Search Results (733)

Brominated flame retardants (BFRs) are widely used in automotive polymers and electronic components, yet vehicles remain an under-characterized and potentially high-exposure microenvironment, particularly in hot climates. This study provides the first comprehensive assessment of BFR occurrence, sources, and exposure risks in vehicle dust from Saudi Arabia, addressing a critical regional data gap. This study systematically investigates the occurrence, compositional patterns, sources, and human exposure risks of polybrominated diphenyl ethers (PBDEs) and selected alternative BFRs in dust from 80 vehicles (domestic cars and taxis; model years 2015–2022) operating in Jeddah, Saudi Arabia. Dust samples were collected using a standardized vacuuming protocol, extracted and cleaned using solvent extraction and silica SPE, and analyzed via GC–NCI–MS. Both legacy PBDE congeners and emerging alternatives (including DBDPE and TBB) were consistently detected, with BDE-209 dominating the overall BFR burden with mean concentrations of 6560 ng/g in domestic vehicles and 5454 ng/g in taxis, with maximum values reaching 220,860 ng/g. Lower-brominated PBDEs occurred at substantially lower concentrations, reflecting the ongoing global transition away from Penta- and Octa-BDE formulations. Taxis exhibited generally higher concentrations than domestic vehicles, likely due to prolonged occupancy, increased usage intensity, and enhanced dust resuspension dynamics. Multivariate analysis (PCA and correlation) revealed two distinct source categories: (i) legacy Penta-BDE-related congeners associated with polyurethane foam and textile materials and (ii) high-brominated PBDEs and DBDPE linked to hard plastics and electronic components. Human exposure assessment demonstrated that dust ingestion is the dominant exposure pathway, while dermal and inhalation routes contribute minimally. Non-carcinogenic hazard indices (HI) were well below unity for all compounds (HI < 1.67 × 10⁻⁶), and incremental lifetime cancer risks (ILCR) for BDE-209 remained within or near accepted risk thresholds (7.52 × 10⁻⁶–1.04 × 10⁻⁵), although occupational exposure among taxi drivers was consistently higher. Overall, the results demonstrate that modern vehicle cabins act as significant microenvironments for chronic BFR exposure, particularly under high-temperature conditions. Despite generally low estimated risks, the combined effects of chemical persistence, bioaccumulation potential, and mixture toxicity—amplified by extreme in-cabin temperatures—highlight vehicles as overlooked yet significant exposure environments. These findings provide the first comprehensive dataset for the Arabian Peninsula and emphasize the need for climate-sensitive exposure assessment, safer material design, and targeted mitigation strategies in vehicle interiors. Full article

This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters (α =1.1758, β =2.9608) based on observed hospital choices and construct travel time and distance matrices from observed trips. Unlike existing Huff-based FCA approaches that assume parameter values, BCEC-2SFCA jointly estimates the attractiveness elasticity and distance-decay coefficient directly from local healthcare travel behavior and integrates these calibrated probabilities into a 2SFCA structure where hospital catchments are endogenously generated rather than exogenously imposed. Compared with conventional Gaussian 2SFCA, the BCEC-2SFCA model produces a continuously varying and behaviorally plausible accessibility surface and better replicates the relative order of hospital attractiveness (ρ = 0.527, p < 0.05), although its RMSE is slightly higher (0.02700 vs. 0.02211) while MAPE is clearly lower (32.17% vs. 42.12%). Robustness checks using all 22 hospitals confirm stable estimates, and subgroup analyses show consistent advantages across hospital scales. The framework is specifically designed for high-order medical services with strong inter-facility competition—such as tertiary hospitals—and its applicability to proximity-based services is limited. Full article

Urban festivals attract substantial numbers of tourists, which consequently imposes significant strain on host cities through spatial overcrowding, uneven pressure on infrastructure, and diminished quality of the visitor experience. Destination management organizations (DMOs) require effective tools to redistribute tourist flows; however, the influence of social media on tourists’ actual destination choices remains insufficiently understood. We ask whether social media discussion intensity (“buzz”) causally influences tourists’ destination choices and whether the effect grows stronger during festivals when information asymmetry is at its peak. Combining 95,692 taxi GPS trajectories with 5995 geotagged Twitter records from the 2019 Songkran Festival in Bangkok, we constructed an exponentially weighted moving average (EWMA) buzz variable with a temporal lag that establishes causal ordering. A conditional logit model shows that district-level buzz significantly raises destination choice probability and that the effect is amplified during the festival. Causal identification rests on a triangulated strategy that combines temporal lag, placebo permutation, and Bartik shift-share instrumental variables. The festival-period IV-corrected estimate (${\widehat{\beta }}^{\mathrm{IV}}=+0.019$, $p<0.001$) is 51% larger than the within-period OLS estimate (${\widehat{\beta }}^{\mathrm{OLS}}=+0.012$, $p<0.001$), a gap consistent with classical measurement-error attenuation in sparse social-media data, and a panel 2SLS analysis at the district–day level isolates a causal visitation channel confirming that cascades reinforce spatial concentration at the tourist-flow level. The aggregate Gini coefficient of spatial concentration declines over the study window in a statistically significant monotonic trend. The positive district-level correlation between buzz and congestion does not survive district and date fixed effects, which indicates that it reflects underlying differences in attractiveness across districts rather than a direct within-district channel. These findings provide an empirical foundation for information-based visitor flow management by identifying the underlying behavioral mechanism rather than evaluating a designed intervention. Full article

