Volume 13 | Issue 8 |

 Abstract

This study evaluates the cognitive level of textual questions in the Algebra unit of the Class X Mathematics textbook prescribed by the Mizoram Board of School Education, using Revised Bloom's Taxonomy (RBT). A content analysis of end-of-chapter questions across five chapters (Polynomials, Linear Equations in Two Variables, Quadratic Equations, Arithmetic Progression, and Sets) was conducted. Results indicate a predominant focus on lower-order thinking skills (LOTS), with 81.9% of questions categorized as "Applying" (C3), while higher-order thinking skills (HOTS) such as "Analyzing" (C4) and "Evaluating" (C5) constituted only 18.1%. Notably, "Creating" (C6) questions were absent. The findings highlight an imbalance in cognitive demand, underscoring the need for curriculum revisions to incorporate more HOTS-based questions to foster critical thinking and problem-solving abilities among students.

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Algebra, Cognitive Level, Revised Bloom's Taxonomy, Mathematics Textbook, Mizoram Board.

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This paper analyzes N.K. Jemisin's Broken Earth trilogy as a feminist ecopolitical allegory that links environmental injustice to systemic oppression. Drawing on ecofeminist theory, postcolonial critique, and narrative analysis, the study explores how Jemisin's depiction of the Anthropocene foregrounds the intertwined exploitation of the Earth and marginalized bodies, particularly women and orogenes. The Sanzed Empire's extractive systems mirror real-world structures of capitalism, colonialism, and racial hierarchy, while the trilogy's resistance movements center care, reciprocity, and community-based governance. Through narrative strategies such as second-person perspective and the reimagining of the Earth as an active political subject, Jemisin reframes liberation as ecological as well as social. The analysis argues that the trilogy's vision resonates with contemporary climate justice movements, calling for the dismantling of intersecting systems of domination and the cultivation of sustainable, reciprocal relationships with the planet.

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Keywords: Feminist Ecopolitics, Environmental justice, Political allegory, Anthropocene, Ecofeminism, Climate justice, Postcolonial critique, Speculative fiction Resistance, Narrative perspective

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The monetary growth of a country mainly depends upon the agricultural conditions of that country. Meeting the food needs of the current population is becoming more difficult due to population growth, climatic fluctuations, and resource inadequacy. Smart farming, additionally referred to as precision agriculture, and has appeared as an advanced technique to tackle modern difficulties in order to provide agricultural sustainability. Precision agriculture (PA) is a cutting-edge farming method for maximizing crop yield, minimizes waste, and enhances sustainability by utilizing technology. PA increases productivity and resource efficiency by endowing farmers to make knowledgeable decisions that are customized to individual areas within their fields through the use of data from soil sensors, satellite imagery, and drones. The shift is greatly aided by machine learning (ML), which analyzes and discerns patterns in enormous amounts of agricultural data to provide useful insights. An orderly analysis on the applications of machine learning has been presented by the author in this article. Various ML algorithms like deep learning, regressions analysis, CNN have been applied to monitor the crop health and its yield prediction. It also includes the crop disease prediction at earlier stage. In order to predict outcomes like when to plant, when to water, and whether to apply pesticides, these algorithms can process inputs from a variety of sources, including climatic data, soil composition, and real-time sensor data. They do this by learning from past trends. Farmers can apply site-specific management techniques with the integration of ML, optimizing crop yield while reducing environmental impact. This article examines the limitations and challenges encountered by precision agriculture regarding management of data, technology adoption, and cost-effectiveness.

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Precision Agriculture, Machine Learning, Smart Farming, Artificial Intelligence

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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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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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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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