Search Results (7,277)

We present the simulation-guided design and experimental demonstration of high-speed 940 nm vertical-cavity surface-emitting lasers (VCSELs). Utilizing established device optimization principles, a simulation study was conducted focusing on the number of oxide layers and the aperture size, which predicted a maximum modulation bandwidth of over 35 GHz. To validate the simulation, a device with a 4-μm double-oxide aperture was fabricated and characterized. Additionally, a Zn-diffusion process was incorporated during fabrication to reduce p-DBR resistance and suppress higher-order transverse modes. The fabricated device achieved an experimental modulation bandwidth of 34 GHz and demonstrated successful 100 Gbit/s PAM-4 data transmission. The close agreement between the simulated and measured performance highlights the successful practical integration of these techniques for developing high-speed optical interconnects. Full article

Dust pollution induced by blasting during tunnel construction via the drill-and-blast method poses a severe threat to workers’ health and construction safety. To address this issue, a wet chord grid dust removal and purification device adaptable to deep-buried long tunnels was developed in this study. The device integrates dust control and removal functions, featuring mobility, high purification efficiency, and water recycling capability. Through experimental tests, the optimal operating parameters of the system were determined: the dust removal efficiency reached a peak of 94.3% (laboratory optimal value from the basic parameter optimization test) when the frequency of the extraction axial flow fan was set to 30 Hz and the cross-sectional wind speed of the chord grid reached 3.34 m/s. The circulating water tank achieved the optimal water treatment performance under the conditions of a relative buried depth of 0.42 for the water inlet, a volume ratio of 1:2 for the sedimentation area to the clear water area, and a relative baffle height of 0.65. Numerical simulations based on CFD software (2021) revealed that the on-site dust removal efficiency of the device reached 79.86% and 87.9% under the working conditions where the tunnel face was 10 m and 100 m away from the connecting passage, respectively, which are in good agreement with the field measurement results. In the practical application at the Shierpo Tunnel of the Guangxi Tianba Expressway, the device achieved an average total dust removal efficiency of 78.4%, with 81.2% removal efficiency for PM₁₀ and 76.5% for PM_2.5, demonstrating excellent engineering applicability and dust removal performance for respirable dust. This study provides effective technical support and a theoretical basis for improving the construction environment of drill-and-blast tunnels. Full article

Optimizing neuromuscular strength and balance is essential for performance and injury prevention in elite Paralympic sport. However, limited evidence describes how these parameters change over time during specific phases of the training season in athletes with lower limb deficiencies. This retrospective case series aimed to describe longitudinal changes in neuromuscular and balance performance during the fundamental preparation period in elite athletes using prosthetic devices. Routinely collected performance data from five international-level Paralympic athletes (Para-swimming and Para-athletics) were retrospectively analyzed across two preparatory observation windows conducted in consecutive competitive seasons. Neuromuscular performance was assessed using countermovement jump variables, while static balance was evaluated through Inertial Measurement Unit-derived sway metrics. Within-athlete changes were examined using descriptive and exploratory analyses. At the group level, changes were observed in selected neuromuscular and balance outcomes over time, including jump height and path length. Individual analyses revealed substantial inter-athlete variability in the magnitude and direction of changes across all outcomes. Overall, the findings indicate that neuromuscular and postural performance may fluctuate meaningfully during preparatory phases in elite athletes with lower limb deficiencies. This study provides exploratory insights derived from real-world training settings and highlights the value of longitudinal monitoring to support individualized performance management in Paralympic sport. Full article

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area coverage capabilities, offer an innovative architecture for low-latency and highly reliable edge services. However, the practical deployment of such systems faces a highly complex multi-objective optimization problem featured by the tight coupling of task offloading decisions, UAV trajectory planning, and edge server resource allocation. Conventional optimization methods are difficult to adapt to the dynamic and high-dimensional characteristics of this problem, leading to suboptimal system performance. To address this critical challenge, this paper constructs an intelligent collaborative optimization framework for UAV-assisted edge computing systems and formulates the system quality of service (QoS) optimization problem as a mixed-integer non-convex programming problem with the dual objectives of minimizing task processing latency and reducing overall system energy consumption. A multi-agent joint task association and trajectory optimization (MA-JTATO) algorithm based on hybrid reinforcement learning is proposed to solve this intractable problem, which innovatively decouples the original coupled optimization problem into three interrelated subproblems and realizes their collaborative and efficient solution. Specifically, the Advantage Actor-Critic (A2C) algorithm is adopted to realize dynamic and optimal task association between UAVs and edge servers for discrete decision-making requirements; the multi-agent deep deterministic policy gradient (MADDPG) method is employed to achieve cooperative and energy-efficient trajectory planning for multiple UAVs to meet the needs of continuous control in dynamic environments; and convex optimization theory is applied to obtain a closed-form optimal solution for the efficient allocation of computational resources on edge servers. Simulation results demonstrate that the proposed MA-JTATO algorithm significantly outperforms traditional baseline algorithms in enhancing overall QoS, effectively validating the framework’s superior performance and robustness in dynamic and complex scenarios. Full article

