VOLUME 12, ISSUE 11, NOVEMBER 2025

Abstract: Accurate modeling of electromagnetic wave propagation in the troposphere is crucial for enhancing the reliability and efficiency of communication systems. This study proposes a mathematical model based on the fractal method, specifically the Weierstrass-Mandelbrot function, to represent the fluctuations of dielectric permittivity in the troposphere. Using the fractal approach, the atmospheric medium was modeled as a multi-scale surface with inherent spatial variability. Based on the proposed model, the trajectory of electromagnetic wave propagation was calculated and simulated under various test conditions. Increasing antenna height and signal frequency significantly affects the variability of the wave path in a fractal medium. The scientific significance of this algorithm lies in its ability to model wave propagation with high precision in unstable and uncertain media. The findings can be effectively applied in meteorological monitoring, above-ground communication systems, and military signal transmission networks. This research contributes to developing more robust and adaptable algorithms for modeling electromagnetic wave behavior in real atmospheric environments, especially in the presence of turbulence and structural irregularities inherent to the troposphere.

Keywords: Weierstrass-Mandelbrot, fractal function, Electromagnetic Wave Propagation, Tropospheric Environment.

Abstract: Background: Despite significant national progress, Ghana continues to face disparities in maternal mortality, largely concentrated in rural areas. The Ashanti Region, a cultural and economic hub, exemplifies this paradox, with relatively strong health infrastructure yet persistent socio-cultural barriers to skilled midwifery care. Methods: This desk review synthesizes current data from the Ghana Demographic and Health Survey (2022), Ghana Health Service Annual Reports (2022-2023), and recent peer-reviewed literature to analyze the socio-cultural determinants influencing the utilization of midwifery services in rural Ashanti. Results: Key barriers identified include a strong cultural preference for Traditional Birth Attendants (TBAs) as spiritual and cultural custodians, matrilineal and gerontocratic decision-making structures that disempower pregnant women, spiritual interpretations of obstetric complications, and logistical challenges exacerbated by cultural norms of modesty. Ghana Health Service strategies, such as the revised CHPS model and TBA integration programs, show promise but require deeper cultural integration. Conclusion: The findings underscore that improving maternal health outcomes in such contexts requires moving beyond infrastructural investment to implement culturally intelligent policies that respectfully engage with traditional norms, leverage existing community structures, and redefine skilled birth attendance within a local socio-cultural framework.

Keywords: Maternal Health, Midwifery, Socio-Cultural Barriers, Traditional Birth Attendants, Ashanti Region, Ghana, Health Systems, CHPS

Abstract: Despite significant national efforts to improve maternal health, Ghana continues to face challenges in reducing maternal mortality, with disparities concentrated in rural areas. While geographic and economic barriers are well-documented, socio-cultural factors remain less quantified yet critically influential. Methods: This desk review synthesizes current qualitative data from the Ghana Demographic and Health Survey (GDHS), the Ghana Maternal Health Survey (GMHS), and program evaluations from the Ghana Health Service (GHS) and its partners (2018-2023). The analysis focuses on thematic findings related to gender norms, cultural beliefs, and community practices in rural Greater Accra and Central Regions. Findings: Key socio-cultural barriers include: (1) male-dominated decision-making power, limiting women's autonomy to seek care; (2) deep-seated cultural beliefs privileging Traditional Birth Attendants (TBAs) and perceiving pregnancy as a natural state; (3) pervasive mistrust in the formal health system fueled by fears of disrespectful abuse, Caesarean sections, and HIV testing stigma; and (4) the influential role of older female relatives in reinforcing traditional norms. These barriers persist despite relative geographic proximity to health facilities in some areas. Conclusion: Improving maternal health outcomes in rural Ghana requires a paradigm shift beyond infrastructure investment. Policy and programming must explicitly integrate community-level socio-cultural interventions, including male engagement, respectful maternity care training, and the respectful integration of traditional systems, to build trust and empower women.

Keywords: Maternal Health, Socio-Cultural Barriers, Rural Ghana, Health Equity, Qualitative Research, Traditional Birth Attendants, Gender Power Dynamics.

Abstract: High rates of attrition within Ghana's civil service present a significant policy challenge, undermining institutional memory and the capacity to implement national development programs. While competitive remuneration is often the focal point of policy debates, this article argues that sustainable solutions lie in strengthening the governance of human resource systems. Through a qualitative analysis of Ghanaian government strategies, including the Civil Service Medium-Term Development Plan, and secondary data on employee sentiment, this article identifies a critical gap between policy intent and frontline experience. It reveals that perceived inequities in career progression, a lack of transparent performance management, and weak mechanisms for professional recognition are primary drivers of turnover. We propose a four-pillar policy framework focused on strategic workforce planning, modernized compensation governance, enhanced talent development systems, and credible performance management. The article concludes that for Ghana to achieve its developmental goals, public sector reform must prioritize governance interventions that rebuild employee trust and demonstrate a tangible commitment to their value and growth.

Abstract: Plant-mediated synthesis of zinc oxide nanoparticles (ZnO-NPs) using leaf extracts is an active research area because phytochemicals serve as eco-friendly reducing and capping agents and because ZnO-NPs show promising antibacterial and antioxidant activities. This manuscript-length summary presents a compact, standardized template that synthesizes common experimental practices used when researchers compare ZnO-NPs produced from five representative leaves (Moringa oleifera, Azadirachta indica (neem), Ocimum spp / Ocimum sanctum (tulsi), Hibiscus cannabinus / Hibiscus spp., and Citrus aurantium / Citrus spp. (orange). It describes comparative study on step-by-step extract preparation and nanoparticle formation (precursors, pH control, reaction conditions, isolation and calcination), recommended characterization workflows (UV-Vis, XRD, FTIR, and FESEM), and standardized biological testing (antibacterial activity against Staphylococcus aureus and E. coli bacteria, while antioxidant activity studied against the DPPH free radical with IC₅₀ reporting). Typical physicochemical signatures of biosynthesized ZnO-NPs are summarized (UV absorption ~ 320-380 nm; XRD wurtzite peaks at 2θ ≈ 31.7°, 34.4°, 36.2°, etc.; FTIR bands indicating phytochemical capping and Zn-O vibrations at 400-600 cm⁻¹). Finally, we present concise conclusions and a recommended reporting checklist to improve reproducibility and enable quantitative cross-study comparison.

Keywords: ZnO Nanoparticles, Moringa oleifera, Azadirachta indica (neem), Ocimum spp. (tulsi), Hibiscus spp., and Citrus aurantium.

Abstract: In recent years, cybersecurity threats have grown exponentially due to the increasing interconnection of digital systems. Traditional intrusion detection systems (IDS) rely on predefined signatures and often fail to detect novel or evolving attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the accuracy and adaptability of IDS. This paper explores the integration of AI techniques-such as machine learning (ML), deep learning (DL), and neural networks-into intrusion detection frameworks. The proposed system aims to detect, classify, and prevent cyberattacks in real time. The results demonstrate that AI-based IDS provide better accuracy, reduced false alarm rates, and improved detection of zero-day attacks compared to traditional methods.

Keywords: Artificial Intelligence (AI), Cybersecurity, Intrusion Detection System (IDS), Machine Learning, Deep Learning, Network Security.

