Search Results (2,349)

Background: Early identification of balance impairments is critical for detecting the fall risk in older adults. Force plates are the standard for measuring postural sway, but are restricted in practice because they are cumbersome and expensive. The Balance Mat is a portable device that requires comprehensive validation against force plates and clinical benchmarks in older adult populations. Objective: The objective of this study was to evaluate the technical validity and clinical discriminative ability of the Balance Mat against a laboratory-grade force plate, clinical tests, and the fall history in an older adult cohort. Methods: Fifty-six community-dwelling older adults performed static balance assessments across six stance conditions. Postural sway data were recorded simultaneously using the Balance Mat and a force plate. The technical validity was assessed using Spearman’s rank correlation and intraclass correlation coefficients. Linear regression models were applied to calibrate the Balance Mat outputs against the force plate. The diagnostic accuracy for classifying the fall risk against the timed up and go test, the Falls Efficacy Scale-International, and the retrospective fall history was evaluated using an area-under-the-curve analysis. Results: The Balance Mat demonstrated strong associations with force plate measurements, particularly for the sway path/velocity, variance and Area95 ($r>0.80$). Following calibration, the absolute agreement for the sway path/velocity reached excellent levels ($ICC=0.93$) and good levels for Area95 and RMS ($ICC>0.75$), whereas the mean sway demonstrated a poor agreement and was excluded. For fall-risk classification, the calibrated Balance Mat achieved a fair accuracy for the retrospective fall history and a high Falls Efficacy Scale-International concern (area under the curve, 0.76-0.78), and a fair accuracy for the timed up and go thresholds (area under the curve, 0.70). Conclusions: The calibrated Balance Mat provided valid measurements of postural sway that aligned with the force plate parameters, particularly for the sway path/velocity and Area95. The within-stance agreement for the sway path/velocity ranged from ICC= 0.44 to 0.88, and the pooled value should not be interpreted as the uniform performance across all stance conditions. Given its fair diagnostic accuracy, the device is best utilised as a portable screening tool in combination with standard clinical assessments and the fall history rather than as a standalone diagnostic test. Full article

Induced-junction silicon photodiodes based on SiO₂/SiN_x surface passivation are attractive for high-accuracy radiometry, but their use in the deep ultraviolet is limited by UV-induced degradation of the dielectric stack. In this work, we investigate the degradation of SiO₂/SiN_x-passivated p-type silicon photodiodes under UV irradiation and evaluate strategies for improving stability through shallow implanted junctions and oxide processing. Capacitance–voltage measurements on MIS capacitors and lifetime measurements on symmetrically passivated wafers show that UV exposure causes a rapid reduction in effective dielectric charge and carrier lifetime, followed by saturation at higher dose, consistent with filling of a finite population of electrically active trap states. Induced-junction photodiodes exhibit rapid photocurrent loss at 222 nm and, in some cases, eventual collapse, indicating that the remaining effective dielectric charge is insufficient to sustain the induced junction. To maintain junction functionality after UV exposure, shallow As- and Sb-implanted junctions are employed, resulting in an initial reduction during 222 nm exposure followed by stabilization at around 80–85% of the initial value up to the highest tested dose of 200 J/cm². Further improvement is achieved by stripping and regrowing the implanted screen oxide before SiN_x deposition, yielding nearly unchanged photocurrent after prolonged 222 nm exposure up to ca. 500 J/cm². These results show that UV stability can be substantially improved by reducing device dependence on dielectric-induced inversion and by improving post-implantation interfacial oxide quality. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

Metal oxide nanomaterials tailored at the nanoscale are opening new avenues for advanced electroanalytical sensing devices with enhanced properties, including improved electrocatalytic activity. In this work, a novel Mn₂Mo₃O₈/MnO-MWCNT nanocomposite was employed to modify a screen-printed carbon electrode, enabling the fabrication of an amperometric sensor for H₂O₂ operating at relatively low applied potential due to the catalytic activity of the nanocomposite. Further functionalization of this nanostructured surface with glucose oxidase allowed the construction of an electrochemical glucose biosensor, where the Mn₂Mo₃O₈/MnO-MWCNT material acted as an efficient electrocatalyst for hydrogen peroxide detection. The H₂O₂ sensor exhibited a linear response from 0.06 mM to 3.00 mM, with a sensitivity of (2.22 ± 0.02) µA mM⁻¹ and a detection limit of 22 µM. The glucose biosensor showed a linear response in the range from 0.10 mM to 18.9 mM glucose, with a sensitivity of (0.345 ± 0.005) µA mM⁻¹, and a detection limit of 29 µM. The biosensor displayed excellent selectivity and high stability and was successfully applied to the determination of glucose in lactose-free skimmed milk. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Background/Objectives: Digital, remote, and ecological tools may complement clinic-based assessment in multiple sclerosis (MS), but the distribution of evidence across fatigue/fatigability, mobility, and real-world functional activity remains unclear. This scoping review mapped tools, metrics, constructs, contexts of use, and reported clinical utility in adults with MS, with attention given to whether the evidence was balanced across domains. Methods: Following Joanna Briggs Institute guidance and PRISMA-ScR/PRISMA-S reporting standards, five databases were searched on 14 March 2026. After deduplication, title/abstract screening, full-text assessment, and manual extraction and verification, the findings were synthesized descriptively without formal critical appraisal. Results: Of 3100 records identified, 1433 unique records were screened and 125 sources were included. Gait was the most frequently assessed domain (105/125), followed by fatigue/fatigability (33/125), physical activity (29/125), and sleep (2/125). The most frequent technologies were wearable devices (60/125), accelerometry (54/125), remote/home-based/telemonitoring modalities (52/125), and inertial measurement units (42/125). Conclusions: The evidence is predominantly gait- and mobility-focused, while fatigue/fatigability and broader real-world functional activity are less consistently represented. Reported clinical utility was usually framed around functional assessment, longitudinal/remote monitoring, rehabilitation planning, patient stratification, and decision support, but these characteristics were extracted as reported and were not independently appraised. Full article

Background and Objectives: Assessing knee extensor (KE) strength is important for detecting muscle weakness in older adults, yet dynamometry is often impractical in community settings. This study examined whether smartphone-derived kinematics during the Five Times Sit-to-Stand Test (FTSST) could predict seated isometric KE strength. Materials and Methods: A cross-sectional study included 105 community-dwelling older adults (68.19 ± 5.85 years). A smartphone application extracted rising time, vertical velocity, and smartphone-estimated relative vertical power during the FTSST. KE strength was measured as maximum voluntary isometric contraction (MVIC) using fixed-frame dynamometry with a Lafayette dynamometer head. Bioelectrical impedance-derived body composition variables were reported descriptively but excluded from the primary prediction models to maintain a transparent movement-based model independent of device-specific body-composition estimates. Hierarchical regression models used smartphone-derived variables and transparent non-BIA covariates. Agreement was examined using Bland–Altman analysis. Results: Smartphone-estimated relative vertical power showed the strongest correlation with MVIC (r = 0.787, p < 0.001). The combined model including sex, age, femur length, and smartphone-estimated relative vertical power explained 71.6% of MVIC variance (adjusted R² = 0.716, SEE = 3.276 kg), outperforming vertical velocity, rising time, and total FTSST time models. Internal validation using repeated 10-fold cross-validation showed CV-R² = 0.701, CV-adjusted R² = 0.689, CV-RMSE = 3.343 kg, and CV-MAE = 2.739 kg. Bland–Altman analysis showed minimal mean bias (0.00 kg), 95% limits of agreement from −6.296 to 6.296 kg, and significant proportional bias (slope = −0.172, p = 0.002), indicating overestimation in weaker individuals and underestimation in stronger individuals. Conclusions: Consistent with our hypothesis, smartphone-estimated relative vertical power was the strongest kinematic predictor of seated isometric KE strength among the evaluated FTSST-derived variables. This approach may support community screening and monitoring, but it should not replace standardized dynamometry for precise individual-level strength quantification. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article

