Search Results (185)

Ultra-high molecular weight polyethylene (UHMWPE) is the preferred bearing material for total knee arthroplasty, yet its limited service life (15–20 years) remains a clinical challenge. This study investigates graphene oxide (GO) nanoplatelets as reinforcing fillers to enhance the mechanical and tribological performance of UHMWPE for longer-lasting implants. GO/UHMWPE composites with 0–1 wt% GO were fabricated via solution blending and hot compression molding. Direct SEM imaging combined with oxygen elemental mapping confirmed uniform GO dispersion up to 0.5 wt%, whereas higher loadings induced agglomeration. Dynamic mechanical analysis showed that the storage modulus at 37 °C increased with GO content, peaking at 0.5 wt% (improved by ~28% over neat UHMWPE), then decreased due to aggregation. Tribological tests under dry reciprocating sliding revealed that GO progressively reduced the wear rate (up to ~45% at 1.0 wt%), but also raised the steady-state friction coefficient from 0.13 to 0.19, attributed to molecular chain anchoring. The optimal balance of enhanced stiffness and wear resistance, with only a marginal friction increase, was achieved at 0.5 wt% GO. The reinforcement mechanism involves efficient stress transfer to rigid GO sheets and reduced surface peeling. This work provides a robust processing route and direct dispersion evidence, offering practical guidance for designing high-performance UHMWPE composites for orthopedic applications. Full article

Understanding the mechanical behaviour of blocky materials and structures is critical in engineering fields dealing with rock masses, masonry, ice crust, and fault gouge. The diverse block kinematics make mechanical responses difficult to predict. An important mechanism controlling deformation of block assemblies (e.g., blocky rock mass) is so-called block elbowing, a process in which rotating blocks push neighbouring blocks apart. Previously, this mechanism has been studied using 1D chain models; however, the effect of higher dimensionality has not been fully understood. In this paper, a two-dimensional blocky structure (block panel) is analysed. It is found that adding a kinematic degree of freedom fundamentally alters structural behaviour. The rotation angle of blocks remains the same such that the assembly breaks into layers sliding over each other; however, the mutual sliding is complex and dependent on frictional conditions. First, the structure becomes skewed, with the sliding directions both subhorizontal and/or subvertical. Second, the sliding modes are not necessarily purely subhorizontal or purely subvertical; combined sliding patterns are more prevalent. These observations highlight the fundamental role of elbowing in governing the block kinematics. In frictionless conditions, coordinated block rotations and complex block sliding are observed. The only mechanism capable of producing this behaviour is elbowing. In frictional conditions, sliding is considerably restrained and the assembly tends to rotate as a whole. These findings contribute to the understanding that neglecting block elbowing may overlook critical deformation mechanisms. The results provide a mechanical basis for analysing blocky structures in rock engineering and related geophysical systems. Full article

Liquid water in hydrogels exhibits an adsorption-desorption dynamic equilibrium with the surrounding environment, which leads to the instability of mechanical properties. To address this limitation, we propose an innovative design of conductive composite organogels by incorporating compatible linear lauryl alcohol (LA) and multi-walled carbon nanotubes (CNTs) into a poly(butyl methacrylate) (PBMA) network. Carbon chains of LAform physical crosslinks with PBMA side chains, effectively replacing inherent polymer chain entanglements. This structural innovation facilitates rapid chain rotation and sliding during stretching, so that the gel has a super stretching property of up to 2460%. At elevated temperatures, weakened interactions between LA–PBMA and PBMA–PBMA chains reduce physical confinement of CNTs within the PBMA network. Simultaneously applying a directional electric field, CNTs undergo rotation and translation to reconstruct an optimized conductive pathway, granting the composite distinctive temperature-sensitive electrical conductivity. Critically, all components in the PBMA/LA/CNTs (PLCs) exhibit low volatility and hydrophobicity. These characteristics enable the organogel to retain excellent flexibility and stable electrical performance after prolonged immersion in deionized water, exposure to vacuum, and even under extreme conditions at 120 °C. Such comprehensive stability suggests promising applications in deep-sea exploration and aerospace engineering. Full article

