The agriculture sector is currently facing several challenges, including the growing global human population, depletion of natural resources, reduction of arable land, rapidly changing climate, and the frequent occurrence of human diseases such as Ebola, Lassa, Zika, Nipah, and most recently, the COVID-19 pandemic. These challenges pose a threat to global food and nutritional security and place pressure on the scientific community to achieve Sustainable Development Goal 2 (SDG2), which aims to eradicate hunger and malnutrition. Technological advancement plays a significant role in enhancing our understanding of the agricultural system and its interactions from the cellular level to the green field level for the benefit of humanity. The use of remote sensing (RS), artificial intelligence (AI), and machine learning (ML) approaches is highly advantageous for producing precise and accurate datasets to develop management tools and models. These technologies are beneficial for understanding soil types, efficiently managing water, optimizing nutrient application, designing forecasting and early warning models, protecting crops from plant diseases and insect pests, and detecting threats such as locusts. The application of RS, AI, and ML algorithms is a promising and transformative approach to improve the resilience of agriculture against biotic and abiotic stresses and achieve sustainability to meet the needs of the ever-growing human population. In this article covered the leveraging AI algorithms and RS data, and how these technologies enable real time monitoring, early detection, and accurate forecasting of pest outbreaks. Furthermore, discussed how these approaches allows for more precise, targeted pest control interventions, reducing the reliance on broad spectrum pesticides and minimizing environmental impact. Despite challenges in data quality and technology accessibility, the integration of AI and RS holds significant potential in revolutionizing pest management.

Municipal solid waste (MSW) management is a critical global challenge due to increasing urbanization and population growth. This context highlights the challenges faced by urban societies in handling municipal solid waste and innovative solutions and technologies employed for efficient waste management. Integrated MSW management strategies aim to maximize resource recovery while minimizing the environmental and social impacts. It emphasizes the potential benefits of adopting advanced waste management and recycling practices both in terms of environmental conservation and socio-ecological viability. Collaborative efforts between governments, industries, and communities are crucial and mandatory for the successful implementation of comprehensive MSW management and recycling programs.

Pasteurization of orange juice is a critical food safety measure commonly achieved through heat treatment of the same at 92 °C for 20 s so as to secure microbial inactivation to provide better shelf life. Pasteurization, being an energy intensive process, consumes 0.932 MJ/kg of thermal energy which causes considerable amount of exergetic degradation. In light of this, the objective of present research endeavor is to get in – depth understanding of energy, exergy and exergo-economic aspects of pasteurization activity by way of unearthing all associated derivatives such as energy efficiency, exergy efficiency, energy improvement potential, exergy improvement potential and sustainability index of the Orange Juice Pasteurization Plant. The specific application of second law of thermodynamics along with its integration with economic formulations from Specific Exergy Costing Method (SPECO) generates a platform where thermodynamic and thermo-economic outcomes are summarized for better diagnosis of operational deficiencies. Further, the exergy efficiency, specific exergy destruction and specific exergy improvement potential were computed as 75.97%, 125.22 kJ/kg and 74.88 kJ/kg while, thermo-economic derivatives such as levelized cost rates, specific cost of exergy destruction, cost rate of exergy destruction, total operating cost rate and specific manufacturing cost, were computed as 44.89 Rs./hr., 0.026 Rs./kJ, 1700.06 Rs./hr., 1744.95 Rs./hr. and 3.29 Rs./kg respectively. The present assessment develops a clear picture of energy related degradation along with detailed information about energy saving potentials available with the plant. It is pertinent to mention that a systematic approach aimed at energy conservation would consequently lead to improvement in sustainability as well as economic characteristics of plant.

The Hot Metal Ladle Cars (HMLC) is frequently employed for transportation of molten iron at elevated temperatures from furnace to Steel Production Units (SPU). It moves along the railways with the help of traction equipment and tilted with a winch or crane to pour the liquid metal at designated place. It basically consists of bearing frame, railway carriages, ladle and automatic-coupler. The current research involves the design modification of the body of auto-coupler attached to the HMLC; to prevent its frequent breakdowns and increase its life and structural stability, which leads to reduction in reducing repair costs and circuit time. In connection to the same, auto-coupler body has been simulated and structurally analyzed for three configurations incorporating Rectangular Slot (RS), Circular Slot (CS) and Ellipsoid Slot (ES) under equivalent static loading 3 MN Compressive as well as 2.5 MN Tensile. It has been identified from the detailed diagnosis that auto-coupler body having ellipsoid slot, adjudged to be most stable amongst all aforementioned configurations owing to least value of resultant stress (327 MPa and 272 MPa) under the action of compressive and tensile loadings respectively. Besides that, the highest fatigue life (5186 cycles) was reported for the same configuration which generates an aggregate economic savings of 25.02 Crores per annum or 2,428,578 Euro per annum.

In the present era, lifestyle changes, increased exposure to water, unhygienic ear practices, and environmental factors have contributed to a rising incidence of external ear infections. Otitis externa is a common inflammatory condition of the external auditory canal characterised by pain, itching, discharge, swelling, and tenderness. Conventional management includes topical antibiotics, antifungal agents, and analgesics, which often provide temporary relief and may lead to recurrence or adverse effects with prolonged use. In Ayurvedic literature, otitis externa can be correlated with Karnasrava, described under Karnarogas.1 These conditions are mainly caused by vitiation of Vata and Kapha Doshas along with Krimi and Kleda involvement. Ayurveda emphasises local therapeutic procedures along with Dosha pacification. Karnadhoopana2 is one such classical procedure indicated in ear disorders, possessing antimicrobial, anti-inflammatory, and analgesic properties. The present case study evaluates the effect of Karnadhoopan using drugs such as Hingu, Sarjarasa, Haridra, Sarshapa, Vacha, Ghrita, Karpura, Vidanga, and Guggulu in the management of otitis externa. Significant improvement was observed in Ear Discharge, pain, itching, swelling, and tenderness after completion of therapy.

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector Machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7 ^% , followed by KNN at 96.7 ^% and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain.