Volume 13 | Issue 11 |

 Abstract

Gold has high value and portability. This makes it vulnerable to theft, loss, and tampering in personal, industrial, and financial settings. Traditional security measures include vaults, guards, and CCTV. These provide deterrence and evidence after incidents. But they fail to offer real-time visibility of asset locations. This paper proposes the Gold Lost GPS System. It is a hybrid architecture that combines GPS and GNSS tracking with IoT smart sensors. It also uses secure communication protocols and cloud-based analytics. All this enables continuous monitoring, anomaly detection, and rapid recovery workflows. We present an expanded system design here. We include methodology for evaluation, implementation details, and application scenarios. These cover supply chains, banking, mining, and personal ownership. A comparative analysis looks at RFID, CCTV, and blockchain-only solutions. It shows the proposed systems strengths in global coverage and event-driven intelligence. There are trade-offs in power management and indoor occlusions though. We address challenges like jamming, battery life, cost, and reliability. We also cover ethical and legal considerations such as privacy, data governance, and consent. Future enhancements integrate AI for threat prediction, biometric access control, blockchain provenance, and 5G or satellite IoT for global interoperability. Case studies and a hypothetical recovery scenario illustrate practical outcomes. A user-centric interface design underscores usability and accessibility. We conclude with a vision for democratizing gold security. This happens via unobtrusive wearables and standardized platforms. The aim is to strengthen trust, reduce losses, and improve stakeholder confidence.

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 Keywords

The Gold Lost GPS System integrates hardware trackers, sensors, communication modules, cloud infrastructure, and user-facing applications. It forms a modular stack that supports secure, real-time asset visibility.

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 Abstract

The northeastern states like Assam, Arunachal Pradesh, Manipur, Mizoram, Meghalaya, Nagaland, Tripura and Sikkim are suitable for the production of different horticultural crops such as different varieties of fruits. The region has sub-tropical climate with hot, humid summers, severe monsoons and mild winters. The hilly areas of northeastern states are suitable for the production of different types of fruits such as apple, kiwi, litchi, citrus, orange, banana, pineapple, plum, guava, peach, jack fruit, mango, grapes, papaya, lemon, passion fruit, dragon fruit, pear, walnut, strawberry etc. This paper is an effort to study the growth trends in the area harvested and production of fruit crops in the northeastern region of India from the year of 2004-05 to 2023-24. As per the findings of the study, the fitted trend line of the variable of the area harvested under the fruit crops and total production of fruit crops in the northeast region of India has recorded increasing trends over the period under the study. The estimated compound annual growth rates of area harvested under fruit crops cultivation and the total production of fruit crops in the northeast region are found significant over the study period.

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 Keywords

Fruit Crops, Area Harvested, Production, Northeastern Region.

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Creative Commons Attribution 4.0 and The Open Definition

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This paper provides an academic overview of the debate on personal identity, focusing on three main approaches: the Psychological, Biological, and Hybrid theories. It examines the Psychological Approach, which grounds identity in psychological continuity such as memory and consciousness, illustrated by the Transplant Intuition. The Biological Approach argues for identity based on the persistence of the human organism, challenging psychological criteria and rejecting the Transplant Intuition. The paper discusses key objections to the Biological Approach, including its counterintuitive implications and methodological concerns. The Hybrid Approach is presented as a middle ground, integrating both biological and psychological elements, though it faces unresolved questions about their interaction. The paper also addresses the "Too Many Minds" objection, which challenges non-biological accounts by highlighting problems with overlapping identities. The conclusion emphasizes the complexity of personal identity, noting that purely biological continuity fails to fully capture what matters for survival, and calls for a nuanced understanding of the relationship between persons and human animals.

