Search Results (183)

For aircraft departure, which is a process of multi-resource coordination, strict time limitations, and complex condition constraints, the optimization of taxi-out time prediction is critical for enhancing airport surface operational efficiency, optimizing runway slot utilization, and reducing aircraft ground delay and fuel consumption. By combining aircraft taxi path and network traffic flow features, a refined airport road-level transport network model is constructed to accurately characterize the taxi path topology and node-edge attributes. On this basis, two new micro-features are introduced: estimated taxi time and the number of handovers. Experimental results show that after the introduction of the micro-features, the prediction accuracy of the taxi-out time prediction model within the error of 1 min increases from 49.29% to 54.41%, and the prediction accuracy within the error of 5 min reaches 99.42%. This method effectively addresses the limitations of traditional models that focus solely on the overall taxiing process while neglecting microscopic airfield network dynamics and time consumption during control handover procedures. The method can be integrated into the Airport Collaborative Decision Making (A-CDM) system to provide minute-level support for departure taxi-out time prediction, thereby providing a more precise and operationally aligned temporal benchmark for intelligent apron operations scheduling, aircraft sequencing optimization, and other collaborative decision making processes. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

Turbulent diffusion plays a critical role in atmospheric pollutant dispersion, particularly in complex environments such as urban areas. This study proposes a novel theoretical approach to enhance the calculation of the turbulent diffusion coefficient in pollutant dispersion models. We propose a new expression for the turbulent diffusion coefficient (KC), which incorporates both hydrodynamic and turbulence-related time scales. This formulation links the turbulent diffusion coefficient to pollutant travel time and turbulence intensity, offering more accurate predictions of pollutant concentration distributions. By addressing the limitations of existing empirical models, this approach improves the parameterization of turbulence and reduces uncertainties in predicting maximum individual exposure under various atmospheric conditions. The study presents a theoretical model designed to advance the current understanding of atmospheric dispersion modeling. Experimental validation, while recommended, is beyond the scope of this work and is suggested as a direction for future empirical research to confirm the practical utility of the model. This theoretical formulation could be integrated into urban air quality management frameworks, providing improved estimations of pollutant peaks in complex environments. Full article

This study focuses on the mid- and low-midlatitude ionospheric response to the 2024 Mother’s Day superstorm, utilizing ground-based and Swarm satellite observations. The ground-based ionosonde measured F1, F2-layer, B0 and B1 parameters, as well as isodensity data, were used. The ionospheric absorption was investigated with the so-called amplitude method, which is based on ionosonde data. Auroral sporadic E-layer was the first time ever recorded at Sopron. Moreover, the auroral F-layer appeared at exceptionally low latitude (35° mlat, over San Vito) during the storm main phase. These unprecedented detections were confirmed by optical all-sky cameras. The observations revealed that these events were linked to the extreme equatorward shift of the auroral oval along with the midlatitude trough. As a result, the midlatitude ionosphere became confined to the trough itself. Three stages of F2-layer uplift were identified during the night of 10/11 May, each caused by different mechanisms: most probably by the effect of prompt penetration electric fields (PPEFs) (1), the travelling ionospheric disturbances (TIDs) (2) and the combination of electrodynamic processes and decreased O/N₂ ratio (3). After a short interval of G-condition, an unprecedented extended disappearance of the layers was observed during daytime hours on 11 May, which was further confirmed by Swarm data. This phenomenon appeared to be associated with a reduced O/N₂ along with the influence of disturbance dynamo electric fields (DDEFs) and it cannot be explained only by the increased ionospheric absorption according to the results of the amplitude method. Full article

