Search Results (11,754)

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46$\mathrm{s}$ settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

Vertical docking of the autonomous underwater shuttle (AUS) for deep-sea data relay relies heavily on ultra-short baseline (USBL) acoustic positioning, whose measurements can be intermittently unavailable and contaminated by outliers in complex underwater environments. This paper proposes a neural network-enhanced robust navigation framework to improve AUS navigation reliability during acoustically guided vertical docking under USBL outages. First, a model-aided batch maximum a posteriori trajectory estimation method (MA-BMAP) is developed to generate learning quality supervision under sensor-limited conditions. Based on the estimated trajectories, a long short-term memory (LSTM)-based horizontal velocity predictor is integrated into a robust fusion filter with online ocean current estimation, enabling stable state estimation during USBL outages and robust rejection of abnormal USBL measurements. The proposed framework is validated through simulations and field trials in lake and sea environments. In sea trials, during two representative 200 s USBL outage intervals, the end-of-window horizontal position errors are 7.86 m and 4.14 m, respectively, corresponding to AUS-to-docking station distances of 244 m and 51 m. In addition, the introduced USBL outliers are successfully detected and rejected. The results indicate that the proposed method enables accurate and stable navigation during USBL unavailability and rapid recovery once USBL measurements resume, demonstrating its practicality for vertical docking missions. Full article

A compact dual-polarized beam-steerable patch antennas with filtering characteristics is proposed in this paper. By digging two orthogonal coupling slots on the ground plate, dual polarization is achieved while ensuring the isolation between the ports. By constructing properly arranged parallel microstrip resonators and open-circuited stubs, the effect of suppressing a broad stopband is produced. The beam steering characteristic is accomplished through the integration of a driven patch antenna with two dual-element metallic walls, each incorporating PIN diodes for electronic tuning. A prototype antenna has been fabricated to substantiate the efficacy of the proposed methodology. The simulated and measured results agree well, demonstrating good performance in terms of impedance bandwidth, stopband suppression, isolation and beam-steering capability. Under six radiation states, the proposed antenna operates from 2.3 GHz to 2.5 GHz with isolation exceeding 20 dB. Additionally, the antenna gain remains below −10 dBi over the 2.6 GHz to 10 GHz band, achieving out-of-band suppression greater than 15.8 dB within the wide stopband. When port 1 is excited, the antenna generates three distinct radiation patterns, enabling beam scanning at 0° and ±30° in the yoz plane. Similarly, exciting port 2 yields three radiation patterns, allowing beam scanning at 0° and ±30° in the xoz plane. This work presents the first integration of dual-polarized, beam-steering, and filtering characteristics into a single compact antenna. Full article

In this study, a thermally crosslinking clay-free weak gel water-based drilling fluid based on salt-responsive polymers and crosslinking agents was investigated as a promising and feasible strategy. Firstly, a salt-tolerant polymer was synthesized using N,N-dimethylacrylamide (DMAA), [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfonopropyl)ammonium hydroxide (DMAPS), and acrylamide (AM). BPEI_10,000 was selected as the thermal crosslinking agent. The optimal crosslinking was achieved at 180 °C and 36% NaCl, with RMFL at 2.0% and BPEI_10,000 at 0.1%. Performance evaluation demonstrated that the crosslinking between RMFL and BPEI_10,000 could enhance the AV, PV, and YP of the RMFL(BPEI_10,000)/CF-WBDFs after aging at 180 °C for 16 h and reduce FL_API. The RMFL(BPEI_10,000)/CF-WBDFs exhibited appropriate shear-thinning behavior, viscoelasticity, thixotropy, and recoverable viscosity under high-temperature, high-salinity, and high-pressure conditions. Mechanism analysis revealed that RMFL and BPEI_10,000 could form a predominantly negatively charged, three-dimensional crosslinking weak gel at high temperatures. The crosslinking weak gel could form dense filter cakes, improving rheological properties and reducing filtration loss of CFWBDFs in high-temperature, high-salinity environments. This paper proposed a novel method to address the technical challenge of rheological performance failure of CFWBDFs, offering valuable insights for subsequent investigations. Full article

