Authors: Dr. Ishwar Singh
Abstract: The NMR, IR and Electronic spectra studies on biologically active complexes of iron (II) have been reported. The bands observed and discussed assuming the molecule under CS point group symmetry. The electronic study in nujol phase has been calculated. The IR spectral studies of this compound have been discussed.
DOI: https://doi.org/10.5281/zenodo.17974049
Authors: Harsh Parashar, Kartik Sharma, Rehansh Mohta
Abstract: The increasing reliance on web-based systems for critical operations—such as financial transactions, healthcare data management, government services, and e-commerce—has augmented the need for reliable web security mechanisms. Modern cyberattacks increasingly exploit browser vulnerabilities rather than server-side weaknesses. According to recent research, over 72% of web- based attacks target insecure browser environments through injection, manipulation, session hijacking, or redirection techniques. This creates a significant attack surface where traditional backend security mechanisms are insufficient. This research paper provides a deep analysis of HTTP security headers, an often overlooked yet highly effective method of browser protection. When properly implemented, security headers can reduce the likelihood of browser-based attacks by more than 70%. The headers examined include Content- Security-Policy (CSP), Strict-Transport-Security (HSTS), X-Frame-Options, Referrer-Policy, X- Content-Type-Options, and Permissions-Policy. Each header's function, implementation method, security impact, and limitations are investigated. The research further incorporates threat modeling using the STRIDE framework, comparative analysis of websites with and without headers, real-world case studies, and AI-based automation concepts for detecting missing headers. The findings indicate that security headers are both low-cost and highly impactful, making them one of the most practical defenses for modern web applications. The paper concludes by proposing future AI-driven methodologies that can automatically analyze, predict, and configure optimal security headers.
Authors: Himanshu Kaspate, Tejas More, Atharv Sanas, Pruthviraj Sarade, Vijay Mohite
Abstract: Indian agriculture increasingly relies on mechanization to improve productivity; however, access to agricultural machinery remains a major challenge for small and marginal farmers due to high purchase costs, maintenance expenses, and unorganized rental practices. In recent years, several agricultural equipment rental and machine finding systems have been proposed to address these challenges through web and mobile-based platforms. This survey paper reviews and analyzes existing agricultural machinery rental systems, focusing on their architecture, functionalities, and technological approaches. The study examines key features such as equipment listing, location-based search, online booking, scheduling, payment mechanisms, and user feedback systems. A comparative analysis of existing research reveals significant limitations, including lack of real-time availability tracking, limited support for rural connectivity constraints, low digital literacy consideration, and absence of intelligent machine discovery mechanisms. Based on the identified research gaps and insights from the literature, this paper highlights the need for a centralized, farmer-friendly, and scalable machine finding platform. The survey provides the foundation for KhetSetGo, a proposed digital machine finding and rental application aimed at improving equipment accessibility, reducing operational costs, enhancing resource utilization, and supporting sustainable digital transformation in Indian agriculture.
Authors: Nikitha B
Abstract: Fluoride-rich industrial effluents originating from sectors such as metal smelting, fertilizer production, glass manufacturing, and battery industries pose significant environmental and public-health concerns due to the risk of dental and skeletal fluorosis. Precipitation, ion-exchange, and membrane filtration are examples of conventional treatment techniques that are less appropriate for large-volume or variable-composition industrial wastewaters due to their high operating costs, sludge production, fouling, or poor selectivity. Biosorption, which uses natural, waste-derived, or biologically modified materials, has become a viable, affordable, and sustainable method of removing fluoride. This review offers an extensive review of biosorbents reported for fluoride remediation, including engineered bio-chars, metal-loaded bio-composites, raw biomass (plant fibers, algae, and agricultural waste), and advanced hybrid materials like MOF-based bio-adsorbents. The majority of research reports Langmuir-type monolayer adsorption behavior, with pseudo-second-order kinetics suggesting chemisorption, particularly for biosorbents impregnated with metals. High removal efficiencies (up to ~90%) and significant adsorption capacities have been attained under ideal laboratory conditions, but problems still exist, including limited regeneration data, narrow effective pH ranges, high adsorbent modification costs, and a lack of validation using actual industrial effluents containing competing ions. This review identifies these knowledge gaps and makes recommendations for future research, such as mechanistic evaluation under actual wastewater matrices, pilot-scale continuous-flow studies, hybrid treatment processes, and regeneration optimization. As long as future studies concentrate on scalability, long-term stability, and practical application, biosorption has great potential as a cost-effective and ecologically friendly technique for fluoride removal.
DOI: https://doi.org/10.5281/zenodo.17962912
Authors: Meenu Bose
Abstract: Pharmaceuticals and personal care products (PPCPs) have been among the emerging contaminants of water and wastewater systems in the recent years. These substances have found their way into the environment on a continuous basis as they are widely used and they are not fully eliminated in the traditional treatment procedures. Although in extremely low levels, their long-term occurrence and biological effects may be hazardous to aquatic ecosystems and human health. This review presents the key contributors to PPCPs, their presence in water bodies, physicochemical behaviour, detection methods, and the treatment technologies. Special attention is paid to adsorption processes, the use of advanced oxidation processes, membrane-based treatment, and combined treatment procedures. Further problems and gaps in research and directions are also presented to promote better water management practices.
