisor

Satyendra Kumr Sharma

RMIT Supervisor

Dr Su Nguyen

Other Supervisor BITS

Other Supervisor RMIT

Prof. Prem Chhetri

Required discipline background of candidate

Project Description

Additive manufacturing (AM) process introduces unique defects in the parts, such as the lack of fusion defect and delamination between the layers in metallurgically dissimilar AM parts, making them inferior in fatigue and fracture resistance, which are critical for Aerospace structures. The precise mechanical behaviour of additively manufactured parts for known defect types and loading conditions is unknown. A trained deep learning network, such as vision deployed Pulse-Coded Neural Networks (PCNN), on the effect of defects on mechanical behaviour can benefit the structural health monitoring of bespoke AM structures in aerospace applications. The present research aims to develop numerical models to predict the effect of known defects on mechanical behaviour, taking into account microstructural and macrostructural characteristics. The findings will be mapped to the laser power and rasterization parameters of the AM method itself. Validate the numerical models using a limited number of experiments, generate sufficiently large data using the validated numerical models and train machine learning (ML) models. Finally, we will test the ML models on known AM process-defect-mechanical performance mapping. The methodology involves AM of tensile and flexural specimens with known defects and conducting tensile and 3-point bending tests. Use suitable sensors and computational algorithms to determine fracture initiation and propagation and relate them to defects and overall strength.

BITS Supervisor

Prof. Srinivasa Prakash Regalla Dean (Institute-wide), Practice School Division, BITS Pilani

RMIT Supervisor

Sabu John, Professor

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Aim: To design a neural network system for an automatic diagnosis based on limited diagnostic data from different modality sensors. Methodology: We will design an algorithm that makes the medical diagnosis based on the combined information from multiple sources, such as different modality sensors or imaging methods. The data can have either single or multiple labels, such as, for example, the type of the disease and the stage of the disease. Current trends are to build very complex single-network multimodal and multi-label structures. The data, training time, and hardware requirements are incredibly high. We propose to replace it with a modular system of interconnected classifiers, each working on a different sub-task. The modules will have simple structures and relatively low data and training requirements. The final decision will be based on the type of connections and information flow between these modules. Each module can be easily re-trained; modules can be switched on or off depending on the application requirements.

BITS Supervisor

Abhijit Das, Dr

RMIT Supervisor

Margaret Lech, Professor

Other Supervisor BITS

Aritra Mukherjee

Other Supervisor RMIT

Richardt Wilkinson, Dr

Required discipline background of candidate

Project Description

The recent technological shifts have led to a tremendous rise in the miniaturised devices, components, features and functional surfaces which find their applications in space, optics, electronics, defence, medical and automotive industries. The surface functions, including its physical and chemical behaviour, can be controlled by creating microstructures on the surface and, therefore, are in huge demand. Various micro-manufacturing techniques, including electric discharge machining, laser-based micromachining, Lithography, electroplating and moulding (LIGA), deep reactive ion etching, deep UV lithography or mechanical micromachining are popular methods for generating the micron-sized features on the substrate materials or producing the functional surfaces. Mechanical micromachining offers high precision, flexibility in the choice of work-piece materials and increased efficiency attributable to very low cycle time and ease of material removal. Owing to continuous involvement between the cutting tool and the workpiece materials during the machining of microfeatures, the cutting edge is subjected to high stresses and consequent tool wear while machining high-strength materials. As the tool wear increases, the material removal mechanisms also tend to change, consequently affecting surface integrity. The surface may get affected in the form of defects such as micron-size burrs, chattering marks and tool marks. The proposed work suggests the modelling-based approach to predict tool wear mechanisms, material removal mechanisms and the effects of cutting forces and vibrations on the quality of the generated surface. Numerical models and tool wear models are proposed to simulate the micromachining process for cutting forces. The preliminary experiments will be carried out to validate the simulated results. The functional surface desired to perform a specific function (wettability or tribology-related) will be fabricated using mechanical micromachining. Post fabrication, surface quality will be thoroughly investigated regarding the machined features' size, surface finish and subsurface damage. Aims and Methodology: The research project aims to gain insights into the process, model validation, and optimise the micro-machining process. There is a great need to examine the effect of these parameters on cutting forces, tool wear, surface roughness, and damage. This research project aims to investigate the mechanical micromachining process through theoretical and exper

BITS Supervisor

ANUJ SHARMA

RMIT Supervisor

Songlin Ding, Professor

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

The architecture and functioning of cGMP signaling is well characterized in metazoans, but poorly understood in protozoans. Some intracellular parasitic protists harbor chimeric proteins embodying P4-type ATPase and guanylate cyclase domains. Such proteins – the actuator of cGMP signaling in this group of clinically relevant important pathogens – are unusual in their sheer size, modus operandi, and evolutionary repurposing. Much like the mythological Sphinx, a human-lion chimeric creature that posed challenging riddles, the P4-type ATPase–guanylate cyclase chimeras present structural and functional conundrums. In this project, we combine the expertise of two PIs and two co-PIs at BITS-RMIT to address the function, topology, mechanism, and intramolecular coordination of the flippase-conjugated guanylate cyclase from the model protozoan pathogen Toxoplasma gondii. We will express and purify the functional domains in bacterial and mammalian systems and then crystallize them to resolve the mechanism and functionality of this protein. In parallel, we will also aim to perform structure determination of the native protein in complex with interaction partners isolated from T. gondii.

BITS Supervisor

Nishith Gupta, Professor

RMIT Supervisor

Paul Ramsland, Professor

Other Supervisor BITS

Ratnesh Kumar Srivastav, Dr.

Other Supervisor RMIT

Christian Doerig, Professor

Required discipline background of candidate

Project Description

Due to global warming, climate change, and the increased need for sustainability, the current power grid needs to be transformed by integrating more renewable energies. However, unlike traditional fossil-fuel-based power sources, renewable energy sources (RES) fluctuate with weather, are variable and are non-dispatchable. This variability and stochastic nature of RESs is a significant challenge for power grid integration. One possible solution is using large-scale energy storage systems (ESS), which will store and dispatch energy when necessary, thus reducing this variability. However, they still need to be cost-friendly. Microgrids, which are small formulations of the main grid grids with smaller RESs and ESS sizes, are becoming popular for integrating more renewable energies. Microgrids can operate independently or be connected to the main power grid. Designing and sizing an optimal microgrid involves many engineering constraints and parameters like voltage fluctuation, frequency deviations, system reliability, cost, and solving nonlinear power flow equations, which presents a complex optimisation problem. This PhD project aims to find the best solution to this optimisation problem. The objective is to identify and apply an efficient and robust optimisation algorithm to handle all the uncertainties related to the RES and tackle the engineering constraints and solve nonlinear power flow equations fast enough for better convergence.

BITS Supervisor

Pratyush Chakraborty

RMIT Supervisor

Dr Manoj Datta, Senior Lecturer

Other Supervisor BITS

HITESH DATT MATHUR

Other Supervisor RMIT

Required discipline background of candidate

Project Description

The electrocatalytic reduction of gaseous CO2 and NOx, powered by renewable electricity, is a promising strategy to not only mitigate the pollutants from the atmosphere, but also to valorize the pollutants to high energy-density fuels, like alcohol and ammonia. However, the state of the art is far from being optimal and the level of understanding of the mechanistic pathways is very poor at present. Also, there are still considerable breakthroughs to be made before it can be considered as a viable economical process. Moreover, the reactions suffer from low activity and poor product selectivity primarily due to the competitive hydrogen evolution reaction. Our proposal focuses on designing and development of high surface area, porous and highly conducting metal organic framework derived single atom catalysts (SACs) for the catalytic processes. The experienced collaboration will facilitate fine tuning of the appropriate SACs for efficient and selective electroreduction with high Faradaic Efficiency. In-situ spectroscopic studies along with DFT calculations will be made towards understanding the molecular mechanism with respect to the structural, morphological, and electronic properties of the synthesized SACs. The final aim will be to develop high-fidelity techno-economic-analysis and life cycle-analysis models to evaluate the economic and environmental benefits along with feasibility and scalability of the process.

BITS Supervisor

Prof. Sounak Roy

RMIT Supervisor

Prof. James Tardio

Other Supervisor BITS

Prof. B M Reddy

Other Supervisor RMIT

Prof. Suresh Bhargava

Required discipline background of candidate

Project Description

Electrochemical energy storage (EES) systems are pivotal in transforming our lifestyle, as they are integrated into electronic components and electric vehicles (EVs). They also enhance the reliability of renewable energy production systems, such as fuel cells, solar cells, wind and tidal power, by providing a platform for large-scale energy storage. Among various EES systems, batteries and supercapacitors (SCs) are the primary systems capable of large-scale energy storage. However, they face challenges related to poor power and energy densities, respectively, which are primarily due to the limitations of the electrodes. Furthermore, issues with the long-term stability of electrode materials can lead to rapid degradation of storage cells, necessitating replacement after a limited number of cycles. The limited lattice space in bulk electrode materials restricts ion insertion, resulting in slow charge-discharge rates, poor power density, and electrode failure. While energy density can be increased by maximising ionic storage, bulk materials only offer a finite number of intercalation sites, and their surface is not fully available for charge storage. Additionally, the reversible intercalation of ions leads to the expansion and contraction of electrode materials, causing mechanical stresses that can result in electrode cracking or delamination from the current collectors. Some materials also undergo phase transformations that produce redox-inactive phases, reducing capacity. These mechanical stresses and phase changes significantly impact the efficiency and lifecycle of EES systems. Therefore, to enhance the stability and cycle life of electrode materials, their phase transformation reactions should be perfectly reversible, and there should be sufficient space to accommodate the resulting stress, which is only possible with atomic-level reactions on planar surfaces. Two-dimensional (2D) materials provide a promising platform for designing new electrode materials to overcome the limitations of various energy storage devices, particularly SCs and batteries. This project aims to develop heterostructures of these 2D materials with perfect face-to-face heterointerfaces at individual flakes. Both wet-chemical and physical methods will be employed to develop materials and explore their performance for different battery chemistries, such as sodium, potassium, zinc, and others.

BITS Supervisor

Sandip S. Deshmukh

RMIT Supervisor

Nasir Mahmood

Other Supervisor BITS

R. Parameshwaran

Other Supervisor RMIT

Muhammad Waqas Khan

Required discipline background of candidate

Project Description

Building Integrated Photovoltaic Systems, besides bringing the obvious benefits such as energy and economy to the table, has to contend with the prospects of partial shading and corresponding energy loss, in addition to dynamic irradiance conditions and complex geometries. It is statistically reported in that the energy loss due to partial shading can be upto 70% under urban environments and Artificial Intelligence (AI) techniques can come to the rescue under partial shading conditions. AI optimization routines help search for maxima points among the electrical interconnections of the modules. This project proposes to simulate and analyze BIPV modules for optimum electrical performance under partial shading conditions wherein specific geometric design and optimization of photovoltaic installations and their electrical layout will be considered. The electrical simulation will be validated for both flat designs as well as flexible thin-film structures under dynamic irradiance and partial shading conditions. This will be further extended to include multiple inverter configurations and module layouts for optimization at the system level. The model will be tested for loss analysis both at the module and system level.

