Search Results (219)

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

The increasing use of AI systems for face, object, action, scene, and emotion recognition raises significant privacy risks, particularly when processing Personally Identifiable Information (PII). Current privacy-preserving methods lack adaptability to users’ preferences and contextual requirements, and obfuscate user faces uniformly. This research proposes a user-centric, context-aware, and ontology-driven privacy protection framework that dynamically adjusts privacy decisions based on user-defined preferences, entity sensitivity, and contextual information. The framework integrates state-of-the-art recognition models for recognising faces, objects, scenes, actions, and emotions in real time on data acquired from vision sensors (e.g., cameras). Privacy decisions are directed by a contextual ontology based in Contextual Integrity theory, which classifies entities into private, semi-private, or public categories. Adaptive privacy levels are enforced through obfuscation techniques and a multi-level privacy model that supports user-defined red lines (e.g., “always hide logos”). The framework also proposes a Re-Identifiability Index (RII) using soft biometric features such as gait, hairstyle, clothing, skin tone, age, and gender, to mitigate identity leakage and to support fallback protection when face recognition fails. The experimental evaluation relied on sensor-captured datasets, which replicate real-world image sensors such as surveillance cameras. User studies confirmed that the framework was effective, with over 85.2% of participants rating the obfuscation operations as highly effective, and the other 14.8% stating that obfuscation was adequately effective. Amongst these, 71.4% considered the balance between privacy protection and usability very satisfactory and 28% found it satisfactory. GPU acceleration was deployed to enable real-time performance of these models by reducing frame processing time from 1200 ms (CPU) to 198 ms. This ontology-driven framework employs user-defined red lines, contextual reasoning, and dual metrics (RII/IVI) to dynamically balance privacy protection with scene intelligibility. Unlike current anonymisation methods, the framework provides a real-time, user-centric, and GDPR-compliant method that operationalises privacy-by-design while preserving scene intelligibility. These features make the framework appropriate to a variety of real-world applications including healthcare, surveillance, and social media. Full article

With the rapid development of deep-sea resource exploration and marine scientific research, wheeled remotely operated vehicles (ROVs) have become crucial for seabed operations. However, under complex seabed conditions, traditional ROV control systems suffer from insufficient trajectory tracking accuracy, poor disturbance rejection capability, and low dynamic torque distribution efficiency. These issues lead to poor motion stability and high energy consumption on sloped terrains and soft substrates, which limits the effectiveness of deep-sea engineering. To address this, we proposed a comprehensive motion control solution for deep-sea wheeled ROVs. To improve modeling accuracy, a coupled kinematic and dynamic model was developed, together with a body-to-terrain coordinate frame transformation. Based on rigid-body kinematics, three-degree-of-freedom kinematic equations incorporating the slip ratio and sideslip angle were derived. By integrating hydrodynamic effects, seabed reaction forces, the Janosi soil model, and the impact of subsidence depth, a dynamic model that reflects nonlinear wheel–seabed interactions was established. For optimizing disturbance rejection and trajectory tracking, a hierarchical control method was designed. At the kinematic level, an improved model predictive control framework with terminal constraints and quadratic programming was adopted. At the dynamic level, non-singular fast terminal sliding mode control combined with a fixed-time nonlinear observer enabled rapid disturbance estimation. Additionally, a dynamic torque distribution algorithm enhanced traction performance and trajectory tracking accuracy. Full article

