Search Results (4,492)

Tropical cyclones are among the most destructive meteorological systems on Earth. Accurate track forecasting of tropical cyclones remains a core challenge in atmospheric science, and it is of great significance for disaster prevention and mitigation. This study targets the critical limitations of existing tropical cyclone track forecasting models: the insufficient ability to extract non-linear spatiotemporal features from 3D atmospheric circulation fields and the long-standing bottlenecks in multi-source heterogeneous meteorological data fusion. To address these issues, we propose a Dual-Stream Gated Fusion Network (DGF-Net), a high-precision track forecasting method tailored to the Northwest Pacific basin. The proposed framework takes the Best Track dataset and ERA5 Reanalysis Dataset as primary inputs: a Bidirectional Gated Recurrent Unit (Bi-GRU) is adopted to capture the temporal evolution characteristics of 2D tropical cyclone trajectory sequences, and a SpatioTemporal Convolutional Gated Recurrent Unit (STConvGRU) is used to extract complex non-linear features from 3D atmospheric environmental fields. Then, a multimodal fusion module integrating gating and attention mechanism is constructed to achieve deep fusion of cross-dimensional features, which effectively mines the intrinsic physical correlations between tropical cyclone track evolution and environmental driving factors. Comparative experiments based on historical observational datasets of the Northwest Pacific show that DGF-Net achieves superior forecasting performance, with the 6 h, 12 h, and 24 h Great Circle Distance (GCD) errors of 35.62 km, 43.53 km, and 135.49 km, respectively. The results significantly outperform mainstream baseline models, which validates the effectiveness of DGF-Net in feature extraction and multimodal fusion and provides solid technical support for tropical cyclone disaster prevention and operational decision-making. Full article

Owing to their high-altitude vantage point and maneuverability, unmanned aerial vehicles (UAVs) have emerged as an effective technical solution for real-time parking detection in expressway scenarios. Using UAV cruise-perspective images, this paper proposes an unauthorized parking detection method by analyzing the time-series variations in the relative distances between the moving vehicle and static structure as a reference. Firstly, vehicle and static structure targets are recognized and tracked by the DeepSort, and a Vehicle–Structure (V-S) distance matrix is further constructed to describe their frame-wise relative positions in the pixel coordinate system. Then, to eliminate the radial scale errors caused by perspective distortion, a scale factor (SF) index is introduced to correct the original V-S matrix and provide a more accurate spatiotemporal representation. Finally, the stationarity of the distance series in the V-S matrix is tested using the Augmented Dickey–Fuller (ADF) test, and a parking detection method is proposed by introducing the parking support ratio (PSR) to establish a multi-structure joint decision scheme. Experimental results show that the corrected V-S matrix can faithfully describe the spatial positional relationship between road vehicles and static structures. With the optimal PSR threshold ${\psi }_0$ and time window T, the proposed method achieves better overall parking-detection performance in terms of accuracy, precision, recall, and F1-score in comparison with a traditional speed threshold approach. Full article

High-speed solid particles ejected during battery thermal runaway pose severe safety threats, yet their velocity measurement is hindered by high density, microscopic size, and intense glare. This study proposes a non-intrusive velocimetry framework that integrates an enhanced single-stage object detector with a structural similarity matching algorithm. The detector incorporates specialized feature extraction modules and a high-resolution layer to identify microscopic targets in extreme environments, while the matching algorithm employs adaptive direction constraints to ensure precise trajectory tracking. Experimental validation demonstrates that the framework achieves a mean average precision of 92.7% and supports real-time processing. The method successfully quantifies a three-stage velocity evolution in battery failure events, identifying a peak particle speed exceeding 120 m/s. These findings provide critical kinematic data for optimizing battery safety structures and modeling fire propagation mechanisms. Full article