BITS Supervisor

Dr Gomathi Bhavani Rajagopalan

RMIT Supervisor

Rebecca Yang

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Ovarian cancer (OC) is an extremely lethal form of cancer, where tumour reduction and platinum-based chemotherapy are the main treatment approaches. Nevertheless, the development of acquired platinum resistance presents a significant obstacle in managing patients. The landscape of metastatic ovarian cancer treatment has been significantly transformed by the introduction of poly (ADP-ribose) polymerase inhibitors (PARPis) as a maintenance therapy following platinum-based chemotherapy. The FDA-approved drug, olaparib, being the initial and effective PARPi developed so far for this specific disease, has played a pivotal role in this paradigm shift. However, the side effects of olaparib include gastrointestinal symptoms like nausea, vomiting, and decreased appetite; fatigue; muscle and joint discomfort; and reduced blood cell counts such as anemia, occasionally leading to leukemia. Hence, the urgent challenges lie in enhancing the effectiveness of current treatments, minimizing side effects, and exploring new therapeutic options. In this regard, we propose the design and development of novel PARP inhibitors based on the molecular structure of olaparib. Both olaparib and rucaparib contain a fluorine atom in the benzene ring which plays a pivotal role in the biological activity of these two drugs. It is also well-known that the incorporation of a fluorine atom or a trifluoromethyl (CF3) group into a drug molecule increases its biological activities such as to a great extent, thus making it a much better or superior drug. In this project, we intend to develop novel PARP inhibitors including some fluoro- or fluoroalkyl analogues of olaparib which would have superior anti-ovarian cancer activity than the parent molecule and minimum side effects. The ultimate aim of this project is to discover a novel and superior drug for the effective treatment of ovarian cancer. The objectives of this proposal are: 1. Design of new and novel PARPi including the fluoro/fluoroalkyl analogues of olaparib 2. Cost-effective and green synthesis of the designed new PARP inhibitors 3. In-vitro evaluation of anti-ovarian cancer activities of the newly synthesized drug molecules and identification of the lead molecule 4. In-vivo evaluation of anti-ovarian cancer activity of the lead molecule. The synthesis of the designed new PARPis, including the fluoro-/fluoroalkyl analogues of olaparib will be carried out by following a recent literature protocol (Green Chem., 2023, 25, 9097–9102).

BITS Supervisor

Dr. Tanmay Chatterjee, Associate Professor

RMIT Supervisor

Professor Magdalena Plebanski

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Applications of IoT are rapidly being adopted in all daily activities in India as well as in Australia through smart homes, smart buildings, smart campuses, smart cities, etc. Such applications are composed of several devices, sensors, and people. To connect and communicate with each other, Low-power Wide Area Network (LPWAN) technology is required that provides a low-power, low data-rate, long-range, energy efficient, reliable, and secure communication. LoRaWAN is a link-layer technology that facilitates aforesaid requirements. LoRaWAN standard defines several PHY and MAC sub-layer specifications for LPWAN. Based on unique features of LoRaWAN, several IoT applications have been realized. However, there are several security vulnerabilities in LoRaWAN. In this proposal, we explore the vulnerabilities of the following LoRaWAN defined joining procedures for the end-devices: 1) Over-the-Air Activation (OTAA), and 2) Activation by Personalization (ABP). Prior works assume that LoRaWAN end-devices are already activated by the network server. Further, the performance of LoRaWAN activation is not widely studied. We aim to analyze the activation delay and power consumption in large-sized LoRa networks. Moreover, from our initial study, we observed the need for solutions to address the collision problem during OTAA and develop new techniques to define time back-off sequence for join-request retransmissions.

BITS Supervisor

Dr. Jay Dave

RMIT Supervisor

Prof. Iqbal Gondal, Associate Dean, Cloud, Systems & Security

Other Supervisor BITS

Dr Nikumani Choudhury

Other Supervisor RMIT

Dr. Golnoush Abaei

Required discipline background of candidate

Project Description

There are many approaches for automated marking of assessments, but they typically limited to questions that assume answers strictly fitting to a particular structure, e.g., some approached work well for checking programming code. However, when we need to provide quick feedback on artefacts like product/sprint backlogs, Trello boards, etc., the situation is more complex. This project is focused on elaboration of a framework for analysis and assessment Requirements Engineering (RE) and Project Management artefacts, as well as providing corresponding feedback to students with the references what exactly material student has to re-watch/re-read. Methodology of the project will include the following core phases: 1. A systematic literature review will be conducted on the related works, 2. A set of artefacts will be selected, and their correctness and completeness properties will be specified formally. On this basis, an algorithm for automated analysis will be developed. This will create the core of the proposed framework. Analysis of the artefacts might require application some AI and NLP approaches. 3. The framework will be implemented (preferably as a cloud-based solution) and evaluated both from correctness and usability perspective. Correctness of the implemented framework will be evaluated using a number of case studies. Usability of the framework will be evaluated by conducting in-depth interviews with educators/ practitioners/ students.

BITS Supervisor

Dr Prajna Upadhyay, A/Prof

RMIT Supervisor

Dr Maria Spichkova, Senior Lecturer

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and also quality of life and health has risen in India and Australia. As an enabling technology, IoT facilitates several advanced applications with varied QoS requirements for smart cities. However, IoT devices are resource constrained in terms of power, memory, and computation. Low-power radio standards used in these devices further constrain the network interfaces. In general, such devices are often equipped with an IEEE 802.15.4 radio which enables low-power low-data-rate wireless networks. 6TiSCH is an emerging technology that aims to in bringing IPv6 to industrial low-power networks, namely IEEE 802.15.4-based IoT networks. 6TiSCH is a networking technology that is being standardized by IETF and having performance benefits of IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) with interoperability of IPv6. 6TiSCH facilitates low-power wireless technology for Industrial Internet of Things to support routing and multi-hop networks. However, several security vulnerabilities have been observed in joining process. In this proposal, we aim to define a minimal secure joining framework for a new device joining an existing 6TiSCH network. Secondly, the devices in such a network are resource constrained. So we aim to work on a resource allocation mechanism to schedule the transmission slots in such networks.

BITS Supervisor

Dr. Jay Dave

RMIT Supervisor

Prof. Iqbal Gondal, Associate Dean, Cloud, Systems & Security

Other Supervisor BITS

Dr Nikumani Choudhury

Other Supervisor RMIT

Dr. Xiaoyu Xia

Required discipline background of candidate

Project Description

Psoriasis, a chronic autoimmune condition, affects 2-3% of the global population. In India, prevalence ranged from 0.4% to 0.54% between 2010 and 2020, according to the Global Psoriasis Atlas. Developing targeted medications with the advancements in drug delivery technologies, for localized administration of anti-psoriatic medications, has the potential to enhance treatment outcomes. Incorporating photodynamic therapy as an additional approach in psoriasis treatment can offer promising results by synergistically targeting both the immune response and the proliferation of skin cells. A comprehensive approach with advanced technology using integrated photodynamic therapy and anti-psoriatic agent is essential for improving treatment outcomes. Advanced technologies such as nanocarriers-based drug delivery show promise in improving the efficacy and safety of topical psoriasis treatments by enabling enhanced drug penetration, prolong drug retention at the site of action, and reduce systemic exposure. Therefore the objective of the proposed research work is to explore the combined chemo-photodynamic therapy for psoriasis treatment with nanocarriers for site specific topical delivery. To achieve this, in-vitro investigation of combination index for chemo and photodynamic agent will be performed. The selected combination will be loaded to in-house design nanocarriers system. Further, therapeutic agents loaded nanocarriers will be evaluated for physicochemical characterization, in-vitro cell line study, ex-vivo permeation and in-vivo efficacy in the disease-induced animal model. It is expected that the proposed strategy will provide industrial feasible nanoformulation with improved therapeutic efficacy in the treatment of psoriasis.

BITS Supervisor

Gautam Singhvi

RMIT Supervisor

Rajesh Ramanathan, Professor

Other Supervisor BITS

Other Supervisor RMIT

Vipul Bansal

Required discipline background of candidate

Project Description

The conventional solar energy storage systems are capable of storing only the thermal energy obtained from the solar radiation. However, they do suffer from huge thermal losses of the order of 15 °C to 20 °C with low energy density, self-discharge, requirement of huge volumes with associated high cost of construction and so on especially when the sensible heat storage materials are utilized. In this context, the well-organized group of investigators from the RMIT and BITS will thoroughly address the above-mentioned fundamental challenges with their complementary expertise gained over the years in this field of research. For the very first time, this research team aims at the development of a low cost and highly efficient next generation smart phase transition film (SPTFi) enriched with bio-polymers and nanomagnetic particles. This collaborative research is immensely significant, that adds value to the field of research in terms of achieving the following salient aspects (but not limited to): • Enhanced solar photo-to-thermal energy conversion efficiency (>85 %) using biopolymers. • Enhanced heat conduction and swift transfer of heat through the incorporation of nanomagnetic particles. • High thermal energy storage capability (>95 %) • Reliability up to 20000 thermal cycles. • Reduction in its overall cost by 30 % (when compared to the existing sensible heat storage materials). The research team will construct a small-scale workable model of the proposed smart phase transition film (SPTFi)-based energy storage system to demonstrate its real-time application for efficient conversion of solar photo-to-thermal energy without sacrificing the above-mentioned salient points.

BITS Supervisor

R. Parameshwaran

RMIT Supervisor

Dr Hamid Arandiyan

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Many energy sectors depend on fossil fuels, which release three-quarters of greenhouse gas emissions and lead to severe climate changes, bringing about catastrophic environmental changes and rising average atmospheric temperature. Hence, the research community is searching for energy transformation using renewable energy sources. Governments worldwide have set ambitious targets to achieve net-zero emissions by 2050, necessitating groundbreaking innovations and widespread deployment across various sectors. Key areas of focus include advancements in battery technology, the utilization of renewable hydrogen, and the development of CO2 capture and utilization technologies aimed at significantly curtailing CO2 emissions. Among these innovations, catalytic conversion processes for CO2 utilization have emerged as a promising avenue for reducing CO2 accumulation while yielding valuable chemicals. Notably, methanol synthesis through CO2 hydrogenation represents one such process garnering considerable attention for its potential to mitigate CO2 emissions and produce high-value products. The conversion of CO2 to methanol is still a great challenge because of CO2 low reactivity and thermodynamic stability. The methanol from CO2 is exothermic and thermodynamically favorable at low temperatures and high pressures. However, considering the reaction kinetics and the stable nature of CO2, a high reaction temperature is required. In addition, the reverse water gas shift (RWGS) reaction is endothermically favorable at high temperatures and low pressures that result in low methanol selectivity and low CO2 conversions. Moreover, the water from CO2 hydrogenation leads to catalyst deactivation and sintering. These challenges could be addressed by developing new catalysts for CO2 hydrogenation, leading to high methanol selectivity and high CO2 conversions. The proposed research aims to develop stable catalyst such as Cu with Zn, Ni, is supported on perovskite oxides and will be tested for CO2 hydrogenation. Bimetallic catalysts have shown a more synergetic effect than monometallic catalysts and can enhance physical and chemical properties like dispersion, active surface area, surface basicity, and CO2 adsorption results in high methanol selectivity with enhanced CO2 conversion.