Autonomous vehicle navigation requires low-latency and energy-efficient machine learning models capable of operating in dynamic and resource-constrained environments. Conventional deep learning approaches are often unsuitable for real-time deployment on embedded edge devices due to their high computational and memory demands. In this work, we propose a unified TinyML-optimized navigation framework that integrates a lightweight convolutional feature extractor (MobileNetV2) with a metric-based few-shot learning classifier to enable rapid adaptation to unseen driving scenarios with minimal data. The proposed framework jointly combines feature extraction, few-shot generalization, and edge-aware optimization into a single end-to-end pipeline designed specifically for real-time autonomous decision-making. Furthermore, post-training quantization and structured pruning are employed to significantly reduce the memory footprint and inference latency while preserving the classification performance. Experimental results demonstrate that the proposed model achieved a 93.4% accuracy on previously unseen road conditions, with an average inference latency of 68 ms and a memory usage of 18 MB, outperforming traditional CNN and LSTM models in efficiency while maintaining a competitive predictive performance. These results highlight the effectiveness of the proposed approach in enabling scalable, real-time navigation on low-power edge devices. Full article

The problem of seepage beneath dams represents a major technical and economic challenge, particularly for countries such as Algeria, where agricultural and industrial development depends heavily on the management of water resources stored in reservoirs. Such seepage can not only cause significant water losses but also jeopardize the stability of the structure, particularly through the piping phenomenon, which poses a risk of sudden failure. Moreover, the evaluation of seepage becomes critical when it exceeds admissible thresholds, thereby requiring the search for solutions to ensure the waterproofing of foundations. Consequently, the design and optimization of devices such as cutoff walls or drainage systems aim to simultaneously reduce three key parameters: the leakage discharge, the uplift pressure, and the downstream hydraulic gradient, in order to guarantee the safety and durability of the infrastructure. The existing literature on cutoff walls beneath concrete dams does not allow for a comprehensive evaluation of the combined effects of geometric and operational parameters. This study aims to address this gap by systematically analyzing the interaction of these factors and their influence on the hydraulic response of the system. Numerical modeling was carried out using the Plaxis 2D software, considering various geometric and parametric configurations. The results indicate that the position, depth, and inclination of the cutoff wall significantly affect the hydraulic performance of the structure. Full article

With the changing demands of society, gears, as fundamental components of mechanical devices, are evolving towards higher reliability and longer service life. To address the issue of thermal scuffing at the gear meshing interface, we propose the introduction of micro/nano-textures to improve the thermal elastohydrodynamic lubrication characteristics of the meshing surfaces, thereby enhancing the lubrication performance and anti-scuffing load capacity of the gear surfaces. First, finite element models with different microstructural features were established. Then, numerical calculations were conducted using computational fluid dynamics (CFD) software to analyze the impact of various micro-texture configurations on the lubrication performance of the tooth surface. Finally, an orthogonal experiment was performed to optimize the groove length, groove width, and areal density of the micro-textures in order to obtain the best processing parameters. The results show that, compared with the triangular, rectangular and trapezoidal micro-textures, the wedge-shaped micro-texture produces the largest pressure difference at the meshing-in and meshing-out points of the texture grooves, which causes the dynamic pressure effect to be more obvious. Compared with the triangular, rectangular and trapezoidal micro-textures, the wedge-shaped micro-texture has the largest bearing capacity and the smallest friction coefficient, so it has better bearing capacity and anti-friction and wear performance. The process parameters were optimized through orthogonal experiments, and the optimal combination of process parameters was obtained as the areal density of 50%, the depth of micro-pits of 12 µm, and the width of micro-pits of 200 µm. Under these optimal parameters, the pressure difference at the meshing-in and meshing-out points of the wedge micro-texture increased significantly by 255.6% compared to the initial model, and the oil film friction coefficient decreased by 17.857% relative to the initial model. These results demonstrate that the micro-texture with optimal parameters significantly enhances the lubrication and anti-friction/wear performance of the tooth surface. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

