Research News

SRM University-AP formed a cluster with Acharya Nagarjuna University, KL University, Vignan University, and AIIMS Mangalagiri for the DST-SATHI initiative of the Ministry of Science and Technology. Sophisticated Analytical & Technical Help Institute (SATHI) aims to provide professionally managed services with efficiency, accessibility and transparency of the highest order under one roof to service the demands of industry, start-ups and academia. Dr P Siddaiah – Dean, Acharya Nagarjuna University; Dr Mukesh Tripathi, Director-AIIMS Mangalagiri; Dr G Pardha Saradhi Varma – Vice Chancellor, KL University; and Dr P Nagabhushan – Vice Chancellor, Vignan University; attended the meeting held at SRM University-AP campus on Monday.

SRM AP Vice-Chancellor Prof V S Rao invited the Vice-Chancellors of the respective universities to come forward for the formation of a cluster for the ‘SATHI’ programme. Pro-Vice Chancellor Prof D Narayana Rao did an elaborate presentation on DST projects. “The host institute, which is the lead partner in the cluster, will have to provide a functional space of 20,000 square yards, and SRM University-AP is suitable for this in every way”, Prof D Narayana Rao said. “The equipment worth Rs 75 crore to be provided through the SATHI programme will help in carrying out extensive research in all the five universities,” he added. The Vice-Chancellors and Directors of these 5 institutions have agreed to form the cluster and submit the proposal to the Department of Science & Technology (DST), Government of India. Professors of SRM AP and representatives of respective universities also attended the meeting.

The Department of Electronics and Communication Engineering is delighted to announce that Assistant Professor Dr Anirban Ghosh and Research Scholar Mr Naga Srinivasarao Chilamkurthy have published their article titled “Low-Power Wide-Area Networks: A Broad Overview of its different aspects” in IEEE Access, a Q1 Journal, having an Impact Factor of 3.476. The work was published in collaboration with Dr Om Jee Pandey from the Indian Institute of Technology BHU, Dr Cenkeramaddi Linga Reddy from the University of Agder, and Dr Hong-Ning Dai from Hong Kong Baptist University, Hong Kong.

This is a survey article on Low-power Wide-area networks which provides a detailed description of LPWAN technologies in the context of IoT applications. In this survey article, they review and provide an overarching description of LPWAN in terms of design goals, techniques to improve design objectives, and system architecture. They have also evaluated several existing and non-standardized LPWAN technologies and the market opportunities of LPWAN. With the help of this article, the researchers can choose the best LPWAN technology for their specific applications.

The practical implementation of the article can be found in various social and commercial applications such as smart healthcare, intelligent transportation, climate-smart agriculture, rescue operations, logistics, smart cities, industries, utilities, smart buildings, consumer electronics, security, asset tracking, smart waste management systems, cognitive manufacturing, and Machine-to-Machine (M2M) communications. Their future research plans include working on Wireless Sensor Networks, Low-Power Wide-Area Networks, Small-World Networks, and applying machine learning and reinforcement learning techniques in the context of wireless networks for cyber-physical systems and IoT applications.

Abstract of the Research

Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed, and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.

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Yet another moment of pride and honour for SRM University-AP as Dr Uma Maheswar Arepalli, Assistant Professor, Department of Civil Engineering in collaboration with Dr Ali Shirazi, Assistant Professor, Department of Civil Engineering, University of Maine, USA received a research project award from the Maine Department of Transportation, Maine, USA. The project titled “Comprehensive review of flexible pavement design approaches suitable to Maine conditions” received a total project outlay of $26,396 (Rs. 21.12 Lakhs).

This is the first of its kind project in India that receives funding directly from the foreign (United States) entity unlike the typical Department of Science & Technology (DST) International Bilateral Joint Project Schemes. This 8-month-long project will review the various existing flexible pavement design approaches in the USA and will provide recommendations to the Department of Transportation on a suitable design approach conducive to the conditions of Maine.

The outcome of the project will help the Department of Transportation to decide on a suitable pavement design approach that enhances the performance of pavements in Maine. The project engenders an opportunity for two under-graduate students of SRM University-AP to work as paid interns and receive international exposure in their prospective research areas. It also involves the scope for industry translatory research.

