Search Results (319)

The proliferation of biometric technology across various domains including user identification, financial services, healthcare, security, law enforcement, and border control introduces convenience in user identity verification while necessitating robust protection mechanisms for sensitive biometric data. While chaos-based encryption systems offer promising solutions, many existing chaos-based encryption schemes exhibit inherent shortcomings including deterministic randomness and constrained key spaces, often failing to balance security robustness with computational efficiency. To address this, we propose a novel dual-layer cryptographic framework leveraging a four-dimensional (4D) Qi hyperchaotic system for protecting biometric templates and facilitating secure feature matching operations. The framework implements a two-tier encryption mechanism where each layer independently utilizes a Qi hyperchaotic system to generate unique encryption parameters, ensuring template-specific encryption patterns that enhance resistance against chosen-plaintext attacks. The framework performs dimensional normalization of input biometric templates, followed by image pixel shuffling to permutate pixel positions before applying dual-key encryption using the Qi hyperchaotic system and XOR diffusion operations. Templates remain encrypted in storage, with decryption occurring only during authentication processes, ensuring continuous security while enabling biometric verification. The proposed system’s framework demonstrates exceptional randomness properties, validated through comprehensive NIST Statistical Test Suite analysis, achieving statistical significance across all 15 tests with p-values consistently above 0.01 threshold. Comprehensive security analysis reveals outstanding metrics: entropy values exceeding 7.99 bits, a key space of ${10}^{320}$, negligible correlation coefficients (<${10}^{-2}$), and robust differential attack resistance with an NPCR of 99.60% and a UACI of 33.45%. Empirical evaluation, on standard CASIA Face and Iris databases, demonstrates practical computational efficiency, achieving average encryption times of 0.50913s per user template for 256 × 256 images. Comparative analysis against other state-of-the-art encryption schemes verifies the effectiveness and reliability of the proposed scheme and demonstrates our framework’s superior performance in both security metrics and computational efficiency. Our findings contribute to the advancement of biometric template protection methodologies, offering a balanced performance between security robustness and operational efficiency required in real-world deployment scenarios. Full article

The incorporation of digital health into the medical curricula is becoming more important to better prepare doctors in the future. Digital health comprises a wide range of tools such as electronic health records, health information technology, telemedicine, telehealth, mobile health applications, wearable devices, artificial intelligence, and virtual reality. The present study aimed to explore the medical and biomedical students’ perspectives on the integration of digital health in medical curricula. A cross-sectional study was conducted on the medical and biomedical undergraduate students at the College of Medicine and Health Sciences at Sultan Qaboos University. Data was collected using a self-administered questionnaire. The response rate was 37%. The majority of respondents were in the MD (Doctor of Medicine) program (84.4%), while 29 students (15.6%) were from the BMS (Biomedical Sciences) program. A total of 55.38% agreed that they were familiar with the term ‘e-Health’. Additionally, 143 individuals (76.88%) reported being aware of the definition of e-Health. Specifically, 69 individuals (37.10%) utilize e-Health technologies every other week, 20 individuals (10.75%) reported using them daily, while 44 individuals (23.66%) indicated that they never used such technologies. Despite having several benefits, challenges exist in integrating digital health into the medical curriculum. There is a need to overcome the lack of infrastructure, existing educational materials, and digital health topics. In conclusion, embedding digital health into medical curricula is certainly beneficial for creating a digitally competent healthcare workforce that could help in better data storage, help in diagnosis, aid in patient consultation from a distance, and advise on medications, thereby leading to improved patient care which is a key public health priority. Full article

Introduction: Adolescents and young adults (AYAs), especially those from underserved communities, often face barriers to sexual and reproductive health (SRH). This pilot study evaluated the implementation of mobile health technologies to promote SRH care, including the integration of the Rapid Adolescent Prevention Screening^TM (RAAPS) and the Health-E You/Salud iTu^TM (Health-E You) app at a School-Based Health Center (SBHC) in Los Angeles using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Methods: This multi-method pilot study included the implementation of an integrated tool with two components, the RAAPS electronic health screening tool and the Health-E You app, which delivers tailored SRH education and contraceptive decision support to patients (who were sex-assigned as female at birth) and provides an electronic summary to clinicians to better prepare them for the visit with their patient. Quantitative data on tool usage were collected directly from the back-end data storage for the apps, and qualitative data were obtained through semi-structured interviews and in-clinic observations. Thematic analysis was conducted to identify implementation barriers and facilitators. Results: Between April 2024 and June 2024, 60 unique patients (14–19 years of age) had a healthcare visit. Of these, 35.00% used the integrated RAAPS/Health-E You app, and 88.33% completed the Health-E You app only. All five clinic staff were interviewed and expressed that they valued the tools for their educational impact, noting that they enhanced SRH discussions and helped uncover sensitive information that students might not disclose face-to-face. However, the tools affected clinic workflows and caused rooming delays due to the time-intensive setup process and lack of integration with the clinic’s primary electronic medical record system. In addition, they also reported that the time to complete the screener and app within the context of a 30-min appointment limited the time available for direct patient care. Additionally, staff reported that some students struggled with the two-step process and did not complete all components of the tool. Despite these challenges, clinic staff strongly supported renewing the RAAPS license and continued use of the Health-E You app, emphasizing the platform’s potential for improving SRH care and its educational value. Conclusions: The integrated RAAPS and Health-E You app platform demonstrated educational value and improved SRH care but faced operational and technical barriers in implementing the tool. These findings emphasize the potential of such tools to address SRH disparities among vulnerable AYAs while providing a framework for future implementations in SBHCs. Full article