Carotid artery stenosis remains a major cause of ischemic stroke worldwide, and its management continues to evolve in parallel with advances in surgical, endovascular, and medical therapies. Carotid endarterectomy (CEA) was established as the standard of care for symptomatic high-grade stenosis following landmark randomized trials, while carotid artery stenting (CAS) subsequently emerged as a less invasive alternative for appropriately selected patients. This review aims to summarize the historical evolution of carotid artery stenting, critically appraise evidence from major clinical trials comparing CAS and CEA, and examine contemporary practice patterns in the era of intensive medical therapy. A comprehensive review of randomized trials, registries, guideline statements, and recent literature was performed to synthesize current evidence regarding procedural outcomes, patient selection, and emerging technologies, including transcarotid artery revascularization (TCAR). Large, randomized trials have demonstrated comparable long-term composite outcomes between CAS and CEA in selected patients, although peri-procedural risk profiles differ, with higher stroke risk observed after CAS and higher myocardial infarction rates after CEA. Technological advancements in embolic protection devices, stent platforms, and alternative access strategies have further refined endovascular approaches. Concurrently, improvements in intensive medical therapy—including lipid-lowering, antiplatelet therapy, blood pressure control, smoking cessation, and lifestyle modification—have substantially reduced overall stroke risk, particularly in asymptomatic patients. In the contemporary era, optimal stroke prevention requires individualized, multidisciplinary decision-making that integrates symptom status, anatomical complexity, comorbid conditions, procedural expertise, and sustained long-term vascular risk factor management following revascularization. Full article

Background: Cleft lip and/or palate (CLP) is a common craniofacial malformation with aesthetic, functional, and psychosocial impacts. Although surgical repair is performed early in life, scar tissue formation may intensify maxillary deformities. Presurgical orthopedic interventions have therefore been introduced to optimize anatomical conditions prior to surgery. This scoping review aimed to systematically map presurgical orthopedic approaches described in the literature for patients with CLP. Methods: A Scoping Review was conducted in accordance with PRISMA-ScR guidelines. The protocol was registered in the Open Science Framework. Searches were performed in PubMed, Embase, Web of Science, and Cochrane databases without language or date restrictions. Two independent reviewers assessed the articles and extracted data. Results: A total of 207 studies were included, with a predominance of case series, case reports, and cohort studies, reflecting a generally low level of evidence. Nasoalveolar molding (NAM) was the most frequently reported intervention, while other appliances such as the Hotz plate and Latham device were considerably less represented. Across studies, reported outcomes included reduction of the alveolar cleft, improved nasal symmetry, and facilitation of feeding; however, variability in protocols and outcome measures limited comparability. Conclusions: The available evidence is heterogeneous and largely based on observational designs, which restricts definitive conclusions regarding the comparative effectiveness of presurgical orthopedic approaches. The predominance of NAM in the literature may reflect clinical preference rather than superior evidence, highlighting the need for standardized protocols and higher-quality studies. Full article

With the growing demand for advanced passive cooling technologies in fields such as building energy efficiency, thermal protection of electronic devices, and personal thermal comfort, radiative cooling materials have garnered considerable attention due to their ability to achieve cooling without external energy input. In this study, TiO₂ hollow microspheres with a core–shell structure were successfully synthesized via a solvothermal method using TiCl₄ as the titanium source and (NH₄)₂SO₄ and CO(NH₂)₂ as structure-directing agents. The effects of reaction temperature (120–200 °C) and reaction time (0.5–36 h) on the morphology, crystal phase, specific surface area, pore structure, and infrared optical properties of the microspheres were systematically investigated. The results indicate that all prepared samples consisted of anatase-phase TiO₂, with the microstructure significantly influenced by the synthesis conditions. An increase in reaction temperature promoted the transition from solid to hollow structures; the microspheres exhibited the most regular morphology and the largest specific surface area at 180 °C. Prolonging the reaction time facilitated the Ostwald ripening process, leading to a more complete hollow structure at 24 h. Infrared optical performance analysis revealed that all samples exhibited high emissivity approaching 100% in the 8–15 μm wavelength range, attributed to the intrinsic lattice vibration absorption of TiO₂. In the 3–8 μm range, however, the emissivity was strongly modulated by the microstructure. Samples synthesized at 180 °C for 12–24 h demonstrated stable emissivity characteristics owing to their dense shells, uniform particle size, and well-defined hollow structures. This study elucidates the intrinsic relationship between microstructural evolution and infrared emission performance in TiO₂ hollow microspheres, providing a theoretical foundation and process optimization strategy for their application in radiative cooling coatings, device thermal protection, and personal thermal management textiles. Full article