Abstract: The Unified Payments Interface (UPI) has rapidly emerged as a disruptive, low-cost digital payment infrastruc- ture that transforms mobile and merchant payments in India. This paper presents an in-depth empirical and technological analysis of UPI - its architecture, adoption trajectory, compar- ative performance across bank categories, and socio-economic impact. Using secondary data from industry reports (NPCI, RBI) and synthesized bank-level statistics for FY 2022-23, we apply descriptive and inferential analyses to evaluate growth trends, sectoral contributions, and operational challenges. Three visualizations (transaction growth, bank-wise volumes, and bank- type market share) are included to illustrate trends. The study highlights UPI's role in accelerating financial inclusion, identifies technical and regulatory challenges, and outlines strategic path- ways for internationalization and resilience through AI-driven fraud prevention and enhanced infrastructure.

Keywords: UPI, Unified Payments Interface, digital pay- ments, fintech, financial inclusion, bank performance, India.

Abstract: Artificial Intelligence (AI) has rapidly become an integral part of daily life, influencing nearly every sector from communication to healthcare. This paper explores the diverse applications of AI in everyday environments and examines how these technologies improve efficiency, accuracy, and decision-making. The study discusses AI's impact in smart homes, education, finance, healthcare, and transportation, emphasizing real-world use cases like voice assistants, predictive text, and autonomous vehicles. Moreover, it addresses ethical and societal challenges such as data privacy, job automation, and algorithmic bias. The purpose of this paper is to provide a comprehensive understanding of AI's transformative role in modern society and to analyze its potential future advancements that could shape human lifestyles in the coming decades.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Automation, Smart Systems, Everyday Applications

Abstract: High-rise buildings are more susceptible to vibrations caused by wind loads, earthquakes, and other dynamic forces. Excessive vibrations may cause discomfort to occupants and structural damage. The Tuned Mass Damper (TMD) is an effective vibration control device that mitigates structural responses by tuning its frequency close to that of the primary structure. This paper presents an analytical study of vibration control in multi-storey buildings using TMDs. The objective is to enhance the seismic and wind performance of structures by optimizing the damper's mass, damping ratio, and tuning frequency. The study reviews various modeling techniques, simulation results, and practical applications to demonstrate the effectiveness of TMD systems in improving the overall stability and safety of multi-storey buildings.

Keywords: Tuned Mass Damper (TMD), Vibration Control, Seismic Response, Structural Dynamics, Civil Engineering, Multi-Storey Building.

Abstract: The use of computed tomography (CT) scans enables physicians to diagnose patients without relying solely on anatomy to identify injuries. Instead, they can directly understand the cause of disease through these scans. Since CT scans are simply a series of two-dimensional images, this study aims to reconstruct a three-dimensional holographic model from these CT scans to provide medical staff with a reference for judgment and diagnosis. This increases diagnostic accuracy and ease of use, and enables physicians to better understand injuries.

Abstract: Due to rapidly growing population human activity in building Construction has been most important factor nowadays. Concrete is widely used and reliable material for construction. Some challenges in industry is global warming and insufficiency of construction material. One of the methods for replacing concrete constituent is the used of geopolymer which are using very less quantity of cement in concrete. Rapid infrastructure development taking place nowadays, Portland cement concrete is the most popular and widely used building material. However, due to the restriction of the manufacturing process and the raw materials there are two major drawbacks with respect to sustainability. About 1.5 tons of raw materials are needed in the production of every ton of Portland cement, at the same time about one ton of carbon dioxide (CO2) is released into the environment during this production. The coal fired thermal power plants generate solid waste in the form of fly ash and pond ash. Disposal of these wastes is a major engineering challenge. Today research has combined sustainability with waste management leading to a wonderful product called geopolymer concrete. Modern-day geopolymer concrete are mostly made from low calcium fly ash and other waste materials activated by alkaline solutions using (NaOH or KOH with Na2SiO3 or K2SiO3). However, it should be noted that with the variation in the parameters such as Na2SiO3/NaOH ratio, Molarity of NaOH, curing temperature and curing time leads to changes in the strength. Geopolymer materials represent an innovative technology that is generating considerable interest in the construction industry, particularly in light of the ongoing emphasis on sustainability. This paper briefly reviews the studies such as molarity of sodium hydroxide and other ingredients in developing compressive strength of geopolymer concrete.

Keywords: Ground granulated blast furnace slag (GGBS). Fly ash, Eco-friendly concrete, sustainable concrete, fly ash-based geopolymer, Geopolymer binder, Sodium hydroxide (NaOH), Sodium silicate (Na₂SiO₃), geopolymer concrete.

Abstract: Accurate sales forecasting plays a crucial role in the retail industry by enabling effective inventory management, resource allocation, and demand planning. This study presents a hybrid time series forecasting approach to predict daily store sales by combining statistical and machine learning models. The proposed method integrates Linear Regression, Ridge Regression, and Facebook Prophet models to capture both linear and nonlinear dependencies in the sales data. Historical store sales records are preprocessed by handling missing values and extracting time-based features such as day, month, year, and day of the week. The ensemble prediction is obtained by averaging the outputs of all three models. Experimental results demonstrate that the ensemble model achieves low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), effectively capturing seasonal variations and trends in store-level sales data.

Keywords: Time series forecasting, sales prediction, Linear Regression, Ridge Regression, Prophet, ensemble model.

Abstract: The growing demands of modern communications, radar, and satellite systems require power amplifiers that deliver high output power, high efficiency, and compact form factor. Wide-bandgap Gallium Nitride (GaN) high-electron-mobility transistors (HEMTs) have become the preferred technology for high-power RF and microwave applications due to their high breakdown voltage, high electron saturation velocity, and strong thermal robustness. This paper presents a comprehensive design methodology and simulation validation for a GaN-based Class-E high-power RF amplifier targeted at sub-1 GHz and S-band operation. The design flow includes device selection, large-signal behavioral modeling, harmonic-balance simulation, impedance matching, thermal considerations, and layout recommendations. Simulated results demonstrate output power above 12 W, small-signal gain ≈28 dB, and Power-Added Efficiency (PAE) exceeding 80% under idealized conditions; the paper also compares these results with recent published GaN PA developments and discusses reliability and manufacturability considerations for tape-out and prototyping. Recent experimental and review literature is cited to contextualize design choices and highlight gaps for further work.

Keywords: Gallium Nitride (GaN), HEMT, RF power amplifier, Class-E, Power-Added Efficiency (PAE), MMIC, thermal management.

Abstract: Contemporary silvicultural restoration has witnessed substantial technological advancement through Unmanned Aerial Vehicle (UAV) integration, representing a significant departure from labour-intensive methodologies and mechanised approaches compromising ecological integrity. Notable developments include ultralight pneumatic seedling deployment systems (under 25 kilograms) employing compressed air propulsion with telescoping actuators for placement and soil consolidation. Modular geospatial control frameworks optimize seed distribution by distinguishing suitable planting zones, demonstrating potential 40% reductions in resource expenditure, while gravity-assisted dispersal mechanisms with pulse-width modulation enable precision applications. Site selection employs convolutional neural networks, achieving classification accuracies exceeding 93% across land cover categories. Post-establishment monitoring utilizes photogrammetric algorithms to quantify canopy metrics and enumerate saplings from UAV imagery, particularly valuable in telecommunications-limited regions. Three-dimensional photogrammetric reconstruction integrated with geographic information systems enables comprehensive visualization and quantitative assessment, supporting evidence-based management strategies that minimize soil disruption and enhance pedological characteristics conducive to vegetation establishment in disturbed landscapes.