Smart healthcare establishes a safe, reliable, and efficient medical information system for the public with the help of the Internet of Things, cloud storage, and other Internet technologies. To enable secure data sharing and case-matching functions in smart healthcare, we construct a revocable identity-based matchmaking encryption with an equality test (RIBME-ET) scheme for smart healthcare. Our scheme not only ensures the confidentiality and authenticity of messages and protects the privacy of users, but also enables a cloud server to perform equality tests on encrypted ciphertexts from different identities to determine whether they contain the same plaintext and protects the confidentiality of data in the system through a user revocation mechanism. Compared with the existing identity-based encryption with equality test (IBEET) and identity-based matchmaking encryption with equality test (IBME-ET) schemes, we have improved the efficiency of the scheme and reduced communication overhead. In addition, the scheme’s security is proven in the random oracle model under the computational bilinear Diffie–Hellman (CBDH) assumption. Finally, the feasibility and effectiveness of the proposed scheme are verified by performance analysis. Full article

Recently, solar energy technologies are a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. However, in many rural areas of developing countries, unreliable electricity severely impacts healthcare delivery, resulting in reduced medical efficiency and increased risks to patient safety. This review explores the transformative potential of solar energy as a sustainable solution for powering healthcare facilities, reducing dependence on fossil fuels, and improving health outcomes. Consequently, energy harvesting is a vital renewable energy source that captures abundant solar and thermal energy, which can sustain medical centers by ensuring the continuous operation of life-saving equipment, lighting, vaccine refrigeration, sanitation, and waste management. Beyond healthcare, it reduces greenhouse gas emissions, lowers operational costs, and enhances community resilience. To address this issue, the paper reviews critical solar energy technologies, energy storage systems, challenges of energy access, and successful solar energy implementations in rural healthcare systems, providing strategic recommendations to overcome adoption challenges. To fulfill the aims of this study, a focused literature review was conducted, covering publications from 2005 to 2025 in the Scopus, ScienceDirect, MDPI, and Google Scholar databases. With targeted investments, policy support, and community engagement, solar energy can significantly improve healthcare access in underserved regions and contribute to sustainable development. Full article

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

Point-of-care (POC) diagnostic technologies have become essential for the real-time monitoring and management of chronic wounds, where maintaining a moist environment and controlling pH levels are critical for effective healing. In this study, a flexible pH sensor based on a graphene/molybdenum disulfide (graphene/MoS₂) composite interdigitated electrode (IDE) structure was fabricated using pulsed laser ablation. The pH sensor, with an active area of 30 mm × 30 mm, exhibited good adhesion to the polyethylene terephthalate (PET) substrate and maintained structural integrity under repeated bending cycles. Precise ablation was achieved under optimized conditions of 4.35 J/cm² laser fluence, a repetition rate of 300 kHz, and a scanning speed of 500 mm/s, enabling the formation of defect-free IDE arrays without substrate damage. The influence of laser processing parameters on the surface morphology, electrical conductivity, and wettability of the composite thin films was systematically characterized. The fabricated pH sensor exhibited high sensitivity (~4.7% change in current per pH unit) across the pH 2–10 range, rapid response within ~5.2 s, and excellent mechanical stability under 100 bending cycles with negligible performance degradation. Moreover, the sensor retained > 95% of its stable sensitivity after 7 days of ambient storage. Furthermore, the pH response behavior was evaluated for electrode structures with different pitches, demonstrating that structural design parameters critically impact sensing performance. These results offer valuable insights into the scalable fabrication of flexible, wearable pH sensors, with promising applications in wound monitoring and personalized healthcare systems. Full article