As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems. Full article

Ride-hailing services have reshaped urban commuting patterns, yet the spatiotemporal mechanisms linking built environment features to ride-hailing demand remain underexplored. Existing studies often overlook the joint effects of origin–destination visual walkability. This study integrates ride-hailing GPS trajectories and geospatial data to quantify mobility patterns and built-environment indicators in Chengdu, China. A dual analytical framework combining global regression and localized modeling was applied to disentangle spatial–temporal influences of urban form and socioeconomic factors. The results reveal that population density, floor–area ratio, and housing prices positively correlate with demand, while road density and distance to city center exhibit negative associations. Visual walkability metrics show divergent effects: psychological greenery and pavement visibility reduce ride-hailing usage, whereas outdoor enclosure enhances it. Temporal analysis identifies time-dependent impacts of built environment variables on main urban area travel. Housing price effects demonstrate spatial globality, while population density and city-center proximity exhibit geographically bounded correlations. Notably, improved visual walkability in specific zones reduces reliance on ride-hailing by facilitating sustainable alternatives. These findings provide empirical support for optimizing urban infrastructure and land-use policies to promote equitable mobility systems. The proposed methodology offers a replicable framework for assessing transportation–land-use interactions, informing targeted interventions to achieve metropolitan sustainability goals through coordinated spatial planning and pedestrian-centric design. Full article

This study investigates the factors influencing the energy consumption and reliability of haul trucks in open-pit mines and quarries, where fuel costs and the environmental impact are significant. Traditional analysis of haulage systems often overlooks crucial aspects such as energy efficiency in the specific mining environment and the effect of road configurations on truck performance. As sustainability becomes increasingly important, reducing fuel consumption not only reduces costs but also reduces greenhouse gas emissions. A key focus of the study is the link between haul truck reliability and overall efficiency. Frequent breakdowns increase maintenance costs, lead to unplanned downtime, and increase fuel consumption, all of which have an impact on the environment. Reliable transport systems, on the other hand, improve efficiency, reduce costs, and support sustainability goals. The authors analyze the energy consumption of trucks in relation to vehicle performance parameters and transport route characteristics. Discrete modeling of the transport system showed the impact of the operating environment on the variability of energy consumption and vehicle reliability. The study highlights the importance of understanding specific energy consumption in order to optimize the choice of transport system, as transport costs are a major cost of resource extraction. By analyzing the effect of road quality on vehicle performance, the authors suggest that improvements to the road surface can more easily improve vehicle reliability and energy intensity than changes to other road design elements. The study presents a quantitative analysis of the impact of haul road conditions on the operational efficiency of haul trucks in mining environments. Through discrete simulation models, two scenarios were analyzed. Total operational time decreased by 11.2% when road quality improved, demonstrating the critical role of surface maintenance. Additionally, breakdown times were reduced by 44%, maintenance by 15%, and empty travel by 9% in the optimized scenario. These findings underscore the necessity of maintaining optimal road conditions to prevent substantial efficiency losses and increased maintenance costs. Full article

Cervical cancer is the leading cause of cancer-related deaths among women in sub-Saharan Africa, especially in rural areas with limited access to screening. This study explored factors influencing rural Kenyan women’s willingness to self-collect samples for HPV-DNA testing. Data were drawn from a mixed methods study in two Kenyan rural counties, including surveys with 174 women and interviews with 21 participants. The mean age of the survey sample was 45.2 (SD = 13.2) years. Only 6.4% had ever been screened, yet 76.9% expressed willingness to self-collect samples for testing. Increased willingness was associated with cervical cancer awareness (OR = 3.49, 95% CI = 1.50–8.11), relying on health workers as primary sources of health information (OR = 1.88, CI = 1.23–2.86), or the news media (OR = 2.63, CI = 1.27–5.48). High cervical cancer stigma (OR = 0.71, CI = 0.57–0.88) and longer travel times of 30–120 min to a health facility (OR = 0.44, CI = 0.20–0.93) were linked to reduced willingness. Integration of the findings showed that comprehensive health promotion—through education, health worker endorsement, and mass media campaigns—may improve HPV self-sampling uptake and reduce the cervical cancer burden in rural Kenya. Full article

