Search Results (2,559)

The gecko-inspired crawling robot driven by water hydraulic artificial muscles (WHAMs) incorporates the stable structural characteristics of geckos, making it particularly suitable for operation in aquatic environments. Conventional crawling robots typically employ electric or oil hydraulic actuation systems, which require complex sealing and waterproof designs when working in water. This study presented a bionic quadruped robot actuated by WHAMs that fundamentally circumvents waterproofing challenges. Although the joint module can dynamically adjust its output torque according to requirements, there has been a lack of theoretical basis for load adjustment. This research established the relationship between the leg joint load and the WHAM pressure difference, resulting in a pressure difference–load model for the leg joint. Through gait planning analysis, the maximum supporting force during robot motion was determined. Experimental tests on a single-leg prototype demonstrated a maximum static load capacity of 23 kg under stationary conditions, while during cycloidal motion the dynamic load capacity reached 10 kg. Both values satisfied the supporting force requirements of the planned gait. Furthermore, the pressure difference–load model showed good agreement with experimental results, providing theoretical guidance for load adjustment in leg joints. Full article

This study examines the torsional behavior of an additively manufactured bushing featuring a unique topology, which includes a flexible gyroid core and rigid inner and outer sleeves. The bushing is designed and fabricated using two materials: thermoplastic polyurethane (TPU) and polylactic acid (PLA), which are interpenetrated in successive layers throughout the bushing’s thickness. First, tensile mechanical tests are conducted on both materials with different infill patterns. The 45/135 infill proves to be the most suitable, providing good stiffness, strength, ductility, and data reproducibility. Additionally, the effectiveness of the interlocking created between the two materials through the printing process is evaluated by testing different overlap lengths. With an overlap of 2 mm, the extrusion process remains unaffected, minimizing voids and defects while ensuring strong interlayer bonding. Next, the designed bushing is subjected to torsional loading under both single and repetitive angular rotations, and its response is measured in terms of torque. The aim of this study is to evaluate the suitability of TPU and PLA materials for developing a design intended for dynamic mechanical environments, serving as a proof of concept. The quasi-static results indicate the presence of local damages and a viscoelastic response of the bushing during twisting, while also demonstrating its strong ability to withstand significant angles of rotation. Quasi-static results indicate local damage and the bushing’s viscoelastic response during twisting, as well as its ability to withstand significant angles of rotation. Full article

This study proposes a field-calibrated, NDT-integrated BIM modeling framework to improve the reliability of post-earthquake assessment for reinforced concrete (RC) buildings. The approach combines destructive and nondestructive testing (NDT) data—including core drilling, Schmidt hammer, ultrasonic pulse velocity (UPV), and Windsor probe—through a site-specific WinSonReb regression model. The calibrated material properties (average compressive strength ≈ 18.6 MPa, CoV > 20%) were embedded into a Building Information Modeling (BIM) environment, producing an as-is, NDT-calibrated BIM model representing a Level-2 static digital twin of the structure. Nonlinear static pushover analyses performed in accordance with TBDY-2018 and ASCE 41-17 showed that the calibrated model exhibits a fundamental period of 0.85 s—approximately 18% longer than the uncalibrated BIM model. This elongation increased displacement demand and caused a shift in performance classification: while the uncalibrated model indicated Life Safety (LS), the calibrated model predicted behavior approaching Collapse Prevention (CP) in the Y direction. Furthermore, calibration reversed the predicted damage hierarchy, from ductile beam hinging to brittle column- and wall-controlled failure near elevator openings, consistent with post-event observations from the 2023 Kahramanmaraş earthquakes. These results demonstrate that integrating field-calibrated NDT data into BIM-based seismic models fundamentally alters both strength estimation and failure-mechanism prediction, reducing epistemic uncertainty and providing a more conservative basis for retrofit prioritization. Although demonstrated on a single case study, the proposed workflow offers a realistic and scalable pathway for NDT-supported seismic performance assessment of existing RC buildings. Full article

Path planning in complex, dynamic environments presents a significant challenge. Deep Reinforcement Learning (DRL) offers an end-to-end solution but suffers from critical sample inefficiency and a “cold-start” problem. Imitation Learning (IL) accelerates training but is constrained by a performance ceiling and poor generalization. To address these limitations, we propose a novel Imitation–Reinforcement Hybrid Machine Learning Algorithm (Hybrid IL-RL). This framework balances exploration and performance via a two-stage process: First, an offline pre-training phase uses Behavioral Cloning (BC) with “non-expert” A* data from static environments for a “warm start”. Second, an online fine-tuning phase uses a DRL algorithm (SAC) to adapt this policy in complex, dynamic environments, allowing the agent to surpass the teacher’s limitations. Simulation experiments validate the approach. The framework demonstrates significantly faster convergence than DRL algorithms trained from scratch. Most critically, in the dynamic environment, our Hybrid IL-RL algorithm achieved the highest success rate (82.4%), while pure IL methods (BC, GAIL) failed due to poor generalization (e.g., 82.1% collision rate) and pure DRL methods struggled (approx. 51–56% success rate). Our results confirm the hybrid framework effectively solves the cold-start problem while using DRL to break the IL performance ceiling. Full article