BITS Supervisor

Srinivas Appari & Associate Professor

RMIT Supervisor

Prof. Suresh Bhargava

Other Supervisor BITS

Other Supervisor RMIT

Jampaiah Deshetti

Required discipline background of candidate

Project Description

Automated processing of natural language requirements has been explored for over a decade [1]. These techniques cover different aspects of requirements engineering such as requirements analysis, elicitation, or quality assessment, including compliance checks. When the requirements come from different contexts (countries, organizations, or situations), it becomes challenging to perform a compliance check, and it is extremely crucial that we also refer to the regulations [2]. Regulatory text is hard to read and understand for laymen, and companies may have to pay fines if they cannot prove to ensure compliance with the existing regulations during auditing procedures. There exist NLP techniques that make understanding regulatory text easier using automated summarization [3] and check compliance of requirements with a set of regulations [5]. A formal framework for dealing with diversity in requirements is proposed in [2], which can be further automated using NLP techniques. More specifically, we aim to use NLP techniques to convert regulatory text into a semi-formal representation, which can be used to assist in the development of formal models like [2] for compliance. Attempto Controlled English [4] is a popular semi-formal representation of legal text. Our goal through this proposal is to develop a system that takes diverse regulations and the requirements as natural language text as input and (i) converts regulations into Attempto Controlled English, a semi-formal representation (ii) analyzes the diversity of requirements, points of ambiguity, and contradictions using (i) and develop formal methods, and (iii) to improve the task of requirements analysis by developing a system to retrieve information regarding different scenarios. 1. R. Sonbol et al. The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review. Tech. Rep. 2021. 2. M. Spichkova et al. Structuring Diverse Regulatory Requirements for Global Product Development. ReLaw 2015. 3. S. Klaus et al. Summarizing Legal Regulatory Documents using Transformers. SIGIR 2022. 4. http://attempto.ifi.uzh.ch/site/ 5. O. Amaral et al. AI-Enabled Automation for Completeness Checking of Privacy Policies. IEEE Trans. on SE. Nov. 2022, pp. 4647-4674, vol. 48

BITS Supervisor

Dr Prajna Upadhyay, A/Prof

RMIT Supervisor

Dr Maria Spichkova, Senior Lecturer

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

The proposed PhD project focuses on sustainable water desalination by integrating a Membrane Distillation (MD) system for mineral recovery and achieving a Zero Liquid Discharge (ZLD) process. The main objectives of the study are to develop an innovative and environmentally friendly approach to desalination, one that not only produces fresh water but also recovers valuable minerals from the brine and eliminates liquid discharge, thus minimizing environmental impact. Aims: The primary aim of the research is to design and optimize a Membrane Distillation system for desalination, mineral recovery and zero liquid discharge. In conventional desalination processes, the concentrated brine, enriched with valuable minerals, is often discarded as waste, causing environmental concerns. The proposed project seeks to develop methods for the recovery of these minerals from the brine stream, turning a waste product into a valuable resource. Methodology: Literature Review: The initial phase of the research will involve an in-depth literature review to gain a comprehensive understanding of existing MD systems, mineral recovery techniques, and ZLD processes in desalination. MD System Design and Optimization: The researcher will work on designing and optimizing the MD system. This will include selecting appropriate membranes, conducting experiments to determine the best operating conditions, and evaluating the systems energy efficiency and performance. Mineral Recovery Techniques: Various mineral recovery techniques will be explored to extract valuable minerals from the brine. These techniques may include crystallization, precipitation, or other innovative approaches suitable for the specific minerals present. Zero Liquid Discharge Implementation: The researcher will focus on developing and implementing a comprehensive ZLD strategy, ensuring that no liquid discharge occurs throughout the entire desalination process.

BITS Supervisor

Manoj Soni

RMIT Supervisor

Abhijit Date, Associate Professor

Other Supervisor BITS

Other Supervisor RMIT

Kiao Inthavong

Required discipline background of candidate

Project Description

Phenomena of friction and wear in mechanical contacts are particularly important in the field of dynamic systems. There are different efforts have been made to know their properties in real time but sometime it is very difficult to achieve specially during operations. Again we know friction and wear both are interdependent on noise and vibration but most of the studies treated friction, wear mechanics, acoustic noise and Vibration separately. In view of this there is a requirement to make studies to develop interdependencies between friction, wear, noise and vibration. It may be possible by studying some developed mathematical models of friction and wear and establish the relation with vibration response and acoustic emission.

BITS Supervisor

Arun Kumar Jalan

RMIT Supervisor

Reza Nakhaie Jazar

Other Supervisor BITS

SHARAD SHRIVASTAVA ASSOCIATE PROFESSOR

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Electric vehicles (EVs) have been considered an efficient solution to alleviate the intensifying energy and environmental crisis. Due to the high energy density, long cycling life, and high efficiency, lithium-ion batteries (LIBs) are selected as the primary power source for EVs. However, the performance of the LIBs is greatly affected by their operating temperature. During the charge and discharge processes, LIB's reasonable working temperature range is 25–40 ?C, and the temperature difference of the battery module should be within 5?C. The power capacity of the LIB decreases at low temperatures, while the risk of thermal runaway increases at high temperatures. Therefore, a battery thermal management system (BTMS) is significant in ensuring the thermal safety of the battery and enhancing the battery's electrical performance in the operating process. According to the different cooling mediums, BTMS can be classified into three methods: air cooling, liquid cooling, phase change material (PCM), and a combination of them. Although the PCM increases the installation space, weight, and cost of the BTMS, the higher thermal conductivity of PCM-based hybrid cooling excels in its efficacy over the air or liquid cooling approaches. As the PCM experiences continuous phase changes, an innovative cold plate design is essential to encapsulate the PCM and maximize the heat transfer. Therefore, it is decided to indigenously design a triply periodic minimal surface (TPMS) based cold plate to encapsulate the PCM. The following objectives are proposed to investigate the thermal management system of lithium-ion batteries integrated with TPMS structures. • Numerically explore the ideal cold plate design for battery thermal management using various sheet-based TPMS structures. • Development of TPMS structures-based cold plates for thermal management of Li-ion battery pack. • Explore the durability and thermal stress of the cold plate for volatile operating conditions. • Explore the performance enhancement during hybridization of the cold plate with phase change materials for thermal management of the battery pack.

BITS Supervisor

Santanu Prasad Datta

RMIT Supervisor

Dr. Maciej Mazur Senior Lecturer

Other Supervisor BITS

NITIN RAMESHRAO KOTKUNDE

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Virtual Reality (VR) can be a great tool for developing simulators for education purposes, but most of the time it loses out on realism in the environment that it is supposed to bring in. The problem usually lies in rendering algorithms as existing shaders require much more computational resources than are available in standard smartphones. The need of the hour is to develop game and VR engines that exploit generative models to render realistic environments in real time, with resource constraints. We plan to achieve this by smartly using traditional photogrammetry and homographic transformations along with deep-learned generative models to mitigate unwanted artifacts by contextual inpainting. Our goal is to develop lightweight algorithms that can run on smartphones to make the technology accessible to masses by just using Google cardboard like headsets. The methodology can be summarized for development of three major components: A photosphere based look-up approach to render realistic backgrounds with seamless stitching across tiles, powered by generative models. A real time photorealistic rendering system using GAN models over traditional blender mesh-maps animated by logic specific to the story-board. An environment with easy to use storyboard based simulation studio (to be used by teachers and course designers) for generating educational programs for virtual reality The project aims to deliver a novel VR engine with photorealistic rendering capacity leveraging the emerging power of generative models augmenting existing shader algorithms.

BITS Supervisor

Aritra Mukherjee

RMIT Supervisor

Fabio Zambetta

Other Supervisor BITS

Abhijit Das, Dr

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Due to efficiency and latency issues, Machine Learning (ML) services using resources at the Internet-of-Things network edge near the data sources has emerged as edge intelligence (EI). EI is expected to push Deep Learning (DL) computations from the cloud to the edge thus enabling various distributed, low-latency, high-throughput and reliable ML services. The research challenges facing Edge DL for ubiquitous intelligence are training, inference, optimizing the ML on Edge. Suitable network architecture, hardware and software support are required for the DL computations in ML services. In this project, we will apply Adversarial Machine Learning (AML) to data streams collected for EI. Deep transfer learning strategies will be researched to facilitate AML on such resource-constrained edge devices. Storing, reusing and transferring information and knowledge from previous datasets and tasks has the potential to improve sample efficiency in AML. Domain adaptation is a research area in transfer learning that can model distributional shifts in data available for validating the robustness of AML algorithms. It will result in AML paradigms such as incremental learning, utility learning, reinforcement learning and online learning with class and cost distribution information for the transferable feature representations in data streams. Tensor decomposition is useful to analyze the transferable feature representations in such AML formulated as a non-linear optimization problem. The resultant optimization algorithms can be sped up with randomized algorithms that select columns according to a biased probability distribution for tensor decompositions. They can be interpreted as generalizations of low-rank tensor approximation methods representing statistically significant portions of the training data obtained from real world processes. We shall work on transfer metric learning to analyze the low-rank approximations of tensors in EI data streams with optimization methods such as canonical correlation maximization. They will be integrated with existing efforts for running DL on the edge such as Model Input narrowing down the ML searching space, Model Structure of specialized models that waive the generality of standard ML models in exchange for faster inference, Model Selection in the edge by combining different compression techniques in DL models balancing between performance and resource constraints.

