Volume 14 | Issue 8 |

 Abstract

: Steel is still the reinforcement of choice in most concrete work, mainly because it is strong and behaves predictably, but rising steel prices and ongoing corrosion problems have pushed researchers to look elsewhere. This study looks at jute, a cheap, renewable fibre that is widely available in India, as a way to cut down on steel use without giving up too much performance. Jute Fibre Reinforced Concrete (JFRC) was prepared with fibre contents of 0.25%, 0.50% and 0.75% by weight, and cube specimens were tested for compressive and split tensile strength at 7, 14 and 28 days to find the fibre content that works best. Jute Fibre Reinforced Polymer (JFRP) bars, made from treated jute fibre, binding wire and an epoxy resin coating, were then cast into beam specimens as a partial substitute for conventional steel bars, and their flexural behaviour was compared against beams reinforced entirely with steel. Across every age tested, 0.50% jute fibre gave the best compressive and split tensile results, with the 28-day compressive strength going up by close to 19% over the plain concrete control, while 0.75% fibre content actually performed worse than the control, most likely because the fibres were not dispersing properly at that dosage. Beams cast with 0.50% jute fibre concrete and 50% steel + 50% JFRP bar reinforcement reached a flexural strength of 17.83 MPa, recovering most of the capacity lost when half the steel was swapped out for jute bars. Overall, the results suggest that jute fibre reinforced concrete combined with JFRP bars is a workable, low-cost way to reduce dependence on steel in reinforced concrete members, though the trade-off in stiffness and long-term durability still needs closer study before it can be used in structural applications.

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 Keywords

- Jute fibre, JFRP bars, natural fibre reinforced concrete, compressive strength, split tensile strength, flexural strength, sustainable construction.

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Inclusive education in India is frequently addressed through policy directives, yet there is limited empirical research on pedagogical practices that facilitate inclusion in everyday classroom settings (Rakshit & Yadav, 2025). This study examines inclusive pedagogical practices in special education classrooms through the lens of Indian Knowledge Systems (IKS). Utilizing a qualitative research method, the research focuses on three classrooms and six teachers within a special education context in Vadodara, Gujarat. The classrooms and teachers were selected through purposive sampling according to criteria such as experience in special education and familiarity with inclusive teaching practices, ensuring a comprehensive understanding of how these methods are applied. Data from classroom interaction transcripts and teacher narratives were subjected to thematic analysis, which entailed coding, pattern identification, and theme development to capture the core aspects of inclusive pedagogy. The analysis shows that inclusive pedagogy is enacted through individualized instruction, experiential and multi-sensory learning, relational care, peer-supported interaction, and adaptable assessment strategies. These methods emphasize learner readiness, participation, and emotional well-being over standardized academic outcomes. When interpreted through the IKS framework, the findings align substantially with indigenous pedagogical principles, such as learner readiness (adhik?ra), experiential learning (anubhava and abhy?sa), and ethical commitment in the teacher-learner relationship. This study delivers empirical evidence to the discussion of inclusive education by demonstrating how Indian Knowledge Systems can inform a culturally rooted, practice-oriented model of inclusive pedagogy.

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 Keywords

Inclusive pedagogy, Indian Knowledge Systems, Special education, Classroom practices, NEP 2020

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Security Operations Centers (SOCs) today face an overwhelming number of security alerts due to the rapid growth of cyber threats and widespread use of monitoring tools. While these alerts play a crucial role in identifying potential attacks, many of them turn out to be false positives or low-risk events. This often leads to alert fatigue, making it difficult for analysts to focus on real threats and respond efficiently. To address this issue, this project proposes an AI/ML-based alert prioritization system that intelligently filters and ranks security alerts from multiple sources such as SIEM systems, intrusion detection systems, firewalls, and endpoint security tools. The system processes alerts through stages like preprocessing, feature extraction, and supervised machine learning classification to identify high-risk incidents. A centralized dashboard displays prioritized alerts along with risk scores, helping analysts make faster and better decisions. By reducing unnecessary alert noise and improving visibility, the system enhances SOC performance, minimizes response time, and supports proactive threat detection in a secure and controlled environment.