Memristors, as the fourth fundamental circuit element, have attracted significant interest for their potential in analog signal processing, computing, and memory storage technologies. However, physical memristor implementations still face challenges in reproducibility, scalability, and integration with standard CMOS processes. Memristor emulator circuits, implemented using analog, digital, and mixed components, have emerged as practical alternatives, offering tunability, cost effectiveness, and compatibility with existing fabrication technologies for research and prototyping. This review paper provides a comprehensive analysis of recent advancements in memristor emulator design methodologies, including active and passive analog circuits, digital implementations, and hybrid approaches. A critical evaluation of these emulation techniques is conducted based on several performance metrics, including maximum operational frequency range, power consumption, and circuit topology. Additional parameters are also taken into account to ensure a comprehensive assessment. Furthermore, the paper examines promising healthcare applications of memristor and memristor emulators, focusing on their integration into biomedical systems. Finally, key challenges and promising directions for future research in memristor emulator development are outlined. Overall, the research presented highlights the promising future of memristor emulator technology in bridging the gap between theoretical memristor models and practical circuit implementations. Full article

Despite advances in cancer care, disparities persist. The collection of the social determinants of health (SDOH) is fundamental to addressing disparities. However, SDOH are inconsistently collected in many regions of the world. This two-phase multiple methods study examined patient and community perspectives regarding SDOH data collection in Canada. In phase 1, a survey was administered to patients at a cancer centre (n = 549) to assess perspectives on an SDOH data collection tool. In phase 2, broader perspectives were sought through a community consultation with patient partners experiencing structural inequality (n = 15). Most participants were comfortable with SDOH data collection. Of survey respondents, 95% were comfortable with the collection of language, birthplace, sex, gender, education, and disability, and 82% to 94% were comfortable with SES, sexual orientation, social support, and race/ethnicity. Discomfort levels did not differ across subgroups, except women were more uncomfortable disclosing SES (OR: 2.00; 95%CI: 1.26, 3.19). Most (71%) preferred face-to-face data collection with a healthcare professional and only half were comfortable with storage of SDOH in electronic health records. Open-ended survey responses (n = 1533) and the community consultation revealed concerns about privacy, discrimination, relevance to care, and data accuracy. SDOH data collection efforts should include a clear rationale for patients, training for providers, strong data privacy and security measures, and actionable strategies to address needs. Full article

From an occupational health perspective, if not stored, handled, and disposed of properly, laboratory chemicals exhibit hazardous properties such as flammability, corrosion, and explosibility. Additionally, they can also cause a range of health effects in handlers, including irritation, sensitization, and carcinogenicity. Additionally, the chemical waste generated during the planned assay is a significant byproduct and, if left untreated, can cause detrimental effects on both living organisms and non-living elements when released into the environment. Chemically, laboratory waste contains reagents, organic and inorganic compounds, and diagnostic stains. These agents are more toxic and hazardous than residential waste and affect the personnel handling them and the environments in which they are released. Considering this, it is crucial to adhere to waste management regulations during the various stages including generation, segregation, collection, storage, transportation, and treatment. This is extremely important and necessary if we are to avoid harm to individuals and environmental contamination. This review encompasses the examination of laboratory medical waste, various categories of chemical waste, and strategies to minimize and ensure the safe disposal of these toxic agents. As far as the authors are aware, this is the first review that focuses on the effects of laboratory-generated chemical wastes and environmental ethics. This is a neglected topic in healthcare education, and this review will serve as a valuable resource for students. Full article

Medical clinical images have a larger number of bits, and real-time and robust medical encryption systems with a high security level, a large key space, high unpredictability, better bifurcation behavior, low computational complexity, and good encryption outcomes are significant design challenges. Chaotic medical image encryption (MIE) has become an important research area in advanced MIE strategies. Chaotic MIE technology can be used in medical image storage systems, cloud-based medical systems, healthcare systems, telemedicine, mHealth, picture archiving and communication systems, digital imaging and communication in medicine, and telehealth. This study focuses on several basic frameworks for chaos-based MIE. Multiple chaotic maps, robust chaos-based techniques, and fast and simple chaotic system designs of chaos-based MIE are demonstrated. The major technical notes, features and effectiveness of chaos-based MIE are investigated for future research directions. The chaotic maps of MIE are illustrated, and security evaluation methods for chaos-based MIE are explored. Design issues in the implementation of chaos-based MIE are demonstrated. The findings can inspire researchers to design an innovative, advanced chaos-based MIE system to better protect MIs against attacks and ensure robust MIE. Full article

