Research News

The Sundarbans represent the largest mangrove system on Earth, covering >10,000 km2. These mangroves can export a vast amount of aquatic carbon that can be potentially sequestered for millennia. Dr Kousik Das, Assistant Professor at the Department of Environmental Science and Engineering, collaborates with Southern Cross University, Australia and IIT Kharagpur to conduct breakthrough research to analyse and estimate the carbon flux between the Sunderbans and the Bay of Bengal. He has published a research paper titled “Groundwater discharge and bank overtopping drive large carbon exports from Indian Sundarban mangroves” in the Q1 journal Science of The Total Environment, having an impact factor of 8.2.

Abstract

We estimate porewater-driven carbon exchange between the Sundarbans and the Bay of Bengal using high-resolution time series and a radon groundwater mass balance approach spanning a neap-spring tidal cycle. Submarine groundwater discharge (SGD) increased from neap to spring tides by 352 % up to a maximum of 65.6 cm d−1 largely driven by creek bank overtopping after the mid-tide. Exports of dissolved organic and inorganic carbon and alkalinity doubled between neap and spring, likely due to the ‘first flush’ of older porewater in the mangrove flats. Groundwater discharge was a significant driver of the net carbon export, contributing up to 86.7 % of DIC and 74.0 % of alkalinity during the spring tide while contributing a lower proportion of DOC (4 %–23 %). If these results are representative of the Sundarbans more broadly, carbon fluxes from the Sundarbans would be more than an order of magnitude higher than some of the world’s largest rivers on an areal basis, highlighting the importance of Sundarbans mangroves to global oceanic carbon budgets.

Practical Implementation/Social Implications of the Research

This study shows the global importance of the Sundarbans mangrove system, with significant dissolved inorganic carbon and total alkalinity fluxes to the Bay of Bengal. It also shows that mangrove SGD is an important driver of dissolved inorganic carbon, dissolved inorganic carbon (DIC) and total alkalinity (TAlk) exports and that bank overtopping during mid and spring tides drives a ‘first flush’ of carbon from groundwater. When carbon fluxes from the study site were upscaled to the entire inundated area of the Sundarbans, DIC and TAlk exports were smaller than some of the world’s largest rivers, however when adjusted to the catchment size (assuming the Sundarbans mangrove catchment area is the extent of the mangroves; 10,200 km2), the areal carbon fluxes from the mangroves are more than an order of magnitude higher than these river systems

Dr Das aims to continue his research and further explore the large flux of carbon export due to tropical cyclones from the Indian Sundarbans to the Bay of Bengal.

Link to the article

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With the advent of cutting-edge technology, Dr Abhijit Dasgupta, Assistant Professor at the Department of Computer Science and Engineering, has conducted breakthrough research in understanding biochemical systems with limited data in hand. His research has been published as a paper titled “Efficient Parameter Estimation in Biochemical Pathways: Overcoming Data Limitations with Constrained Regularization and Fuzzy Inference” in the Elsevier journal Expert Systems With Applications, having an impact factor of 7.5.

Abstract

This study introduces a new method to estimate parameters in biochemical pathways without relying on experimental data. The method called the Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF) uses fuzzy logic to estimate parameters based on known but imprecise relationships between molecules. To handle complex and unstable data, the method incorporates Tikhonov regularisation, improving accuracy and stability. CRFIEKF was tested on several pathways, including glycolysis and JAK/STAT signalling, and reliable results were obtained. This approach offers a useful tool for estimating parameters in complex biochemical systems, especially when experimental data is limited.

Explanation of the Research in Layperson’s Terms

This research is about finding a new way to predict how living cells work, especially when scientists don’t have enough data from experiments. Normally, to understand how cells function, scientists need to collect a lot of information over time, which can be costly, difficult, or even impossible.

To solve this, the researchers developed a new method that doesn’t need as much experimental data. Instead of relying on exact measurements, their method uses fuzzy logic, which is like making smart guesses based on patterns and relationships we know, even if we don’t have perfect information. They also used a technique to keep these guesses steady and reliable, even when the data is messy or incomplete.

They tested this method on different biological processes, such as how cells turn food into energy (a process called glycolysis) and how cells send signals using proteins (like in the JAK/STAT pathway). The method worked well and gave accurate results.

In simple terms, this research helps scientists predict how cells behave without needing a lot of expensive and hard-to-get data, making it easier to study the complex systems inside living organisms.

Practical Implementation/Social Implications of the Research

The practical implementation of this research lies in its ability to accurately predict how biological systems, such as cells, function without relying heavily on time-consuming and costly experimental data. This method can be applied in various fields, including drug development, personalised medicine, and agriculture, where understanding complex biological processes is crucial. For instance, pharmaceutical companies could use this technique to model how a drug will interact with different biological pathways, speeding up the drug discovery process. Similarly, it could help tailor medical treatments to individual patients by predicting how their unique biological makeup will respond to specific therapies.