Precise object tracking of space objects is an image acquisition method that uses the mount of the telescope to orient the instrument in real time towards the target to be tracked, compensating for the target’s motion. Using this method, the object of interest will appear as a circular or point-like shape in the acquired image, while the background stars will appear as streaks. Using precise object tracking, the light from a faint object accumulates in the same region of the image, increasing the chance of observation, but longer exposures also increase the length of the background star streaks and makes the astrometric calibration difficult. This paper presents a method for the automatic processing of image sequences acquired in precise object tracking mode. Our proposed method includes a filtering mechanism that will ensure local maxima in the center of star streaks in order to allow for a publicly available astrometric calibration software to work even if the stars are not point-like, a weighted stacking mechanism to increase the signal-to-noise ratio for faint targets while excluding the stars, an automatic object detection and astrometric reduction mechanism and a constraint-based filtering of outliers for the final generation of the tracklet. The method was tested on multiple observation sessions for surveying the CLUSTER II highly eccentric orbit satellites, including the CLUSTER II FM5 satellite (Rumba) on its final passes before reentry, and the accuracy of the measurements was estimated based on ground truth from ESA’s reentry team. The method was also tested on lower orbit objects and found to be accurate for objects with ranges of more than 1300 km from the observer. Full article

Background/Objectives: Schemas support memory, but it is unclear how aging affects remembering when events deviate from schemas in different ways. We tested whether age differences depend on the integrability of schema-deviant object configurations and whether eye movement evidence sampling tracks retrieval decisions. Methods: Young and older adults completed a Restructured Object Memory Task. They encoded objects that were non-restructured (schema-consistent), reasonably restructured (deviant but integrable), or unreasonably restructured (deviant and non-integrable). At retrieval, participants made three-alternative forced-choice judgments while eye movements were recorded. Subjective ratings assessed perceived deviation, and representational similarity analysis related eye-movement patterns to memory confusions. Results: Older adults showed poorer cue discrimination and object memory, with the largest deficits for restructured objects. Their errors were schema-consistent, often selecting the typical object when targets were restructured. Ratings confirmed the intended deviation ordering, but older adults differentiated conditions less. Eye movements showed that young adults showed the highest target viewing proportion for reasonably restructured targets, and longer fixation durations for unreasonably restructured targets. In young adults, eye-movement representational structure tracked memory confusions. In older adults, early orienting and fixation duration were less predictive of choices, consistent with weaker coupling between sampling and decision. Conclusions: Aging was associated with poorer memory for schema-deviant objects, consistent with reduced representational fidelity and reduced flexibility in how online visual evidence is sampled and used when prior knowledge conflicts with new configurations. Full article

Chemical monitoring of pollutants and hazardous materials in water supply systems traditionally depends on centralized laboratories, advanced instrumentation, and trained personnel, limiting accessibility and preventing real-time, on-site analysis. This work presents an alternative cost-effective, field-deployable approach that uses genetically engineered bioluminescent bioreporters, encapsulated in self-sufficient alginate capsules and integrated with an optoelectronic detection circuit, to detect and quantify target materials in water. We have developed a scalable single-channel prototype featuring four sensing tracks—two for sample measurement, one for clean water, and one for a standard reference solution. The latter employs the standard ratio (SR) method to ensure robust quantification, compensating for batch variability and environmental effects. System characterization showed high uniformity across tracks. Validation with nalidixic acid (NA) demonstrated reliable quantitative performance, with a blind test estimation of 5.6 mg/L for a true concentration of 5 mg/L, well within the calibration error range. Additional sensitivity testing confirmed detection of mitomycin C (MMC) at concentrations as low as 50 µg/L. Overall, the results highlight the potential of bacterial chemical sensing as a practical and scalable tool for real-time, in situ water quality monitoring networks. Full article

In dynamic and complex real-world environments, artificial intelligence (AI) vision systems continue to face significant challenges in accurately detecting and tracking small objects. The core difficulty lies in the fact that small targets usually exhibit limited spatial and textural features, while dynamic backgrounds often generate numerous misleading motion cues, thereby interfering with reliable discrimination between targets and backgrounds. Inspired by the remarkable capability of the insect brain in detecting small moving objects, this study proposes a visual neural network model enhanced by a feedback mechanism. By adaptively responding to temporal variations, the proposed model is able to more precisely distinguish small targets from background-induced false targets. The network architecture consists of two main pathways: a motion detection pathway that extracts motion-related features from minute targets, and a feedback attention pathway that enhances the focus on true targets by leveraging the feature differences between real and false motion signals. In addition, a global inhibition module is incorporated to reduce the false alarm rate by filtering out background-induced false positives, thereby improving the overall detection performance of the model. Experimental results demonstrate that the proposed model achieves a detection rate of 0.81 in complex visual scenarios, whereas the compared models all achieve detection rates below 0.59, indicating a significant improvement in detection performance. Meanwhile, in terms of Precision and F1-score, the proposed model achieves values of 0.0648 and 0.12, respectively, while the compared models obtain values lower than 0.0077 and 0.015, further validating the superiority of the proposed method in detection accuracy and robustness. Full article

Spaceborne infrared small-target detection plays a critical role in space-sky early warning, disaster rescue, and reconnaissance tracking, benefiting from all-time, all-weather, and wide-area monitoring capabilities. The deployment of high-spatial-resolution infrared payloads (ground sampling distance, GSD < 10 m) has introduced pronounced scale diversity among targets, leading to size-sensitive performance degradation in existing detectors and heightened risks of missed detections or false alarms in mixed-size scenarios. Furthermore, multi-frame infrared small-target detection methods often face challenges in maintaining consistent temporal coherence during feature propagation across sequences. To overcome these limitations in high-resolution spaceborne infrared videos, we propose DTRFR, an end-to-end unified detection framework built on an enhanced recurrent feature refinement architecture. This approach incorporates a realistic SITP-QLSD dataset derived from QLSAT-2 infrared backgrounds, featuring diverse scenes, multi-size small targets, and a dedicated generalization sub-test set with extremely small targets partially unseen in training; a multi-scale IRFeatureExtractor leveraging parallel convolutions and dilated receptive fields for improved cross-scale discrimination and clutter suppression; and an adaptive gating pyramid deformable alignment module to optimize sequence alignment and enhance temporal consistency, enabling robust performance across various clutter levels and dynamic backgrounds. Extensive evaluations on SITP-QLSD demonstrate that DTRFR attains competitive performance, achieving mIoU of 74.32% and Pd of 94.51% on the main set, with strong robustness on the generalization sub-test set (Pd = 92.37%). Compared to single-frame and multi-frame baselines, the proposed method achieves higher detection accuracy with significantly reduced false alarms, benefiting from multi-scale feature extraction that enables robust detection of small targets of different sizes in infrared videos. Full article

Huntington’s disease (HD) is characterized by progressive striatal atrophy and complex proteomic changes in the central nervous system. Using the ultrasensitive Next-Gen Ultra-Sensitive Immunoassay (NULISA) proteomic platform, we analyzed cerebrospinal fluid (CSF) from 88 persons with HD to dissect the biological correlates of gray matter loss. Our findings reveal a distinct “Two-Track” model of pathology. The first track, marked by the axonal damage protein neurofilament light chain (NEFL), showed a strong inverse correlation with putamen volume (Pearson r = −0.53, p < 0.001), reinforcing its utility as a proxy for structural neurodegeneration. The second track was defined by a positive association between the immune regulator TNFRSF8 (CD30) and putamen volume (Pearson r = 0.36, p < 0.001), reflecting a decline in active immune-regulatory signaling as striatal atrophy advances. Given its established role in immune modulation, TNFRSF8 was pre-specified for follow-up to further interrogate this neuro-immune axis. Crucially, TNFRSF8 maintained an independent association with striatal volume (Beta = 0.24, p = 0.008) even after controlling for NEFL, genetic burden (CAG-Age Product score), and sex. Supplementary analyses confirmed that this structural–immune axis is localized specifically to the striatum—showing no association with generic structural control regions—and is driven by CAG repeat length rather than chronological aging. Furthermore, bidirectional mediation analysis supported an atrophy-driven model, where striatal volume statistically mediates the relationship between genetic burden and downstream immune dysregulation (p = 0.010). These results demonstrate that maladaptive immune signaling is a distinct pathological correlate in HD, separable from general cytoskeletal damage. This dual-axis framework warrants evaluation in larger longitudinal and interventional studies to guide future biomarker-driven patient stratification and target engagement. Full article

Mountain ecosystems, strongly affected by climate-related variability and human impact, are degrading faster than other terrestrial ecosystems. Currently, the United Nations (UN) utilizes Sustainable Development Goal (SDG) 15: Life on Land (Target 15.4 and Sub-indicators 15.4.2a and 15.4.2b), along with the System for Earth Observation Data Access, Processing and Analysis for Land Monitoring, commonly referred to as SEPAL, to track mountain degradation. This SEPAL analysis does not include soil data, which is critical to understanding mountain degradation. The present research focuses on improving the tracking and evaluation of mountain land degradation (LD) utilizing soil data in the state of Colorado (CO) in the United States of America (USA) as an example. Total anthropogenic LD affects an estimated 19% of Colorado’s territory as of 2024, driven mainly by agricultural activities (80%). Between 2001 and 2024, overall LD in CO decreased (−0.4%), but LD from development increased by 23.3%. For mountain areas in CO, the mountain green cover index (MGCI) was 96% for 2024, and it decreased (−0.4%) between 2001 and 2024. The mountain LD proportion was 2.5% as determined by the SEPAL method compared to 4.4% by LULC analysis. Incorporation of soil data into LULC analysis found that between 2001 and 2024 LD increased to 6.6%. All soil types in the mountains exhibited anthropogenic LD due to development with a total developed area of 1385.1 km². Current total mountain LD (inherent + anthropogenic) in CO may be as high as 38.9%. Future estimates of total mountain LD should include both inherent and anthropogenic LD. Full article

Accurate multi-target filtering is crucial for low-altitude surveillance, where point and group targets often coexist. Poisson multi-Bernoulli mixture (PMBM) filters provide a unified Bayesian framework for the joint filtering of point and group targets under the assumptions of independent target dynamics and standard measurement models. However, in practical scenarios, group targets may generate new targets through member separation, while point targets may produce multiple measurements due to multi-beam sensing and micro-Doppler signatures. These phenomena violate the assumptions of existing PMBM filters and lead to degraded state estimation and target-type inference. To address these challenges, this paper proposes a modified PMBM filter with group target spawning and generalized measurement models for coexisting point and group targets. Specifically, a group-dependent spawning model is incorporated into the prediction step to enable timely detection of newly spawned targets. In addition, a generalized update function is developed to support point-target density updates with measurement sets of arbitrary cardinality, and a measurement-rate-based correction factor is introduced to improve target-type estimation under nonstandard measurement conditions. Furthermore, an efficient Poisson multi-Bernoulli approximation is derived to reduce computational complexity. The effectiveness of the proposed filter is verified through simulation and experimental results. Full article

Unmanned aerial vehicle (UAV) guidance systems must operate reliably under significant uncertainties, such as sensor noise, target maneuvers, and environmental disturbances. Traditional guidance methods like proportional navigation (PN), while computationally efficient, often struggle to maintain performance under such challenging conditions. To overcome these limitations, this study proposes a robust UAV guidance framework based on deep reinforcement learning (DRL), specifically utilizing the soft actor–critic (SAC) algorithm. The UAV–target tracking problem is formulated as the Markov decision process (MDP) for both two-dimensional (2D) and three-dimensional (3D) scenarios. A deep neural network policy is trained in noisy environments to generate acceleration commands that minimize the zero-effort miss (ZEM). Extensive numerical simulations conducted using the OpenAI Gym validate effectiveness of the proposed method under previously unseen initial conditions and increased noise levels. The results demonstrate that the SAC-based policy achieves higher tracking success rates than the PN, particularly under strict terminal conditions and observation noise. Full article

