Search Results (71)

Gas pipelines are a critical means of transportation in industrial production. To detect gas pipeline leaks, ultrasonic transducers with specific center frequencies and high sensitivity are required. This paper proposes a novel air-coupled ultrasonic transducer design based on a horn-type matching layer and a bending-mode type of piezoelectric material, specifically tailored for gas leak detection scenarios. The transducer design is optimized by the finite element method, focusing on the basic components of the piezoelectric bimorph, the horn and the supporting tube. First, the influence of various dimensional parameters of the piezoelectric bimorph on the bending vibration mode was analyzed. Then, the effects of the other two components, the horn and the supporting tube, on the piezoelectric bimorph vibration mode were investigated. A parametric scan on the dimensions of these components was conducted to optimize the transducer’s output. Finally, ultrasonic transducers using PMN-PT and PZT were fabricated and tested. The results show that the sensitivity of those transducers surpasses that of similar commercial transducers, especially the PMN-PT one with a center frequency of 40 kHz and a peak receiving sensitivity of −51.1 dB. This transducer, benefiting from the high-performance piezoelectric material and the bending vibration mode, proves to be a promising candidate for high-precision leak detection in gas pipelines. Full article

The integration of autonomous vehicles (AVs) into road transport requires robust experimental tools to analyze the human–machine interaction, particularly under conditions of system disengagement. This study presents the primary controls calibration and virtual scenario validation of the EVACH autonomous driving simulator, designed to reproduce the SAE Level 2 and Level 3 driving modes in rural road scenarios. The simulator was customized through hardware and software developments including a dedicated data acquisition system to ensure the accurate detection of braking, steering, and other critical control inputs. Calibration tests demonstrated high fidelity, with minor errors in brake and steering control measurements, consistent with values observed in production vehicles. To validate the virtual driving rural environment, comparative experiments were conducted between naturalistic road tests and simulator-based autonomous driving, where five volunteers participated in the preliminary pilot test. Results showed that average speeds in the simulation closely matched those recorded on real roads, with differences of less than 1 km/h with minimum standard deviation and confidence values. These findings confirm that the EVACH simulator provides a stable and faithful reproduction of autonomous driving conditions. The experimental platform offers valuable support for current and future research on the safe deployment of automated vehicles. Full article

As a core device in the natural gas supply chain, the compressor unit of the gas storage reservoir is crucial to the improvement of storage and transportation efficiency through its operation optimization. Based on the basic structure, working principle, and layout mode of the compressor unit of the gas-storage reservoir, this paper establishes a mathematical model for the operation optimization of the compressor unit, proposes an optimization method for the series-parallel operation of the compressor unit, and develops optimization software for the matching operation of the compressor unit. Aiming at the compressor unit used in the gas-storage reservoir with the largest gas injection and production capacity in China, this paper analyzes the variation laws of the compressor inlet temperature and the inlet and outlet pressures during the gas injection cycle, conducts research on the operation-matching optimization of the compressor unit within a one-month long cycle, and obtains the optimization scheme of the series-operation of the compressor unit and the energy-consumption results. Compared with the actual operation data, the monthly power consumption is reduced by 5.12%. The operation optimization situation of the compressor unit on typical days is analyzed to obtain the operation optimization law of the series-connected compressor unit. This research provides a theoretical basis and practical guidance for the operation-scheme optimization of the compressor unit of the gas-storage reservoir and has important practical application value. Full article