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 Keywords

Personal Identity, Psychological Approach Psychological Continuity, Consciousness, Biological Continuity, Identity Persistence

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Creative Commons Attribution 4.0 and The Open Definition

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Electronic voting (e-voting) systems are designed to make elections faster, more efficient, and more transparent compared to traditional paper-based voting. However, many existing systems still face problems like voter impersonation, multiple voting, and tampering with voting data.To solve these issues, this research proposes a secure e-voting system that uses fingerprint-based biometric authentication. In this system, each voter's fingerprint is captured and converted into a unique encrypted template, preventing duplication or forgery.When a voter comes to vote, their fingerprint is matched with the stored record to ensure that only the right person can vote--and only once. To protect the security of the votes, encryption methods such as AES or RSA are used during both data transmission and storage.The system also generates a unique transaction ID as a receipt for each voter. This allows the voter to confirm that their vote was recorded, without revealing who they voted for.Overall, the proposed system increases transparency, blocks fraudulent activities, and provides strong end-to-end security throughout the election process. Experimental results show that combining biometric authentication with encryption greatly improves the accuracy of voter verification and the overall reliability of the system compared to traditional e-voting methods.

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 Keywords

Biometric authentication, Cryptography, Electronic voting (E-voting), Fingerprint recognition, Security, Vote verification, Data encryption.

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Abstract Background: Cold water immersion (CWI) and intermittent fasting (IF) are increasingly adopted as hormetic strategies to improve stress resilience, autonomic regulation, and sleep quality. Methods: A systematic review and meta-analysis of controlled trials in healthy adults (>=18 years) synthesized data from 11 CWI and 23 IF studies assessing stress markers, heart rate variability (HRV), and sleep outcomes. Standardized mean differences with 95% confidence intervals were calculated. Results: CWI produced a large time-dependent reduction in stress at 12 hours post-exposure, alongside significant improvements in vagally mediated HRV indices and slow-wave sleep with fewer nocturnal arousals. IF, particularly 16:8 time-restricted eating, improved HRV over 8-12 weeks and induced adaptive endocrine changes, including increased cortisol and reduced triiodothyronine. Direct evidence for combined CWI+IF protocols was limited to a single pilot study. Conclusions: CWI and IF independently benefit stress reactivity, autonomic nervous system function, and sleep via hormetic mechanisms, with preliminary theoretical support for synergistic effects when combined. High-quality trials on combined protocols, diverse populations, and long-term safety are warranted.

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Cold water immersion, intermittent fasting, hormesis, stress reactivity, heart rate variability, sleep architecture, autonomic nervous system, parasympathetic activity

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Creative Commons Attribution 4.0 and The Open Definition

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With the rapid expansion of digital payments and e-commerce platforms, online financial transactions have become deeply integrated into everyday life. This growth, however, has been accompanied by a significant rise in fraudulent activities, including identity theft, card-not-present transactions, account takeovers, and phishing-driven attacks. Conventional rule-based fraud detection systems struggle to adapt to evolving fraud patterns and often produce high false-positive rates. To address these challenges, this research proposes TransGuard-AI, an Artificial Intelligence (AI)-driven fraud detection framework that employs supervised machine learning techniques to identify anomalous transaction behavior. The proposed system integrates Logistic Regression, Decision Tree, and Random Forest models, enabling comparative evaluation and ensemble-based insights. A structured feature engineering pipeline is designed to extract transactional attributes such as amount frequency, spending velocity, merchant category patterns, and geolocation deviation scores. Experiments were conducted using a benchmarked credit card fraud detection dataset containing anonymized real-world transaction records with highly imbalanced class proportions. To mitigate class imbalance, Random Under-Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE) were applied. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix analysis. Results show that Random Forest outperformed the baseline models, achieving high detection accuracy and improved recall for the minority (fraud) class, while effectively reducing false alarms. Logistic Regression demonstrated faster inference suitability for real-time processing, whereas Decision Tree offered interpretability and rule extraction capabilities. Overall, TransGuard-AI presents a robust and scalable approach for real-time fraud detection in financial systems. The integration of machine learning algorithms, advanced preprocessing, and anomaly-centric feature engineering significantly enhances detection capability, making the system suitable for deployment in modern transaction monitoring infrastructures.

