Research News

  • Navigating the Future: Advancements in NLP for Enhanced Healthcare June 19, 2024

    In a significant contribution to the intersection of technology and healthcare, Dr V M Manikandan, Assistant Professor in the Department of Computer Science and Engineering along with a team of dedicated undergraduate students, has co-authored a pivotal book chapter. The chapter, titled “Advancements and Challenges of Using Natural Language Processing in the Healthcare Sector,” has been published in the insightful book “Digital Transformation in Healthcare 5.0.”

    The collaborative effort by Dr Manikandan, Mr Shasank Kamineni, Ms Meghana Tummala, Ms Sai Yasheswini Kandimalla, and Mr Tejodbhav Koduru delves into the innovative applications and potential hurdles of implementing natural language processing (NLP) technologies in healthcare. Their work highlights the transformative power of NLP in analysing vast amounts of unstructured clinical data, thereby enhancing patient care and medical research.
    This academic achievement showcases the expertise and commitment of the faculty and students and underscores the institution’s role in driving forward the digital revolution in healthcare. The chapter is expected to serve as a valuable resource for researchers, practitioners, and policymakers interested in developing smarter, more efficient healthcare systems.

    Introduction of the Book Chapter

    “Digital Transformation in Healthcare 5.0: IoT, AI, and Digital Twin” delves into how advanced technologies like IoT, AI, and digital twins are reshaping healthcare. It provides a comprehensive look at the integration challenges and technological advancements aiming to modernise medical practices. The chapter “Advancements and Challenges of Using Natural Language Processing in the Healthcare Sector” specifically explores how NLP processes vast data in healthcare to transform it into actionable insights, enhancing efficiency and patient care while highlighting the implementation challenges of these technologies. This book is crucial for healthcare and technology professionals interested in the future of digitally enhanced healthcare.

    Significance of the Book Chapter

    The chapter “Advancements and Challenges of Using Natural Language Processing in the Healthcare Sector” is significant because it encapsulates my interest and expertise in harnessing NLP to enhance healthcare operations. It showcases the potential of technology in transforming healthcare data into valuable, actionable insights, directly aligning with my focus on improving patient outcomes through technological innovation.

    Link to the Book Chapter

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  • Dr Maheshwar Publishes Significant Research in Materials Letters June 19, 2024

    In a remarkable achievement, Dr Maheshwar Dwivedy, Associate Professor in the Department of Mechanical Engineering, has made a significant contribution to the field of materials science with his latest publication. The paper, entitled “Understanding heterogeneity and anisotropy of duplex stainless steel elastic/plastic nature through property mapping technique,” has been published in the esteemed journal Materials Letters, which boasts an impact factor of 3.0.

    Dr Dwivedy’s research provides insightful analysis of the complex behaviours of duplex stainless steel, a material known for its high strength and corrosion resistance. By employing a property mapping technique, the study reveals the intrinsic heterogeneity and anisotropy of the material’s elastic and plastic properties. This groundbreaking work not only advances the understanding of duplex stainless steels but also opens up new possibilities for their application in various industries.
    The publication of this paper in a journal with a significant impact factor is a testament to the quality and importance of the research conducted by Dr Dwivedy and his team. It underscores SRM University – AP’s commitment to fostering cutting-edge research and innovation.

    Abstract
    Accelerated property mapping, an advanced indentation technique, was used to describe the nanomechanical behaviour of duplex stainless steel (DSS) surfaces prior to and post-heat treatments. Heterogeneity in deformation responses and relative elastic and/or plastic nature of DSS was assessed on longitudinal and transverse directions through load-displacement curves, property maps, histograms of hardness (H), modulus (E) and indentation works. Empirical ratios such as H/E, (H/E)1/2, H3/E2 and plasticity index were employed to understand the anisotropy across the directions. It is crucial that for structural designing, heterogeneity and anisotropy of mechanical behaviour need to be accounted for improved property–optimisation.