The climate crisis exposes the inadequacy of modern urban paradigms grounded in the separation between nature and built form. In response, this paper reframes streetscapes as architectural and urban spaces where ecological performance and spatial composition are conceived as mutually constitutive. Rather than treating Nature-Based Solutions (NBS) as isolated techno-performative devices, the research interprets them as design components capable of shaping section, threshold, and relational depth within the street. Building on two European-funded research projects, the ClimaScapes research—which unfolds into the Climate-Adaptive Nature-Based Urban Regeneration (CANBUR) Framework—through the different phases of Research about Design, Research by Design and Research for Design, thus develops the design-driven Operational Methodology. The paper, repositioning streetscapes as strategic fields for urban and architectural design, presents (i) the tools developed within it and (ii) its application inside a neighborhood of Matera (Italy). The findings demonstrate that integrating NBS within coherent spatial configurations enables a shift from environmental optimization toward architectural composition, offering a transferable yet context-sensitive methodology for climate-adaptive regeneration in Euro-Mediterranean and comparable urban contexts. This approach suggests streetscapes evolve into resilient, climate-adaptive urban commons, reinforcing community ties, ecological sustainability, and the broader goal of future-proof cities. Full article

Thick-film organic photovoltaics (OPVs) are key for scalable manufacturing, but increasing active-layer thickness usually lowers power conversion efficiency (PCE) due to charge recombination and limited carrier extraction. We report high-efficiency thick-film OPVs fully processed in air by doctor blading using non-halogenated solvents (o-xylene with 3.5% tetralin) for two non-fullerene acceptor systems: PM6:ITIC-4F and PTQ-10:ITIC-4F. Active layers (100–500 nm) were fabricated by adjusting the coating speed while keeping the ink concentration and gap constant. Under mild drying (40 °C, 2 min), both systems exhibited significant efficiency losses at 1 sun (AM1.5G) as the thickness increased, whereas performance was largely preserved under indoor LED illumination (200 lx and 1000 lx), enabling high performance for thick films. Short thermal post-annealing (80–140 °C, 2 min) further improved PCE by reducing bimolecular recombination and enhancing nanostructure. Optimized PM6:ITIC-4F devices reached 10.2% (300 nm) under 1 sun and 14.78% at 200 lx; PTQ-10:ITIC-4F achieved 11.3% (500 nm) under 1 sun and up to 15.71% at 200 lx. Morphological and structural analysis indicates that the superior thick-film performance of PTQ-10:ITIC-4F is linked to favorable phase behavior, polymer-rich surface composition, and preferential face-on molecular orientation, promoting charge collection. These results demonstrate that low-cost PTQ-10 and non-halogenated air processing can enable industrially relevant, high-performance thick-film OPVs. Full article

Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article

Rapid increases in distributed photovoltaic (PV) penetration have brought additional challenges to distribution network planning and operation. Meanwhile, flexible interconnection devices such as soft open point integrated with battery energy storage system (E-SOP) can significantly enhance the regulatory capability and operational adaptability of the distribution system and have been widely applied in recent years. First, to improve both economic performance and voltage quality, a coordinated planning method for the multi-region flexible interconnected distribution system based on E-SOP is proposed. Second, with the ongoing growth of interconnected distribution networks, centralized optimization methods exhibit limitations in computational efficiency and privacy protection. To address this, the planning model is decomposed into several subproblems by applying the Alternating Direction Method of Multipliers (ADMM), allowing each region to optimize its local subproblem in a fully distributed manner. Additionally, a Shapley value-based cost allocation mechanism is applied to ensure fair and rational cost distribution among different distribution networks. Finally, case studies are conducted to validate the effectiveness of the proposed method. Case studies show that the proposed method reduces the system’s total annual cost by 14.90% and the electricity purchase cost by 28.61% compared with the pre-planning case. Meanwhile, the maximum voltage imbalance is reduced to within the standard range. These results validate the effectiveness of the proposed method in enhancing both economic efficiency and power quality for flexible interconnected distribution systems. Full article