Search Results (1,555)

Diabetic chronic wounds, characterized by persistent inflammation and a complex microenvironment, pose a major challenge to global healthcare. Traditional dressings act merely as passive physical barriers, lacking the ability to sense biochemical fluctuations or respond to dynamic pathological changes. Therefore, developing smart platforms for in situ, continuous, and non-invasive monitoring is crucial for early warning and precision intervention. This review systematically explores recent advances in high-fidelity wound monitoring, focusing on the deep integration of “front-end interface engineering” and “back-end data analysis”. We first analyze the specific physicochemical and biochemical abnormalities of the diabetic wound microenvironment. Next, we discuss how advanced material designs, such as active fluid management, anti-biofouling zwitterionic networks, and nanozyme-based reactive oxygen species (ROS) scavenging, ensure the long-term stability of sensing interfaces against complex microenvironmental interference. Building on this hardware foundation, we summarize in situ sensing strategies and multiparameter decoupling techniques tailored for key biomarkers, including pH, temperature, glucose, ROS, and MMP-9. Furthermore, we highlight cutting-edge developments in signal digitization, emphasizing the pivotal role of portable devices and machine learning algorithms in extracting high-dimensional features and translating complex multimodal signals into objective clinical metrics. By outlining this comprehensive technological closed-loop, this review aims to provide a systematic theoretical framework for the development and clinical translation of next-generation smart wound monitoring platforms. Full article

The explosive growth of the Internet of Things (IoT) has brought an array of resource-constrained devices to domains such as smart homes, industrial automation, and healthcare, raising substantial cybersecurity challenges. Lightweight wireless protocols, such as Thread, Zigbee, and Z-Wave, are integral to IoT connectivity, but the degree to which their embedded cryptographic mechanisms satisfy formal cybersecurity certification schemes remains underexplored. This work draws primarily on recent peer-reviewed publications and major conference proceedings to rigorously evaluate Thread, Zigbee, and Z-Wave against the Common Criteria (CC) Functional Requirements for Cryptography (FCS) as specified in CC:2022 and the EU cybersecurity certification scheme on Common Criteria (EUCC). The assessment focuses on essential CC cryptographic components, including key generation (FCS_CKM.1), secure key distribution (FCS_CKM.2), agreement protocols (FCS_CKM_EXT.7), cryptographic operations (FCS_COP.1), and random bit generators (FCS_RBG.1). The analysis reveals that Thread demonstrates the strongest alignment with CC requirements by leveraging Advanced Encryption Standard—Counter with CBC-MAC mode (AES-CCM) authenticated encryption and Elliptic Curve Diffie-Hellman (ECDH)-based key exchange within a decentralized trust framework. Zigbee matches this cryptographic strength at the primitive level, but its dependency on a centralized Trust Center for key management complicates full compliance with key lifecycle and distribution controls. Z-Wave, especially through its S2 Security framework, improves by incorporating authenticated ECDH exchanges, though proprietary constraints and limited protocol transparency remain obstacles to independent assurance. This comparative study concludes that while all three protocols provide a baseline of robust cryptographic security, only Thread currently aligns with CC and EUCC certification schemes. Zigbee and Z-Wave will require additional protocol hardening and enhancement of cryptographic key lifecycle management to achieve comparable assurance levels. Ensuring conformance with formal cybersecurity standards is imperative for building trust and resilience across critical IoT infrastructures. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

Mycotoxins are one of the biggest threats to global food safety, public health, and economic stability. More than 400 mycotoxins have been found to be secondary metabolites of toxigenic fungi, mostly from the genera Aspergillus, Fusarium, Penicillium, and Alternaria. Aflatoxins (AFs), ochratoxin A (OTA), deoxynivalenol (DON), zearalenone (ZEA), fumonisins (FBs), patulin (PAT), and T-2/HT-2 toxins are the most dangerous to the health of people and animals. Conventional physical and chemical decontamination methods are only partially effective and can reduce food quality, leave toxic residues, or be too expensive for smallholder food systems. Recent studies have shown that the application of lactic acid bacteria (LAB) as a biological detoxification method is a safe, cost-effective, and environmentally friendly option, and has a long history of safe use in fermented foods. Selected strains or taxonomic units have been granted GRAS status by the FDA or QPS (Qualified Presumption of Safety) status by EFSA. However, their use for mycotoxin detoxification still requires strain-level safety assessment and efficacy validation in the intended food matrix. There are several mechanisms by which LAB employ to reduce the bioavailability of mycotoxins in food systems: (i) physical adsorption via cell wall components such as peptidoglycan, teichoic acids, and exopolysaccharides; (ii) enzymatic biotransformation that may produce non-toxic or less-toxic metabolites, though the safety of degradation products requires case-by-case toxicological assessment; (iii) antifungal metabolite production that inhibits fungal growth and mycotoxin biosynthesis; and (iv) competitive exclusion of toxigenic fungi during fermentation. This comprehensive review examines the existing evidence on the detoxification of major food mycotoxins by LAB, with an emphasis on mechanisms, strain-specific efficacy, food-matrix applications, and factors that affect detoxification efficacy. Discussion has also been made of translating in vitro findings to in vivo settings and food-scale applications, alongside regulatory frameworks, current challenges, and future research directions. The review also suggests ways to combine LAB with new technologies, such as encapsulation, genetic engineering, and fermentation optimization, to make food systems safer by synergistically controlling mycotoxins. Full article