    Explanation of the Research in Layperson’s Terms

    Duplex stainless steel (DSS), a unique category of steel, contains almost equal amounts of ferrite and austenite phases within its microstructure. These are employed in applications like boilers, pressure vessels, heat exchangers, etc., as they offer superior strength, ductility, toughness, and corrosion resistance properties compared to other steels. Mechanical characteristics of DSS are significantly influenced by manufacturing protocols including heat treatments. It is believed that anisotropy and heterogeneity in mechanical behaviour can be driven by microstructures post-material processing.

    A comprehensive understanding of DSS material behaviour at the macroscopic scale is not feasible without knowledge of its features and their properties locally. Although the mechanical properties of DSS have been widely explored from a macroscopic perspective as well as innovative nano-scale property mapping techniques, the number of studies addressing anisotropy seen through small-scale characterization is rather restricted. In general, the preliminary assessment of the mechanical behaviour commonly done through hardness (H) and modulus (E) properties. For estimating the elasticity and/or plasticity of material or any surface, different empirical ratios were adopted namely H/E, H3/E2 and (H/E)1/2.

    Practical Implementation or the Social Implications Associated
    • This study demonstrates the notable differences in mechanical properties in longitudinal and transverse directions along with heterogeneity before and after heat treatments.
    • It is felt crucial that for structural designing, heterogeneity and anisotropy of mechanical behaviour need to be accounted for improved property-optimisation.

    Link to the article

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  • Significant Advancement in Analytical Detection of NFZ by the Department of Chemistry and RARE Lab June 13, 2024

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    The Department of Chemistry and RARE Lab are excited to announce a groundbreaking advancement in the field of analytical detection. Researchers Dr Rajapandiyan Panneerselvan, Asst. Professor and Ph.D scholars, Ms Arunima Jinachandran and Ms Jayasree Kumar have developed a novel method for detecting nitrofurazone (NFZ) using three-dimensional silver nanopopcorns (Ag NPCs) on a flexible polycarbonate membrane (PCM) in their paper “Silver nanopopcorns decorated on flexible membrane for SERS detection of nitrofurazone” published in Microchimica Acta. This innovative technique leverages the power of surface-enhanced Raman spectroscopy (SERS) to provide a highly sensitive and practical solution for detecting NFZ on various surfaces, including fish.

    Nitrofurazone (NFZ) is an antibiotic commonly used in veterinary medicine that poses significant health risks if residues enter the food chain. Despite regulatory bans, its illegal use continues, necessitating highly sensitive detection methods. While effective, traditional methods such as high-performance liquid chromatography and mass spectrometry are often costly and labor-intensive. The new SERS-based method offers a more efficient and straightforward alternative.

    Abstract

    The synthesis of three-dimensional silver nanopopcorns (Ag NPCs) onto a flexible polycarbonate membrane (PCM) for the detection of nitrofurazone (NFZ) on fish surfaces by surface-enhanced Raman spectroscopy (SERS) is presented. The proposed flexible Ag-NPCs/PCM SERS substrate exhibits significant Raman signal intensity enhancement with a measured enhancement factor of 2.36 × 10^6. This enhancement is primarily attributed to the hotspots created on Ag NPCs, which include numerous nanoscale protrusions and internal crevices distributed across the surface. The detection of NFZ using this flexible SERS substrate demonstrates a low limit of detection (LOD) of 3.7 × 10^−9 M and uniform, reproducible Raman signal intensities with a relative standard deviation below 8.34%. The substrate also exhibits excellent stability, retaining 70% of its efficacy even after 10 days of storage. Notably, the practical detection of NFZ in tap water, honey water, and fish surfaces achieves LOD values of 1.35 × 10^−8 M, 5.76 × 10^−7 M, and 3.61 × 10^−8 M, respectively, highlighting its effectiveness across different sample types. The developed Ag-NPCs/PCM SERS substrate presents promising potential for the sensitive SERS detection of toxic substances in real-world samples.

