Search Results (332)

Composite materials, particularly polymers reinforced with carbon, glass, and aramid fibers, enable the development of lightweight yet mechanically robust structures that enhance user comfort and functional performance. Their high strength-to-weight ratio and fatigue resistance make them ideal for applications requiring repetitive movements in rehabilitation and assistive robotics. However, challenges remain related to cost-effective production, durability under complex loading conditions, and ergonomic fit to human anatomy. Recent advances in materials science and smart materials are expanding the possibilities of multifunctional composites with embedded sensors. Furthermore, machine learning methods are increasingly being used to optimize material selection and structural design. Future advances are expected to improve scalability, personalization, and system integration, positioning composite fibers as a key assistive technology in next-generation robotic systems. Full article

With the rapid development of wireless communications, electromagnetic interference (EMI) in complex environments has become a critical factor affecting communication quality. Addressing the EMI issues caused by multi-band coexistence in indoor scenarios, traditional metallic resonant structures, while effective in filtering, often compromise optical transparency due to light blockage. To resolve this trade-off, this paper proposes a dual-ring resonant frequency-selective surface (FSS) based on Indium Tin Oxide (ITO) films. This design aims to achieve efficient transmission in specific C-band frequencies and suppress out-of-band interference, realizing excellent optical transmittance while ensuring electromagnetic shielding effectiveness. The designed metasurface targets a passband of 5.35–5.40 GHz for sub-6 GHz indoor communications. Experimental results confirm superior transmission in this range and significant out-of-band suppression. Furthermore, featuring high optical transparency, the structure can be directly integrated onto glass surfaces. It is not only suitable for optically transparent devices but also provides a compact passive solution for anti-EMI applications in smart buildings and sub-6 GHz indoor communications. Full article

This study evaluated the performance of thermochromic windows as dynamic envelopes for smart greenhouses, focusing on the light environment and cooling load under peak summer conditions. Four covering materials, glass, Low-E glass, polycarbonate, and thermochromic windows, were compared using EnergyPlus (v9.2.0) simulation for an 8-span greenhouse with a floor area of 1008 m² in Gwangju, South Korea, on a representative summer day of 21 July. Thermochromic properties were modeled with temperature-dependent SHGC variations from 0.521 at 25 °C to 0.425 at 85 °C. Results showed that thermochromic windows reduced noon illuminance by 75% compared to conventional glass, from 26,482 lux to 6628 lux, while maintaining adequate light levels above the compensation point for tomato and paprika cultivation. Simultaneously, peak cooling load decreased by 13.1%, from 537,929 W to 467,477 W, outperforming Low-E glass at 9.2% and polycarbonate at 7.0%. At peak hours of 1:00 p.m., when the glass surface temperature reached 60.5 °C, the thermochromic glazing reduced transmitted solar radiation by 37.8% per unit area compared to conventional glass. This study demonstrates that thermochromic windows effectively balance photosynthetic light provision and cooling energy reduction in smart greenhouses, offering a viable design solution for controlled environment agriculture under extreme summer conditions. Full article

This research focuses on creating a real-time facial emotion recognition system for smart glasses designed for visually impaired users. By adapting a contextually adaptive attention mechanism (CBAM) in a lightweight classification header with a pre-trained deep learning model, we obtain a model capable of successfully predicting emotions from facial features. The model will be complemented by a comprehensive preprocessing pipeline that includes face detection and alignment, standard normalization, and data augmentation for underrepresented classes. The model was trained on a merged benchmark dataset (FER24, RAF-DB, CK+) and evaluated across basic emotion classes: surprise, happiness, disgust, fear, sadness, neutral, and anger. Two models were compared: FaceNet–CBAM and EmoFormer–CBAM (a ViT-Base model enhanced with a 1D-CBAM attention module). EmoFormer–CBAM achieved 98% and ~72% test accuracy on new dataset (CleanFER25_RAF_CK) and AffectNet, respectively. In addition, a small set of external real-world images is used as a pilot qualitative evaluation to assess robustness under unconstrained conditions. A detailed analytical study of both models was performed to determine the impact of their structural components on overall performance using the available data. Based on the results, the most successful model under all conditions, EmoFormer–CBAM, was selected as the prototype for the smart glasses for the visually impaired. The necessary mechanisms for future deployment and implementation of the smart glasses prototype for the target users were also studied, in accordance with the ethical approvals previously obtained from Gazi University in Türkiye. Full article

Radio network planning is critical for 5G deployments, particularly for temporary installations in rural areas where terrain and vegetation significantly impact signal propagation. While empirical path loss (PL) models characterize propagation environments through scenario-specific parameters—leading to inherently noisy predictions at individual sites—machine learning (ML) approaches can predict site-specific path loss from multiple features simultaneously. This study conducts a systematic literature review of rural path loss prediction methods and introduces a novel dataset collected via a 5G nomadic measurement platform in a vineyard environment, capturing real-world propagation characteristics. We present a comprehensive comparison of machine learning and interpretable machine learning techniques, demonstrating that vegetation dynamics (quantified through the Normalized Difference Vegetation Index, NDVI) is an important driver of path loss variability when combining data across seasonal campaigns—though not within individual campaigns, where distance dominates. Cross-campaign NDVI transfer, however, is sensitive to satellite resolution, which appears to conflate vine canopy with seasonally managed inter-row ground cover. In cross-campaign transfer, XGBoost proves substantially less susceptible to NDVI-induced degradation than Explainable Boosting Machines (EBM), and a hybrid Log-Normal Shadowing (LNS) and XGBoost model confirms that NDVI captures seasonal variability more effectively than empirical path loss parameters alone. Still, the data captured the expected seasonal trend between April and June 2025, from which our interpretable models derived useful propagation insights. Tree-based models like Random Forest and XGBoost achieved the highest prediction accuracy ($R^2$ up to 0.924 on individual campaigns, 0.891 on combined data, and up to 0.945 (individual) and 0.907 (combined) with antenna pattern-corrected path loss), while explainable boosting machines achieved near-parity ($R^2$ up to 0.919; 0.876 on combined data) with the advantage of interpretability. Among individual campaigns, June—with densest canopy cover—yielded the highest $R^2$ values. These findings provide actionable insights for optimizing temporary 5G networks in precision agriculture and other rural applications. Full article

The rapid expansion of wireless communication in urban environments requires antenna systems that balance high electromagnetic performance with stringent aesthetic and security constraints. This review examines recent advances in concealed antenna technologies integrated into building structures, with a focus on performance variation, material-induced attenuation, and emerging concealment strategies. Techniques such as transparent conductors on glass, structural embedding within walls, and camouflage-based designs are shown to significantly influence resonance behavior, radiation efficiency, and pattern characteristics compared to free-space operation. Despite these challenges, optimized solutions including transparent conductive oxide arrays, wideband embedded antenna geometries, and metasurface-enhanced window structures can partially recover performance while maintaining optical transparency above 70%. Material loading effects are found to induce resonant frequency shifts of approximately 10–44%, depending on dielectric properties and environmental conditions. Transparent antenna arrays achieve gains ranging from 0.34 to 13.2 dBi, while signal-transmissive wall systems demonstrate transmission improvements of up to 22 dB relative to untreated building materials. These technologies enable a wide range of applications, including 5G and beyond-5G cellular networks across sub-6 GHz and millimeter-wave bands, as well as Internet of Things systems and smart city infrastructure. However, key challenges remain, including the need for comprehensive characterization of building material electromagnetic properties, optimization of multilayer structural environments, and the development of standardized design and evaluation methodologies. This review provides a unified framework for understanding the tradeoffs associated with antenna concealment and identifies critical research directions for the development of building-integrated wireless systems in next-generation communication networks. Full article

We designed and implemented facial recognition smart glasses to assist visually impaired individuals in recognizing people and navigating their environment safely and independently. The smart glasses utilize a Raspberry Pi 4 as the processing unit, integrating a Pi Camera for facial recognition and a USB camera for object detection. A face recognition library is employed to extract 128-dimensional facial embeddings using a convolutional neural network, enabling real-time face identification at close range (100–500 cm) under proper lighting conditions. Object detection is performed using a YOLOv5-based model, while ultrasonic sensors provide proximity alerts through audio feedback. Real-time processing is optimized to minimize latency and protect user privacy. The smart glasses were tested on participants with varying levels of visual impairment, including low vision, legal blindness, and total blindness. The system achieved an overall facial recognition accuracy of 88.89% and an object detection accuracy of 61.11%. The results demonstrate the viability of edge-AI wearable devices in assistive technology, with user feedback highlighting strengths in audio feedback and recognition accuracy, as well as areas for improvement, such as device comfort and low-light performance. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Digital inspection technologies are increasingly being adopted in the construction industry to improve efficiency, decision quality, and sustainability performance. Mixed reality (MR) systems can reduce rework, minimise human error, and support resource-efficient inspection processes. However, empirical evidence on how perceptions of efficiency and safety influence professional acceptance of MR technologies remains limited. This study investigates the adoption of MR for construction inspection using an extended technology acceptance model (TAM) that incorporates task efficiency and safety perception as domain-specific human factors. A within-subjects scenario-based experimental design was applied, in which 103 construction professionals evaluated four inspection modalities: HoloLens MR, smart glasses, tablet-based systems, and traditional paper-based methods. Data was analysed using linear mixed-effects models, structural equation modelling, mediation analysis, and dominance analysis. The results show that HoloLens MR achieved the highest perceived efficiency and safety perception, while imposing the lowest cognitive demand. Perceived efficiency was a strong predictor of device preference and significantly predicted perceived usefulness (β = 0.322, p < 0.001), which fully mediated its effect on behavioural intention. Safety perception accounted for a substantial proportion of the variance in user evaluations (η² = 0.237). These findings indicate that sustainable adoption of MR in construction inspection depends on combined perceptions of efficiency gains, usability, and safety support. Full article

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests. Full article

Mesoporous bioactive glasses (MBGs) represent a significant advancement in bioactive glass technology, combining the well-established osteoconductive and osteoinductive properties of traditional bioactive glasses with the structural precision provided by highly ordered mesoporosity. Their characteristic architecture, defined by uniform pores typically ranging from a few to several tens of nanometers and exceptionally high surface areas reaching several hundred m²/g, enables enhanced drug-loading capacity, controlled therapeutic ion release, and accelerated tissue regeneration. In this work, we emphasize how the synthesis of these materials is predominantly governed by structure-directing agents, which critically influence the pore size, mesophase ordering, surface area, and structural stability. Additionally, we discuss how compositional tailoring, particularly through therapeutic ion doping with elements such as Sr, Cu, Zn, or B, can impart osteogenic, angiogenic, antibacterial, or antioxidant functionalities. Moreover, we illustrate how these functionalities can be further expanded and enhanced by employing a comprehensive suite of characterization tools to establish robust correlations between synthesis parameters, mesostructural features, and biological performance. Improving the above functionalities enables the MBGs to exhibit exceptional versatility across biomedical applications, notably in bone tissue engineering (as hierarchical or composite scaffolds), controlled drug delivery (anticancer, antibiotic, and anti-inflammatory agents), wound healing, dental therapy, and bioactive implant coatings. Finally, we acknowledge that despite their broad potential, several associated challenges remain, including the synthesis scalability, batch-to-batch reproducibility, mechanical fragility of pure MBGs, and the complexity of predicting in vivo degradation and ion-release behaviors. We believe that emerging research directions, including eco-friendly synthesis routes, stimuli-responsive smart MBGs, multifunctional theranostic platforms, and patient-specific additive manufacturing, are poised to overcome current limitations and drive the next generation of MBG-based biomedical technologies. Full article

To investigate the dynamic behavior of smart composite structures with embedded heat sources over a wide temperature range, this study employed thermoplastic polypropylene as the matrix, combined with glass/carbon fiber prepregs and Ni80Cr20 alloy heating wires, and fabricated functional laminated specimens with integrated heating elements via a prepreg molding process. Using a self-developed variable-temperature cantilever beam vibration testing system, the evolution of natural frequencies and damping ratios from room temperature to 140 °C was systematically examined. Results indicate that temperature-induced thermal softening of the polypropylene matrix reduces the effective bending stiffness of the composites, leading to a decline in natural frequencies across all modes. For example, the first-order natural frequency of the sample decreased from approximately 30.8 Hz at room temperature to about 28.3 Hz at 140 °C, representing a reduction of approximately 8.12%. The second-order reduction reached about 8.99%, and the third-order reduction was approximately 9.65%. Carbon fiber-reinforced specimens exhibited relatively smaller frequency reductions due to the high modulus of the fibers. Concurrently, elevated temperatures enhance molecular chain mobility and interfacial viscoelastic dissipation at the fiber–matrix interface, causing a sharp increase in damping ratios at high temperatures (>100 °C). For instance, the damping ratio of the first-order mode increased significantly from approximately 1.02% at room temperature to about 2.9% at 140 °C. By comparatively analyzing carbon fiber and glass fiber systems, the study elucidated the distinct mechanisms underlying the “fiber-dominated” stiffness retention effect and the “resin/interface-dominated” damping dissipation effect under thermal influence. These findings provide critical experimental data and theoretical references for the active thermal regulation of structural performance in thermoplastic composite structures with integrated heat sources, thereby mitigating damage caused by external disturbances. Full article