The broader social impact of this research is significant. By reducing the need for extensive experimental data, this method can lower the cost and time required for scientific discoveries in healthcare and biotechnology. This could lead to faster development of new medicines, more affordable healthcare solutions, and personalised treatments that improve patient outcomes. In agriculture, this method can help optimise crop growth and resilience, contributing to food security. Overall, this research provides a pathway for more efficient and cost-effective advancements in biology, healthcare, and environmental sciences, ultimately benefiting society by improving health and sustainability.

Collaborations

1. St Jude Children’s Research Hospital, Memphis, USA

2. University of California, San Diego, USA

3. Columbia University, New York, USA

4. Nantes Université, France

5. University of North Carolina at Chapel Hill, USA

6. Institute of Himalayan Bioresource Technology, Palampur, India

7. Indian Statistical Institute, Kolkata, India

8. Aliah University, Kolkata, India

9. Michelin India Private Limited, Pune, India

10. Gitam University, Bangalore, India

Future Research Plans

Building on the foundation laid by this research, the next steps will involve expanding its applications to more complex biological systems and personalised medicine. The following outlines a future roadmap:

1. From Time-Course Data to Pathway Enrichment and Single-Cell Modeling:

The current method, which estimates parameters without relying on time-course data, can be adapted to use time-course data when available. Time-course data captures how biological processes change over time, offering valuable insights. By integrating this data, we can refine the parameter estimation and achieve a more precise pathway enrichment analysis. This approach can be particularly beneficial in single-cell studies, where understanding the variability in cellular responses within complex diseases like cancer, diabetes, or neurodegenerative disorders is crucial. Modelling these pathways at the single-cell level will enable us to capture heterogeneity within tissues and improve disease understanding.

2. Simulating Pathways to Identify Drug Targets:

Once the pathways are enriched and modelled, we can simulate these biological networks to predict how different interventions—such as drugs—might influence the system. This simulation can help identify potential drug targets, particularly those that are critical in disease progression. For instance, by manipulating the modelled pathways, we can observe how specific proteins or molecules influence the disease state, providing insights into where a drug could be most effective.

3. Predicting Drug Side Effects:

After identifying potential drug targets, the next step is to predict the side effects of these interventions. The same model can simulate unintended consequences by analysing how modifying a target impacts other connected pathways. This simulation can provide early warnings about potential side effects, reducing the risk during later stages of drug development. Understanding these off-target effects at an early stage will be crucial for designing safer drugs.

4. Predicting Drug Molecules Using Generative Adversarial Networks (GANs):

Incorporating machine learning, particularly Generative Adversarial Networks (GANs), can take this research to the next level. GANs can be trained to generate new drug molecules by learning from existing drug databases. By feeding the pathway model’s identified targets into the GAN, we can generate candidate drug molecules that are predicted to interact with these targets effectively. This approach can significantly speed up the drug discovery process by automating the design of new drug candidates tailored to specific biological pathways.

5. Integration with Omics Data for Personalized Medicine:

The future of personalised medicine relies on integrating various layers of biological data—such as genomics, transcriptomics, proteomics, and metabolomics—into a cohesive model. By integrating pathway data with these other “omics” layers, this research will facilitate a more comprehensive understanding of individual patient biology. This integration allows for tailored treatment strategies, making personalised medicine more achievable. For instance, based on an individual’s genetic makeup and biological pathways, we can predict how they will respond to specific drugs and design personalised therapies with minimal side effects.

6. Pathway-Based Drug Design and Validation:

Once potential drug molecules are identified using GANs, they can be simulated within the enriched pathways to test their efficacy in silico. These simulations will allow researchers to understand how the drug interacts with the target pathway and its downstream effects. If the simulation shows promising results, the drug candidates can be prioritised for lab testing and clinical trials. This systematic approach, from modelling to simulation, could drastically reduce the time and cost associated with traditional drug discovery processes.

Summary

This plan transforms the current research from a powerful parameter estimation technique into a comprehensive framework for personalized medicine and drug discovery. By expanding to time-course data, single-cell modeling, pathway simulation, and integrating cutting-edge AI techniques like GANs, we can predict drug molecules tailored to individual biological systems. This personalized approach not only streamlines drug discovery but also enhances the safety and effectiveness of treatments, paving the way for more efficient and precise medical interventions.

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In a society where sustainability is the watchword, Dr Jaidev Kaushik, Assistant Professor at the Department of Chemistry introduces an innovative method for producing acetanilide derivatives, using waste-derived copper flakes as a catalyst. This environmentally friendly approach enables a single-step synthesis while promoting a “waste to wealth” philosophy. His paper titled, “Waste-derived Copper Flakes for Solvent-Free Reductive Acetamidation of Nitroarenes,” featured in the Q1 Journal, Langmuir (ACS).