DOI: http://doi.org/10.5281/zenodo.17961994
Authors: R Swetha
Abstract: Concrete construction largely depends on natural river sand as fine aggregate. However, continuous extraction of river sand has resulted in serious environmental issues such as riverbed erosion, groundwater depletion, and ecological imbalance. At the same time, copper industries generate large quantities of copper slag as an industrial waste, which poses disposal and environmental challenges. This experimental study focuses on evaluating the suitability of copper slag as a partial replacement for fine aggregate in concrete. Concrete mixes were prepared by replacing river sand with copper slag at proportions of 0%, 10%, 20%, 30%, 40%, and 50% by weight. The fresh properties, strength characteristics, and selected durability parameters of concrete were studied. The results show that concrete containing copper slag exhibits improved strength and durability up to an optimum replacement level of about 20–30%. Beyond this level, a reduction in strength was observed. The study concludes that copper slag can be effectively utilized as an alternative fine aggregate, contributing to sustainable and eco-friendly concrete construction.
Authors: Sagar Shivaji Thakare
Abstract: This qualitative research paper explores the transformative impact of digital marketing on business practices in India. With rapid advancements in internet penetration, smartphone usage, and social media engagement, Indian businesses—both large and small—have increasingly adopted digital marketing tools to reach wider audiences. The study draws upon qualitative insights from existing literature, expert interviews, and case studies to understand how businesses leverage digital platforms for branding, customer engagement, and sales growth. The findings reveal that digital marketing enhances competitiveness, fosters innovation, and supports customer-centric strategies. However, challenges such as digital literacy, regulatory issues, and high competition remain significant. The paper concludes with recommendations to improve digital marketing adoption, emphasizing education, government support, and local innovation.
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Authors: Darshana N V
Abstract: This review paper explores low-cost methods and technologies for monitoring and enhancing indoor air quality (IAQ) in residential and institutional buildings. It highlights the importance of maintaining healthy IAQ for occupant health, comfort, and productivity, especially amid growing urbanization and environmental concerns. The paper systematically examines affordable sensor technologies, measurement approaches, and intervention strategies that provide effective IAQ management without significant financial investment. By evaluating recent innovations and practical applications, this review offers a comprehensive overview of accessible solutions aimed at improving indoor environments, supporting occupant well-being, and advancing sustainable building practices.
Authors: Sreehari K B, Deepakumar M
Abstract: Early disease prediction is a crucial aspect of modern healthcare systems, as it enables timely medical intervention, improves patient survival rates, and reduces long-term healthcare costs. Many chronic and life-threatening diseases such as diabetes, cardiovascular disorders, cancer, and neurological conditions develop gradually and often remain asymptomatic during their early stages. Traditional diagnostic approaches, which rely on clinical rules, physician experience, and fixed statistical thresholds, are often inadequate for detecting these early-stage disease patterns. and neurological disorders progress slowly over time and are often diagnosed only at advanced stages. Late diagnosis significantly reduces treatment effectiveness and increases mortality rates. With the growing global disease burden and aging population, early detection has become a priority in modern healthcare systems. Advancements in healthcare digitization have led to the availability of large-scale medical data, including Electronic Health Records (EHRs), laboratory reports, and medical imaging. These datasets provide valuable insights into patient health patterns and disease progression, enabling the development of predictive models for early diagnosis. With the rapid digitization of healthcare, vast amounts of medical data are generated through Electronic Health Records (EHRs), laboratory test reports, diagnostic imaging, and wearable health devices. This has created opportunities for Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze complex and high- dimensional medical data efficiently. Existing AI- based disease prediction systems have demonstrated improved accuracy compared to conventional methods; however, many of these systems suffer from limitations such as reliance on single-modal data, centralized data storage, poor generalization across healthcare institutions, severe class imbalance, and lack of interpretability. This project proposes an AI-based early disease prediction framework that addresses these limitations through the integration of multimodal clinical data, privacy-aware learning mechanisms, imbalance-sensitive training strategies, and explainable AI techniques. The proposed system learns complex patterns from longitudinal patient data and generates calibrated risk scores to support early diagnosis and preventive care. By improving transparency, robustness, and clinical trust, the proposed framework aims to provide an effective and scalable solution for early disease prediction in real-world healthcare environments.
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Authors: Akshay Bhangade, Dr. Pushpa Pathak
Abstract: The rapid expansion of higher education institutions has intensified the need for efficient, intelligent, and scalable student management solutions. Traditional college management systems often suffer from fragmented data handling, limited automation, and insufficient analytical capabilities, leading to administrative inefficiencies and suboptimal academic decision-making. This paper presents an enhanced Student College Management System that integrates intelligent academic automation, centralized student information management, and data-driven performance analytics within a unified architectural framework. The proposed system leverages automation to streamline core academic processes such as admissions, course registration, attendance tracking, assessment management, and result processing, thereby reducing manual intervention and operational errors. A centralized database architecture ensures secure, consistent, and real-time access to comprehensive student records across departments. Furthermore, advanced analytics modules utilize historical and real-time data to evaluate student performance, identify learning patterns, predict academic risks, and support evidence-based decision-making for faculty and administrators. The system architecture emphasizes modularity, scalability, and interoperability, enabling seamless integration with existing institutional platforms and future technological enhancements. By combining intelligent automation with robust analytics, the proposed solution enhances administrative efficiency, improves academic monitoring, and supports personalized student development. This integrated approach contributes to improved institutional governance, better learning outcomes, and a data-driven academic ecosystem aligned with modern higher education requirements.