    Methodology

    The synthesis involves creating silver nanopopcorns on a flexible polycarbonate membrane using a simple chemical method. The resulting Ag NPCs exhibit high surface roughness with numerous nanoscale features that enhance the Raman signal. This flexible substrate can easily collect samples from irregular surfaces without requiring extensive preparation.

    This SERS substrate can detect NFZ in various real-world samples, including:

    • Tap water
    • Honey water
    • Fish surfaces

    The method’s sensitivity and ease of use make it a promising tool for ensuring food safety and monitoring environmental contaminants.

    The Department believes this development will significantly impact public health by providing a reliable and accessible method for detecting harmful substances in the food chain.

    Read more

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  • Innovative System for Detection and Classification of Manufacturing Defects in PCB June 13, 2024

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    Dr Ramesh Vaddi, Associate Professor & Head of the Department of Electronics and Communication Engineering, along with his PhD Scholar Mr A Vinod Kumar has developed a new system for real-time and accurate detection and classification of manufacturing defects in Printed Circuit Boards (PCBs). This groundbreaking invention has been filed and published with Application Number: 202441021739 in the Patent Office Journal.

    Abstract

    This study presents a new system for real-time detection and classification of defects in Printed Circuit Boards (PCBs), which are critical in electronic products and systems. It employs an efficient model with pre-trained weights to detect defects for enhanced quality control. The model is initially trained and fine-tuned on a computer and then deployed on a compact computing board. For real-time imaging, a high-definition USB camera is connected to the system, allowing direct defect identification without the need for external devices. The output is shown on a monitor, with the PCB image featuring clearly labelled boxes to indicate the type and location of defects. This method offers a streamlined approach to defect classification, helping to improve the quality control process in electronics manufacturing.

    Explanation of the Research in Layperson’s Terms

    This research focuses on finding defects in PCBs, which are essential for most electronic devices like computers and phones. The system uses a powerful computer model to quickly identify any defects in real time. The model is trained on a regular computer to recognise normal PCBs and various defects. Once ready, it is transferred to a small, efficient computer board. A camera captures images of the PCBs, and the system analyses these images to identify defects. The results are displayed on a screen, clearly marking where the defects are and what types they are. This helps companies quickly and accurately detect defects in their electronics manufacturing process, saving time, reducing waste, and improving product quality

    Practical Implementation/Social Implications of the Research

    The practical implementation of this research involves deploying a system for real-time detection and classification of defects in PCBs, essential components in nearly all electronic devices. Using advanced deep learning techniques, the system can quickly identify manufacturing defects early in the production process. This leads to significant improvements in quality control, reduced waste, and lower production costs. By improving quality control in electronics manufacturing, the system helps reduce electronic waste, a significant environmental concern. Early detection of defects also decreases the chances of faulty electronic products reaching consumers, enhancing safety and reducing the need for product recalls. The system’s efficiency and accuracy could lead to more reliable electronics, fostering greater consumer trust in electronic products. This, in turn, encourages companies to invest in higher-quality manufacturing processes, ultimately leading to a more sustainable and responsible electronics industry.

    Collaborations

    To develop this system, the research team first trained a computer model to recognise defects in PCBs. The training involved feeding the model a large dataset of PCB images, some with defects and some without. The model learned to identify common defects by analysing these examples. Once trained, the model was implemented in a real-time setting and integrated with equipment to inspect PCBs during production. The system used a camera to capture images of each PCB and applied the trained model to analyse these images for defects. Running in real-time, the system could immediately detect issues and alert the manufacturing team, allowing them to correct problems on the spot. This approach improved product quality, reduced the chances of defective electronics reaching consumers, sped up the quality control process, and reduced waste, making the manufacturing process more efficient.