Abstract:

Amides are the most vital chemical moieties used in organic and biological processes and as a starting material in many industrial sectors like pharmaceuticals, agro-based, polymers, solvents, dyes, pigments, etc. The most common synthesis method of amides is by first reducing nitroarenes to their corresponding amines, followed by acylation with the required acyl group, a two-step process involving expensive metal catalysts. Given this, we have utilized waste-derived copper (Cu) flakes as a cost-effective heterogeneous catalyst for solvent-free and one-pot reductive acylation of nitroarenes. Metallic zerovalent copper flakes (f-ZCu) were isolated from waste copper Cu scrap/flakes/turnings generated after the grinding and cutting from the Cu industries. Moreover, the same procedure was also utilized to produce various substrates, including the gram-scale synthesis of the well-known crucial antipyretic drug, paracetamol.

Practical / Social Implications:

The proposed copper waste-derived heterogeneous catalyst can replace toxic and heavy metal catalysts from various organic synthesis reactions and can be utilized in the large-scale synthesis of important drugs such as paracetamol under more optimized conditions.

Collaborations:

Dr Sumit Kumar Sonkar (MNIT Jaipur, India)

Future Research Plans:

1. The adsorption/photodegradation-assisted quick and efficient removal of next-generation advanced pollutants such as microplastic, pesticides, pharmaceutical waste, etc. by hydrophobic carbon aerogel and their doped and functionalized versions.

2. Utilising waste-derived heterogeneous catalysts in organic transformation reactions.

3. Selective sensing of toxic metal ions/biomarkers/biomolecules using fluorescent nanomaterials.

4. Upcycling of carbonates/CO2 via photo/thermal assisted reactions to get C1 and C2 hydrocarbons (green fuel).

 

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It is pivotal for multinational companies to opt for sustainable growth while expanding their domains. In this regard, Dr Manisha Kumari, Assistant Professor from the Department of Management, Paari School of Business, has published her paper titled “The Impact of Corporate Environmental Reporting on the Financial Performance of Listed Manufacturing in Ghana” along with her research scholars Mr Musah Mohammed Saeed and Ms Mohammed Shahin Sultana in the Q1 journal Corporate Social Responsibility and Environmental Management with the impact factor 8.3.

Abstract

A growing number of businesses are facing criticism for engaging in environmentally damaging practices. Despite advancements in technology and operational efficiency, the environmental challenges confronting businesses have become increasingly urgent. As disclosure requirements have expanded, the importance of reporting standards for environmental sustainability has risen. This study explores the impact of corporate environmental reporting on the financial performance of listed manufacturing firms in Ghana. It analyses ten years (2012-2021) of annual reports from 20 publicly traded manufacturing companies, using panel regression and content analysis to assess the data. The findings reveal that environmental sustainability disclosure (ENVD) has a positive and significant effect on return on equity (ROE) and net profit margin (NPM). Furthermore, disclosures related to health, safety, and community development initiatives have a strong positive impact on ROE. The study recommends that policymakers develop guidelines, especially for environmental reporting, to aid firms in preparing their annual reports. It also suggests that corporate accountants expand their expertise and collaborate with environmental and ecological experts. This research offers valuable insights for policymakers and provides a foundation for further investigation into the effects of corporate environmental reporting (CER) on the performance of listed firms in sub-Saharan Africa.

Explanation of the Research in Layperson’s Terms

In simple terms, this study looks at how companies are being criticised for harming the environment, even though they have access to better technology and more efficient ways of working. Environmental problems for businesses are getting more serious, and as governments and organisations require companies to share more information about how they affect the environment, the standards for reporting this information have become more important.

The research focuses on how reporting environmental activities affects the financial success of manufacturing companies in Ghana. It examines reports from 20 publicly traded companies over ten years (2012-2021). The analysis shows that companies that disclose their environmental efforts, such as how they manage health, safety, and community projects, tend to have better financial outcomes, particularly in terms of the profit they return to shareholders and how much profit they make overall.

The study recommends that governments create clear guidelines for environmental reporting to help companies include this information in their annual reports. It also suggests that accountants should work more closely with environmental experts to improve their knowledge. The research provides important information for policymakers and encourages further exploration into how reporting on environmental activities affects company performance in Africa.

Practical Implementation/Social Implications of the Research

The study shows that environmental sustainability disclosures (ENVD) positively impact net profit margin (NPM) and return on equity (ROE) but have minimal effect on return on assets (ROA). This emphasises the need for stricter regulations to ensure comprehensive sustainability reporting, with policymakers incentivising better practices to boost financial performance.

• Energy disclosure (END) positively affects NPM, ROA, and ROE, suggesting mandatory energy-efficiency reporting. Regulators should set standardised metrics and offer tax incentives to encourage energy-efficient technologies.
• Mandatory environmental reporting by companies can help achieve environmental goals, gain importance in securing trade partnerships, and attract global capital.
• Health and safety disclosures (HSD) improve financial metrics, underscoring the need for stricter workplace safety regulations and rewarding safety-focused companies to enhance both worker safety and financial outcomes.
• Community involvement disclosure (CID), which has a negative but insignificant effect on financial performance, highlights the need for standardised reporting and alignment with core business strategies to show long-term value.
• To boost transparency, integrated reporting should be adopted nationwide, embedding environmental and social disclosures into financial reports to better demonstrate their long-term impact. Strengthening Ghana’s weak regulatory framework and aligning it with international standards (e.g., IFRS S1 and S2) would improve corporate social responsibility (CSR) practices, particularly in community development.
• Fiscal incentives, such as tax breaks, could encourage firms to invest in long-term sustainability projects, such as education and healthcare. Capacity building within firms, including sustainability training, would improve both financial and environmental outcomes.
• Standard method of accounting for environmental reporting can bring consistency in the market. Also, they required a proper set of guidelines that can attract environmental investors.

Overall, policy reforms focused on transparency, regulation, and aligning sustainability with financial goals would significantly benefit Ghana’s corporate sector.

Future Research Plans

Dr Manisha plans to shift her focus from traditional accounting standards to the growing field of sustainability reporting. This transition aligns with the increasing global emphasis on environmental, social, and governance (ESG) issues, which have become central to corporate accountability and transparency. Sustainability reporting has been a topic of ongoing discussion, reflecting the need for businesses to disclose not only their financial performance but also their impact on the planet and society.

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Dr Mohammad Abdussami, an Assistant Professor in the Department of Computer Science and Engineering, has made a significant contribution to the field of Internet of Things (IoT) security with the publication of his paper titled “APDEAC-IoT: Design of Lightweight Authenticated Key Agreement Protocol for Intra and Inter-IoT Device Communication Using ECC with FPGA Implementation.” This groundbreaking research has been published in the esteemed Q1 journal Computers and Electrical Engineering, which boasts an impact factor of 4.

Dr Abdussami’s research addresses critical security challenges faced by IoT devices, particularly in facilitating secure communication between intra and inter-device networks. The lightweight authenticated key agreement protocol he has developed utilizes Elliptic Curve Cryptography (ECC) and Field-Programmable Gate Array (FPGA) implementation to enhance the security framework of IoT ecosystems.

As the adoption of IoT devices continues to expand across various sectors, the importance of robust security protocols cannot be overstated. Dr Abdussami’s work is poised to make a substantial impact on how devices communicate safely and efficiently, ensuring the integrity and confidentiality of data transmitted over the network.

As the demand for secure IoT solutions continues to grow, Dr Abdussami’s research stands as a beacon for future developments in this crucial area, potentially paving the way for safer and more efficient IoT interactions globally.

Abstract:

In this research work, we proposed a fog-enabled network architecture integrated with IoT devices (Intra and Inter-domain IoT devices) and developed the DEAC-IoT scheme using Elliptic Curve Cryptography (ECC) for secure authentication and key agreement. Our protocol is designed to protect device-to-device communication from security threats in resource-constrained IoT environments.

Citation format:

Abdussami Mohammad, Sanjeev Kumar Dwivedi, Taher Al-Shehari, P. Saravanan, Mohammed Kadrie, Taha Alfakih, Hussain Alsalman, and Ruhul Amin. “DEAC-IoT: Design of lightweight authenticated key agreement protocol for Intra and Inter-IoT device communication using ECC with FPGA implementation.” Computers and Electrical Engineering 120 (2024): 109696.

Explanation of the Research in Layperson’s Terms:

With more and more devices connecting wirelessly through the Internet of Things (IoT) (think of smart home gadgets, wearables, etc.), keeping their communications secure has become a big priority. However, many current communication methods for IoT devices don’t provide strong enough security. This leaves them open to cyber-attacks.

The challenge is to create a security system that is safe from attacks and doesn’t require too many computations. This is important because IoT devices often have limited resources (like low battery power or slower processors).

In this research, the authors have devised a solution: a new type of network setup (called fog-enabled architecture) that connects IoT devices with each other and with external devices. They’ve also developed a security protocol called DEAC-IoT, which uses Elliptic Curve Cryptography (ECC)—a highly efficient method for securing communications.

Their system makes it easier for IoT devices to authenticate (verify each other’s identity) and securely exchange keys (used to encrypt data), all while being lightweight enough to run on devices that don’t have a lot of processing power or energy.

In short: the paper offers a way to securely connect IoT devices with minimal computations, making communication between devices safe from hackers, even in environments where cyber threats are common.

Practical Implication and Social Implications Associated:

The practical implementation of this research can strengthen the security of IoT devices across many sectors, from homes and cities to healthcare and industries. The proposed DEAC-IoT scheme can also be used to implement vehicle to vehicle secure communication in autonomous vehicles, VANETs and Internet of Vehicles scenario.

Socially, it can enhance trust in IoT technology, protect privacy, safeguard critical infrastructure, and promote economic and technological development—while ensuring security remains affordable even in resource-constrained environments.

In Industrial IoT (IIoT) Scenario: In industries where machines are connected via IoT (such as in factories), devices need to communicate securely to ensure the smooth running of production lines. The DEAC-IoT protocol could secure these communications, preventing industrial espionage or sabotage.

