Volume 13 | Issue 8 |

 Abstract

This literature review examines the integration of artificial intelligence (AI), sensor networks, and IoT technologies in modern agriculture, with a focus on weed detection, animal intrusion prevention, irrigation automation, and soil health monitoring. It analyzes recent journal publications (2020-2025) that utilize deep learning models such as CNN and YOLOv5/YOLOv8 for real-time object detection, along with smart sensors for environmental data collection. Mobile applications and microcontrollers like Raspberry Pi and NodeMCU are also evaluated for automation and farmer interaction. The review highlights the limitations of existing systems--including poor cross-domain performance, reliance on internet connectivity, and lack of regional language support--and identifies the need for an integrated, edge-based, multi-functional solution like the Krishi ????? - The AgriFusion project. This study is intended to guide future work on accessible and sustainable smart farming platforms for smallholder farmers.

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 Keywords

Weed Detection, IoT in Agriculture, Smart Irrigation, Animal Intrusion Detection, CNN

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Sphaeranthus Indicus Linn or East Indian Globe Thistle is a multi-branched aromatic herb consisting of various medicinal properties, and is used in the treatment of skin conditions, as an antimicrobial, immunomodulator, immunostimulant and in wound healing traditionally.[9] All the parts of the Sphaeranthus Indicus have been utilized for the treatment of various disease conditions out of which the marketed herbal formulations of Sphaeranthus Indicus were investigated for its toxicity profile which showed dose dependent toxicity. Toxicity profile of this marketed formulation was not investigated in previous studies Aim: - Explore the acute and subacute toxicity profile of the following two marketed herbal formulation of Sphaeranthus indicus linn for the safe use of traditional medicine and explore its potential for further effectiveness. Method: - In Acute toxicity study of both herbal formulations, Total no. of animals used were 24 male Swiss albino mice, weighing 25-30g, randomly assigned to experimental groups of 5mg/kg,50mg/kg, 300mg/kg and 1000mg/kg with 3 mice each and receiving single dose of the extract at 5mg/kg b.w,50mg/kg,300mg/kg and 1000mg/kg b.w respectively. Animals were monitored for 14 days for signs of toxicity, behavioral changes, mortality etc. In Sub-acute toxicity study, Total no of animals used were 70 Swiss albino mice weighing 25-30 g consisting of male and female Swiss albino mice randomly assigned to experimental groups of 50mg/kg, 1000mg/kg ,2000mg/kg with control group been given distilled water. The extract of herbal formulation of Sphaeranthus indicus was given at the doses of 50mg/kg,1000mg/kg,2000mg/kg per body weight of the mice for 28 consecutive days. The studies were conducted in compliance with OECD guidelines 423 and 407. Results: - Acute toxicity study showed no mortality up to the dose of 1000mg/kg, but mortality rates increase with higher doses. In subacute toxicity study, there was no mortality, up to the dose of 2000mg/kg with increase in deaths at higher doses. The hematological and biochemical analysis of the blood parameters don't show much significant differences. The histopathological analysis of liver shows marked lesions at higher doses. Conclusion: - It is concluded that the oral administration of doses of herbal formulation of Sphaeranthus indicus in acute study don't cause any mortality or other changes up to the level of 1000mg/kg. At higher doses there are possibilities of finding gross changes. In subacute study, no significant changes in the hematological, biochemical, and histopathological parameters were observed. There is mild grade of lesion with repeated and daily dosing of the drug at higher dose of 2000mg/kg indicating that at higher doses with repeated dosing there is mild level development of toxicity signs.

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 Keywords

Sphaeranthus indicus Linn, Hematology, Biochemical, Histopathology, toxicity

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Integrating IoT, Blockchain, and Deep Neural Networks (DNNs) creates a secure, intelligent, and decentralized data ecosystem. IoT devices collect real-time data, while blockchain ensures tamper-proof, transparent, and trusted data exchange. DNNs analyze this data to extract insights and enable predictive decision-making. Blockchain smart contracts automate secure communication and access control, while federated learning allows DNNs to train without exposing raw data. This synergy enhances scalability, privacy, and resilience, making it ideal for applications like smart cities, healthcare, and industrial automation. The model overcomes traditional ML limitations by distributing intelligence and trust across the network.

