Search Results (720)

Hospital design plays a pivotal role in improving patient outcomes, enhancing clinical efficiency, and strengthening infection control. Since the outbreak of COVID-19, research in this field has expanded significantly, showing a marked trend toward interdisciplinary integration. In this study, bibliometric analysis was conducted using CiteSpace (version 6.2.R3) as the primary tool, with Excel and Tableau (version 2024.3) as supplementary software. A total of 877 documents on hospital design published between 1932 and 2025 were retrieved from the Web of Science Core Collection and analyzed from multiple perspectives. The analysis examined publication trends, collaborative networks, co-citation structures, disciplinary evolution, and keyword dynamics. The results indicate that the field has entered a phase of rapid development since 2019. Global collaboration networks are becoming increasingly multipolar; yet, institutional and author-level connections remain decentralized, with relatively low overall density. Evidence-based design (EBD) continues to serve as the theoretical foundation of the field, while emerging themes such as healing environments, biophilic design, and patient-centered spatial strategies have become major research hotspots. Increasingly, the field reflects deeper integration across disciplines, including architecture, medicine, nursing, and environmental science. This study provides a clearer picture of the developmental trajectory, knowledge base, and future directions of hospital design research, offering systematic insights and theoretical guidance for both scholars and practitioners. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across workstations while minimizing total operation time and accounting for directional switching time between disassembly and assembly phases. To solve this problem, we propose an improved reinforcement learning algorithm, IQ($\lambda$), which extends the classical Q($\lambda$) method by incorporating eligibility trace decay, a dynamic Action Table mechanism to handle non-conflicting parallel tasks, and switching-aware reward shaping to penalize inefficient task transitions. Compared with standard Q($\lambda$), these modifications enhance the algorithm’s global search capability, accelerate convergence, and improve solution quality in complex DALBP scenarios. While the current implementation does not deploy live IoT infrastructure, the architecture is modular and designed to support future extensions involving edge-cloud coordination, trust-aware optimization, and privacy-preserving learning in Industrial Internet of Things (IIoT) environments. Four real-world disassembly-assembly cases (flashlight, copier, battery, and hammer drill) are used to evaluate the algorithm’s effectiveness. Experimental results show that IQ($\lambda$) consistently outperforms traditional Q-learning, Q($\lambda$), and Sarsa in terms of solution quality, convergence speed, and robustness. Furthermore, ablation studies and sensitivity analysis confirm the importance of the algorithm’s core design components. This work provides a scalable and extensible framework for intelligent scheduling in cyber-physical manufacturing systems and lays a foundation for future integration with secure, IoT-connected environments. Full article

The rapid development of spacecraft on-orbit services has increased the requirements for docking technology, especially for large-scale payloads that exceed the launch envelope. Docking technology based on astronaut extravehicular activities is one of the most promising directions for on-orbit services. In view of this, this paper designs and characterizes a handheld double-point docking mechanism for assembling large-scale payloads that is suitable for extravehicular activity (EVA) in dual-astronaut collaborative operations. It achieves the functional decoupling of docking, locking, unlocking, and separation throughout the whole process. The mechanism also has excellent design for human factors engineering, allowing astronauts to change hands, operate with one hand, and apply limited force. The mechanism adopts a dual-point probe–drogue configuration, while the misalignment tolerance design guarantees the docking accuracy and the operating range, and forms a rigid structural connection through a force amplification mechanism. Theoretical analysis and numerical simulations are implemented to estimate the dynamics, statics, and kinematics of the docking process. Corresponding experiments of the prototype are also conducted, including high–low temperature dynamics, docking tests, and kinematic tolerance experiments. The experiments validate the finite element analysis and verify the actual performance of the mechanism. The designed handheld dual-point docking mechanism was successfully applied for the first time by the Shenzhou 15 crew on China’s Space Station in March 2023. This paves a new road for spacecraft on-orbit service of large-scale payloads by EVAs, providing guidance as well as a technical foundation for the on-orbit construction of large spacecraft in the future. Full article