Automated guided vehicle (AGV) path planning aims to obtain an optimal path from the start point to the target point. Path planning methods are generally divided into classical algorithms and reactive algorithms, and this paper focuses on reactive algorithms. Reactive algorithms are classified into swarm intelligence algorithms and artificial intelligence algorithms, and this paper reviews relevant studies from the past six years (2019–2025). This review involves 123 papers: 81 papers are about reactive algorithms, 44 are based on the swarm intelligence algorithm, and 37 are based on artificial intelligence algorithms. The main categories of swarm intelligence algorithms include particle swarm optimization, ant colony optimization, and genetic algorithms. Neural networks, reinforcement learning, and fuzzy logic represent the main trends in artificial intelligence–based algorithms. Among the cited papers, 45.68% achieve online implementations, and 33.33% address multi-AGV systems. Swarm intelligence algorithms are suitable for static or simplified dynamic environments with a low computational complexity and fast convergence, as 79.55% of papers are based on a static environment and 22.73% achieve online path planning. Artificial intelligence algorithms are effective for dealing with dynamic environments, which contribute 72.97% to online implementation and 54.05% to dynamic environments, while they face the challenge of robustness and the sim-to-real problem. Full article

Resource-constrained sensor nodes in Internet-of-Things (IoT) and embedded sensing applications frequently rely on low-cost microcontrollers, where even basic algorithmic choices directly impact latency, energy consumption, and memory footprint. This study evaluates six sorting algorithms—Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and Heap Sort—in the restricted environment that microcontrollers provide. Three Arduino boards were used: Arduino Uno, Arduino Leonardo, and Arduino Mega 2560. Each algorithm was implemented in its unoptimized form and tested on datasets of increasing size, emulating buffered time-series sensor readings in random, ascending, and descending order. Execution time, number of write operations, and memory usage were measured. The tests show clear distinctions between the slower $O\left(n^2\right)$ algorithms and the more efficient $O(nlogn)$ algorithms. For random inputs of $n=1000$ elements, Bubble Sort required 1,958,193.75 $\mathsf{\mu }$son average, whereas Quick Sort completed it in 54,260.50 $\mathsf{\mu }$s and Heap Sort in 92,429.00 $\mathsf{\mu }$s, i.e., speedups of more than one order of magnitude compared to the quadratic baseline. These gains, however, come with very different memory footprints. Merge Sort kept the runtime below 100,000 $\mathsf{\mu }$s at $n=1000$ but required approximately 2023 bytes of additional static random-access memory (SRAM), effectively exhausting the 2 kB SRAM of the Arduino Uno. QuickSort used approximately 311 bytes of extra SRAM and failed to process larger ascending and descending datasets on the more constrained boards due to its recursive pattern and stack usage. Heap Sort offered the best overall trade-off: it successfully executed all tested sizes up to the SRAM limit of each board while using only about 12–13 bytes of additional SRAM and keeping the runtime below 100,000 $\mathsf{\mu }$s for $n=1000$. The results provide practical guidelines for selecting sorting algorithms on 8-bit AVR Arduino-class microcontrollers, which are widely used as simple sensing and prototyping nodes operating under strict RAM, program-memory, and energy constraints. Full article

Floating photovoltaic (FPV) systems are increasingly deployed in offshore environments. Among various FPV concepts, membrane-type platforms offer distinct advantages, including reduced weight, lower material consumption, and cost-effectiveness. This study investigates the hydrodynamic response of a membrane-type offshore FPV system through a 1:40 scale physical model test based on the Ocean Sun prototype. Static-water free-decay tests were first conducted to determine the natural periods and damping characteristics in heave, surge, and pitch motions. Subsequently, irregular-wave tests were performed under seven sea states representative of an offshore demonstration site. Free-decay results show model-scale natural periods of approximately 1.0 s for heave, 0.8 s for pitch, and 15 s for surge. The long surge natural period avoids resonance with short-period waves, while the high damping in heave and pitch effectively limit dynamic amplification. Under irregular waves, heave and pitch motions remain small, whereas surge motion exhibits pronounced long-frequency excursions. Spectral analysis reveals a dominant low-frequency surge peak at f ≈ 0.067 Hz (corresponding to the natural period of 15 s), superimposed with higher-frequency components associated with wave-induced motions. A strong correlation is observed between low-frequency surge and mooring tensions. Across Sea States 1–6, the motion responses increase gradually, while a marked rise in the exceedance probability of mooring forces occurs only in the most severe sea state. Weibull extreme-value fits show good linearity, indicating that the measured extremes are statistically consistent. The results provide experimental data and design insights for membrane-type FPV systems, establishing a foundation for future hydroelastic studies. Full article

