Search Results (1,642)

Accurate and smooth path tracking is important for autonomous tracked agricultural robots operating in greenhouse-like environments. Existing adaptive look-ahead pure-pursuit methods mainly adjust the look-ahead distance according to vehicle speed or path geometry, while the influence of time-varying localization reliability has not been sufficiently considered. This study proposes a noise-aware adaptive pure-pursuit controller that combines Extended Kalman Filter (EKF) estimation with causal Savitzky–Golay (SG) endpoint smoothing. A bounded look-ahead law is designed by jointly considering normalized vehicle speed, lateral error, path curvature, and an innovation-derived localization-noise indicator. Numerical simulations were conducted on straight, circular, S-shaped, and U-shaped reference paths under prescribed localization disturbances. Under the 0.5 m positional-noise condition, the proposed method achieved an root mean square error (RMSE) of 0.087 m and an angular-velocity root mean square (RMS) of 0.28 rad/s, compared with 0.112 m and 0.36 rad/s, respectively, for conventional fixed-look-ahead pure pursuit. Compared with proportional-integral-derivative (PID), Stanley, model predictive control (MPC), and conventional pure-pursuit controllers, the proposed method provides a favorable balance between tracking accuracy and control smoothness. It also has better computational efficiency than MPC while retaining the low-computational-burden advantage of geometric control. In the sensitivity analysis, the relative RMSE increase from 0.1 to 0.8 m was 36.5% for the proposed method and 103.4% for conventional pure pursuit. These results indicate that the proposed lightweight noise-aware control strategy can improve tracking accuracy, control smoothness, and tolerance to localization disturbances under the specified numerical conditions, providing a practical design reference for low-speed greenhouse agricultural robots. Full article

Developing new quality productive forces (NQPF) is an important direction for accelerating China’s high-quality development and advancing Chinese-style modernization. Based on the connotation and characteristics of NQPF, this study constructs an urban evaluation index system from three aspects of niche width, height, and overlap, and uses panel data of 283 Chinese prefecture-level cities over 2010–2023 to examine the spatiotemporal evolution patterns and key influencing factors of urban NQPF. Results are as follows. Firstly, urban NQPF exhibits an overall upward trajectory with an average annual growth rate of about 4.89%, showing phased features of rapid growth, moderated growth, and slight decline, and a spatial gradient of Eastern leadership, Central catch-up, and relatively low levels in the Western and Northeastern zones, with high-level cities growing into the largest group. Secondly, niche width, height, and overlap all show long-term upward trends. The energy level of factor resources and external resource acquisition capacity grow persistently. Inter-city competition evolves from moderate to high intensity, and regional disparities gradually converge. Thirdly, niche width and height are significantly and positively associated with NQPF development, whereas overlap is significantly and negatively associated, with marked regional heterogeneity. Combinatorial pathway analysis reveals that dominant pathways shift from “LLL + MLM” to “HHH + MMH” types, with HHH dominating high-level cities, MMH prevailing among medium-level ones, and low-level cities shifting from LLL to LLH. Improving energy level of urban new qualitative factor resources, strengthening spatial interaction level of new qualitative factors among cities, and avoiding adverse effects caused by excessive competition among cities should become an important content of optimizing the spatial layout of NQPF factor resources in China. Full article

Activity–travel pattern reconstruction infers latent paths, destinations, activities, and timing from incomplete mobility observations and supports travel-demand analysis and activity-based simulation. A structured search and citation tracking identified 157 core studies. Existing studies, however, remain fragmented across data sources, local reconstruction tasks, modeling techniques, and evaluation settings. This survey develops an integrated framework linking observation mechanisms, mathematical models, real-data applications, and performance evaluation. It first formulates reconstruction as inference over a latent activity–travel chain conditioned on partial observations and contextual information. Major mobility data sources are then compared according to their Eulerian or Lagrangian observation mechanisms and their spatial, temporal, and semantic information. Reconstruction methods are organized into model-driven, data-driven, and hybrid approaches, with emphasis on their mathematical structures, real-data applications, and ability to represent network, temporal, behavioral, and uncertainty constraints. Evaluation methods are reviewed at the element, chain, and population levels, while distinguishing missing-only performance from full-output performance. This review identifies four priorities for future research: joint reconstruction of complete chains, principled multi-source data fusion, calibrated uncertainty representation, and transferable benchmarks with realistic missingness and independent testing. This framework clarifies the current state of the field and supports the development of more reliable and behaviorally meaningful reconstruction methods. Full article