Background: The framework of measuring effective coverage is conceptually straightforward, yet translation into a single metric is quite intractable. An estimation of a metric linking need, access, utilization, and service quality is imperative for measuring the progress towards Universal Health Coverage. A coverage metric obtained from a household survey alone is not succinct as it only captures the service contact which cannot be considered as actual service delivery as it ignores the comprehensive assessment of provider–client interaction. The study was thus conducted to estimate a one-composite metric of effective coverage by linking varied datasets. Methods: The study was conducted in a rural, remote, and fragile setting in India. Tools encompassing a household survey, health facility assessment, and patient exit survey were administered to ascertain measures of contact coverage and quality. A gamut of techniques linking the varied surveys were employed such as (a) exact match linking and (b) ecological linking using GIS approaches via administrative boundaries, Euclidean buffers, travel time grid, and Kernel density estimates. A composite metric of effective coverage was estimated using linked datasets, adjusting for structural and process quality estimates. Further, the horizontal inequities in effective coverage were computed using Erreygers’ concentration index. The concordance between linkage approaches were examined using Wald tests and Lin’s concordance correlation. Results: A significantly steep decline in measurement estimates was found from crude coverage to effective coverage for an entire slew of linking approaches. The drop was more exacerbated for structural-quality-adjusted measures vis-à-vis process-quality-adjusted measures. Overall, the estimates for effective coverage and inequity-adjusted effective coverage were 36.4% and 33.3%, respectively. The composite metric of effective coverage was lowest for postnatal care (10.1%) and highest for immunization care (78.7%). A significant absolute deflection ranging from −2.1 to −5.5 for structural quality and −1.9 to −8.9 for process quality was exhibited between exact match linking and ecological linking. Conclusions: Poor quality of care was divulged as a major factor of decline in coverage. Policy recommendations such as bolstering the quality via the effective implementation of government flagship programs along with initiatives such as integrated incentive schemes to attract and retain workforce and community-based monitoring are suggested. Full article

The stochastic routing transportation network problem presents significant challenges due to uncertainty in travel times, real-time variability, and limited sensor data availability. Traditional adaptive routing strategies, which rely on real-time travel time updates, may lead to suboptimal decisions due to dynamic traffic fluctuations. This study introduces a novel routing framework that integrates traffic sensor data augmentation and deep learning techniques to improve the reliability of route selection and network observability. The proposed methodology consists of four components: stochastic traffic assignment, multi-objective route generation, optimal traffic sensor location selection, and deep learning-based traffic flow estimation. The framework employs a traffic sensor location problem formulation to determine the minimum required sensor deployment while ensuring an accurate network-wide traffic estimation. A Stacked Sparse Auto-Encoder (SAE) deep learning model is then used to infer unobserved link flows, enhancing the observability of stochastic traffic conditions. By addressing the gap between limited sensor availability and complete network observability, this study offers a scalable and cost-effective solution for real-time traffic management and vehicle routing optimization. The results confirm that the proposed data-driven approach significantly reduces the need for sensor deployment while maintaining high accuracy in traffic flow predictions. Full article

Background: Geographic access to healthcare services can impact cancer outcomes. This paper reviews and updates the current evidence and gaps in the literature on the associations between travel distance and cancer stage. Methods: A search of electronic databases (PubMed, SpringerLink, and Science Direct) was conducted to identify studies published between 2015 and 2025. Studies examining the association between travel distance and cancer stage at diagnosis were included in this article. Results: From 19,197 studies, 11 articles met the inclusion criteria. In summary, four articles reported significant associations between travel distance/time and cancer stage, while six other articles did not report any association. Significant associations were observed in sub-Saharan Africa. In contrast, studies from Scotland, Canada, and the United States did not show significant relationships, while results from Japan varied, with papers showing either no significant impact of travel distance or indicating a correlation with advanced stages. Conclusions: This study suggests that longer travel distance is associated with advanced cancer stage in countries with healthcare access challenges and highlights the importance of healthcare accessibility in improving early cancer detection. Full article

