Search Results (1,393)

Understanding complex vessel behaviors in port waters is important for maritime traffic supervision, navigation safety management, and intelligent maritime decision-making. However, existing AIS-based studies often focus on isolated behavior recognition or trajectory-level analysis, with limited integration of vessel attributes, navigation scenarios, motion states, and temporally organized behavioral processes. We develop a knowledge graph-driven framework for complex vessel behavior recognition and frequent behavior sequence pattern mining using AIS data. BehaviorEvents are constructed from continuous-navigation segments and integrated with water-area scenarios, motion states, vessel attributes, and temporal relationships to form a unified semantic representation. Based on this representation, interpretable semantic rules are used for event-level complex behavior recognition, while PrefixSpan is applied to Scene–SpeedState–TurningState token sequences to discover recurrent multi-event behavior patterns. Independent expert evaluation, semantic ablation, and sensitivity analyses are used to assess recognition credibility, contextual semantic constraints, and robustness, while a vessel-level Discovery–Validation strategy evaluates the reproducibility of frequent patterns. Experiments on AIS data from Xiamen Port waters involve 16,400 vessels, 239,877 continuous-navigation segments, and 2,457,965 BehaviorEvents, of which 624,561 match at least one predefined semantic rule or candidate condition. Independent expert evaluation of R1–R7 yields a macro-average confirmation rate of 92.11% and a Cohen’s κ of 0.746. Semantic ablation shows that Scene and VesselTypeClass provide important contextual constraints on broad motion-based rule activations, while sensitivity analyses indicate that the main recognition results remain stable under perturbations of motion-state, duration, and temporal-segmentation parameters. From 75,144 valid compressed behavior-token sequences, PrefixSpan identifies recurrent patterns involving medium-speed transit with course adjustments, low-speed–stop combinations, and maneuvering-related behaviors. The dominant Top-20 patterns showed substantial overlap and broadly consistent ranking across the vessel-level Discovery and Validation subsets, with a Jaccard overlap of 0.9048 and a Spearman rank correlation of 0.9654. Comparative evaluation with a normalized relational representation further shows equivalent analytical results, while the knowledge graph provides explicit organization of semantic relationships, temporal paths, and event-level traceability. These results indicate that the proposed framework provides a unified and interpretable semantic basis for connecting event-level complex vessel behavior recognition with sequence-level frequent behavior pattern mining in complex port environments. Full article

Background: Herniated intervertebral disc (HIVD) with radiculopathy and foot drop can lead to substantial functional impairment. Although both pharmacopuncture and motion-style acupuncture treatment (MSAT) have demonstrated therapeutic potential individually, evidence on their combined use remains limited. In this single-case report, we described the clinical course of a patient with HIVD with radiculopathy and foot drop who underwent a novel approach combining ultrasound (US)-guided Shinbaro 2 pharmacopuncture targeting multiple nerve roots with tibialis anterior muscle motion-style acupuncture treatment (TA MSAT). Case Presentation: A 40-year-old man with lower back pain (LBP), radiculopathy, and foot drop attributed to acute HIVD was treated with high-dose US-guided Shinbaro 2 pharmacopuncture and TA MSAT. Outcomes were measured using the Numeric Rating Scale (NRS) for LBP, L5, and S1 radiculopathy; Manual Muscle Testing (MMT) for dorsiflexion and great toe extension; the Oswestry Disability Index (ODI) for lumbar function; the EuroQol Five-Dimension (EQ-5D) Index for quality of life; and the Patient Global Impression of Change (PGIC) for satisfaction. After 14 weeks of treatment, NRS scores for LBP, L5, and S1 radiculopathy decreased from 5 to 1, 8 to 1, and 8 to 1, respectively. MMT grades for dorsiflexion and great toe extension improved from 3 to 5 and 2 to 5, respectively, indicating full functional recovery. ODI and EQ-5D scores improved from 62.22 to 6.67 and 0.344 to 1.0, respectively. The PGIC score was 1, indicating clinical improvement. Conclusions: Our integrative approach involving US-guided pharmacopuncture and TA MSAT demonstrates therapeutic potential for the management of acute HIVD. Full article