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IoT, Blockchain, Deep learning, Deep Neural Network, ML, Edge Computing, Federated Learning

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India's rich flora, including medicinal plants, is rapidly disappearing due to over-exploitation. To manage this resource, mass propagation of crops using plant tissue culture strategies is necessary. The conservation and domestication of endangered and threatened species is a critical global issue due to habitat destruction, fragmentation, degradation, and human-wildlife conflict. This review provides an overview of the conservation and domestication status of these species, their successes and challenges, and future directions for conservation and domestication. The National Medicinal Plants Board (NMPB) in New Delhi has been instrumental in conservation and cultivation of these species, addressing habitat loss, biotic and abiotic interference, and unsustainable harvesting. The NMPB has funded numerous projects for the conservation and cultivation of endangered and threatened medicinal plants. Future directions for conservation and domestication include technology, community engagement, and policy support.

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Over-exploitation, Plant Tissue Culture, Conservation, Domestication, Endangered Species, Threatened Species, Habitat Destruction, Fragmentation, Degradation, Human-Wildlife Conflict, Habitat Loss, Biotic Interference, Abiotic Interference, Unsustainable Harvesting, Technology, Community Engagement etc

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Artificial Intelligence refers to the capability of machines to perform tasks that typically need human intelligence such as learning, problem solving, and decision making. It incorporates a wide range of technologies and techniques that enable computers to mimic cognitive functions and exhibit human like capabilities. Attitude is a mental tendency that how someone thinks, feels, or behaves towards something's or someone in a positive or negative way. Attitudes towards AI are influenced by perceived usefulness, ease of use, and the risks associated with AI. These attitudes, in turn, influence an individual's intentions to use AI or not. Undergraduate students play a vital and growing role in the field of artificial intelligence contributing across academics, research, skill development and real world innovation. Therefore, their attitudes are very important in this perspective. The objective of this article is to examine college students' attitudes toward artificial intelligence. Stratified random sampling was used for selecting the sample comprising of 203 (Male=58 and female=145) undergraduate students from Serampore College, affiliated to University of Calcutta, West Bengal, India. Descriptive survey research design was used. The General Attitudes towards Artificial Intelligence Scale (GAAIS) was used to collect data. Data were analysed by using Mean, SD, t-test, ANOVA. The result indicated that undergraduate students display positive attitudes towards AI. The finding discovered that no significant difference in regard to attitudes towards artificial intelligence between male and female students. The result also displayed that there was a significant difference in regard to residence of student's attitudes towards artificial intelligence in positive subscale but no significant difference in regard to residence of student's attitudes in negative subscale.

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Artificial Intelligence, attitudes, undergraduate students, GAAIS.

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High-temperature superconductors (HTS) have emerged as pivotal materials in modern science and engineering due to their ability to conduct electricity without resistance at relatively higher temperatures compared to conventional superconductors. Since the discovery of cuprate superconductors in the late 1980s, materials such as YBa?Cu?O?-? (YBCO), BSCCO, and iron-based compounds have demonstrated critical temperatures well above the boiling point of liquid nitrogen, making them more viable for practical applications. This paper explores the theoretical foundations, material properties, and real-world uses of HTS, focusing on sectors such as energy transmission, magnetic levitation transport, medical imaging, and quantum computing. Through a detailed review of literature, analysis of technical data, and case studies, the research identifies both the transformative potential and the key challenges associated with HTS technologies. It highlights issues like material brittleness, fabrication complexity, and cryogenic cooling requirements, while also presenting current advancements in flux pinning, coated conductor technology, and scalable engineering solutions. The findings indicate that while widespread adoption remains constrained, on-going innovations are steadily bringing HTS systems closer to commercial viability, with profound implications for sustainable and high-performance technologies of the future.