By allowing continuous connectivity, automation, and data-driven decision-making across these areas, Internet of Things (IoT) has transformed certain facets of daily life, including home automation and healthcare, as well as business operations like supply chain management and smart manufacturing. IoT systems are susceptible to different cyberattacks, though, because of different designs, lack of funds, and inadequate security policies, which creates major security issues given their fast growth. Covering important topics including protocols, architectures, attack classification, detection methods, countermeasures, and research issues, this paper offers a thorough study of IoT security. Emphasizing their relevance in enhancing the security of IoTs, the article offers a thorough analysis of machine and deep learning-based detection techniques. It also offers recommendations for future paths to handle changing risks by means of particular proposals and provides tools and datasets required for IoT security studies. When considering recent progress, however, there are still some major limitations in scaling, real-time detection, dataset availability, and versatility of current solutions. We identified these issues and provided guidance on future research; we also offered a selected set of tools and datasets for further research. Additionally, this paper provides an overview of the most important issues related to IoT security as documented in the current literature, providing a framework for developing resilient and adaptable IoT security solutions in the future. Full article

Background/Objectives: The global health landscape is currently confronted with dual challenges from both infectious diseases and chronic conditions. Medical–preventive integration has emerged as a pivotal strategy to address these issues, aiming to create a comprehensive, closed-loop framework that spans disease prevention, treatment, rehabilitation, and healthcare, ultimately improving population health outcomes. In the Chinese context, existing policies remain fragmented, scattered across various healthcare-related regulations, and lack systematic and comprehensive analysis. This policy fragmentation may impede the creation of synergistic effects essential for the effective implementation of integrated healthcare strategies. Methods: This study adopted a mixed-methods approach to analyze 85 national policies: a three-stage coding process identified 1088 policy nodes, and a three-dimensional framework (policy instruments (X) × full-cycle health service (Y) × integration stages (Z)) was applied to uncover systemic imbalances. Social network analysis and Latent Dirichlet Allocation (LDA) topic modeling were utilized to map interagency collaboration patterns and thematic shifts, which were visualized using Gephi and Sankey. Results: The analysis revealed that policy instruments are predominantly supply-side (45.04%) and environmental-side (40.35%), with demand-side instruments (14.61%) being notably underutilized, particularly in health financing. Rehabilitation services, representing just 8.27% of the policy focus, were identified as a significant gap in the comprehensive health service cycle. While 44.58% of the instruments facilitated collaboration of medical and preventive services, integration of medical–preventive management stagnated at 25.28%, reflecting institutional inertia that impedes the redistribution of cross-sector resources. Agency collaboration evolved from a siloed approach (2015–2018) to a networked structure (2019–2021) and transitioned to centralized governance post-2022. Thematic shifts in policy discourse moved from a “Healthy China” focus toward pandemic-driven disease surveillance, culminating in the recent development of smart health ecosystems. Conclusions: China’s policies for medical–preventive integration demonstrate notable structural imbalances, particularly in the economic instruments related to health financing and the private-sector participation in healthcare. These imbalances may impede the effective allocation of healthcare resources and hinder the seamless transition toward integrated care. Future policy efforts should focus on optimizing the structure of policy instruments, addressing gaps in the full lifecycle of health services, advancing integration reforms, and promoting the transformation of the healthcare system through enhanced collaborative governance among key stakeholders. Full article

