Search Results (6,209)

Traffic state estimation (TSE) is a core task in intelligent transportation systems (ITSs) that seeks to infer key operational parameters—such as speed, flow, and density—from limited observational data. Existing methods often face challenges in practical deployment, including limited estimation accuracy, insufficient physical consistency, and weak generalization capability. To address these issues, this paper proposes a hybrid estimation framework that integrates multi-agent reinforcement learning (MARL) with the Lighthill–Whitham–Richards (LWR) traffic flow model. In this framework, each roadside detector is modeled as an agent that adaptively learns fundamental diagram (FD) parameters—the free-flow speed and jam density—by fusing local detector measurements with global CAV trajectory sequences via an interactive attention mechanism. The learned parameters are then passed to an LWR solver to perform sequential (rolling) prediction of traffic states across the entire road segment. We design a reward function that jointly penalizes estimation error and violations of physical constraints, enabling the agents to learn accurate and physically consistent dynamic traffic state estimates through interaction with the physics-based LWR environment. Experiments on simulated and real-world datasets demonstrate that the proposed method outperforms existing models in estimation accuracy, real-time performance, and cross-scenario generalization. It faithfully reproduces dynamic traffic phenomena, such as shockwaves and queue waves, demonstrating robustness and practical potential for deployment in complex traffic environments. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article

The Canadian Prairies are a region of critical importance to continental hydroclimate and agriculture, exhibiting high sensitivity to variability in atmospheric moisture transport. This study investigates the seasonal and interannual variability of integrated moisture flux over the Canadian Prairie region (96° W–114° W, 49° N–53° N) using the National Centers for Environmental Prediction (NCEP) Reanalysis dataset from 1979 to 2023. We employ a combination of composite analysis and Empirical Orthogonal Function (EOF) analysis to identify the dominant modes of variability and their associated large-scale synoptic drivers. Our results confirm a strong seasonal reversal: winter moisture flux is predominantly zonal (westerly), contributing an average of 90% to total inbound flux, while summer flux is primarily meridional (southerly), contributing a dominant 72.6%. Composite analysis of extreme moisture years reveals that anomalously high-moisture winters are associated with an intensified Aleutian Low and a strengthened pressure gradient off the North American west coast, facilitating enhanced westerly flow. Conversely, a strengthened continental high-pressure system characterizes anomalously low-moisture winters. During summer, high-moisture years are driven by an enhanced southerly component of the flow, likely linked to a strengthened Great Plains Low-Level Jet (GPLLJ). The first EOF mode for winter explains 43% of the variance in eastward flux and is characterized by a pattern consistent with the El Niño Southern Oscillation (ENSO) teleconnection pattern. These findings underscore the control of Pacific-centric circulation patterns on Prairie hydroclimate in winter and have significant implications for predicting seasonal water availability. Full article

Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency. Full article

Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article

Kratom (Mitragyna speciosa (Korth.) Havil.) oxindole alkaloids remain underexplored compared to the well-studied indole constituents mitragynine and 7-hydroxymitragynine. Previous research has primarily focused on phytochemical identification and preliminary pharmacology, with limited pharmacokinetic insight. This study pioneers an in silico ADMET modeling analysis of 27 kratom-derived oxindole alkaloids using ADMET Predictor™ v3.0, delivering the first comprehensive predictions of their physicochemical properties, CYP450/UGT enzyme interactions, transporter affinities, permeability, and pharmacokinetic parameters. Representative compounds such as speciophylline, isomitraphylline, and isospeciophylline displayed notably favorable predicted jejunal permeability and moderate metabolic stability, suggesting promising oral drug-like characteristics. Across the dataset, high CYP3A4 substrate affinity (98% confidence), variable CYP3A4, CYP2D6, CYP2C19 inhibition, strong P-gp substrate potential, and differential BBB penetration probabilities (46–99%) were observed. These findings provide a foundational computational framework to guide future experimental validation and rational drug development of kratom oxindole alkaloids. Full article

Background: Not all ambulance missions result in patient transport, often referred to as non-conveyance. However, in some cases, patients discharged at the scene may require further examination and treatment. Patient sex, age, and psychiatric disease seem to be factors associated with non-conveyance. This study aimed to identify and characterise patients not transported following an urgent ambulance mission, and to examine subsequent hospital admission and mortality rates. In addition, we wanted to examine their reasons for calling the Emergency Medical Communication Centre (EMCC). Methods: This retrospective study was conducted for the emergency medical system of Norway’s second-largest city. Data, including information from non-conveyed patients involved in acute or urgent ambulance missions over 1 year, were obtained from the EMCC. The frequency of non-conveyance, patient demographics, and incidence of hospital admissions within 72 h were analysed. Furthermore, the 30-day mortality, predictive factors, and reasons for contacting the EMCC were determined. Results: Out of a total of 22,183 ambulance missions, 7.3% of patients were not conveyed, of whom 5.8% were admitted to hospital within 72 h. The 30-day mortality rate among all non-conveyed patients was 2.4%, whereas 2.1% of hospitalised patients died within 30 days. Psychiatric conditions were frequently observed in both groups. The mortality rate increased significantly with age but was not associated with the number of ambulance requests. Furthermore, 30-day mortality was not significantly associated with sex, time of day, day of the week, or rurality. Conclusions: Our data suggests that there is no difference between the short-term outcomes of non-conveyed and conveyed patients; both groups are equally likely to come to harm. Therefore, the factors influencing non-transportation decisions warrant further investigation. Subsequent events following patient discharge should be routinely collected by ambulance services and monitored for learning and to improve the quality of patient care. Full article