Introduction and Clinical Significance: Descemet membrane detachment (DMD) is an uncommon but potentially vision-threatening complication of anterior segment surgery. Although immune-mediated graft rejection is a recognized cause of postoperative graft edema following arcuate keratotomy (AK) in eyes with previous penetrating keratoplasty (PKP), structural complications such as DMD may present with similar clinical findings and require fundamentally different management. We report a case of DMD initially misdiagnosed as acute graft rejection following manual AK in a post-PKP eye. Case Presentation: A 47-year-old female with a 20-year history of PKP in the right eye (RE) presented with decreased vision three weeks after undergoing manual AK using a diamond blade to correct high astigmatism. The procedure was complicated by an intraoperative wound leak requiring suturing. Postoperatively, she developed corneal edema and was initially misdiagnosed with acute graft rejection. Despite treatment with topical and systemic corticosteroids, her condition did not improve. Upon referral to our clinic, slit-lamp examination and anterior segment optical coherence tomography (AS-OCT) revealed a near-total, nonplanar DMD. The patient underwent a single air descemetopexy session involving three sequential intracameral air injections, resulting in Descemet membrane apposition and improvement in graft clarity. At 3-month follow-up, visual acuity had improved from hand motion to 20/200, with IOP within normal limits. Longer-term follow-up was unavailable. Conclusions: DMD should be considered as a cause of graft edema after AK in post keratoplasty eyes especially in the presence of complications. Full article

A recurrent challenge in scaling ultra-wideband (UWB) motion-capture systems is interference management when many ranging transactions coexist in time and space. To address this, we study a two-layer localization architecture that separates field-level player localization from local on-body pose tracking, allowing the two tasks to operate with different communication regimes and spatial-reuse policies. A stochastic-geometry framework is used to map sport-dependent parameters, including player density, field size, tag count, anchor count, update rates, and ranging airtime, to reliability and update-rate tradeoffs. The analytical model is parameterized using controlled experiments that characterize ranging success under temporal overlap, player distance, and variable-delay scheduling. These measurements inform the design of a proximity-aware local coordination strategy. We apply our proposed approach to soccer, volleyball, and ice hockey as representative use cases. Our results show that proximity-aware coordination can provide a scalable and lightweight interference management mechanism. Coordination is activated only where local player clustering creates strong interference, while spatially separated players continue to share resources without coordination. For the highest-density scenario tested, this increases the local-layer ranging success from below $50\%$ without coordination to over $80\%$ in four- and eight-player congestion clusters, while avoiding network-wide coordination overhead. Full article

Reliable wearable motion tracking depends not only on nominal sensor precision but on whether calibration remains valid after re-donning, anatomical misalignment, magnetic disturbance, temperature change, and prolonged operation. This structured critical review treats calibration as a system-level engineering decision spanning intrinsic inertial measurement unit (IMU) correction, sensor-to-segment alignment, flexible-sensor calibration, drift management, and complementary or external references. A reproducible Scopus core search conducted in May 2026 identified 807 records. Three co-authors screened titles, abstracts, and keywords using explicit eligibility criteria, with full-text inspection when required; the final 110-source reference corpus combines eligible core records with supplementary and contextual sources identified through coverage checks in IEEE Xplore, Web of Science, ScienceDirect, and the MDPI database. Unlike prior reviews that mainly treat individual calibration layers or inertial-error mechanisms, this review jointly compares sensor-level, anatomical, multimodal, adaptive, and external-reference routes within a common deployment-oriented framework. Evidence is coded by failure mechanism, calibration route, sensing modality, application context, validation context, and deployment constraint. No universally superior route is supported: static and functional alignment differ in burden and anatomical sensitivity; magnetometer use depends on field reliability; vision and ultra-wideband (UWB) references can restore absolute observability but add infrastructure dependence; and direct IMU–flex calibration remains comparatively sparse. The resulting decision framework prioritizes reproducibility, robustness, calibration stability, and fitness for real-world deployment over isolated best-case accuracy. Full article

