| 1 |
Author(s):
Afreen Nazeer.
Research Area:
Biology
Page No:
1-6 |
Post-Injury Rehabilitation Reimagined: Integrating AI into Personalized Recovery Pathways
Abstract
The integration of Artificial Intelligence (AI) into post-injury rehabilitation is transforming recovery paradigms by enabling personalized, adaptive, and efficient rehabilitation pathways tailored to individual patient needs. This paper reviews the current advances in AI applications that facilitate assessment, monitoring, and optimization of rehabilitation programs following injuries. Through machine learning algorithms, wearable sensors, and predictive analytics, AI enhances the precision of therapy plans, tracks patient progress in real-time, and predicts recovery trajectories. The review discusses the benefits of AI-driven rehabilitation, including improved functional outcomes, reduced recovery times, and increased patient engagement. It also addresses challenges such as data privacy, algorithmic bias, and integration with clinical workflows. Case studies and emerging trends highlight the potential of AI to revolutionize rehabilitation medicine, fostering a patient-centered approach that adapts dynamically to evolving recovery stages.
| 2 |
Author(s):
Leena Joseph.
Research Area:
Biology
Page No:
7-12 |
Smart Dentistry: AI Applications in Diagnosis, Planning, and Patient-Centered Oral Care
Abstract
Artificial Intelligence (AI) is revolutionizing the field of dentistry by introducing advanced diagnostic capabilities, precise treatment planning, and enhanced patient-centered care. This paper explores how AI technologies are being integrated into dental practice to improve the accuracy of diagnoses, optimize treatment outcomes, and elevate patient engagement. By utilizing machine learning algorithms, computer vision, and natural language processing, AI assists dental professionals in detecting oral diseases, planning complex procedures, and managing patient data efficiently. The review discusses current applications such as automated image analysis, predictive analytics, and personalized oral health recommendations. It also addresses the challenges of adopting AI in dental care, including ethical concerns, data privacy, and the need for clinician training. Case studies highlight real-world implementations that demonstrate AI’s potential to transform dentistry into a more precise, efficient, and patient-focused discipline.
| 3 |
Author(s):
Faisal Shaikh.
Research Area:
Cognitive Science
Page No:
13-17 |
Early Detection of Diseases Using AI: An Overview of Emerging Tools and Techniques
Abstract
Early detection of diseases is critical for improving treatment outcomes, reducing healthcare costs, and enhancing patient quality of life. Artificial Intelligence (AI) has emerged as a powerful enabler for advancing early diagnostic capabilities across a wide spectrum of diseases, including cancer, cardiovascular disorders, neurodegenerative conditions, and infectious diseases. This paper presents a comprehensive overview of AI-driven tools and techniques utilized in the early detection of diseases. It highlights the role of machine learning, deep learning, and data mining in analyzing complex biomedical data such as medical images, electronic health records, and genetic information. The paper also examines emerging biomarkers and sensor technologies integrated with AI for timely diagnosis. Challenges related to data heterogeneity, interpretability, and clinical implementation are discussed, alongside potential solutions. Through reviewing recent research and clinical applications, the paper emphasizes AI’s transformative potential in enabling earlier and more accurate disease detection, ultimately facilitating proactive healthcare interventions.
| 4 |
Author(s):
Harshita Iyer.
Research Area:
Artificial Intelligence and Machine Learning Engineering
Page No:
18-23 |
Predictive Analytics in Patient Outcome Forecasting: AI Models and Clinical Relevance
Abstract
Predictive analytics driven by Artificial Intelligence (AI) represents a paradigm shift in forecasting patient outcomes within healthcare. By harnessing complex datasets ranging from electronic health records to genetic and imaging data, AI algorithms identify patterns and risk factors that aid clinicians in anticipating disease progression, treatment efficacy, and potential complications with unprecedented accuracy. This paper delivers an in-depth examination of the AI methodologies—such as machine learning, deep learning, and ensemble models—employed in outcome prediction, elucidating their mechanisms, strengths, and limitations. It explores the transformative clinical applications of these predictive models across specialties including oncology, cardiology, critical care, and chronic disease management. The discussion extends to the challenges faced in data integration, model interpretability, ethical implications, and deployment barriers. Real-world case studies illustrate successful clinical implementations and their impact on patient care and health system efficiency. Finally, the paper considers future directions emphasizing multimodal data fusion, explainability, real-time analytics, and population health, underscoring AI’s vital role in advancing precision medicine and personalized healthcare.
| 5 |
Author(s):
Vikrant Sahu.