This study, for the first time, investigates and simulates ballistic-transport-induced avalanche behavior in two-dimensional materials. Using a technology computer-aided design simulation platform, a device model for ballistic avalanche transport is systematically established. By accurately calibrating the material parameters of two-dimensional materials and selecting appropriate physical models, the key features of the ballistic avalanche effect are successfully reproduced, including low threshold voltage and high gain. The simulation results show good agreement with experimental data. Furthermore, mechanism-based analysis is performed to clarify the influence of critical design parameters on the avalanche threshold and multiplication gain. Finally, based on the same physical models and mechanistic understanding, the operational paradigm and performance of ballistic-transport avalanche photodetectors based on two-dimensional materials are predicted. This work provides a reliable theoretical foundation and a robust simulation framework for the optimized design of high-performance and low-power avalanche photon devices. Full article

This study presents the development, calibration, and validation of cohesive zone models (CZMs) for epoxy-based adhesive joints connecting stainless steel and CFRP composites. The objective of this study is to develop and rigorously validate cohesive zone models for epoxy-based adhesive joints between stainless steel and CFRP composites, ensuring their reliability for numerical simulations of structural failure under quasi-static and large-deformation conditions. The work focuses on accurately describing the quasi-static behaviour and failure mechanisms of hybrid adhesive interfaces, which are crucial for multimaterial structures in modern transportation systems. Experimental tests in Mode I (DCB) and Mode II (ENF) configurations were performed to determine the cohesive parameters of the structural adhesive SikaPower 1277. The obtained data were further analysed through analytical formulations and validated numerically using PAM-CRASH. Excellent agreement was achieved between experiments, analytical predictions, and simulations, confirming the reliability of the proposed material definitions under large deformations. The validated models were subsequently implemented in a large-scale numerical simulation of a bus rollover according to UN/ECE Regulation No. 66, demonstrating their applicability to real structural components. The results show that the developed cohesive zone models enable accurate prediction of failure initiation and propagation in adhesive joints between dissimilar materials. These findings provide a robust foundation for the design of lightweight, crashworthy structures in transportation and open new perspectives for integrating epoxy-based adhesives into additively manufactured hybrid metal–composite systems. Full article

Skin cancer (SC) is the most prevalent malignancy worldwide, with subtypes varying in aggressiveness: basal cell carcinoma tends to be locally invasive, squamous cell carcinoma has a higher metastatic risk, and melanoma remains the deadliest form. Current treatments such as surgery, radiotherapy, and systemic chemotherapy are associated with aesthetic and functional morbidity, recurrence, and/or systemic toxicity. Although targeted therapies and immunotherapies offer clinical benefits, their high cost and limited accessibility underscore the need for innovative, affordable alternatives. Metal-based compounds (metallopharmaceuticals) are promising anticancer agents due to their ability to induce oxidative stress, modulate redox pathways, and interact with DNA. However, clinical translation has been limited by poor aqueous solubility, rapid degradation, and low skin permeability. This review discusses the most recent preclinical findings on gold, silver, platinum, palladium, ruthenium, vanadium, and copper complexes, mainly in topical and systemic treatments of SC. Advances in chemical and physical enhancers, such as hydrogels and microneedles, and in drug delivery systems, including bacterial nanocellulose membranes and nanoparticles, as well as liposomes and micelles, for enhancing skin permeation and protecting the integrity of metal complexes are also discussed. Additionally, we examine the contribution of photodynamic therapy to SC treatment and the use of mathematical and computational modeling to simulate skin drug transport, predict biodistribution, and support rational nanocarrier design. Altogether, these strategies aim to bridge the gap between physicochemical innovation and clinical applicability, paving the way for more selective, stable, and cost-effective SC treatments. Full article

Daqu is the core saccharifying and fermenting starter for strong-flavor Baijiu, and its quality is strongly shaped by the workshop microenvironment. Here, mature Daqu from a newly built workshop and a long-established workshop within the same distillery were compared under identical raw materials and process conditions. Physicochemical properties, volatile flavor compounds (HS-SPME-GC–MS), bacterial and fungal communities (16S/ITS sequencing), and Tax4Fun-predicted functions were jointly analyzed. The quality indicators of the Daqu in the new workshop are qualified, but the acidity (and moisture) is higher, and the fermentation, saccharification and liquefaction abilities are lower. The Daqu in the old workshop is rich in esters, the aroma is more mature, and the total ester content is about twice that of the new workshop. Both Daqu types shared similar core taxa, but the new workshop was dominated by a simpler Weissella–Thermomyces consortium, while the old workshop was enriched in Bacillus, lactic acid bacteria, Rhizomucor, Saccharomycopsis, and Wickerhamomyces. Correlation and network analyses linked these old-workshop core genera to key ethyl esters, higher alcohols and pyrazines, and Tax4Fun indicated a stronger bias toward amino acid/carbohydrate metabolism and membrane transport in the old workshop. These results show that workshop age reshapes Daqu quality by co-modulating physicochemical traits, microbial consortia and functional potential, and suggest microbial and functional targets for accelerating the “maturation” of new workshops. Full article