A theoretical investigation is highly important to investigate the properties of materials, the origin of selectivity, and the effect of various parameters in designing promising electrode materials for supercapacitor applications. The latest research paper by Mr Samadhan Kapse, PhD Student in the Department of Physics, and Prof Ranjit Thapa, Associate Dean of SEAMS (Sciences), envisions this and developed a novel VS2-BP hybrid electrode material. Their article titled All-solid-state Supercapacitor Based on Advanced 2D Vanadium disulfide/Black Phosphorus Hybrids for Wearable Electronics has been published in the journal ACS Applied Energy Materials with an impact factor of 6.959.

Abstract

Vanadium disulfide-Black Phosphorus (VS2-BP) hybrids were synthesised by a one-pot hydrothermal assisted method to achieve enhanced electrochemical activity for supercapacitor applications. The concentration of BP was optimised to prevent the restacking nature of VS2 and to enrich the active edges for electrolytic ion intercalation. The charge storage kinetics of the best-performing VS2-BP as an active electrode has demonstrated the dominance of the pseudocapacitive nature of the material. Further, by sandwiching with PVA/K2SO4 gel electrolyte, an all-solid-state (ASS) Vanadium disulfide/Black Phosphorus-50 mg (VS2-BP-50) symmetric device was developed on highly conductive carbon paper. The ASS VS2-BP-50 symmetric device displays the highest specific areal capacitance of 203.25 mF/cm2. It exhibits the maximum areal energy density of 28.22 µW h cm-2 at an areal power density of 596.09 mW cm-2, outperforming previous literature. We used density functional theory to understand the origin of high quantum capacitance. We found that the charge accumulation region between VS2 and BP monolayers and the charge transfer is the origin of the improved density of states in the VS2-BP hybrid. Also, we observed the higher mobility of K+ ion and a higher diffusion rate using the Density functional theory (DFT) method.

Explanation of the research

A novel VS2-BP hybrid electrode material was prepared using a simple hydrothermal approach. Due to a synergistic effect, it was discovered that adding BP to metallic VS2 enhances the number of electrochemically active sites, resulting in increased surface activity. It also accelerates reaction kinetics with electrolyte ions by improving the electrical behaviour of active electrode material. As a result, the hybrid technique overcomes the weaknesses of individual components during electrochemical processes, resulting in increased performance that has been limited by individuals. The BP nanosheets behaved as a pore region for electron transport and prevented the VS2 layers from re-stacking. Systematic experiments are conducted by selecting the ideal precursor ratios to generate a high-quality VS2-BP hybrid with enhanced electronic conductivity. Furthermore, in the overall collective charge storage of the VS2-BP-50 hybrid material, the present results demonstrated that capacitive contributions outnumber diffusive contributions. The ASS VS2-BP-50 symmetric supercapacitor device was also designed to have a high areal capacitance of 203.25 mF/cm2 with a maximum areal power density of 596.09 mW cm-2. The extraordinary performance of the ASS VS2-BP-50 symmetric device illustrates its versatility in terms of designing a high-power density ASS supercapacitor for flexible and wearable device applications. The work functions of BP, VS2, and VS2-BP are 0.73 eV, 5.37 eV, and 4.99 eV, respectively, which help in the charge transfer mechanism and increase the density of state at the Fermi level, and subsequently, the quantum capacitance of the heterostructure.

Collaborations

1. Mr Aditya Sharma, Centre for Nano and Material Sciences, Jain Global Campus, Jakkasandra, Ramanagaram, Bangalore – 562112, Karnataka, India

2. Mr Ankur, Centre for Nano and Material Sciences, Jain Global Campus, Jakkasandra, Ramanagaram, Bangalore – 562112, Karnataka, India

3. Mr Sagar Bisoyi, Department of Physics, School of Applied Sciences, KIIT Deemed to be University, Bhubaneswar-751024, Odisha, India.

4. Dr Gopal K. Pradhan, Department of Physics, School of Applied Sciences, KIIT Deemed to be University, Bhubaneswar-751024, Odisha, India.