BITS Supervisor

Dr. Aneesh Chivukula

RMIT Supervisor

Professor Jenny Zhang

Other Supervisor BITS

Dr Nikumani Choudhury

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Environmental pollution due to the burning of fossil fuels is a global phenomenon. Additionally, the resources of fossil fuel are depleting at a high rate and will be exhausted soon. As a result, it becomes pertinent to seek alternative clean-renewable energy sources, and hydrogen can be considered the most environmentally friendly potential fuel due to the carbon-free emission it produces. H2 can be produced from biomass or biomass-derived hydrocarbons using different catalytic reforming methods. Among these routes, the catalytic steam reforming (SR) of raw bio-oil (obtained from fast pyrolysis or hydrothermal liquefaction of biomass) is considered an attractive and viable technology offering twin benefits of waste management and sustainable energy production. Challenges regarding hydrogen production technology are lowering the cost of production at least by a factor of 3-4 and improving production rates. Deactivation of the catalyst is one of the cost-intensive problems. The present project aims at developing catalytic systems to study their performance and stability in steam reforming of bio-oil to produce green hydrogen. The global surplus agro-residue (AR) generation rate is around 3300 MT, which is underutilized and could be the cause of environmental pollution from stubble burning. We utilize this AR to synthesize high surface area and porosity carbonaceous materials (AC, GO, offering twin benefits of waste management and sustainable energy production) and then use it for the production of cost-effective catalysts (bimetallic Ni; such as Ni-Co and Ni-Sn) on AC or GO. The catalysts will be synthesized by solution chemical routes. At this point, we will study simulated bio-oil (aqueous mixture of phenol, acetic acid, furfural Loba, and hydroxy acetone) as the precursors for hydrogen production. The thermocatalytic performance will be studied in a custom-made reactor at 600-900C. The liquid phase and the gas product from the reactor will be analyzed by GC-MS-MS and GC respectively.

BITS Supervisor

Prof. Banasri Roy

RMIT Supervisor

Prof. Kalpit Shah

Other Supervisor BITS

Dr. Somak Chatterjee

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Carboxylesterases (CEs) play a primary role in metabolism and aberrant CE activity has been directly linked to numerous diseases including obesity, cancer, hepatic steatosis, etc. Sensors for the detection of esterase activity are important tools for the study of drug metabolism and disease progression. The objective of this study is to design and synthesize red-emissive probes with suitable ligands for targeting mitochondria and endoplasmic reticulum, and their use in the efficient detection of intracellular carboxylesterase activity. To push the boundaries of research further, these probes will be encapsulated within cubosomes, enhancing their intracellular delivery efficiency. To further advance the frontiers of research, these probes will undergo encapsulation within cubosomes, thereby amplifying their efficacy in intracellular delivery. This pioneering method holds the potential to unveil a new area of selective and sensitive detection capabilities, elucidating the intricate mechanisms of esterase enzymes within various subcellular compartments. The study aims to: Aim or Objectives • Design and synthesis of red-emissive probes for carboxylesterase enzymes. • Encapsulation of the probes within cubosomes to enhance stability and facilitate intracellular delivery. • Validate the performance of the cubosome-delivered probes for detecting esterase activity in vitro and in cellular models. • Explore potential biomedical applications of the organelle-targeted probes for intracellular imaging and disease diagnosis. Methodology: Probe Design and Synthesis: • Suitable ?-elongated AIE-active aromatic azines capable of red/NIR-emission with large Stokes shifts will be designed and synthesized. • The aromatic ring will be functionalized by organelle-targeting ligands to enable enhanced localization in mitochondria and endoplasmic reticulum. • These probes will be modified by esterase-responsive functionalities and will remain in turn-off state. • The esterase-specific recognition motifs ensure selectivity towards CE enzymes and offer a turn-on response by CE-mediated hydrolysis leading to cleavage of the recognition motif. Initial sensing studies: • The sensing ability of the probe will be assessed in the solution phase using CEs as the analyte and fluorescence spectrophotometer for detection purposes. • Equivalent, selectivity and LOD studies will be conducted. • Evaluation of targeting efficiency and specificity using fluorescence microscopy and subcellul

BITS Supervisor

Mainak Banerjee

RMIT Supervisor

Sampa Sarkar

Other Supervisor BITS

AMRITA CHATTERJEE

Other Supervisor RMIT

Prof. Charlotte Conn

Required discipline background of candidate

Project Description

Summary : Over the past 50 years, the UAE has witnessed a significant transformation in its road network, providing citizens with well-paved highways and flyovers. However, escalating traffic demand, construction projects, and rising temperatures pose challenges, particularly road pavements. The expected temperature increase can impact pavement structural performance of asphalt pavements, which make up for more than 90% of the roads in the UAE. To address this, continuous monitoring is essential, moving from traditional periodic inspections to intelligent remote monitoring using IoT and embedded sensing technologies. A Digital Twin Model (DTM) is crucial, synchronizing real-time data from sensors to create a virtual representation of the pavement structure. This approach, gaining popularity across various infrastructure sectors, enables dynamic updates for timely maintenance decisions. A Digital Twin Model (DTM), as demonstrated for an asphalt pavement in Ireland, is essential, synchronizing real-time data from sensors to create a virtual representation of the pavement structure. The primary objective of this project is to develop a 3D digital twin model of asphalt pavement structure, to monitor the structural changes under real-time loading and environmental conditions. This can be achieved through the following research objectives. Objectives : 1) Develop a DTM using a 3D Finite Element Method (FEM) based model to monitor the critical pavement responses 2) Develop machine-learning techniques to predict the heat distribution in pavement layers from weather data and temperature probes 3) Embed temperature and strain sensors at various depths of pavement layers 4) Establish the physical-to-virtual connection to update the DTM model parameters 5) Update the structural reliability, and propose the optimal timings for pavement maintenance Methodology : The development of the DTM to monitor the structural responses of the pavement is carried out in two phases in this project. Phase 1: a thermal digital twin model is developed to predict the predict heat distribution in the pavement layers. Phase 2: the predicted temperature distribution will be used as input, along with the field strain measurements, to build the 3D FEM model. Steps : 1) Get telemetry data from the physical road structure 2) Detect and diagnose 3) Develop real-time FEM-based model 4) Model Calibration 5)Result Collection 6)Decision support based on AI algorithm

BITS Supervisor

Deepthi Mary Dilip

RMIT Supervisor

Mojtaba Mahmoodian Dr

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

To meet the ever-growing demand for electrical energy, there is a need to harvest energy from sustainable sources, such as solar, thermal and wind. For these energy harvesting methods to succeed, materials with suitable properties and energy efficiencies are required. This project aims to investigate new materials for their potential energy applications. We will focus on 2D van der Waals materials, which can be used in low dimensional devices due to their size, flexibility, mechanical stability and unique material properties owing to their dimensionality. Various new 2D materials can be designed by functionalization as well as forming van der Waals heterostructures from the classes of existing 2D materials such as transition metal dichalcogenides, phosphorene, MXene etc. The materials that we will design and study in this project would be semiconducting in nature having a band gap within a specific range and band alignment suitable for photovoltaic and photocatalytic applications. First principles-based density functional theory (DFT) calculations and methods beyond DFT will be adopted for calculating the structural, electronic, optical and vibrational properties of the materials. The materials will be assessed for their suitability in photovoltaic and photocatalytic hydrogen production applications via water splitting and other chemical energy conversion reactions.

BITS Supervisor

Prof. Swastibrata Bhattacharyya

RMIT Supervisor

Prof. Michelle Spencer

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

The emergence of Agriculture 4.0, driven by the rapid advancement of IoT, AI, Blockchain, and Digital Twin technologies, marks a significant evolution towards data-driven agricultural practices. However, the integration of these technologies, particularly AI and blockchain, into Agriculture 4.0 is challenged by issues related to data availability, accuracy, security, and privacy. Agricultural Mobile CrowdSensing (AMCS) is identified as a scalable and cost-effective solution to address these challenges. Despite this, existing centralized AMCS systems are plagued by security vulnerabilities, lack of transparency, and difficulties in data sharing among stakeholders. Additionally, the project aims to tackle two critical issues: the need for a more robust data ownership and transfer framework, and the creation of an incentivization mechanism to encourage farmer participation in data collection. The primary aim of this project is to address the shortcomings of current blockchain-based MCS frameworks that focus predominantly on raw data collection and often neglect the crucial aspect of delivering validated data. This neglect is particularly problematic in the agricultural sector due to the dynamic nature of field properties, making the incorporation of ML/AI methods for data validation a necessity. By designing a holistic, end-to-end architecture that seamlessly integrates IoT, mobile crowdsensing, blockchain, AI, and Digital Twin technologies, this project seeks to revolutionize agricultural data collection, enhance predictive agricultural services through advanced machine learning and deep learning models, and empower farmers with the tools needed for sustainable, productive, and resilient practices, effectively navigating the central challenges of modern agriculture.

BITS Supervisor

Dr. Ashutosh Bhatia

RMIT Supervisor

Abebe Diro

Other Supervisor BITS

Prof. Kamlesh Tiwari

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Objective: Development of novel nanocomposites, with tailor-made heterostructure that will demonstrate high photoelectrocatalytic efficiencies towards (i) water treatment (degradation of antibiotics, small organic pollutants, etc) and (ii) H2 production (which is future clean energy) via water splitting under solar light exposure at a very low external potential bias. Sustainable and affordable technologies for wastewater treatment and clean energy production are now paramount worldwide (Sustainable Development Goal SDG-6 and SDG-7). In this context, photoelectrocatalysis (PEC) process is gaining immense attention. Research gaps: (i) Catalysts for PEC capable of showing high efficiency under solar light at a low bias potential (below 500mV) are in extreme demand and yet to be achieved, (ii) nanocatalysts showing high catalytic activity for both water treatment and H2 production are not yet well developed. Here catalysts will be synthesized by creating all-solid-state Z-Scheme heterojunctions via combining metal sulphide/ metal oxide semiconductor nanoparticles with another semiconductor (e.g., gC3N4 or Potassium poly(heptazineimide)) and integrating an electron transfer mediator (Ti3C2Tx MXene). The new 2-D material MXene as an electron mediator has not yet well explored. Composition and microstructure of the catalysts will be manipulated so that (i) catalyst can generate holes and electrons under solar light exposure, (ii) recombination of photogenerated holes and electrons can be prevented, (iii) energy states of CB edge and VB edge become suitable for water splitting and dye degradation. Methodology: (1) Synthesis, characterizations, and optimization of optoelectronic, and Electrochemical impedance properties of photoelectrocatalysts, (2) Investigations on PEC activities of the catalysts under solar light exposure with an external potential bias for (i) water treatment (degradation of antibiotics, small organic pollutants, etc), and (ii) H2 evolution reaction, (3) DFT calculations for electronic structures of the catalysts, (4) using these catalysts develop membranes/ devices for aforesaid applications. The novelty of the nanocatalysts lies in their ability to decompose organic pollutants in water and their competence in the production of H2 under solar light irradiation with a very low external potential bias (about 500 mV). This work will provide us with an effective strategy for environmental pollution remediation and energy conversion technologies.

