Search Results (524)

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer–Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin–destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures. Full article

Safety climate has been extensively studied using survey-based approaches, providing significant insights into safety perceptions and behaviors. However, understanding its dynamics in construction projects requires methods that address temporal and trade-specific variability. This study employs a longitudinal, mixed-methods design to explore safety climate dynamics. Quantitative data analyzed with ANOVA revealed stable overall safety climate scores across project phases, while Item Response Theory (IRT) identified survey items sensitive to safety climate changes. Positive perceptions were associated with management commitment and regular safety meetings, while negative perceptions highlighted challenges such as workplace congestion and impractical safety rules. Qualitative data from semi-structured interviews uncovered trade-specific and phase-specific safety challenges, including issues tied to site logistics and workforce dynamics. For instance, transitioning from structural to interior work introduced congestion-related risks and logistical complexities, underscoring the need for phase-adapted strategies. This combination of quantitative stability and qualitative variability provides empirical evidence of safety climate dynamics in construction. The findings emphasize the importance of tailoring safety interventions to address trade-specific and phase-specific risks. This study advances the understanding of the safety climate in dynamic work environments and offers actionable recommendations for improving construction safety management through targeted, proactive strategies. Full article

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m⁻³). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. Full article

This study proposes a regression-based approach for calculating the likelihood probability in time-varying multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters. These time-varying MIMO systems often face performance challenges because of the difficulty in tracking changes in the likelihood probability. To address this challenge, the proposed method leverages channel statistics and decoded outputs to refine the likelihood. An optimization problem is then formulated to minimize the mean-squared error between the true and refined likelihood probabilities. A linear regression approach is derived to solve this problem, and a regularization technique is applied to further optimize the calculation. The simulation results indicate that the proposed method improves reliability by effectively tracking temporal variations in the likelihood probability and outperforms conventional methods in terms of performance. Full article

Background: Hand dexterity is affected by normal aging and neuroinflammatory processes in the brain. Understanding the relationship between hand dexterity and brain structure in neurotypical older adults may be informative about prodromal pathological processes, thus providing an opportunity for earlier diagnosis and intervention to improve functional outcomes. Methods: this study investigates the associations between hand dexterity and brain measures in neurotypical older adults (≥65 years) using the Nine-Hole Peg Test (9HPT) and magnetic resonance imaging (MRI). Results: Elastic net regularized regression revealed that reduced hand dexterity in dominant and non-dominant hands was associated with an enlarged volume of the left choroid plexus, the region implicated in neuroinflammatory and altered myelination processes, and reduced myelin content in the left frontal operculum, the region implicated in motor imagery, action production, and higher-order motor functions. Distinct neural mechanisms underlying hand dexterity in dominant and non-dominant hands included the differences in caudate and thalamic volumes as well as altered cortical myelin patterns in frontal, temporal, parietal, and occipital regions supporting sensorimotor and visual processing and integration, attentional control, and eye movements. Although elastic net identified more predictive features for the dominant vs. non-dominant hand, the feature stability was higher for the latter, thus indicating higher generalizability for the non-dominant hand model. Conclusions: Our findings suggest that the 9HPT for hand dexterity might be a cost-effective screening tool for early detection of neuroinflammatory and neurodegenerative processes. Longitudinal studies are needed to validate our findings in a larger sample and explore the potential of hand dexterity as an early clinical marker. Full article

Speech recognition is widely applied in fields like security, education, and healthcare. While its development drives global information infrastructure and AI strategies, current models still face challenges such as overfitting, local optima, and inefficiencies in decoding accuracy and computational cost. These issues cause instability and long response times, hindering AI’s competitiveness. Therefore, addressing these technical bottlenecks is critical for advancing national scientific progress and global information infrastructure. In this paper, we propose improvements to the model structure fusion and decoding algorithms. First, based on the Conformer network and its variants, we introduce a weighted fusion method using training loss as an indicator, adjusting the weights, thresholds, and other related parameters of the fused models to balance the contributions of different model structures, thereby creating a more robust and generalized model that alleviates overfitting and local optima. Second, for the decoding phase, we design a dynamic adaptive decoding method that combines traditional decoding algorithms such as connectionist temporal classification and attention-based models. This ensemble approach enables the system to adapt to different acoustic environments, improving its robustness and overall performance. Additionally, to further optimize the decoding process, we introduce a penalty function mechanism as a regularization technique to reduce the model’s dependence on a single decoding approach. The penalty function limits the weights of decoding strategies to prevent over-reliance on any single decoder, thus enhancing the model’s generalization. Finally, we validate our model on the Librispeech dataset, a large-scale English speech corpus containing approximately 1000 h of audio data. Experimental results demonstrate that the proposed method achieves word error rates (WERs) of 3.92% and 4.07% on the development and test sets, respectively, significantly improving over single-model and traditional decoding methods. Notably, the method reduces WER by approximately 0.4% on complex datasets compared to several advanced mainstream models, underscoring its superior robustness and adaptability in challenging acoustic environments. The effectiveness of the proposed method in addressing overfitting and improving accuracy and efficiency during the decoding phase was validated, highlighting its significance in advancing speech recognition technology. Full article

With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network security while addressing challenges related to data imbalance and long-tail distributions. A dataset containing five types of attacks targeting programmable logic controllers (PLCs) in industrial control systems (ICS) was first constructed. To address class imbalance and challenges posed by complex network traffic, Synthetic Minority Oversampling Technique (SMOTE) and Borderline-SMOTE were applied to oversample minority classes, thereby enhancing their diversity. This paper proposes a dual-channel feature extraction model that integrates a multi-scale one-dimensional convolutional neural network (MS1DCNN) and a Weight-Dropped Transformer (WDTransformer) for IDS. The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail distributions, a custom combined loss function was developed by integrating cross-entropy loss and focal loss to reduce misclassification in minority classes. Experimental validation on the constructed dataset demonstrated that the proposed model achieved an accuracy of 95.11% and an F1 score of 95.12%, significantly outperforming traditional machine learning and deep learning models. Full article

In-depth research on the spatial and temporal evolution of ecosystem service trade-offs and synergistic relationships, scientific identification of ecosystem service bundles, and the main factors affecting the spatial differentiation of ecosystem service bundle provisioning are crucial to enhancing the overall benefits of regional ecosystem services and human well-being. Based on the assessment of the Guangdong–Hong Kong–Macao Greater Bay Area ecosystem service functional system, we combined the correlation analysis method, hierarchical clustering method, and principal component analysis to analyze the trade-offs/synergistic relationships of 11 indicators contained in four major ecosystem service categories of the Guangdong–Hong Kong–Macao Greater Bay Area and explored the study of ecosystem service bundle identification and clustering spatial differentiation. The results of this study showed the following: (1) Between 2000 and 2018, Regulating and Supporting services showed a decreasing trend while provisioning and cultural services showed an increasing trend. Human interference affected the spatial differentiation of ecosystem services provision; the provision of individual ecosystem services was more random, but the geospatial distribution showed a certain degree of regularity. (2) The intrinsic connection of ecosystem services is continuously strengthened, and the other four ecosystem services except industrial products in the provisioning services easily produce synergistic relationships with regulating and supporting services, while industrial products, leisure and recreation, scientific research and education, and other ecosystem services are more likely to produce a trade-off relationship between them. The correspondence among ecosystem service trade-offs, synergistic relationships, and cold/hot spots is not uniform due to spatial scales. (3) The method of combining socio-economic statistics and the InVEST model can identify similar ecosystem service bundle classifications, but there are differences in the performance of some of the roles at different study scales and in different study areas. (4) For complex urban-natural ecosystem services, the classified ecosystem service bundles have broad similarities. The development of high-density city clusters depends on the coordinated development of the population, resources, environment, society, and economy of each city in the region. Full article

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. Full article

Background: Gestational diabetes mellitus (GDM) is a transient elevation of blood glucose during pregnancy. It is typically not associated with diabetic retinopathy. However, certain investigators revealed retinal microvascular injury. In this study, we aimed to assess the ophthalmic findings, optical coherence tomography (OCT) parameters, and retinal vascular thickness and macular density through fundus autofluorescence (FAF). Methods: Prospectively, women diagnosed with GDM were enrolled in this study. All the participants underwent comprehensive ophthalmic examination. Furthermore, macular OCT with analysis of the central subfield thickness (CST) and total thickness was carried out. Moreover, FAF was performed, and the macular density and retinal vascular thickness were extracted using ImageJ software. Results: Thirty-four women were enrolled. The mean maternal age was 32.7 years. No participant had diabetic retinopathy, nine eyes had early cataract, and two eyes had keratoconus. Higher levels for the 1 h oral glucose tolerance test (OGTT) were associated with a drop in the CST and total thickness. Moreover, women who underwent CS had higher levels of total thickness. Higher levels for the fasting OGTT were associated with a thinner inferior temporal retinal artery. Pregnant women with miscarriages had lower macular density on FAF, as represented by lower values of integrated density and mean gray values. Higher levels for the fasting OGTT were associated with higher values of integrated density. Conclusions: Although GDM is typically not associated with diabetic retinopathy, microscopic changes involving the microvascular environment and the macula may occur. Regular ophthalmic screening for women with GDM may be advised. Larger studies with more investigations may reveal further findings. Full article

