Volume 13 | Issue 11 |

 Abstract

Disaster management systems often face challenges such as disrupted communication, inefficient resource distribution, and delayed situational awareness. To address these issues, this paper presents DisasterXAI, an AI- powered, adaptive disaster response and resource management platform designed for real-time operation even in limited connectivity conditions. The system integrates edge-based computer vision, predictive analytics, geospatial optimization, and offline-first communication to assist emergency responders and communities during natural and human-made disasters. By leveraging AI models for object detection, demand forecasting, and intelligent allocation, DisasterXAI ensures timely, data- driven, and resource-efficient response planning

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 Keywords

disaster response, edge AI, offline operation, YOLO, resource allocation, geospatial optimization, on- device inference, adaptive planning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Student council elections play a vital role in promoting leadership, representation, and democratic culture within educational institutions. However, traditional election methods often face challenges such as manual vote counting, impersonation, biased practices, data tampering, and lack of transparency. To address these concerns, this paper presents VoteChain, a secure and intelligent web-based student council election platform that integrates Artificial Intelligence (AI), Machine Learning (ML), and blockchain-inspired ledger mechanisms. The system ensures reliable voter authentication using AI-driven facial or ID verification, encrypted vote processing for privacy, and ML-based fraud detection to identify anomalous or duplicate voting patterns. Additionally, a blockchain-like immutable vote recording system enhances trust and transparency by preventing unauthorized data manipulation. The platform also features a real-time analytics dashboard for vote monitoring, participation insights, and instant result visualization. Experimental evaluation demonstrates that VoteChain significantly improves election integrity, reduces human intervention, accelerates result generation, and encourages wider student participation. This research contributes towards developing a modern, transparent, and secure digital election ecosystem tailored for academic institutions.

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 Keywords

Student Council Election, AI-based Authentication, Machine Learning, Blockchain-Inspired Ledger, Fraud Detection, Secure Online Voting, Real-Time Analytics, Data Integrity.

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In this work, we report the synthesis of the Schiff base compound, (Z)-3-((2-(2,4-dinitrophenyl)hydrazono)methyl)-4-methyl-1H-pyrrolo[2,3-b]pyridine, through the condensation reaction between 4-methyl-1H-pyrrolo[2,3-b]pyridine-3-carbaldehyde and (2,4-dinitrophenyl)hydrazine. Elemental analysis, FT-IR, NMR, and UV-Vis spectroscopic measurements confirmed the structural composition of DMP. FT-IR and NMR measurements provided evidence for the formation of a hydrazone linkage with nitro and aromatic functionalities. The UV-Vis and DFT results revealed a clear signature of intramolecular charge transfer with an estimated HOMO-LUMO energy gap of 3.125 eV, which is in close agreement with the experimental absorption at 390 nm. The molecular electrostatic potential (MEP) map displayed distinct positive and negative potential regions indicating the probable reactive sites. These confirm the structural veracity of the synthesized compound with promises for applications in the area of optoelectronics and sensing.

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dinitrophenyl, elemental, spectroscopy, DFT, HOMO

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Cyclical mastalgia, known in Ayurveda as Stanpeeda, is a common premenstrual condition characterized by breast pain, heaviness, and tenderness due to hormonal variations. In modern medicine, analgesics are frequently prescribed for symptomatic relief; however, its long-term use may lead to androgenic side effects. Ayurveda offers a holistic and safer alternative through fs possessing Shothahara (anti-inflammatory), Vedanasthapana (analgesic), and Stanyashodhana properties.

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Stanpeeda, Cyclical Mastalgia, Nishakanak Kalka Lepa, Ayurveda, Vedanasthapana, Shothahara

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Larvivorous fishes are an important component of integrated pest management of mosquitoes as well as freshwater aquaculture in Madhya Pradesh, India. This study has assessed the haematological and biochemical characters of the four key larvivorous fishes are Gambusia affinis, Poecilia reticulata, Heteropneustes fossilis and Channa striata in seasonal variation in Gurma Dam, Mauganj. There were significant interspecies and seasonal differences in red blood cell count, hemoglobin concentration, hematocrit and white blood cell count: these reflect species-specific physiological adaptations and responses to environmental stressors (e.g. hypoxia, temperature fluctuations). Nutritional and metabolic status changes consistent with environmental conditions were further exemplified by changes in biochemical parameters of serum proteins, serum glucose, liver enzymes (ALT and AST) and minerals. Correlation analysis showed good correlation between dissolved oxygen, pH, carbon dioxide, and the combination of fish health indicators. The study emphasises the need to have region-specific hematological metrics for proper optimization of larval fish health for enhanced vector efficiency, aquaculture productivity. These results presented good baseline data for sustainable fisheries management practices and offered practical suggestions for regular monitoring and environment quality maintenance in order to benefit the double objectives of public health and food security in freshwater ecosystems.

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Keywords: Larvivorous fishes, haematological parameters, biochemical profiles, vector control, Madhya Pradesh.