The rapid integration of AI into IoT systems has outpaced the ability to explain and audit automated decisions, resulting in a serious transparency gap. We address this challenge by proposing a blockchain-based framework to create immutable audit trails of AI-driven IoT decisions. In our approach, each AI inference comprising key inputs, model ID, and output is logged to a permissioned blockchain ledger, ensuring that every decision is traceable and auditable. IoT devices and edge gateways submit cryptographically signed decision records via smart contracts, resulting in an immutable, timestamped log that is tamper-resistant. This decentralized approach guarantees non-repudiation and data integrity while balancing transparency with privacy (e.g., hashing personal data on-chain) to meet data protection norms. Our design aligns with emerging regulations, such as the EU AI Act’s logging mandate and GDPR’s transparency requirements. We demonstrate the framework’s applicability in two domains: healthcare IoT (logging diagnostic AI alerts for accountability) and industrial IoT (tracking autonomous control actions), showing its generalizability to high-stakes environments. Our contributions include the following: (1) a novel architecture for AI decision provenance in IoT, (2) a blockchain-based design to securely record AI decision-making processes, and (3) a simulation informed performance assessment based on projected metrics (throughput, latency, and storage) to assess the approach’s feasibility. By providing a reliable immutable audit trail for AI in IoT, our framework enhances transparency and trust in autonomous systems and offers a much-needed mechanism for auditable AI under increasing regulatory scrutiny. Full article

Objectives: Identification of monosodium urate (MSU) and calcium pyrophosphate (CPP) crystals in synovial fluid should ideally be performed within 24 h to ensure optimal diagnostic accuracy for gout and CPP arthritis. However, crystal identification is often delayed in community-based healthcare facilities due to limited access to specialists or necessary equipment. This study aimed to determine whether MSU and CPP crystals remain detectable in synovial fluid after two weeks of storage at 4 °C and −20 °C. Methods: Anonymized synovial fluid samples were obtained from Thammasat University Hospital between February and March 2024. All samples underwent an initial round of crystal identification using compensated polarized light microscopy, conducted by two experienced examiners blinded to the clinical diagnosis. Following the initial analysis, each sample was divided into two equal portions and placed in ethylenediaminetetraacetic acid (EDTA)-coated tubes. One portion was stored at 4 °C, while the other was frozen at −20 °C. After two weeks, all samples underwent a second round of crystal identification. Results: Forty-nine samples were included for the first evaluation; MSU and CPP crystals were identified in 14 and 6 samples, respectively. On the second examination, MSU crystals were detectable in 13/14 (92.8%) samples stored at 4 °C and 12/14 (85.7%) samples stored at −20 °C. However, CPP crystals were detectable in 2/6 (33.3%) samples stored at both temperatures. No new crystal formation in initially negative samples was observed. Conclusion: MSU crystals remain detectable in synovial fluid for up to two weeks when stored in a standard refrigerator or freezer. However, the identification rate of CPP crystals tends to decline over this period. These findings may help inform best practices for handling synovial fluid samples in cases where immediate access to a specialist or necessary equipment is unavailable. Full article

Fog computing technology grants computing and storage resources to nearby IoT devices, enabling a fast response and ensuring data locality. Thus, fog-enabled IoT environments provide real-time and convenient services to users in healthcare, agriculture, and road traffic monitoring. However, messages are exchanged on public channels, which can be targeted to various security attacks. Hence, secure authentication protocols are critical for reliable fog-enabled IoT services. In 2024, Harbi et al. proposed a remote user authentication protocol for fog-enabled IoT environments. They claimed that their protocol can resist various security attacks and ensure session key secrecy. Unfortunately, we have identified several vulnerabilities in their protocol, including to insider, denial of service (DoS), and stolen verifier attacks. We also prove that their protocol does not ensure user untraceability and that it has an authentication problem. To address the security problems of their protocol, we propose a security-enhanced blockchain-based secure authentication protocol for fog-enabled IoT environments. We demonstrate the security robustness of the proposed protocol via informal and formal analyses, including Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Moreover, we compare the proposed protocol with related protocols to demonstrate the excellence of the proposed protocol in terms of efficiency and security. Finally, we conduct simulations using NS-3 to verify its real-world applicability. Full article

Hospitals require continuous access to water to sustain essential health services, especially when resources are taxed when drought conditions are compounded with other public health emergencies. In mid-2020, we conducted a rapid assessment of 71 hospitals in northern Thailand to evaluate water use and resilience during the concurrent 2019–2020 drought and the early phase of the COVID-19 pandemic in Thailand. While most hospitals reported adequate water availability, many depended on short-term measures such as shallow wells and improvised storage. Water use per bed often exceeded international benchmarks, reflecting broader usage patterns that extend beyond potable consumption. Community hospitals, in particular, reported more limited backup supply and planning capacity. Drawing on both our findings and international guidance, we propose the Hazard Management Model, involving a set of recommendations to strengthen hospital water resilience, including hazard-specific planning, protected infrastructure, emergency storage, and improved efficiency. These insights contribute to the growing body of work on climate-adaptive healthcare, particularly in resource-constrained settings facing intensifying multi-hazard risks. Full article