Search Results (7,911)

Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as “special category” personal data when used for uniquely identifying individuals. Compliance requires meeting strict conditions, including explicit consent and data protection by design. Passkeys, the modern name for FIDO2-based authentication credentials developed by the FIDO Alliance, enable passwordless login using public key cryptography. Its “match-on-device” architecture stores biometric data locally in secure hardware (e.g., Android’s Trusted Execution Environment, Apple’s Secure Enclave), potentially reducing the regulatory obligations associated with cloud-based biometric processing. This paper examines how Passkeys are implemented on Android and iOS platforms and their differences in architecture, API access, and hardware design, and how those differences affect compliance with the GDPR. Through a comparative analysis, we evaluate the extent to which each platform supports local processing, data minimization, and user control—key principles under GDPR. We find that while both platforms implement strong local protections, differences in developer access, trust models, and biometric isolation can influence the effectiveness and regulatory exposure of Passkeys deployment. These differences have direct implications for privacy risk, legal compliance, and implementation choices by app developers and service providers. Our findings highlight the need for platform-aware design and regulatory interpretation in the deployment of biometric authentication technologies. This work can help inform stakeholders, policymakers, and legal experts in drafting robust privacy and ethical policies—not only in the realm of biometrics but across AI technologies more broadly. By understanding platform-level implications, future frameworks can better align technical design with regulatory compliance and ethical standards. Full article

Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and invasive. While vision-based systems offer a more accessible alternative, they have not been extensively explored for eye-writing recognition. Additionally, the natural instability of eye movements and variations in writing styles result in inconsistent signal lengths, which reduces recognition accuracy and limits the practical use of eye-writing systems. To address these challenges, we propose a novel vision-based eye-writing recognition approach that utilizes a webcam-captured dataset. A key contribution of our approach is the introduction of a Discrete Fourier Transform (DFT)-based length normalization method that standardizes the length of each eye-writing sample while preserving essential spectral characteristics. This ensures uniformity in input lengths and improves both efficiency and robustness. Moreover, we integrate a hybrid deep learning model that combines 1D Convolutional Neural Networks (CNN) and Temporal Convolutional Networks (TCN) to jointly capture spatial and temporal features of eye-writing. To further improve model robustness, we incorporate data augmentation and initial-point normalization techniques. The proposed system was evaluated using our new webcam-captured Arabic numbers dataset and two existing benchmark datasets, with leave-one-subject-out (LOSO) cross-validation. The model achieved accuracies of 97.68% on the new dataset, 94.48% on the Japanese Katakana dataset, and 98.70% on the EOG-captured Arabic numbers dataset—outperforming existing systems. This work provides an efficient eye-writing recognition system, featuring robust preprocessing techniques, a hybrid deep learning model, and a new webcam-captured dataset. Full article

Content Delivery Networks (CDNs) have evolved to meet surging data demands and stringent low-latency requirements driven by emerging applications like high-definition video streaming, virtual reality, and IoT. This paper proposes a hybrid CDN architecture that synergistically combines edge caching, Multi-access Edge Computing (MEC) offloading, and reinforcement learning (Q-learning) for adaptive routing. In the proposed system, popular content is cached at radio access network edges (e.g., base stations) and computation-intensive tasks are offloaded to MEC servers, while a Q-learning agent dynamically routes user requests to the optimal service node (cache, MEC server, or origin) based on the network state. The study presented detailed system design and provided comprehensive simulation-based evaluation. The results demonstrate that the proposed hybrid approach significantly improves cache hit ratios and reduces end-to-end latency compared to traditional CDNs and simpler edge architectures. The Q-learning-enabled routing adapts to changing load and content popularity, converging to efficient policies that outperform static baselines. The proposed hybrid model has been tested against variants lacking MEC, edge caching, or the RL-based controller to isolate each component’s contributions. The paper concludes with a discussion on practical considerations, limitations, and future directions for intelligent CDN networking at the edge. Full article

Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility to many patients and rehabilitation centers. This work presents the design and development of a low-cost walking exoskeleton, conceived to offer an affordable and simple alternative. The system uses a compact eight-bar mechanism with only one degree of freedom per leg, drastically simplifying motorization and control. The exoskeleton is customized for each patient using a synthesis process based on evolutionary algorithms to replicate a predefined gait. Despite the reduced number of degrees of freedom, the resulting mechanism perfectly matches the desired ankle and knee trajectories. The device is designed to be lightweight and affordable, with components fabricated using 3D printing, standard aluminum bars, and one actuator per leg. A working prototype was fabricated, and its functionality and gait accuracy were confirmed. Although limited to a predefined gait pattern and requiring crutches for balance and steering, this exoskeleton represents a promising solution for rehabilitation centers with limited resources, offering accessible and effective gait assistance to a wider population. Full article

Background/Objectives: Increased screen time partially replaces social interaction, physical activity, and outdoor play in kindergarten children, leading to a risk of decreased cognitive, emotional, and motor skills. Children with high motor skills are more likely to have access to challenging joint activities that promote their cognitive and emotional development. This study examines the moderating role of motor competence in the relationship between executive function skills and emotion comprehension. Methods: A sample of 220 kindergarten children (101 girls, 119 boys) completed the NEPSY-II subtests and the ‘Dimensional Change Card Sort’ tool for executive function skills assessment, the Movement Assessment Battery for Children—Second Edition (MABC-2) for motor competence, and the Test of Emotion Comprehension (TEC) for emotion comprehension. Executive function skills and motor competence were assessed when children were in their penultimate year of kindergarten (children were aged on average 5 years 10 months), and emotion comprehension was assessed one year later, when children were in their final year of kindergarten. When children were in their penultimate year of kindergarten, caregivers also reported on children’s passive and active screen time, maternal education, and family income, which were used as control variables. Results: For girls, motor competence moderated the relationship between cognitive flexibility and later emotion comprehension. High motor competence amplified this relationship (B = 0.171; SE = 0.066; 95% CI [0.041, 0.302]; p = 0.011). For boys, there were no significant moderation effects. Conclusions: High motor competence can improve emotion comprehension in kindergarten girls. Emotional development may benefit from effective shared motor interventions for children. Full article

Anxiety and depression are mental disorders with significant global impact, and are especially prominent in women during times of hormonal fluctuations. The microbiota–gut–brain axis (MGB axis) has emerged as a crucial pathway in the pathogenesis of these disorders, as it directly influences the production of neurotransmitters such as serotonin (5-HT), gamma-aminobutyric acid (GABA) and dopamine (DA). In addition, they have shown estrogenic effects through enzymes such as β-glucuronidase, which modulate hormone metabolism and consequently mood. A comprehensive search of recent preclinical studies has found that probiotic intake in female rats led to significant improvements in anxiety- and depression-related behaviors. Similarly, clinical trials in certain populations, particularly women with hormonal imbalances during menopause or premenstrual syndrome, have shown promising results. However, there are still significant problems, such as the individual variability of responses and the need for controlled long-term studies. The development of specific probiotics for hormonal modulation and the implementation of personalized approaches integrating omics and neuroimaging technologies to optimize therapeutic interventions in the field of mental health are promising. Accordingly, a comprehensive search was conducted in scientific databases such as PubMed, ScienceDirect, Scopus and Web of Science. Preclinical studies investigating the estrogenic effects of different probiotic strains in animal models and in controlled clinical trials during chronic treatment were selected, excluding those studies that did not provide access to full text. Full article

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

(1) Background: Working from home is becoming increasingly common and has many advantages, but also negative consequences such as reduced physical activity, poor ergonomics and stress. Many people find it difficult to attend in-person rehabilitation sessions due to transportation barriers and limited access to rehabilitation facilities. Therefore, the aim of our study was to investigate whether our online preventive exercise programme effectively improves the musculoskeletal and mental health of sedentary workers. (2) Methods: The study participants worked from home on a long-term basis. Our research group consisted of 30 people, 16 in the intervention group and 14 in the control group. The intervention group members participated in a complex preventive exercise programme three times a week for 10 weeks. The training took place online. Before and after the exercise programme, various tests were used to assess the functional status of the participants’ spine, the strength and flexibility of their core muscles (Core, Kempf and Kraus–Weber tests) and their mental state (Beck Depression Inventory). (3) Results: Significant improvement was observed in the intervention group in the Core, Kempf, Kraus–Weber and Beck tests. (4) Conclusions: Our online complex training programme proved to be effective during the evaluation. It is crucial to tailor remote rehabilitation services to the individual needs and preferences of patients and healthcare professionals. Full article

