Search Results (21,376)

Every day, countless individuals with hearing or speech disabilities struggle to communicate effectively, as their conditions limit conventional verbal interaction. For them, sign language becomes an essential and often sole tool for expressing thoughts and engaging with others. However, the general public’s limited understanding of sign language poses a major barrier, often resulting in social, educational, and professional exclusion. To bridge this communication gap, the present study proposes a smart wearable glove system designed to translate Arabic sign language (ArSL), especially Moroccan sign language (MSL), into a written alphabet in real time. The glove integrates five MPU6050 motion sensors, one on each finger, capable of capturing detailed motion data, including angular velocity and linear acceleration. These motion signals are processed using an Artificial Neural Network (ANN), implemented directly on a Raspberry Pi Pico through embedded machine learning techniques. A custom dataset comprising labeled gestures corresponding to the MSL alphabet was developed for training the model. Following the training phase, the neural network attained a gesture recognition accuracy of 98%, reflecting strong performance in terms of reliability and classification precision. We developed an affordable and portable glove system aimed at improving daily communication for individuals with hearing impairments in Morocco, contributing to greater inclusivity and improved accessibility. Full article

Oral administration remains the most patient-friendly drug delivery route, yet its efficacy is limited by physiological barriers including gastric degradation and inefficient cellular uptake. pH-responsive nanogels have shown promise for gastrointestinal drug delivery, though their effectiveness is often constrained by poor membrane interaction. Inspired by natural membrane-anchoring mechanisms, a series of comb-like anionic polymers were designed via grafting alkylamines of different chain lengths (C₁₀, C₁₄, C₁₈) at varying densities (10–30%) onto a biodegradable poly(L-lysine isophthalamide) (PLP) backbone. These pH-responsive comb-like polymers self-assembled into nanogels for loading the hydrophobic chemotherapeutic agent camptothecin. The alkyl length and grafting density significantly influenced pH-responsive behavior, membrane disruption, and drug release profiles. The optimal formulation—the nanogel prepared with PLP grafted 30% C₁₄—achieved a high drug-loading capacity, ideal particle size and stability, and offered superior protection in acidic conditions (only 7 ± 5% release at pH 1.2 over 24 h), while enabling rapid intestinal release (78 ± 2% at pH 7.4 within 24 h). The nanogels significantly enhanced cellular uptake, cytoplasmic delivery, and cytotoxicity against colorectal carcinoma cells. This study demonstrates the key role of hydrophobic modification in designing effective oral nanocarriers, providing a promising platform for the treatment of intestinal diseases. Full article

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most studies have focused either on stationary applications or on mobility, seldom addressing their integration withing a single framework. In particular, the potential of Metal Hydride (MH) tanks remains largely underexplored in the context of sector coupling, where the same storage unit can simultaneously sustain household demand and provide in-house refueling for light-duty fuel-cell vehicles. This study presents the design and analysis of a residential-scale HRES that combines photovoltaic generation, a PEM electrolyzer, a lithium-ion battery and MH storage intended for direct integration with a fuel-cell electric microcar. A fully dynamic numerical model was developed to evaluate system interactions and quantify the conditions under which low-pressure MH tanks can be effectively integrated into HRES, with particular attention to thermal management and seasonal variability. Two simulation campaigns were carried out to provide both component-level and system-level insights. The first focused on thermal management during hydrogen absorption in the MH tank, comparing passive and active cooling strategies. Forced convection reduced absorption time by 44% compared to natural convection, while avoiding the additional energy demand associated with thermostatic baths. The second campaign assessed seasonal operation: even under winter irradiance conditions, the system ensured continuous household supply and enabled full recharge of two MH tanks every six days, in line with the hydrogen requirements of the light vehicle daily commuting profile. Battery support further reduced grid reliance, achieving a Grid Dependency Factor as low as 28.8% and enhancing system autonomy during cold periods. Full article

With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations. Full article

In deepwater oil and gas transportation, Pipe-in-Pipe (PIP) systems are an effective solution for mitigating external loads while preserving internal thermal integrity. A finite element model with ITT elements and nonlinear spring contacts was developed in ABAQUS to simulate thermal expansion and contraction under extreme conditions. The coupled mechanical response of double-layer pipelines under non-uniform temperature fields and internal pressure was analyzed, focusing on stress distribution and deformation coordination between the inner and outer pipes. The inner pipe primarily sustains compressive or tensile stress depending on the thermal load direction, while the outer pipe experiences opposing stresses due to mechanical coupling. Distinct stress transfer zones are present near the pipe ends, governed by pipe-soil interaction and internal bending moments. The proposed model for double-layer pipelines under coupled thermal and internal pressure loads demonstrates a prediction accuracy within 5% as compared with benchmark numerical solutions. The simulations capture axial stress variations of up to 68% between extreme thermal expansion and contraction scenarios, with radial deformation ranging from 0.9 mm to 3.4 mm. These findings provide valuable insights into the safe and efficient design of subsea PIP systems, particularly for optimizing material selection and structural configuration in high-temperature, high-pressure environments. Full article