Metallized pellets are spherical iron-bearing burden materials obtained by treating iron ore pellets through processes such as direct reduction. They contain a certain proportion of metallic iron and mainly consist of metallic iron, incompletely reduced oxides, gangue, and other nonmetallic components. They are one of the commonly used iron-bearing materials in electric smelting furnaces. The melting process of metallized pellets not only affects the melting efficiency of the charge but is also accompanied by slag–metal separation and gangue separation, which is directly related to mass transfer, heat transfer, and production efficiency during the smelting process. However, existing studies have mainly focused on the melting behavior of pellets in a single-phase molten pool, while studies on gangue separation, interfacial migration, and slag–metal separation during pellet melting at the slag–metal two-phase interface remain rare. Because this region involves complex interfacial heat transfer, fluid flow, and interfacial interactions, investigating only the overall melting process of pellets is insufficient to reveal the actual gangue separation mechanism. Therefore, a systematic investigation of the melting and separation processes of pellets at the slag–metal interface is necessary. Based on the principle of similarity, a water–oil–ice three-phase physical model was employed in this study, in which water, silicone oil, and ice balls containing dyed silicone oil samples were used to simulate molten iron, slag, and pellets, respectively. The dyed silicone oil is specially designed to simulate the gangue in the pellet. Visualization experiments were conducted to investigate the evolution of pellet melting morphology, oil droplet (gangue) release behavior, and diffusion characteristics in the oil layer under different initial oil droplet positions and static or parallel flow conditions. The results show that the initial position of the oil droplet and the parallel flow significantly affect the local melting behavior of the ice ball and the oil droplet release process. In particular, the release time of the oil droplet located above the ice ball is significantly longer than that of the oil droplet located below the ice ball. The parallel flow significantly changes the melting sequence of the ice ball and the oil droplet release path by enhancing convective heat transfer in the lower region of the ice ball. According to the initial position of the oil droplet and the flow conditions, the oil droplet release process can be classified into five typical separation types, including (1) lateral release of the upper oil droplet after the ice shells on both sides melt through, (2) release of the upper oil droplet through a hole at the bottom of the upper hemisphere, (3) direct release of the lower oil droplet through a local hole, (4) two-stage release of the lower oil droplet controlled by interfacial constraint, and (5) single-stage release of the oil droplet on the downstream flow side driven by parallel flow. The oil droplet release time in the parallel flow cases is shorter than that in the static conditions. The diffusion behavior of the oil droplet after entering the oil layer is weakly affected by the parallel flow and is mainly characterized by inertial diffusion along the initial release direction, followed by spreading toward the surrounding area. This study reveals the slag–metal separation mechanism during pellet melting at the slag–metal interface under the combined control of flow, ice shell morphology, and interfacial interactions, providing experimental evidence for optimizing the melting and separation behavior of pellet charges in electric smelting furnaces and related smelting processes. Full article

Multi-module DC–DC converters are well suited to shipboard DC power systems with stringent requirements for high power density, operational safety, and continuous power supply. By distributing the system voltage, current, and power among multiple submodules (SMs), the modular architecture reduces device stresses and facilitates capacity expansion, maintenance, and redundant operation. Among the available modular configurations, the input-series output-parallel (ISOP) structure is particularly suitable for interfacing high-voltage DC buses with low-voltage, high-current loads. However, conventional voltage- or current-sharing strategies generally neglect efficiency differences among SMs. Under equal power sharing, low-efficiency SMs generate greater losses and experience higher thermal stress, resulting in thermal imbalance and accelerated aging. To address this issue, an efficiency-consensus-based power-distribution strategy is proposed for ISOP LLC-DAB hybrid converters. A distributed efficiency observer based on multi-agent consensus theory dynamically regulates the power references according to the relative efficiencies of the SMs, allowing high-efficiency modules to process more power while reducing the loading of low-efficiency modules. Experimental results obtained from a three-module prototype include comparative efficiency measurements and temperature-distribution tests. The results demonstrate that the proposed strategy improves the efficiency consistency among the three SMs, redistributes power according to their relative efficiency states, and reduces the temperature difference among the modules, thereby mitigating localized loss concentration and thermal imbalance. The proposed method provides a feasible solution for improving the electrothermal operating conditions of modular DC–DC converters. The achieved reduction in thermal imbalance may contribute to enhanced long-term reliability by alleviating uneven electrothermal stress. Full article