Background/Objectives: The COVID-19 pandemic has significantly intensified global concerns surrounding antimicrobial resistance (AMR), particularly in relation to tuberculosis (TB). In the European Union (EU), the reallocation of healthcare resources towards managing COVID-19 led to a de-prioritization of TB surveillance and control. This shift contributed to delays in TB diagnosis and treatment, creating conditions favorable for the emergence and spread of drug-resistant TB strains. This meta-analysis aims to assess epidemiological trends of drug-resistant TB across EU countries before, during, and after the pandemic and quantify the impact of COVID-19 on Mycobacterium tuberculosis resistance patterns. Methods: Data were obtained from the European Centre for Disease Prevention and Control (ECDC) covering 2015 to 2022. Following the TB incidence, the multidrug-resistant TB (MDR-TB) and rifampicin-resistant/MDR-TB (RR/MDR-TB) cases, as well as treatment success rates over 12- and 24-month periods, were analyzed. The analysis included 31 EU countries across three-time frames: pre-pandemic (2015–2019), pandemic onset (2020), and post-pandemic transition (2020–2022). Results: The pandemic was associated with a decrease in reported TB cases but a simultaneous increase in the proportion of MDR and RR/MDR cases. Treatment success rates showed a modest rise for 24-month regimens, while outcomes declined for 12-month therapies. Conclusions: These findings underscore the pandemic’s disruptive impact on TB control and highlight the need for renewed investment in diagnostic capacity, treatment access, and antimicrobial stewardship, in order to reduce antimicrobial resistance occurrence. Continued monitoring beyond 2022 is essential to fully understand long-term effects and inform future public health strategies. Full article

The incorporation of bioactive natural compounds into biomedical applications offers a promising route to enhance therapeutic efficacy while supporting sustainability. In this study, we investigated the synergistic potential of Sericin, a silk-derived biopolymer, and Chelidonium majus L. (C. majus), a medicinal plant with a diverse phenolic profile, in relation to biological activities relevant for wound care and infection control. A combined experimental strategy was applied, integrating detailed chemical characterization of C. majus extracts with antimicrobial and cytocompatibility assays across different Sericin–plant extract ratios (1:1, 1:2, 2:2, and 2:1). Phytochemical analysis identified and quantified 57 phenolic compounds, including high levels of flavonoids (quercetin, kaempferol, isorhamnetin) and phenolic acids (caffeic and ferulic acid). Salicylic acid (123.6 µg/g), feruloyltyramine (111.8 µg/g), and pinocembrin (98.4 µg/g) were particularly abundant, compounds previously reported to disrupt microbial membranes and impair bacterial viability. These metabolites correlated with the strong antimicrobial activity of C. majus against Gram-positive strains (MIC = 5–10 mg/mL). In combination with Sericin, antimicrobial performance was ratio-dependent, with higher proportions of C. majus (2:1) retaining partial inhibitory effects. Cytocompatibility assays with HFF1 fibroblasts demonstrated low antiproliferative activity across most formulations (GI₅₀ > 400 µg/mL), supporting their potential safety in topical applications. Collectively, the results indicate a concentration-dependent interaction between C. majus phenolics and the Sericin protein matrix, reinforcing their suitability as candidates for natural-based wound healing materials. Importantly, the valorization of Sericin, an underutilized byproduct of the silk industry, together with a widely accessible medicinal plant, underscores the ecological and economic sustainability of this approach. Overall, this work supports the exploration of the development of biomaterials with potential for advancing tissue repair and wound management. Full article

The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems. Full article

This study presents a loop-shaping methodology for the attitude control of a fixed-wing unmanned aerial vehicle (UAV). The proposed controller design focuses on achieving desired frequency–domain characteristics—such as specified phase and gain margins—to ensure stability and robustness. Unlike many existing approaches that rely on oversimplified plant models or involve mathematically intensive robust-control formulations, this work develops controllers directly from a high-fidelity six-degree-of-freedom UAV model that captures realistic aerodynamic and actuator dynamics. The loop-shaping procedure translates multi-objective requirements into a transparent, step-by-step workflow by progressively shaping the plant’s open-loop frequency response to match a target transfer function. This provides an intuitive, visual design process that reduces reliance on empirical PID tuning and makes the method accessible for both hobby-scale UAV applications and commercial platforms. The proposed loop-shaping procedure is demonstrated on the pitch inner rate loop of a fixed-wing UAV, with controllers discretized and validated in nonlinear simulations as well as real flight tests. Experimental results show that the method achieves the intended bandwidth and stability margins on the desired design target closely. Full article