Systolic anterior motion (SAM) of the mitral valve remains a clinically relevant complication after mitral valve repair and may result in dynamic left ventricular outflow tract (LVOT) obstruction, SAM-associated mitral regurgitation, and hemodynamic instability. Despite advances in surgical techniques and perioperative imaging, SAM remains an important cause of difficult separation from cardiopulmonary bypass and postoperative circulatory compromise. The development of SAM is multifactorial and results from the interaction between mitral valve anatomy, ventricular geometry, surgical repair characteristics, and perioperative hemodynamic conditions. Contemporary evidence has identified several echocardiographic predictors, including excessive posterior leaflet height, elongated anterior leaflets, reduced coaptation–septal distance, a narrow mitro–aortic angle, basal septal hypertrophy, and small hyperdynamic left ventricles. Recognition of these risk factors facilitates perioperative risk assessment and pre-repair surgical planning. Transesophageal echocardiography plays a pivotal role throughout the perioperative period, enabling risk assessment before repair, early diagnosis after cardiopulmonary bypass, and guidance of therapeutic interventions. Initial treatment is based on preload optimization, afterload augmentation, withdrawal of inotropic stimulation, and heart rate control, whereas refractory cases may require surgical revision. This narrative review summarizes the current understanding of SAM after mitral valve repair, focusing on pathophysiological mechanisms, echocardiographic predictors, surgical prevention and perioperative management, with particular emphasis on the practical role of cardiac anesthesiologists and mitral valve surgeons. Full article

Hydrogel-and-nanomaterial-integrated wearable biosensor networks are promising platforms for real-time biomedical monitoring because they combine soft biointerfaces, sensitive signal transduction, and wireless data connectivity. Hydrogels provide tissue-like softness, hydration, adhesion, permeability, and biocompatibility, whereas nanomaterials such as graphene, carbon nanotubes, MXenes, metallic nanoparticles, and conductive polymers enhance conductivity, electrochemical activity, optical responsiveness, mechanical durability, and signal amplification. This review summarizes recent advances in hydrogel-and-nanomaterial-integrated wearable biosensors, ranging from soft material interfaces and stand-alone sensing devices to wireless wearable nodes, IoT-connected platforms, and emerging closed-loop sensor–actuator systems. Because these platforms differ substantially in their level of integration and validation, this review distinguishes enabling material and device concepts from fully connected or closed-loop systems. The distinctive contribution of this review is a materials-to-systems, evidence-graded framework that links hydrogel and nanomaterial interface design with sensing mechanisms, wearable sensor-node integration, wireless and IoT connectivity, and closed-loop actuation while distinguishing device-level proof of concept from clinically validated performance. We discuss functional hydrogel design, nanomaterial-based conductive networks, hybrid hydrogel–nanomaterial structures, and key requirements for skin compatibility, adhesion, stretchability, and long-term stability. Major sensing mechanisms and biomedical targets are reviewed, including electrochemical and optical biosensing, mechanical and physiological signal sensing, and sweat biomarker monitoring. We further highlight system-level integration strategies involving wearable sensor nodes, wireless communication, smartphone and cloud connectivity, data processing, power management, security, and reliability. Representative biomedical applications are summarized, including sweat-based metabolic monitoring, smart wound monitoring, hydrogel-based wound dressings, cardiovascular and respiratory monitoring, and motion sensing. Finally, current technical and translational challenges are discussed with emphasis on the distinction between analytical sensing performance, physiological correlation, and clinical validation. Disease-management and closed-loop healthcare applications are discussed as emerging directions that require appropriate human studies, reference-method comparison, agreement analysis, long-term monitoring, and safety validation before clinical implementation. Full article