Keywords: Drone Reforestation, Unmanned Aerial Vehicles (UAVs), Seedling Planting Mechanism, Deep Learning, Geospatial Data, Forest Management

Abstract: This study explores the growing preference for modern digital payment systems over traditional financial services. Findings reveal that most users, especially those aged 18-25 and educated females, actively use and trust financial apps such as Google Pay and PhonePe. Convenience, time efficiency, and ease of use are the main reasons for this shift. However, concerns about security and limited adoption among older or less-educated groups persist. The study concludes that digital payments are rapidly transforming financial behavior, and further efforts are needed to make fintech platforms more secure, inclusive, and user-friendly.

Keywords: digital/modern payments, traditional payments, fintech, financial services.

Abstract: Universal health care is a vital part of Canada's social contract, and its comprehensive integration of midwifery services sets Ontario apart. This study examines how Ontario's healthcare system achieves nearly universal maternal coverage through publicly funded midwifery services, emphasizing accessibility, equity, and continuity of care. Using provincial health reports for 2024-2025, policy documents, and global standards for maternal health, the research critically evaluates funding models, workforce distribution, and access barriers. The findings show that Ontario's contractor-based midwifery system delivers high-quality, equitable maternity care, including for uninsured populations. However, it faces capacity and funding challenges. Lessons from Ontario's approach demonstrate that achieving universal coverage requires not only adequate funding but also systemic flexibility, autonomous operation, and ongoing investment in midwifery infrastructure.

Keywords: Universal health coverage, midwifery, Ontario Health Insurance Plan, maternal health, public health equity, Canada.

Abstract: Maternal emergencies continue to be a significant cause of preventable illness and mortality worldwide. Midwives frequently serve as initial responders, making clinical competence vital for delivering high-quality care. Despite considerable resource disparities, Canada and Ghana are both dedicated to ensuring safe, evidence-based maternal healthcare through continuous professional development.

Keywords: Midwifery; emergency competence; Ghana; Ontario; professional regulation; CPD; maternal mortality; skills certification

Abstract: Ghana's National Health Insurance Scheme (NHIS) and the Free Maternal Health Care Policy (FMHCP) were established to remove financial barriers to maternal and neonatal healthcare. After twenty years, challenges remain in turning policy coverage into real protection. This paper critically assesses Ghana's progress toward universal maternal healthcare from 2010 to 2025, using current data from the Ministry of Health and the World Health Organization (WHO), and comparing outcomes with Ontario, Canada's publicly funded midwifery model. The results show significant improvements in antenatal visits and facility-based deliveries; however, ongoing inequalities persist due to delayed reimbursements, stock shortages, and workforce gaps. Recommendations include adopting digital claims systems, decentralizing funding processes, and expanding community midwifery services.

Keywords: Maternal health coverage, NHIS, free maternal policy, midwifery, Ghana, universal health coverage, equity.

Abstract: The global increase in demand for water, due to population growth and industrialization, puts heavy pressure on the scant available water resources through overuse and contamination. But how efficient is the world in conserving it?. Saving water is now a collective responsibility. In this age of scientific advancements, using technology to solve environmental problems would contribute in making the Earth a better place for all living beings. Thus, this research examines the frontier innovations poised to rewrite the future of global water security. It explores various methodologies of water conservation. The topics covered in these papers include the employment of IoT technology, PLC (Programming Logic Controllers) based monitoring device, Sensor technology, Transmission cloud and Optoelectronic device. It emphasizes improvement in water efficiency in petrochemical industry that influence water conservation, a real-time monitoring of water conservation, a conservancy project, a mobile app to regulate water pumps, smart metering, municipal pipe networking, sediment variation, water consumption proportion. The research also investigates the effects of water conservation on the environment and suggests an information architecture. It addresses the importance of water conservation and the methods to evade disasters like flood and also the effective ways to overcome challenges such as water scarcity and water contamination.

Keywords: IoT Technology, Optoelectronic device, Wireless sensor networks, PLC (Programming Logic Controllers), Smart metering.

Abstract: Surveillance video anomaly detection is vital for enhancing security by identifying unusual activities in video footage. Traditional methods often face challenges with high false alarm rates and scalability. Recent advancements in deep learning, including convolutional neural networks (CNNs) and autoencoders, have improved anomaly detection by analyzing patterns in video data. This paper reviews various approaches to anomaly detection, such as motion analysis and deep learning techniques, while addressing challenges like real-time processing, data imbalance, and the need for large labelled datasets. Finally, we discuss future directions, including multi-modal data integration and more efficient models for diverse surveillance environments.

Keywords: Real-time surveillance, Anomaly detection, Machine learning, Deep learning, Convolutional Neural Networks

Abstract: In growing construction industry demand for sustainable, affordable, and eco-friendly alternatives to traditional materials like steel has increased. The steel offers excellent strength and ductility, it has notable disadvantages such as high embodied energy, substantial carbon emissions, susceptibility to corrosion, and volatile market prices. These problems have led researchers to explore renewable and low-carbon material options. bamboo as a reinforcing material can be a promising option. The use of bamboo in place of steel is used as a whole and as a combination of both bamboo and steel. It is used as such to ensure the reduction in weight and economic advantages with its strength compromised to a slight and safe level. This study investigates use of bamboo's as a reinforcement material in concrete, especially for low-rise buildings. Bamboo, a renewable and fast-growing material with high tensile strength and an excellent strength-to-weight ratio, was tested in both untreated and chemically treated forms to evaluate its structural, mechanical, and durability properties in comparison to steel reinforcement. Test were conducted on M25 grade concrete using cubes, cylinders, and beams reinforced with steel rods and bamboo splints. The Tests find out tensile strength, bond strength, water absorption, flexural performance, and cyclic load behavior. Results showed that although steel-reinforced beams had the highest load capacity and energy absorption, bamboo-reinforced beams performed well, reaching up to 72% of the strength of steel-reinforced concrete and outperforming plain concrete significantly. Coatings like bitumen, epoxy, and boron-based treatments improved the bond between bamboo and concrete and reduced water absorption, thus enhancing durability. The bamboo's properties like light weight, renewability, low embodied carbon, and cost-effectiveness make it a feasible, sustainable substitute for low-cost housing, disaster-resistant structures, and rural infrastructure. Bamboo is an abundant, fast-growing, renewable material with high specific strength and a low embodied-energy profile. This paper reviews recent literature on the feasibility of using bamboo as a reinforcement material in concrete and geopolymer matrices. Key challenges-bonding with concrete, durability, variability between species, and low modulus of elasticity compared with steel-are identified alongside mitigation strategies. The review highlights where bamboo can be a viable low-cost, low-carbon reinforcement and where current evidence still favors conventional steel reinforcement.

Keywords: bamboo reinforcement, bamboo-reinforced concrete (BRC), bond strength, sustainability, durability.

Abstract: With tremendous increase in population across the globe, the amount of waste which is generated every day in very high by each individual. Some of the waste can be recycled and some cannot be done. Waste produced in a country falls into two main types organic waste and inorganic waste. Given the rapid growth of the global population, each person generates a large amount of waste daily. Modern farming faces serious difficulties with managing organic waste because traditional methods are often ineffective and unsustainable. To address these issues, this research proposes a new method for smart composting that merges IoT with gradient-boosting algorithms. The two critical components of sustainable agriculture that complement each other to achieve ecological balance, environmental preservation and the health of the people are organic farming and waste management. Organic farming is focused on application of natural inputs, crop rotation and composting as opposed to fertilizers and pesticides. In contrast, waste management is the process of systematically gathering, recycling and reusing of organic and farming garbage to ensure more soil fertility and less pollution. This paper provides an extensive survey on the inter-relation between practices of organic farming and waste management systems, where a combination of these methods can help in achieving sustainable agriculture and development in rural areas.