The ethical and legal governance of Animal-Assisted Interventions (AAIs) remains conceptually and normatively fragmented. Although animals engaged in therapeutic, educational, and assistive activities make valuable contributions to human well-being, they continue to be defined by law as property or welfare objects, despite their meaningful yet limited forms of relational participation within structured human-controlled environments. This perspective obscures their context-dependent responsiveness and their institutional embeddedness. The present paper addresses this gap by adopting a normative and interdisciplinary approach grounded in relational legal theory and vulnerability scholarship. The concept is developed by drawing on Jennifer Nedelsky’s notion of relational autonomy and Martha Fineman’s theory of universal vulnerability. This results in the conceptualisation of AAI animals as correlated subjects: beings whose ethical and legal significance derives from the relationships and institutional contexts that shape their participation. The analysis identifies weaknesses in current medico-legal practices that frame veterinary certification and welfare assessment as static technical acts, ignoring their relational and systemic dimensions. The paper puts forward a relational ethical–legal framework for Animal-Assisted Interventions, centred on relational vulnerability, context-sensitive oversight and continuous institutional accountability. A number of practical recommendations are put forward, including the introduction of renewable ethical licences, inter-institutional monitoring and the establishment of multidisciplinary oversight mechanisms. By redefining animals’ normative status through relational ethics, in alignment with the interconnected human, animal, and environmental dimensions emphasized by the One Welfare principles, the study advances a shift from welfare-based protection toward a model of justice grounded in interspecies interdependence and institutional responsiveness. Full article

Reconstructing animatable humans, together with their surrounding static environments, from monocular, motion-blurred videos is still challenging for current neural rendering methods. Existing monocular human reconstruction approaches achieve impressive quality and efficiency, but they are designed for clean intensity inputs and mainly focus on the foreground human, leading to degraded performance under motion blur and incomplete scene modeling. Event cameras provide high temporal resolution and robustness to motion blur, making them a natural complement to standard video sensors. We present E-Sem3DGS, a semantically augmented 3D Gaussian Splatting framework that leverages hybrid event-intensity streams to jointly reconstruct explicit 3D volumetric representations of human avatars and static scenes. E-Sem3DGS maintains a single set of 3D Gaussians in Euclidean space, each endowed with a learnable semantic attribute that softly separates dynamic human and static scene content. We initialize human Gaussians from Skinned Multi-Person Linear (SMPL) model priors with semantic values set to 1 and scene Gaussians by sampling a surrounding cube with semantic values set to 0, then jointly optimize geometry, appearance, and semantics. To mitigate motion blur, we derive optical flow from events and use it to supervise image-based optical flow between rendered frames, enforcing temporal coherence in high-motion regions and sharpening both humans and backgrounds. On the motion-blurred ZJU-MoCap-Blur dataset, E-Sem3DGS improves the average full-frame PSNR from 21.75 to 32.56 (+49.7%) over previous methods. On MMHPSD-Blur, our method improves PSNR from 25.23 to 28.63 (+13.48%). Full article

The Agricultural Park Integrated Energy System (APIES) is a key platform for integrating distributed renewable energy (DRE) with agricultural production. However, its economic operation and the stability of crop growth environments are severely challenged by bidirectional uncertainties from external meteorology. These include the inherent variability of wind-solar generation and critical agricultural loads, such as supplementary lighting and temperature control, a challenge that existing models with static environmental parameters fail to address. To solve this, a bi-level optimization scheduling model for APIES considering meteorological uncertainty is proposed. The upper layer minimizes operation costs by quantifying uncertainties via triangular fuzzy chance constraints, with core constraints on DRE output, energy storage charging-discharging, and load shifting, solved by YALMIP-Gurobi linear programming. The lower layer maximizes crop growth environment satisfaction using a dynamic weight adaptive mechanism and NSGA-II multi-objective algorithm. The two layers iterate alternately for coordination. Using a small agricultural park in Xinjiang, China, as a case study, the results indicate that the proposed two-layer optimal scheduling model reduces costs by 10.8% compared to the traditional single-layer optimization model, and improves environmental satisfaction by 4.3% compared to the fixed-weight two-layer optimization model. Full article