Background and Objectives: Reconstruction of extensive acetabular bone defects in revision total hip arthroplasty (THA) remains technically demanding and is associated with a high risk of complications. Custom-made three-dimensional (3D)-printed implants have emerged as a patient-specific option for managing severe bone loss, particularly in Paprosky type 3A and 3B defects. The aim of this multicentre study was to evaluate the early clinical, radiographic, and surgical outcomes following single-stage reconstruction using custom-made 3D-printed acetabular implants. Materials and Methods: This retrospective multicentre study included 62 patients treated between 2021 and 2024 at four orthopaedic departments. All patients had Paprosky type 3A (n = 34) or 3B (n = 28) acetabular defects and underwent reconstruction with custom-made 3D-printed implants. A minimum clinical follow-up of 6 months was required for inclusion. Preoperative computed tomography was used for implant design and surgical planning. Clinical assessment included pain using the Visual Analogue Scale, functional evaluation using the Harris Hip Score and Karnofsky Performance Scale, mobility, range of motion, and use of orthopaedic aids. Radiographic evaluation included plain radiographs and computed tomography. Standardised functional and CT outcome assessments were performed during the first 6 months after surgery, whereas the mean overall follow-up represented subsequent clinical surveillance. Mean follow-up was 27 months. Results: All patients had undergone multiple previous surgical procedures before implantation of the custom-made prosthesis. The interval between primary arthroplasty and implantation of the custom-made implant ranged from 9 to 31 years. During follow-up, improvements in pain, mobility, and functional performance were observed across the entire cohort. The reported functional and CT outcomes refer to the standardised 6-month assessment period. Radiographic evaluation demonstrated satisfactory implant positioning and restoration of hip biomechanics. Progressive radiographic findings suggestive of implant integration were observed during follow-up. The most common complications were postoperative wound-healing disorders and prosthetic dislocation, particularly in patients with more extensive bone defects. Conclusions: Single-stage reconstruction using custom-made 3D-printed implants appears to be a feasible treatment option for patients with Paprosky type 3A and 3B defects, resulting in improvements in pain, mobility, and functional performance. Radiographic assessment suggested stable implant fixation and progressive implant integration during early follow-up. However, postoperative wound-healing disorders, infection, and prosthetic dislocation remain clinically relevant complications, particularly in patients with more extensive bone defects. Further studies with longer follow-up are required to determine long-term implant durability, implant survivorship, and functional outcomes. Full article

Protected horticultural crops are commonly produced at high planting densities and have short production cycles; imbalances in water and fertilizer supply can rapidly affect plant vigor, yield, and quality. Non-destructive diagnostic methods are therefore needed to characterize plant nutritional status under greenhouse conditions. Spectral imaging can simultaneously capture spatial and spectral information associated with pigments, water status, tissue structure, and canopy phenotype. It does not directly detect nutrient ions; rather, it captures physiological and structural responses that may be associated with nutrient status and may also be influenced by water deficit, disease, temperature, salinity, phenology, and genotype. This review focuses on crops grown in soil, substrate, and hydroponic systems under greenhouse conditions. Studies conducted in vertical farms, growth chambers, and open fields are included only as supplementary references for sensor selection, model calibration, and inspection methods. This article synthesizes diagnostic indicators for nitrogen, phosphorus, and potassium, together with their associated physiological responses and spectral characteristics; compares the performance of hyperspectral, multispectral, and machine learning methods at the leaf, plant, and canopy scales; and examines fixed measurement, stop-and-go mobile inspection, continuous motion imaging, and autonomous plant revisitation. Existing studies have established a solid foundation for nutrient content retrieval, deficiency identification, and mobile monitoring. However, several challenges remain inadequately addressed under continuous inspection conditions, including radiometric–geometric joint calibration, plant identity preservation, acquisition of multi-element chemical truth values, model generalization across growth stages and greenhouse types, and long-term performance evaluation. Future work should refine standardized protocols for dynamic data collection and water–fertilizer environmental control, integrate mechanistic constraints with data driven approaches, and incorporate plant re-identification, spatiotemporal registration, uncertainty quantification, and online calibration. These efforts will contribute to constructing a long-term stable and comparable nutritional diagnostic system, thereby advancing the transition of facility vegetable nutritional monitoring from single-time static measurements toward continuous, traceable, and autonomously patrolled systems that may ultimately support precision irrigation and fertilization management after appropriate independent validation. Full article