Brazil leads tropical agriculture, yet annual yield losses from insect pests and concerns over water contamination, non-target impacts, and resistance sustain demand for safer, more efficient control tools. This review synthesizes advances in nanopesticides for Brazil’s major crops (soybean, sugarcane, coffee, and citrus) and is organized into five parts, comprising concepts and definitions; formulation families; modes of action; efficacy evidence from laboratory, greenhouse, and field studies; and environmental and human health risk considerations. Evidence indicates that nano-enabled delivery can increase on-target deposition, prolong residual activity, and match or exceed control at reduced active ingredient loads by improving foliar adhesion, transcuticular transport, plant uptake, and spatiotemporal targeting with pheromone-releasing nanofibers and other dispensers. Because nanoformulations can alter exposure pathways and environmental fate, this review emphasizes nano-specific physicochemical characterization under use conditions, fate and transport in Oxisols and Ultisols, and tropical waters, ecotoxicity panels that include pollinators, aquatic invertebrates, soil biota, and vertebrate surrogates, and scenario-based exposure assessment for applicators, residents, and consumers. The review closes with practical guidance for Brazil: head-to-head efficacy benchmarks against commercial standards, the standardized reporting of release and characterization data, and a nano-specific environmental risk assessment checklist to help realize efficacy gains while protecting environmental and human health. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

Hybrid electric vehicles (HEVs) have gained significant attention for their superior energy efficiency and are becoming a predominant mode of urban transportation. The DC/DC converter plays a critical role in HEV energy management systems, especially in matching the voltage levels between the battery and DC bus. This paper proposes a novel high-gain DC/DC converter with a wide input voltage range based on coupled inductors. The innovation lies in the integration of a resonant cavity and the simultaneous realization of zero-voltage switching (ZVS) and zero-current switching (ZCS), effectively reducing both voltage/current stresses on the power switches and switching losses. Compared with conventional topologies, the proposed design achieves higher voltage gain without extreme duty cycles, improved conversion efficiency, and enhanced reliability. Detailed operating principles are analyzed, and design conditions for voltage stress reduction, gain extension, and soft switching are derived. The simulation model has been conducted in a PSIM environment, and a 300 W experimental prototype, implemented using a dsPIC33FJ64GS606 digital controller, has been established and demonstrates 93% peak efficiency at a 10 times voltage gain. The performance and practical feasibility of the proposed topology have been evaluated by both simulation and experiments. Full article

Background: Armed conflicts impose complex logistical and behavioral challenges on healthcare systems, particularly in managing acute conditions such as ST-elevation myocardial infarction (STEMI) and ischemic stroke. Although both diagnoses require timely intervention, their clinical pathways differ significantly. Few studies have systematically compared their management during active warfare, particularly within the warzone. Methods: This retrospective cohort study was conducted at Soroka University Medical Center (SUMC), the sole tertiary hospital in southern Israel and the main referral center for cardiovascular and neurological emergencies in the region. We included all adult patients (≥18 years) admitted with new-onset STEMI or ischemic stroke during three-month periods of wartime (October–December 2023) and matched routine periods in 2021 and 2022. Patients with in-hospital events, inter-hospital transfers, or foreign citizenship were excluded. Data on demographics, comorbidities, arrival characteristics, treatment timelines, and outcomes were extracted from electronic medical records. Categorical variables were compared using Chi-squared or Fisher’s exact test, and continuous variables using t-tests or Mann–Whitney U tests, as appropriate. Multivariable logistic and linear regression models were adjusted for age, sex, Charlson Comorbidity Index (CCI), and mode of arrival. Interaction terms assessed whether wartime modified the associations differently for STEMI and stroke. Results: A total of 410 patients were included (193 with STEMI and 217 with stroke). Patients with STEMI were significantly more likely to arrive by self-transport during the war (38, 57.6% vs. 32, 25.2%, p < 0.001) and had higher rates of late arrival beyond 12 h (19, 28.8% vs. 13, 10.2%, p = 0.002). These findings support the conclusion that patients were more prone to delayed and unstructured presentations during a crisis. In contrast, patients with stroke showed a reduction of 354 min in symptom-to-door times during the war [median 246 (30–4320 range) vs. 600 min (12–2329 range), p = 0.026]. Regression models revealed longer delays for stroke vs. STEMI in routine settings [β = 543.07 min (239.68–846.47 95% CI), p < 0.001], along with significantly lower in-hospital (OR = 0.39, 95% CI= 0.15–0.97, p = 0.05) and 30-day mortality (OR = 0.43, 95% CI= 0.19–0.94, p = 0.04). However, these differences were no longer significant during wartime. Patients with STEMI showed a trend toward lower 180-day mortality during the war (OR = 0.33, 95% CI = 0.09–0.99; p = 0.07), although this difference did not reach statistical significance. Conclusions: During wartime, patients with stroke arrived earlier and in greater numbers, while patients with STEMI showed reduced admissions and delayed, self-initiated transport. Despite these shifts, treatment timelines and short-term outcomes were maintained. These diagnosis-specific patterns highlight the importance of reinforcing EMS access for STEMI and preserving centralized protocol-based coordination for stroke during crises. Full article