BITS Supervisor

Dr NARENDRA NATH GHOSH and Professor

RMIT Supervisor

Dr Baohua Jia and Distinguished Professor

Other Supervisor BITS

Dr Sudipta Chatterjee and Assistant Professor

Other Supervisor RMIT

Dr Derek Hao and Vice Chancellor’s Postdoctoral Fellowship

Required discipline background of candidate

Project Description

PROJECT DESCRIPTION Considering the onboard difficulties of PEM fuel cells for transportation applications, designing an efficient energy-controlling strategy along with the prognostic health management system will enhance the efficacy as well as effective operational life of the PEM fuel cells significantly. The present strategy aims to operate the PEM fuel cell in its efficient region of operation as a static machine, while the required energy to supply the demand will be externally controlled by the system charging and discharging control. Usually, this strategy will lead to the release of excess energy (Excess energy = Energy of PEM-Energy Demand) which can be routed to an effective energy reservoir that is integrated with the PEM fuel cell stack. Considering the available energy storage systems, especially considering the high energy demand/supply operations, the quick rechargeability of proton flow batteries attracted us to investigate the feasibility and reliability of proton flow batteries as a standalone candidate for an effective energy reservoir system. Since the supplied excess energy will be stored as reserve energy in the proton battery and can be utilized in which system requires peak energy demand or the effective and optimized proton battery can be utilized as a range extender for transportation applications. Additionally, utilizing the PEM unitized regenerative fuel cell (PEM URFC) instead of the PEM fuel cell can extend the operation of the fuel cell by regenerating the hydrogen gas at the external charging mode. Hence, this project will investigate the possible ways of combining a Proton Battery (PB) or Proton Exchange Membrane (PEM) and/or Unitized Regenerative Fuel Cell (URFC) in a hybrid power supply system for automotive applications. Possible combinations are standalone PBs or URFCs, or a conventional PEM fuel cell with a PB or URFC to meet peak demands and to meet the DOE-targeted service life under AST environments. The present study will involve a theoretical analysis of the various hybrid system options combined with simulation, followed by design and testing of small-scale (up to 1 kW) experimental trial systems.

BITS Supervisor

Sudha Radhika, Associate Professor

RMIT Supervisor

John Andrews, Professor

Other Supervisor BITS

Sujith R, Associate Professor

Other Supervisor RMIT

Dr Shahin Heidari

Required discipline background of candidate

Project Description

The proposed project aims to develop an optimal solution towards sustainable electric vehicle (EV) charging infrastructure by introducing renewable energy sources (e.g; solar PV) integrated long-life battery storage (e.g; Vanadium Redox Flow Battery, VRFB), considering local power distribution grid scenarios of India and Australia. A low-cost smart energy management scheme will be developed to meet dynamic EV load demand. The impact of EV penetration and solar PV on distribution grid operation for both the Indian and Australian scenarios will also be investigated in detail. The proposed work will focus on the four major objectives; 1. Design and development of an efficient electrical interfacing system for long-life battery storage (e.g.; VRFB) with solar PV. 2. Development of an optimized battery management system (BMS) for 5kW 30kWh VRFB storage. 3. Development of IoT-based smart energy management scheme. 4. Investigations on the impact of dynamic EV penetration on local distribution grid parameters. The following available facilities will be used during the project; Indian supervisor side: 5kW 30kWh VRFB set-up, Power electronic test-bed, Programmable load bank for emulating EV penetration profiles, Raspberry-Pi communication kit. Australian Supervisor side: Solar and distribution grid simulator, EV lab facilities, DIgSILENT Power Factory software. The methodology is presented by following work packages; WP1: Integrated solar PV – VRFB – distribution grid design and simulation WP2: Efficient BMS design for VRFB storage WP3: IoT-based low-cost smart energy management scheme WP4: Impact of EV penetration on the local distribution grid WP5: Validation of the developed solution for both the Indian and Australian scenarios.

BITS Supervisor

Dr. Ankur Bhattacharjee

RMIT Supervisor

Dr. Kazi Hasan

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

This proposal aims to enhance the capabilities of electrospun polymer fibres in wound healing dressings by surface derivatization with antibiotic-free nano-antimicrobials. Electrospinning offers advantages, and the focus is on incorporating ultrafine gold nanosystems (UGNs) and nano-metal-organic frameworks (nanoMOFs). These nano-antimicrobials exhibit broad-spectrum efficacy against microbes, paving the way for clinical applications due to their oral administration feasibility, easy renal excretion, and selective targeting of prokaryotic cells over eukaryotic cells. In addition to their antimicrobial properties, the derivatized fibers will possess improved biofluid drainage and easy-to-peel characteristics. This multifunctional approach addresses the limitations of conventional wound healing dressings. Notably, nano-antimicrobials differ in their mechanisms of action from traditional antibiotics, suggesting potential advantages in minimizing resistance development. This exploration contributes to innovative wound healing solutions, providing a promising avenue for overcoming microbial infections. The proposed strategy harnesses the synergy of electrospun polymers and advanced nano-antimicrobials, marking a significant step forward in the evolution of wound care technologies. Aims: (1) To develop nanoparticles-anchored electrospun nanofibers (BITS-9 months) (2) To characterize and evaluate in vitro antimicrobial performance of fabricated nanoparticles-anchored electrospun fibers (RMIT-12 months) (3) To evaluate the in vivo effectiveness of nano-antimicrobial electrospun dressing patches for wound healing (BITS-12 months) Methodology: 1. Fabrication and characterization of UGN-anchored electrospun polycaprolactone nanofibers and evaluating their in vitro antibacterial effectiveness. 2. Fabrication and characterization of nanoMOF-anchored electrospun nanofibers and assessing their in vitro antibacterial effectiveness. 3. Comparing the efficacy of both dressing materials with standard dressings under in vivo conditions in a wound-healing mouse model.

BITS Supervisor

Professor Jayati Ray Dutta

RMIT Supervisor

Professor Ravi Shukla

Other Supervisor BITS

Ramakrishnan Ganesan

Other Supervisor RMIT

Required discipline background of candidate

Project Description

As per the Statistical Review of World Energy, the global energy demand rose 1% last year and record growth in renewable sources didn’t affect the dominance of fossil fuels, which still accounted for 82% of supply standing at 137,236 terawatt-hours in the year 2022. With severe air pollution and the imminent climate change hazards, use of alternative fuels and energy sources is inevitable. Fuels produced from biodiesel have demonstrated lowering greenhouse gas (GHGs) emissions, without any engine modification required. Biodiesels are also known for increasing combustion effectiveness and reducing engine emissions as an oxygenated fuel. Rice-bran oil and Corn-Oil have been established as good alternatives for use as individual biofuel blends to replace fossil-based fuel in combustion engines leading to reduction in GHG emissions. As first-of-its-kind study, this project aims to investigate the effect of dual biodiesel-diesel blends derived jointly from rice-bran oil and corn oil, mixed in different proportions with diesel to find the optimum blending composition for minimizing CO and NOx emissions, without considerable penalty in its performance. Method: (a) Thorough literature survey and characterization focused on studies on the thermo-physical properties and blending ratios of Rice-Bran and Corn-Oil derived biodiesels with a focus on their thermo-physical properties and blending ratio delivering optimum performance - BPDC (b) Preparation of Rice-Bran and Corn-Oil derived Dual biodiesels in pre-decided concentrations with diesel through Transesterification process - BPDC (c) Numerical Simulations to estimate the Optimal blending Ratio using Ricardo Wave Simulation Software For Dual Biodiesel - BPDC (d) Experimental Assessment of Performance and Emission Characteristics of Combustion in Single-Cylinder Diesel Engine Setup with AVL Gas Emission Analyzer - BPDC (e) Experimental Assessment of Ignition Delay and Flame Speed (& Development) in Constant Volume Combustion and Shock Tube respectively - RMIT (f) Collation and Analysis of Data, Thesis Writing and Ph.D. Defense - BPDC BPDC - BITS Pilani Dubai Campus RMIT - Royal Melbourne Institute of Technology, Bundoora Campus

BITS Supervisor

Prof. Shashank Khurana, Associate Professor

RMIT Supervisor

Prof. Petros Lappas, Senior Lecturer

Other Supervisor BITS

Prof. Snehaunshu Chowdhury, Associate Professor

Other Supervisor RMIT

Required discipline background of candidate

Project Description

To fulfill the continuously growing need for energy that will not adversely affect the environment, hydrogen has been found to be a clean fuel with zero carbon emissions. However, the conventional hydrogen generation methods leave a carbon footprint. The proposed project aims to generate green hydrogen using photocatalytic water splitting and the consequent development of a hydrogen storage system. A photocatalyst will be designed through density functional theory and will be synthesized experimentally. A nanoporous material for efficient hydrogen storage will be predicted through machine learning, and the adsorption capacity of the porous material will be predicted using molecular simulations. Finally, the project will develop a complete hydrogen generation and storage system for utilization of hydrogen as a clean fuel in our daily life.

BITS Supervisor

Dr. Sarbani Ghosh - Assistant Professor

RMIT Supervisor

Rachel Caruso

Other Supervisor BITS

Prof. Banasri Roy

Other Supervisor RMIT

Dr Haoxin Mai

Required discipline background of candidate

Project Description

Recently drones are gaining popularity for variety of applications viz. military, agriculture, inspection and monitoring etc. Traditionally, IC engines are used to power drones however, they emit greenhouse gases, have vibration, exhibit acoustic and thermal signatures. Electric-power systems such as battery, fuel cell etc. can eliminate these issues. Battery based propulsion systems are quite popular however, they have very less energy density (250 Wh/kg) and less endurance (30 min). Fuel cells are light weight and have high energy density (1000 Wh/kg) and therefore can improve the endurance of drones manyfold. However, fuel cells have low power density and therefore they alone are not sufficient to power the drones during high power requirements. Instead of stand-alone battery or fuel cell, a hybrid energy system can be more effective in powering drones. Therefore, this study aims to develop a fuel cell-battery hybrid energy system for high endurance drone application. The specific objectives are: 1. Development of a fuel cell – battery hybrid energy system. 2. Development of an energy management system (EMS) for optimal control of power splitting between fuel cell and battery. 3. Testing and performance assessment of the hybrid energy system under different operating conditions. 4. Incorporation of the hybrid energy system into drone platform and preliminary flight tests. The proposed hybrid system consists of a fuel cell and a battery connected in parallel. It will be integrated with a DC-bus, by suitable power electronic converters. An EMS will optimize the power split between fuel cell and battery. The scheduling of prioritizing the energy sources will be decided based on the energy management algorithm. The algorithm will be embedded on the low cost Raspberry Pi processor. The associated sensor (voltage, current, temperature) data will be processed by the algorithm and thus enabling the control switches for real time operations. The brushless DC (BLDC) motor will be controlled by the motor controller powered by the DC bus. The BLDC motor will run the propeller. The fuel cell and battery are very sensitive to the operating conditions therefore, the hybrid energy system will be tested at different conditions by placing them in an environmental chamber. The hybrid energy system will be installed onto the drone platform. Initial tests will be conducted to check functionality, endurance, range etc.

