The increasing demand for secure, transparent, and tamper-proof digital credential management systems has highlighted the limitations of traditional certificate verification approaches, which rely on centralized storage, manual validation, and third-party authentication. These conventional methods are often vulnerable to forgery, data manipulation, and verification delays. To address these challenges, this research proposes a Blockchain-Based Digital Certificate Generation and Validation System integrated with Digital Signature Scheme (DSS) and Decentralized Identity (DID) to provide a secure, automated, and trustless verification platform. The proposed system employs cryptographic hashing techniques to convert certificate data into unique digital fingerprints, which are then securely anchored on the Ethereum blockchain through smart contract execution. Digital signature mechanisms ensure certificate authenticity and non-repudiation, while decentralized identity frameworks enable privacy-preserving credential ownership and selective information sharing. A web-based portal facilitates certificate issuance by authorized institutions and enables real-time verification by employers or third-party verifiers without requiring direct communication with the issuing authority.Certificate validation is performed by recalculating the hash of submitted credential data and comparing it with immutable blockchain records. The system provides instant verification results, significantly reducing operational delays and eliminating the risk of fraudulent certificates.

ObjectiveTo examine the legal, ethical, and policy issues associated with gig work in nursing across the United States, the United Kingdom, and Canada.MethodsNarrative review and cross-country comparative analysis.ResultsSixteen studies were included. While the term "gig work" is not generally applied to nursing in both the United Kingdom and Canada, the availability of flexible, temporary, short-term work is common, manifesting primarily through either bank nursing or agency nursing mediated by various digital platforms. The phenomenon of internal banking was observed across all three countries. Despite the increasing trend of gig work, legal ambiguities exist regarding the classification of nurses as employees or independent contractors which has significant ramifications for liability and accountability. With the short-term nature of gig work, patient safety concerns also exist, particularly for nurses navigating new healthcare contexts. Compounding these challenges, many gig platforms lack standardized mechanisms to verify nurses' credentials or enforce compliance with scope-of-practice regulations. Ethically, this regulatory vacuum perpetuates systemic inequities, as gig nurses may face substandard wages, exclusion from benefits, and exploitative contractual terms.ConclusionWhile gig work offers nurses unprecedented autonomy and flexibility, its unchecked growth risks normalizing precarious labor conditions, eroding workplace protections, and raising patient safety concerns. To sustainably integrate the gig model, legislators must close classification loopholes. Healthcare institutions should implement registries for vetted gig workers and enforce standardized onboarding protocols to maintain care quality. Simultaneously, gig platforms require regulatory oversight to mandate real-time credential verification, wage guarantees, and scope-of-practice safeguards.

The Medibox — Voice-Controlled Smart Medical Assistant is an IoT-based embedded healthcare system designed to improve medication management by ensuring timely and accurate medicine intake. The system addresses common issues such as missed doses, incorrect medication usage, and lack of monitoring, particularly among elderly and chronically ill patients. It utilises voice recognition technology to allow users to register medication details, such as dosage, timing, and expiry date, via simple voice commands, enhancing accessibility and ease of use. The Raspberry Pi acts as the central controller, integrating various components, including an RFID system using the MFRC-522 module operating at 13.56 MHz for medicine identification, an RTC for precise scheduling, and servo motors for automated medicine dispensing. An IR sensor is used to detect whether the medicine has been taken, providing real-time verification and improving reliability compared to traditional reminder-based systems. In case of missed doses, the system sends alerts to caregivers via email using the Gmail API, enabling remote monitoring and timely intervention. The system also includes an expiry validation feature that prevents the dispensing of expired medicines, thereby ensuring patient safety. By combining automation, intelligent control, and user-friendly interaction, the Medibox system reduces human errors and enhances medication adherence. The proposed solution demonstrates high accuracy, reliability, and efficiency, making it suitable for both home and healthcare environments.