Open Ground-Storey (OGS) buildings, widely adopted for functional openness, are highly vulnerable to seismic collapse due to stiffness irregularity at the ground storey (GS). The magnification factor (MF), defined as the amplification applied to GS column design forces, acts as a practical strengthening measure to enhance GS stiffness and thereby mitigate the soft storey failure mechanism. While earlier studies recommended fixed MF values, their lack of adaptability often left stiffness deficiencies unresolved. This study develops a rational framework to quantify and predict the required MF for OGS columns, enabling safe yet functionally efficient design. A comprehensive set of three-dimensional reinforced concrete OGS models was analyzed under seismic loads, covering variations in plan geometry, ground-to-upper-storey height ratio (Hr), and GS infill percentage. Iterative stiffness-based evaluations established the MF demand needed to overcome stiffness deficiencies. To streamline prediction, advanced machine learning (ML) models were applied. Among these, black-box models achieved high predictive accuracy, but Symbolic Regression (SR) offered an interpretable closed-form equation that balances accuracy with transparency, making it suitable for design practice. A sensitivity analysis confirmed the Hr as the most influential parameter, with additional contributions from other variables. Validation on additional OGS configurations confirmed the reliability of the SR model, while seismic response comparisons showed that Modified OGS (MOGS) frames with the proposed MF achieved improved stiffness, reduced lateral displacements, uniform drift distribution, and shorter fundamental periods. The study highlights the novelty of integrating interpretable ML into structural design, providing a codifiable and practical tool for resilient OGS construction. Full article

This paper analyzes the seismic performance and damage characteristics of high-rise frame-core-tube structures on soft soil, explicitly incorporating dynamic soil–pile–structure interaction (SSI). A refined 3D finite element model of a 52-storey soil–pile–structure system was developed in ABAQUS, utilizing viscous-spring boundaries and the equivalent nodal force method for seismic input. Nonlinear analyses under six seismic waves were compared to a fixed-base model neglecting SSI. Key findings demonstrate that SSI significantly alters structural response; it amplifies lateral displacements and inter-storey drift ratios throughout the structure, particularly at the top level. While total base shear decreased, frame column base shear forces substantially increased. SSI also reduced peak top-storey accelerations, diminished short-period spectral components, and prolonged the predominant period of response spectra. Analysis of member damage revealed SSI generally reduced compressive and tensile damage in core walls, floor slabs, and frame beams. Principal compressive stresses at the base of frame columns increased under SSI. These results highlight the necessity of including dynamic SSI in seismic analysis for high-rises on soft soil, specifically due to its detrimental amplification of forces in frame columns. Full article

Obstacle avoidance during high-speed, low-altitude flight remains a significant challenge for unmanned aerial vehicles (UAVs), particularly in unfamiliar environments where prior maps and heavy onboard sensors are unavailable. To address this, we present an entropy-aware deep reinforcement learning framework that enables fixed-wing UAVs to navigate safely using only monocular onboard cameras. Our system features a lightweight, single-frame depth estimation module optimized for real-time execution on edge computing platforms, followed by a reinforcement learning controller equipped with a novel reward function that balances goal-reaching performance with path smoothness under fixed-wing dynamic constraints. To enhance policy optimization, we incorporate high-quality experiences from the replay buffer into the gradient computation, introducing a soft imitation mechanism that encourages the agent to align its behavior with previously successful actions. To further balance exploration and exploitation, we integrate an adaptive entropy regularization mechanism into the Proximal Policy Optimization (PPO) algorithm. This module dynamically adjusts policy entropy during training, leading to improved stability, faster convergence, and better generalization to unseen scenarios. Extensive software-in-the-loop (SITL) and hardware-in-the-loop (HITL) experiments demonstrate that our approach outperforms baseline methods in obstacle avoidance success rate and path quality, while remaining lightweight and deployable on resource-constrained aerial platforms. Full article

Character toys are toys identifiable by name, personality, and visual traits. They represent specific characters derived from or associated with popular culture. This entry explores adult engagement with character toys, or toy play, through a multidisciplinary lens, emphasizing playability, materiality, affect, creativity, and sociocultural meaning. Drawing on earlier toy research on dolls, action figures, figurines, and soft toys—those with a face and assigned personality—it considers how adults collect, customize, create stories, and critique societal conditions through toys for personal enrichment, identity work, and community formation. The toy play of adults is framed as a legitimate and complex form of self-expression and cultural participation that intersects with object play, creative fandoms, and political resistance. Full article