Liquid coolant jets are commonly used to remove excess heat from workpieces during grinding. There is a pressing need to reduce energy waste that contributes to environmental heat pollution and to limit the spread of oil-based coolants and mist formation. As a liquid jet issues from a nozzle and enters the surrounding air, surface instabilities develop, causing the jet to break into droplets. This breakup diminishes the jet’s ability to deliver maximum momentum to the workpiece and grinding wheel in grinding operations, thereby reducing cooling efficiency. The presence of moving ambient air near the workpiece and rotating grinding wheel further complicates cooling. First, the study investigates jet breakups in stationary air, predicting breakup lengths with reasonable agreement to experiments at varying jet velocities using the Reynolds Averaged Navier–Stokes (RANS) method equipped with Shear Stress Transport (SST) k-ω model of turbulence. The coolant jet breakup length for a jet normal to the grinding wheel is different from that for a free jet and affected by the proximity of grinding wheel to nozzle that was not evaluated in prior studies. Simulations were performed using Ansys Fluent software 2023R1, with careful tuning of numerical schemes and selection of breakup criteria. The results include analysis of jet breakup phenomena in presence of rotating grinding wheel and workpieces, determination of breakup lengths across a range of Weber numbers, and effects of nozzle design. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

Seepage–dissolution in carbonate rock fractures serves as the core driver governing the evolution of key engineering projects, including reservoir dam stability, CO₂ geological sequestration, and unstable rock collapse mitigation strategies. While physical heterogeneity (e.g., fracture aperture, mineral distribution) is widely recognized as a critical factor regulating dissolution processes, the specific influence of mineral distribution heterogeneity on dissolution rates still lacks quantitative quantification. To address this gap, this study focuses on limestone fractures and employs multi-component reactive transport numerical simulations to model acidic fluid (pH = 5.0) seepage–dissolution under two Darcy flux conditions (37.8/378 m·yr⁻¹). It investigates the controlling mechanisms of fracture roughness (λ_b = 0.036~0.308) and calcite contents (55%, 75%, 95%) on dissolution dynamics, and analyzes spatial variations in local Darcy velocity, reaction rate, and effective dissolution rate (R_eff,i). Results demonstrate that mineral distribution heterogeneity directly induces pronounced spatial heterogeneity in dissolution behavior: diffusion dominates under low flux (simulation duration: 48.3 days), forming discrete reaction fronts (~15 mm) controlled by mineral clusters; advection prevails under high flux (simulation duration: 4.83 days), generating alternating dissolution–deposition zones (~7.5 mm) with R_eff,i one order of magnitude greater than that under low flux. Notably, 55% calcite content yields the highest R_eff,i (1.87 × 10⁻¹¹ mol·m⁻²·s⁻¹), 0.94 orders of magnitude greater than that at 95% calcite content. A strong linear correlation (R² > 0.98) exists between the Damköhler number (Da_I) and Reff,i at the same calcite content. Furthermore, the synergistic interaction between fracture aperture and mineral heterogeneity amplifies dissolution complexity, with high roughness (λ_b= 0.308) coupled with 55% calcite content achieving the highest R_eff,i of 2.1 × 10⁻¹¹ mol·m⁻²·s⁻¹. This study provides critical theoretical insights and quantitative data support for fractured rock mass evolution prediction models, geological hazard prevention, and geological carbon sequestration optimization. Full article

To achieve energy savings, reduce consumption, and support the “dual-carbon” strategy in China, this study applies digital twin technology to investigate the centralized air-conditioning water system of a metro-station HVAC installation and develops a high-fidelity digital twin model to reveal the thermal delay and thermal attenuation characteristics of the pipeline network. Using the noncausal modeling approach of the Modelica language, a full digital twin representation of the centralized air-conditioning water network is constructed by covering chillers, cooling towers, pumps, terminal units, the pipeline network, etc. The model is validated against real operation data to ensure high fidelity. Validation shows the predicted chilled water flow rate of the digital twin model agrees well with the measured chilled water flow rate with an RMSE of 0.27 kg/s. Validation also shows the difference is about 0.3 °C between the digital twin prediction and the measurement in the main pipe. Based on the validation digital twin model, the thermal delay and thermal attenuation characteristics of the centralized air-conditioning water system are seriously evaluated. The results indicate that branch K3, due to its longest transport distance, exhibits a delay of 227 s. The overall thermal delay of the system reaches 7.5 min. The temperature attenuation of this water system is about 0.2 °C due to heat loss through pipe walls. The findings may offer theoretical support for the optimal regulation and control, fault detection, and anomaly identification of this centralized air-conditioning water system. Full article