Given the potential risks of unknown and emerging infectious respiratory diseases, prioritizing an appropriate ventilation strategy is crucial for controlling aerosol droplet dispersion and mitigating cross-infection in hospital wards during post-epidemic periods. This study optimizes the layout of supply diffusers and exhaust outlets in a typical two-bed ward, employing a downward-supply and bottom-exhaust airflow pattern. Beyond ventilation, implementing strict infection control protocols is crucial, including regular disinfection of high-touch surfaces. CO₂ serves as a surrogate for exhaled gaseous pollutants, and a species transport model is utilized to investigate the airflow field under various configurations of vents. Comparisons of CO₂ concentrations at the respiratory planes of patients, accompanying staff (AS), and healthcare workers (HCWs) across nine cases are reported. A discrete phase model (DPM) is employed to simulate the spatial-temporal dispersion characteristics of four different particle sizes (3 μm, 12 μm, 20 μm, and 45 μm) exhaled by the infected patient (Patient 1) over 300 s. Ventilation effectiveness is evaluated using indicators like contaminant removal efficiency (CRE), suspension rate (SR), deposition rate (DER), and removal rate (RR) of aerosol droplets. The results indicate that Case 9 exhibits the highest CRE across all respiratory planes, indicating the most effective removal of gaseous pollutants. Case 2 shows the highest RR at 50.3%, followed by Case 1 with 40.4%. However, in Case 2, a significant portion of aerosol droplets diffuse towards Patient 2, potentially increasing the cross-infection risk. Balancing patient safety with pollutant removal efficacy, Case 1 performs best in the removal of aerosol droplets. The findings offer novel insights for the practical implementation of ventilation strategies in hospital wards, ensuring personnel health and safety during the post-epidemic period. Full article

In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series in video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures of the number of people and image occupancy are extracted at regular intervals, which are then analyzed to obtain trends and anomalous behaviors. Specifically, through temporal decomposition and residual analysis, intervals or specific situations of unusual behaviors are identified, which can be used in decision-making and the improvement of actions in sectors related to human movement such as tourism or security. This methodology introduces a novel, privacy-focused approach by analyzing anonymized metrics rather than tracking or recognizing individuals, setting a new standard for ethical crowd monitoring. Applied to the webcam of Turisme Comunitat Valenciana in the town of Morella (Comunitat Valenciana, Spain), this approach has shown excellent results, correctly detecting specific anomalous situations and unusual overall increases during the previous weekend and during the October 2023 festivities. These results have been obtained while preserving the confidentiality of individuals at all times by using measures that maximize anonymity, without trajectory recording or person recognition. Full article

To balance the real-time and robustness of UAV visual tracking on a single CPU, this paper proposes an object tracker based on spatio-temporal context (STCT). STCT integrates the correlation filter and Siamese network into a unified framework and introduces the target’s motion model, enabling the tracker to adapt to target scale variations and effectively address challenges posed by rapid target motion, etc. Furthermore, a spatio-temporal regularization term based on the dynamic attention mechanism is proposed, and it is introduced into the correlation filter to suppress the aberrance of the response map. The filter solution is provided through the alternating direction method of multipliers (ADMM). In addition, to ensure efficiency, this paper proposes the average maximum response value-related energy (AMRE) for adaptive tracking state evaluation, which considers the time context of the tracking process in STCT. Experimental results show that the proposed STCT tracker can achieve a favorable balance between tracking robustness and real-time performance for UAV object tracking while running at ∼38 frames/s on a low-cost CPU. Full article

In this paper, the results of a Particle-in-Cell (PIC) simulation of electrons accelerated using a 10 fs Top-hat (TH) beam with a limited pulse energy of 85 mJ, focused on a double nozzle gas target using an off-axis parabola (OAP), an axiparabola (AXP), and an axiparabola with additional spatial–temporal coupling (AXP+STC), are discussed. The energy of accelerated electrons was predominantly determined through self-focusing and the ionisation injection effects of the laser beam propagating in plasma. The maximal energy of electrons accelerated using an AXP+STC could be higher by 12% compared to the energy of electrons accelerated by the regular OAP. Full article