BITS Supervisor

Naveen Kumar Shrivastava

RMIT Supervisor

Arash Vahidnia

Other Supervisor BITS

Dr. Ankur Bhattacharjee

Other Supervisor RMIT

Required discipline background of candidate

Project Description

The liquid crystalline phase has generated significant attention from researchers worldwide due to its fascinating properties to explore in the drug delivery field. Lyotropic liquid crystals (LLCs) manifest their liquid crystalline behavior depending on the surrounding solvent, paving the way for innovative drug delivery and tissue regeneration applications. Management of chronic wounds is increasingly recognized as a significant global health challenge. Effective treatment necessitates establishing a conducive healing environment for tissue remodeling and sustained delivery of therapeutic molecules, including antibacterial agents, angiogenic molecules, and growth factors. Using innovative materials like LLCs has emerged as a promising trend in chronic wound management. LLC structures offer numerous advantages, such as cost-effectiveness, ordered architecture, and efficient payload loading. LLCs create a moist wound environment and facilitate the release of the payload sustainably, which is essential for delivering biological macromolecules, such as proteins, peptides, and nucleic acid molecules. In this proposal, we plan to explore lyotropic liquid crystalline formulation to deliver antibacterial tissue regenerative payload or nucleic acids for faster wound healing and tissue regeneration in diabetic wound healing. Nanozymes, specific metal nanoparticles consisting of ceria, zinc, or copper, exhibit several catalytic activities, including catalase, superoxide dismutase, and peroxidase. Using nanozymes for healing wounds is an emerging approach that addresses antimicrobial resistance and provides alternate antibiotic therapy. Therefore, this proposal aims to develop lyotropic liquid crystalline formulations of antibacterial agents and nucleic acids as a self-healing spray dressing to provide multifunctional benefits for tissue regeneration, bactericidal, and angiogenic potential. The depth of knowledge acquired in developing lyotropic liquid crystalline formulation would help us use the platform technology to develop liquid crystalline nanoparticles, cubosomes to deliver nucleic acids, and hydrophobic anticancer drugs to synergize anticancer therapy. The fabricated spray and the nanoparticles systems will be characterized physicochemically, and in the in vitro and in vivo assay systems.

BITS Supervisor

Swati Biswas

RMIT Supervisor

Sampa Sarkar

Other Supervisor BITS

Other Supervisor RMIT

Prof. Charlotte Conn

Required discipline background of candidate

Project Description

Metamaterials represent a frontier in material science, engineered to possess properties not found in natural materials. These materials exhibit unique acoustic, and mechanical behaviours, including exceptional impact and blast resistance. Despite their widespread applications, the underlying physics of wave propagation through metamaterials remains an intricate puzzle, bridging the gap between physics and materials science. This project aims to unravel the complex nature of wave propagation and scattering in elastic metamaterials through a comprehensive research strategy that integrates theoretical analysis, numerical simulation, and experimental validation. It specifically seeks to elucidate the mechanisms by which elastic waves are filtered or attenuated within these metamaterials under conditions of impact loading and acoustic exposure. By harnessing the expertise from the Birla Institute of Technology and Science (BITS) in wave propagation phenomena and RMIT University in metamaterials characterization, this interdisciplinary approach aims to deepen our understanding of how elastic waves can be manipulated or attenuated in the metamaterials. The aim and objectives of the proposed work are the following: Aim 1: To thoroughly understand the behaviour of the elastic wave propagation in the metamaterials under of impact loading and acoustic exposure. The project will employ experimental investigations, including scattering parameter measurements, and transmission/reflection coefficients to examine the elastic wave propagations in the metamaterial structures. The metamaterials in focus will include Bio-inspired Layered Metamaterials, Layered Metamaterials, and Metasurface-Layered Metamaterial Hybrids. Aim 2: To leverage computational methods for the predictive analysis of the wave propagation. Advanced simulation tools, such as the Finite Element Method (FEM), will be utilized to model the behaviour of metamaterials. This will enable the prediction and optimization of their properties for specific applications. Aim 3: To deepen the theoretical understanding of wave propagation in the metamaterial Develop the analytical models that describe wave propagation and scattering in metamaterials. These models should take into account the unique properties of metamaterials, such as negative refraction, and bandgap behavior.

BITS Supervisor

Prof. Sumit Kumar Vishwakarma

RMIT Supervisor

Dr Ngoc San Ha

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Summary: Moisture sensors have applications in various sectors including agriculture, automobiles, food processing industry, medical equipment, pharmaceutical and waste water treatment plants, to name but a few. Traditional moisture content estimation includes thermogravimetric analysis and multi-spectral remote sensing-based approaches relying on absorptive properties of water in near-infrared or shortwave-infrared wavelengths. However, the conventional moisture measurement methods involve cumbersome steps and lack sensitivity. Hence, there is an unmet need to come up with a simple and sensitive tool for real time quantification of moisture. Aims: 1. Investigation of suitable coatings on sensor surfaces for more moisture adsorption sites 2. Development of cost-effective and scalable sensor for rapid and real time monitoring of moisture 3. Comparison of the developed moisture sensor with the state-of-the-art techniques Methodology: Quartz crystal resonator (QCR) is entirely electronic and simple in configuration and has therefore gained wide attention as a rapid and on-line detection sensor in both liquid and gaseous mediums [1]. Adoption of fixed frequency drive (FFD) method in conjunction with microfluidics and a temperature controlled QCR will be an exemplary application for real-time monitoring of moisture. In FFD method, a QCR is driven continuously at a fixed frequency and amplitude and the response is analytically interpreted to obtain resonance frequency and dissipation shifts employing imaginary and real components of experimentally recorded electrical impedance respectively. FFD technique will be applied to detect moisture employing coated QCRs which are obtained using either drop casting, spin coating or spray coating techniques. Various coatings including graphene oxide and indium oxide quantum dots, to name but a few will be explored for the project. The interaction between coating modified QCR surface and moisture will be quantified in terms of QCR resonance frequency and dissipation (acoustic energy loss) shifts with respect to baseline signal devoid of moisture. Initial experiments will be carried out using 14.3 MHz QCRs. QCRs with higher fundamental frequencies ranging from 50 to 250 MHz will be explored for sensitive detection of moisture. Reference [1] A. Alassi, M. Benammar, and D. Brett, “Quartz crystal microbalance electronic interfacing systems: A review,” Sensors (Switzerland), vol. 17, no. 12, pp. 1–41, 2017, doi: 10.3390/s1712

BITS Supervisor

Dr Arnab Guha, Assistant Professor

RMIT Supervisor

Dr Henin Zhang, Senior Lecturer

Other Supervisor BITS

Other Supervisor RMIT

Dr Amirali Khodadadian Gostar, Senior Lecturer

Required discipline background of candidate

Project Description

The co-delivery of chemotherapeutic drugs with DNA is a promising strategy in cancer therapy, capable of suppressing multiple disease pathways simultaneously and improving the efficacy of treatment. However, a delivery vector is required to facilitate uptake of the large, negatively charged DNA across the cell membrane. The aim of this project is to assess the performance of hybrid organic-inorganic nanoparticles (NPs) for the co-delivery of p53 plasmid DNA and the chemotherapeutic drug, paclitaxel. The nanoparticles consist of a mesoporous silica nanoparticle (MSN) core decorated with a bilayer (formed from either a cationic one-head-two-tail biodegradable surfactant or an ionizable lipid) which improves biodegradability and reduces burst release of the drug. We will synthesize and test two biodegradable surfactants of various hydrocarbon chain lengths (N,N-dimethyl-N,N-bis[2-(alkyloxycarbonyl)ethyl]ammonium bromide (2xCnE-Br) and N,N-dimethyl-N,N-bis[2-(alkylcarbamoyl)ethyl]ammonium bromides (2xCnA-Br) in combination with MSNs. Two commercially available ionizable lipids will be used in this project: ALC-0315 (used in the Pfizer/BioNTech vaccine) and SM-102 (used in the Moderna SpikeVax vaccine). Lipid combinations based on these lipids may also be used, including cholesterol and helper lipids such as DOPC and DOPE. Biodegradable surfactants will be synthesized following reported methods and characterized by 1H NMR, 13C NMR, FTIR, and HR mass spectroscopic methods. Ionizable lipids may be bought commercially. The size, internal nanostructure, surface charge, and morphology of the hybrid NPs will be determined using a combination of synchrotron small-angle X-ray scattering, dynamic light scattering, and cryo-transmission electron microscopy. SANS method with contrast matching will be used to determine the core-shell morphology of the particle and the location of individual components including the DNA. The extent of adsorption of lipids/biodegradable surfactants on the surfaces of silica nanoparticles will be assessed by measuring the nitrogen weight percentage (N%) using EDAX method. The DNA binding capacity of the formulation will be characterized via a gel retardation assay, steady-state and time-resolved fluorescence, and fluorescence anisotropy methods. The % compaction of DNA will be estimated by fluorescence microscopy. Transfection efficiency will be assessed both in vitro and in vivo in mice.

