Volume 14 | Issue 4 |

 Abstract

Crinum latifolium is an ornamental and medicinal plant widely grown in various Indian regions and worldwide. In aurveda, this plant having a significant medicinal importance. In present study, this plant was used in green synthesis of CuO nanoparticles using Copper chloride. However before the green synthesis of CuO nanoperticles, Plant leaves were extracted in double distilled water and methanol. Qualitative phytochemical screening for presence of bioactive compounds and antimicrobial activity against gram negative bacteria (E.coli and Salmonella), gram positive bacteria (K. pneumoniae) and fungi (Fusarium oxysporum) were analysed. After green synthesis of CuO nanoparticles, were characterized using different available techniques UV visible, FTIR and XRD. Moreover about this the synthesized CuO nanoparticles were investigated for antimicrobial potentiality and antioxidant activity.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Crinum latifolium, Phytochemicals, Antioxidants, CuO nano particles and Antimicrobial activity.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The deployment of autonomous vehicles represents one of the most consequential applications of artificial intelligence, with profound implications for public safety, urban mobility, and societal trust in AI systems. This paper proposes a comprehensive trust-centric ethical AI architecture specifically designed for self-driving cars, addressing the critical gap between technical capability and ethical accountability in autonomous decision-making. The architecture integrates five core dimensions: transparent decision-making through explainable AI mechanisms, value-aligned behavior encoded through machine ethics frameworks, safety assurance via formal verification and redundant systems, privacy preservation through federated learning and differential privacy, and human oversight mechanisms maintaining meaningful human control. A multi-layered framework encompasses the perception layer with bias-mitigated sensor fusion, the ethical reasoning layer implementing structured moral decision frameworks, the action selection layer with constraint-based planning, and the accountability layer enabling post-incident analysis through immutable logging. The proposed architecture addresses the trolley problem variants specific to autonomous driving by prioritizing legally compliant outcomes while avoiding algorithmic discrimination. Simulation-based evaluation demonstrates that the trust-centric approach achieves safety performance comparable to conventional architectures while providing superior transparency, auditability, and ethical alignment. The paper concludes with policy recommendations for regulatory frameworks governing autonomous vehicle deployment and identifies open research challenges in aligning AI systems with diverse human values across cultural contexts.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autonomous Vehicles, Ethical AI, Self-Driving Cars, Machine Ethics, Trust Architecture, Explainable AI, AI Safety, Algorithmic Accountability, Moral Decision-Making, Value Alignment, Autonomous Systems.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Santali Folks Literature

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Santali Folks Literature

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The recent global re-emergence of Monkeypox (Mpox) necessitates rapid, non-invasive diagnostic tools to differentiate its dermatological manifestations from visually similar diseases such as Chickenpox and Measles. While deep learning models possess high theoretical accuracy for skin lesion classification, standard architectures frequently fail in clinical deployment due to "black-box" opacity and their structural vulnerability to out-of-distribution (OOD) inputs--often generating high-confidence false-positive diagnoses when presented with non-medical imagery. In this paper, we propose a robust, multi-stage Explainable Artificial Intelligence (XAI) framework to resolve these limitations. The core mathematical feature extractor utilizes an EfficientNetB0 architecture trained across five distinct classes, including a dynamic "Others" category to compartmentalize generic skin anomalies. To enforce diagnostic safety, a lightweight MobileNetV2 semantic sentry is integrated to instantaneously reject OOD inputs (such as animals or household objects) prior to inference, alongside an 80% algorithmic confidence threshold. Finally, the framework employs Gradient-weighted Class Activation Mapping (Grad-CAM) to visually project the specific convolutional features influencing the model's prediction, thereby enforcing clinical accountability. By combining high-fidelity transfer learning with explicit OOD defense mechanisms and real-time visual explainability, our proposed system bridges the critical gap between deep learning theoretical accuracy and practical, trustworthy medical deployment.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Monkeypox Detection, Deep Learning, EfficientNetB0, Explainable AI (XAI), Grad-CAM, Out-of-Distribution (OOD) Detection, Computer-Aided Diagnosis (CAD).