Tissue regeneration is essential for wound healing, organ function restoration, and overall patient recovery. Its success significantly impacts medical procedures in fields like internal medicine and orthopedics, enhancing patient quality of life. Recent advances in regenerative medicine, particularly the combination of advanced drug delivery systems (DDS) and bioengineering, have enabled customized methods to improve tissue regeneration outcomes. However, conventional tissue engineering techniques have drawbacks, often using static scaffolds that lack the dynamic properties of real tissues, leading to subpar healing outcomes. The use of 3D printing and other advanced scaffolding techniques allows for the creation of bio functional scaffolds that deliver bioactive molecules at precise locations and times. The optimal integration of biological systems with enhanced material properties for personalized treatment options remains unclear. There is a need for more research into the complex interactions between cellular biology, drug delivery, and material technology to improve tissue regeneration. Despite progress in developing bioactive scaffolds and localized drug delivery methods, the interactions among different scaffold materials, bioactive agents, and cellular behaviors within the regenerative ecosystem are not fully understood. While there is extensive research on 3D-printed scaffolds in tissue engineering, there is a lack of studies integrating bio printing with in vivo biological reactions in real time. Limited research on the dynamic integration of patient-specific parameters in regeneration methods highlights the need for customized approaches that consider individual physiological differences and the complex biological environment at injury sites. Additionally, challenges arise when translating laboratory results into effective therapeutic applications, underscoring the necessity for interdisciplinary collaboration and innovative design approaches that align advanced material properties with biological needs. Full article

Ensuring workers’ safety is a critical component of social sustainability in the construction industry. Accidents that occur while workers are walking on construction sites constitute a significant portion of overall accidents, yet they are often overlooked in conventional task-oriented safety risk assessments. This study proposes novel Accident-During-Walk (ADW) risk indices, hierarchical and data-driven metrics designed to quantify workers’ accident risk during walks. The indices are built on Association Rule Mining and utilize structured accident data, accounting for both environmental and work-related attributes. By integrating these indices with project-specific work schedules and worker allocation plans, this study establishes an automated method for daily and weekly look-ahead ADW risk monitoring aligned with construction progress. Case studies on two construction projects validate the discriminative power of the proposed method. The results demonstrate that the indices effectively capture risk fluctuations driven by concurrent multi-trade operations and environmental severity. Notably, the analysis reveals counterintuitive patterns where adverse weather conditions paradoxically reduce risk values by constraining worker mobility, a nuance often missed by static assessments. Ultimately, this framework serves as a data-driven decision-support tool, enabling safety managers to transition from uniform inspections to targeted interventions during high-risk periods, thereby fostering a safer and more socially sustainable construction environment. Full article

Swarm unmanned surface vehicles (USVs) have been increasingly explored for maritime defense and security operations, particularly in scenarios requiring the rapid detection and interception of multiple attackers. The target detection reliability and defender–target assignment stability are significantly crucial to ensure quick responses and prevent mission failure. A key challenge in such missions lies in the assignment of targets among multiple defenders, where frequent reassignment can cause instability and inefficiency. This paper proposes a novel ETA-hysteresis-guided reinforcement learning (RL) framework for continuous multi-target hunting with swarm USVs. The approach integrates estimated time of arrival (ETA)-based task allocation with a dual-threshold hysteresis mechanism to balance responsiveness and stability in multi-target assignments. The ETA module provides an efficient criterion for selecting the most suitable defender–target pair, while hysteresis prevents oscillatory reassignments triggered by marginal changes in ETA values. The framework is trained and evaluated in a 3D-simulated water environment with multiple continuous targets under static and dynamic water environments. Experimental results demonstrate that the proposed method achieves substantial measurable improvements compared to basic MAPPO and MAPPO-LSTM, including faster convergence speed (+20–30%), higher interception rates (improvement of +9.5% to +20.9%), and reduced mean time-to-capture (by 9.4–19.0%), while maintaining competitive path smoothness and energy efficiency. The findings highlight the potential of integrating time-aware assignment strategies with reinforcement learning to enable robust, scalable, and stable swarm USV operations for maritime security applications. Full article

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

An MMC is widely applied to the HVDC power transmission system. With a large number of insulated gate bipolar transistors (IGBTs) utilized in MMC-HVDC converter stations, an extremely complicated EM environment is generated due to the dv/dt and di/dt during the IGBT switching process. A magnetic field simulation model is proposed to calculate the magnetic field generated by a 4.5 kV/5 kA IGBT-based MMC submodule under the DPT, with the dynamic switching behavior of IGBTs considered. Firstly, a behavior model of 4.5 kV/5 kA IGBTs is built with the help of commercial software. To validate its effectiveness, a DPT simulation model is built. A comparison between the simulation result and the measured data is performed. Finally, a quasi-static Maxwell model is utilized to approximate the near field caused by the current Ic of the DPT. The simulation result of the magnetic field strength at the point near the gate driver PCB is verified by the measurement data. The proposed magnetic field simulation model can help to analyze the EMI behavior and EMI design for MMC-HVDC submodules under DPT. Full article