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis. Full article

Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research. Full article

Robotic assembly of electrical connectors enables the automation of high-efficiency production of electronic products. A rigid gripper is adopted as the end-effector by the majority of existing works with a force–torque sensor installed at the wrist, which suffers from very limited perception capability of the manipulated objects. Moreover, the grasping and movement actions, as well as the inconsistency between the robot base and the end-effector frame, tend to result in angular misalignment, usually leading to assembly failure. Bio-inspired by the human finger, we designed a tactile finger in this paper with three characteristics: (1) Compliance: A soft ‘skin’ layer provides passive compliance for plenty of manipulation actions, thus increasing the tolerance for alignment errors. (2) Tactile Perception: Two types of sensing elements are embedded into the soft skin to tactilely sense the involved contact status. (3) Enhanced manipulation force: A rigid fingernail is designed to enhance the manipulation force and enable potential delicate operations. Moreover, a tactile-based alignment algorithm is proposed to search for the optimal orientation angle about the z axis. In the application of U-disk insertion, the three characteristics are validated and a success rate of 100% is achieved, whose generalization capability is also validated through the assembly of three types of electrical connectors. Full article

As a key transportation infrastructure, it is of great significance to ensure the seismic safety of the high-speed railway hub station. Taking Changde high-speed railway hub station as background, a comprehensive 3D numerical model of the high-speed railway station structure is proposed to consider the engineering geological characteristics of the site, soil nonlinearity, and pile-soil interactions. The results show that the hub station structural system, considering pile-soil interaction, presents the ‘soft-upper-rigid-down’ characteristics as a whole, and the natural vibration is lower than that of the station structure with a rigid foundation assumption. Under the action of three strong seismic motions, the nonlinear site seismic effect is significant, the surface acceleration is significantly enlarged, and decreases with the buried depth. The interaction between pile and soil is related to the nonlinear seismic effect of the site, which deforms together to resist the foundation deformation caused by the strong earthquake motions, and the depth range affected by the interaction between the two increases with the increase of the intensity of earthquake motion. Among the three kinds of input earthquake motions, the predominant frequency of the Kobe earthquake is the closest to the natural vibration of the station structure system, followed by the El Centro earthquake. Moreover, the structures above the foundation of the high-speed railway hub station structural system are more sensitive to the spectral characteristics of Taft waves and El Centro waves compared to the site soil. This is also the main innovation point of this study. The existence of the roof leads to the gradual amplification of the seismic response of the station frame structure with height, and the seismic response amplification at the connection between the roof and the frame structure is the largest. The maximum story drift angle at the top floor of the station structure is also greater than that at the bottom floor. Full article

With the increasing significance of deep-sea resource development, Forward-Looking Sonar (FLS) has become an essential technology for real-time environmental mapping and navigation in deep-sea mining vehicles (DSMV). However, FLS images often suffer from a limited field of view, uneven imaging, and complex noise sources, making single-frame images insufficient for providing continuous and complete environmental awareness. Existing mosaicking methods typically rely on external sensors or controlled laboratory conditions, often failing to account for the high levels of uncertainty and error inherent in real deep-sea environments. Consequently, their performance during sea trials tends to be unsatisfactory. To address these challenges, this study introduces a robust FLS image mosaicking framework that functions without additional sensor input. The framework explicitly models the noise characteristics of sonar images captured in deep-sea environments and integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy during the feature-matching stage. For image fusion, a radial adaptive fusion algorithm with a protective frame is proposed to improve edge transitions and preserve structural consistency in the resulting panoramic image. The experimental results demonstrate that the proposed framework achieves high robustness and accuracy under real deep-sea conditions, effectively supporting DSMV tasks such as path planning, obstacle avoidance, and simultaneous localization and mapping (SLAM), thus enabling reliable perceptual capabilities for intelligent underwater operations. Full article