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Security Operations Center, Alert Prioritization, Artificial Intelligence, Machine Learning, SIEM, Cybersecurity Automation.

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Airborne contaminants and global warming are both of the leading severe and interrelated environmental issues impacting people worldwide in the 21st century. Together, these factors have a significant effect on healthy ecosystems, ecological sustainability, human well-being, and especially impact on vulnerable communities in developed as well as developing nations. Human-caused processes such as burning of fossil fuels, industrial development, urbanization, transport and agricultural methods have resulted in a substantial rise in atmospheric levels of particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), carbon monoxide (CO), volatile organic compounds (VOCs) and black carbon. Around a comparable time, climate change influences weather trends due to higher temperatures, different patterns of rainfall, enhanced atmospheric equilibrium, burning forests, and severe thunderstorms, all of which affect the production, transportation and scattering of harmful substances, and the human inhalation of these substances. This article critically examines the connections among air pollution, global warming, human contact, health effects, and government policies. It covers the main sources of atmospheric pollutants, routes of exposure, biological systems, and the effects of air pollutants on pulmonary, cardiovascular, nervous system, metabolism, reproduction, and psychological health, cancer, and premature death. It also assesses the effect of airborne pollutants on DALYs, YLL and shortfalls in life cycle, along with health disparities in various regions and socioeconomic categories. This article also discusses how global warming can worsen airborne pollutants episodes due to enhanced photochemical ozone synthesis, wildfire emission levels, urban heat island phenomenon, and changes in airflow. The success rate, difficulties with implementation, and potential outcomes of various national and international policy proposals, such as the World Health Organization's (WHO) air quality standards, the UN Sustainable Development Goals (SDGs), Paris Agreement, as well as India's National Clean Air Program (NCAP) were assessed critically. Technological advances including satellite-based remote sensing; cost-effective network sensors, AI, machine learning, and high-level precision health care are introduced as revolutionary instruments for risk assessment and ecological governance. Strengthening multidisciplinary studies, expanding long-term exposure observation, improving surveillance frameworks, and implementing scientifically supported environmental regulations are critical to reducing disease burdens and building adaptability to global warming. A coordinated worldwide approach of atmospheric science, epidemiological research, public health and environmental policies will be required to achieve sustainable progress and protect subsequent generations.

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Keywords: Air pollution, Climate change, Human health burden, particulate matter, Air quality management, Sustainable development, Human health policies

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Keywords - Lithium-Ion Battery, State of Health (SOH), Remaining Useful Life (RUL), Attention-BiLSTM, CNN-LSTM, GRU, Deep Learning, Internet of Things (IoT), Battery Management System (BMS), Predictive Maintenance.

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Densities, viscosities of some substituted heterocyclic compounds in 1, 4 dioxane were measured in the temperature range 303.15K to 323.15K. The viscosity was determined by John-Dole equation. The data has been used to calculate Gibb's free energy change (?G), entropy change (?S), and enthalpy change (?H). ?G and ?H are negative and ?S is positive which is indicate the spontaneity of reaction according to thermodynamics.

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Substituted heterocyclic compound, Viscosity, Thermodynamic parameters.

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Microneedle (MN) technology has emerged as a promising strategy for transdermal drug delivery by combining the advantages of hypodermic injections and conventional transdermal systems. MNs are minimally invasive, painless, and capable of enhancing the delivery of small molecules, peptides, proteins, vaccines, and biologics through the skin barrier. This review summarizes skin anatomy relevant to MN-mediated delivery, types of microneedles, fabrication techniques, evaluation parameters, clinical applications, regulatory considerations, and recent advancements in smart and responsive systems. Conventional fabrication methods such as micromolding, photolithography, and laser ablation, along with modern approaches including 3D printing and femtosecond laser micromachining, are discussed. Current challenges involving drug loading, manufacturing scalability, formulation stability, and regulatory approval are also highlighted. Future developments in nanotechnology, biodegradable polymers, biosensors, and artificial intelligence are expected to improve the clinical translation and commercialization of MN-based systems. Overall, microneedles represent a patient-friendly and efficient platform for next-generation transdermal drug delivery and personalized medicine.