Keywords: Organic Farming, Waste Management, Composting, Sustainable Agriculture, Environment, Recycling.

Abstract: Environmental factors such as dust and sand storms significantly influence the performance of microwave and millimetre-wave communication systems. This paper presents a comprehensive theoretical analysis of electromagnetic wave propagation under dust- and sand-laden conditions in the coalfield region of Surguja district, Chhattisgarh. Various analytical and modeling techniques have been reviewed and applied, including Mie Scattering Theory, Effective Medium Approximations (Maxwell-Garnett and Bruggeman models), and the Radiative Transfer Equation (RTE) for dense particle media. In addition, empirical and semi-empirical attenuation models have been discussed to provide practical estimation approaches based on visibility and dust density data. To complement the theoretical framework, reference is made to experimental validation methods such as the Vector Network Analyzer (VNA), Point-to-Point Analyzer (PPA), Infinite Sample Method, and Two-Point Dielectric Method, which are commonly employed in similar studies. Theoretical simulations indicate that attenuation and scattering effects increase with both frequency and particle concentration, making millimetre-wave bands (above 30 GHz) particularly vulnerable in dusty or coal-dust-dominated environments. The study highlights the distinct electromagnetic behavior of coal dust, attributed to its higher dielectric loss compared to sand, and underscores the need for empirical validation to refine propagation models for mining regions.

Keywords: Microwave propagation, Millimeter-wave, Dust and sand storms, Coal dust attenuation, Mie scattering theory, Effective medium theory, Radiative transfer, Surguja coalfield, Theoretical modeling, Electromagnetic wave propagation

Abstract: Vednahari is a topical Ayurvedic preparation that contains various active ingredients such as menthol crystal (pudina phool), thymol crystal (ajwain phool), and edible camphor (desi kapoor). Ayurveda is the world's most ancient science of holistic healing. Today, due to fast and faulty lifestyles and different dietary patterns, people are suffering from many diseases. There is an increasing need for instant relief. There is a common belief that Ayurveda does not provide quick relief, but in Ayurveda, Siddhyoga Sangraha explains "Karpurdhara," also known as Jeevanarkrasayan, which gives instant relief in some conditions like rhinitis and gastritis. It is a preparation made from thymol, menthol, and camphor. This formulation is used as a proprietary medicine in our institute under the name Vednahari.

Keywords: Carom seeds, Camphor, Menthol.

Abstract: This paper presents a comprehensive analysis of blockchain-enabled electronic voting systems, focusing on how decentralization, smart contracts, and advanced cryptographic techniques can enhance trust, security, and transparency in modern elections. By examining key research contributions-including Ethereum-based protocols like Ques-Chain, decentralized systems such as the Open Vote Network, permissioned blockchain evaluations using Hyperledger Fabric, and cutting-edge privacy solutions employing zero-knowledge proofs and zk-SNARKs-the work highlights both the promise and limitations of blockchain as an e-voting foundation. The study explains how wallet-based voter authentication, Merkle tree validation, and self-tallying mechanisms can enforce one-time voting, preserve voter anonymity, and eliminate reliance on central authorities. At the same time, the analysis acknowledges persistent challenges: scalability constraints, governance complexities, and the critical vulnerability posed by insecure end-user devices. The paper concludes that while blockchain provides a mathematically robust and auditable architecture for secure e-voting, achieving a truly trustless, large-scale national implementation requires addressing real-world security issues, performance bottlenecks, and user-centric barriers that remain unresolved today.

Keywords: Blockchain, E-Voting, Smart Contracts, Zero-Knowledge Proofs, zk-SNARKs, Merkle Tree, Wallet-Based Authentication, Decentralization, Cryptographic Voting, Hyperledger Fabric, Election Security, Self-Tallying Protocols1. Introduction: The Imperative for Secure E-Voting in the Digital Age

Abstract: Organic farming aims for human welfare without harming the environment and follows the principles of health, ecology, fairness and care for all, including soil. Organic farming is an agricultural production system that avoids the use of pesticides and fertilizers. It emphasizes the use of natural inputs ensuring that nature stays clean and rich. It promotes and enhances agro-ecosystem health, including biodiversity and biological cycles. The contemporary definition of organic agriculture combines indigenous wisdom with scientific progress and technology. It emphasizes a method of food production that is sustainable and environmentally friendly, focusing on the conservation of natural resources and pollution reduction. Organic agriculture includes not only the production of crops, but also the efficient management of crops post-harvest, proper storage, and marketing development to maintain product quality and provide farmers with profitable options to earn a living. This study reviews the key elements, challenges, and advantages of organic agriculture enhancing soil fertility, improving food quality, and mitigating greenhouse gas emissions. It will further discuss newly emerging ideas like the bioeconomy, waste recycling, and the soft systems methodology as a potential means to manage the future sustainability of agriculture. For these reasons, organic agriculture not only provides nutrition food it also engages in the stewardship of the environment and the economic well-being of rural communities.

Keywords: Sustainable Agriculture, Long Short-Term Memory (LSTM), ICT in Agriculture, Data Mining, SWOT, Digital Marketing

Abstract: India's electric vehicle (EV) market is at a critical juncture, reflecting the nation's ambition to achieve sustainable economic growth while addressing environmental concerns. This study explores the interplay of marketing dynamics and sustainability factors shaping the EV market, focusing on the perspectives of three key stakeholders: customers, retailers, and manufacturers. The research employs a mixed-method approach, incorporating quantitative data from structured questionnaires and qualitative insights to understand the factors driving EV adoption. The findings highlight the pivotal role of consumer awareness, government incentives, and charging infrastructure in influencing EV purchase decisions. From the customer perspective, key factors include affordability, range anxiety, and perceived environmental benefits. Retailers emphasize supply chain efficiency, after-sales service, and promotional efforts as essential components of market penetration strategies. Manufacturers, on the other hand, underscore the importance of aligning production capabilities with sustainability goals through innovation and compliance with evolving regulatory frameworks. Using factor analysis, the study identifies and categorizes sustainability drivers such as technological innovation, policy support, and green marketing strategies. The analysis reveals that while customer-centric marketing efforts are instrumental, systemic challenges like inadequate infrastructure and high upfront costs remain significant barriers. Retailers and manufacturers stress the need for a collaborative approach to overcome these hurdles, advocating for stronger public-private partnerships and enhanced policy implementation. The research provides actionable insights into sustainable marketing practices that can accelerate EV adoption in Karnataka. Recommendations include targeted marketing campaigns to raise awareness, competitive pricing strategies, and the expansion of charging networks. Additionally, the study emphasizes the integration of circular economy principles, such as battery recycling and energy-efficient manufacturing processes, to reinforce sustainability. This paper contributes to the ongoing discourse on sustainable marketing by offering a comprehensive analysis of the EV ecosystem in India. It highlights the interconnectedness of stakeholder roles and suggests strategic directions for aligning economic objectives with environmental goals. The findings are especially relevant for policymakers, marketers, and industry leaders seeking to foster a robust and sustainable EV market in India.

Keywords: Electric vehicles, sustainability, marketing dynamics, consumer behavior, stakeholder perspectives, government policies, green marketing, charging infrastructure, technological innovation, India.