Oscillating water column (OWC) wave energy converters equipped with dielectric elastomer generators (DEGs) represent a promising technology for harnessing ocean wave energy. This study emphasises the critical role of functional specifications in guiding the development of these devices from initial concept to full-scale deployment. A comprehensive analysis of key design parameters that influence the performance and efficiency of flexible OWCs with DEG-based power take-off systems is presented. This investigation focuses on the effects of draft, membrane diameter, deformation characteristics, number of layers, and membrane thickness on power output. Utilising a combination of analytical tools, including Wave Venture software, MATLAB, and Abaqus, detailed simulations and analyses are conducted to optimise these parameters. Our results demonstrate that increasing the DEG diameter significantly enhances power output, with diameters between 5 and 12 m showing optimal efficiency. A critical strain threshold of approximately 32% is identified, beyond which power output efficiency diminishes. Furthermore, the study reveals that multi-layer DEG configurations can substantially increase energy production, with thinner membranes generally yielding higher outputs. These findings provide valuable insights for developing functional specifications that balance performance, manufacturability, and long-term reliability in marine environments. This research advances OWC technology by offering a parameter-screening framework to guide device design towards optimised configurations and to accelerate the path to commercial viability in the wave energy sector. Full article

Background: Carbapenem-resistant K. pneumoniae (CRKP) represents a major challenge in hospitalized patients because of its association with healthcare exposure, restricted antimicrobial options, and adverse clinical outcomes. Microbiological isolation alone does not define invasive disease; therefore, clinical interpretation requires separation of colonization, localized infection, invasive infection, and carbapenem-resistant Enterobacterales (CRE)-associated sepsis. This study evaluated epidemiological features, resistance phenotypes, treatment adequacy, and clinical outcomes among hospitalized adults with K. pneumoniae isolates, using a clinical framework that distinguishes colonization from active infection and invasive disease. Methods: This single-center retrospective observational cohort study included 157 consecutive adults admitted between January and July 2025 to a tertiary-care hospital with at least one microbiologically confirmed K. pneumoniae isolate recovered from clinical specimens and/or CRE surveillance rectal swabs. Isolates were assigned hierarchically to four mutually exclusive phenotypic groups: carbapenem-susceptible K. pneumoniae (CSKP), extended-spectrum beta-lactamase (ESBL)-producing carbapenem-susceptible K. pneumoniae (ESBL), carbapenem-resistant non-carbapenemase-producing K. pneumoniae (CRKP), and carbapenemase-producing K. pneumoniae (CP-KP). A prespecified secondary analysis compared carbapenem-resistant isolates (CRKP + CP-KP) with non-carbapenem-resistant isolates (CSKP + ESBL). Clinical adjudication distinguished colonization-only cases, non-invasive infection, bloodstream infection, device-associated infection, and CRE-associated sepsis; ventilator-associated pneumonia (VAP) was considered when source data allowed reliable attribution. Sepsis was defined according to Sepsis-3 criteria; quick Sequential Organ Failure Assessment (qSOFA) was used only as a bedside screening tool. Statistical tests were selected according to variable type, distribution, and expected cell counts. Results: The cohort comprised 157 unique patients, with a median age of 71 years (interquartile range [IQR], 61–76). Current CRE colonization was documented in 79/154 patients with available colonization status (51.3%). Complete-case in-hospital mortality was higher in the carbapenem-resistant group (CRKP + CP-KP, n = 46) than in the non-carbapenem-resistant group (CSKP + ESBL, n = 111): 11/42 (26.2%) versus 5/108 (4.6%; Fisher exact odds ratio (OR) 7.31, 95% confidence interval (CI) 2.36–22.65; p < 0.001); overall complete-case mortality was 16/150 (10.7%). Multivariable logistic regression for carbapenem resistance (N = 150; five prespecified covariates; events per variable (EPV) = 9.0) identified age 65 years or older (adjusted odds ratio [aOR] 3.78, 95% CI 1.32–10.86), recent hospitalization within 30 days (aOR 2.56, 95% CI 1.16–5.63), and current colonization (aOR 2.96, 95% CI 1.24–7.05) as independent predictors. CRE-associated sepsis was excluded a priori because of definitional circularity with the case definition. Male sex showed a non-significant protective trend (aOR 0.50, 95% CI 0.22–1.12). CRE-associated sepsis showed a strong bivariate association with carbapenem resistance (OR 9.90, 95% CI 3.91–25.09; p < 0.001), and this association is reported descriptively because the variable was excluded from the multivariable model owing to definitional circularity. Model performance was acceptable, with area under the curve (AUC) 0.77, Hosmer–Lemeshow p = 0.95, and Nagelkerke R² = 0.25. Of 99 molecularly characterized isolates, OXA-48-like was detected in 78 (78.8%), NDM in 71 (71.7%), KPC in 6 (6.1%), and NDM + OXA-48-like dual production in 54 (54.5%); VIM and IMP were uniformly negative. Conclusions: In this high-risk hospital cohort, carbapenem resistance in K. pneumoniae was associated with advanced age, recent healthcare exposure, current CRE colonization, and a pronounced unadjusted mortality signal. Interpretation of sepsis and mortality requires explicit separation of colonization from active infection and invasive disease. These findings support intensified CRE surveillance, source-specific clinical interpretation, rapid resistance detection, and risk-adapted empirical antimicrobial strategies in high-risk hospital settings. Full article