BITS Supervisor

Prof. Subit Kumar Saha

RMIT Supervisor

Prof. Charlotte Conn

Other Supervisor BITS

Prof. Kumar Pranav Narayan

Other Supervisor RMIT

Professor Calum Drummond

Required discipline background of candidate

Project Description

This study delves into the complex interrelationships among environmental, social, and governance (ESG) practices, board diversity, ownership structure, and firm performance within the Indian business landscape. By analyzing financial reports spanning from 2010 to 2023, the research aims to uncover the implications, benefits, and challenges associated with integrating ESG considerations into business operations using robust panel data econometric models to control for industry and time effects as well as the potential endogeneity issues. Moreover, it emphasizes the necessity of a comprehensive approach to corporate sustainability. As businesses increasingly recognize the importance of sustainable practices, this study provides valuable insights for policymakers, corporate leaders, and scholars. Furthermore, the research examines the influence of board gender diversity and ownership structure on the relationship between ESG practices and firm performance. It investigates how the gender composition of the board and ownership attributes shape overall firm performance dynamics. Specifically, the study explores the impacts of regulatory measures such as the Companies Act (2013) on board gender diversity and ownership structure and their subsequent effects on firm performance. Additionally, it assesses whether these measures contribute to sustainable growth and improved social practices, considering the different perspectives on corporate

BITS Supervisor

R. L. MANOGNA

RMIT Supervisor

Dr. Muhammad Safiullah, CPA

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Quality control measures for alcoholic beverages, especially wines, are of paramount importance to ensure that wines are safe for consumption. This includes monitoring for contaminants such as pesticides, heavy metals, and microbiological hazards, which could pose health risks to consumers if present above permissible levels. This also ensures that each batch of wine maintains consistency in flavour, aroma, and overall sensory characteristics. This is particularly important for brands that strive to offer a distinct taste profile, as any deviation from the expected flavour can disappoint consumers and harm brand loyalty. Furthermore, proper quality control measures help ensure that wines are aged and stored correctly, preserving their quality over time. Factors such as temperature, humidity, and exposure to light can affect the aging process and ultimately affect the taste and quality of wine. Quality control should extend beyond the winery to the entire supply chain, including grape growers, suppliers of equipment and packaging materials, and distribution channels. By implementing rigorous quality control measures at every stage, wineries can ensure that only the highest quality ingredients and materials are used in the production process. The adulteration of alcoholic beverages, including wine, can occur through various means. The practice of adding water or excess sugar without regard to quality can lead to imbalances in flavour and may violate regulatory standards. Sometimes, despite their hazardous impact on the body, artificial colourants or synthetic flavour enhancers are used to improve the taste of low-quality wines or to cover undesirable characteristics. Some producers add chemical preservatives, such as sulfites, sugar, acids, and glycerin, to prolong the shelf life of alcoholic beverages and prevent spoilage. This project aims to develop optical sensors for the detection and estimation of chemical adulterants and preservatives in commercially available alcoholic beverages such as wine. In addition to detection, we will attempt to remove artificial adulterants from alcoholic beverages without hampering their flavour, aroma, and taste. This low-cost sensor will enable the preservation of the taste and quality of beverages. Additionally, the sensors and technologies developed through this project will be benchmarked with industry standards in collaboration with industry partners from the RMIT supervisor.

BITS Supervisor

Nilanjan Dey

RMIT Supervisor

Rajesh Ramanathan, Professor

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

A green supply chain is critical to achieve and maintain environmentally friendly manufacturing, particularly in automotive sector as about 40 to 60 percent of auto-components are outsourced. Weight reduction of auto- components using natural fiber-based material will reduce overall vehicle weight resulting in significant reduction in fuel/energy consumption as well as increase road infrastructure lifespan. Natural fiber-based composite part fabrication offers potential solution to environmental issues and more flexibility to utilize locally available agricultural resources. Further, using additive manufacturing technique addresses the challenges associated with part production complexity. Thus, possible green manufacturing with green supplier will provide an auto-manufacturer an alignment with NET ZERO global mission and sustainability. Developing auto-parts production practices using additive manufacturing will give a supplier competitive advantage. Therefore, this proposed work aims to provide a sustainable business model for automobile sector by utilizing green raw material and processing. Initially, it will study the existing supply chain of selected automotive components to understand carbon footprints and other related environmental impact both in India and Australia. It will further explore the production of selected components using natural fiber-based materials considered in design specifications. The selected part(s) will be fabricated using natural fiber-based composite through additive manufacturing. The developed part(s) will be characterised and tested as per the ASTM standards. Comparative life cycle assessment of the developed products and existing products will be conducted and optimized for business continuity. Finally, the supply chain and its decarbonization, a framework for upscaling and training module for capacity building will be developed.

BITS Supervisor

Professor Srikanta Routroy

RMIT Supervisor

Muhammad Abdulrahman

Other Supervisor BITS

Professor Radha Raman Mishra

Other Supervisor RMIT

Kamrul Ahsan

Required discipline background of candidate

Project Description

A new theme in nature conservation is rapidly emerging: Nature Positive. The Nature Positive movement emerged as an aspirational goal of the Convention on Biological Diversity’s Global Biodiversity Framework, and its objective is to halt and reverse nature loss by 2030 on a 2020 baseline, and achieve full recovery by 2050. One critical component of Nature Positive is its focus on achieving this goal via economic mechanisms. The theme has become a priority of global conservation governance institutions, such as the International Union for Conservation of Nature, and was cemented into Australian government policy in the Nature Repair Market Bill in 2023. If implemented as anticipated, it could have important consequences for businesses, especially the agriculture industry. Yet, it remains to be seen how such new policies and frameworks can be translated into practice, whether they can achieve biodiversity gains, how they might be biased by the values and interests of different stakeholder groups, and what they mean for communities affected by them. Australia has been a leader in developing biodiversity metrics and accounting, pioneering biodiversity offsetting schemes that are likely to inform how Nature Positive is implemented. Biodiversity offsetting has been heavily criticised, and yet is being rolled out in other places across the globe, in part due to lack of alternative, more effective mechanisms to balance economic development and biodiversity conservation. This research presents an opportunity to work internationally, identifying how countries such as Australia and India take different approaches to the Nature Positive movement, and how they can learn from each other to achieve ecological and social benefits. Drawing on economics, policy theory, and social psychology, this project will take the following approach: - Conduct a rapid review of economic policy and frameworks that have emerged around the world in response to the GBF Nature Positive goal. Identify trends and potential strengths and weaknesses. - Develop case studies on Australian and Indian interpretation and implementation of Nature Positive. This will be achieved through policy review and interviews with key stakeholders such as public servants, private industry, and other individuals or groups who are working on or towards Nature Positive initiatives in Australia and India. This will identify strengths, weaknesses, and opportunities to achieve stronger environmental and social outcomes

BITS Supervisor

Professor Vivekananda Mukherjee

RMIT Supervisor

Dr Lily van Eeden

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Political scientists and researchers are concerned about the impact of fake news on democracy. On the other hand, fake news could also escalate to life-threatening problems, for example, UNESCO states that during COVID-19 pandemic fake news is putting lives at risk, like Coronavirus (COVID-19) pandemic. Fake news continues to spread, so does people's behaviour and emotions about the fake news via social media platforms. Fake news via social media platforms. This opens up the back door for cyber-criminals to entice people (i.e., taking advantage of victims' emotional and behavioural aspects) to click on links (e.g., phishing links) associated with fake news when reading. Therefore, in this project we investigate how people's emotional and behavioural features influence reading and diffusing fake news in social media and proposes a fake news detection model incorporating people's behavioural features and their emotions to better detect fake news in social media. Furthermore, we study how individuals are emotionally manipulated to perform various malicious behavioural activities such as clicking on a link associated with fake news. The project will also explore fake news diffusion patterns and attacker strategies in social media. The research findings can be used in a risk prediction model to predict the risk score of any news content being fake in social media. Eventually, a browser plugin can be implemented through persuasive design principles for social media platforms that alert the potential risks to users when dealing with (fake) news. Methodology: The proposed methodology consists of several steps. First, we extract textual sentiment, visual sentiment, behavioural and metadata features from fake and real news in a dataset. Then, we analyse the data and evaluate the correlations between these set of features, as well as compare features between real and fake content. Based on the analysis, we identify important and impactful features for fake news detection, i.e., to distinguish between real and fake news. Then, we build a classifier using machine learning techniques, which will try to classify a post as fake or not, using these features as training data. In the following subsections, we will describe our methodology in detail. Finally, a browser plugin is implemented through persuasive design principles (i.e., HCI methods) for social media platforms that alert the potential risks to users when dealing with (fake) news.

BITS Supervisor

Yashvardhan Sharma and Professor, Department of Computer Science & Information Systems & Faculty In-

RMIT Supervisor

Nalin Asanka Gamagedara Arachchilage

Other Supervisor BITS

Other Supervisor RMIT

Prof. Matthew Warren

Required discipline background of candidate

Project Description

With the rise in energy demand, exploring efficient and economical ways of sustainable energy production techniques to reduce our dependency on fossil fuels, the primary source of the global greenhouse effect and climate change is essential. The energy requirement perceived an unparalleled growth in renewable energy production, with wind energy as a leader among others. In 2019, 7.3% of U.S. energy requirements were met by wind energy, with that percentage forecasted to increase to 20% by 2030 and 35% by 2050. In the wind turbine system, the mechanism involves the rotation of the rotor, which is directly connected to a generator through different bearings and a series of gearboxes (which speed up the rotation). The transition of wind energy to the rotation of a generator is what produces electricity. The premature failures (i.e., surface-initiated fatigue, white etching cracks [WECs], scuffing/smearing, spalling, and dents/indentations) of bearing in wind turbines are the prime concerns and significant causes of downtime in energy production. For this reason, the project aims to develop a high entropy alloy (HEA) coating with constituent elements of equal molar ratio, which will be fabricated using the magnetron sputtering technique. The investigating hypothesis will be the effect of metal disulfide content on the microstructure, composition, phase constitution, and wear and corrosion phenomenon on the magnetron sputtering HEA coatings. A tribological system consists of two same/different materials rolling/sliding against each other in dry or lubricating conditions. The investigating hypothesis will be how HEA hard-coated and lubricated tribological pairs could improve the endurance limit of the wind turbine bearings. In this, the P.I. aims to develop a hard and lubricated coated tribological pair that can support heavy axial and radial turbine load with minimum friction. The principal investigator will focus on developing an advanced tribological system consisting of Titanium alloy-coated bearing rollers in linear contact with the metal disulfide-coated raceways in dry and lubricated rolling conditions. The lubricant incorporated with metal disulfide nanoparticles as a friction modifier will be further tested for its auxiliary lubricating behavior in lubricated conditions. Thus, developing and investigating effective and economically advanced tribological systems for wind energy turbines for controlling turbine bearing failures are highly desirable.

BITS Supervisor

Piyush Chandra Verma

RMIT Supervisor

Nasir Mahmood

Other Supervisor BITS

Himanshu Aggarwal

Other Supervisor RMIT

RAJ DAS

Required discipline background of candidate

Project Description

Harmful Algal Blooms (HABs) are increasingly recognised as critical global environmental issues, with significant repercussions for ecosystems, economies, and public health. These blooms, often exacerbated by the influx of nutrients from agricultural runoff, sewage discharge, and the broader impacts of climate change, lead to the proliferation of toxic algae in water bodies. Such proliferation not only diminishes water quality but also threatens aquatic life through oxygen depletion and toxin release, posing serious risks to human health, affecting the livelihoods dependent on fisheries, and undermining recreational water-based activities. Traditional methods for monitoring HABs, which typically involve in-situ sampling followed by laboratory analyses, are not only resource-heavy but cannot provide a comprehensive picture of the blooms' spatiotemporal dynamics, making it challenging to respond effectively to their rapid development and spread. The emergence of remote sensing technologies marks a paradigm shift in detecting and monitoring HABs. Equipped with advanced high-resolution multispectral and hyperspectral sensors mounted on satellites and Unmanned Aerial Vehicles (UAVs), these technologies offer a synoptic, timely, and cost-efficient means to observe and analyse the extent and biomass of algal blooms across vast and often inaccessible aquatic environments. Such technological advancements enable the continuous surveillance of water bodies, offering a valuable tool for environmental monitoring and management practices aimed at mitigating the impacts of HABs. This research aims to refine remote sensing techniques for enhanced HAB monitoring by tackling challenges in detecting blooms in turbid and coastal waters and developing precise models for estimating HAB characteristics. It will improve atmospheric correction algorithms, create and test new analytical and machine learning models, and integrate multisource remote sensing data for detailed HAB analysis. Cloud computing will enable efficient processing of large datasets for timely global HAB surveillance. Additionally, standardising methodologies and reporting in HAB remote sensing studies will boost the reliability and comparability of findings.