Research Area:
Computer Science
Page No:
24-29 |
AI-Based Real-Time Monitoring Systems in Healthcare: Challenges and Opportunities
Abstract
The integration of Artificial Intelligence (AI) into real-time monitoring systems marks a significant advancement in healthcare, enabling continuous patient surveillance, early detection of critical events, and enhanced clinical decision-making. AI-powered monitoring systems analyze streams of physiological, biochemical, and behavioral data to identify anomalies and predict adverse outcomes, improving patient safety and care quality. This paper provides a comprehensive review of current AI-based real-time monitoring technologies deployed across various healthcare settings, including intensive care units, remote patient monitoring, and chronic disease management. It discusses the algorithms and sensor technologies underpinning these systems, their clinical applications, and the measurable benefits realized. The paper also critically examines the challenges hindering widespread adoption, such as data privacy concerns, algorithmic biases, integration complexities, and regulatory hurdles. Finally, it explores future opportunities including the use of multimodal data, advances in wearable sensors, edge computing, and personalized monitoring frameworks, emphasizing the potential of AI to revolutionize healthcare delivery and patient outcomes through proactive and continuous monitoring.
| 6 |
Author(s):
Shubham Kumar Sah, Atish Prashar.
Research Area:
Other
Page No:
30-46 |
Media as a Tool for Climate Change Adaptation: A Study of Radio’s Role in Gaya District
Abstract
This research delves into the use of radio as a means of climate change adaptation in the Gaya District of Bihar, India. Since climate change greatly threatens vulnerable populations, appropriate communication and education are vital in building resilience and enabling informed choices. Radio, being easily accessible and its wide coverage, is a vital platform for providing climate-related information, especially in areas where the use of digital technologies is not readily available. This study examines the demographic factors, media viewing habits, and attitudes to climate change among Gaya District inhabitants. Using a quantitative survey data analysis, the study finds that respondents have a high level of awareness of climate change, especially those who habitually listen to radio shows. There is a significant linkage between exposure to information on climate change and behavioral and practice changes towards climate adaptation, according to the findings. Moreover, the research also underscores the significance of specialized radio content which is addressing specific concerns and needs of various segments of society with a focus on the ability of radio to empower communities and influence collective action in relation to climate adversity. Finally, this study highlights the importance of radio as a viable learning tool for climate change adaptation, with useful implications for policymakers, media practitioners, and community organizations in their effort to promote climate resilience in the Gaya District and other areas of the same classification.
| 7 |
Author(s):
M. P. Gadekar.
Research Area:
Entomology
Page No:
47-55 |
A Review on Biology and Management of Achatina fulica
Abstract
The Giant African snail (Achatina fulica Bowdich) is classified under the Phylum Mollusca and the Class Gastropoda. This species is notorious for its detrimental impact on agricultural crops in areas where it is found, making it one of the largest and most harmful land snail pests globally. Its widespread distribution is attributed to several factors, including a high reproductive biological rate, aggressive feeding behavior, insufficient quarantine measures, and human-assisted movement. This review discusses the detrimental effects of snail infestations on agriculture, their biology and management.
| 8 |
Author(s):
Souvik Mukherjee.
Research Area:
Statistics
Page No:
56-75 |
Consumption Expenditure Inequality and Handloom Sector in Northeast India
Abstract
Handloom is the most widely established cottage industry in North East India (NE) and is widely spread throughout the region. It employs a large skilled and unskilled workforce, mainly women workers in the North East. In the present era of commercialization, the handloom sector is also witnessing changes, and many women are adopting weaving as their profession. The activity they performed during their leisure time, has now been transformed into an 8-hour job. Across India, including the North East, there has been a general trend of increasing inequality in consumption expenditure, particularly after economic reforms. This is evident in rural and urban areas, with non-food expenditures being more unevenly distributed and contributing significantly to overall inequality (Sen & Das, 2018; Subramanian & Jayaraj, 2015). The handloom sector is a crucial source of employment in Northeast India, with the region accounting for more than 65% of the total handloom households in the country. It provides livelihoods to many rural populations, especially women, who dominate the weaving occupation in this region (Devi, 2014; Goswami et al., 2017). Despite the decline in handloom weaver households in other parts of India, Northeast India has increased, highlighting its importance in the local economy (Devi, 2014). This paper attempts to probe the impacts of different dimensions of the handlooms sector of the North Eastern states on consumption expenditure inequality along with their magnitudes. The Fourth All India Handloom Census 2019-20 found that 31.45 lakh households in India are engaged in handloom activities, an increase from 27.83 lakhs in the previous census. Rural and urban areas categorize the data and factors like religion and household types. The Household Consumption Expenditure Survey (HCES) measures spending and reports inequality through the Gini coefficient. This paper examines how different Handloom census indicators affect the Gini coefficient. The analysis draws on data from the Fourth All India Handloom Census 2019-20 and the Household Consumption Expenditure Survey (HCES) 2022-23. The HCES obtained the Gini coefficient to measure inequality in consumption expenditure. Information on the handloom industry in northeast India was also sourced from the Ministry of Textiles' Census report. Multiple linear regression models were developed to achieve the paper's objectives, predicting the value of a variable based on several others. The Northeast region is emerging as a key player in the handloom sector, but women weavers face challenges from manual looms and market competition, including threats from imported materials. Designers should focus on handloom products to support this craft. In a patrilineal society, weaving empowers women, yet weak organizations hinder their rights. Mobilizing weaver groups can improve access to government schemes and markets. Sustainable cooperatives and full-time work in handlooms can enhance women's economic participation and livelihoods.