Background/Objectives: Obesity and type 2 diabetes mellitus (T2DM) profoundly disrupt lipid metabolism within local microenvironments of skeletal muscle and its associated connective tissues, including adipose tissue, bone, and fascia. However, the role of local communication between skeletal muscle and its proximal connective tissues in propagating metabolic dysfunction is incompletely understood. This narrative review synthesizes current evidence on these local metabolic interactions, highlighting novel insights and existing gaps. Methods: We conducted a comprehensive literature analysis of primary research published in the last decade, sourced from PubMed, Web of Science, and ScienceDirect. Studies were selected for relevance to skeletal muscle, adipose tissue, fascia, and bone lipid metabolism in the context of obesity and T2DM, with emphasis on molecular, cellular, and paracrine mechanisms of local crosstalk. Findings were organized into thematic sections addressing physiological regulation, pathological remodeling, and inter-organ signaling pathways. Results: Our synthesis reveals that local lipid dysregulation in obesity and T2DM involves altered fatty acid transporter dynamics, mitochondrial overload, fibro-adipogenic remodeling, and compartment-specific adipose tissue dysfunction. Crosstalk via myokines, adipokines, osteokines, bioactive lipids, and exosomal miRNAs integrates metabolic responses across these tissues, amplifying insulin resistance and lipotoxic stress. Emerging evidence highlights the underappreciated roles of fascia and marrow adipocytes in regional lipid handling. Conclusions: Collectively, these insights underscore the pivotal role of inter-tissue crosstalk among skeletal muscle, adipose tissue, bone, and fascia in orchestrating lipid-induced insulin resistance, and highlight the need for integrative strategies that target this multicompartmental network to mitigate metabolic dysfunction in obesity and T2DM. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

Loess has poor engineering properties, including wet subsidence and dynamic fragility, and the dynamic stability of the loess subgrades can be improved by compacted modified loess mixing industrial wastes such as fly ash and lignin. However, the performance of the modified loess under complex environmental conditions, including dry and wet cycles, as well as freeze-thaw cycles, remains unclear. In this study, the dynamic and structural characteristics of modified loess mixing fly ash and lignin under the coupling effect of dry-wet/freeze-thaw cycles were investigated through laboratory tests, including dry-wet–freeze/thaw cycle tests, dynamic triaxial tests, and scanning electron microscope tests. The cumulative plastic deformation characteristics of the improved loess under different dry-wet cycles and freeze-thaw cycles were analyzed. Combined with the scanning electron microscope test results, the attenuation mechanism of the strength of the improved loess under dry-wet/freeze-thaw coupling was analyzed. The results show that the dry-wet/freeze-thaw cycles have a significant effect on the dynamic deformation of the improved loess. With the increase in dry-wet/freeze-thaw cycles, the cumulative plastic deformation of the improved loess increases logarithmically with the rise in vibration times. With the increase in the number of dry-wet/freeze-thaw cycles, the improved loess becomes loose. The micro-cracks formed in the modified loess due to the connection and directional arrangement of the pores, and become wider and wider with the increase in dry-wet/freeze-thaw cycles. The apparent porosity, average porous diameter, and pore fractal dimension of the improved loess increase, while the probability entropy decreases. Compared with freeze-thaw cycles, dry-wet cycles had a greater effect on the microstructure of the improved loess, which made the deterioration of the dynamic stability of the improved loess more obvious. Full article

Understanding psychological change requires a quantitative framework capable of capturing the complex and dynamic relationships among personal constructs. Personal Construct Psychology emphasizes the hierarchical reorganization of bipolar constructs, yet existing qualitative methods inadequately model the reciprocal and graded influences involved in such change. This paper introduces the Presence–Balance (PB) space, a centrality measure for constructs represented within Fuzzy Cognitive Maps (FCMs). FCMs model cognitive systems as directed, weighted graphs, allowing for nuanced analysis of construct interactions. The PB space operationalizes two orthogonal dimensions: Presence, representing the overall connectivity and activation of a construct, and Implication Balance, quantifying the directional asymmetry between influences exerted and received. By formalizing Hinkle’s hierarchical theory within a rigorous mathematical framework, the PB space enables precise identification of constructs that drive or resist transformation. This dual-dimensional model provides a structured method for analyzing personal construct systems, supporting both theoretical exploration and clinically relevant interpretations in the study of psychological change. Full article