Practising the calculation of determinants is important in linear algebra. A pioneering study involving 580 first-year university students evaluated the impact of game-based learning in higher mathematics education. The participants formed two groups: an experimental group of 279 students and a control group of 301. The experimental group students played the non-digital educational card game DETerminator, designed to help students learn and practise calculating determinants in small square matrices. In contrast, the control group received no intervention, allowing for a clear outcome comparison. Students in the experimental group worked in smaller teams during didactic gameplay sessions that involved solving matrix-determinant problems in a competitive and collaborative classroom setting, enhancing their understanding through interaction and teamwork. The objective of this paper is to provide a detailed presentation of the DETerminator game and showcase its integration as an effective teaching tool for practising essential concepts and theorems related to determinants. Moreover, a quasi-experiment was conducted to explore how incorporating game-based learning can lead to successful and enjoyable mathematical education experiences for students. We used a quantitative approach to assess the effectiveness of the card game on academic achievement. At first, a pre- and post-test design was employed with the experimental group of 279 participants to evaluate the short-term effects of game-based learning. The Wilcoxon test was utilised for hypothesis testing, revealing a large effect size of 0.63. Moreover, the results from related midterm exam problems were statistically analysed to obtain the medium-term impact. The outcomes were compared using the Mann–Whitney U-test. The results demonstrated that the experimental group statistically outperformed the control group, but achieving a small effect size of 0.16, with a mean score of 3.14 out of 7 on the designated midterm exam tasks, compared to the control group’s mean score of 2.5. The small effect size suggests that, although the intervention had a positive effect, it is worth considering what other options there are for increasing the medium-term effect. A Likert-scale questionnaire was used to evaluate students’ attitudes towards the game. Our findings show the importance of incorporating game-based learning strategies in mathematics education at the university level, especially for enhancing students’ proficiency in key topics such as the determinant of a matrix. Full article

Accurate semantic segmentation of high-resolution remote sensing imagery is challenged by substantial intra-class variability, inter-class similarity, and the limitations of single-modality data. This paper proposes MMFNet, a novel multimodal fusion network that leverages the Mamba architecture to efficiently capture long-range dependencies for semantic segmentation tasks. MMFNet adopts a dual-encoder design, combining ResNet-18 for local detail extraction and VMamba for global contextual modelling, striking a balance between segmentation accuracy and computational efficiency. A Multimodal Feature Fusion Block (MFFB) is introduced to effectively integrate complementary information from optical imagery and digital surface models (DSMs), thereby enhancing multimodal feature interaction and improving segmentation accuracy. Furthermore, a frequency-aware upsampling module (FreqFusion) is incorporated in the decoder to enhance boundary delineation and recover fine spatial details. Extensive experiments on the ISPRS Vaihingen and Potsdam benchmarks demonstrate that MMFNet achieves mean IoU scores of 83.50% and 86.06%, outperforming eight state-of-the-art methods while maintaining relatively low computational complexity. These results highlight MMFNet’s potential for efficient and accurate multimodal semantic segmentation in remote sensing applications. Full article

We report the design, fabrication, and experimental characterization of an asymmetric loop-terminated Mach–Zehnder interferometer (a-LT-MZI) realized on a silicon nitride (SiN) platform for refractive index (RI) sensing. The LT-MZI architecture incorporates a Sagnac loop that enables bidirectional light propagation, effectively doubling the interaction length without enlarging the device footprint, enhancing sensitivity and improving stability against environmental noise. Subwavelength grating (SWG) waveguides were integrated into the sensing arm to further strengthen light-matter interaction. The fabricated devices exhibited stable and well-defined interference fringes, with uniform wavelength shifts that scaled linearly with changes in the surrounding refractive index. Standard a-LT-MZI structures (ΔL = 300 μm) achieved experimental sensitivities of 288.75–301.25 nm/RIU, while SWG-enhanced devices reached 496–518 nm/RIU, confirming the effectiveness of refractive index engineering. Comparative analysis against previously reported MZI-based sensors highlights the competitive performance of the proposed design. By combining the scalability and CMOS compatibility of silicon nitride with the sensitivity and robustness of the a-LT-MZI architecture, this device provides a compact and versatile platform for next-generation lab-on-chip photonic sensors. It holds strong potential for applications in biochemical diagnostics, medical testing, and environmental monitoring. Full article