Industry 5.0 is composed of a variety of complex tasks and challenging processes requiring specialized labor and multidisciplinary coordination. Specifically, when it comes to shipbuilding, shipyards leverage advanced technologies, seeking to replace operations that continue to rely on traditional methods, such as 2D blueprints and paper-based documentation, which can lead to inefficiencies and alignment errors in precision-dependent tasks. For this reason, this article focuses on embracing Mixed Reality (MR) technologies to address these challenges in the context of electrical outfitting tasks. The design, development and evaluation of a MR application tailored for HoloLens 2 smart glasses aims to streamline the workflow for operators, reducing reliance on paper-based documentation and enhancing the precision of assembly processes. The proposed system allows for the precise positioning of 3D models in the real environment, ensuring accurate alignment during assembly. Additionally, it incorporates automatic dimension generation between objects in the scene. To further enhance usability, the application integrates a Galician on-device Automatic Speech Recognition (ASR) system, allowing operators to interact seamlessly with the MR interface using voice commands. The whole system has been exhaustively tested, both through usability and functionality evaluations, which validate MR as a viable tool for shipyard assembly and inspection tasks. Full article

The thermally efficient and lightweight TRC/CLCi composite panels for functional and smart building envelopes, funded by the iclimabuilt project (Grant Agreement no. 952886), offer innovative solutions to sustainably address common failure risks in facade systems. This work specifically emphasizes strategies for mitigating structural, thermal, and fire-related failures through targeted material selection, advanced design methodologies, and rigorous validation protocols. To effectively mitigate structural failures, high-pressure concrete (HPC) reinforced with carbon fibers is utilized, significantly enhancing tensile strength, reducing susceptibility to cracking, and improving overall durability. To counteract thermal bridging—a critical failure mode compromising energy efficiency and structural integrity—the panels employ specially designed glass-fiber reinforced pins connecting HPC outer layers through the cellular lightweight concrete (CLC) insulation core that has a density of around 70 kg/m³ and a thermal conductivity in the range 35 mW/m∙K comparable to those of expanded polystyrene and Rockwool. These connectors ensure effective load transfer and maintain optimal thermal performance. A central focus of the failure mitigation strategy is robust fire behavior. The developed panels undergo rigorous standardized fire tests, achieving an exceptional reaction to fire classification of A2. This outcome confirms that HPC layers maintain structural stability and integrity even under prolonged fire exposure, effectively preventing catastrophic failures and ensuring occupant safety. In conclusion, this work highlights explicit failure mitigation strategies—reinforced concrete materials for structural stability, specialized glass-fiber connectors to prevent thermal bridging, rigorous fire behavior protocols, and comprehensive thermal performance validation—to produce a facade system that is robust, energy-efficient, fire-safe, and sustainable for modern buildings. Full article

Artificial Intelligence (AI) is increasingly shaping materials research, particularly in the development and optimization of Glass Fibre Reinforced Polymer (GFRP) bars used as innovative alternatives to steel reinforcement. Despite this growing intersection, no prior bibliometric study has systematically mapped how AI contributes to the advancement of GFRP technologies. This paper fills this gap through a comprehensive bibliometric analysis based on 102 Scopus-indexed publications from 2015 to 2025. Following PRISMA guidelines, the study combines performance analysis and science mapping using VOSviewer to identify publication dynamics, leading journals, key contributors, and thematic clusters. The results reveal a tenfold growth in annual output (compound annual growth rate, CAGR = 10.1%) and five dominant research directions: (1) machine learning in structural analysis, (2) AI-driven composite materials modeling, (3) smart damage detection, (4) mechanical characterization, and (5) advanced deep learning frameworks. China, India, and the United States collectively account for more than half of global publications, highlighting strong international collaboration. The findings demonstrate that AI has evolved from an exploratory tool to a transformative driver of innovation in GFRP research. This study provides the first quantitative overview of this emerging field, identifies critical gaps such as sustainability integration and standardization, and proposes future directions to foster cross-disciplinary collaboration toward intelligent and sustainable composite structures. Full article