In 1982 a conservation project was initiated to restore glade communities in the Missouri Ozarks with a special emphasis on collared lizards (Crotophytus collaris), a state threatened species at the time. Starting in 1984, collared lizards were translocated onto restored glade habitats on Stegall Mountain in the Ozarks. The populations persisted but did not colonize other glades on Stegall until 1994, when prescribed fires included not only the glades, but also the surrounding woodland. Starting in 1999, landscape-level burns connected Stegall to three nearby mountains. The goal of this study was to test the hypothesis that expanding prescribed burns to the forested woodlands between Stegall Mountain and nearby mountains would create dispersal corridors for the colonization of glades on new mountains. Indeed, all three mountains were colonized and direct dispersal of marked lizards from Stegall was observed following the prescribed fires. Inter-mountain colonization dynamics and patterns varied: one was absorbed into the Stegall meta-population, another experienced a founder event followed by rapid colonization, and the third underwent repeated failed colonization attempts before successful establishment years later. These diverse patterns were consistent with differences in landscape resistance in the inter-mountain corridors rather than in geographical distance. Intra-mountain colonization patterns indicated that lizards assessed glade quality based on multiple factors. High landscape resistance resulted in a young age structure in the initial colonizing population. This young age structure interacted with the territorial behavior of collared lizards, age of reproduction, and probability of dispersal. This strong interaction between landscape resistance in the dispersal corridor and social behavior in the colonizing population is a novel factor in predicting colonization dynamics. Moreover, these results highlight the importance of social behavior on dispersal decisions versus habitat quality alone. Full article

Axon polarization is a fundamental process in neuronal development, providing the structural basis for directional signaling in neural circuits. Precise control of axon specification is, thus, essential for the bottom-up construction of neuronal networks with defined architecture and connectivity. Although neurite length and elongation timing have both been implicated as determinants of axonal fate, their relative contributions have remained unresolved due to technical limitations in manipulating these factors independently in conventional culture systems. Here, we developed a constructive neuroengineering platform based on modifiable agarose gel microstructures that enables dynamic, in situ control of neurite outgrowth length and timing during neuronal cultivation. This approach allowed us to directly address whether axon polarization depends primarily on neurite length or the order of neurite extension. Using a single-neurite elongation paradigm, we quantitatively defined two length thresholds for axon specification: a critical length of 43.3 μm, corresponding to a 50% probability of axonal differentiation, and a definitive length of 95.4 μm, beyond which axonal fate was reliably established. In experiments involving simultaneous or sequential elongation of two neurites, we observed that neurite length—not elongation order—consistently predicted axonal identity, even when a second neurite was introduced after the first had already begun to grow. The presence of a competing neurite modestly elevated the effective critical length, suggesting inhibitory interactions that modulate length thresholds. These findings provide the first direct experimental confirmation that neurite length is the primary determinant of axon polarization and demonstrate the utility of constructive microfabrication approaches for dissecting fundamental principles of neuronal polarity. Our platform establishes a powerful experimental foundation for future efforts to achieve complete control over axon and dendrite orientation during the engineered construction of functional neuronal circuits. Full article

Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of adaptive autonomy for path planning. This approach allows the system to autonomously select the best path-planning strategy. The technique utilizes dynamic programming with an adaptive memory size, leveraging a cellular decomposition technique to divide the map into convex cells. The path is divided into three segments: the first segment connects the starting point to the center of the starting cell, the second segment connects the center of the goal cell to the goal point, and the third segment connects the center of the starting cell to the center of the goal cell. Since each cell is convex, internal path planning simply requires a straight line between two points within a cell. Path planning uses an improved $A^{\ast }$ (I-$A^{\ast }$) algorithm, which evaluates the feasibility of a direct path to the goal from the current position during execution. When a direct path is discovered, the algorithm promptly returns and saves it in memory. The memory size is proportional to the square of the total number of cells, and it stores paths between the centers of cells. By storing and reusing previously calculated paths, this method significantly reduces redundant computation and supports long-term sustainability in mobile robot deployments. The final phase of the path-planning process involves pruning, which eliminates unnecessary waypoints. This approach obviates the need for repetitive path planning across different scenarios thanks to its compact memory size. As a result, paths can be swiftly retrieved from memory when needed, enabling efficient and prompt navigation. Simulation results indicate that this algorithm consistently outperforms other algorithms in finding the shortest path quickly. Full article