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High-Temperature Superconductors; YBCO; BSCCO; Iron-Based Superconductors; Zero Resistance; Power Transmission; Maglev Trains; MRI; Quantum Computing; Materials Science; Cryogenic Technology

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Seed testing is crucial for modern agriculture, as it ensures the quality, purity, and viability of seeds before planting. By evaluating parameters like purity, germination potential, moisture content, and seed health, seed testing provides vital information for farmers and seed producers to make informed decisions about planting and crop performance. It also helps prevent the spread of diseases and pathogens, contributing to sustainable farming practices and increased productivity. Mainly there are two methods of seed testing, destructive testing and non-destructive testing. As it clear that sample is destroyed in destructive testing method and based on its result batch of that seeds quality estimated. In non-destructive testing method seed samples are tested using computer vision and electronics sensors. In this this paper various seed testing methods like computer vision, image processing, spectral analysis of non-destructive testing are explore with key benefit and their limitations.

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NDT of seed, Image processing, Seed quality testing, Multispectral testing, Computer vision.

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Ketotifen hydrazine, a tricyclic antihistamine, is a structurally distinct compound from ketotifen, possessing an additional diazene moiety. Although identified as an H?-antihistamine and mast cell stabilizer, its preparations remain pending formal approval from major regulatory agencies, including the FDA and EMA. Despite sharing a similar mechanism of action with ketotifen fumarate, ketotifen hydrazine has not been marketed as a standalone therapeutic agent globally. This hypothetical analysis aims to explore the pharmacological and therapeutic potential of ketotifen hydrazine, addressing the knowledge gaps in its development and potential applications. By examining its structural uniqueness and pharmacological profile, this study provides a comprehensive understanding of ketotifen hydrazine's possibilities, limitations, and future directions, ultimately informing regulatory decisions and guiding its potential entry into the global market.

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Ketotifen hydrazine, antihistamine, hypothetical.

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INTRODUCTION: Immunization is one the most cost-effective health services to prevent chroni illness that contribute to child mortality in the country, particular in environment where malnourished children, overcrowding, poverty and illiteracy reign childhood immunization has been an important part of maternal and child health services. Objective: To assess the knowledge level regarding revised Immunization among mother of under five children. Methods: The quantitative research approach and survey research design was used to collect the data. The total 60 samples was collected by purposive sampling technique. Demographic and structured questionnaires was used to collect the data. Result: The Result shows that Majority of the mothers were between 20-35 years of age, All are 100% (100) were female, 57% (23) were having secondary school certificate, Majority of 70% (28) under-five mothers were housewife, 80% (34) religion I Hindu, 82.5% (33) were married, 50% (20) were nuclear family,45% (18) under-five mothers having the information about immunization. The level of knowledge of mothers pretest was categorized in poor, good and average. The result shows that 50% mothers of under five children have good knowledge, 45%have average knowledge and 5% mothers f under five children have poor knowledge.

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Knowledge, under five children mothers, revised immunization

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Financial success is important for companies looking to grow in the challenging marketing environment. To increase profitability and remain competitive in the marketplace, businesses must effectively adapt to changes in market conditions. The key operational decision that ultimately affects expectations for performance is to make the three key financial decisions regarding investment, financing, and dividends. This study investigates the link between capital structure and profitability in a sample of manufacturing enterprises, with a particular focus on financing decisions. Over a ten-year period from 2011 to 2020, it examined five industries that produce manufactured goods: iron and steel, metals, oil and gas, medicines, and electricity generation and distribution. Using the "CAPITALINE" database, the National Stock Exchange (NSE) provided the secondary data for this investigation, including sales turnover. The generalized method of moments (GMM) was used to analyze the data within the parameters of a dynamic panel regression model. For the sample of firms, the study's findings demonstrated a strong correlation between capital structure choices and manufacturing environment profitability.

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Capital Structure, GMM Method, Dynamic Panel Regression, National Stock Exchange (NSE)

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