Future Research Plans

1. Design of group key authentication protocols for IoT devices communication.
2. Design of handover authentication protocols for Fog-enabled IoT devices communication.
3. Design of quantum safe authentication protocols for vehicle-to-vehicle communication

Collaborations:

1. Dr Sanjeev Kumar Dwivedi, Centre of Artificial Intelligence, Madhav Institute of Technology and Science (MITS), Gwalior, Madhya Pradesh 474005, India.
2. Dr Taher Al-Shehari, Computer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia.
3. Dr Mohammed Kadrie, Computer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia
4. Dr P Saravanan, Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, India.
5. Dr Ruhul Amin, Department of Computer Science & Engineering, IIIT Naya Raipur, Naya Raipur 493661, Chhattisgarh, India
6. Dr Taha Alfakih, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
7. Dr Hussain Alsalman, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

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Surface-Enhanced Raman Spectroscopy (SERS), a technique that helps scientists detect tiny amounts of substances, is used for checking pollutants in our environment and the food we eat. However, using this method can be tricky because sometimes other substances can interfere. To overcome these challenges, scientists are working on better ways to prepare samples and analyse the data with a quick and easy way to find harmful pollutants called PFOSA in human urine, soil, and water using a fish scale-based substrate. This remarkable research titled, “Ag nanoparticle-embedded fish scales as SERS substrates for sensitive detection of forever chemical in real samples” by faculty members from the Department of Chemistry and Department of Biological Sciences, Dr J P Raja Pandiyan and Dr Anil K Suresh, along with their research scholars, Ms Jayasree K and Ms Arunima J, have opened up new avenues, demonstrating a significant advancement in the field of science.

Abstract:

Surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most promising analytical tools in recent years due to its advantageous features such as high sensitivity, specificity, ease of operation, and rapid analysis. These attributes make SERS particularly well-suited for environmental and food analysis. However, detecting target analytes in real samples using SERS faces several challenges, including matrix interference, low analyte concentrations, sample preparation complexity, and reproducibility issues. Additionally, the chemical complexity of pollutants and environmental factors can impact SERS measurements. Overcoming these hurdles demands optimised experimental conditions, refined sample preparation methods, and advanced data analysis techniques, often necessitating interdisciplinary collaborations for effective analysis. Therefore, our focus lies in the development of various methods for fabricating SERS substrates, pretreating analytes, and devising sample preparation strategies. These efforts aim to enable the detection of analytes like Perfluorooctane sulfonamide (PFOSA) – a toxic environmental pollutant within complex real samples, including human urine, lake water, and soil samples.

Practical / Social Implications:

SERS Community: Introducing a facile fabrication method for developing filter paper-based substrates, utilizing evaporation-induced self-assembly methods with the aid of 96-well plates. These substrates boast exceptional sensitivity and uniformity, exhibiting a relative standard deviation (RSD) of 8.2%. They offer easy fabrication and serve as effective SERS substrates for various applications.

Industry and Government Bodies: This invention plays a pivotal role in assessing contamination in food and water bodies, serving as a crucial tool in monitoring
environmental contamination through on-site analysis with portable instruments. It ensures adherence to regulatory standards and safeguards public health.

Research: Beyond its practical applications, the invention supports scientific research endeavours focused on identifying microplastic contaminants in real-world samples using portable Raman spectrometers. This not only aids ongoing research but also paves the way for future studies in this critical field.

Collaborations:

1. Dr Hemanth Noothalapati Raman Project Center for
Medical and Biological
Applications, Shimane
University, Matsue 690-8504,
Japan

2. Dr Murali Krishna C. Advanced Centre for
Treatment, Research and
Education in Cancer, Tata
Memorial Centre, Navi
Mumbai 410210, India

3. Dr Soma Venugopal University of Hyderabad, India

Future Research Plans:

Harnessing SERS for the Detection of Emerging Contaminants in Environmental and Food Matrices

 

 

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Dr Sriramulu Bojjagani, Assistant Professor, Department of Computer Science and Engineering and his research scholar, Ms Praneeta Supraneni, have proposed a secure and novel way to safeguard cars from being hacked, data breaches, and unauthorised access. Their research paper titled “Handover-Authentication Scheme for the Internet of Vehicles (IoV) using Blockchain and Hybrid Computing” will now improve transparency and traceability of your cars. Read the interesting abstract to learn more!