The tourism sector thrives on a comprehensive understanding of the factors that motivate individuals to explore new destinations. Identifying the push and pull factors that drive travel decisions is essential for analyzing tourist behavior and recognizing the external constraints that tourism enterprises and destinations must consider. Adopting a sustainable approach to these motivational forces underscores the need to balance tourism growth with the preservation of destinations, the well-being of local communities, and responsible travel practices. Push and pull factors in tourism are inherently linked to the emotional states that travelers experience throughout the decision-making process, from the initial intention to travel to the post-trip evaluation. The sector prospers by understanding the reasons that inspire individuals to discover new places. Determining these motivational factors is crucial for comprehending tourist behavior and addressing the external limitations that tourism businesses and destinations must navigate. A sustainability-focused approach highlights the significance of aligning tourism growth with destination preservation and community well-being, ensuring a responsible and enduring tourism model. This study aims to examine the impact of positive and negative emotions on push and pull motivational factors across different phases of the COVID-19 pandemic, adopting a sustainability perspective. The research was structured into four empirical studies: (i) pre-pandemic phase, involving a sample of 508 tourists; (ii) pandemic phase, with data collected from 507 participants; (iii) post-pandemic phase, comprising 488 respondents; (iv) comparative analysis, assessing variations across the three periods. The results indicate that emotional states exert a significant influence on push and pull motivational factors, with variations observed depending on the period of data collection: before, during, and after the COVID-19 pandemic. However, while emotions exhibited fluctuations across the three phases, push and pull factors demonstrated relative stability over time. These findings emphasize the critical role of emotional experiences in shaping travel motivations, highlighting the interplay between psychological drivers and destination attributes. This understanding is essential for tourism businesses and policymakers to develop strategies that align with evolving traveler expectations while promoting sustainable and responsible tourism practices. Full article

A two-dimensional longitudinal profile model was used to evaluate groundwater flow along a 48 km flowline in the Southeastern Hueco Aquifer, extending from the Diablo Plateau in Texas to the Sierra de San Ignacio in Chihuahua, Mexico. The model, incorporating geologically distributed permeability values, closely matched the predevelopment potentiometric surface. Predicted recharge rates and travel times aligned with published estimates and environmental isotopes, suggesting potential transboundary groundwater movement. The model estimated recharge rates needed to reach flow capacity, or the maximum volume a system can transmit, typically saturating the water table. Current moisture levels are insufficient, but flow capacity may have been reached during late Pleistocene pluvial periods. Required recharge rates were 297% higher than initial calibration in the U.S. and 1080% higher in Mexico, with only U.S. estimates appearing plausible for the Pleistocene–Holocene transition. These findings are relevant to regional waste disposal considerations because water tables near land surface present a risk to groundwater resources. A transient simulation modeled hydraulic head decay due to recharge abatement linked to climate change over 14,000 years. It simulated a decrease from a “flow capacity” recharge rate of 10.4 mm/year to 3.5 mm/year today. The modeling simulations ended with the hydraulic head remaining only 20 m above current levels, suggesting a minimal-to-negligible fossil hydraulic gradient in the low-permeability flow system. Full article

Chimera states and bump states are collective synchronization phenomena observed independently (in different parameter regions) in networks of coupled nonlinear oscillators. And while chimera states are characterized by coexistence of coherent and incoherent domains, bump states consist of alternating active and inactive domains. The idea of multistable plasticity in the network connections originates from brain dynamics where the strength of the synapses (axons) connecting the network nodes (neurons) may change dynamically in time; when reaching the steady state the network connections may be found in one of many possible values depending on various factors, such as local connectivity, influence of neighboring cells etc. The sign of the link weights is also a significant factor in the network dynamics: positive weights are characterized as excitatory connections and negative ones as inhibitory. In the present study we consider the simplest case of bistable plasticity, where the link dynamics has only two fixed points. During the system/network integration, the link weights change and as a consequence the network organizes in excitatory or inhibitory domains characterized by different synaptic strengths. We specifically explore the influence of bistable plasticity on collective synchronization states and we numerically demonstrate that the dynamics of the linking may, under special conditions, give rise to co-existence of bump-like and chimera-like states simultaneously in the network. In the case of bump and chimera co-existence, confinement effects appear: the different domains stay localized and do not travel around the network. Memory effects are also reported in the sense that the final spatial arrangement of the coupling strengths reflects some of the local properties of the initial link distribution. For the quantification of the system’s spatial and temporal features, the global and local entropy functions are employed as measures of the network organization, while the average firing rates account for the network evolution and dynamics. In particular, the spatial minima of the local entropy designate the transition points between domains of different synaptic weights in the hybrid states, while the number of minima corresponds to the number of different domains. In addition, the entropy deviations signify the presence of chimera-like or bump-like states in the network. Full article