An in-beam PET scanner has been developed within the PETITION (PET for InTensive care units and Innovative protON therapy) collaboration to exploit the full potential of proton therapy by enabling either real-time range verification of proton beams or daily biological adaptation of treatment plans based on PET imaging acquired in treatment position before delivery. To optimize the design, the performance of various scanner geometries was first investigated using Monte Carlo simulations in GATE (v8.2), assessing sensitivity and spatial resolution with a 1 MBq 22 Na point source and evaluating image reconstruction using a uniform 1 MBq activity-filled water cylinder and a proton-irradiated head phantom. Based on these studies, a novel C-shaped scanner was developed and simulations were compared with the experimental performance. The prototype achieved a peak simulated sensitivity of 2.9%, with measured sensitivity of 2.1%. Spatial resolution was 2.0 mm FWHM tangentially and axially, and 3.5–4.0 mm radially, showing good agreement between simulations and measurements. Multi-angle acquisition and reconstruction improved image quality, reducing artefacts and enhancing similarity to reference scans in the water cylinder study. Activity distributions from the proton-irradiated head phantom showed alignment with expected activation profiles, demonstrating reliable reconstruction of clinically relevant signals. These findings confirm the feasibility of an open-ring PET scanner for proton therapy with performance comparable to clinical PET systems while uniquely enabling in-treatment position imaging and online beam range verification. Therefore, it supports novel approaches for adaptive and biologically guided proton therapy.

ESPiezometer: ESP32-based field tool for installation and validation of piezometric sensors for groundwater level monitoring.

The pace with which digital news media and social media started proliferating has intensified the rate of misinformation, bringing significant problems with the reliability of information and the credibility of the people. Traditional fake news detection systems rely on the traditional way of machine learning systems, which are fed by predefined data sets, which restricts its flexibility to new events and real-time changes. Also, stand-alone large language models (LLMs) are likely to be susceptible to failing to base their responses on the existing evidence, which in turn leads to a high risk of hallucination and contextual bias. The aim of the paper is to suggest a real-time AI-based News Verification System that will be built by incorporating Retrieval-Augmented Generation (RAG) with a Large Language Model (Gemini 2.0 Flash) to achieve context-sensitive and explainable news content verification. It is founded on a modular architecture of a rest-based architecture written in FastAPI as a backend and Next.js as a frontend, MongoDB as a persistence layer and JWT as an authentication. The Tavily Search API retrieves real-time contextual evidence and then uses it together with the logic of LLM to ensure they become more credible and less groundless. The framework generates ordered output in terms of classification label (Real/Fake), credibility score (0-100%), summary of explanation and identification of suspicious phrases. Performance assessment identifies a mean of 2.8 seconds response latency time when the system is stable with simultaneous API requests. The suggested architecture offers an architecture which offers scalability, modularity, and production readiness to detect misinformation in real-time in dynamic digital environments.

This study presents a secure machine learning model for drug authentication to combat the proliferation of counterfeit pharmaceuticals. A web-based system was developed by integrating a FastTree binary classification model in ML.NET with biometric facial recognition for user authentication. The system enables real-time verification of pharmaceutical products using structured metadata, including batch numbers, expiration dates, manufacturer identifiers, and barcodes. A dataset obtained from the U.S. Food and Drug Administration’s OpenFDA repository was used for model training and evaluation, with preprocessing implemented through a C# schema class. To enhance system security and prevent unauthorized access, a facial recognition module was incorporated as an additional authentication layer. Performance evaluation using 10-fold cross-validation yielded strong results, achieving 97.8% accuracy, an F1-score of 96.8%, and an AUC of 0.981. The proposed system provides a lightweight, scalable, and secure framework that integrates machine-learning-based drug authentication with biometric access control. This approach enhances the reliability, integrity, and security of pharmaceutical verification systems. Future work may explore larger datasets and blockchain integration for improved traceability