Abstract:

The advancements in telecommunications are significantly benefiting the Internet of Vehicles (IoV) in various ways. Minimal latency, faster data transfer, and reduced costs are transforming the landscape of IoV. While these advantages accompany the latest improvements, they also expand cyberspace, leading to security and privacy concerns. Vehicles rely on trusted authorities for registration and authentication processes, resulting in bottleneck issues and communication delays. Moreover, the central trusted authority and intermediate nodes raise doubts regarding transparency, traceability, and anonymity. This paper proposes a novel vehicle authentication handover framework leveraging blockchain, IPFS, and hybrid computing. The framework uses a Proof of Reputation (PoR) consensus mechanism to improve transparency and traceability and the Elliptic Curve Cryptography (ECC) cryptosystem to reduce computational delays. The suggested system assures data availability, secrecy, and integrity while maintaining minimal latency throughout the vehicle re-authentication process. Performance evaluations show the system’s scalability, with creating keys, encoding, decoding, and registration operations done rapidly. Simulation is performed using SUMO to handle vehicle mobility in IoV environment. The findings demonstrate the practicality of the proposed framework in vehicular networks, providing a reliable and trustworthy approach for IoV communication

Practical Implementation / Social Implications:

The practical application of this research can significantly improve the safety and reliability of autonomous vehicles and connected vehicle networks. By securing the handover process, it reduces the risk of hacking, data breaches, and unauthorized access, making connected vehicle systems safer for the public and contributing to the development of smart transportation infrastructures.

Future Research Plans:

Moving forward, we plan to focus on optimizing blockchain solutions for large-scale IoT and smart city applications, with a particular interest in improving consensus mechanisms and security protocols for real-time operations, such as autonomous driving and smart energy grids.

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Today, we approach the topics of caste and religion with great sensitivity, aware of the deep-rooted complexities they carry. Yet, here was a character who transcended these societal boundaries, evolving into the embodiment of ‘Moner Manush’—a figure who rose above the constraints of identity to embrace a higher sense of spiritual unity and inclusiveness. Dr Sayantan Thakur, Assistant Professor at the Department of Literature and Languages closely reads into the intricacies of Lalon’s conceptualisation of man and the caste barriers in his research paper.

Abstract:

The paper entitled ‘Beyond ‘Jaat’ and Dharma: Exploring the Evolution of Lalon’s Idea of ‘Moner Manush’ delves into an in-depth exploration of Lalon’s conceptualization of ‘Moner Manush,’ transcending the conventional confines of ‘Jaat’ (caste) and Dharma (religion). Through a nuanced analysis of Lalon’s evolving perspectives, the study traces the transformative journey of the idea of ‘Moner Manush.’ By dissecting the lyrical and philosophical aspects, the paper illuminates how Lalon’s spiritual musings challenge societal norms, promoting a universal ethos that goes beyond distinctions. This inquiry aims to unravel the evolving nature of Lalon’s concept of ‘Moner Manush’ and its enduring significance in fostering inclusivity and spiritual interconnectedness, surpassing the limitations of caste and religion.

Practical Implementation and Social Implications:

The practical implementation of my research on “Beyond ‘Jaat’ and Dharma: Exploring the Evolution of Lalon’s Idea of ‘Moner Manush'” has profound social implications, particularly in fostering inclusivity and breaking down societal barriers. By promoting Lalon’s vision of transcending caste (jaat) and religious (dharma) divisions, this research advocates for a more egalitarian society where people are valued for their inner virtues, not external identities. In practical terms, this philosophy can be integrated into education, community-building, and social reform initiatives to encourage tolerance, empathy, and unity among diverse groups.

In multicultural societies, teaching Lalon’s ideas in schools and community programs can help dismantle deep-seated prejudices and promote cross-cultural understanding. Socially, the emphasis on the Moner Manush—the ideal human being—can encourage individuals to focus on self-reflection, moral development, and compassion, creating a more harmonious coexistence. Additionally, his philosophy can inform contemporary debates on identity politics, helping people prioritize human connections over rigid societal structures.

Future Research Plans

Regional Literature in Translation

Tantric Tradition and Eastern Indian Literature

Folk Music of Bengal

Indian Philosophy, Aesthetics & Literature

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Mr M Ratna Raju, Assistant Professor in the Department of Computer Science and Engineering, has achieved a remarkable milestone by publishing a research paper titled “Service caching and user association in cache-enabled multi-UAV assisted MEN for latency-sensitive applications” in the esteemed Q1 journal, Computers and Electrical Engineering, which boasts an impact factor of 4.0.
The paper explores innovative strategies for improving service caching and user association in multi-unmanned aerial vehicle (UAV) networks, addressing challenges faced by latency-sensitive applications. Mr Raju’s research contributes significantly to the field of computer science, particularly in enhancing the efficiency of UAV-assisted networks.

This publication not only highlights Mr Raju’s dedication to cutting-edge research but also reinforces SRM University-AP’s commitment to fostering academic excellence and innovation in technology. As the demand for efficient communication networks continues to grow, findings from this study are poised to play a critical role in shaping the future of network architecture and UAV applications.
The academic community and students alike look forward to Mr Raju’s further contributions as he continues to lead impactful research initiatives at SRM University-AP.

Abstract of the Research

The evolution of 5G (Fifth Generation) and B5G (Beyond 5G) wireless networks and edge IoT (Internet of Things) devices generates an enormous volume of data. The growth of mobile applications, such as augmented reality, virtual reality, network gaming, and self-driving cars, has increased demand for computation-intensive and latency-critical applications. However, these applications require high computation power and low communication latency, which hinders the large-scale adoption of these technologies in IoT devices due to their inherent low computation and low energy capabilities.