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Esomeprazole, Domperidone, Method Validation

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Nadi shuddhi : Hathayoga or Ayurvedic Drishti se ek Samnvit Adhyayan

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Nadi shuddhi : Hathayoga or Ayurvedic Drishti se ek Samnvit Adhyayan

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The present study focuses on the development and validation of a Reverse Phase High-Performance Liquid Chromatography (RP-HPLC) method for the simultaneous estimation of Losartan Potassium and Enalapril Maleate in pharmaceutical dosage forms. The analytical method was developed to provide a simple, precise, accurate, and cost-effective technique suitable for routine quality control analysis. Chromatographic separation was achieved using a C18 column with a mobile phase consisting of buffer and acetonitrile (60:40 v/v, pH 4.5 adjusted with orthophosphoric acid) at a flow rate of 1.0 mL/min. The detection wavelength was set at 235 nm, providing well-resolved and symmetrical peaks for both analytes with retention times of 3.15 min for Enalapril Maleate and 5.42 min for Losartan Potassium. The developed method was validated as per ICH Q2 (R1) guidelines, fulfilling parameters such as specificity, linearity, precision, accuracy, robustness, and system suitability. Linearity was established in the range of 5-15 ?g/mL for Enalapril Maleate and 25-75 ?g/mL for Losartan Potassium, with correlation coefficients exceeding 0.999. The recovery results were within 98-102%, confirming method accuracy and reproducibility. The validated RP-HPLC method proved to be reliable for the simultaneous determination of Losartan and Enalapril, making it highly applicable for routine pharmaceutical quality control.

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Losartan Potassium, Enalapril Maleate, Method Validation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study evaluates the risk-adjusted performance of five mid-cap mutual funds in India over the period 2020-2025. Using Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Beta, R-squared, and a volatility-adjusted alpha derived from GARCH (1,1) modeling, the paper integrates macroeconomic volatility--interest rates, inflation, and exchange rates--into fund performance analysis. The findings reveal that funds with lower beta and higher adjusted alpha outperform during macroeconomic stress, offering strategic insights for investors and fund managers in emerging markets.

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Mutual Funds, Mid Cap, Interest Rates, Inflation, Exchange Rates

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 Abstract

Construction sites are among the most hazardous working environments, with frequent accidents caused by the absence or improper use of Personal Protective Equipment (PPE) such as helmets, safety vests, goggles, gloves, and boots. Traditional safety monitoring is often carried out by supervisors through manual inspections, but this process is inefficient, prone to human error, and difficult to manage in large-scale projects where many workers operate simultaneously. In this context, the use of Artificial Intelligence (AI) offers a more effective solution for ensuring worker safety and compliance with safety regulations. The proposed system utilizes deep learning and computer vision techniques to automatically detect both workers and their safety gear in real time. High-resolution video streams from surveillance cameras are processed using state-of-the-art object detection models such as YOLO or Faster R-CNN. These models are trained on annotated datasets containing images of workers with and without PPE under various conditions, including different lighting, weather, and occlusion scenarios. Once a person is detected, the system verifies the presence of required safety gear by associating PPE detections with the corresponding worker. If non-compliance is identified, the system immediately triggers alerts through audio signals, on-site alarms, or notifications to supervisors. Beyond real-time detection, the system provides a centralized dashboard for site managers. This dashboard offers detailed compliance statistics, incident logs, and trend analysis to support decision-making and safety training initiatives. The solution can be deployed flexibly, either on cloud servers for centralized processing or on edge devices such as NVIDIA Jetson boards for low-latency, offline operation. The expected outcomes of this project include improved worker safety, reduction in accident rates, and enhanced operational efficiency on construction sites. By automating PPE compliance monitoring, the system minimizes reliance on manual inspections and ensures continuous, unbiased supervision. Furthermore, the recorded data and compliance reports can assist construction companies in meeting legal safety requirements and reducing insurance liabilities. In conclusion, this AI-based PPE detection system represents a step toward smarter and safer construction sites. Its integration of computer vision, real-time alerting, and compliance analytics provides a comprehensive framework for worker protection. With further enhancements such as helmet color recognition, fall detection, and hazardous zone monitoring, the system can evolve into a complete safety management platform, significantly contributing to the creation of accident-free workplaces in the construction industry.

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 Keywords

Artificial Intelligence, Computer Vision, Personal Protective Equipment (PPE), Safety Monitoring, Deep Learning, Object Detection, YOLO, Faster R-CNN, Worker Safety, Construction Site Safety, Real-time Detection, Machine Learning, Edge Computing, Cloud Deployment, Video Analytics, Automated Surveillance, Safety Compliance, Accident Prevention, Occupational Health and Safety (OHS), Workplace Monitoring, Human Detection, Smart Construction, Hazard Detection, Safety Management Systems, Predictive A

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Creative Commons Attribution 4.0 and The Open Definition