Background: Healthcare chatbots have emerged as a promising application of artificial intelligence in healthcare, offering potential benefits in accessibility, efficiency, and patient engagement. However, despite their growing adoption, limited research has examined the factors that determine their success from the user’s perspective. Objective: This study aimed to evaluate the success of a health chatbot service by applying the updated DeLone and McLean Information Systems Success Model augmented with a trust construct, examining the effects of information quality, system quality, service quality, and trust on intention to use, user satisfaction, and net benefits. Methods: An online survey design was employed, utilizing a structured questionnaire with 28 items measuring seven constructs on a seven-point Likert scale. Data were collected electronically from residents of Saudi Arabia between July and September 2024 using convenience sampling. Eligible participants were adults aged 18 years or older who had previously used the health chatbot service. A total of 321 valid responses were obtained. Partial Least Squares Structural Equation (PLS-SEM) was conducted using SmartPLS 3.3 software for measurement and structural model analysis. Results: The measurement model demonstrated acceptable reliability and validity, with composite reliability values exceeding 0.90 and average variance extracted values above 0.70 for all constructs. Structural model analysis supported eight of ten hypotheses. Trust exhibited the strongest effect on intention to use (β = 0.359, p < 0.001), followed by system quality (β = 0.234, p < 0.001) and information quality (β = 0.147, p < 0.01). Intention to use significantly predicted user satisfaction (β = 0.620, p < 0.001) and net benefits (β = 0.278, p < 0.001). User satisfaction demonstrated a strong positive effect on net benefits (β = 0.610, p < 0.001). The model explained 67.6% of the variance in intention to use, 72.7% in user satisfaction, and 71.4% in net benefits. Conclusions: Trust emerged as the most influential factor affecting intention to use the healthcare chatbot service, underscoring its critical role in user acceptance of health chatbot services. Information quality, system quality, and service quality exerted small to moderate effects on behavioral outcomes. These findings suggest that healthcare organizations deploying chatbot services should prioritize building user trust alongside ensuring high system and information quality to maximize user satisfaction and realized net benefits. Full article

Brown seaweeds are recognized for their rich content of phlorotannins with promising antidiabetic properties through multi-targeted modulation of glucose metabolism. This study investigated the antidiabetic potential of the ethyl acetate fraction of Ecklonia cava (EC-ETAC) and its major phlorotannin, dieckol, focusing on inhibition of carbohydrate-digesting enzymes, intestinal glucose absorption, dipeptidyl peptidase-IV (DPP-IV) activity, and hepatic glucose metabolism. EC-ETAC potently inhibited α-glucosidase (IC₅₀ = 2.2 ± 0.2 µg/mL) and α-amylase (IC₅₀ = 41.0 ± 1.2 µg/mL), outperforming acarbose by 26-fold and 6-fold, respectively. Pure dieckol showed strong activity with IC₅₀ values of 2.213 ± 0.04 µM (α-glucosidase) and 156.87 ± 0.124 µM (α-amylase). In differentiated Caco-2 cells, both EC-ETAC and dieckol downregulated SGLT1 and GLUT2 protein expression to ~0.5-fold of control and suppressed 2-NBDG glucose uptake by 46–53% over 120 min, effects not seen with acarbose. Dieckol inhibited DPP-IV activity (IC₅₀ = 12.12 ± 0.021 µM), reducing in situ activity to 53.89% at 25 µM without changing DPP-IV protein levels. Molecular docking revealed high-affinity binding of dieckol to DPP-IV (−10.396 kcal/mol), directly occluding the catalytic triad (Ser630, His740). In insulin-resistant HepG2 cells, dieckol restored glucose uptake to 108.97% of control via AMPK activation (1.21-fold), GLUT2 normalization (0.84-fold), and PGC-1α recalibration (0.96-fold), matching or surpassing 1 mM metformin. These results demonstrate dual-inhibition mechanism combined with hepatic AMPK restoration, establishing dieckol as a promising marine-derived multi-targeted agent for T2DM management. Full article

The explosive growth of the Internet of Things (IoT) has expanded the attack surface across industrial systems, smart cities, healthcare, and homes, motivating a synthesis of recent advances in machine learning for IoT security and a clear statement of remaining gaps. This review conducted a systematic search of MDPI, IEEE Xplore, Nature, ScienceDirect, and SpringerLink for publications from 2023 to 2025, screening them for domain relevance and organizing findings into a taxonomy of ML methods, threat types, and deployment contexts, with particular attention to datasets, edge constraints, and privacy considerations. We find that the field is shifting from signature-based detection to supervised and deep learning approaches that report high accuracy on benchmark traffic, while federated learning enables privacy-preserving, distributed intrusion detection with near-real-time edge performance. Across domains, prevalent threats include DDoS, unauthorized access, and malware; persistent challenges include device heterogeneity, rapid exploit weaponization, nonstandardized evaluation, concept drift, adversarial/poisoning risks, and governance and privacy constraints that hinder real world rollouts. We conclude that ML materially strengthens IoT resilience but requires rigorous, industry-scale validation, lightweight and explainable models, protocol-aware designs, robust federated aggregation, and SDN/NFV orchestration; we outline benchmark and deployment priorities to translate laboratory gains into operational security. Full article