Background: This study explored how Taiwanese schoolchildren perceive the appearance of their peers with and without cleft lip and palate (CLP) and whether this perception affects social interactions. We specifically focused on early adolescents with surgically repaired CLP to assess the impact of residual craniofacial deformities. Methods: A cross-sectional design was used, analyzing three-dimensional (3D) surface images of twenty patients with repaired CLP and five without. A total of 91 schoolchildren (40 with CLP, 51 without) served as raters. Participants used a Likert scale to rate images on facial appearance and perceived social acceptance. The study also measured the reliability of its questionnaires using Cronbach’s alpha. Results: All participants successfully differentiated between images of children with and without CLP, though non-cleft participants had significantly better distinguishing abilities. Non-cleft raters consistently gave more positive appearance ratings to non-cleft images, a pattern less evident among cleft raters. While differences in awareness and acceptance between the two groups were not statistically significant, over half of all responses regarding social interaction were neutral. The questionnaires demonstrated high reliability, with Cronbach’s alpha values greater than 0.85. Conclusions: Despite the ability to perceive residual craniofacial differences, appearance alone did not significantly affect social interactions for early adolescent children with surgically repaired CLP in Taiwan. This suggests that other factors may play a larger role in social dynamics within this population. Full article

Climate change mitigation for the built environment has become a subject of greatest urgency, as buildings account for nearly 40% of total energy consumption and nearly one-third of total CO₂ emissions. While environmental, social, and governance (ESG) indicators are increasingly used to monitor sustainability performance, their collective role in impacting building-related emissions is yet largely under-investigated. The current research closes that gap through an examination of the ESG dimension–CO₂ emissions intersection of 180 nations from 2000 to 2022, in the hope of illuminating how environmental, social, and governance elements interact to facilitate decarbonization. The research is guided by a multi-method design, including econometric examination, cluster modeling, and machine learning techniques, which provide causal evidence and predictive analysis, respectively. The findings reveal that the deployment of renewable energy significantly reduces emissions, while per capita energy use and PM_2.5 air pollution exacerbate this effect. The social indicators show mixed results: learning, women’s parliamentary representation, and women’s workforce representation reduce emissions, while food production and growth among the lowest-income individuals demonstrate higher emissions. Governance demonstrates mixed results as well, with good regulation reducing emissions under specific conditions yet primarily supporting high-income countries with superior infrastructure. The examination of clusters reveals that ESG-balanced performance is retained by countries in the low-emission clusters, whereas decentralized ESG pillars are associated with higher emissions. Machine learning confirms the existence of non-linear effects and identifies PM_2.5 exposure and renewable energy deployment as the strongest predictors of the relationship. In summary, the findings suggest that successful policies for decarbonizing the built environment are constructed upon the consistency of environmental, social, and governance plans, rather than single steps. Full article

Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents a novel system that combines a patient-centred chatbot with a deep learning framework to support early diagnosis, symptom triage, and health education. The system integrates convolutional neural networks, class activation mapping, and natural language processing within a conversational interface. Five deep learning models were evaluated (CNN, DenseNet121, DenseNet169, DenseNet201, and InceptionV3) using two balanced public datasets. Model performance was assessed using accuracy, sensitivity, specificity, diagnostic odds ratio (DOR), and Cohen’s Kappa. InceptionV3 consistently outperformed the other models across these metrics, achieving the highest diagnostic accuracy (77.6%) and DOR (20.67), and was selected as the core engine of the chatbot’s diagnostic module. The deployed chatbot provides real-time image assessments and personalised conversational support via multilingual web and mobile platforms. By combining automated image interpretation with interactive guidance, the system promotes timely consultation and informed decision-making. It offers a prototype for a chatbot, which is scalable and serves as a low-cost solution for underserved populations and demonstrates strong potential for integration into digital health pathways. Importantly, the system is not intended to function as a formal screening tool or replace clinical diagnosis; rather, it provides preliminary guidance to encourage early medical consultation and informed health decisions. Full article