Pile–raft foundations are widely used in soft soil engineering due to their good integrity and high stiffness. However, traditional design methods independently design pile–raft foundations and superstructures, ignoring their interaction. This leads to significant deviations from actual conditions when the superstructure height increases, resulting in excessive costs and adverse effects on building stability. This study experimentally investigates the interaction characteristics of pile–raft foundations and superstructures in soft soil under different working conditions using a 1:10 geometric similarity model. The superstructure is a cast-in-place frame structure (beams, columns, and slabs) with bamboo skeletons with the same cross-sectional area as the piles and rafts, cast with concrete. The piles in the foundation use rectangular bamboo strips (side length ~0.2 cm) instead of steel bars, with M1.5 mortar replacing C30 concrete. The raft is also made of similar materials. The results show that the soil settlement significantly increases under the combined action of the pile–raft and superstructure with increasing load. The superstructure stiffness constrains foundation deformation, enhances bearing capacity, and controls differential settlement. The pile top reaction force exhibits a logarithmic relationship with the number of floors, coordinating the pile bearing performance. Designers should consider the superstructure’s constraint of the foundation deformation and strengthen the flexural capacity of inner pile tops and bottom columns for safety and economy. Full article

X-ray imaging of articular cartilage could be a breakthrough for the early diagnosis of tissue degeneration. This approach relies on radiopaque contrast agents to enhance the visualization of soft tissues. The potential impact of contrast agents on the mechanical response of articular cartilage should be considered in the frame of both clinical and research applications. Attention has been drawn to a solution containing molecules with six iodine atoms and four positive charges (CA4+), which has been shown to improve the X-ray visibility of articular cartilage. This study aimed to determine the effects of a CA4+ solution on tissues’ mechanical properties. An experimental pipeline based on indentation tests was applied to paired samples of articular cartilage before and after the immersion in either CA4+ or phosphate-buffered saline solution, maintained at a temperature of 22 ± 2 °C, for 22 h to determine the differences in instantaneous, viscous, and equilibrium responses between the articular cartilage of the two groups. The 22 h immersion of articular cartilage in either CA4+ or phosphate-buffered saline solution had a significant detrimental effect on the overall response, including the instantaneous, viscous, and equilibrium responses, of the articular cartilage. However, this detrimental effect was greater with exposure to the CA4+ solution. Specifically, the articular cartilage was found to be less stiff in both the instantaneous response (approximately −25%) and the equilibrium response (approximately −38%). The softening effect could be attributable to an alteration of the interaction between the proteoglycans of articular cartilage, induced by the positive charges within the CA4+ contrast agent. Further investigations are needed to elucidate whether this hypothesized mechanism is reversible. Full article

The shallow, soft subsea formations, characterized by low strength and poor stability, lead to complex interactions between the screw motor drilling tool and the wellbore wall during directional drilling, complicating the accurate evaluation of the tool’s deflection capability. To address this issue, this paper proposes an integrated mechanical analysis method combining three-dimensional finite element analysis and transient dynamic analysis. By establishing a finite element model using 12-DOF (degree-of-freedom) spatial rigid-frame Euler–Bernoulli beam elements, coupled with well trajectory coordinate transformation and Rayleigh damping matrix, a precise description of drill string dynamic behavior is achieved. Furthermore, the introduction of pipe–soil dynamics and the p-y curve method improves the calculation of contact reaction forces between drilling tools and formation. Case studies demonstrate that increasing the tool face rotation angle intensifies lateral forces at the bit and stabilizer, with the predicted maximum dogleg severity within the first 10 m ahead of the bit progressively increasing. When the tool face rotation angle exceeds 2.5°, the maximum dogleg severity reaches 17.938°/30 m. With a gradual increase in the drilling pressure, the maximum bending stress on the drilling tool, maximum lateral cutting force, and stabilizer lateral forces progressively decrease, while vertical cutting forces and bit lateral forces gradually increase. However, the predicted maximum dogleg severity increases within the first 10 m ahead of the bit remain relatively moderate, suggesting the necessity for the multi-objective optimization of drilling pressure and related parameters prior to actual operations. Full article