MEC (mobile edge computing) is a prominent solution that improves the quality of service by offloading the services near the users. Besides, in emergencies where network failure exists due to natural calamities, UAVs (Unmanned Aerial Vehicles) can be positioned to reinstate the networking ability by serving as flying base stations and edge servers for mobile edge networks. This article explores computation service caching in a multi-unmanned aerial vehicle-assisted MEC system. The limited resources at the UAV node induce added problems of assigning the existing restricted edge resources to satisfy the user requests and the associate of users to utilise the finite resources effectively. To address the above-mentioned problems, we formulate the service caching and user association problem by placing the diversified latency-critical services to maximise the time utility with the deadline and resource constraints.

The problem is formulated as an integer linear programming (ILP) problem for service placement in mobile edge networks. An approximation algorithm based on the rounding technique is designed to solve the formulated ILP problem. Moreover, a genetic algorithm is designed to address the larger instance of the problem. Simulation results indicate that the proposed service placement schemes considerably enhance the cache hit ratio, load on the cloud and time utility performance compared with existing mechanisms.

Explanation of the Research in Layperson’s Terms

The rapid growth of 5G (Fifth Generation) and B5G (Beyond 5G) wireless networks, along with edge IoT (Internet of Things) devices, is creating a massive amount of data. As mobile applications like augmented reality (AR), virtual reality (VR), online gaming, and self-driving cars become more popular, there’s a greater need for fast, powerful computing. However, IoT devices typically have limited computing power and energy, making it hard to run these advanced applications. Mobile Edge Computing (MEC) offers a solution to this problem by offloading tasks to servers located closer to users, reducing delays and improving performance.

In cases of emergency where network failure occurs due to natural disasters, Unmanned Aerial Vehicles (UAVs) can be used to restore connectivity. UAVs can act as flying base stations and edge servers, helping mobile edge networks continue functioning. This research focuses on improving how computation services are cached and handled in a system that uses multiple UAVs to assist MEC. Since UAVs have limited resources, there’s a challenge in efficiently assigning these resources to meet user demands. The research proposes a solution by formulating this problem as an integer linear programming (ILP) problem, aiming to place services in a way that maximises performance while considering deadlines and resource limits. To solve this complex issue, we use two approaches. First, they apply an approximation algorithm based on a rounding technique to solve the ILP problem. Then, for larger problems, they use a genetic algorithm. Their simulation results show that these service placement strategies significantly improve metrics like cache hit ratio, load reduction on the cloud, and time utility, compared to existing methods.

Practical Implementation and the Social Implications Associated

The practical implementation of this research lies in enhancing the efficiency of real-time, computation-intensive applications like augmented reality (AR), virtual reality (VR), autonomous driving, and network gaming in mobile edge computing (MEC) environments, particularly in scenarios involving Unmanned Aerial Vehicles (UAVs). By optimizing how services are cached and distributed in a multi-UAV-assisted MEC system, the research enables faster data processing and lower latency, which is crucial for applications where even slight delays can cause major issues, such as in self-driving cars or real-time remote surgeries. In emergency situations, such as natural disasters, where ground-based networks may be damaged or overloaded, the deployment of UAVs as flying base stations and edge servers could restore network connectivity quickly and provide essential services. This research ensures that even under such constraints, services are efficiently distributed, enhancing responsiveness and reliability.

Social Implications:

Disaster Relief: UAVs with MEC support could be deployed during natural calamities to restore communication services, helping rescue teams coordinate better and saving lives.
Smart Cities and Autonomous Vehicles: The work contributes to making smart cities more responsive, with real-time data processing and seamless service delivery. Autonomous vehicles, for instance, would benefit from reduced latency, leading to safer and more efficient navigation.
Healthcare: Applications such as telemedicine and remote surgery could operate more effectively with lower latency, improving healthcare delivery in remote or disaster-affected regions.

Collaborations

1. Manoj Kumar Somesula and Banalaxmi Brahma from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Punjab 144008, India.
2. Mallikarjun Reddy Dorsala from Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh 517646, India.
3. Sai Krishna Mothku from National Institute of Technology, Tiruchirappalli 620015, India

Future Research Plans

In future, we plan to consider the unique challenge of making caching decisions while accounting for the limited energy capacity of UAVs, mobility of UAVs, network resources, and service dependencies, which introduces new complexities in algorithm design minimising the overall service delay while adhering to constraints such as energy consumption, UAV mobility, and network resources. This would require the joint optimisation of service caching placement, UAV trajectory, UE-UAV association, and task offloading.

Link to the Article

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In a significant advancement for neurodevelopmental research, Dr Nitul Dutta, Associate Professor in the Department of Computer Science and Engineering, along with PhD scholar Ms Surya Samantha Beri and BTech student Mr Nallamothu Sai Karthik, have successfully filed and published a patent titled “A System for Autism Spectrum Disorder Detection.” The application, numbered 202441053505, has been officially documented in the Patent Office Journal.