Chitooligosaccharides (COS) are short-chain chitosan derivatives with a wide range of biomedical, agricultural, and environmental applications, including antimicrobial therapy, wound healing, and pollutant removal. Reliable quantification of COS is essential but currently relies on high-performance liquid chromatography, mass spectrometry, or capillary electrophoresis, which require costly equipment, complex sample preparation, and are unsuitable for routine or on-site applications. This study reports a rapid, solvent-free, colorimetric assay for COS based on the reaction of 5% aqueous ninhydrin with free amino groups in McIlvaine buffer. The assay was optimized using glucosamine as a model analyte, yielding maximal sensitivity at pH 7.0. The chromophore generated (Ruhemann’s purple) remained stable for over 120 min after reaction, allowing measurements to be taken without strict time constraints. Calibration was linear from 0.4 to 2.2 mM (R² = 0.9926), with low limits of detection (0.006 mM) and quantification (0.018 mM). Increasing absorbance with COS polymerization degree (DP1–DP6) demonstrates specificity for free amino groups, while N-acetyl glucosamine showed a negligible response. Furthermore, the assay was successfully adapted for solid-phase detection on ninhydrin-pretreated filter paper and nitrocellulose, with enhanced sensitivity. This simple, efficient, and low-cost method provides an accessible alternative to instrumental techniques, supporting COS monitoring in laboratory workflows and enabling portable applications in biomedicine, agriculture, and environmental diagnostics. Full article

As a global environmental problem, biological invasion poses a serious threat to natural ecosystems. To explore the influence mechanism of Alternanthera philoxeroides (Mart.) Griseb on the growth and development of landscape plants, this study systematically analyzed the effects of extracts from different organs (stems, leaves, and roots) of A. philoxeroides on the seed germination and seedling growth of Zinnia elegans Jacq. by combining the Petri dish filter paper method with a pot experiment to reveal the potential mechanism of allelopathy. The results showed that the aqueous extract of A. philoxeroides inhibited the seed germination and seedling growth of Z. elegans. The high concentration (100 mg·mL⁻¹) of stem and leaf extracts significantly reduced the germination rate (by 99.10% and 90.65%) and seedling morphological parameters. The allelopathic inhibition increased with an increase in concentration, and the inhibitory effect of stem and leaf extracts was significantly stronger than that of root extracts. Aqueous extracts from the roots, stems, and leaves of A. philoxeroides at three concentrations (25, 50, and 100 mg·mL⁻¹) induced oxidative stress in seedlings, as evidenced by the elevated malondialdehyde (MDA) content and dysregulated activities of antioxidant enzymes. Specifically, superoxide dismutase (SOD) and catalase (CAT) activities exhibited a concentration-dependent trend of initial induction followed by subsequent inhibition, while root activity was significantly suppressed (p < 0.05), ultimately impairing seedling growth. The aqueous extracts of A. philoxeroides showed a concentration-dependent inhibitory effect on the seed germination and seedling growth of Z. elegans. High concentrations of stem and leaf extracts exerted a significant inhibitory effect on seedling growth, and this growth suppression was attributed to the induction of oxidative stress by the extracts. This study elucidated the phytotoxicity degree and physiological response mechanisms underlying the biochemical allelopathy of A. philoxeroides on Z. elegans. The findings provide a theoretical foundation for the selection of horticultural plant cultivars resistant to allelopathic stress and the development of management strategies for invasive plants. Full article

In complex traffic environments such as low light, strong glare, occlusion and at night, systems that rely solely on visible light single sensors for pedestrian detection have drawbacks such as low detection accuracy and poor robustness. Based on the YOLOv8 convolutional network, this paper adopts a dual-branch structure to process visible light and infrared images simultaneously, fully utilizing feature information at different scales to effectively detect pedestrian targets in complex and changeable environments. To address the issues of insufficient interaction of modal feature information and fixed fusion weights, a cross-modal feature interaction and enhancement mechanism was introduced. A modal-channel interaction block (MCI-Block) was designed, in which residual connection structures and weight interaction were added within the module to achieve feature enhancement and filter out noise information. Introduce a dynamic weighted feature fusion strategy, adaptively adjusting the contribution ratio of different modal features in the fusion process, aiming to enhance the discrimination ability of the key pedestrian area. The training and testing of the network designed in this paper were completed on the visible light and infrared pedestrian detection dataset LLVIP and Kaist. At the same time, the test results of the dual-branch model and the model designed in this paper were further verified in actual traffic scenarios. The results show that the dual-branch YOLOv8 network for visible light and infrared images, which was constructed in this paper, can reliably enhance the detection performance of pedestrian targets in complex traffic environments, including accuracy, recall rate, and mAP@0.5, etc., thereby improving the robustness of pedestrian detection. Full article

