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Wireless capsule endoscopy (WCE) is medical examination process for gastrointestinal tract (GIT). This noninvasive multi advantageous procedure can be made more popular by overcoming the problem of prolonged analysis time. Video summarization is a concise and meaningful representation of a video. Along with automated detection and segmentation methods, summarized video will serve as an additional source for confirming the analysis of WCE video before the final diagnosis without any dropouts. Nowadays, Internet of Things (IoT) environments are predominant in healthcare sectors. Considering limited resources of smart phones and long duration of WCE process, it is impractical to send all the WCE data to health-care centers or gastroenterologists. This paper reviews video summarization types and the techniques used for WCE video summarization by various researchers. Feature set selection, clustering methods and key frame selection techniques play important role in performance of summarization technique.

Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

Book review. Food, Agri-Culture and Tourism: Linking Local Gastronomy and Rural Tourism: Interdisciplinary Perspectives

The increasing demand for efficient and cost-effective energy storage systems has pushed extensive research into improved materials for next-generation energy storage devices. This study discusses the crucial significance of material advances in boosting the performance and reducing the costs of storage technologies such as batteries and supercapacitors. Conventional energy storage systems face limits in energy density, charge or discharge rates, and scalability, which impede their broad implementation. Advanced materials, including nanomaterials, solid-state electrolytes, and innovative electrode compounds, offer solutions to these difficulties by enhancing energy efficiency, power output, and overall longevity. Additionally, the use of plentiful and low-cost materials, such as sodium-ion and aluminium-based compounds, presents prospects for significant cost savings. This research analyzes current trends, issues in material manufacturing, and future perspectives for energy storage systems, concentrating on balancing efficiency improvements with cost-effectiveness to enable the rising integration of renewable energy sources. The development of these materials is important to creating sustainable, scalable, and economical energy storage systems for the future.

The worldwide energy market is intricate and unstable, shaped by several aspects including geopolitical occurrences, supply-demand variations, and regulatory modifications. Precisely forecasting energy prices and trends is essential for stakeholders, such as energy producers, dealers, and policymakers. This study investigates the utilization of artificial intelligence (AI) and machine learning (ML) to improve energy price forecasting models. Conventional forecasting methods frequently fail to account for the dynamic and non-linear characteristics of energy markets; however, AI/ML techniques, including neural networks, decision trees, and reinforcement learning, provide enhanced prediction precision. By including external variables such as meteorological conditions and economic metrics, AI models can produce more accurate and useful insights. Case studies illustrate the effective implementation of AI in energy markets, showcasing its capacity to surpass traditional methods. This article addresses difficulties such as data quality and computing expenses while delineating potential developments in AI-driven energy market forecasts.

This paper investigates the potential of blockchain technology to transform energy markets through peer-to-peer (P2P) energy trading. Blockchain enables decentralized, transparent, and secure energy transactions, allowing consumers to trade electricity directly, thereby reducing reliance on traditional centralized systems. The study examines the main challenges facing current energy markets, including inefficiencies, pricing complexities, and the integration of renewable energy sources. By utilizing smart contracts, blockchain automates and secures energy exchanges, giving consumers a more active role in the market. The paper also explores the technical aspects of implementing blockchain in energy trading, such as infrastructure needs and scalability issues, while addressing the regulatory, legal, economic, and environmental implications of this technology. Real-world examples and case studies underscore the potential of blockchain to foster more resilient, efficient, and sustainable energy markets.

Diabetes mellitus commonly considered as clandestine killer is a metabolic disorder that affects people all over the world and is a silent killer. A variety of treatments are available, including insulin and oral anti-diabetic medications for regulating blood sugar levels in the body but every treatment option has certain unfavorable side effects that might cause abnormalities with the macro and microvascular systems in the human body. Kale (Brassica oleracea var. acephala) from the brassica family is gaining popularity due to its phytochemical content and ability to prevent many ailments, notably those brought on by oxidative stress. The current study assessed Brassica oleracea var acephala of the UT of Kashmir locally known as khanyari as it is loaded with various bioactive substances including total phenols, flavonoids, and glucosinolates that impart antioxidant and antidiabetic properties. Kale(K-29) showed impressive results in normalizing the blood glucose levels(102.30mg/dl) as well as improving the lipid profile such as cholesterol(76.22mg/dl), HDL(36.4mg/dl), LDL(36.1mg/dl), VLDL(21.2mg/dl) and triglycerides(85.40mg/dl) that is usually accompanied with hyperglycemia. The treatment of K-29 also showed improvisation in the various hepatic parameters such as S.G.P.T(91.14), S.G.O.T (41.18), Alkaline phosphatase (102.32U/L), Albumin (6.12g/dl), S. bilirubin direct(0.6mg/dl), S. bilirubin indirect(0.43mg/dl).

Integrating carbon footprint reduction into contemporary supply chain networks necessitates strong cybersecurity and data integrity protocols to safeguard communication infrastructures against threats. This study examines the use of blockchain technology as a cybersecurity and traceability framework in supply chain systems, using simulated data that represents logistical information, shipping quantities, carbon emissions, and blockchain transactions. An investigation indicates varied carbon-emitting mechanisms, with air freight exhibiting the greatest emissions and rail transit the lowest. Transportation nodes in Supply varying emission rates, with urban distribution centres generating more emissions than rural logistics hubs, underscoring disparities in carbon footprints across different areas. Moreover, risks such data breaches, illegal access, and cyberattacks underscore the fragility of centralized supply chain systems. Blockchain transactions exemplify secure and transparent exchanges throughout all of the supply chain, highlighting blockchain’s capacity to provide safe and verifiable monitoring of carbon emissions inside the network. This research Supply the potential of blockchain technology to diminish carbon footprints in supply chains, emphasizing its ability to enhance data security, authenticate carbon emission assertions, and augment traceability. The findings highlight the need for customized cybersecurity frameworks for Supply demonstrate blockchain’s capacity to improve the transparency, security, and sustainability of global supply chain operations.

This study uses econometric modeling to assess carbon tax programs’ efficacy in reducing carbon emissions across economic sectors. Multiple case studies, tax incentive analysis, and energy-efficient technology promotion reduced carbon intensity per unit of production by 25%. Additionally, dynamic pricing schemes matched with carbon taxation frameworks reduced emissions by 15%. These policies integrated into economic models outperformed standard regulatory methods, reducing emissions by 30% while maintaining or improving economic efficiency. The policy ideas have been tested in real-world settings, proving their practicality and applicability across sectors. This work contributes to climate change mitigation discourse and helps policymakers and business stakeholders combine environmental responsibility with economic development in a constantly changing global context.

This research used system dynamics modeling to assess the synergies generated by industrial symbiosis in food supply chains, focusing on resource recovery and waste reduction. The examined symbiotic systems included food waste valorization, energy recovery, and by-product interchange across sectors including agricultural, food processing, and bioenergy production. The findings indicated that the agriculture sector achieved a 15% decrease in waste via effective by-product exchange. Food processing showed a 20% reduction in energy use via waste-to-energy conversion. The statistical study revealed a substantial association between enhanced symbiotic relationships and improved resource efficiency. Furthermore, the significance of inter-industry cooperation was paramount, since food waste was reused more efficiently in areas with robust industrial networks. Moreover, system performance was affected by external influences, including market demand and regulatory rules. These results provide significant insights into the capacity of industrial symbiosis to improve circular economy practices within food supply chains, hence promoting sustainability and resource efficiency.