The rapid spread of misinformation in digital communication environments presents significant challenges to information integrity, particularly in emerging media ecosystems such as Nigeria. Recent developments in generative artificial intelligence (GenAI) have introduced new possibilities for detecting and managing misleading information across digital platforms. This study investigates how generative AI can contribute to governing information integrity within Nigeria’s misinformation ecosystem from a communication perspective. Using an exploratory qualitative approach, the study draws on in-depth interviews with experts in artificial intelligence, machine learning, digital media, and information governance. The findings reveal that generative AI can enhance the monitoring of misinformation by identifying misleading narratives, analyzing persuasive message patterns, tracking the spread of viral content, and supporting real-time verification processes in journalism and fact-checking. However, the study also shows that the effectiveness of AI technologies depends on contextual adaptation, ethical governance, and collaboration among stakeholders. AI systems alone cannot fully address misinformation challenges without the support of media literacy initiatives and institutional communication strategies. The study concludes that generative AI can play a significant role in strengthening information integrity within digital public communication.

Abstract - The AI Powered Virtual Attendance System is an intelligent solution designed to automate attendance management using artificial intelligence and computer vision. Traditional attendance processes—such as manual roll calls, ID card scanning, or signature- based methods—are slow, error-prone, and vulnerable to proxy attendance. The proposed system uses face recognition technology to identify individuals in real time and mark their attendance automatically. The process includes capturing a live facial image, extracting key facial features, comparing them with a pre-trained dataset, and updating the attendance record in a secure database. The system also integrates modules for user authentication, dataset storage, attendance logs, and real-time verification. By reducing human intervention, the system increases accuracy, eliminates impersonation, and saves valuable classroom time. This AI-based solution provides a fast, secure, and scalable attendance mechanism that supports digital transformation in educational institutions. Key Words - AI-powered attendance system, Facial recognition, Convolutional Neural Networks (CNN), Automated attendance, Computer vision, Deep learning, Cloud database, Real-time monitoring, Virtual attendance.

The increasing demand for secure, efficient, and tamper-proof digital payment systems in educational institutions has necessitated the development of automated receipt management solutions. This study presents the design and implementation of a secure web-based Receipt Generation and Verification System that integrates One-Time Password (OTP) authentication and QR code technology to enhance payment transparency and fraud prevention within the School of ICT, Federal Polytechnic Bida. The study adopted a hybrid Software Development Life Cycle (SDLC) approach, combining Waterfall for structured planning and Agile methodology for iterative development and refinement. System requirements were gathered through observation of existing manual receipt processes and stakeholder consultation. The system was developed using HTML, CSS (Bootstrap), JavaScript, PHP, and MySQL, with SMTP integration for automated email delivery of digital receipts. OTP authentication was implemented to secure login and payment authorization processes, while dynamically generated QR codes were embedded in each receipt to enable real-time verification. Testing results demonstrate that the system effectively eliminates delays associated with manual teller submission, reduces the risk of receipt forgery, prevents data loss due to fading ink, and improves financial record accuracy. The QR-based verification mechanism ensures authenticity, while OTP authentication significantly enhances access control and transaction security. The study concludes that integrating OTP and QR code technologies into institutional payment systems improves operational efficiency, transparency, and security. It is recommended that educational institutions adopt secure digital receipt platforms and continuously upgrade encryption and authentication mechanisms to mitigate emerging cybersecurity threats.

Purpose: The rapid expansion of remote and digital hiring has increased incidents of candidate impersonation, credential fraud, and identity substitution within enterprise recruitment systems. This study proposes a real-time candidate identity verification framework designed to strengthen hiring integrity in U.S. enterprises. Methodology: This research adopts a design science methodology to develop a conceptual, event-driven identity continuity framework. The study synthesizes digital identity standards (NIST SP 800-63), AI risk management principles, and HR technology architectures to construct a scalable fraud detection model embedded across recruitment workflows. Findings: The proposed framework demonstrates that continuous identity assurance, behavioral signal correlation, and dynamic risk scoring can proactively detect hiring fraud prior to onboarding. The model integrates privacy-by-design, explainability, and human oversight mechanisms, reducing false positives while maintaining compliance with employment and data protection regulations. Unique contribution to theory, practice and policy: This study contributes a novel reference architecture that bridges HR technology, cybersecurity governance, and responsible AI in talent acquisition. It advances theory by conceptualizing identity continuity as a lifecycle control rather than a point-in-time verification step, offering practical and policy-relevant implications for secure digital hiring.