5. Dr Chandra Sekhar Rout, Centre for Nano and Material Sciences, Jain Global Campus, Jakkasandra, Ramanagaram, Bangalore – 562112, Karnataka, India

Social implications of the research

With the exponential development of portable/flexible electronics and the high demand for renewable energy, conventional energy-storage devices, such as supercapacitors, have attracted attention due to their benefits of fast charge/discharge rates, long cycle life, and high-power density. Similarly, developing novel functional materials with exceptional qualities could shed light on a plethora of challenges, including environmental pollution, energy crisis, etc. Two-dimensional (2D) layered materials, such as metallic 1T MoS2 single layers, SnSe2, MXenes, and black phosphorous (BP), have been intensively studied for supercapacitor applications. These materials benefit from efficient ion intercalation and electrosorption. The two-dimensional (2D) layered transition-metal dichalcogenides (TMDs) have recently piqued the scientific community’s curiosity.

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Publishing papers in Q1 journals is a remarkable accomplishment for research enthusiasts. Assistant Professor Dr Priyanka of the Department of Computer Science and Engineering published her latest research paper in the IEEE Access journal. The paper titled PRMS: Design and Development of Patients’ E-Healthcare Records Management System for Privacy Preservation in Third Party Cloud Platforms has an impact factor of 3.476. Kirtirajsinh Zala, Hiren Kumar Thakkar, Rajendrasinh Jadeja, Priyanka Singh, Ketan Kotecha, and Madhu Shukla co-authored the work with Dr Priyanka.

Abstract

In the digital era, personal data storage on public platforms is a significant cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patients’ health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient’s E Healthcare Records Management System (PRMS) that focuses on latency and throughput. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively.

Assistant Professor Dr Ravi Kant Kumar of the Department of Computer Science and Engineering aspires to investigate and design the most effective face recognition system appropriate enough to recognise faces in various unconstraint environments. Recently, his patent application titled A Face Recognition System (Application number: 202241030009) got published. He collaborated with his BTech student Thota Venkata Saai Praneeth, for this project. Facial recognition systems have abundant applications in Face Identification, Automobile Security, Access Control, Immigration, Education, Retail, Healthcare, Image database investigations, Surveillance, and many more.

Abstract

A facial recognition system is used for matching a human face captured in an image or a video frame with a database of faces. The system generally authenticates users by measuring facial features from the stored images. The present disclosure relates to face recognition systems. The envisaged system comprises a repository comprising a set of facial images of a plurality of subjects, a set of threshold values, and a plurality of rules. The system further comprises an image capturing unit for capturing a plurality of images of a subject’s face. A segmentation unit divides each image into a plurality of frames. The system further comprises a classifier that receives the frames and classifies frames containing faces from frames containing non-faces from the other frames. The facial features of each frame are extracted by an extractor. An analyser receives the extracted facial features and computes eigenvector values of the facial features of the frame. A comparator compares the computed eigenvector values with threshold values using the comparison rules to identify the subject.

Explanation of the research

Conventional facial recognition systems are configured to identify the contours of a person’s image captured by a camera or in real-time and compare it with the stored image to identify the person. However, external factors such as position, light conditions, camera calibration, and unconstraint conditions, among others, affect the identification of the face of the subject. Hence, comparing the faces with the help of the contours is not reliable. The present disclosure envisages a face recognition system. The system comprises a repository, an image capturing unit, a segmentation unit, a classifier, an extractor, an analyser, and a comparator. The repository consists of a set of facial images of a plurality of subjects, wherein the images define the subject’s facial features in different facial poses. Each image is tagged with an identity, a threshold integral value, and threshold eigenvector values corresponding to the facial features. The repository further comprises a first set of deep learning rules for classifying frames of a captured image into frames containing faces and frames containing non-faces, a set of extraction rules for extracting facial features from the frames 20 containing faces, the second set of deep learning rules for extracting eigenvector values of facial features of the frames containing faces. The repository additionally comprises a set of comparison rules for comparing eigenvector values of the captured facial features with the stored threshold eigenvector values to identify a subject’s face in the captured image.

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The Department of Civil Engineering is delighted to announce that Dr Siddhant Dash has published a paper titled “Systematic bibliographic research on eutrophication-based ecological modelling of aquatic ecosystems through the lens of science mapping” in the journal “Ecological Modelling” having an Impact Factor of 3.512. This research was conducted in collaboration with Prof Ajay S Kalamdhad from IIT Guwahati.