In live-streaming e-commerce systems, massive volumes of user-generated danmaku constitute a critical yet underutilized source of business intelligence. However, transforming such unstructured, noisy, and highly context-dependent textual data into structured and actionable knowledge remains a fundamental challenge for enterprise information systems. To address this issue, this study proposes TaSC-LLM, an LLM-enabled topic recognition method for constructing interpretable topic measurements from unstructured user-generated content. The proposed framework integrates topic taxonomy construction and zero-shot classification into a unified semantic reasoning pipeline. Unlike conventional topic modeling or supervised classification approaches, TaSC-LLM leverages chain-of-thought reasoning, multi-stage taxonomy induction, sliding window context modeling, and self-consistency verification to eliminate reliance on predefined label spaces and annotated training data. This design allows the system to dynamically construct and update topic taxonomies while ensuring interpretability, robustness, and cross-scenario adaptability. Empirical evaluation on three large-scale live-streaming e-commerce danmaku datasets shows that TaSC-LLM achieves strong taxonomy coverage, classification accuracy, and agreement with expert annotations. The findings suggest that LLM-based reasoning can help convert unstructured user-generated text into interpretable topic measures for downstream empirical and managerial analysis. While the present evaluation is conducted offline, TaSC-LLM provides a methodological foundation for future business applications that can be further examined under multi-session, multi-platform, and deployment-oriented conditions. Full article

Background: Autopsy pathology remains the reference standard for determining the cause of death, reconstructing disease and injury mechanisms, ensuring diagnostic quality, and supporting medical education and forensic investigations. However, declining autopsy rates, workforce shortages, biosafety concerns, increasing diagnostic complexity, and the rapid evolution of digital technologies have stimulated the development of complementary investigative approaches. This review critically examines whether artificial intelligence (AI) and emerging technologies are driving a genuine paradigm shift toward digitally integrated death investigation. Methods: A structured narrative review informed by a systematic literature search was conducted in PubMed/MEDLINE, Embase, Scopus, and Web of Science, covering publications from January 2000 through June 2026. Evidence addressing postmortem imaging, virtopsy, digital pathology, computational pathology, molecular autopsy, robotics, artificial intelligence, machine learning, and emerging omics technologies was critically appraised. Owing to the methodological heterogeneity of the available literature, findings were synthesized qualitatively according to technological maturity, forensic applicability, validation status, and implementation readiness. Results: The reviewed evidence demonstrates substantial progress in postmortem computed tomography, postmortem CT angiography, postmortem magnetic resonance imaging, whole-slide imaging, molecular autopsy, robotic-assisted postmortem procedures, three-dimensional reconstruction, and AI-assisted forensic analysis. These technologies enhance trauma evaluation, vascular imaging, ballistic reconstruction, digital documentation, remote consultation, diagnostic reproducibility, and multimodal integration of forensic evidence. Nevertheless, the level of evidence varies considerably across technological domains. Postmortem imaging represents the most mature and extensively validated technology, whereas most AI applications remain supported predominantly by retrospective proof-of-concept studies with limited multicenter external validation. Current systematic evidence further indicates that AI should presently be regarded as an assistive technology that augments expert forensic interpretation rather than replacing conventional autopsy or autonomous medicolegal decision-making. Conclusions: Contemporary autopsy pathology is evolving toward a hybrid model of digitally integrated death investigation in which conventional autopsy, imaging, digital pathology, molecular diagnostics, robotics, and AI function as complementary components of a unified forensic workflow. Current evidence supports a conceptual paradigm shift characterized by transformation of evidence acquisition, preservation, interpretation, and integration, while reaffirming that conventional autopsy remains the indispensable biological reference standard for the development, validation, and medicolegal interpretation of all emerging technologies. Future implementation should prioritize multicenter validation, standardized forensic datasets, explainable AI, digital chain-of-custody procedures, and robust regulatory governance to ensure safe and scientifically reliable integration into forensic practice. Full article