To overcome the challenges of conventional low-carbon retrofits for existing buildings—such as high construction volume, cost, and implementation difficulty—this study proposes a minimally invasive design and optimization method for Photovoltaic–Energy Storage–Direct Current–Flexible (PEDF) systems. The goal is to maximize energy savings and economic benefits while minimizing physical intervention. First, the minimally invasive retrofit challenge is decomposed into two coupled problems: (1) collaborative PV-ESS layout optimization and (2) flexible energy management optimization. A co-optimization framework is then developed to address them. For the layout problem, a model with multiple constraints is established to minimize retrofitting workload and maximize initial system performance. A co-evolutionary algorithm is employed to handle the synergistic effects of electrical pathways on equipment placement, efficiently obtaining an optimal solution set that satisfies the minimally invasive requirements. For the operation problem, an energy management model is developed to maximize operational economy and optimize grid interactivity. A deep reinforcement learning (DRL) agent is trained to adaptively make optimal charging/discharging decisions. Case simulations of a typical office building show that the proposed method performs robustly across various scenarios (e.g., office, commercial, and public buildings). It achieves an energy saving rate exceeding 20% and reduces operational costs by 10–15%. Moreover, it significantly improves building–grid interaction: peak demand is reduced by 33%, power fluctuations are cut by 75%, and voltage deviation remains below 5%. The DRL-based policy outperforms both rule-based strategies and the DDPG algorithm in smoothing grid power fluctuations and increasing the PV self-consumption rate. Full article

Urban green spaces are essential for promoting human health and well-being, especially in cities facing increasing noise pollution and ecological stress. This study investigates the effects of audio-visual interaction on restorative outcomes across three soundscape types (park, residential, and street), focusing on the compensatory role of positive visual stimuli in low-quality soundscape environments. Thirty-two university students participated in a controlled evaluation using soundscapes and corresponding visual materials derived from 30 urban green spaces. A two-way repeated measures ANOVA revealed significant main effects of soundscape type and modality (auditory vs. audio-visual), as well as a significant interaction between these factors. Audio-visual conditions consistently outperformed auditory conditions, with the strongest restorative effects observed in noisy street soundscapes when paired with positive visual stimuli. Further analysis highlighted that visual cleanliness and structural clarity significantly enhanced restorative outcomes in challenging environments. These findings align with existing theories of sensory integration and extend their application to large-scale urban settings. This study shows that multi-sensory optimization can mitigate urban environmental stressors, supporting healthier, more resilient, and sustainable urban environments. Future research should explore long-term and cross-cultural applications to inform evidence-based urban planning and public health policies. Full article

Settlement-relevant load combinations play a critical role in the serviceability design of buildings, particularly for structures on soils with time-dependent deformation behavior. While permanent loads must be fully considered, the contribution of variable actions depends on their duration relative to soil response. This study investigates suitable settlement-relevant load combinations and their influence on the restrained load redistribution within buildings, based on parametric finite element analyses of wall-type and frame-type structures on sand, silt, and clay using PLAXIS 3D (Version 2024.3). Results show that structural stiffness significantly affects force redistribution due to settlements: stiffer structures exhibit greater redistribution, while soft soils generate higher absolute restraining forces but are less sensitive to load combinations. Based on these findings, the reduced characteristic load combination (including α_n) is recommended for coarse-grained, drained soils, as it balances safety and realistic deformation. For fine-grained, low-permeability soils, the quasi-permanent combination should be applied to capture long-term consolidation effects. Short-term load variations after consolidation have negligible impact and should be addressed through safety factors rather than separate settlement analyses. These recommendations provide a clear and practical framework for selecting settlement-relevant load combinations, enhancing reliability and efficiency in structural design. Full article

Background/Objectives: Family physicians are key stakeholders in the implementation of cancer prevention strategies, including risk factor assessment, lifestyle counseling, and early detection. Despite this, integration of personalized prevention into routine practice remains limited. This study aimed to explore family physicians’ perspectives on barriers, training needs, and collaboration opportunities in cancer prevention. Methods: A mixed-methods study was conducted using an exploratory sequential design. The qualitative phase involved semi-structured interviews with 12 family physicians from the North-West Region of Romania. Thematic analysis was employed to identify main challenges and opportunities. Findings informed the development of a structured online survey completed by 50 family physicians. Descriptive and comparative statistical analyses were applied to assess trends and subgroup differences. Results: Interviews and survey data revealed multiple barriers to cancer prevention in primary care: insufficient consultation time, limited access to diagnostic tools, administrative workload, and low patient health literacy. Physicians reported moderate familiarity with personalized prevention but expressed strong interest in further training, particularly through flexible and interactive learning formats. Collaboration with cancer centers was considered suboptimal; participants emphasized the need for streamlined referral pathways and improved communication. Conclusions: The study highlights systemic and educational gaps affecting cancer prevention efforts in family medicine. Tailored training programs, digital integration with cancer centers, and targeted policy adjustments are needed to enhance prevention capacity within primary care. Full article