The fast progression of accounting informatization has resulted in the widespread use of cloud platforms to handle and process large-scale financial data. Nevertheless, depending on cloud environments makes accounting systems vulnerable to data tampering, unauthorized access, and loss of data integrity; thus, they are able to undermine in a significant way not only financial accuracy but also ‍‌trust. To address these challenges, this research proposes a Deep Reinforcement Learning (drl)-Based Cloud Data Integrity Verification Algorithm (diva) designed to ensure secure, adaptive, and real-time verification of financial data (n = 1900) integrity in accounting informatization systems. The proposed model integrates the Adaptive Deer Hunting Optimized Double Deep Q-Network (ADHO-DDQN) model to enhance autonomous decision-making in identifying and responding to integrity breaches. The DRL agent learns optimal verification policies through continuous interaction, dynamically adjusting parameters and optimizing resources during audits. DDQN ensures stable decision-making, while ADHO adaptively refines parameter selection for efficiency. Preprocessed accounting logs enable the model to detect subtle inconsistencies or tampering patterns effectively. Principal Component Analysis (PCA), which keeps the most important patterns while lowering the number of dimensions in the data, is used. Experimental evaluations using real-world financial datasets demonstrate that ADHO-DDQN achieves superior performance with a high integrity verification accuracy (98.9%) compared to conventional static verification methods using Python 3.8.10. The findings highlight that integrating DRL into cloud data verification frameworks, also enhances adaptive learning capabilities in accounting systems. This research contributes a scalable and intelligent integrity assurance mechanism for secure and transparent accounting informatization in the digital era.

Hearing loss affects approximately two thirds of adults in the United States aged 70 years or older and frequently remains untreated despite its well-documented harms, including accelerated cognitive decline, increased caregiver burden, and higher health care expenditures. We examine the major barriers to accessing high-quality hearing care, with particular attention to the complex and fragmented landscape of insurance coverage across Medicare, Medicaid, the US Department of Veterans Affairs, private plans, and over-the-counter (OTC) products. We review key legislative and regulatory developments over the past decade, most notably the 2022 establishment of OTC hearing aids, and summarize early opportunities and remaining gaps. We then propose targeted reforms to improve access and affordability, including more consistent Medicaid benefits, selective Medicare expansion, integration of teleaudiology, and strengthened oversight and consumer protections for OTC devices. Finally, we advance a technology-driven policy framework that integrates artificial intelligence-supported risk prediction, teleaudiology, real-time insurance verification, and a transparent device marketplace to modernize delivery and evaluation. Together, these strategies can catalyze a fundamental rethinking of how hearing health is prioritized and managed within the broader United States health care ecosystem. (Am J Public Health. 2026;116(3):387-396. https://doi.org/10.2105/AJPH.2025.308298).

Development of a motor bearing fault diagnosis system based on an auxiliary classifier generative adversarial network

In a variety of contexts, including banking, legal documents, and financial transactions, signature verification is essential to identity authentication. Conventional signature verification techniques depend on manual inspection, which is frequently laborious and prone to human error. Deep learning methods have become viable options for automated signature verification with the development of artificial intelligence. The extensive learning-based online signature verification method presented in this work examines the dynamic and visual characteristics of signatures to ascertain their legitimacy. After processing input signatures, the system determines whether they are authentic or fraudulent by identifying key characteristics. To enable users to upload signatures and obtain real-time verification results, a web-based interface is created. The suggested approach has great reliability and precision in differentiating between real and fake signatures, according to experimental data. For safe authorization apps, the system offers a scalable and effective solution.