This study investigates the tribological performance of gum arabic (GA)-reinforced epoxy (EP) overlays on EN8 steel. Four GA concentrations (0.25, 0.5, 1, and 3 wt.%) were incorporated into the epoxy matrix to prepare overlays designated as EPGA1–EPGA4. Tribological performance was evaluated using a reciprocating tribometer under varying loads (5–20 N), sliding frequencies (1–2.5 Hz), and temperatures (40–70 °C). An L16 orthogonal array based on the Taguchi method was used to design the experimental matrix, and multi-criteria decision-making using the TOPSIS technique was employed to identify the optimum tribological condition based on minimum coefficient of friction (COF) and specific wear rate (SPWR). The optimum condition was obtained for the EPGA3 overlay (1 wt.% GA) at 5 N, 2 Hz, and 60 °C, which exhibited the lowest COF of 0.0567 ± 0.0021 and negligible wear. In contrast, the pure epoxy overlay showed severe adhesive wear, catastrophic delamination, a high COF of 1.15 ± 0.0023, and a wear rate of 163 × 10⁻⁸ mm³/Nm. Thermal characterization showed that GA improved the thermal stability and thermal transition behaviour of the epoxy matrix. Thermogravimetric analysis revealed an increase in onset degradation temperature from 320 °C for pure EP to 342 °C for EPGA4, while differential scanning calorimetry showed that EPGA3 exhibited the highest glass transition temperature (~118 °C), indicating improved interfacial interactions and restricted polymer-chain mobility. Nanoindentation and pull-off adhesion tests further confirmed the improved mechanical integrity and interfacial adhesion of the GA-reinforced overlays, demonstrating its potential as a sustainable reinforcement for tribological coating applications. Full article

Semantic segmentation technology based on Inverse Synthetic Aperture Radar (ISAR) images can provide crucial perception and analytical capabilities for intelligent safety maintenance of on-orbit spacecraft. However, conventional semantic segmentation methods suffer from three main limitations: firstly, the lack of modeling for radar physical characteristics in the “image first, segment later” pipeline leads to loss of scattering information and phase details; secondly, reliance on extensive pixel-level manual annotation increases application costs; thirdly, ineffective utilization of spacecraft structural priors fails to guide networks to focus on the main body and edges of spacecraft segmentation. To address these issues, this paper proposes a complex-domain semantic segmentation framework named One-Stop Segmentation (OSS) based on ISAR echoes. The framework incorporates two innovative modules: an Automatic ISAR Labeling (AIL) method designed based on ISAR scattering characteristics to generate labels corresponding to ISAR echoes, and a complex-domain semantic segmentation network named One-Stop Segmentation Network (OSSNet) that performs semantic segmentation directly on echoes, avoiding information loss from imaging while shortening the data processing chain. Core contributions of OSSNet include: (1) a Domain Alignment Module (DAM) to effectively mitigate domain mismatch caused by data distribution differences between raw echo signals and labels; (2) a Multi-Perspective Attention (MPA) framework incorporating a Sliding Correlation Attention (SCA) module and a Subdomain Balanced Attention (SBA) module, lever-aging spacecraft structural priors to guide the network’s focus on main structures and edge details from complementary perspectives, significantly improving segmentation ac-curacy. Experimental results on a simulated ground-based radar dataset demonstrate that the proposed OSS framework achieves a mean Intersection over Union (mIoU) of 92.13% and a mean F1-score of 95.75% in ISAR spacecraft semantic segmentation tasks, outperforming existing methods. Full article

This technical note aims to present a theoretical analysis for cybertronics engineering (CE) for a class of dynamic supply chains for artificial intelligence (AI)-based products, services or hybrid solutions. The cybertronics-based analysis encompasses three classes of supply chains: (1) energy-based dynamic supply chains (DSC); (2) semiconductor manufacturing and quantum supply chains; and (3) retailing of the AI-based DSC solutions generated. Considering the nonlinear nature of DSC, to provide solutions for products and services based on AI-chained supply chains, novel robust control is addressed via sliding mode control (SMC) with proper stability analysis for the DSC. Full article