Since the publication of the “Review of Embedded Artificial Intelligence Research” in 2023, driven by innovations in hardware architectures, advances in lightweight algorithms, and the maturation of edge–cloud collaboration technologies, embedded artificial intelligence (embedded AI) has progressed from “technically feasible” to “large-scale deployment”. As a continuation of that review, this article systematically surveys the core advances in embedded AI from 2023 to 2026. At the hardware level, it examines engineering progress in non-von Neumann architectures such as compute-in-memory and neuromorphic chips, as well as heterogeneous integration technologies. At the algorithmic level, it covers dynamic adaptive lightweighting, specialized edge-side optimization of large models (including on-device large language model fine-tuning and edge diffusion models), and lightweight multimodal approaches. In terms of deployment paradigms, it discusses edge-side full training, federated edge learning, edge–cloud collaborative intelligence, and emerging paradigms. At the application level, it illustrates the “perception–decision–execution” pipeline in industrial IoT, wearable healthcare, autonomous driving, embodied intelligence, and smart agriculture. The article also analyzes core challenges including ultra-low-power design for extreme scenarios, cross-platform standardization, edge-side data security and privacy, and model robustness in complex environments. Based on these findings, four research directions are proposed to guide future work. Full article

Background: Although technology is rapidly transforming many industries, the healthcare industry remains comparatively conservative and slow to adopt new technologies due to patient safety concerns. Notwithstanding the abundance of research on technology acceptance, most studies overlook departmental variations, making it impossible to enhance technology adoption in the medical sector. Thus, the purpose of this study is to bridge this gap by concentrating on the emergency department (ED). Methods: This study examined the factors influencing Malaysian ED healthcare professionals’ acceptance of new medical technology by introducing organisational support and training with the Technology Acceptance Model (TAM). The study’s target population comprised ED healthcare professionals in Malaysian hospitals who were at least 25 to 60 years old. In total, 140 valid surveys were gathered by email and WhatsApp from Malaysian hospital EDs, and SPSS and SmartPLS were utilised for analysis. Results: Perceived usefulness and training have a significant impact on attitude towards use, whereas attitude towards use is the sole variable that directly influences behavioural intention to use and acts as a mediator in certain paths. Conclusions: Hospital administration should concentrate on the actual needs of ED healthcare professionals, improve their understanding of technology, and offer targeted training in order to promote its effective adoption and utilisation. In the meantime, technology providers should improve the innovation’s design to make it more accessible to EDs. These findings also show that incorporating organisational support and training enhances TAM’s explanatory power and reveals its flexibility in high-stress, fast-paced environments. Full article

A clustering analysis was conducted to identify distinct patient subgroups with White Blood Cells (WBC) count alongside Age and Total Immunoglobulin E (IgE) biomarkers. All data were obtained from a coordinated primary care network operating in Apulia (Southern Italy). We analyzed 300 patient records, performed preprocessing and exploratory data analysis, and then applied unsupervised clustering directly to the standardized three-variable feature space (Age, WBC, and Total IgE), followed by supervised validation steps. Several algorithms were applied for clustering. Among the evaluated methods, K-means and Spectral Clustering showed the most favorable internal validation profiles, based on Silhouette Score (SS), Calinski–Harabasz Index (CH), and Davies–Bouldin Index (DB). K-means achieved the best scores (SS = 0.406, CH = 190.00, DB = 0.900), closely followed by Spectral Clustering (SS = 0.398, CH = 182.57, DB = 0.936), outperforming Agglomerative Clustering (SS = 0.361, CH = 160.41, DB = 1.016) and Gaussian Mixture Models (SS = 0.233, CH = 103.89, DB = 1.289). Post-clustering ANOVA analyses indicated significant differences in WBC, age, and total IgE across the five consensus clusters. An evaluation of cluster internal separability occurred through the training of a Random Forest classifier to predict cluster membership. The results indicate internal cluster separability within the analyzed dataset, but more external verification and clinical evidence are necessary for validation. The research group established clinical descriptions along with suggested treatment plans and detected co-existing diseases to help validate model-based findings. A simplified cluster-informed clinical summary based on biomarker ranges was derived to support interpretation of the identified patient profiles. This integrated method preliminarily suggests that patient strata may be identified from routine clinical variables, while highlighting the importance of internal validation and clinical interpretability in clustering research. Full article