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 Keywords

Fraud Detection Machine Learning TransGuard-AI Logistic Regression Decision Tree Random Forest Feature Engineering Class Imbalance SMOTE Random Under-Sampling (RUS) Anomaly Detection Transaction Monitoring Credit Card Fraud ROC-AUC Real-Time Detection

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A supportive learning environment and enrichment programmes are essential for holistic development of children in primary education (ages 5-11). This paper examines key components of emotional safety, classroom climate, teacher support, peer collaboration, learning corners, enrichment interventions, observation-based assessment, feedback cycles, creativity promotion, learning ownership, curiosity-based pedagogy, and community support. Research indicates that enriched environments increase engagement, reduce learning fear, enhance literacy and numeracy stamina, nurture divergent thinking, and improve social belonging. Recommendations include teacher scripting literacy bridges, scaffold feedback loops, task-mode choices, sampling portfolios, low-barrier instruction, and structured enrichment governance.

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Creative Commons Attribution 4.0 and The Open Definition

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Learning Environment, Enrichment Programmes, Classroom Climate, Primary Holistic Growth

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Creative Commons Attribution 4.0 and The Open Definition

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The main aim of these paper is to present some fixed point theorems for self mappings satisfying certain contraction which is involving an auxiliary function. Also ,the results is obtained for the existence of a common fixed point and coincidence point for generalize and ordered complete b- metric space ,Our results generalizes and extends some well-known results existing in the literature.

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Keywords : Ordered b- metric space, Rational type generalized (?,?) contraction mapping ,fixed point ,complete metric space, weakly compatible mapping.

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Creative Commons Attribution 4.0 and The Open Definition

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Inclusive education in the primary phase ensures meaningful and barrier-free learning for children of diverse developmental, sensory, cognitive, linguistic, socio-economic, and neuro-developmental backgrounds. This paper examines evidence-based classroom strategies that promote differentiated learning, peer-assisted collaboration, Universal Design for Learning (UDL) integration, assistive learning scaffolds, behavior-positive pedagogies, multimodal communication, teacher-mediated emotional support, inclusive assessment, and community participation. Guided by sociocultural, constructivist, and learner variability frameworks, research suggests that inclusion enhances participation, motivation, literacy progression, early numeracy understanding, emotional safety, error tolerance, and learning ownership. The paper concludes by recommending teacher training, structured sampling assessments, low-barrier learning design, and digital inclusion equity to operationalize respectful, child-centered classrooms without compromising academic rigor.

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: Inclusive Pedagogy, Differentiation, UDL, Special Needs Support, Primary Participation, Assistive Tools

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Creative Commons Attribution 4.0 and The Open Definition

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The modernization of democratic elections through digital technologies offers vast opportunities but also introduces complex challenges. Traditional electronic voting (e-voting) mechanisms often suffer from vulnerabilities such as centralized data control, opacity in verification processes, and high susceptibility to manipulation or cyberattacks. To address these limitations, this study presents a hybrid framework integrating blockchain and artificial intelligence (AI) to achieve enhanced security, transparency, and scalability in e-voting. The blockchain layer ensures immutability and distributed trust through consensus-based verification, while the AI layer provides adaptive intelligence for real-time fraud detection, biometric voter authentication, and anomaly analysis. The designed framework integrates several essential modules for voter registration, biometric verification, vote casting, AI-driven fraud monitoring, blockchain anchoring, and audit visualization. Experimental simulations with over one million synthetic votes demonstrate fraud detection accuracy of 98.5%, biometric verification accuracy of 99.1%, and an average vote confirmation latency of 1.7 seconds, confirming the framework's feasibility for secure, large-scale elections. The results highlight that the synergy between blockchain's decentralization and AI's adaptivity can form the foundation for a next-generation digital democracy.

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 Keywords

E-voting; Blockchain; Artificial Intelligence; Cybersecurity; Smart Contracts; Fraud Detection; Biometric Authentication; Anomaly Detection.

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Creative Commons Attribution 4.0 and The Open Definition