Abstract: This study presents a descriptive assessment of Wesleyan University-Philippines' One-Stop-Shop (OSS) Enrollment System, implemented under the new president's leadership. The research involved 361 university students across various programs, selected through simple random sampling from a population of over 5000 college students. The Raosoft online calculator was utilized to determine the sample size, ensuring a 5% margin of error. The researcher-developed questionnaire, evaluated by six experts in education and business, demonstrated high reliability (Cronbach's alpha = 0.912). Findings reveal the OSS Enrollment System's resounding success, which simplifies processes and fosters a positive student environment. Enrollees express complete satisfaction, emphasizing the system's convenience and efficiency. Furthermore, the helpful feedback from the student respondents reflects a collective effort to enhance the system, showing dedication to ongoing improvement and receptiveness to consumer needs. This research contributes relevant insights into the effective implementation and ongoing improvement of WU-P enrollment systems.

Keywords: One-Stop-Shop Enrollment System, Resounding Success, Satisfaction, Streamline, Wesleyan University-Philippines

Abstract: Driver drowsiness is a major contributor to road accidents worldwide, leading to numerous injuries and fatalities each year. This project aims to develop a real-time drowsiness detection system using Python and Google's MediaPipe framework. By analyzing facial landmarks, particularly the eyes, the system can detect signs of drowsiness such as frequent blinking or prolonged eye closure. The Eye Aspect Ratio (EAR) is calculated to monitor eye state, triggering alerts when drowsiness is detected. This low-cost, non-invasive solution enhances road safety by preventing accidents due to driver fatigue. The project emphasizes real-time processing, ease of deployment, and accuracy.

Keywords: Drowsiness detection, MediaPipe, Python, Eye Aspect Ratio (EAR), Driver safety, Realtime monitoring, Facial landmarks, Computer vision, OpenCV.

Abstract: Traditional authentication systems relying on static credentials or fixed biometrics are increasingly vulnerable to credential theft, phishing, and spoofing. Behavioral biometrics such as keystroke dynamics and mouse movements provide a more secure alternative but often lack adaptability and add friction. This paper proposes an AI-driven adaptive authentication system that fuses behavioral biometrics with contextual data including device information, location of login, and date and time of login to compute a dynamic trust score. The system adjusts authentication requirements in real time, providing stronger security while maintaining usability. Experimental analysis and literature review suggest that multimodal behavioral and contextual fusion reduces error rates, improves robustness against spoofing, and provides resilience in real-world deployment scenarios.

Keywords: Adaptive authentication, behavioral biometrics, keystroke dynamics, mouse dynamics, risk scoring, privacy- preserving security, multi-factor authentication, continuous authentication.

Abstract: This work presents a fuzzy logic-based closed-loop system for automated blood glucose regulation using continuous glucose monitoring (CGM) and intelligent insulin delivery. Unlike conventional PID controllers that rely on fixed gains and linear assumptions, the proposed controller uses linguistic rules and adaptive membership functions to relate glucose tracking error and its rate of change to appropriate insulin infusion rates. A validated glucose-insulin dynamic model is used to simulate patient response under realistic meal disturbances, and performance is assessed using metrics such as RMSE, MARD, and Time in Range. Results show that the fuzzy controller significantly reduces postprandial glucose excursions, improves average glycemic control, and lowers the frequency of hypoglycemic events compared with both open-loop operation and PID control. Sensitivity analysis further confirms the robustness of the fuzzy architecture to variations in patient parameters and measurement noise. These findings suggest that fuzzy inference offers a promising alternative to fully model-based control strategies for artificial pancreas applications, especially when physiological variability and uncertainty make precise mathematical modeling difficult.

Keywords: Fuzzy logic controller, Blood glucose regulation, Artificial pancreas, Insulin infusion control, Continuous glucose monitoring, Glucose-insulin model, Glycemic variability, Intelligent control

Abstract: Curcumin hybrids combine the pharmacophore of curcumin with other bioactive molecules, such as metals, synthetic drugs, phytoconstituents, peptides, or Nano carriers, resulting in synergistic pharmacological effects and enhanced pharmacokinetic profiles. Recent studies demonstrate that these hybrids exhibit superior enzyme inhibition (α-glucosidase, α-amylase, DPP-4), improved glucose uptake, enhanced insulin sensitivity, and significant antioxidant activity compared to native curcumin. Moreover, many hybrid derivatives show improved stability, metabolic resistance, and controlled-release behavior. This review summarizes the chemistry of curcumin, types of curcumin hybrids, synthesis strategies, and mechanisms of antidiabetic action, pharmacokinetic benefits, and current research limitations. A detailed discussion on future opportunities including advanced hybrid design, Nano-hybrid systems, molecular docking, and clinical translation is also provided. Curcumin hybrids thus represent a highly promising next-generation therapeutic approach for effective and safer diabetes management. Curcumin hybrids produce synergistic pharmacological effects and improved pharmacokinetic profiles by combining the pharmacophore of curcumin with other bioactive molecules, such as metals, synthetic medicines, phytoconstituents, peptides, or Nano carriers. According to recent research, these hybrids outperform natural curcumin in terms of enzyme inhibition (α-glucosidase, α-amylase, DPP-4), glucose absorption, insulin sensitivity, and antioxidant activity. Additionally, a lot of hybrid derivatives exhibit enhanced controlled-release behaviour, metabolic resistance, and stability. The chemistry of curcumin, types of curcumin hybrids, synthesis techniques, mechanisms of antidiabetic action, pharmacokinetic advantages, and present research limits are all summarized in this paper. Future prospects, such as enhanced hybrid design, Nano-hybrid systems, molecular docking, and clinical translation, are also thoroughly discussed. Thus, curcumin hybrids offer a very promising next-generation treatment strategy for managing diabetes in a safer and more effective manner.

Keywords: Curcumin hybrids, Antidiabetic agents, Type 2 diabetes mellitus, α-amylase inhibition, α-glucosidase inhibition, DPP-4 inhibition, Insulin sensitization, β-cell protection, GLUT-4 translocation, Anti-inflammatory activity, Antioxidant mechanisms, Nanotechnology, Nano-hybrids, Metal-curcumin complexes

Abstract: The red bed sands of the Visakhapatnam coastal region form one of the most distinctive Late Quaternary sedimentary archives along the East Coast of India. These ferruginous deposits, characterized by their red to reddish-brown colours imparted by iron bearing minerals of hematite and goethite coatings, exhibit a multicyclic and polygenetic origin caused by deep weathering of Eastern Ghats crystalline rocks, fluvial transport, aeolian reworking, and coastal geomorphic adjustments. Four major litho-units-yellow sands, reddish-brown sands, brick-red sands, and light-yellow sands reflect marked variability in grain size, sorting, heavy-mineral assemblages, and pedogenic features such as duricrusts and calcretes. Textural and petrographic characteristics indicate that the basal yellow sands were deposited under high-energy fluvial conditions, whereas the overlying reddish-brown and brick-red sediments record enhanced chemical alteration of Fe-bearing minerals and semi-arid pedogenic episodes. The upper light-yellow sands, resembling modern coastal dune deposits, reflect aeolian reworking during phases of lowered oxidation. The combined influence of monsoonal climate, sea-level oscillations, neotectonic uplift, and coastal processes contributed to the complex stratigraphic architecture of these red sands. This review synthesizes existing sedimentological, mineralogical, and geomorphic studies to clarify the origin, weathering pathways, and depositional environments of the Visakhapatnam red sands, while identifying key research gaps for future geochemical, chronological, and provenance analyses.