The innovative system aims to enhance the early detection of Autism Spectrum Disorder (ASD), providing a more efficient and accessible method for diagnosis. By integrating advanced algorithms and machine learning techniques, the system promises to analyse behavioural data effectively, allowing for timely interventions and support for individuals on the spectrum. Dr Dutta emphasised the importance of early detection, stating, “The earlier we can identify ASD, the better the outcomes for individuals and their families. Our system is designed to make this process more accurate and user-friendly.”

Ms Beri and Mr Karthik contributed significantly to the research, which reflects a collaborative effort between academia and technology. Their work not only demonstrates the potential for technological solutions in healthcare but also highlights the critical role of interdisciplinary approaches in addressing complex challenges.

This patent represents a crucial step forward in the field of autism research and is expected to pave the way for further innovations aimed at improving the lives of those affected by ASD.

Abstract of the Research

The system for autism spectrum disease detection incorporates a server with a hybrid application comprising several key modules: a capturing module receiving images from image-capturing devices , a data collection module gathering a dataset of images from multiple capturing devices, and a pre-processing module standardising and normalising images to generate a standardised dataset. Additionally, a feature extraction module collaborates with the pre-processing module to identify autism-indicative features in standardised images, preparing labelled standardised images stored in the data collection module.

Furthermore, a data segmentation module segments standardised images into training and testing data, including a training module for real-time training of a convolutional neural network model and a testing module to evaluate the convolutional neural network model’s accuracy in detecting autism based on testing data.

Explanation of the Research in Layperson’s Terms

The background information herein below relates to the present disclosure but is not necessarily prior art. Autism spectrum disorder (ASD) is a neurological or developmental disorder that profoundly impacts communication skills, social interaction, and cognitive abilities in individuals. Those with Autism Spectrum Disorder (ASD) often exhibit challenges in social interaction, limited eye contact, difficulty understanding social cues, and impaired language skills. Additionally, repetitive behaviours and sensory sensitivities are common characteristics.

The disorder arises from developmental changes in brain structure and can have various causes, including genetic factors, familial history of autism spectrum disorder (ASD), advanced parental age, or low birth weight. The prevalence of autism spectrum disorder (ASD), as reported by the World Health Organization (WHO), stands at one in every 160 children. Early detection and intervention are crucial for managing autism spectrum disorder (ASD) effectively, as interventions such as medical and neurological examinations, cognitive and language assessments, and frequent observations, including blood and hearing tests, can significantly improve outcomes. Detecting Autism Spectrum Disorder (ASD) in children below the age of 10 is comparatively easier than in adults, underscoring the importance of early diagnosis to facilitate timely interventions.

Current diagnostic processes for autism spectrum disorder (ASD) often present significant challenges, particularly for young children, due to their limited ability to communicate and cooperate during assessments. Traditional diagnostic methods rely heavily on structured interviews, behavioural observations, and standardised tests, which can be daunting and stressful for children, leading to inaccurate results. Moreover, these procedures are time-consuming and often require multiple visits to specialised clinics or healthcare facilities, causing inconvenience and financial strain for families. Also, the cost associated with autism spectrum disorder (ASD) diagnosis can be prohibitive for many families.

Therefore, there is a pressing need for more accessible, less intrusive, and cost-effective methods for detecting autism spectrum disorder (ASD) in its early stages to ensure timely and effective intervention. Therefore, there is a need for a system for autism spectrum disorder detection that alleviates the drawbacks.

Practical and Social Implications Associated with the Research

Current diagnostic procedures for Autism Spectrum Disorder (ASD) face considerable technical challenges, particularly concerning young children’s limited ability to engage in conventional assessment methods. These methods typically rely on structured interviews, behavioural observations, and standardised tests, all of which can be arduous and distressing for children with Autism Spectrum Disorder (ASD), potentially leading to unreliable outcomes. Furthermore, these procedures are resource-intensive, requiring multiple visits to specialised clinics or healthcare facilities, thereby causing logistical challenges and financial burdens for families. Current diagnostic processes for Autism Spectrum Disorder (ASD) often present significant challenges, particularly for young children, due to their limited ability to communicate and cooperate during assessments. Traditional diagnostic methods rely heavily on structured interviews, behavioural observations, and standardised tests, which can be daunting and stressful for children, leading to inaccurate results. Moreover, these procedures are time-consuming and often require multiple visits to specialised clinics or healthcare facilities, causing inconvenience and financial strain for families. Also, the cost associated with Autism Spectrum Disorder (ASD) diagnosis can be prohibitive for many families. Therefore, there is a pressing need for more accessible, less intrusive, and cost-effective methods for detecting Autism Spectrum Disorder (ASD) in its early stages to ensure timely and effective intervention.

Collaborations

This research was done in collaboration with Professor George, Brunel University, London, United Kingdom

Future Research Plans

In the future, we will also try to diagnose the disorder by speech therapy using Natural Language Processing and integrate it with real-time industry in health care, which can be used by many doctors in their respective practices

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