Objective: The current investigation aims to assess the clinical efficacy of intraoral photographic assessment in detecting dental caries of varying severity and to assess different variables, such as the type of dentition, examiner experience, and the type of imaging equipment, on evaluative clarity. Methods: This meta-analysis of the PRISMA-DTA systematic review and diagnostic test accuracy was conducted. They searched electronic databases such as PubMed, Web of Science, Scopus, Embase, and Cochrane Library from the beginning of time up to January 2025. The studies had to have evaluated intraoral photographic caries because they were required to have compared it with clinical intraoral examination and provide extractable tooth-level 2 × 2 data. Enamel (ICDAS 1 3), dentine (ICDAS 4 6), and any caries (ICDAS 1 6) were analyzed separately in a meta-analysis. A random-effects model was used to compute pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves. Subgroup analysis was done on a pre-specified basis according to dentition, type of examiner, and imaging device. This study has been registered in PROSPERO with reference number 2026 CRD420261330820. Results: Twenty-three studies were retrieved through a comprehensive search and were stratified by severity into three categories. In the case of enamel caries, sensitivity was 0.65 (95% CI: 0.62–0.68), specificity was 0.95 (95% CI: 0.94–0.95), DOR was 36.74 (95% CI: 12.44–108.49), and the AUC was 0.87. In the case of dentine caries, the pooled sensitivity and specificity were 0.86 (95% CI: 0.85–0.87) and 0.96 (95% CI: 0.96–0.97), respectively, which produced the DOR of 176 (95% CI: 91.2–339.6) and the AUC of 0.94. Any caries had a pooled sensitivity of 0.81 (95% CI: 0.80–0.83), specificity of 0.97 (95% CI: 0.96–0.97), DOR of 64.04 (95% CI: 11.65–351.94), and AUC of 0.888. Subgroup analyses revealed that diagnostic accuracy was greater when the lesions were severe. Conclusions: Intraoral photographic assessment has a moderate level of accuracy in detecting enamel lesions and has a clinically acceptable level of accuracy in detecting dentine caries. The clinical efficacy increased with the severity of lesions and was consistent with high specificity at all levels of threshold. Imaging on smartphones could be a promising method for caries screening. Full article

The built-in electric field induced by polarization in ZnO/Mg_0.2Zn_0.8O quantum wells can be screened to modulate the conduction-band potential profile and intersubband energy levels. To optimize the screening of the built-in electric field, we analyze the influence of an external electric field, temperature, and modulation doping. The position of the doped layer is varied within the heterostructure to improve field compensation, providing additional control over electron localization and intersubband energy separation. In this work, within the effective mass approximation and by self-consistently solving the Poisson and Schrödinger equations using the finite-difference method, we calculate the electronic structure and nonlinear optical response of an n-type doped ZnO/Mg_0.2Zn_0.8O quantum well heterostructure. Our results indicate a strong dependence of the confinement potential on the applied external electric field and the electrostatic potential arising from the doped layer. We demonstrate electronic Raman gain values on the order of ${10}^3$–${10}^4$ cm⁻¹ for specific values of field strength, temperature, and doped-layer position. This approach enables fine-tuning of the nonlinear optical response, which is crucial for the development of ZnO-based optoelectronic devices. Full article