Keywords: Red bed sands of Visakhapatnam, Eastern Ghat Mobile Belt, polygenic origin, bad land topography, tropical weathering, depositional environment.

Abstract: The integration of cloud-native technologies with edge computing is transforming the deployment of 5G applications by enabling ultra-low latency, scalable, and reliable services. This paper provides an overview of cloud-native edge computing in the context of 5G networks, discussing its architecture, benefits, challenges, security concerns, and real-world applications.

Keywords: 5G, Cloud-Native, Edge Computing, Kubernetes, Microservices, IoT, Low Latency

Abstract: The runaway path of global urbanization combined with unstable climate change has subjected municipal water infrastructure to record stress. Conventional reactive water management frameworks based on static regulations are inherently unsuitable for providing the required levels of efficiency and robustness to contemporary urban settings. One of the prime symptoms of this systemic failure is the existence of Non-Revenue Water (NRW), which undetermined the commercial feasibility of utilities and environmental abundance of portable water. Predictive flow control through Artificial Intelligence (AI) is a required technology transformation, redefining the fundamental operations of utility companies from passive monitoring to self-optimizing systems. This survey lays out the critical role of AI incorporation to enable sustainable urban water management, outlining its technical underpinnings in the Internet of Things (IoT) and Supervisory Control and Data Acquisition (SCADA) systems. The central application, Dynamic Pressure Management (DPM), delivers measurable worth by facilitating the reduction of NRW by large amounts estimated at 0.79 gallons per service connection per day for each PSI decrease and garnering enormous Operational Expenditure (OpEx) savings through optimized pumping. In addition, AI improves the longevity of infrastructure by preventing pressure transients and extends its applications to crucial flood risk reduction, ensuring overall urban climate resilience. Strategic investment in such a transition requires careful financial planning using models such as Performance-Based Service Contracting (PBSC) and strict governance practices to take on the amplified cybersecurity threats, especially those to data integrity and autonomous control loops. Index Terms-Artificial Intelligence (AI), Dynamic Pressure Management (DPM), Non-Revenue Water (NRW), Internet of Things (IoT), SCADA, Urban Resilience, Cybersecurity.

Keywords: Internet of things (IoT) sensor, Real-time Monitoring, Artificial Intelligence, Smart Metering, Leak Detection, Predictive Maintenance.

Abstract: The increasing reliance on digital classrooms, virtual meetings, and multimedia content has created a strong demand for systems that can quickly convert long audio-video streams into structured and meaningful information. This paper introduces a unified, AI-driven transcription and summarization framework that functions seamlessly across a Windows-based standalone desktop application for real-time system audio transcription using Stereo Mix, a Chrome browser extension that performs tab-level audio capture and streaming transcription through a floating overlay interface; and a docker-containerized Flask web application deployed on Google Cloud Run. that supports file uploads, URL processing, AI-driven summarization, translation, and subtitle generation (SRT/VTT). The system captures audio from multiple sources - system level outputs, active browser tabs, uploaded media files, and external URLs - and transforms them into accurate transcripts through an optimized pipeline featuring chunk-based processing, adaptive buffering, low-latency data streaming, and efficient WebSocket/SSE communication. Real-time transcription is delivered through tokenized streaming, while Google Gemini generates multilingual summaries, context-aware descriptions, and synchronized subtitles. Reliability is strengthened through UUID-based storage, parallel chunk processing, and noise-resilient preprocessing. The entire pipeline is powered by Soniox Speech-to-Text (STT) and Google Gemini models. Experimental evaluation confirms that the architecture successfully handles long-form recordings, noisy audio streams, browser restrictions, and fluctuating network conditions. The proposed solution provides a scalable and flexible platform suitable for students, educators, content creators, and accessibility-driven applications, enabling fast transcript generation, cross-platform usability, and intelligent AI-powered summarization.

Keywords: Real-time transcription, audio processing, speech-to-text, Multilingual summarization, Server-Sent Events (SSE), AI-based summarization, browser extension, Flask web application, desktop transcription application, WebSocket streaming, cloud deployment, Docker, Soniox STT.

Abstract: Service station wastewater, typically rich in oil, grease, and detergents, poses a serious environmental threat when discharged untreated. This study explores a sustainable and low-cost phytoremediation approach using Eichhornia crassipes (water hyacinth) to treat wastewater from a vehicle washing station at SSM Polytechnic College, Tirur. A controlled laboratory experiment was conducted for 21 days using a 100 L tank containing wastewater collected from the service station. Key parameters-Biochemical Oxygen Demand (BOD), turbidity, pH, chloride, alkalinity, and oil and grease-were analyzed on Days 3, 14, and 21. Results revealed significant pollutant removal efficiencies: BOD (74%), turbidity (78%), oil and grease (65%), and chloride (58%), with pH stabilizing around 7.2. The findings demonstrate that water hyacinth provides an effective, eco-friendly solution for small-scale wastewater treatment, aligning with the goals of sustainable water management and SDG 6. The study highlights the potential for decentralized green wastewater treatment systems adaptable for institutional and semi-urban applications.

Keywords: Water hyacinth, Phytoremediation, Service station wastewater, Biochemical oxygen demand, Sustainable treatment, Green technology.

Abstract: Unicorn platforms in the gig economy-particularly Zomato and Swiggy-have transformed the food-delivery landscape in Bengaluru, yet growing concerns persist regarding the working conditions of delivery personnel. This study empirically examines the lived experiences of gig workers associated with these platform-based unicorns. Primary data were collected from 107 delivery employees using a structured questionnaire that captured key dimensions such as working hours, earnings and incentives, job security, occupational safety, algorithmic control, customer interactions, physical and psychological stress, and overall job satisfaction. The responses were analysed using descriptive statistics, cross-tabulations, and reliability assessments. The findings suggest that although gig work offers flexibility and minimal entry barriers, most delivery workers face long and irregular working hours, unstable and fluctuating incomes, high work pressure, and substantial physical strain resulting from prolonged riding and time-sensitive deliveries. Key concerns raised by respondents include income volatility, lack of insurance coverage, rising fuel and vehicle maintenance costs, and inadequate support during breakdowns. The study further highlights the impact of algorithmic management-specifically order allocation systems, performance ratings, and penalty mechanisms-which contributes to heightened stress and dissatisfaction. Safety risks related to traffic exposure, accidents, and occasional customer hostility also negatively affect the well-being of workers. Overall, the study concludes that despite their integration into technologically advanced unicorn platforms, gig workers continue to experience precarious and vulnerable working conditions. The results emphasise the need for policy reforms, fair compensation structures, improved safety and welfare measures, and strengthened worker representation to ensure more sustainable, equitable, and dignified gig work in metropolitan cities such as Bengaluru. These insights carry significant implications for platform management, regulatory frameworks, and academic research focused on the future of gig work.

Keywords: Gig economy; Unicorn startups, Food-delivery platforms, Working conditions, Delivery employees, Zomato; Swiggy, Job satisfaction; Work stress; Platform-based employment; Bengaluru; Workforce well-being; Incentive structure; Occupational safety.