This work proposes an Adaptive Neural Control for the asymptotic stabilization of a chain of integrators at the origin. The proposed approach addresses the stabilization of the integrator chain by means of a control law whose applied signal is structurally bounded to $(-1,1)$ by the hyperbolic tangent architecture, i.e., $u\left(t\right)=tanh\left(z\right)$, where z represents a weighted linear combination of the system states and a bias term. Furthermore, an adaptation law for the weights is proposed, based on the classical backpropagation algorithm for neural networks. The stability analysis is conducted using singular perturbation theory, demonstrating that, under a sufficiently high learning rate, the closed-loop system exhibits a Standard Singular Perturbation Form. This formulation allows for the analysis of the system across two distinct time scales: the adaptation dynamics (fast subsystem) and the state dynamics (slow subsystem). Based on this formulation, explicit conditions on the learning rate and the initial conditions are derived to guarantee local asymptotic stability using Tikhonov’s theorem. These conditions characterize the region of attraction and ensure that the adaptive neural controller stabilizes the system. Numerical simulations were carried out to evaluate the controller’s performance under three different scenarios: ideal conditions, initialization outside the region of attraction, and a low learning rate. These scenarios illustrate the closed-loop system behavior and validate the theoretical conditions required for asymptotic stability. Furthermore, comparative numerical simulations were conducted on an Inverted Pendulum on a Cart system to benchmark the proposed Adaptive Neural Control against Linear Quadratic Regulator, Sliding Mode Control, and Nested Saturation Function controllers. Based on the Integral of Time-weighted Squared Error performance index, the Adaptive Neural Control demonstrated a significant reduction in control effort, achieving performance improvements of up to 95.02% compared to the aforementioned strategies. Full article

Rainstorm-induced disaster chains are characterized by high suddenness, immense destructive power, and complex chain propagation mechanisms. Traditional static assessment methods rely on fixed parameters and struggle to depict the dynamic evolution of such disasters. Existing dynamic models are mostly based on predefined structures and lack the capability to integrate multi-source data and quantify uncertainty, thereby constraining the accurate prediction of rainstorm disaster chains. To address these issues, this study proposes a rainstorm disaster chain prediction model (SW-DBN) that integrates a large language model (LLM)-optimized sliding window mechanism with a dynamic Bayesian network (DBN). The model first performs dynamic segmentation and feature extraction on multi-source time-series data through the sliding window mechanism and constructs an LLM-driven module for semantic understanding of multi-source information and latent parameter mining. By leveraging the LLM’s in-depth analysis of data pattern variations within the window, the model excavates latent parameters, adaptively adjusts the DBN network topology, and feeds back to optimize the window width and sliding step, thereby maintaining adaptive alignment between the sliding window’s feature extraction and the dynamic evolution of the disaster chain. Ultimately, the cascade propagation process of the rainstorm disaster chain is modeled, reasoned, and validated through the DBN, forming an integrated prediction framework of “perception–reasoning, dynamic regulation, and cascade verification.” A case study in the Xi’an area demonstrates that the proposed model can effectively simulate the temporal evolution of rainstorm disaster chains. The average prediction accuracy for four key types of disaster nodes reaches 84.8%, representing an improvement of 7.5 percentage points over the standard DBN model, with clear advantages in early warning timeliness for critical nodes. The proposed model provides technical support for the probabilistic prediction of rainstorm disaster chains and disaster prevention decision-making, featuring both dynamic adaptability and interpretability. Full article

Rockfall from slope unstable rock masses is a typical geological hazard induced by brittle failure, with abrupt occurrence, limited macroscopic deformation before failure, and a short warning lead time. Conventional static analysis methods are useful for design-stage stability checks, but they cannot continuously capture structural-plane damage or update the stability state in real time. Dynamic evaluation based on structural dynamics links measurable parameters such as natural frequency, damping ratio, mode shape, vibration trajectory, wave velocity, and energy dissipation to the degradation of structural planes. This review synthesizes the dynamic behavior mechanism, parameter system, theoretical models, sensing technologies, and engineering applications for slope unstable rock masses. Different from previous reviews that mainly summarize rockfall monitoring or conventional slope stability analysis, this paper organizes the literature by failure mode, monitoring scale, model assumptions, field validation, uncertainty sources, and engineering applicability. The single-degree-of-freedom models for sliding-, toppling-, and falling-type rock masses, multi-block chain-collapse models, and data-physics dual-driven surrogate models are compared critically. Contact monitoring based on MEMS sensors, non-contact LDV monitoring, acoustic emission, microseismic monitoring, coda wave interferometry, and cloud-edge early-warning architectures are further reviewed. Key challenges include field-scale validation under heterogeneous and anisotropic geological conditions, environmental compensation, robust threshold calibration, and probabilistic linkage between dynamic indicators and failure probability. The review provides guidance for selecting dynamic evaluation models, designing field monitoring systems, and developing full-life-cycle digital-twin platforms for rockfall risk mitigation. Full article