(WMoTaNb)SiN refractory high-entropy nitride films were deposited via magnetron cosputtering, and the Si content was systematically regulated by varying the Si target power to investigate its influence on the microstructure, mechanical properties, oxidation resistance, and oxyhydrogen-flame ablation behavior. All the films exhibited dense columnar architectures with a distinct FCC + BCC dual-phase structure, whereas increasing the Si target power led to a gradual increase in the deposition rate and Si incorporation. The mechanical properties displayed a non-monotonic relationship with the Si target power, with film applied at an intermediate level of Si target power showing the highest hardness, approximately 28.5 GPa, and improved elastic recovery. Tribological evaluations using a GCr15 steel ball revealed that this film exhibited the lowest wear rate of 4.1 × 10⁻⁶ mm³·N⁻¹·m⁻¹ and a narrower wear track, which was attributed to reduced plastic deformation and the development of an oxygen-enriched tribofilm during sliding. High-temperature oxidation at 1000 °C in air revealed that Si incorporation significantly modified oxide-scale evolution by refining the oxidation products and altering the scale architecture, while the protection of the scale was governed by its continuity and compactness rather than its thickness alone. Oxyhydrogen-flame ablation tests revealed that the degradation behavior was primarily driven by the competition between oxidation-induced mass increase and ablation-induced material loss, with localized film disruption and substrate exposure playing a decisive role. In summary, the findings illustrate that an optimal Si target power establishes a favorable equilibrium between mechanical strength, tribological efficiency, oxidation resistance, and ablation performance, underscoring the potential of (WMoTaNb)SiN films for protective applications in complex mechanical and extreme thermal environments. Full article

The transition from lower to upper secondary education is a critical developmental stage, requiring decisions with long-term academic and professional consequences. Addressing a gap in evidence that often treats counselling, family educational capital, and place of residence separately, this study examines how these factors jointly relate to students’ high school track/profile choice and their intention to pursue higher education in the Romanian educational transition. Using a standardized questionnaire, we conducted a cross-sectional survey of 1392 lower secondary students (aged 13–14) from Brașov County, Romania, to map preferred tracks, influencing factors, perceptions of high school, and the values framing decision-making. High school track/profile choice emerged as a central “decision node”, strongly associated with participation in counselling p < 0.001; Cramer’s V = 0.678) and significantly related to parents’ educational level and university intentions. Substantial urban–rural differences were observed in track/profile choice (p < 0.001; V = 0.442), with urban students selecting the “real” track more frequently (≈68%) than rural students (≈37%). University intention was high overall, with a small but significant urban–rural difference (≈89.7% vs. ≈86.9%; p = 0.028; V = 0.072). Findings support integrating counselling into coherent adolescent career development models and expanding services to reduce contextual disparities through stronger school–family–community partnerships. This evidence is relevant for education policy and practice by supporting the scaling of school-based career guidance and targeted measures to reduce rural–urban disparities. Full article

This study examines whether targets’ environmental, social, and governance (ESG) performance is associated with acquirers’ post-merger ESG outcomes and market valuation over the merger year and the subsequent two years. We treat controversies-adjusted ESG scores (ESGC) as outcome-based indicators. Using a global panel of 4572 acquirer-year observations from 47 countries between 2002 and 2023, we analyze the association between targets’ ESGC and acquirers’ post-merger ESG trajectories and market value. Tobit estimations trace combined and pillar-level ESG dynamics over the merger year and the first two post-merger years. The results indicate that target ESG performance is associated with persistent improvements in acquirer sustainability, with the strongest effects in social and environmental dimensions and more gradual adjustments in governance, reflecting institutional and organizational integration complexity. Heterogeneity analyses reveal that cross-border within-industry acquisitions generate the largest ESG gains, whereas domestic within-industry transactions are associated with ESG deterioration. Regarding market valuation, acquirers’ own ESG performance is reflected in Tobin’s Q, while target ESG becomes value-relevant with a one-year lag, highlighting a two-stage valuation mechanism linked to post-merger absorption and institutionalization. Adopting a multi-period perspective, the study shows that ESGC track post-merger sustainability outcomes in ways consistent with learning, institutionalization, and legitimacy-based interpretations. Full article