This paper presents an integrated validation framework for EV taxi charging-hub plans that combines spatial accessibility, grid deployability, and operational performance. Candidate hub configurations are first generated through a demand-weighted p-median model based on 175 taxi stands and 2825 cooperative members in Belém, Brazil. The assigned demand is then translated into charger requirements through stochastic sizing, and the resulting infrastructure is screened against the available headroom of 12,905 medium-voltage transformers. Finally, the selected solution is evaluated through an Erlang-C queueing model under peak-demand concentration. The final plan, obtained with 14 hubs, achieved a weighted mean distance of 0.724 km and a weighted P95 distance of 1.488 km, while requiring 46 chargers and 2610 kW of installed capacity. Of these, 45 chargers were successfully allocated in the grid-screening stage, corresponding to a placement rate of 97.83%. Full article

In this work, a dynamic polymer electrolyte membrane (PEM) fuel cell system is modelled in Modelica using the in-house developed, open-source library ThermoFluidStream. The focus lies on the fuel cell stack, the hydrogen fuel supply and the air supply. Additionally, the thermal management and the power electronics are considered in a simplified manner. Dynamic simulations are carried out for this system over an exemplary aircraft gate-to-gate mission. Simultaneously, a baseline control scheme is developed to provide the fuel cell with sufficient product gases in a suitable state regarding the temperature, pressure and relative humidity. The results indicate that the fuel cell system performs well with standard PI controllers. Only when strong dynamics occur, such as when going from taxi to take-off, does the control scheme show some weaknesses, as expected. This fuel cell system together with its control is a powerful baseline for future investigations. Full article

Potato early blight (EB), caused by Alternaria, is an economically devastating fungal disease affecting global potato production. Using a hybrid population derived from distantly related varieties, we combined resistance evaluation, histological analysis, Bulked Segregant Analysis sequencing, RNA sequencing and molecular dynamics simulation, which successfully identified key candidate resistance genes. Genetic mapping localized three major resistance-associated regions on chromosome 8 spanning positions 25.07–29.20 Mb, 38.05–38.80 Mb, and 39.40–40.78 Mb. Through candidate gene analysis, we identified CND41, encoding an aspartic protease, as the prime candidate. This gene exhibited significantly higher basal expression levels and stronger pathogen-induced upregulation in resistant genotypes. Molecular dynamics simulations further identified six crucial non-synonymous mutations in the TAXI-N domain that likely contribute to enhanced resistance by destabilizing the susceptibility-associated protein conformation. Transient overexpression of CND41 provided functional evidence supporting its likely involvement in early blight resistance (EBR). These findings contribute valuable genetic resources and a strong candidate gene for molecular breeding toward EBR potato varieties. Full article

Extended Reality (XR) and Artificial Intelligence (AI) are increasingly converging within cyber–physical infrastructures, including digital twins, the Spatial Web, and smart-city systems. These environments require new frameworks for understanding how human performance emerges through sustained interaction with immersive interfaces and adaptive computational agents. This paper introduces the TAXI–XI-CAP framework, a two-layer model that links psychobiological mechanisms of XR–AI interaction to higher-level, experimentally testable capability constructs. The TAXI layer defines 42 mechanisms spanning perception, cognition, physiology, sensorimotor control, and social coordination, while XI-CAP organizes these into capability patterns such as remote dexterity, distributed cognition, and adaptive workload regulation. Derived through a theory-guided synthesis across XR, neuroscience, and human–automation interaction, the framework models performance as emerging from interacting mechanisms under real-world constraints. A validation-oriented research agenda is proposed, emphasizing mechanism-level measurement, capability-level evaluation, and longitudinal testing. The TAXI–XI-CAP framework provides a structured basis for hypothesis generation, comparative analysis, and empirical validation of XR–AI systems, supporting the development of reliable, scalable, and human-centered Extended Intelligence infrastructures. Full article