End-of-life (EoL) electric vehicle (EV) batteries comprise numerous interconnected components with complex topological and precedence relationships. These constraints significantly increase the difficulty of disassembly sequence planning (DSP), as feasible sequences must satisfy multiple dependency requirements. Moreover, the large number of possible disassembly alternatives creates a vast search space, making sequence optimisation susceptible to combinatorial explosion and convergence to local optima. Therefore, effective DSP requires both robust constraint-handling mechanisms to ensure sequence feasibility and efficient optimisation strategies to identify high-quality solutions. To address these challenges, this paper proposes a disassembly sequence optimisation method that integrates a hard-constraint rule base, the linear upper confidence bound (LinUCB) algorithm, and the Bees Algorithm (BA). First, a disassembly-oriented hard-constraint rule base is developed to standardise the identification of component topological relationships and precedence constraints, thereby ensuring the generation of feasible disassembly sequences. A LinUCB-based contextual adaptive operator-selection mechanism is subsequently introduced to dynamically select neighbourhood operators according to the current search state. A weighted multi-criteria evaluation function incorporating disassembly time, payment cost, and human–robot utility is integrated into the BA. Two representative EoL-EV battery case studies with different levels of structural complexity are used for validation. Across 50 independent runs, LinUCB-BA reduced the mean normalised weighted objective value by 39.01% and 28.12% relative to simplified swarm optimisation (SSO) and teaching–learning-based optimisation (TLBO), respectively, in the 27-component case, and by 7.19% and 2.33% in the 16-component case. Compared with the enhanced discrete Bees Algorithm (EDBA) ablation baseline, further reductions of 1.98% and 0.43% were achieved, together with lower run-to-run variability. These results indicate that the proposed framework is effective for the two investigated battery disassembly scenarios, while broader validation across additional battery architectures and operating conditions remains necessary. Full article

Oral squamous cell carcinoma (OSCC) is a common malignancy that can develop from precancerous conditions such as oral lichen planus (OLP). This case report describes the clinical management and complication reporting of a patient with OLP who developed an exophytic lesion on the anterior maxillary gingiva, diagnosed as well-differentiated OSCC (cT1N0M0). Surgical treatment included anterior maxillectomy and reconstruction using an iliac crest bone graft housed in a customized CAD-CAM titanium mesh, covered by a facial artery musculomucosal (FAMM) flap. Postoperatively, the patient experienced significant surgical complications, including FAMM flap dehiscence and subsequent necrosis of the underlying bone graft, leading to total graft failure. Management of these adverse events required aggressive wound care, hyperbaric oxygen therapy, and complete removal of the exposed titanium mesh under local anesthesia, classified as a Grade IIIad complication according to the Clavien–Dindo system. To address the functional and aesthetic deficits resulting from the surgical failure, the patient was successfully rehabilitated using a removable partial prosthesis. This report highlights the novel clinical lesson that while CAD-CAM mesh reconstruction is innovative, high soft-tissue tension in dynamic maxillary regions represents a critical failure risk; it demonstrates that conventional removable prosthetics served as a successful, low-morbidity rescue strategy in this patient when complex surgical reconstructions failed. Full article

Background: Ultra-high-frequency ultrasound (UHFUS), performed in the present study using 48 and 70 MHz transducers, enables high-resolution assessment of superficial tissues and may provide useful information on the morphology, internal architecture, vascularity, and anatomical relationships of oral cavity lesions. Methods: This retrospective single-center study included consecutive patients examined between January 2025 and June 2026. Of 137 eligible cases, five were excluded because of inadequate image or cine-loop quality, leaving 132 lesions for analysis. All examinations were performed by the same experienced radiologist using 48 and 70 MHz linear probes. Each lesion was assessed in B-mode in at least two orthogonal planes and with Color Doppler. Archived anonymized examinations were reviewed using a predefined structured form. Morphology, margins, echogenicity, echotexture, composition, posterior acoustic features, vascularity, dimensions, presumed plane of origin, and involvement of adjacent anatomic layers were evaluated. The qualitative synthesis was performed jointly by a radiologist and an oral pathologist, with disagreements resolved by consensus. Results: UHFUS allowed detailed visualization of mucosal, submucosal, muscular, and bone/periodontal interfaces and enabled qualitative characterization of lesion boundaries, internal structure, vascular patterns, and local extension. The combined use of 48 and 70 MHz probes provided complementary information according to lesion depth and tissue composition. Five representative histologically confirmed cases from the study cohort illustrated reactive/inflammatory, benign neoplastic, and malignant conditions. One additional clinically confirmed gingival fistula, not included in the analytical cohort, was presented solely as an illustrative example of a superficial fistulous tract detectable with UHFUS. Conclusions: UHFUS provides detailed, layer-based assessment of oral cavity lesions and may complement clinical examination and histopathology in lesion characterization and preoperative evaluation. Sonographic findings should be interpreted within a multimodal diagnostic framework rather than as stand-alone criteria. Full article