    Future Research Plans

    The research team has outlined several future plans to enhance and expand their defect detection system for PCBs:

    • Model Optimization: Refining the machine learning model to improve accuracy and speed, experimenting with different architectures and training techniques to boost performance.
    • Expanded Defect Library: Gathering a more extensive dataset of PCB defects to enable the model to identify a wider range of issues, making the system more versatile for various manufacturing environments.
    • Real-World Testing: Testing the system in a broader range of manufacturing settings to ensure robustness and adaptability, understanding performance in diverse scenarios, and fine-tuning for optimal results.
    • Integration with Manufacturing Systems: Aiming to integrate the system with other manufacturing processes and technologies for seamless communication between defect detection and other quality control systems, enhancing overall workflow.
    • Automation and Robotics: Exploring the use of automation and robotics to streamline the defect detection process, potentially leading to a more automated manufacturing line with reduced human intervention and errors.
    • Collaboration and Partnerships: Planning to collaborate with more industry partners and academic institutions to accelerate research and development, gaining valuable insights and resources to advance the system.
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  • India’s Tryst with Globalization: A Study on Growth and Poverty Nexus June 12, 2024

    The poor in India has continuous future benefits from globalization through improved healthcare – A new research from SRM University-AP economists
    A new research from SRM University-AP economistsIn a groundbreaking study, Dr. Manzoor H. Malik, an Assistant Professor in the Department of Economics, and Sodiq O. Bisiriyu, a Research Scholar, have developed a comprehensive framework known as the globalization-growth-poverty (GPP) triangular nexus.

    This innovative model examines the interplay between globalisation waves, economic growth trends, and their collective impact on poverty reduction in India, aligning with the nation’s commitment to achieving Goal 1 of the Sustainable Development Goals.

    The researchers used three different indicators of poverty, encompassing the monetary and non-monetary constructs. The research controls for the impact of government development spending and domestic investment in the GPP nexus. The result of the research validates the significant globalization-led growth hypothesis in the short run and establishes the existence of the GPP triangular nexus in India. Additionally, the researchers found positive association of globalization and growth with monetary and non-monetary poverty measures in the short-run while the impact on monetary measure in the long-run is ambiguous.

    Finally, the results offer significant insight into the impact of government development spending and domestic investment on poverty. The results prove that government development expenditure in India is detrimental to non-monetary poverty while domestic investment have insignificant impact on all poverty constructs.

    From policy perspective, the researcher said, “the short-run validation of globalization-led growth hypothesis for India is challenging and calls for government broad-based framework to tackle impediments of long-term globalization-led growth and monetary poverty benefits”. They emphasized that only sustained long-term growth can accelerate the vision of India as stated in “Viksit Bharat 2047” agenda. Published in the esteemed ‘Quality & Quantity’ journal by Springer, with an impact factor of 2.87, this study provides valuable insights for academics, practitioners, and policymakers. It sheds light on the complex dynamics shaping contemporary socio-economic environments and offers evidence-based analysis supported by an extensive review of relevant literature.

    Link to the Article

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  • Professor and Scholar Duo filed Patent for Employee Productivity Enhancement System June 10, 2024

    In a significant development, Prof. Kamesh AVS, Professor at Paari School of Business, along with his PhD Scholar, Ms. Ashrafunnisa Mohammed, has successfully published and filed a groundbreaking patent titled “SYSTEM AND METHOD TO DETECT BOTTLENECKS AND ENHANCE EMPLOYEE PRODUCTIVITY” with the Application Number: 202441014093 A in the Patent Office Journal.

    This pioneering system aims to revolutionise the way organisations detect and alleviate bottlenecks within their operations, consequently boosting overall employee productivity. The patent’s unique approach and methodology signify a remarkable stride towards fostering efficiency and performance in the workplace. Prof. Kamesh AVS and Ms. Ashrafunnisa Mohammed’s collaborative effort underscores their commitment to innovation and research excellence. Their accomplishment not only reflects their expertise in the field but also highlights the spirit of ingenuity and problem-solving that drives academia at Paari School of Business.

    The SRM AP community extends its heartfelt congratulations to Prof. Kamesh AVS and Ms Ashrafunnisa Mohammed on this remarkable achievement and eagerly anticipates the positive impact their invention will have on the realm of employee productivity and operational efficiency.