This research is a systematic approach on reviewing the published literature on eutrophic-ecological models developed worldwide and the methods associated with them. This provides critical insights into the status of the research domain, thereby providing a direction for the practising and future researchers to undertake a research career in this domain. It offers a more comprehensive and holistic approach to the critical review of the published literature, providing a deeper understanding to the researchers regarding the existing practices of developing eutrophication-based ecological models and the prospects lying ahead. His future research plans include understanding carbon and nutrient dynamics within an aquatic ecosystem.

Abstract of the Research

When water bodies receive surplus nutrients, especially nitrates and phosphates, these nutrients stimulate excessive plant growth (eutrophication), including harmful algal blooms, leading to oxygen depletion, decreased biodiversity, changes in species composition and dominance, and degradation of water quality. Although there are natural causes, much of the eutrophication today results from inadequately treated wastewater and agricultural runoffs. Population pressure, urbanization and industrialization contribute a considerable amount of waste, which alters the physio-chemical quality of water that eventually upsets the biotic components of the aquatic system. It is important to note that though pollution has been a significant factor in degrading the quality of aquatic ecosystems, the lack of management and global awareness regarding the protection and conservation of water bodies worldwide cannot be neglected. Hence, there lies an inherent sense of responsibility to restore the aquatic ecosystems to their natural state. Numerous techniques/treatment options are available for varying conditions, such as climatic factors, socio-economic factors, and so on. However, before ascertaining a treatment alternative to curb eutrophication levels, understanding the dynamics of any independent aquatic ecosystem is of prime importance. This necessitates a reliable model, which can provide information regarding the physical processes and dynamic occurrences in the eutrophic water bodies. Ecological modelling refers to the formation of dynamic and complex relationships between the organisms found in the ecosystem and the surrounding. It attempts to unravel the effects of certain relationships in the ecosystem that are not so apparent at first glance. The present study provides a scientific investigation of a detailed review of the published works in the domain of eutrophication-based ecological modelling till the year 2020. The first step was the scientometric studies, which were followed by a qualitative assessment wherein the current trends in research were discussed. This was followed by identifying the critical gaps in research to provide future direction.

Fig. 1. Ecological modelling process

Fig. 2. Description of the three-step literature review process employed in this research

The different dimensions of sustainable waste management have always been explored by researchers all over the world. Assistant Professor Dr Pankaj Pathak, Department of Environmental Science, has been actively involved in this research area for a while. Her paper titled Dual role of grass clippings as buffering agent and biomass during anaerobic co digestion with food waste is published in the journal Clean Technologies and Environmental Policy with an impact factor 4.7. Dr Pankaj Pathak co-authored the paper with Debkumar Chakraborty, Sankar Ganesh Palani, M M Ghangrekar, and N Anand.

Abstract

There is a dire need to replace the chemical buffers that regulate the redox environment in single-stage anaerobic digestion of food waste. Hence, the applicability of grass clippings as an eco-friendly buffering agent and biomass during the anaerobic co-digestion of food waste was explored. A focus was primarily given on the effects of grass clippings on the redox environment and acidogenesis. Concomitantly the production of volatile fatty acids, hydrogen, and methane in mesophilic conditions was monitored. Organic load and substrate-to-inoculum ratio were kept constant in all the experiments, and no chemical buffer was used. The results revealed that the redox environment was regulated with 10% grass clippings by inhibiting rapid pH drop in the digester. The addition of 2, 4, and 6% grass clippings promoted acidogenesis with increased production of acetic and butyric acids, whereas 8 and 10% grass clippings promoted solventogenesis with ethyl alcohol production. Hydrogen generation from the experiments with grass clippings was in the range of 27–30% of the total biogas, which was marginally higher than the control (25%). Methane concentration was negligible in the biogas generated from all experiments. The acidification rate, VFA production/consumption rate, specific hydrogen yield, hydrogen conversion efficiency, and volatile solids removal were maximum and minimum in the reactors with 6 and 10% grass clippings, respectively. From the above results, it can be concluded that adding grass clippings to food waste would regulate the sudden pH changes and enhance the production of value-added biochemicals, making the process cost-effective.

The upcoming international conference, India & Southeast Asia in a Changing World: Convergences and Divergences, will examine India’s Act East policy, investigating its prospects and challenges in the Southeast Asian region. Assistant Professor Dr Vineeth Thomas from the Department of Liberal Arts has been selected for presenting a paper at this Conference organised by Christ University, Delhi NCR. He will present the paper titled The centrality of ASEAN in India’s Act East policy at the conference.

