Search Results (253)

The Offset Elliptical Normal (OEN) mixture model represents the seasonal–diurnal surface wind vector for wind engineering design applications. This study upgrades the parameterization of OEN by accounting for changes in format of the global database of surface observations, improving performance by eliminating manual supervision and extending the scope of the model to include skewness. The previous coordinate transformation of binned speed and direction, used to evaluate the joint probability distributions of the wind vector, is replaced by direct kernel density estimation. The slow process of sequentially adding additional components is replaced by initializing all components together using fuzzy clustering. The supervised process of sequencing each mixture component through time is replaced by a fully automated unsupervised process using pattern matching. Previously reported departures from normal in the tails of the fuzzy-demodulated OEN orthogonal vectors are investigated by directly fitting the bivariate skew generalized t distribution, showing that the small observed skew is likely real but that the observed kurtosis is an artefact of the demodulation process, leading to a new Offset Skew Normal mixture model. The supplied open-source R scripts fully automate parametrization for locations in the NCEI Integrated Surface Hourly global database of wind observations. Full article

Transient increments of X-ray radiation and extreme ultraviolet (EUV) during solar flares are strong drivers of thermospheric dynamics on Mars, yet their class-dependent impacts remain poorly measured. This work provides the first direct, side-by-side study of Martian thermospheric reactions to flares X8.2 on 10 September 2017 and M6.0 on 17 September 2017. This study shows nonlinear, class-dependent effects, compositional changes, and recovery processes not recorded in previous investigations. Species-specific responses deviated significantly from irradiance proportionality, even though the soft X-ray flux in the X8.2 flare was 13 times greater. Argon (Ar) concentrations rose 3.28× (compared to 1.13× for M6.0), and radiative cooling led CO₂ heating to approach a halt at ΔT = +40 K (X8.2) against +19 K (M6.0) at exobase altitudes (196–259 km). N₂ showed the largest class difference, where temperatures rose by +126 K (X8.2) instead of +19 K (M6.0), therefore displaying flare-magnitude dependent thermal sensitivity. The 1.95× increase in O concentrations during X8.2 and the subsequent decrease following M6.0 (−39 K cooling) illustrate the contradiction between photochemical production and radiative loss. The O/CO₂ ratio at 225 km dropped 46% during X8.2, revealing compositional gradients boosted by flares. Recovery timeframes varied by class; CO₂ quickly re-equilibrated because of effective cooling, whereas inert species (Ar, N₂) stabilized within 1–2 orbits after M6.0 but needed >10 orbits of the MAVEN satellite after the X8.2 flare. The observations of the X8.2 flare came from the western limb of the Sun, but the M6.0 flare happened on the far side. The CME shock was the primary driver of Mars’ EUV reaction. These findings provide additional information on atmospheric loss and planetary habitability by indicating that Mars’ thermosphere has a saturation threshold where strong flares induce nonlinear energy partitioning that encourages the departure of lighter species. Full article

With the rise in short-term intercultural mobility programs, enhancing students’ adaptive capacity through structured experiential learning has become a key concern. Using constructivist grounded theory, this study draws on extending the situated learning and embodied cognition theories as analytical frameworks to explore international students’ cross-cultural adaptation within the context of study tours. It develops a three-phase framework (pre-departure, on-site immersion, and post-tour reflection) to trace their transition from cultural distance to adaptation. The findings reveal that the process through which international students shift from cross-cultural distance to multidimensional adaptation can be further accelerated by environmental settings, situational behaviors, and short-term emotional responses within study tour contexts. Moreover, culture-led and nature-led environments evoke distinct patterns of participation and emotional responses, facilitating varying degrees and dimensions of adaptation across psychological, social, and cultural domains. The study extends situated and embodied learning perspectives by conceptualizing study tours as dynamic, context-sensitive learning sites. By considering adaptation processes context-dependent, this study deepens the understanding of how learning, emotion, and environment interact to shape intercultural development and offers practical insights for designing responsive, stage-sensitive study tour programs. Full article

Airports are facing significant challenges due to the increasing number of air travel passengers. After a significant downturn during the COVID-19 pandemic, airports are implementing measures to enhance security and improve their level of service in response to rising demand. However, the rising passenger volume has led to increased congestion and longer waiting times, undermining operational efficiency and passenger satisfaction. While most previous studies have focused on static modeling or infrastructure improvements, few have addressed the problem of dynamically allocating passengers in real-time. To tackle this issue, this study proposes a mathematical model with a dynamic rescheduling framework to balance the workload across multiple departure areas where security screening takes place, while minimizing the negative impact on passenger satisfaction resulting from increased walking distances. The proposed model strategically allocates departure areas for passengers in advance, utilizing data-based predictions. A mixed integer linear programming (MILP) model was developed and evaluated through discrete event simulation (DES). Real operational data provided by Incheon International Airport Corporation (IIAC) were used to validate the model. Comparative simulations against four baseline strategies demonstrated superior performance in balancing workload, reducing waiting passengers, and minimizing walking distances. In conclusion, the proposed model has the potential to enhance the efficiency of the security screening stage in the passenger departure process. Full article