In a large deep lake, the generation of internal Kelvin waves and internal Poincaré waves due to wind stress on the lake surface is a significant phenomenon. These internal waves play a crucial role in material transport within the lake and have profound effects on its ecosystem and environment. Our study, which investigated the modes of internal waves in Lake Biwa using the vertical temperature distribution from field observations, has yielded important findings. We have demonstrated the applicability of the frequency equation solutions, considering the Coriolis force. The period of the internal Poincaré waves, as observed in the field, was found to match the solutions of the frequency equation. For example, observational data collected in late October revealed excellent agreement with the theoretical solutions derived from the frequency equation, showing periods of 14.7 h, 11.8 h, 8.2 h, and 6.3 h compared to the theoretical values of 14.4 h, 11.7 h, 8.5 h, and 6.1 h, respectively. However, the periods of the internal Kelvin waves in the field observation results were longer than those of the theoretical solutions. The Modified Mathew function uses a series expansion around $q_i=0$, making it difficult to estimate the periods of internal Kelvin waves under conditions where $\left|q_i\right|>1.0$. Furthermore, in lakes with an elliptical shape, such as Lake Biwa, the elliptical cylinder showed better reproducibility than the circular cylinder. These findings have significant implications for the rapid estimation of internal wave periods using the frequency equation. Full article

The aim of this paper is to discuss both higher-order asymptotic expansions and skewed approximations for the Bayesian discrepancy measure used in testing precise statistical hypotheses. In particular, we derive results on third-order asymptotic approximations and skewed approximations for univariate posterior distributions, including cases with nuisance parameters, demonstrating improved accuracy in capturing posterior shape with little additional computational cost over simple first-order approximations. For third-order approximations, connections to frequentist inference via matching priors are highlighted. Moreover, the definition of the Bayesian discrepancy measure and the proposed methodology are extended to the multivariate setting, employing tractable skew-normal posterior approximations obtained via derivative matching at the mode. Accurate multivariate approximations for the Bayesian discrepancy measure are then derived by defining credible regions based on an optimal transport map that transforms the skew-normal approximation to a standard multivariate normal distribution. The performance and practical benefits of these higher-order and skewed approximations are illustrated through two examples. Full article

Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. Full article

Intelligent and information systems in transportation record and accumulate large volumes of raw data on dynamic transportation processes. However, these data are not fully utilized for forecasting, real-time planning, and transportation management. Spatio-temporal graphs allow describing simultaneously both the structure of transportation systems of different modes of transportation and the dynamics of transportation flows. Optimization of such graphs makes it possible to justify management decisions in real time, as well as to forecast the parameters of traffic flows and transportation processes. The purpose of the study is to identify trends in the use of spatio-temporal graphs for solving various problems in transportation, as well as the most common methods of optimization of such graphs. The sample papers studied include 114 publications from the Scopus database over 25 years, from 1999 to 2024. First, a bibliometric analysis was conducted to establish the increase in the number of publications, journals, countries, institutions, subject areas, articles, authors, and keyword matches, to understand the amount of literature generated. Secondly, a literature review was conducted based on content analysis to predict future research directions in the field. We have found that the development of deep learning methods and approaches for designing graph neural networks based on spatio-temporal graphs is a promising direction. Such methods are mostly used to solve the tasks of real-time control of urban transportation systems. There are fewer publications in areas that require in-depth knowledge of transportation technology, such as air, sea, and rail transportation. This study contributes to the expansion of scientific knowledge about methods of spatio-temporal optimization of transport systems based on bibliometric analysis. Full article