    Abstract of the Research
    SYSTEM AND METHOD TO DETECT BOTTLENECKS AND ENHANCE EMPLOYEE PRODUCTIVITY
    The present disclosure relates to a system for detecting bottlenecks and enhancing employee productivity. The system broadly includes an input module (106), a pre-processing module (108), a segmentation module (110), a feature selection module (112), a feature extraction module (114), a data training module (116), and a classification module (118). The system also consists of a data repository (102), a processor (104), and an evaluation module (120). The present disclosure aims to examine counterproductive work behaviors of employees through the effort-reward imbalance model by considering dark triad traits as moderators. The dark triad traits can indulge the employees more in unethical practices in organizations and one among those traits is Narcissism which negatively affects counterproductive work behaviors. Organizations can predict the Dark Triad traits in individuals, and they can avoid unethical practices in organizations and counterproductive work behaviors.

    Explanation of the Research in Layperson’s Terms:

    The present process aims to examine counterproductive work behaviors through the effort-reward imbalance model by taking into consideration Dark triad traits as moderators. The Dark Triad traits can indulge the employees more in unethical practices in organizations and one among those traits is Narcissism which negatively affects counterproductive work behaviors. Organizations can predict the Dark Triad traits in individuals, and they can avoid unethical practices in organizations and can avoid counterproductive work behaviors. There is a notable knowledge gap concerning the moderating role of Dark Triad traits in the relationship between efforts and rewards imbalance and counterproductive work behaviors, even though there is existing research on both the association between Dark Triad traits and counterproductive work behaviors and the relationship between efforts and rewards imbalance and counterproductive work behaviors.

    Practical Implementation or Social Implications Associated with the Research:

    • The Invention aims at identifying and eliminating the consequences of the Dark Triad Traits while performing Human Resources Practices like Recruitment, Training and Development, Performance Analysis, and Compensation and Reward Management system.
    • The Invention either weakens or eliminates the Counter Productive work behaviors as a potential consequence of springing up of Dark Triad traits during the execution of various Human Resource practices in the organizations and this leads to achieving the estimated Organizational Productivity as per the strategy.
    • Organizations can identify the impact of the Dark triad traits on employees and take steps to reduce the stress that leads to unproductive work behaviors.
    • Organizations can eliminate the job stressors which are seen as the main sources of
    • Counterproductive work behaviors can indicate the Dark triad traits’ influence on individuals.
    • Management should investigate the causes of rudeness in the workplace to improve morale and productivity.
    • During the hiring process, management should use a battery of psychological tests to gain insight into candidates’ personalities.
    • Management can formulate standard policies to eliminate malicious activities and avoid effort-reward imbalances and counterproductive work behaviors.
    • Understanding the causes of counterproductive workplace behavior is vital because it has become a widespread and expensive problem for businesses worldwide.

    Future Research Plans
    To Incorporate AI and ML tools to identify the Dark Triad traits, thereby enhancing the employee evaluation process and mitigating counterproductive work behaviors within organisations.

    Link to the article

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  • Innovative Insights: Rupesh Kumar’s Book Unveils Advances in Wireless Technologies June 10, 2024

    In a remarkable stride for the field of wireless communication, Prof. Rupesh Kumar from the Department of Electronics and Communication Engineering has authored a pivotal book that promises to redefine our understanding of radar and RF systems. The book, entitled “Radar and RF Front End System Designs for Wireless Systems,” is the latest gem in the prestigious “Advances in Wireless Technologies and Telecommunication (AWTT)” series.

    With his profound expertise, Prof. Kumar navigates through the complexities of designing state-of-the-art front-end systems, offering readers a treasure trove of knowledge that bridges theory and practical application. This book is set to become an essential read for aspiring engineers and seasoned professionals alike, enriching the academic and industry landscape with its innovative approach.
    Join us in celebrating Prof. Kumar’s exceptional contribution to the world of electronics and communication engineering. Dive into the depths of this masterful work and emerge with insights that could shape the future of wireless systems.