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cystic fibrosis patients frequently suffer from chronic pulmonary infections caused by multidrug-resistant pathogens, among which the Burkholderia cepacia complex represents one of the most clinically devastating. These infections are associated with rapid lung function decline, limited antibiotic responsiveness, and poor transplant outcomes, underscoring an urgent need for alternative therapeutic strategies. The present study adopts a structure-guided drug repurposing approach to identify FDA-approved drugs capable of targeting key druggable proteins of Burkholderia cepacia. Essential and virulence-associated bacterial proteins were systematically identified and evaluated for druggability using structural and functional criteria. High-confidence targets were subjected to virtual screening against a curated library of FDA-approved compounds to rapidly uncover candidates with strong binding potential. Molecular docking and interaction profiling were employed to characterize binding modes, followed by molecular dynamics simulations to assess complex stability under physiological conditions. This integrative in silico framework prioritizes clinically actionable drug candidates capable of disrupting critical bacterial pathways while bypassing early-stage drug development bottlenecks. The findings provide a rational basis for repurposing existing drugs against Burkholderia cepacia infections and offer a scalable strategy for combating antimicrobial resistance in cystic fibrosis.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cystic Fibrosis, Burkholderia cepacia complex, Drug Repurposing, Multidrug Resistance, Molecular Docking, Virtual Screening, FDA-approved Drugs, Antimicrobial Resistance

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Crop combination and diversification are fundamental concepts in agricultural geography that help in understanding spatial patterns of land use and regional agricultural development. This study examines crop combination and diversification patterns in Dhule district, Maharashtra, for the period 2013-2016 using Doi's modified minimum deviation method. The analysis is carried out at both tahsil and circle levels to identify dominant crop associations and regional variations. The findings indicate significant spatial heterogeneity in cropping patterns, with cotton dominating monoculture regions and diversified cropping systems prevalent in tribal and high rainfall areas. The study highlights the influence of physical and socio-economic factors such as rainfall, soil fertility, irrigation, and market accessibility on crop distribution. It also emphasizes the importance of crop diversification as a strategy for risk mitigation, resource optimization, and sustainable agricultural development.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Crop combination, crop diversification, agricultural regionalization, Doi's method, Dhule district

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Water scarcity and inefficient irrigation practices remain critical constraints to sustainable agricultural production, particularly in horticulture. This study examines the impact of water availability and irrigation systems on horticultural crop production in the Baruipur subdivision of West Bengal, India. Based on primary data collected from 300 farm households during 2024-2025, complemented by secondary sources, the study analyzes irrigation sources, adoption of micro-irrigation (MI), farmer perceptions, and agro-economic outcomes. Descriptive statistics and comparative analysis indicate that pond irrigation (64.33%) is the dominant source, followed by tubewells (18.67%). Approximately 43.33% of farmers reported moderate to severe water scarcity. Adoption of micro-irrigation significantly improves water-use efficiency, yield, and profitability. However, high initial costs, institutional barriers, and infrastructure limitations constrain adoption. The study suggests that strengthening policy support, improving irrigation infrastructure, and enhancing farmer awareness are essential for sustainable horticultural development.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords : Water availability; Micro-irrigation; Horticulture; Water-use efficiency; Farmer perception; Sustainable agriculture;

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nebivolol Hydrochloride, Ramipril, first derivative, second derivative, validation, UV Spectrophotometric

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

International organizations play a crucial role in maintaining global peace and security in an increasingly interconnected world. These institutions facilitate cooperation among nations, prevent conflicts, manage crises, and promote sustainable peace through diplomatic negotiations, peacekeeping missions, and humanitarian assistance. Organizations such as the United Nations, North Atlantic Treaty Organization, and European Union contribute significantly to conflict resolution and peace building initiatives. This research paper examines the role of international organizations in maintaining world peace by analyzing their functions, achievements, challenges, and future prospects. The study highlights the importance of collective security, diplomacy, and international cooperation in promoting global stability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

International organizations United Nations, International cooperation ,peacekeeping

  License

Creative Commons Attribution 4.0 and The Open Definition