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a⁻¹ (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. Full article

The square steel tube–timber–concrete composite L-shaped columns are lighter in weight due to the inclusion of wood and exhibit superior seismic performance. This combination not only reduces transportation and labor costs but also enhances earthquake resistance. The wood contributes lightness and flexibility, the steel provides strength, and the concrete offers excellent compressive performance, thereby achieving an optimized design for performance. To investigate the axial compression performance of square steel tube–timber–concrete composite L-shaped short columns, axial compression tests were conducted on eight groups of L-shaped columns. The study examined ultimate load, failure modes, load–displacement relationships, initial stiffness, ductility, and bearing capacity improvement factors under different slenderness ratios, steel tube wall thicknesses, and wood content rates. The results show that the mechanical performance of the composite columns is excellent. Local buckling of the steel tube is the primary failure mode, with ‘bulging bands’ forming at the middle and ends. When the wood content reaches 25%, the synergy between the steel tube, concrete, and wood is optimal, significantly enhancing ductility and bearing capacity. The ductility of the specimen increased by 31.1%, and the bearing capacity increased by 4.14%. The bearing capacity increases with the steel tube wall thickness but decreases with increasing slenderness ratio. Additionally, based on the Mander principle and considering the partitioned constraint effects of concrete, a simplified calculation method for the axial compressive bearing capacity was proposed using the superposition principle. This method was validated to match well with the test results and can provide a reference for the design and application of these composite L-shaped columns. Full article

The ScanSAR mode image obtained by the Gaofen-3 (GF-3) satellite has an imaging width of up to 130–500 km, which is of great significance in monitoring oceanography, meteorology, water conservancy, and transportation. To address the issues of subswath misalignment and the inability to correct in the processing of GF-3 ScanSAR images in coastal areas using software such as PIE, ENVI, and SNAP, a method for mosaicking and correcting GF-3 ScanSAR images with subswaths that overlap within specified range constraints is proposed. This method involves correlating the coefficients of each subswath thumbnail image in order to determine the extent of the overlap range. Given that the matching points are constrained to the overlap between subswaths, the normalized cross-correlation (NCC) matching algorithm is utilized to calculate the matching points between subswaths. Subsequently, the random sampling consistency (RANSAC) algorithm is employed to eliminate the mismatching points. Subsequently, the subswaths should be mosaicked together with the stitching translation of subswaths, based on the coordinates of the matching points. The image brightness correction coefficient is calculated based on the average grayscale value of pixels in the overlapping region. This is performed in order to correct the grayscale values of adjacent subswaths and thereby reducing the brightness difference at the junction of subswaths. The entire ScanSAR slant range image is produced. By employing the Range–Doppler model for indirect orthorectification, corrected images with geographic information are generated. The experiment utilized three coastal GF-3 ScanSAR images for mosaicking and correction, and the results were contrasted with those attained through PIE software V7.0 processing. This was conducted to substantiate the efficacy and precision of the methodology for mosaicking and correcting coastal GF-3 ScanSAR images. Full article

Transport mode is one of the important travel characteristics for citizens, which is crucial to the planning and management of urban transportation. With the natural advantages of large sample sizes and a wide coverage of people, more and more researchers adopt mobile phone signaling data (MSD) to detect transport modes. However, due to their low positioning accuracy and temporally irregular nature, identifying transport modes with similar spatiotemporal features, such as the bus and car modes, is particularly challenging. We propose a transport detection framework using MSD combined with bus GPS data to distinguish between the car and bus modes. First, a trajectory matching algorithm is proposed to obtain the most probable bus that mobile phone users may take. Then, more features are mined to improve the accuracy of transport mode detection with different classification models. Furthermore, for signaling trajectories identified as the bus mode, more bus travel information is recognized, including the boarding and alighting station and timestamp. Finally, we built a ground truth dataset and compared the recognition accuracies under different features and classification models. The result shows that the transport mode detection accuracies of the proposed framework with the GBDT, XGBoost, and LightGBM algorithms are all higher than 94%. Full article