In multi-beam low-Earth-orbit (LEO) satellite communication networks, high-mobility aerial users, such as unmanned aerial vehicles (UAVs), high-speed aircraft, and near-space vehicles, may traverse multiple satellite beams within a short period of time. When the precise position of a target user is not continuously available to the network, the network needs to determine the set of beams in which the user is likely to be located when a paging request arrives. The corresponding communication satellites then transmit paging messages within these candidate beams to reach the target user. If the selected beam set does not cover the user’s actual position, the paging attempt fails; however, excessively enlarging the paging region or frequently updating the user’s location information introduces additional signaling and management overhead. Therefore, the key problem is to construct an accurate and adaptive paging region under the joint mobility of the user and LEO satellite beams. To address this problem, this paper proposes a network-side sensing- and prediction-aided dynamic location-area management method for high-mobility users. First, based on a three-stage motion model of high-mobility users, LEO satellite ephemeris information, and beam coverage parameters, the coverage performance during the whole flight process of high-mobility users is analyzed. Second, a high-mobility user state prediction mechanism integrating a three-stage motion model and square-root cubature Kalman filtering (TSM-SRCKF) is proposed. This mechanism can adaptively adjust the weights of different motion models according to the current motion state of the high-mobility user and suppress the influence of abnormal measurements during the measurement update process, thereby obtaining more reliable position prediction results and error covariance information. Finally, a TSM-SRCKF-aided dynamic location-area management method is proposed. Simulation results show that the root-mean-square error of the high-mobility user position under the proposed mechanism is only 23.3% of that of the comparison mechanism. Compared with the traditional velocity-based dynamic location area design method, the proposed method improves the paging success probability by about 60.1% and reduces the cumulative total management overhead by about 73%. Full article

Vehicle-to-everything (V2X) connectivity provides electrified vehicles with previews of traffic signals, road geometry, surrounding traffic, and route conditions, yet control benefit arises only when these data are converted into variables that can shape motion and powertrain decisions. This review presents a structured, framework-driven narrative synthesis organized along an information–motion–energy chain: external preview, control-oriented prediction, energy-aware speed planning, trip-level energy and state-of-charge scheduling, power-source allocation, cross-layer coordination, and staged validation. The reviewed studies are compared in terms of coupling depth, from traffic-layer optimization and sequential speed–energy management strategy (EMS) schemes to hierarchical/weakly coupled and joint/tightly coupled formulations. Across hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicle/hybrid electric vehicle (FCEV/FCHEV) platforms, the information interface is broadly shared, whereas energy-replenishment, thermal, and component-health constraints require powertrain-specific formulations. The evidence base also shows a persistent maturity gap between algorithmic simulation and hardware or vehicle validation. Key needs are uncertainty-aware closed-loop design, physically interpretable model–data fusion, fallback control under information degradation, standardized cross-layer benchmarks, and staged validation that reports both control performance and evidence level. Full article

Model Predictive Control (MPC) offers a structured approach for autonomous-vehicle motion planning by optimizing predicted vehicle behavior over a finite horizon. As learning-based components become increasingly integrated into autonomous systems, interpretable interfaces between decision-making and control become increasingly important. Motivated by the need to examine how predictive depth should vary with maneuver context, this paper proposes and evaluates an adaptive phase-aware horizon-selection formulation within a hybrid Maximum Entropy Deep Inverse Reinforcement Learning-Model Predictive Control (MEDIRL-MPC) framework. The formulation conditions prediction depth explicitly on the recognized maneuver phase, providing an interpretable scheduling signal rather than maintaining the horizon as a globally fixed controller parameter. The considered architecture combines a MEDIRL-informed driving cost, a sampling-based Cross-Entropy Method planner, a kinematic vehicle model, and a scenario manager that identifies the current lane-change phase and provides the corresponding reference information. Controlled experiments are conducted in the CARLA simulator using a static-obstacle lane-change scenario. Fixed-horizon baselines, phase-wise analysis, common-state counterfactual comparisons, and controlled robustness experiments are used to evaluate the effect of prediction depth. The results indicate that the prediction horizon length greatly affects closed-loop behavior and computational demand, and that the relative suitability of different horizons varies across each scenario phase. The phase-aware policy further demonstrates that predictive depth can be allocated selectively across maneuver phases while preserving successful maneuver execution, and remains successful and lane-safe under controlled variations in target speed, obstacle distance, and activation distance. These findings support maneuver phase as an interpretable context for prediction-horizon adaptation within the evaluated architecture, while limiting the conclusions to the investigated scenario and experimental setting. Full article

