Abstract

Lumpy Skin Disease (LSD) is a viral infection that impacts cattle. This may result in financial setbacks in the dairy and livestock sectors. Timely identification of the illness is essential for improved treatment and for halting its transmission. Conventional diagnostic approaches, including clinical assessments and lab examinations, require considerable time and resources. Recent advancements in artificial intelligence, particularly in image processing through machine learning, offer efficient methods for automated LSD detection. This evaluation provides an examination of existing techniques, contrasting their advantages and disadvantages. We examine key obstacles in practical implementation and propose avenues for future studies to enhance the precision and effectiveness of LSD detection systems. 

Abstract

Artificial intelligence (AI) may be a game-changer for farmers coping with the escalating problems of climate change. AI models can predict and mitigate the wide-ranging impacts of climate change on agriculture, providing farmers with state-of-the-art tools to aid in their decision-making. As environmental issues intensify, AI integration is starting to shift the game for climate-resilient agriculture. In order to manage the complexities of climate unpredictability, the chapter discussed how AI may assist farmers in making adaptive decisions in this scenario. The advantages of AI and climate research working together in concert to identify climate-related risks, such as extreme weather, changed precipitation patterns, and new pest concerns It also highlights the impact of AI on smallholder and rural farmers in an effort to increase overall resilience. Someone has to take initiative, manage irrigation, distribute resources, and pick crops carefully. A thorough analysis is conducted of the possible advantages and challenges of widespread AI application in various agricultural situations. For scholars, policymakers, and business leaders who want to address resilient and sustainable farming practices for the benefit of future generations while simultaneously advancing AI in agriculture Crop yields, soil health, and water availability will all be impacted by climate change, which presents significant difficulties for global agriculture. Artificial intelligence (AI) and machine learning (ML) are emerging as game-changing technologies for developing climate-resilient agriculture in response to these problems. AI-powered technologies that combine computer vision, deep learning, reinforcement learning, and predictive analytics provide precise climate forecasting, early disease detection, and economical resource utilization. Machine learning techniques such as support vector machines (SVMs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) enhance crop monitoring, yield prediction, and soil quality assessment. Additionally, reinforcement learning and Internet of Things (IoT) integration enable smart irrigation systems and adaptive decision-making in unexpected climate conditions. This research provides a comprehensive analysis of the use of AI and ML in precision agriculture, climate-smart farming, and sustainable land management. We discuss the most recent advancements in self-sufficient farming, remote sensing, and geospatial analysis that contribute to increased climate change resilience. The study also looks at ethical concerns with the use of AI, interpretability of models, and data scarcity. Edge AI, blockchain-based agricultural intelligence, and federated learning are some of the developing concepts that the report highlights as potentially useful for future climate-resilient farming systems.

Abstract

The present work elaborates the ongoing work in banana fiber reinforced in bio plastic for the making of green and sustainable composite. Non-woven banana fiber was used as reinforcement in bio polymer known as poly lactic acid (PLA). The mat of non-woven banana fiber rinsed in the melt PLA was pressed against the moving hydraulic press in a specific die of compression moulding machine. Fiber loading in PLA resin was meticulously selected as 20, 25, 30, 35 and 40 % of the total composition. The results obtained for the prepared composites shows that significant improvement was observed in the mechanical properties such as tensile, impact and flexural strength till 35 % of banana fiber reinforcement. At 40 % reinforcement of banana fiber, the improvement in tensile, impact and flexural strength was marginal. Hardness value of prepared composites found to be decreased with the addition of banana fiber. Among all the reinforcing conditions, tensile strength was noticed as64 MPa (maximum), and highest flexural strength was found to be 49 MPa, both at 35 % loading of banana fiber. Impact energy was also observed to be highest among all at 35 % of banana fiber loading which is found to be 2.2 J. The addition of banana fiber in PLA results in lower onset temperature of PLA and early degradation of composite. The obtained results aresufficientfor the development of low-cost products used in daily basis such as basins, basket, kitchens, and toilet accessories. It may also beneficial to expedite the development of sustainable and durable composite. The development of banana-reinforced PLA composite would be beneficial in meeting the needs of environmental safety agencies at a lower cost.

Abstract

Functional genomics aims to understand the dynamic aspects of gene expression and function at a systems level. Network biology offers a powerful framework to uncover functionally coherent gene modules by integrating various types of biological data. This review summarizes the current tools and computational techniques used for functional gene module discovery, highlighting their theoretical foundations, strengths, and limitations. We also discuss recent advances in integrating multi-omics data, single-cell analyses, and the application of machine learning. Emphasis is placed on the biological relevance and translational potential of identified gene modules in areas such as disease mechanism elucidation, biomarker discovery, and therapeutic target identification.

Abstract

Messenger RNA (mRNA)–based vaccines have revolutionized modern immunization by offering a flexible and rapid platform for combating infectious diseases. When combined with lipid nanoparticles (LNPs), these vaccines gain enhanced stability, targeted delivery, and efficient cellular uptake. The integration of mRNA technology with lipid-based nanocarriers has opened new possibilities for developing broad-spectrum vaccines capable of inducing strong and durable immune responses against multiple viral pathogens.

This innovative hybrid approach enables the delivery of multiple antigen-encoding mRNAs within a single formulation, promoting both humoral and cellular immune responses. Moreover, lipid nanoparticles protect the fragile mRNA from enzymatic degradation and facilitate endosomal escape, ensuring efficient protein expression in host cells. Such designs can be fine-tuned to address emerging viral variants, including influenza, coronavirus, and other zoonotic threats.

Beyond their immediate role in pandemic preparedness, mRNA–lipid hybrid nanovaccines represent a transformative step toward personalized immunization and universal antiviral defense. Continued research into optimizing lipid composition, immune adjuvants, and storage stability will be critical to realizing their full potential in global public health.

Abstract

The human gut microbiota plays a foundational role in health, particularly influencing physical performance, metabolism, and cognitive well-being. This paper explores how insights from gut microbiota science can be integrated with physical education, especially in the Indian context where nutrition, hygiene, and exercise patterns vary widely across regions. With rising rates of non-communicable diseases (NCDs) and mental health challenges among Indian youth, a multidisciplinary model linking microbiome awareness with physical education is both timely and transformative. The study also emphasizes the role of AI, digital health platforms, and indigenous health knowledge systems in promoting sustainable well-being, aligned with India's National Education Policy (NEP 2020) and the UN Sustainable Development Goals (SDGs).