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies. Full article

Single-lead electrocardiogram (ECG) is widely used in wearable devices for atrial fibrillation (AF) screening. Nevertheless, subtle pathological characteristics like P-waves and f-waves in practical signals are vulnerable to noise contamination. Meanwhile, the scarcity of high-quality annotated abnormal data instances leads to severe class imbalance. To mitigate these issues, we present an end-to-end framework designed for arrhythmia diagnosis using single-lead ECG signals, which integrates quality-aware data augmentation with a Peak-Enhanced attention mechanism. First, to mitigate the problem of data imbalance, a Quality-Aware Generative Adversarial Network (QA-GAN) is designed. This network integrates a signal quality evaluation module based on signal kurtosis, together with a dynamic soft-label training scheme, guiding the generator to prioritize learning high-quality morphological features, thereby synthesizing high-fidelity minority class samples. Second, to accurately capture subtle pathological features in electrocardiograms, a Peak-Enhanced Attention Convolutional Network (PEAC-Net) classification model is proposed. This model incorporates a Peak-Enhanced Attention (PE-Att) module, which employs learnable derivative convolutional kernels to precisely identify the transition points in the ECG signal. Furthermore, by integrating one-dimensional multi-scale dilated convolution (DSGC1D) with bidirectional LSTM, the model achieves effective capturing of both fine-grained local morphological features and long-range global rhythm patterns. Experimental results on the PhysioNet 2017 dataset indicate that the presented model attains an accuracy of 0.902 and a macro-F1 score of 0.880, respectively, outperforming other state-of-the-art models and also exhibiting robust data adaptability on the MIT-BIH dataset. Full article

Cyber-physical systems (CPSs) based on the Internet of Things (IoT) form the backbone of modern smart infrastructures, including smart cities, healthcare monitoring, industrial automation, and intelligent transportation. However, connecting many resource-limited IoT devices makes them more vulnerable to cyber threats, particularly quantum attacks. This review comprehensively examines quantum-secure communication (QSC) frameworks for IoT-enabled CPS, focusing on Quantum Key Distribution (QKD), post-quantum cryptographic (PQC) algorithms, and hybrid quantum–classical security models suitable for constrained devices. A PRISMA-guided search of the Scopus and Google Scholar database was conducted in January 2026 using three keyword groups related to hybrid security, artificial intelligence, and cyber-physical systems. Based on the evaluation, 6008 publications have been identified between 2001 and 2026. The first-round screening was performed for 4948 articles, after excluding duplicates. During the screening stage, 348 articles were selected for abstract scrutiny, 115 records were excluded due to no direct focus on CPS/IoT applications, 52 studies were excluded because these papers relied on traditional security models, 25 studies were excluded due to insufficient relevance to the review objectives, and 15 additional non-English studies were removed. Following the screening stage, 141 studies were selected for full-text eligibility. Out of those, 86 studies were removed due to a lack of specific evaluation metrics or not being published in a peer-reviewed venue. Furthermore, the publications are classified as QKD-based secure CPS and QSC for industrial IoT, AI-Assisted Secure Communication for CPS Networks, and hybrid PQC-QKD models for CPS/IoT devices. This article investigates recent advancements in secure data transmission, verified protocols, and AI-driven anomaly detection customized to CPS/IoT environments. In addition, operational hurdles, interaction with open innovations, real-time deployment, and secure edge-cloud integration are highlighted. By analyzing recent developments and identifying research gaps, this review provides a structured roadmap for designing secure, scalable, and quantum-safe IoT-based CPS frameworks capable of withstanding next-generation cyber threats. This systematic review was performed and reported according to the PRISMA 2020 guidelines. Full article