Skeleton-based human action recognition can support digital construction and renovation monitoring by converting worker motions into structured information for safety and process management. However, workers are often occluded by tools, materials, furniture, and temporary structures, resulting in incomplete skeleton sequences and reduced recognition accuracy. In this study, we propose a recognition-guided generative skeleton imputation approach for robust action recognition under severe occlusion. Our method first evaluates an incomplete skeleton and applies imputation only to samples with uncertain recognition results. For these samples, a pretrained OmniControl model generates multiple plausible motion candidates from the observed skeleton information and action descriptions. The candidate most consistent with the observed motion is selected and used only to fill missing regions while preserving reliable joints, and the completed skeleton is then re-evaluated by the same recognition model. Experiments on 565 collected indoor renovation clips and the NW-UCLA dataset, using four recognition models and nine occlusion conditions, demonstrated overall improvements in recognition robustness, with average accuracy increasing from 61.01% to 65.00% on the collected dataset and from 42.63% to 50.31% on NW-UCLA. These results demonstrate the potential of selective generative imputation for improving action recognition under incomplete skeletal observations. Full article

In long-aperture forward-looking radar, nonlinear Doppler mismatch caused by target relative motion can lead to positioning deviation and image defocusing. To address this issue, an adaptive segmented Doppler compensation method based on phase error constraints is proposed. As synthetic aperture time increases, high-order terms in the slant range history broaden the Doppler spectrum and enhance spatially variant phase errors. Conventional global compensation cannot achieve stable focusing, and fixed-length segmentation fails to adapt to varying motion nonlinearity. Accordingly, the high-order nonlinear characteristics of the slant range are first analyzed, and an adaptive sub-aperture partitioning criterion constrained by second-order phase error is derived, ensuring each sub-aperture satisfies the local quasi-linear hypothesis. A cross-segment mapping relationship between different sub-apertures is then established, and the compensation process is formulated as a two-dimensional separable operator. To manage the high computational complexity of solving spatially variant mapping under long apertures, the Alternating Direction Method of Multipliers (ADMM) is introduced to iteratively optimize the operator, achieving phase alignment and coherent reconstruction among sub-apertures. Simulation and experimental results show that the proposed method effectively suppresses nonlinear defocusing under long-aperture conditions. Compared with conventional global methods, it achieves superior energy concentration and focusing resolution in extended target scenarios. Full article

Incorporating energy generated from regular human movement into wearable electronics offers a promising alternative to conventional batteries, enabling devices to power themselves by harvesting energy from motion and the surrounding environment. Ambient energy harvesting provides a pathway toward limitless, self-sustaining power and supports the development of cleaner, smarter wearable systems. Among various approaches, integrating hybrid mechanisms into footwear represents a transformative solution for sustainable power generation. In this work, a piezoelectric generator (PEG), an electromagnetic generator (EMG), and a triboelectric generator (TEG) were hybridized within a single architecture to harvest biomechanical energy from walking, jogging, and running. The device incorporates pressure-sensitive Lead Zirconate Titanate (PZT) sheets, a spiral spring with dual neodymium (NdFeB) magnets with wound copper coils, and a nickel foam with polytetrafluoroethylene (PTFE) for triboelectricity operating in contact–separation mode. A dedicated energy-management circuit comprising independent rectification, DC bus energy aggregation, supercapacitor storage, and voltage regulation was implemented to efficiently utilize the harvested energy. The system was optimized through simulation using SOLIDWORKS 2023 and validated experimentally by using LabVIEW-NI myRIO FPGA system and treadmill. The proposed hybrid device achieved an exceptional peak output power of 58 mW and a voltage of 7.4 V, enough to charge low-power wearable devices, significantly surpassing the performance of most reported standalone and hybrid insole energy harvesters. These results demonstrate the effectiveness of multimodal integration in broadening operational bandwidth, increasing energy density, and enhancing compatibility with wearable applications. Piezoelectric, Electromagnetic and Triboelectric Insole Energy Harvesting (PET-IEH) establishes a new benchmark in biomechanical energy harvesting and paves the way for next-generation self-powered and sustainable wearable electronics. Full article