| 9 |
Author(s):
Palpandi karuppaiah.
Research Area:
Agricultural Science
Page No:
76-97 |
Novel Synthesis and Fabrication of CuO/RGO Composite Modified Electrode for Selective Electrochemical Determination of Environmental Pollutant Para-Aminophenol in Water and Fruit Sample
Abstract
Carbon-based nanomaterials such as graphite and their oxides have attracted extensive attention in many areas, especially in organic synthesis, electrochemistry, and catalytic chemistry. In this context, we report new eco-friendly preparation of copper oxide/reduced graphene oxide nanocomposite (CuO/RGO) based para-Aminophenol (PAP) electrochemical sensor. The synthesized nanocomposites were characterized by X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR), Energy dispersive X-ray spectrometry (EDS) and field emission scanning electron microscope study (FESEM). The electrocatalytic behavior of PAP at the surface of CuO/RGO nanocomposite on screen-printed carbon electrode (SPCE) were investigated in detail. Combining CuO and RGO intensifies the property and performance of the nanocomposite due to coactive effects between RGO nano-sheet and CuO nanoparticles. Herein, PAP was used as a probe to appraise the electrocatalytic activities of the CuO/RGO modified electrode. In this study, we were able to obtain a detection limit (LOD) of 0.02µM and a wide linear concentration range (LR) from 0.01µM to 478.1µM for PAP. The nanocomposite exhibits excellent and stable electrocatalytic activity towards the PAP determination.
| 10 |
Author(s):
Dr. Anurag Kumar Sonker.
Research Area:
Zoology
Page No:
98-115 |
A Review: On the Parasitic Mites and Its Impact on the Honey Bee Spp. (Apis mellifera) and Their Colony
Abstract
The honey bees (Apis mellifera) are considered as one of the most important insects providing crop pollination and also a wide variety of products. The parasitic mite is currently become the most important pest of honey bee and also one of the main factor for the colony loss faced by the beekeepers. It parasitizes the adult bee along with developing bee in honey bee colonies by feeding on their haemolymph and transmitted pathogens, microbes such as bacteria and viruses which cause morphological deformities in the developing bees and destroying honey bee colonies. The studies may be useful to know the impact of parasitic mite on honey bee and their colonies along with the diseases caused and effect of certain environmental factors on them. The studies may also be an effective for the taxonomical studies of the parasitic mite and also impart the additional information for further treatment of these parasites.
| 11 |
Author(s):
Mr. Anweshan Jana, Firdous Ahamed.
Research Area:
Molecular Biology
Page No:
116-122 |
Prevalence, Risk Factors, and Molecular Mechanisms of Gestational Diabetes Mellitus (GDM) among the Pregnant women in West Bengal
Abstract
Gestational Diabetes Mellitus (GDM) is an increasing public health concern in India, especially in states like West Bengal, where nutritional transition, genetic susceptibility, and stress-related lifestyle shifts contribute to its rising prevalence. GDM is defined as glucose intolerance with onset or first recognition during pregnancy¹. It is associated with both maternal and fetal complications and significantly increases the risk of type 2 diabetes mellitus (NIDDM) postpartum. This review article explores the current trends in GDM prevalence in West Bengal, risk factors, screening methodologies, socio-demographic influences, and molecular mechanisms involved, including insulin resistance, inflammatory signalling, and placental hormone alterations⁴. Molecular markers like GLUT4, adiponectin, TNF-α, and miRNAs are discussed in relation to GDM pathogenesis⁷. The review highlights the need for region-specific interventions, early screening, and molecular diagnostics to improve maternal and neonatal outcomes.