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Microneedles, Transdermal Drug Delivery, Skin Anatomy, Drug Delivery Systems, Dissolving Microneedles, 3D Printing, Biosensors

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 Abstract

Bahuvishayak Shiksha Ke Sandarbh Mein 64 Kalaon Ka Vishleshanatmak Adhyayan

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Bahuvishayak Shiksha Ke Sandarbh Mein 64 Kalaon Ka Vishleshanatmak Adhyayan

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Precision agriculture is moving from isolated sensing and threshold-based automation toward integrated platforms that jointly interpret soil condition, irrigation-water quality, weather variability, crop response, and disease pressure. This paper presents a structured integrative review and an implementable framework for a Plant and Ground Monitoring System (PGMS) that combines calibrated field sensing, edge intelligence, cloud analytics, remote sensing, machine-learning inference, and a multilingual web dashboard. The review synthesizes evidence on soil nutrient monitoring, crop and fertilizer recommendation, evapotranspiration-informed irrigation, image-based plant disease diagnosis, and edge-cloud deployment. The resulting architecture uses an ESP32-S3 field node to acquire soil moisture, soil pH/electrical conductivity/nutrients, irrigation-water pH/electrical conductivity, atmospheric measurements, and leaf imagery; local rules provide safe operation during connectivity loss, while cloud services perform time-series analysis and decision fusion. Reported literature demonstrates approximately 97% crop-classification accuracy in one hybrid MLP-LSTM study, soil-parameter deviations below 13.26% against laboratory measurements, and water savings reported up to 70% in reviewed smart-irrigation deployments. These outcomes are not treated as results of the proposed prototype; instead, they define evidence-based performance targets and a staged validation protocol. The review identifies interoperability, field robustness, calibration drift, uncertainty communication, and secure deployment as the principal barriers to practical adoption. PGMS addresses these gaps through multimodal fusion, confidence-aware recommendations, offline control, and traceable human override.

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 Keywords

Climate-smart agriculture, edge computing, Internet of Things, irrigation scheduling, plant disease detection, precision agriculture, soil fertility.

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The fig-fig wasp interaction represents one of the most fascinating examples of co-evolution and ecological specialization in nature, where fig trees (Ficus spp.) and their pollinating wasps (Family: Agonidae) maintain a highly specific and mutualistic relationship. The study investigates the ecological interactions between fig trees and their associated faunal diversity, with special emphasis on fig- fig wasp relationships among four Ficus species: Ficus racemosa, F. benghalensis, F. religiosa, and F. benjamina. Field observations and laboratory analysis were conducted over a period of three-months in and around Jnanasiri campus, Tumkur University. Different developmental phases (A-E) of syconia were dissected and examined to document floral morphology, wasp developmental stages, and faunal associations. A total of 35 faunal species of invertebrates and vertebrates were recorded to be interacting with Ficus trees, highlighting its role as ecological hubs and keystone species. Hymenopterans (fig wasps) constituted the most diverse group, comprising pollinating fig wasps (PFWs) and non-pollinating fig wasps (NPFWs) exhibiting parasitic, gall-inducing, inquiline, and parasitoids behaviours. Species specific pollination mutualisms were confirmed, by Ceratosolen fusciceps with F. racemosa and Eupristina masoni with F. benghalensis. The study emphasized on fig- fig wasp systems as intricate micro-ecosystems governed by highly specialized and co-evolved interactions, while simultaneously supporting various faunal diversity. These findings reinforce the ecological significance of Ficus as keystone species and highlight the utility of fig - fig wasp systems as versatile models for evolutionary and micro-ecological research.

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 Keywords

Ficus, Fig wasps, Microcosm, Co-evolution, Keystone species

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