BITS Supervisor

Anirban Roy, Associate Professor

RMIT Supervisor

Dr Amirali Khodadadian Gostar, Senior Lecturer

Other Supervisor BITS

Snehanshu Saha and Professor

Other Supervisor RMIT

Dr. Sara Vahaji

Required discipline background of candidate

Project Description

The requirement for heavy-duty vehicles for passenger and commercial transportation has increased drastically with the rapid population growth around the globe. It represents a significant part of global oil and natural gas consumption and is therefore responsible for greenhouse emissions, air pollution, global warming, and oil depletion. Fuel cell electric vehicles (FCEV) are one of the solutions proposed to tackle this global warming and energy crisis. As it is eco-friendly and decreases air pollution, it is expected to be more prevalent in the near future. The FCEV consists of different subsystems like proton exchange membrane fuel cell (PEMFC), battery, and electric machine. Each of these subsystems requires a dedicated thermal management system apart from the cabin HVAC module. Therefore, the knowledge about the interaction of each subsystem with each other in terms of an integrated thermal management system is essential for the entire hybrid powertrain. It will help to enhance the overall performance of the vehicle, its energy efficiency, and sustainability, which will also fulfil the global goal of electrifying all commercial vehicles by 2050 with a new mobility policy. Although a few investigations by considering standalone subsystems like fuel cells, batteries, or electrical controls have been done in the past, an integrated energy management analysis for the entire powertrain, including the cabin, with realistic drive cycles in line with city, highway, and urban driving conditions, are yet to explore. Based on these aforementioned observations, the objectives of the present investigation are formulated as follows, • Develop an integrated energy management system including a fuel cell, battery, electrical machine, and cabin for a heavy-duty vehicle and optimize its performance for different drive cycles. • Explore the energy efficiency and temperature stability of the proposed thermal management strategy on large-scale battery packs for both hot and cold ambient conditions. • Develop a regenerative and dynamic air humidification cycle by utilizing the pure wastewater from the fuel cell stack to optimize the reaction rate and energy output to drive the powertrain. • Expose the optimally designed electrical powertrain under different faulty conditions imitating the on-road accidental conditions like overcharging, overheating, collision, etc., and study the corresponding thermal runaway alongside the multi-state reliability of the entire subsystem.

BITS Supervisor

Santanu Prasad Datta

RMIT Supervisor

Bahman Shabani, PhD

Other Supervisor BITS

Suparna Chakraborty

Other Supervisor RMIT

John Andrews, Professor

Required discipline background of candidate

Project Description

Solar energy harvesting is the process of extracting the power from solar energy (sunlight) and converting it into DC power and using this power to drive electrical devices. As the technology is evolving towards 6G and the number of electric-based devices is increasing every year, solar energy scavenging is bound to become an alternative means for powering the devices by utilizing the solar energy from the surrounding environment. Major applications of this technology are the replacement of batteries, powering sensors in challenging environments, etc. Solar energy scavenging is accomplished using special devices known as Optical rectennas. which consists of the antenna-coupled diode (a rectifying network) operating at optical frequencies. Optical rectennas are devices designed to convert high-frequency electromagnetic waves, particularly in the terahertz range, into direct current (DC) electricity. This technology holds potential for various applications, including solar energy conversion. Optical rectennas can potentially achieve higher efficiency in converting certain wavelengths of electromagnetic radiation into electricity compared to traditional solar panels. An optical rectenna incorporates a submicron antenna and an ultra-high-speed diode. The optical rectenna absorbs electromagnetic radiation and converts it to current. A diode rectifies the AC, providing DC electrical power. Compared to conventional solar cells, which absorb photons and generate electron-hole pairs to provide electrical power, rectennas seem to rely on a classical electromagnetic wave view of light. Optical rectennas may require fewer materials compared to traditional solar panels, which often use semiconductor materials like silicon. This could lead to cost savings and a smaller environmental footprint.

BITS Supervisor

Runa Kumari

RMIT Supervisor

Stefania Castelletto

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

To address today’s energy crisis with a sustainable solution, Zn metal batteries show promise for large-scale energy storage due to their inherent safety, eco-friendliness, and affordability. However, challenges such as hydrogen evolution, Zn corrosion, and dendrite growth hinder their practical use. To address these issues, researchers are tasked with developing functional materials to improve the specific capacity and cyclic stability of Zn-air/ion batteries. Metal–organic frameworks (MOFs) have emerged as promising materials for energy applications thanks to their high surface area, porous structure, and customizability. However, pristine MOFs often suffer from low electronic conductivity and chemical instability, limiting their large-scale use. On the other hand, MXene, with abundant surface terminations and high metallic conductivity, can be a good substrate or filler material for MOFs to improve their stability and conductivity compared to their pristine counterparts. This project aims to ameliorate the electrochemical properties of diverse tailored MOF/MXene nanoarchitectures for developing novel high-performance counter electrodes for rechargeable Zn-air and Zn-ion batteries. The primary objectives of the project are given below; (1) To develop different dimensional, highly porous, and high surface areas containing tailored MOFs and high-conducting MXenes for MOF/MXene nanostructures (2) Nanoarchitectonics to optimize high-performance nanocomposite for improved electrochemical properties (3) Using MOF/MXene nanostructures as electrode materials in Zn-ion/Zn-air batteries (4) Development of prototype coin cell and pouch cell Zn-batteries Different dimensional MOFs with variable metals and ligands will be synthesized. These MOFs will have no free coordination sites to provide the best stability with a wide range of solvents, pH conditions, and thermal and aqueous stability. The synthesized MOF will be directly composited with developed high-conducting MXenes (like V2CTx or Ti3C2Tx, etc.) by an in-situ synthesis process to grow MOF on conducting MXene layers. The electrochemical properties of the nanocomposite electrode will be thoroughly evaluated. The electrochemical oxygen evolution and reduction reactions (the main reactions at the air cathode responsible for the charging and discharging of the Zn-air battery) will be evaluated and optimized. The nanostructured composite electrode will be used to fabricate coin cell and pouch cell Zn-battery.

BITS Supervisor

Dr Chanchal Chakraborty

RMIT Supervisor

Nasir Mahmood

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

Aim: Developing scalable technology for removing dye and heavy metals from tannery industry effluents using biosurfactant-mediated low-cost nano-adsorbents. Methodology: 1) Literature relevant to the proposed research topic will be comprehended, and a detailed methodology for synthesizing biosurfactant-functionalized nano-adsorbents will be developed. 2) Metal oxide nanoparticle synthesis mediated by biosurfactants in varying proportions will be carried out at batch scale under a controlled environment using co-precipitation, hydrothermal, ultrasonic methods, etc. 3) Detailed characterization (XPS, SEM, XRD, Surface area analysis, etc.) to determine synthesized nano-adsorbents' properties and identify the suitable candidate with desired physicochemical characteristics. 4) Experiments on the dye and heavy metal removal from simulated effluent will be performed to measure the efficacy of developed nano-adsorbents. Further, modifications will be made in the synthesis process using advanced methods of experiment design. 5) Potential nano-adsorbents will be used for the treatment of tannery industry wastewater samples in a continuous column and then at a pilot scale design that considers 4E (energy, exergy, economic, and environmental) analysis. 6) Regeneration study will be performed on the synthesized nano-adsorbents using thermal, chemical, and electrochemical methods. 7) Thermodynamic and kinetic models will be developed and verified with the experimental data to better understand the process.

BITS Supervisor

Prof Amit Jain

RMIT Supervisor

Nicky Eshtiaghi and Professor

Other Supervisor BITS

Prof Suresh Gupta

Other Supervisor RMIT

Required discipline background of candidate

Project Description

This project aims to investigate the security implications of generative AI technologies by utilizing system dynamics modelling. Our approach involves conceptualizing the multifaceted interactions between generative AI applications and cybersecurity frameworks to predict and mitigate potential threats dynamically. The project expects to generate new knowledge in the domain of AI security by using the innovative approach of system dynamics. It is inherently interdisciplinary, incorporating insights from cybersecurity, artificial intelligence, and systems theory. By employing system dynamics modelling, we aim to uncover the nonlinear behaviours and feedback loops that characterize the interaction between generative AI systems and their potential security vulnerabilities. Expected outcomes of this project include the development of a robust theoretical model for understanding the security dynamics of generative AI systems, enhanced capacity for anticipating security threats in AI deployment, and the establishment of cross-disciplinary collaborations between AI researchers, cybersecurity experts, and system theorists. These outcomes will provide a foundation for developing more resilient AI systems and cybersecurity policies. This project should provide significant benefits, including improved predictive tools for identifying and mitigating security threats in generative AI, a comprehensive framework for policymakers and technologists to understand and address AI security risks, and the promotion of safer AI development and deployment practices industry wide. By enhancing the understanding of how generative AI systems can be exploited or become insecure through system dynamics, stakeholders can proactively address vulnerabilities, contributing to a safer digital ecosystem.

BITS Supervisor

Amit Dua

RMIT Supervisor

Abebe Diro

Other Supervisor BITS

Other Supervisor RMIT

Required discipline background of candidate

Project Description

This project aims to explore the integration and application of Generative Artificial Intelligence (AI) and Digital Twin technology within the healthcare sector, specifically focusing on their potential to revolutionize medical research, diagnosis, and treatment methodologies. Generative AI, a cutting-edge technology capable of producing images, text, and various media forms from human prompts, holds significant promise in healthcare innovation. It can contribute to personalized medicine, predictive diagnostics, and tailored treatment plans. Concurrently, Digital Twin technology, which involves creating virtual replicas of physical entities, presents an opportunity to advance patient care by developing individualized digital counterparts. These digital twins can simulate patients' health conditions in real time, offering unprecedented insights into disease progression, treatment outcomes, and personalized healthcare interventions. Incorporating these technologies into the Smart City paradigm, this project envisions developing a robust healthcare system that leverages the capabilities of Generative AI and Digital Twin technology. This system will enhance patient care, improve health outcomes, and optimize resource allocation in future smart cities. By harnessing the synergy between these advanced technologies, the project aims to establish a healthcare framework that is innovative and responsive to the dynamic needs of urban populations, setting a new standard for healthcare delivery in the digital age.

BITS Supervisor

Vinay Chamola

RMIT Supervisor