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table. Full article

In the rapid process of urbanization, urban agglomerations have become a key driving factor for regional development and spatial reorganization. The formation and development of urban agglomerations rely on communication between cities. However, the spatiotemporal characteristics of intercity travelers are not fully grasped throughout the entire trip chain. This study proposes a spatiotemporal analysis method for intercity travel in urban agglomerations by constructing origin-to-destination (OD) trip chains using smartphone data, with the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employed Cramer’s V and Spearman correlation coefficients for multivariate feature selection, identifying 12 key variables from an initial set of 20. Then, optimal cluster configuration was determined via silhouette analysis. Finally, the K-prototypes algorithm was applied to cluster 161,797 intercity trip chains across six transportation corridors in 2019 and 2021, facilitating a comparative spatiotemporal analysis of travel patterns. Results show the following: (1) Intercity travelers are predominantly males aged 19–35, with significantly higher weekday volumes; (2) Modal split exhibits significant spatial heterogeneity—the metro predominates in Beijing while road transport prevails elsewhere; (3) Departure hubs’ waiting times increased significantly in 2021 relative to 2019 baselines; (4) Increased metro mileage correlates positively with extended intra-city travel distances. The results substantially contribute to transportation planning, particularly in optimizing multimodal hub operations and infrastructure investment allocation. Full article

Various platforms, such as Google Maps, provide information about the services of public transport companies worldwide. Operators publish the planned (static) timetable using the General Transit Feed Specification (GTFS) format, while the GTFS Realtime (GTFS-RT) specification provides live (dynamic) information about the services. In this paper, we present our dataset that was built by retrieving and pre-processing the data sources of the open data platform of BKK Futár, hosted by the Centre for Budapest Transport Company (BKK). The paper contains a well-detailed description of our methods for retrieving and pre-processing the data among statistical features. The dataset covers a one-year period in which the data collection mechanism used for realtime data was continuously improved from collecting only live vehicle positions to covering all the available feeds and increasing the query frequency. We merged the static data with the vehicle positions to filter them, yielding a clean set of tracked trips. As a result, more than 90% of the daily planned trips could be reconstructed from the responses. We provide an interactive web-based visualization for the analysis of the GTFS schedule’s, and the GTFS-RT Vehicle Positions feed’s, geospatial features. The dataset and also our methodology can serve as input for various research studies to investigate the common characteristics of delays and disruptions or predict real departure times based on the current vehicle positions and historical data. Full article

The scheduling of carrier-based aircraft departure operations is subject to stringent temporal, spatial, and resource constraints. Conventional approaches struggle to yield exact solutions or provide a comprehensive mathematical description of this complex, dynamic process. This study proposes a simulation-based optimization method, establishing a high-fidelity simulation model for aircraft departure scheduling. To address the coupled challenges of path planning under spatial constraints and station matching/sequencing under operational constraints, we developed (1) a deep reinforcement learning (DRL)-based path planning algorithm (AAE-SAC), and (2) an enhanced particle swarm optimization (PSO) algorithm (LTA-HPSO). This integrated two-stage framework, termed LTA-HPSO + AAE-SAC, facilitates efficient, collision-free departure scheduling optimization. Simulation experiments across varying sortie scales were conducted to validate the framework’s effectiveness and robustness. Notably, for a complex scenario involving 24 aircraft with diverse priorities and stringent spatial constraints, LTA-HPSO + AAE-SAC achieved an average solution time of 185.19 s, reducing scheduling time by 26.18% and 49.54% compared to benchmark algorithms (PSO + Heuristic and PSO + SAC, respectively). The proposed LTA-HPSO + AAE-SAC framework significantly enhances the quality and robustness of carrier-based aircraft departure scheduling. Full article

The Hurst phenomenon is regarded as an intrinsic characteristic of many natural processes closely related to high uncertainty and long-term persistence. Temperature and precipitation are the two important meteorological factors characterizing the climate conditions of different regions. Analyzing the Hurst phenomenon in precipitation and temperature are crucial for understanding the long-term dynamics of our climate system. This study examines the annual mean temperature (AMT) and annual total precipitation (ATP) series for regions across all the land areas of the world, using both gridded climate data and ground station records. The results demonstrate that, in most regions, the Hurst exponent of AMT is higher than that of ATP, particularly with larger spatial scales of averaging. Like ATP, the Hurst exponents of AMT also increase with the spatial scale of averaging. Unlike AMT, ATP is more controlled by local meteorological conditions which tend to weaken its long-term persistence. Moreover, the cumulative departure from the mean series of ATP is much more variable across different regions, whereas those of AMT for different regions are more similar. What is identified for the first time in this study is the strong similarity in the cumulative departure from the mean patterns of regionally averaged and individual stations’ ATP and AMT series over many regions of the world. At most of these regions and stations where such similarities are identified, more than half have confirmed that AMT is the Granger cause of ATP variations. Moreover, the fluctuation functions obtained in multifractal detrended cross-correlation analysis exhibit approximately linear behavior in the log–log spaces across all regions at both global and continental scales, indicating that ATP and AMT series are long-range cross-correlated. Full article