Abstract: The efficiency of Optical Character Recognition (OCR) decreases significantly when dealing with handwritten text, low-quality scans, and complex backgrounds, often resulting in fragmented, noisy, and syntactically incorrect output. These limitations affect the accuracy of subsequent Natural Language Processing (NLP) tasks such as summarization, information extraction, and automated document analysis. To address these issues, this research work proposes an combined OCR-NLP method that automatically detects text type using DenseNet-121 and applies either Tesseract or OCRSpace based on whether the input contains printed or handwritten text. The raw OCR output is then refined using the Phi-3 language model to correct grammar, enhance readability, and restore contextual meaning. Experimental results on mixed printed and handwritten datasets show a substantial improvement in accuracy, with reduction in Character Error Rate (CER) and Word Error Rate (WER) after NLP post-processing. The proposed system demonstrates a robust, scalable, and automated pipeline suitable for educational digitization, archival processing, and large-scale text-driven applications.

Keywords: OCR, NLP, DenseNet-121, Handwritten Recognition, Printed Text Recognition, Phi-3, Post-Processing.

Abstract: The present research work was undertaken with the objective of formulating and evaluating a polyherbal facewash containing Ocimum sanctum (Tulsi), Azadirachta indica (Neem), Aloe barbadensis (Aloe vera), and Citrus limon (Lemon peel) to provide an effective, mild, and skin-friendly cleansing preparation with anti-acne, antimicrobial, and skin-nourishing benefits. Synthetic facewash formulations often incorporate chemical surfactants and preservatives that may cause long-term skin sensitivity or irritation. Therefore, the present study was aimed at developing a natural herbal alternative enriched with phytoconstituents that exhibit synergistic therapeutic actions for maintaining skin hygiene and improving overall skin health. Each selected herb possesses well-documented medicinal value in traditional and modern skincare systems. O. sanctum is rich in eugenol and rosmarinic acid, imparting antimicrobial, antioxidant, and anti-inflammatory properties beneficial for reducing acne and purifying the skin. A. indica contains azadirachtin and nimbidin, effective against acne-causing bacteria and inflammation, contributing to clearer and infection-free skin. Aloe vera provides hydration, healing, and soothing effects due to its polysaccharides and glycosides, making the formulation suitable for sensitive and irritated skin. Lemon peel, a natural source of vitamin C and citric acid, supports gentle exfoliation, skin brightening, and reduction of pigmentation and blemishes. The combination of these herbs enhances cleansing efficiency while preserving the natural moisture balance of the skin. The polyherbal facewash was formulated using Carbopol 940 as a gelling agent, glycerin as a humectant, sodium lauryl sulphate as a mild foaming agent, rose water as a toning component, and sodium benzoate as a preservative. The prepared formulation was evaluated for organoleptic properties, pH, spreadability, foaming ability, washability, stability, and skin irritation potential. The facewash exhibited an appealing light-green colour with a smooth, uniform gel-like consistency and a pleasant herbal aroma. The pH of the formulation was found to be 5.8 ± 0.2, which lies within the skin-compatible range and ensures minimal irritation. The foam height measured 1.8 ± 0.1 cm, indicating adequate foaming and cleansing ability.

Keywords: Herbal facewash, Skin care, Formulation, Evaluation, Antimicrobial, Anti-acne, Ocimum sanctum (Tulsi), Azadirachta indica (Neem), Aloe barbendensis

Abstract: This study evaluates the impact of training programs on the professional performance, motivation, and teaching effectiveness of private school teachers in Mysore District. Data were collected from 170 respondents (150 teachers and 20 administrators) using structured questionnaires and interviews. Descriptive statistics and chi-square tests were used to analyse the relationship between training participation and performance outcomes. The findings show that over 70% of teachers rated pedagogical and digital training programs as moderately to highly effective, while 52% reported significant improvement in classroom delivery and instructional clarity. Digital training enhanced technological competence for 48% of participants, especially in post-pandemic hybrid classrooms. However, 15% indicated that training lacked depth, and over 40% reported insufficient follow-up evaluation. Chi-square results confirmed a significant positive association between frequency of training and improved teaching performance. The study underscores the need for continuous professional development, structured feedback mechanisms, and stronger institutional support to maximize long-term training outcomes.

Keywords: Training Programs, Teacher Performance, Professional Development, Digital Competence, Pedagogical Effectiveness, Private Schools, Mysore District

Abstract: Nowadays, the space between building is reduced due to globalization and the construction of high-rise buildings. This leads to increase in the use of non-renewable energy sources, thus, and there is a requirement of new construction technique like green buildings and indoor thermal system. This project focuses on the development and evaluation of transparent concrete, a novel construction material that incorporates light- transmitting optical fibers into a standard concrete matrix. This project aims to develop and analyze transparent concrete as a sustainable and functional construction material. The primary goals are to harness natural daylight to reduce artificial lighting costs in buildings, and to integrate smart sensing capabilities using optical fibers for structural health monitoring. This research will evaluate the mechanical performance of transparent concrete by testing its compressive and tensile strengths, comparing various mix designs including those with different percentages of optical fibers or partial cement replacement with glass powder. Additionally, the project will quantify the material's light-transmitting properties to assess its efficiency in daylighting applications, such as for facade materials, partition walls, and interior cladding. The findings will provide data on the material's viability for both aesthetic and structural purposes, contributing to the development of smart and green building technologies.

Keywords: Light Transmissive, Eco-friendly concrete, Architectural Aesthetic, Stress sensing, Green Architecture Transparent concrete, optical fibres, glass powder, Cement, Sand, Concrete, Glass fibres, Translucent Concrete, Light transmitting Concrete, Energy Saving, Material, Sustainable Concrete, Optical Concrete, Compressive Strength, Tensile Strength.

Abstract: Structural audit is a key step in checking the condition and strength of buildings, especially older ones. This report is meant to look closely at a chosen residential or commercial building to understand its current state. The main purpose is to find any signs of structural problems, check how safe the building is, and suggest needed repairs or improvements. The audit uses methods like visual checks, non-invasive testing, detailed structural analysis, and making sure the building follows all the required standards. The results show parts of the building that need urgent care and steps for ongoing maintenance to keep people safe and the structure strong over time. This study highlights how regular audits are important for managing buildings in a sustainable way.

Keywords: structural audit, non- destructive test, repairs and controls, audit standards.

Abstract: The rapid rise of e-commerce has increased doorstep deliveries, but challenges such as missed deliveries, parcel theft, and dependency on customer availability still persist. This paper presents a Dual Door Smart Locker System that enables secure parcel drop-offs without requiring the owner's physical presence. The system features independent access points for delivery personnel and the owner, where the delivery door is remotely unlocked by the user, and the retrieval door requires secure authentication using a PIN or facial recognition. Additionally, the system includes an integrated smart letter box, where an IR sensor detects newly dropped mail and updates the cloud dashboard with real-time notifications. The architecture uses ESP32 microcontrollers, Firebase cloud services, a Python-based recognition engine, and a web dashboard to enable remote monitoring, access logging, and seamless communication between modules. Experimental results demonstrate reliable authentication, accurate mail detection, and efficient dual-door operation, making the proposed solution a practical and scalable approach for modern home-delivery ecosystems.

Keywords: Smart Locker, IoT, Dual Door Access, Facial Recognition, ESP32, Secure Parcel Delivery, Cloud Integration.