This study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging virtual and augmented reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases generated using Direct Numerical Simulation (DNS) while maintaining visual fidelity and ensuring efficient rendering for user interaction. Fully immersive visualization of supersonic (Mach number 2.86) spatially developing turbulent boundary layers (SDTBLs) over strong concave and convex curvatures was achieved. The comprehensive DNS data provides insights on the transport phenomena inside turbulent boundary layers under strong deceleration or an Adverse Pressure Gradient (APG) caused by concave walls as well as strong acceleration or a Favorable Pressure Gradient (FPG) caused by convex walls under different wall thermal conditions (i.e., Cold, Adiabatic, and Hot walls). The process begins with a .vts file input from a DNS, which is visualized using ParaView software. These visualizations, representing different fluid behaviors based on a DNS with a high spatial/temporal resolution and employing millions of “numerical sensors”, are treated as individual time frames and exported in GL Transmission Format (GLTF), which is a widely used open-source file format designed for efficient transmission and loading of 3D scenes. To support the workflow, optimized Extract–Transform–Load (ETL) techniques were implemented for high-throughput data handling. Conversion of exported Graphics Library Transmission Format (GLTF) files into Graphics Library Transmission Format Binary files (typically referred to as GLB) reduced the storage by 25% and improved the load latency by 60%. This research uses Unity’s Profile Analyzer and Memory Profiler to identify performance limitations during contour rendering, focusing on the GPU and CPU efficiency. Further, immersive VR/AR analytics are achieved by connecting the processed outputs to Unity engine software and Microsoft HoloLens Gen 2 via Azure Remote Rendering cloud services, enabling real-time exploration of fluid behavior in mixed-reality environments. This pipeline constitutes a significant advancement in the scientific visualization of fluid dynamics, particularly when applied to datasets comprising hundreds of high-resolution frames. Moreover, the methodologies and insights gleaned from this approach are highly transferable, offering potential applications across various other scientific and engineering disciplines. Full article

Sign language serves as a vital way to communicate with individuals with hearing loss, deafness, or a speech disorder, yet accessibility remains limited, requiring technological advances to bridge the gap. This study presents the first real-time Greek Sign Language recognition system utilizing deep learning and embedded computers. The recognition system is implemented using You Only Look Once (YOLO11X-seg), an advanced object detection model, which is embedded in a Python-based framework. The model is trained to recognize Greek Sign Language letters and an expandable set of specific words, i.e., the model is capable of distinguishing between static hand shapes (letters) and dynamic gestures (words). The most important advantage of the proposed system is its mobility and scalable processing power. The data are recorded using a mobile IP camera (based on Raspberry Pi 4) via a Motion-Joint Photographic Experts Group (MJPEG) Stream. The image is transmitted over a private ZeroTier network to a remote powerful computer capable of quickly processing large sign language models, employing Moonlight streaming technology. Smaller models can run on an embedded computer. The experimental evaluation shows excellent 99.07% recognition accuracy, while real-time operation is supported, with the image frames processed in 42.7 ms (23.4 frames/s), offering remote accessibility without requiring a direct connection to the processing unit. Full article

Connected Autonomous Vehicles (CAVs) technology holds immense potential for enhancing traffic safety and efficiency; however, its inherent complexity presents significant challenges for conventional autonomous driving. World Models (WMs), an advanced deep learning paradigm, offer an innovative approach to address these CAV challenges by learning environmental dynamics and precisely predicting future states. This survey systematically reviews the advancements of WMs in connected automated driving, delving into the key methodologies and technological breakthroughs across six core application domains: cooperative perception, prediction, decision-making, control, human–machine collaboration, and scene generation. Furthermore, this paper critically analyzes the current limitations of WMs in CAV scenarios, particularly concerning multi-source heterogeneous data fusion, physical law mapping, long-term temporal memory, and cross-scenario generalization capabilities. Building upon this analysis, we prospectively outline future research directions aimed at fostering the development of more robust, efficient, and interpretable WMs. Ultimately, this work aims to provide a crucial reference for constructing safe, efficient, and sustainable connected automated driving systems. Full article

This study offers a novel perspective on the role of bridges as agents of urban transformation by examining their influence on the morphological development of Valencia (Spain) from the 13th century to the catastrophic flood of 1957. Traditionally viewed as mere connective infrastructure, bridges are reframed here as key structuring elements that shaped urban expansion, resilience strategies, and socio-spatial dynamics. Through an innovative classification based on stages of bridges, the research integrates historical cartography, cadastral data, and Geographic Information Systems (GIS) to trace how successive waves of bridge construction aligned with distinct socio-political, environmental, and technological contexts. The study demonstrates that bridge development not only facilitated territorial connectivity but also directed urban growth patterns, enabled functional zoning, and responded adaptively to flood risk and demographic pressure. The case of Valencia is particularly significant in light of contemporary challenges in climate adaptation and sustainable urban planning. By unveiling bridges as morphological and functional drivers of urban form, this research offers transferable insights for cities worldwide grappling with the legacy of riverine geographies and the pressures of resilient transformation. Full article