Understanding how people spend time on daily activities is key to modeling travel behavior. However, accurately estimating the duration of these activities remains a significant challenge, especially when generating synthetic activity-travel data. This article introduces an activity-based approach that addresses this issue by applying statistical and machine learning models to improve the precision of activity duration estimates. The method utilizes real-world Origin-Destination (OD) datasets to generate additional synthetic data that can support transportation planning processes. Unlike conventional approaches that rely solely on OD matrices, this framework incorporates Cox and Cox-based hazard models to more precisely estimate activity durations, as well as arrival and departure times across trip segments. Statistical tests and comparative evaluations show that the proposed method produces more accurate synthetic data than existing open-source tools that do not employ hazard-based modeling. A case study using real-world data from Athens, Greece, demonstrates the effectiveness of the proposed approach. Full article

Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability. Full article

The air–sea transfer of gases is important within climate physics, biogeochemistry and the control of pollutants. A two-layer model of transfer directly across the sea surface underpins most discourse, but an expanding literature also features transfer mediated by “suspended fragments”, either bubbles in the upper ocean or drops and aerosol in the lower atmosphere. In this study, we describe a categorization of process that elucidates departures from two-layer theory and is a starting point for quantification. On counting the distinct phenomena and their application to gases of various solubility, a total of eight categories are identified. Each category has a distinct scaling with respect to the properties of the gas and this is key to the relative importance of different categories and processes. Transfer through sea spray can be an exchange process, but the evaporation of sea spray is more effective and is an ejection process. The reactivity of carbon dioxide in aqueous solution enhances the effect of spray. Exceptional levels of sea spray generation and evaporation are required to be significant for most gases, but moderate levels are sufficient for carbon dioxide and the most soluble pollutants. Full article

This study explores the etymological roots of nature and nature-inspired design within the context of soil stabilisation. It outlines Aristotle’s doctrine of hylomorphism and applies these concepts to develop a pathway for the stabilisation of clays within their original porous or looser structure through interparticle modifications. A biopolymer is introduced to a base clay thorough a procedure that imitates forms, matter, generative processes, and functions of arbuscular mycorrhizal (AM) fungi. For the first time, the void ratio was progressively increased from 0.50 to 0.70, and the air ratio from 0.15 to 0.33, reflecting a systematic transition from a denser to a looser packing state. A 20% increase in shear wave velocity indicated enhanced interparticle engagement following treatment. This reinforcement effect contributed to the preservation of stiffness and residual strength, despite a 120% increase in air ratio and a 63% reduction in degree of saturation, alongside a modest improvement in unconfined compressive strength. The findings presented here mark a departure from both conventional and emerging stabilisation techniques, enabling engineered soil to remain porous, to loosen with time, and to continue delivering engineering and ecological services. Full article

Aircraft boarding is one of the most essential handling processes carried out at an airport. Its importance derives from the fact that it is part of the critical path; that is, the time of its completion determines the aircraft’s departure time. It is desirable to examine how the efficiency of the boarding process changes depending on the disruptions that may occur. It is particularly important to check how they affect existing and partially applied boarding strategies that are assumed to improve the process. This article aimed to develop a microscale model of the boarding process implemented as a hierarchical, timed, colored Petri net (HTCPN). This model makes it possible to consider various disturbances in the boarding process, two of which were the subject of simulation experiments that were realized. As a result, it was found that due to disruption, not only did the effectiveness of boarding strategies change, but also their ordering relative to the total completion time of the process. This led to the conclusion that using models similar to those presented in this article is necessary, where input parameters can be determined dynamically. This means that it can be recommended to observe the currently ongoing boarding and, if any disruption is detected, perform a fast simulation to answer the question about the most advantageous boarding strategy in this situation. Full article

To improve the utilization of port resources and reduce the consumption of resources due to vessel waiting and delays, this paper investigates the Berth Allocation and Quay Crane Assignment Problem (BACAP) in container ports, focusing on the Quay Crane (QC) profile. The objective is to assign berths, berthing times, and QC profiles to vessels arriving at the port within a given planning horizon, thereby extending the traditional BACAP framework. To minimize the sum of idle time costs caused by vessel waiting and delay time costs due to late vessel departures, a mixed-integer linear programming (MILP) model is proposed. Additionally, a variable neighborhood search (VNS) algorithm is designed to solve the model, tailored to the specific characteristics of the problem. The proposed MILP model and VNS algorithm are evaluated using two sets of BACAP instances. The numerical results demonstrate the effectiveness of both the model and the algorithm, showing that VNS efficiently and reliably solves instances of various sizes. Furthermore, each neighborhood structure contributes uniquely to the iterative process. This study also analyzes the impact of different idle and delay costs on BACAP, providing valuable managerial insights. The proposed framework contributes to enhancing operational efficiency and supports sustainable port management. Full article