    About the Book:
    Radar and RF Front End System Designs for Wireless Systems delves into the intricate world of wireless technologies, particularly focusing on radar and RF front-end systems. The advent of wireless communication has ushered in a new era of connectivity, revolutionising various sectors including healthcare, smart IoT systems, and sensing applications. In this context, the role of RF front-end systems, with their reconfigurable capabilities, has become increasingly vital. The impetus behind this book stems from the remarkable surge in innovation witnessed in RF front-end systems. Researchers and practitioners alike have contributed a plethora of new configurations and design architectures, paving the way for unprecedented advancements in wireless systems. Our aim with this publication is to provide a platform for researchers to explore both theoretical insights and practical applications, thereby facilitating the dissemination of the latest trends and developments in the field. The contents of this book cover a wide spectrum of topics, ranging from RF frontend antenna systems to the impact of artificial intelligence and machine learning in system design. With contributions from experts in academia and industry, readers can expect a comprehensive exploration of radar and antenna system design, modeling, and measurement techniques. We envision this book serving as a valuable resource for students, researchers, scientists, and industry professionals seeking to deepen their understanding of RF front-end antenna and radar system designs. Whether it’s exploring reconfigurable antenna systems for 5G/6G networks, or delving into radar modelling and signal processing techniques, this book offers insights that are both timely and relevant.

    Co-author of the Book:

    Shilpa Mehta, a co-author of the book, holds a PhD from Auckland University of Technology, New Zealand. Presently, she serves as a Teaching Assistant at AUT, Auckland. Shilpa received the Summer Doctoral Research Scholarship for her PhD work. Her research spans across projects such as Radio Frequency Integrated Circuits, RF front ends, Optimization, Internet of Things, Wireless Communication, Artificial Intelligence, Healthcare, Radars, Software-defined Radios, and Smart Cities.

    For the Book Chapter Publication, Click the Link

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  • Exploring the Exciting Potential of 6G Networking June 7, 2024

    Patent Published by Dr Manjula

    The Department of Computer Science and Engineering is proud to announce the acceptance of the book chapter titled, Dielectric Characterization of Ovine Heart Tissues at Terahertz Frequencies via Machine Learning: A Use Case for in-vivo Wireless Nano-Communication in the book, “Edge-Enabled 6G Networking: Foundations, Technologies, and Applications.” The book chapter by Dr Manjula R and her students, Ms NSK Sarayu, Ms N Sai Sruthi, Ms D Samaya, and Mr K Tarun Teja from the department caters to UG/PG and PhD students, educational institutions, and medical healthcare sectors. Dr Manjula’s research doesn’t just underscore the significance of understanding the dielectric properties of heart tissues but also highlights the transformative potential of machine learning in predicting, diagnosing and offering therapeutic interventions equipped with real-time monitoring capabilities. The research also lays the groundwork for future advancements in this field, facilitating the development of more efficient and reliable in-vivo sensing technologies.

    Abstract of the Book Chapter:

    A new generation of sensing, processing, and communicating devices at the size of a few cubic micrometers are made possible by nanotechnology. Such tiny devices will transform healthcare applications and open up new possibilities for in-body settings. A thorough understanding of the in-vivo channel characteristics is essential to achieve efficient communication between the nanonodes floating in the circulatory system (here, it is the heart) and the gateway devices fixed in the skin. This entails one to have accurate knowledge on the dielectric properties (permittivity and conductivity) of cardiac tissues in terahertz band (0.1 to 10 THz). This research examines the strength of the machine learning models in accurate calculation of the dielectric properties of the cardiac tissues. Initially, we generate the data using 3-pole Debye Model and then use machine learning models (Linear Regression, Polynomial Regression, Gradient Boosting, and KNN), on this data, to estimate the dielectric properties. We compare the values predicted by machine learning models with those given by the analytical model. Our investigation shows that the Gradient Boosting method has better prediction performance. Further, we have also validated these results using Origin software employing curve fitting technique. In addition, the research also contributes to the study of data expansion by predicting unknown data based on available experimental data, emphasizing the broader applicability of machine learning in biomedical research. The study’s conclusions enhance areas like non-invasive sensing in the context of 6G, which may improve data and monitoring in a networked healthcare environment.