Hyaluronan (HA) receptors are expressed in a wide variety of different tissues and have long been known to support the critical cellular functions of adhesion and motility, in addition to a range of different physiological and pathological processes, including immunity, inflammation and tumour metastasis. In recent years, LYVE-1, an HA receptor largely but not exclusively restricted to the endothelia of lymphatic capillaries, has been shown to mediate the entry of immune cells through lymphatic endothelial junctions by engaging with their surface HA glycocalyx, itself anchored to the immune cell membrane by the closely related receptor CD44. Although similar to CD44 in primary sequence, LYVE-1 is functionally distinct, with a mutually exclusive pattern of tissue expression and a marked dependence on avidity for engagement with the long chains of HA—achieved primarily through receptor clustering. Here, we review key data that have defined the in vitro and in vivo functions of LYVE-1, including recent high-resolution crystal structures that have revealed its unusual and reversible “sliding” mode of interaction with HA, as distinct from the conventional “sticking” interaction in CD44. Lastly, we consider the emerging functions of LYVE-1 in sites beyond the lymphatics, namely tissue-resident macrophages and the specialised blood vessels of certain organs, and its potential as a therapeutic target. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

This study addresses asymmetric large surrounding rock deformation induced by narrow coal pillar instability in a gentle-dipping coal seam gob-side coal roadway (GSCR) under water-immersed and high-humidity conditions. The corresponding instability mechanism and control technology are systematically studied via integrated laboratory, theoretical, numerical and field methods. From constant temperature–humidity rock deterioration tests, SEM and XRD analysis, it is revealed that hydration of hydrophilic minerals (kaolinite, chlorite) in immediate roof mudstone intrinsically drives its macro–micro structural disintegration and mechanical degradation, and the catastrophic chain mechanism of water-induced mudstone weakening–force transmission medium failure of coal pillars and overlying strata–sliding instability of key voussoir beam blocks–linked large surrounding rock deformation is clarified. A mechanical model of the overlying voussoir beam structure for the target roadway is established considering both mudstone weakening and excavation-induced load transfer effects. The sliding criterion of key overlying blocks is derived, which quantitatively confirms that higher mudstone weakening and excavation-induced stress concentration elevate the sliding instability risk of the voussoir beam structure. Based on the findings and field conditions, a combined near-field and low-position field support scheme is proposed, including near-field reinforcement (shotcreting sealing, bolt–cable cascade reinforcement, deep grouting modification) and low-position field pressure relief via liquid CO₂ phase transition long–short boreholes roof cutting. Field application verifies that the maximum roadway deformation is controlled within 172 mm, with excellent surrounding rock control performance. Full article

This paper presents a robust and efficient mmWave radar-based human activity recognition (HAR) framework optimized for practical real-time indoor deployment. Addressing computational inefficiencies and limited recognition scopes in existing systems, the framework introduces two core contributions: Multi-class Spatio-Temporal Network (MuST-Net), a lightweight, multi-class network, and an online detection process for enhanced temporal stability. MuST-Net utilizes a hybrid 2D convolutional neural network and temporal convolutional network architecture to recognize seven distinct classes, significantly broadening the system’s recognition repertoire. The online detection process implements a novel sliding-window post-processing chain that employs an activity-buffering mechanism, which maintains temporal continuity and effectively suppresses spurious detections at activity boundaries. Experimental results demonstrate the superior performance of our unified framework, attaining over 98.6% accuracy for multi-class classification by MuST-Net and achieving at least 97% accuracy for activity detection and a crucial 100% recall for fall detection. Robustness is validated across three distinct indoor environments and nine subjects—with two of the three sites entirely unseen during training—confirming strong generalization under installation, environment, and subject variations. Full article