Mountain catchments integrate environmental gradients, disturbance legacies, and multiscale processes that shape how ecosystem functions vary across space and time. To better understand the role of these spatial processes, we analyzed whether ecosystem-function relationships in Patagonian headwater catchments are organized according to single multifunctionality gradients or are scale-dependent on ecosystem condition and structure. We fitted three expert-constrained Bayesian networks using field-measured ecosystem functions across three spatial resolutions: catchment (n = 12), forest clusters (n = 63), and forest plots (n = 175). Predictive performance varied strongly across scales and functions: at the catchment scale, firewood volume was best predicted (r = 0.79), whereas deadwood carbon stock, tree carbon stock, vascular richness, timber volume, and soil erosion showed negative predictive correlations (r = −0.14, −0.15, −0.33, −0.44, and −0.80, respectively). Specifically, at the forest-cluster scale, firewood volume, live tree carbon stocks, soil erosion, and deadwood carbon stocks were strongly predicted (r = 0.96, 0.81, 0.77, and 0.69). At the plot scale, firewood volume, tree carbon stock, deadwood carbon stock, and understory plant diversity showed the strongest local signal (r = 0.91, 0.67, 0.67, and 0.60). Bayesian networks revealed a recurrent positive wood–carbon structure linking tree carbon stock, firewood volume, deadwood carbon stock, tree carbon sequestration, and some soil responses. In contrast, understory plant diversity was more associated with local drivers such as elevation, canopy cover, slope, and tenure, and erosion control changed direction with scale and forest development stage. Findings show that monitoring should assess carbon- and wood-production-related functions together with biodiversity and soil-related functions across nested spatial scales, because trade-offs and synergies emerge as context- and scale-dependent relationships shaped by shared environmental and management drivers. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Automated wildlife monitoring systems are essential for studying bat populations in natural environments, where nocturnal behavior, high flight speeds, and limited illumination make conventional observation difficult. This paper presents a modular multi-sensor vision system that integrates RGB, near-infrared (NIR), and depth cameras for automated bat monitoring. The proposed architecture consists of one main module and two secondary modules that can be configured into multiple operating modes according to monitoring requirements. The main module operates independently to perform real-time habitat reconstruction using an integrated depth camera or bat detection using a YOLO-based model. When combined with one secondary module, it forms a stereo vision system for three-dimensional localization; when combined with both secondary modules, it generates panoramic images that substantially expand the field of view for monitoring large cave entrances and other complex environments. The proposed modular architecture enables flexible deployment while supporting multiple sensing configurations within a single platform. The modular design provides scalability, geometric consistency through multi-sensor calibration, and flexible deployment, enabling accurate bat detection, habitat reconstruction, and wide-area monitoring within a unified sensing framework. The proposed system provides a versatile and scalable solution for adapting wildlife monitoring to different environmental conditions and observation scenarios. Experimental results demonstrate a detection precision of 0.893, a panoramic field of view of 119°, and real-time processing at 60 fps, validating the effectiveness of the proposed modular architecture. Full article

Vehicular accidents are among the leading causes of fatalities, economic losses, and traffic disturbances worldwide. Understanding the intricacies of accident emergence and propagation is crucial for devising mitigation strategies. Among the different analysis approaches, cellular automata modeling stands out as a powerful tool since it enables the reproduction of complex collective dynamics through simple local interactions. Although numerous CA-based studies have addressed traffic accidents under different conditions, the literature still lacks a comprehensive synthesis dedicated to cellular automata-based vehicle-to-vehicle accident modeling. This systematic review classifies and comparatively analyzes how cellular automata models represent vehicle-to-vehicle collision mechanisms, dangerous traffic states, accident occurrence, and accident-related traffic effects. Through searches in Scopus, Web of Science, TRID, and IEEE Xplore, 740 records were identified and subsequently filtered to 90 papers according to the PRISMA methodology. The selected studies were classified according to a common taxonomy that includes rear-end, head-on, and side-impact collisions, as well as lateral conflicts. The contribution of the proposed taxonomy is threefold: to provide a deeper understanding of the key modeling principles, to review the types of accidents most frequently analyzed, and to identify current research gaps. Overall, this paper serves as a reference point for developing, comparing, and extending cellular automata models for traffic safety analysis. Full article

Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LiFePO₄) batteries is challenging because the voltage feedback used for correction does not provide constant SOC-related information under different operating conditions. Conventional extended Kalman filters (EKFs) usually apply measurement correction based on predefined statistical assumptions, while overlooking variations in voltage-domain observability and innovation reliability. This paper proposes a hybrid estimation framework, termed observability- and innovation–reliability-regulated EKF with reliability-scaled residual learning (OIR-EKF-RSRL). The proposed method retains a first-order RC model and EKF as the physical estimation backbone, while regulating voltage correction according to local OCV-SOC sensitivity and normalized innovation reliability. A reliability-scaled residual learning module is further introduced after physical filtering to compensate for remaining SOC deviations rather than directly predicting SOC. The learned residual correction is modulated by a reliability-dependent scaling coefficient before fusion with the OIR-EKF estimate, after which the final SOC estimate is constrained to the physical interval [0, 1]. The framework is evaluated using the CALCE A123 LiFePO₄ dataset under a frozen temperature-disjoint train–validation–holdout protocol. On the independent holdout set, OIR-EKF-RSRL reduces the RMSE from 3.331 percentage points for the conventional EKF to 2.942 percentage points. The results demonstrate that reliability-aware measurement utilization and reliability-scaled residual compensation provide an interpretable solution for LiFePO₄ SOC estimation under varying voltage-information quality. Full article

Wi-Fi Channel State Information (CSI) provides a privacy-preserving modality for human activity recognition (HAR), particularly in environments where activity classes vary in complexity and temporal scale. This work presents an integrated framework that combines multi-scale Fourier operator learning with manifold-aware density refinement for coarse-grained CSI-based HAR. The adaptive Multi-Scale Fourier Neural Operator (MS-FNO) models CSI trajectories as structured stochastic processes and learns activity mappings directly in the function space. Its multi-scale design enables the model to capture both simple, quasi-periodic macro-activities and more complex multi-person interactions by adjusting its receptive field according to the intrinsic structure of each activity class. To address class imbalance and representation collapse, the framework incorporates a Manifold-Preserving Local Density Oversampling (MPLDO) module that performs locality-constrained interpolation in a correlation-projected latent subspace, followed by classifier-guided pruning to maintain decision-boundary consistency. Experimental results show that the combined MS-FNO and MPLDO pipeline improves class-conditional separability and enhances recognition accuracy across both low-complexity and high-complexity activities. The findings highlight the effectiveness of this integrated operator-learning pipeline for privacy-aware activity monitoring in cafés, elder-care facilities, hospitals, and other real-world environments where coarse-to-complex activity understanding is required. Full article

The growing need to ensure the safety, resilience, and sustainability of existing building structures has accelerated the adoption of artificial intelligence (AI) for structural condition assessment and rehabilitation. This critical narrative review synthesizes 82 retained sources, including 33 application-oriented sources, through a transparent, structured literature search and study-selection process; it is not a formal systematic review or meta-analysis. To organize this fragmented evidence base, the review introduces the Data-to-Decision (D2D) Continuum, a unifying conceptual framework that traces eight engineering stages from data acquisition through damage detection, localization, quantification, condition and performance assessment, prognosis, reliability and risk assessment, to rehabilitation decision support. Classical machine learning, deep and temporal models, physics-guided and probabilistic approaches, and emerging foundation models are examined according to the engineering output required at each stage. The strongest evidence concerns bounded defect detection and localization, whereas uncertainty-aware prognosis, risk-informed rehabilitation selection, multi-site validation, and governed deployment remain markedly less mature. By integrating existing monitoring, digital-twin, life-cycle risk, and maintenance-decision concepts into an interface-centered evidence chain, the D2D framework clarifies what must be validated before an AI output can responsibly influence an intervention. Full article