The rapid evolution of smart cities demands a transition from reactive healthcare systems to proactive, data-driven health management paradigms that support long-term urban sustainability. Predicting population health status based on lifestyle-related behavioral and physiological factors is critical for enabling early intervention, personalized healthcare, and efficient resource allocation directly contributing to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-being; SDG 11: Sustainable Cities and Communities). This study proposes an IoT-enabled Explainable Artificial Intelligence (XAI) framework for predictive health scoring as part of sustainable population health management, integrating real-time data acquisition, cloud-based analytics, and interpretable machine learning. To address the limitations of conventional ensemble models particularly the black-box nature and hyperparameter sensitivity of Extreme Gradient Boosting (XGBoost) a Bayesian optimization strategy is employed to automatically fine-tune model parameters, thereby enhancing predictive accuracy and generalization performance. Furthermore, Shapley Additive Explanations (SHAP) are incorporated to provide transparent, interpretable insights into model predictions by quantifying the contribution of individual lifestyle features. Using a publicly available Kaggle dataset (“Health and Lifestyle Data for Regression”), experimental evaluation demonstrates that the proposed Bayesian-Optimized XGBoost model achieves superior performance (Test R² = 0.878, RMSE = 4.983), outperforming ten benchmark models, including standard XGBoost, which exhibits signs of overfitting (Test R² = 0.832). The results further reveal that Body Mass Index (BMI) and diet quality are the most influential factors affecting health scores, providing actionable insights for urban health policymakers. The proposed framework highlights the synergy between IoT, optimization techniques, and explainable AI to develop transparent, reliable, and scalable predictive health systems. This work provides a practical foundation for next-generation smart healthcare applications and decision-support systems, advancing the vision of sustainable, data-driven, and human-centric smart cities. Full article

Accurate multi-step building load forecasting is critical for optimizing energy management in smart grids and reducing operational costs. However, existing forecasting methods struggle with complex temporal dependencies, seasonal variations, and robust performance under noisy conditions. This paper proposes R-SATNet (Robust Self-Attention Transformer Network), a novel deep learning architecture that integrates multi-head self-attention mechanisms with robust optimization techniques for enhanced building load prediction. The proposed framework incorporates temporal feature extraction modules, adaptive noise suppression layers, and multi-scale attention blocks to capture both short-term fluctuations and long-term seasonal patterns. Extensive experiments on real-world building load datasets demonstrate that R-SATNet achieves superior forecasting accuracy with 15.7% lower RMSE and 12.3% improved MAPE compared to state-of-the-art methods. The model maintains robust performance under various noise conditions and provides reliable multi-step predictions up to 24 h ahead, making it highly suitable for practical smart energy system deployments. The proposed framework is validated across six diverse building datasets spanning commercial, residential, industrial, campus, mixed-use, and healthcare facilities, confirming its generalizability and practical applicability in heterogeneous smart energy environments. Full article

Background: Location decision plays a key role in strategic logistics and business success. The beauty business in Thailand has continuously grown, and medical aesthetics clinic location is one of the critical factors for business success. The problem is also related to sustainable urban service accessibility. Methods: This paper presents, for the first time, a systematic selection of medical aesthetics clinic location as a multi-criteria decision-making (MCDM) problem. The Simple Multi-Attribute Rating Technique (SMART) and Single-Valued Neutrosophic TOPSIS (SVN-TOPSIS) are combined to solve the location selection problem. SMART determines criterion weights, whereas SVN-TOPSIS evaluates alternatives using linguistic terms understandable to non-technical decision makers. Results: The proposed SMART SVN-TOPSIS is applied to a real investment problem in which two investors select the best clinic location from five alternatives with nine criteria. Siam Square—the heart of shopping, fashion, and youth culture in Bangkok—is recommended as the top location. Conclusions: The results indicate that the proposed method is capable of generating a consistent ranking of alternatives and differentiating between locations that exhibit similar evaluation characteristics. The findings may also support sustainable urban service planning and healthcare-related facility location decisions. Full article