Abstract: Animals and birds entering restricted human zones need to be quickly and accurately detected to avoid crop damage, traffic accidents, and conflicts between humans and wildlife. This proposed work describes the development of a useful AI-driven real-time Animals and Birds Detection and Alert System on a custom dataset. The proposed pipeline detects various species of animals and birds present in images, videos, and live streaming feeds. Upon detection of hazards, it automatically triggers user-configurable alerts such as SMS notifications, buzzer/siren sounds, etc. We present the model architecture, training, deployment plan, and performance evaluation on diverse scenes, such as highways and farms. The system allows for live feeds from IP cameras, user uploads, incident review dashboards, and alert logging for future monitoring and analysis. A scalable, modular design allows for future system expansion, integration with IoT components, and effective real-time inference suitable for edge deployment in rural and forest border areas.

Keywords: human-wildlife conflict prevention, bird detection, YOLO, real-time surveillance, Flask, alert system, deep learning, edge AI.

Abstract: This study aimed to propose a model for selecting appropriate assessment methods. One of the principles of high-quality assessment is appropriateness. Specifically, assessment must be constructively aligned with the level of learning outcomes to consider its appropriateness. The proposed model is comprehensible enough to use in identifying the assessment methods to be used. The researchers analyzed the contents about assessment and selected faculty members and school administrators purposively for a focus group discussion for development and validation of the final proposed model. Starting with the levels of learning outcomes under the taxonomy of objectives in the cognitive domain, types of assessment, and examples of assessment methods, the proposed model was carefully designed and developed to establish the alignment of the assessment methods. The proposed model can be used by educators in designing their assessment methods which are properly aligned and appropriate. Also, the proposed model is the first model for selecting appropriate assessment methods.

Keywords: assessment, appropriateness, alignment, learning outcomes, types of assessment

Abstract: This study presents a comprehensive bifurcation analysis of a nonlinear tumor-immune interaction model under varying treatment intensities. The model, governed by two coupled differential equations, captures the complex dynamics between tumor growth, immune response, and treatment-induced cytotoxic effects. Stability and equilibrium analyses reveal critical treatment thresholds that separate tumor persistence, oscillatory remission, and eradication regimes. Numerical simulations, including nullcline plots, bifurcation diagrams, and sensitivity analyses, demonstrate how treatment intensity and immune system parameters influence system behavior. Results indicate that low treatment intensities lead to uncontrolled tumor growth, moderate levels induce oscillatory coexistence through Hopf bifurcation, and higher intensities stabilize the tumor-free equilibrium, signifying successful therapy. The model emphasizes the delicate balance between therapeutic efficacy and immune preservation, offering insights into optimizing treatment strategies for tumor suppression.

Keywords: Tumor-immune interaction, Bifurcation analysis, Nonlinear dynamics, Stability analysis, Hopf bifurcation, Treatment intensity, Mathematical oncology.

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page Smart And Sustainable Food Systems Through Technology and Waste Reduction Harshith Y A, Harshitha Umashankar, Priya Chandrika R, Sai Kiran R D, Shrishail, Roopa K Murthy

Abstract: The global food system faces a growing challenge on how to meet rising demand without exhausting natural resources or worsening environmental impacts. This paper explores on how smart technologies and effective waste reduction strategies can make food systems more sustainable and resilient. It looks at the potential of tools such as Artificial Intelligence (AI), the Internet of Things (IoT), and data-driven decision-making to improve efficiency for exporting food. By integrating these technologies with circular economy principles, it is possible to minimize food loss, reduce carbon emissions, and promote more responsible production and consumption patterns. Real-world examples illustrate how innovations like precision farming, smart sensors, and digital supply chains can cut waste, enhance food quality, and strengthen food security. This study states that combining technology with sustainability practices can help build a more inclusive, efficient, and environmentally friendly food system. These insights offer practical guidance for policymakers, researchers, and industry leaders working towards smarter and more sustainable approaches to food production and distribution.

Keywords: Smart food systems, Sustainability, Food waste reduction, Technology, Circular economy. Downloads: | DOI: 10.17148/IARJSET.2025.1211049 How to Cite: [1] Harshith Y A, Harshitha Umashankar, Priya Chandrika R, Sai Kiran R D, Shrishail, Roopa K Murthy, "Smart And Sustainable Food Systems Through Technology and Waste Reduction," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2025.1211049 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: Columns rest on the beam without foundation are called floating column.They are used commonly in multi-storey buildings which are purposed to hold parking at ground floor or open halls at higher floors. Discontinuation within the load transfer path is seen in this column. Thus, they are designed for gravity loads. But these structures aren't designed for earthquake loads. In present scenario structures with floating column may be a common characteristic in urban India. However, in tectonic areas, this type of structure is not preferred due to discontinuity of load transfer path i.e. whole earthquake load on the structure is shared by the shear walls without any loads on the floating columns. This paper reviews the nature of a multi-storey building under quake forces with and without of floating columns. This analysis focusses the importance of specially identifying the presence of the floating column within the study of the structure, establish its correlation with the building without a floating column using designing software Extended three-dimensional analysis of building systems (ETABS). This paper also discusses the performance of structure having floating column in seismically active areas. Besides this various parameter such as maximum displacement, effect on number of storeys on drift, base shear is also studied.

Keywords: Floating columns, Equivalent static analysis, Storey displacement, Storey drift, Base shear, Etabs

Abstract: Early detection of diabetes is crucial for effective management and prevention of complications. This research presents a robust deep learning framework for predicting diabetes using clinical and lifestyle features. A fully connected neural network model with batch normalization, dropout layers, and residual connections was designed to handle class imbalance and improve generalization. The model was trained on a comprehensive dataset of 100,000 patient records and evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate that the proposed approach achieves a test accuracy of 97.13%, outperforming conventional machine learning models and recent state-of-the-art methods. Confusion matrix and classification reports confirm high predictive performance for both positive and negative classes. This framework provides a scalable, interpretable, and efficient solution for early diabetes screening in healthcare systems.

Keywords: Deep Learning, diabetes prediction, early detection, Heart rate variability, ECG, CNN, LSTM

Abstract: Water quality prediction is essential for sustainable environmental management and public health. Traditional analytical methods are often laborious and inefficient. This research presents an enhanced deep learning framework (EHDL-WQM) for accurate Water Quality Prediction and Monitoring. The framework integrates Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal pattern learning, enhanced by an Attention Mechanism to emphasize significant parameters. The proposed architecture effectively processes multivariate sensor data to predict key indicators, including pH, dissolved oxygen, turbidity, and conductivity. Experimental evaluation demonstrates that EHDL-WQM achieves superior prediction accuracy and faster convergence compared to traditional and baseline deep learning models. The framework provides a scalable, intelligent solution for real-time monitoring and proactive water quality management.

Keywords: Water quality, Machine learning models, Deep learning, Water quality index, Water quality classification

Abstract: Soil fertility plays a critical role in agricultural productivity and food security. With increasing global population and diminishing arable land, optimizing crop yield through intelligent soil management has become imperative. This work presents an enhanced deep learning-based model for soil fertility assessment and crop recommendation using a multi-parameter agricultural dataset. The proposed system integrates key soil features such as N, P, K, pH, EC, micronutrients, and organic carbon, followed by feature scaling and optimized training to achieve improved classification performance. Experimental results demonstrate an accuracy of 88.06% with a macro F1-score of 0.72, indicating strong predictive capability for major soil fertility classes. The confusion matrix confirms high precision and recall for Classes 0 and 1, while Class 2 shows lower performance due to limited representation. Overall, the model provides an efficient and data-driven solution for agricultural decision-making, helping farmers and agronomists select suitable crops based on soil characteristics.

Keywords: Deep Learning, Fertilizer Recommendation, Crop Recommendation, soil data; soil analysis