Background/Objectives: Viral respiratory tract infections (VRTIs) in patients with diabetes mellitus (DM) are characterized by a severity gap rather than an infection gap. This review synthesizes evidence from the 2023–2026 respiratory seasons to provide a post-pandemic framework for managing the synergistic metabolic and viral threats in this population. Materials and Methods: A scoping review of literature from PubMed, Scopus, and Embase (2023–2026) was conducted, focusing on clinical outcomes and mechanistic interactions between DM and emerging respiratory pathogens. Results: Recent data identify human Metapneumovirus (hMPV) and adenovirus as significant threats to diabetic hosts, with mortality risks equivalent to seasonal influenza (HR 1.00 for hMPV vs. influenza). The two-hit model combines a baseline of innate immune paralysis, characterized by impaired neutrophil chemo-taxis and mechanical SP-D dysfunction, with a cellular signaling environment primed for cytokine overreaction by epigenetic metabolic memory. The stress hyperglycemia ratio (SHR) has emerged as a promising predictor of mortality compared to absolute glucose or HbA1c, with a proposed threshold of ≥1.14 identifying patients at 3.5-fold increased risk for mechanical ventilation. Precision management should consider the prudent suspension of SGLT2 inhibitors to mitigate euglycemic DKA risks and considering the early use of GLP-1 receptor agonists for their hypothesized pulmonary anti-inflammatory properties. Conclusions: Closing the mortality gap may require a shift from generic viral care to a precision model that treats metabolic susceptibility with high clinical priority alongside the treatment of the viral pathogen. Full article

Accurate short-term traffic flow prediction in complex urban road networks is of great significance for capacity organization and dispatch optimization in intelligent transportation systems. Using publicly available historical taxi trip records released by the New York City Taxi and Limousine Commission from January to June 2016, this study develops a spatiotemporal fusion framework for short-term traffic flow prediction. To address the nonlinearity, sparsity, and complex spatiotemporal dependencies of traffic flow sequences, the raw trajectory data are first cleaned, spatially gridded, and temporally discretized. Based on the spatial adjacency relationships among grid nodes, a graph structure is then constructed, and a serially coupled graph convolutional network and long short-term memory model is developed to capture spatial dependency features and temporal dynamic features, respectively. Experimental results on the New York City taxi dataset show that, compared with baseline models including the historical average model, long short-term memory network, graph convolutional network, and Transformer, the proposed model achieves better performance in terms of mean absolute error, root mean square error, and coefficient of determination. Furthermore, the SHAP (SHapley Additive exPlanations) method is employed to ANALYZE the differences in feature contributions across nodes in different functional zones from both temporal and spatial perspectives. The results indicate that the model exhibits heterogeneous temporal dependency depths and spatial aggregation patterns across different types of regions within the study area. In addition, regions with high feature contributions show a certain degree of spatial correspondence with the major traffic corridors in Manhattan, suggesting that the model is able to capture part of the spatiotemporal correlation structure of traffic flow in this dataset. Finally, the limitations of the proposed method in terms of static graph structure, response to extreme events, and integration of external factors are discussed. It should be noted that these findings are derived from New York City taxi data from the first half of 2016, and their generalizability to other cities, time periods, or traffic scenarios remains to be further validated. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

This study addresses an integrated airport gate assignment and departure scheduling problem under capacity constraints while explicitly accounting for the operational role of apron resources. A bi-objective mixed integer linear programming model is developed to jointly determine gate or apron assignments and departure times by considering passenger transfer times, taxi operations, runway separation, and schedule deviations. The first objective minimizes a normalized composite measure of passenger transfer burden, taxi penalties, and departure schedule deviation, whereas the second objective minimizes apron usage. The epsilon constraint method is used to generate exact Pareto-efficient solutions. Computational experiments on synthetically generated congested hub airport instances with 20 flights show that increasing physical gate capacity from 3 to 5 improves the average value of Objective 1 from 1.37 to 0.92 and reduces average apron usage from 10.00 to 4.00 flights. In the highlighted 20-flight and 5-gate scenario, increasing apron usage from 3 to 5 assignments reduces the standard deviation of departure time deviations from 8.0 to 7.6 min. The results show that selective apron usage improves system-level schedule stability and that gate capacity and apron flexibility should be evaluated jointly in tactical airport planning. Full article