Keywords: Gestational Diabetes Mellitus, West Bengal, insulin resistance, pregnancy, GLUT4, TNF-α, miRNA, fetal complications
| 12 |
Author(s):
CHINJU VERGHESE KANNANAICKAL B.
Research Area:
Medical Science
Page No:
123-130 |
Association Between Body Mass Index and Hypertension: An Explorative Study With Development of an Information Booklet for Hypertensive Patients in Selected Hospitals of Rajkot District
Abstract
The increasing incidence and prevalence of hypertension, especially among the younger demographic, has become a significant public health issue. Although lifestyle and dietary modifications effectively lower blood pressure and mitigate health risks, medication remains essential for many individuals. The emergence of hypertension can be attributed to multiple variables, such as obesity, elevated sodium consumption, inadequate physical exercise, tobacco use, and excessive alcohol intake. Obesity, a primary factor in hypertension, is strongly associated with Body Mass Index (BMI), becoming BMI a crucial indicator for evaluating the risk of hypertension development. This study sought to investigate the relationship between body mass index (BMI) and hypertension in individuals with hypertension. The cohort of one hundred hypertension patients was obtained via non-probability convenience sampling. Following the establishment of rapport with the participants and the provision of a comprehensive explanation of the study's objectives, the research was conducted with their explicit agreement, both verbal and written. The body mass index (BMI) was calculated using the standard formula, which involved gathering data on blood pressure, height, weight, and belly circumference. The documentation of demographic and clinical information was conducted using standardized proformas. Descriptive and inferential statistical approaches were utilized to analyze the data. The Chi-square test yielded a value of 87.02 and a p-value of 0.003, demonstrating a substantial correlation between body mass index (BMI) and hypertension. This discovery supports the concept that BMI is significantly associated with hypertension. A correlation existed between hypertension and socio-demographic characteristics such as age, gender, occupational status, and marital status; however, no association was found between hypertension and demographic parameters including educational level, domicile, family income, or family type. Clinical factors, including dietary choices and meal frequency, were found to be associated with hypertension. No connection was identified between hypertension and concurrent diseases or cooking methods. This study's findings indicate a substantial link between body mass index (BMI) and hypertension. The study emphasizes the necessity of consistent BMI monitoring and lifestyle modifications for the prevention and management of hypertension.
Keywords : Hypertension, Body Mass Index (BMI), Obesity, Lifestyle Changes, Chi-Square Test, Socio-Demographic Variables, Clinical Variables, Hypertensive Patients, Blood Pressure.
| 13 |
Author(s):
Viraj P. Tathavadekar, Dr. Nitin R. Mahankale.
Research Area:
Information Technology
Page No:
131-146 |
Sustainable Last-Mile Delivery Innovations: Decarbonizing IT Product Logistics Through Smart Technologies
Abstract
The final stage of IT product delivery operations comes under scrutiny by the adoption of smart technologies that have the purpose to reduce carbon emissions and maintain the operational efficiency. The research proposes three innovative technological solutions that revolutionize traditional logistics operations to create sustainable supply chains. A projected analysis up to the year 2030 indicates that the combination of artificial intelligence route optimization with electric delivery vans and micro-fulfillment centers will achieve a 62.8% reduction in emissions and a 43.5% improvement in delivery efficiency. The research assesses the impact of blockchain-based supply chain transparency combined with Internet of Things (IoT) solutions on this correlation. A structural equation model reveals that the use of smart technology helps to break the connection between decarbonization effects and sustainability policy. The research provides fresh theoretical insights to the development of sustainable logistics along with real-life strategies to applying technology within IT product delivery systems.
| 14 |
Author(s):
Dr. Himanshi.
Research Area:
Nanotechnology
Page No:
147-151 |
Understanding Nanobubble–Nanoparticle Systems: Formation, Nucleation, and Submicron Interactions
Abstract
Over the last twenty years, gas-saturated liquids have drawn increasing interest due to the unexpected stability of nanobubbles observed within them. This stability challenges conventional understanding and has prompted investigations into a wide array of potential applications, particularly those involving interactions between nanobubbles and nanoparticles. This review explores the current knowledge surrounding systems where both entities coexist, which is common in many nanobubble-related technologies. We discuss key processes such as the generation of nanoparticles from nanobubbles, the initiation of nanobubbles on nanoparticle surfaces, and the interactions that occur between them. While substantial progress has been made, critical aspects—such as the precise mechanisms underlying nanobubble nucleation and the nature of nanoscale interactions—remain poorly understood. Continued research in this area is vital to deepen our theoretical insight and to harness these phenomena for practical use.
| 15 |
Author(s):
Mr. Faeem Ahmad, Mr. Rajneesh Kumar.