Background/Objectives: Solitary plasmacytoma of bone (SPB) results from abnormal proliferation of plasma cells and accounts for 2–5% of all plasmacytic malignancies. Radiation therapy is the standard of care in treating SPB due to its efficacy in controlling disease progression and optimizing patient survival. However, prior studies have highlighted disparities in radiation therapy receipt among various cancer types. In this study, we aim to investigate whether similar sociodemographic and clinical disparities exist in the treatment of SPB through use of the Surveillance, Epidemiology, and End Results (SEER) database. Methods: The SEER database was queried for biopsy-confirmed cases of SPB between 2000 and 2021 using the ICD-O-3 histology code 9731/3 and primary site codes C40.0–41.9. Chi-square tests, Fisher’s exact tests, and multivariable logistic regression were completed using SPSS v29.0.2, with significance set to p < 0.05. Results: A total of 4139 patients were identified, of which 75.3% received treatment with radiation therapy. Multivariable analysis revealed that low-income patients making less than $74,999 annually (aOR 0.80, 95% CI 0.67–0.97), as well as those from non-Hispanic Asian/Pacific Islander (aOR 0.49, 95% CI 0.33–0.73) and Hispanic (aOR 0.77, 95% CI 0.60–0.98) racial and ethnic groups, were significantly less likely to receive radiation therapy. Conclusions: These findings reveal notable disparities in radiation therapy utilization for SPB patients based on income and race and ethnicity, emphasizing the need for interventions to address systemic inequities, improve access to care, and ensure that all patients receive high-quality cancer care to optimize long-term outcomes. Full article

Particulate matter (PM2.5) is a critical indicator of air quality and has significant health implications. This study presents the development and evaluation of a custom-built PM2.5 device, named the P-Tracker, designed to offer an accessible alternative to commercially available air quality monitors. This paper presents the design framework used to address the requirements of a low-cost, accessible device which meets the performance of existing commercial systems. Step-by step build instructions are provided for hardware and software development and connection to the P-tracker open access website which displays the data and interactive map. To demonstrate the performance, the P-Tracker was compared against leading consumer devices, including the AtmoTube Pro by AtmoTech Inc., Flow by Plume Labs, View Plus by Airthings, and the Smart Citizen Kit 2.1 by Fab Lab Barcelona, across four controlled tests. The tests included: (1) a controlled paper combustion test in which all devices were exposed to combustion aerosols in a sealed environment alongside the DustTrak 8530 (TSI Incorporated, Shoreview, MN, USA), used as the gold standard reference, where the P-Tracker achieved a Pearson correlation of 0.99 with DustTrak over the final measurement period; (2) an outdoor test comparing readings with a stationary reference sensor, Osiris (Turnkey Instruments Ltd., Rudheath, UK), where the P-Tracker recorded a mean PM2.5 concentration of 3.08 µg/m³, closely aligning with the Osiris measurement of 3.53 µg/m³ and achieving a Pearson correlation of 0.77; (3) a controlled indoor air quality assessment, where the P-Tracker displayed stable readings with a standard deviation of 0.11 µg/m³, comparable to the AtmoTube Pro; and (4) a real-world kitchen environment test, where the P-Tracker effectively captured fluctuations in PM2.5 levels due to cooking activities, maintaining a consistent response with the DustTrak reference. The results indicate varied degrees of agreement across devices in different conditions, with the P-Tracker demonstrating strong correlation and low error margins in high-pollution and controlled scenarios. This research underscores the potential of open-source, low-cost, custom-built air quality sensors which may be developed and deployed by communities to provide hyperlocal measurements of air pollution. Full article

The deployment of autonomous vehicles (AVs) depends on the readiness of both physical and digital infrastructure. However, existing national and city-level indices often overlook deficiencies along specific routes, particularly in developing contexts such as Thailand, where infrastructure conditions vary widely. This study develops and applies a corridor-level framework to assess AV readiness on five controlled-access roads in western Bangkok. The framework evaluates key infrastructure dimensions beyond conventional vehicle requirements. In this study, infrastructure readiness means the extent to which essential physical (EV charging capacity, traffic sign visibility, and lane marking retroreflectivity) and digital (5G speed and coverage) subsystems meet minimum operational thresholds required for AV deployment. Data were collected through field measurements and secondary sources, utilizing tools such as a retroreflectometer, a handheld spectrum analyzer, and the Ookla Speedtest application. The results reveal significant contrasts for physical infrastructure, showing that traffic signage is generally satisfactory, but EV charging capacity and road marking retroreflectivity are insufficient on most routes. On the digital side, 5G coverage was generally adequate, but network speeds remained less than half of the global benchmark. Kanchanaphisek Road demonstrated comparatively higher digital readiness, whereas Ratchaphruek Road exhibited the weakest road marking conditions. These findings point out the need for stepwise enhancements to EV charging infrastructure, lane marking maintenance, and digital connectivity to support safe and reliable AV operations. The proposed framework not only provides policymakers in Thailand with a practical tool for prioritizing corridor-level investments but also offers transferability to other rapidly developing urban regions experiencing similar infrastructure challenges for AV deployment. Full article