Urban accessibility is commonly evaluated using static spatial indicators, which assume stable travel conditions throughout the day. Road congestion, network saturation, and service variability change the function and experience of the built environment (BE). This study tests the Temporal City Framework (TCF) by examining how time of day (TOD) reshapes urban accessibility and travel behaviour with varying levels of congestion. Using 30,288 trip records from the 2022 US National Household Travel Survey (NHTS), duration is operationalised as a sixth dimension of the BE. A time-normalised impedance metric, measured in minutes per mile (MPM), is used that captures realised congestion independently of distance. Temporal impedance (TI) varies strongly with TOD, with substantially higher MPM during peak and midday periods than at night. Compared with nighttime conditions, midday travel requires approximately 19% more time per mile. This indicates a measurable contraction in functional accessibility under identical BE conditions. The TI model outperforms duration-only models, with impedance remaining dominant when both measures are included. These results support interpreting duration as a structural dimension of urban accessibility. TI significantly increases the relative likelihood of active and public transport compared to private cars, even after accounting for absolute trip duration. Hired transport modes (taxi and ride-hailing services) are most prevalent at night, reflecting a greater reliance on on-demand services outside regular daytime schedules. This study tests duration as a structural dimension of the BE by operationalising time-normalised TI. Associations are interpreted as trip-level behavioural constraints rather than causal effects. Planning frameworks based on static travel times systematically misrepresent exposure, equity, and travel mode feasibility. Time-stratified accessibility metrics should therefore be integrated into transport and land-use evaluation and associated policies. Full article

Advanced Air Mobility (AAM) includes innovative aviation technologies and services that could alter how people and goods are transported. However, future AAM growth and potential regional integration are uncertain and influenced by a range of factors. In this paper, we report findings from expert interviews (n = 35) and a scenario planning workshop (n = 32 stakeholders), conducted between August 2024 and July 2025, to explore potential alternative futures for AAM at the San Francisco International Airport (SFO) and the greater San Francisco Bay Area. We applied a two-axis framework: regulatory environment (supportive vs. restrictive) and economic conditions (vibrant vs. stagnant). Building on this, we developed four plausible scenarios for the 2025 to 2030 and post-2030 time horizons. We apply the SPELT (social, political, economic, legal, technological) framework to assess cross-cutting drivers, tensions, and indicators across the four scenarios based on two timeframes, i.e., 2025 to 2030 and post-2030. Our analysis of the scenarios reveals that regulatory clarity and macroeconomic conditions are key influencers that define the pace and scale of AAM growth, while community impacts (e.g., noise), public acceptance, and infrastructure availability are constraints. These factors largely determine whether technical readiness can translate into scaled deployment. Cross-cutting themes across all of the scenarios consistently shape the outcomes: (1) equity and community acceptance strongly influence political feasibility; (2) SFO and other airports can serve dual roles as conveners and practical enablers but face risks of stranded assets; and (3) flexible, modular infrastructure and incremental investment strategies reduce uncertainty for SFO and other Bay Area airports and public agencies. Together, the findings suggest that while the future of AAM is uncertain, policy and planning responses can assist airports, local governments, and other public agencies in preparing for potential developments. Full article

Background: Regulations in some countries of the world allow self-driving vehicles (private cars and robo-taxis) to operate on geofenced, public roads, yet governments are slow to plan as how best to use this automated technology. We pose research questions about the Australian government’s preparedness, planning gaps for a transition to an autonomous public transport system, and specific system components that require attention. Method: We review the relevant literature, and podcasts of automobile manufacturing experts, and draw on our extensive professional experience advising governments in applying the systems approach to a planning system that includes autonomous transport. Results: Governments must include risk management in Type-II road corridors; develop mobility hubs that connect terminals for fully self-driving vehicles and robo-taxis to connect with public transport systems; and include body corporates when engaging the community in precinct planning. In the discussion, we argue the case for an autonomous urban public transport system where private ownership of vehicles is progressively reduced. Conclusions: Australian governments are not prepared with a systems-wide urban planning process that includes autonomous transport and self-driving vehicles. During the transition period, the existing and new transport systems must operate together, emphasising the leading role for governments. A roadmap for further research and development is outlined and this could provide the framework for urban planning in other jurisdictions. Full article

To clarify the wear degradation of airport cement concrete pavements under combined environmental and traffic actions, this study established an environment-tire-pavement multi-physics finite element model incorporating surface texture, freeze–thaw deterioration, temperature gradients, and aircraft lift during taxiing. Indoor rapid freeze–thaw tests, accelerated wear tests, and 3D texture scanning were further conducted to calibrate and validate the model. The results show that temperature gradients significantly amplify pavement wear. At 180 km/h and 1.2 million wear cycles, increasing the temperature gradient from 0 to 60 °C/m increased wear depth and wear mass by about 40% and 96%, respectively. Taxiing speed was negatively correlated with wear, mainly because higher speed reduced tire-pavement contact duration and effective vertical load. Freeze–thaw deterioration was the dominant factor affecting wear, and the coupled freeze–thaw–temperature–load condition produced the most severe damage. The experimental and simulation results agreed well, with R² values above 0.98. Based on the combined experimental-simulation dataset, an interpretable CNN-BiLSTM model was developed for wear-depth prediction, achieving RMSE values of 0.019 and 0.035 for the training and test sets, respectively. SHAP analysis further confirmed that freeze–thaw cycles contributed most to wear prediction. This study can provide a quantitative basis for the wear resistance evaluation, life prediction, and maintenance decision-making of airport pavements. Full article