Research Area:
Mechanical Engineering
Page No:
152-157 |
Current Developments and Prospects in Energy Harvesting and Storage
Abstract
Technologies for energy harvesting and storage are leading the way in developing sustainable energy solutions. The process of turning ambient energy—such as vibrations, heat, light, and radio frequency signals—into usable electrical energy is known as energy harvesting. Energy storage technologies, such as batteries and supercapacitors, work in tandem to guarantee the steady and dependable retention of this captured energy. Reducing reliance on fossil fuels, cutting carbon emissions, and improving energy efficiency in a variety of applications—from wearable electronics and Internet of Things devices to large-scale power systems—all depend on the integration of these technologies. Piezoelectric, electromagnetic, electrostatic, thermoelectric, solar, radiofrequency, bioenergy, and hydro energy sources are just a few of the energy harvesting techniques that are examined in this overview, which also highlights contemporary developments and their uses. In the realm of energy storage, significant progress in lithium-ion, solid-state, and flow batteries, as well as supercapacitors, is discussed. For continuous and sustainable power solutions, the research highlights how crucial it is to integrate energy harvesting and storage technologies. Applications of these technologies in smart grids, environmental monitoring, wearable electronics, and remote sensing are investigated. Along with these issues, the evaluation also discusses economic viability, efficiency, scalability, reliability, and safety and environmental issues. New materials and technologies promise better performance in the future, and hybrid systems that combine several energy harvesting and storage techniques provide reliable answers. The contribution of regulatory and policy changes to innovation is also taken into account. In conclusion, the technologies for energy harvesting and storage are developing quickly and will be essential to the shift to sustainable energy systems. The assessment emphasizes that in order to progress the subject and overcome current obstacles, interdisciplinary collaboration is essential.
| 16 |
Author(s):
Mr. Rakesh Kumar, Dr. Rita Kumari Saini, Dr. Mohit Verma.
Research Area:
Computer Engineering
Page No:
158-170 |
Potato Leaf Disease Detection Using Feature-Optimized Machine Learning Models
Abstract
Food security and agricultural output are seriously threatened by potato leaf diseases including Early Blight and Late Blight. Through the use of machine learning (ML) and deep learning (DL) approaches to image processing, this work investigates the automated identification of these disorders. Two models were trained using a preprocessed dataset of potato leaf pictures from the Plant Village repository: a Convolutional Neural Network (CNN) and a K-Nearest Neighbours (KNN) classifier. CNN learnt features directly from raw pictures, but KNN was developed using created features retrieved via colour, texture, and form analysis. The findings indicate that while KNN provides ease of use and interpretability, its scalability is constrained and its accuracy ranges from around 70 to 80%. With accuracy ranging from 90% to 98%, CNN, on the other hand, performs noticeably better than KNN and has remarkable resilience in actual agricultural circumstances. The study demonstrates CNN's supremacy in automated, real-time disease identification and its potential for integration into drones, smartphone applications, and Internet of Things-enabled precision farming systems, providing an effective tool to support farmers in sustainable agriculture and early disease control.
| 17 |
Author(s):
Viraj Tathavadekar, Dr. Nitin R Mahankale.
Research Area:
Environmental Science
Page No:
171-204 |
AI-Powered Sustainable Supply Chains: A Machine Learning Framework for Circular Economy Transitions by 2040
Abstract
Integration of artificial-intelligence (AI) and machine-learning (ML) technologies within sustainable supply chain management (SSCM) represents a paradigm shift toward achieving circular economy (CE) objectives by 2040. This systematic literature review examines the convergence of AI-powered systems and circular supply chains through a comprehensive analysis of 170 peer-reviewed articles from Q1 Scopus-indexed journals published between 2020-2025. The study identifies critical research gaps in AI-driven circular economy transitions and proposes a novel dual-framework approach for implementing intelligent sustainable supply chains. Our findings reveal that while AI applications in supply chain optimization have increased by 300% since 2020, only 23% of current implementations specifically target circular economy principles. The research contributes by developing two innovative frameworks: (1) the AI-Circular Economy Integration Model (AI-CEIM) and (2) the Machine Learning Sustainability Assessment Framework (ML-SAF). Through structural equation modeling (SEM) analysis of 12 organizational case studies, we demonstrate that AI-powered circular supply chains can achieve 45% reduction in waste generation, 38% improvement in resource efficiency, and 52% enhancement in supply chain resilience by 2040. The study provides actionable insights for practitioners and establishes a roadmap for future research in AI-driven sustainable supply chain management.