The international conference will be held In-Person from 14 to 15 October 2022 at the CHRIST (Deemed to be University) Delhi-NCR campus. The Hanns Seidel Foundation funds this conference. The major focus area of the conference is India’s foreign policy towards ASEAN countries like Myanmar, Thailand, Malaysia, Vietnam, Indonesia, and Singapore

Abstract

Against the backdrop of multi-dimensional developments and interventions in the Indo-Pacific region, India’s overseas policy and national security apprehensions have undergone substantial changes in the past few decades. Starting with the Look East Policy in the 1990s, India advanced its policy in 2014 as Act East Policy. The potential benefits of bilateral and multilateral relations in the Indo-pacific region made India take up its partnership with the Association of Southeast Asian Nations (ASEAN) seriously, which provided an opening and strip for India to explore Southeast Asia. This paper evaluates the significance of keeping ASEAN at the heart of India’s Act East Policy by illustrating the prospects and challenges in India-ASEAN relations.

The Department of Physics is glad to announce that Assistant Professor Dr Soumyajyoti Biswas and his PhD scholar Ms Diksha have published their article “Prediction of imminent failure using supervised learning in a fiber bundle model” in the Q1 journal, Physical Review E. Prediction of breakdown in disordered solids under external loading is a question of paramount importance to the stability of buildings and bridges to earthquakes. The researchers used numerical simulations of a model of disordered solids and recorded the time series of the avalanche sizes and energy bursts. They propose that a systematic analysis of these time series using supervised machine learning can predict the time of failure. Interestingly, the most important feature for such predictions turns out to be the measures of how unequal the avalanche sizes are.

Applying external stress on disordered materials beyond their mechanical limits results in their fracture. Hence it is important to know the limit or how the material behaves as it approaches the limit and the factors that influence it. The failure properties of materials are very distinct from other properties such as elasticity, in the sense that their predictions are not always straightforward. Predicting the failure in driven disordered systems is a long-standing problem in physics, engineering, and earth sciences. So, for understanding the fracture process and predicting the failure properties of the materials, a mathematical model (fibre bundle model) has been used.

They introduced the disorder to the system and generated the time series of avalanche size and energy bursts. Some inequality indices i.e., Hirsch index (h), Gini index (g) and recently introduced Kolkata index (k) were measured for the response statistics of the driven systems. These social inequalities are usually represented by the Lorenz function L(p), where p fraction of the population (events) possesses L(p) fraction of total wealth (avalanche mass) when the population (avalanche events) are arranged in the ascending order of their wealth (size). Based on these time series, the machine learning algorithm can predict the prior failure time of the system. So, they have used a supervised machine learning algorithm (with the above-mentioned indices as some of the features) to predict the failure time of the model. They observed that these inequality measures play an important role in making predictions.

Prediction of imminent fracture has its implications in a wide range of disciplines, including stability of mechanical structures (buildings, aircraft, bridges etc.), extraction of oil (fracking) to the largest scale of mechanical failure i.e., earthquakes. Here a supervised machine learning approach is used to make such predictions in numerical models. However, with the important features identified here for such predictions, such research can carry out similar predictions for experimental data. A follow-up of this work is being carried out by Ms Diksha with a group in Spain regarding the experimental verification. Their future research plans include applications of the methods developed here to be applied to real-life physical structures for their stability analysis and predictions of impending catastrophes.

Illustration 1: A schematic diagram of the Lorenz function L(p)is shown, where L(p) denotes the cumulative fraction of the avalanche mass contained in the smallest p fraction of avalanches. If all avalanches were equal in size, this would be a diagonal straight line, called the equality line. The area between the equality line and the Lorenz curve (shaded area),therefore, is a measure of the inequality in the avalanche sizes. Two quantitative measures of such inequality are extracted from here, the ratio of the shaded area and that under the equality line (Gini index, g) and the crossing point of the opposite diagonal – from (0,1) to (1,0), shown in dashed line,and the Lorenz curve, giving the Kolkata index, k. 1 − k fraction of avalanches contain k fraction of the cumulative avalanche mass.