Accurate crop classification is critical for optimizing agricultural resource use and informing production decisions. Deep learning, with its robust feature extraction ability, has become a prevalent technique for remote sensing-based crop classification. However, agricultural landscape complexity poses three key challenges: background noise interference, class confusion from inter-crop spectral similarity, and blurred small-area crop boundaries due to class imbalance. This paper proposes FCR-TransUNet, a TransUNet-based enhanced model integrating three modules: Feature Enhancement Module (FEM) for noise filtering, Class-Attention (CAExperimental results on the Youyi Farm and barley datasets validate the superiority of the proposed model. On the Youyi Farm dataset, FCR-TransUNet achieves an MIoU of 92.2%, representing an improvement of 1.8% over SAM2-UNet and 2.9% over the baseline TransUNet. On the barley dataset, it yields an MIoU of 89.9%. Ablation studies further verify the effectiveness of each designed module. To comprehensively evaluate the classification performance of FCR-TransUNet across the full crop growth cycle, experiments were conducted using remote sensing images from May, July, and August, respectively. The results demonstrate that FCR-TransUNet exhibits strong stability and adaptability at different crop growth stages, providing a reliable solution for precision agriculture and intelligent agricultural production. Full article

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed information, significantly limiting the recognition performance. Conventional image processing methods, while aiming to restore the imaging quality of instrument panels through low-light enhancement, inevitably introduce overexposure and indiscriminately amplify background noise during this process. To address the two key challenges of illumination recovery and noise suppression in the process of restoring panel image quality under non-uniform low-light conditions, this paper proposes a coarse-to-fine cascaded perception framework (CFCP). First, a lightweight YOLOv10 detector is employed to coarsely localize the meter reading region under non-uniform illumination conditions. Second, an Adaptive Illumination Correction Module (AICM) is designed to decouple and correct the illumination component at the pixel level, effectively restoring details in dark areas. Then, an Illumination-invariant Feature Perception Module (IFPM) is embedded at the feature level to dynamically perceive illumination-invariant features and filter out noise interference. Finally, the refined detection results are fed into a lightweight sequence recognition network to obtain the final meter readings. Experiments on a self-built industrial digital instrument dataset show that the proposed method achieves 93.2% recognition accuracy, with 17.1 ms latency and only 7.9 M parameters. Full article

The navigation of ships in complex shallow water areas is constrained by various factors such as water depth, channel boundaries, and environmental interference. Therefore, it is crucial to improve the adaptability and effectiveness of collision avoidance decisions for ships in complex shallow water scenarios. To address these issues, this paper proposes a multi-factor constrained autonomous decision-making method for complex shallow water vessel maneuvering. Firstly, a digital transportation environment was constructed by combining dynamic and static information, such as water depth, tides, channel boundaries, changes in maneuvering characteristics, and navigation rules, and a navigable water area model that was suitable for shallow water was proposed. Then, considering the constraints of ship maneuverability and the navigation environment, a shallow water ship motion model affected by wind flow was developed. A complex shallow water adaptive maneuvering coupled decision-making method was constructed, considering the influence of ship navigation rules and channel constraints. This method utilizes the Kalman filtering algorithm to correct residuals and predict the maneuvering of the target vessel. Integrated improved heading control and guidance algorithms achieved automatic heading control and future position prediction. Through testing and verification in the complex waters of the Yangtze River estuary, the results show that the autonomous collision avoidance decision-making method proposed in this paper can effectively make collision avoidance decisions in complex multi-ship shallow water areas. This study can provide innovative and practical solutions for the technological development of autonomous ship collision avoidance decision-making. Full article

Detecting and disconnecting faults is of utmost importance in power systems to prevent damage, outages and limit the impact on the surrounding grid. However, there are faults that may not be detected by protective functions and therefore do not interrupt the operation. Such faults, which have not been considered during the design of an HVDC system despite causing negative operational impacts, are referred to as abnormal failure modes in this paper. Data from three cases of abnormal failure modes in point-to-point HVDC systems are presented. The first case regards a prolonged subsequential failure of a DC filter capacitor for an LCC-HVDC link. The second case presents a measurement disturbance resulting in power oscillations from a VSC-HVDC link. The third case shares details of an overload scenario of a grounding impedance due to DC voltage unbalance from asymmetric corona discharges. This study shares details from these failures and suggests recommendations based on the presented abnormal failure modes in HVDC applications, including multi-terminal HVDC systems. Full article

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 to 2022. Using firm- and year-fixed-effects panel models, the paper examines whether these candidate proxies show pre-specified directional associations within firms and whether the same ratios are associated with operating margin in parallel models. Instrumental-variable (IV) specifications are reported only as sensitivity analyses, and nearly all are weak by the paper’s reported first-stage diagnostics. Results: Accordingly, most findings are interpreted as associative rather than causal. After false-discovery-rate adjustment and weak-instrument-robust inference, only four firm–proxy pairs meet the paper’s detection criterion; all remaining estimates are treated as non-robust. Conclusions: The contribution is therefore narrow: this is a constrained exploratory screening exercise showing which candidate mappings survive the paper’s inferential filters in this sample and which do not. The results do not establish a validated cross-industry scorecard, a scalable benchmarking framework, or a basis for policy claims. Full article