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  • Examining Karnataka’s Mandate for Signboards: Paper by Dr Vineeth Thomas May 30, 2024

    In a  research paper published in Economic and Political Weekly, Dr Vineeth Thomas, an Assistant Professor at the Department of Liberal Arts, delves into the socio-political and economic implications of the “Karnataka’s Mandate for Kannada on Signboards” amendment. The article highlights the social and cultural significance of the amendment and raises awareness about the potential economic challenges it may pose. This insightful paper not only makes for an engaging read but also serves as a valuable resource for policymakers and politicians, bringing attention to the practical and social implications of the amendment.

    Abstract

    The Kannada Language Comprehensive Development (Amendment) Bill, 2024, ratified by the Karnataka Legislative Assembly and Legislative Council, stands as a legislative landmark with profound implications. This commentary critically analyses the socio-economic-political implications, unravelling the intricate web of influences that the Kannada Language Comprehensive Development Bill introduces within the diverse and dynamic landscape of Karnataka.

    Citation
    Roshmi Antony, Vineeth Thomas, Lulubala Nayak (2024)-, Karnataka’s Mandate for Kannada on Signboards, Economic and Political Weekly, ISSN (Online) – 2349-8846 (SCOPUS /ABDC Indexed)

    Link to the article

     

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  • MOU Signed with IGCAR for Cutting-edge Biomedical Research May 28, 2024

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    This is a remarkable opportunity for our students to enhance their fields of study, gain academic insights from expert scientists and participate in cutting-edge research projects at IGCAR. This will ensure that we at SRM AP nurture students with a great scientific temperament,” stated Prof. Manoj K Arora, Vice Chancellor of SRM University-AP on signing the MOU with IGCAR.

    SRM University-AP has signed a Memorandum of Understanding (MOU) with the Indira Gandhi Centre for Atomic Research (IGCAR) at Kalpakkam, Tamil Nadu, to collaborate on academic and research projects in Biomedical Research, Disaster Management, and other domains. The MOU was signed by Dr B Venkatraman, Director-IGCAR and Prof. Manoj K Arora, Vice Chancellor, SRM University-AP in the presence of Dr Vidya Sundarrajan, Head PHRMD & QAD, IGCAR Kalpakkam, Mrs M Menaka, Head RAMS, RESD, SQRMG, IGCAR, Kalpakkam, Prof. Ranjit Thapa, Dean-Research, SRM AP and Dr K A Sunitha, Associate Professor, SRM AP.

    The MoU underscores a mutually beneficial agreement between the two institutes. On the academic front, the MOU provides internship opportunities, research collaboration for projects and industry visits for the students and faculty of SRM AP. This ensures a knowledge transfer between the two organisations, promoting stellar growth in scientific and technological advancements.

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    SRM AP has already collaborated with IGCAR on a consultancy project in the pioneering field of Biomedical Research last year. The parties have successfully conducted health screening of over 1500 subjects in the Chengalpattu region in Tamil Nadu, with SRM Medical Hospital & Research Centre and AIIMS Mangalagiri as secondary collaborators. Upon the successful completion of the project, IGCAR and SRM University-AP further extend their association with an official MOU for academic and research collaborations. “The MOU with SRM University-AP for translational research will be a huge motivation for the young faculty and scholars to pursue breakthrough research in their scientific domains,” remarked Dr B Venkatraman, Director-IGCAR.

    Both institutes plan to extend their collaborative health screening project to the state of Andhra Pradesh, focusing on the neighbouring villages of SRM AP. Dr K A Sunitha, Project Head from SRM University-AP, opines that this project aims not just the possibility of translational research but also research for the societal cause. This research enables us to understand the correlated factors that influence various health disorders. Prof. Ranjit Thapa, Dean-Research also stated his enthusiasm for the project and expanding their research ventures to other domains.

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