ATLASky-AI: An Autonomous Framework for Physics-Based Trustworthy Verification of LLM-Generated Spatiotemporal Knowledge

In the era of rapidly evolving digital infrastructures, ensuring the scalability and efficiency of technological transactions has become a critical challenge. Traditional blockchain models often suffer from limitations, such as high latency, restricted throughput, and network congestion, particularly under high transaction volumes. This paper proposes a novel dual-layer blockchain architecture designed to address these limitations by segregating transaction processing and consensus mechanisms into two distinct but interoperable layers. The first layer, a lightweight transactional layer, handles real-time data exchange and verification with minimal computational overhead, while the second layer focuses on robust consensus, security, and long-term data immutability. By decoupling these functions, the proposed model significantly improves scalability, reduces latency, and enhances system responsiveness. Experimental simulations demonstrate that the dual-layer approach outperforms conventional single-chain systems in terms of transaction throughput, confirmation time, and scalability under varying loads. This architecture holds promising potential for deployment in sectors requiring high-performance, secure, and decentralized transaction systems, such as finance, supply chain, and smart industry ecosystems.

This research presents the design and implementation of an automatic gate barrier system based on Quick Response (QR) Code, integrated with Optical Character Recognition (OCR) technology for vehicle license plate verification. The system is controlled by an ESP32 microcontroller connected to a web server, enabling real-time data verification through the Internet of Things (IoT) network. The study applies the Waterfall development model, including system requirements analysis, hardware and software design, and comprehensive testing stages. Testing with 54 vehicle data samples showed that the system achieved a 70% success rate in validating both QR Code and license plate data, while 30% failed due to insufficient lighting and improper camera positioning. These results demonstrate that the system significantly improves security, efficiency, and organization of parking management, especially within university environments. The integration of QR Code and OCR technologies provides an effective solution for IoT-based automatic parking systems, enhancing both convenience and reliability for users.

The rapid expansion of remote work, cloud services, and distributed enterprise infrastructures has significantly increased cybersecurity risks, rendering traditional perimeter-based security models inadequate. Zero-Trust Architecture (ZTA) has emerged as a modern security paradigm that assumes no implicit trust and continuously verifies user identity and device integrity. However, conventional authentication mechanisms such as passwords and one-time verification methods are insufficient to ensure persistent security throughout a user session. This paper proposes a Deep Learning-Based Continuous Authentication Framework tailored for Zero-Trust enterprise environments. The system leverages behavioral biometrics, including keystroke dynamics, mouse movements, and user interaction patterns, to continuously verify user identity in real time. Advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are employed to model temporal and spatial behavioral patterns. The framework integrates risk-based scoring and adaptive access control policies to dynamically enforce authentication decisions. Experimental evaluation demonstrates improved detection accuracy, reduced false acceptance rates, and minimal user disruption compared to traditional authentication systems. The proposed solution strengthens enterprise security by enabling adaptive, non-intrusive, and real-time identity verification aligned with Zero-Trust principles

Modern passport systems face significant challenges in secure data sharing, real-time verification, and user-controlled authorization, particularly in cross-border scenarios. Existing digital passport solutions, often built on permissioned blockchains, suffer from limited transparency, scalability, and high operational costs. This paper proposes a decentralized passport management system based on an Ethereum Layer 2 architecture that combines global governance with high-throughput and cost-efficient passport operations. The system adopts a hybrid design in which a Global Passport Registry smart contract is deployed on the Ethereum mainnet for cross-country coordination, while passport issuance, access control, and identity management are handled on Layer 2 networks through country-operated Passport Managers and user-specific Personal Passport smart contracts. Extensive performance evaluations show that Ethereum Layer 1 throughput saturates at approximately 40–50 transactions per second (TPS), whereas the proposed Layer 2 deployment consistently exceeds 150 TPS and reaches up to 300 TPS under higher-performance environments, significantly surpassing the estimated system requirement of 70 TPS. These improvements result in faster response times, reduced congestion, and substantially lower transaction costs, demonstrating that public Ethereum Layer 2 infrastructures can effectively support a scalable, self-sovereign, privacy-preserving, and globally verifiable digital passport system suitable for real-world deployment.