Search Results (3,379)

The ORCI project aims to develop an AI-based decision-support tool to assist air traffic controllers in complex TMA operations, taking Barcelona’s transitions as the primary use case. Using historical radar data, the tool has been trained to predict spacing between consecutive arrivals based on real-time vectoring commands. A data-processing pipeline was developed to clean, classify and validate flight trajectories, and synthetic samples were generated to enable a wider variety of situations. Explainable ML models achieved a mean absolute error of around 0.38 NM, demonstrating strong predictive capability. The results show the potential of ORCI to improve sequencing efficiency and runway throughput. Full article

To reduce the conservatism of local trajectory planning in dynamic road scenarios caused by redundant projection of predicted trajectories, this paper proposes a hierarchical hybrid trajectory-planning framework with a time-to-arrival (TTA)-driven dynamic risk-filtering mechanism. In the Frenet coordinate system, road boundaries, ego states, and static and dynamic obstacles are represented uniformly to construct an S–L fused risk field and an S–T spatiotemporal interaction graph, enabling the filtering of temporally irrelevant conflict regions based on TTA relationships. At the path-planning layer, risk-guided adaptive sampling is integrated with dynamic programming and quadratic programming to improve search efficiency and trajectory quality. At the speed-planning layer, spatiotemporal coordination is achieved through non-uniform discretization, safe-corridor extraction, and speed-profile optimization. Simulation results show that the proposed method generates safe, smooth, continuous, and executable local trajectories in scenarios involving static-obstacle avoidance, adjacent-vehicle cut-ins, non-motorized road-user crossings, and mixed multi-obstacle interactions, while reducing unnecessary deceleration and detours. Ablation results further indicate that adaptive sampling reduces the number of DP search nodes by approximately 50% and the average planning time by about 30%, while maintaining a nearly unchanged minimum safety distance. These findings demonstrate that the proposed framework effectively suppresses redundant conflict regions and improves planning efficiency, solution feasibility, and motion continuity without compromising safety. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

“The knock” refers to the moment when the police first arrive at a house to investigate suspected sexual offending by a member of that family. This paper examines the levels of trauma, wellbeing and crisis support for partners, both at the time of the knock and at the current time. Forty-eight participants who had experienced the knock completed an online survey. All respondents were female; they had experienced the knock 0–20 years previously (M = 3.88 years). Participants provided demographics and completed the World Health Organisation (WHO) Wellbeing Index, the International Trauma Questionnaire (ITQ) and the Crisis Support Scale (CSS) for (i) retrospective (at the time of the knock) and (ii) current levels of social support. Participants reported lower levels of wellbeing and higher levels of trauma in comparison with general population norms. Approximately 40% of participants’ scores on the ITQ exceeded the criterion for post-traumatic stress disorder (PTSD), with a further 25% of participants meeting the criterion for Complex PTSD. Levels of trauma were negatively correlated with wellbeing and with both retrospective and current self-reported crisis support. Qualitative analysis of open-ended questions explored participants’ experiences of the knock, their perceptions of police conduct, and the personal, relational, and practical consequences that followed. The findings highlight substantial and enduring harm among partners and are discussed in relation to implications for current policy and practice. Full article

Uncertainty in vessel arrival times can substantially reduce the efficiency of berth planning in port operations. To address this issue, this study proposes a unified, data-driven, predict-then-optimize framework that explicitly links vessel arrival time (VAT) prediction with downstream continuous berth allocation optimization. In the prediction stage, heterogeneous maritime data, including port call records, AIS trajectories, and vessel physical characteristics, are integrated to construct VAT prediction models. In the optimization stage, the predicted VAT is embedded into a continuous berth allocation problem (BAP) model to support berth scheduling decisions. To better reflect real operations, a two-stage evaluation framework is further developed, in which berth plans generated from estimated arrival times (ETAs) or predicted VATs are re-evaluated under realized actual arrival times while preserving the original temporal and spatial service order. Experimental results show that the proposed framework improves VAT prediction accuracy substantially, reducing the MAE and RMSE from 4.795 h and 7.255 h for the vessel-reported ETAs to 2.844 h and 4.934 h, respectively. More importantly, the predicted-VAT-based BAP consistently outperforms the ETA-based benchmark, yielding an overall 35.96% reduction in objective value across tested scenarios. These findings demonstrate that improved VAT prediction can be effectively translated into meaningful operational gains in berth allocation. Full article

Detection of trace gases with high sensitivity and weak excitation power is highly desired for long-range remote sensing. Here, we report the detection of the greenhouse gas nitrous oxide (N₂O) with the power of excitation light down to picowatts, by converting the mid-infrared laser to near-infrared photons through an intra-cavity-enhanced sum-frequency upconversion system. The intra-cavity-enhanced pumping power of 1064.0 nm reaches about 200.0 W, resulting in the conversion of the 4514.6 nm mid-infrared laser to 861.1 nm with an efficiency up to 73.4% under optimal conditions. The upconverted light is then detected by a single-photon avalanche detector, followed by a time-correlated single-photon counting module, which can measure the arrival time of each upconverted photon. By performing discrete Fourier transformations of the arrival time of the detected photons, the frequency spectrum can be determined. By using frequency modulation, this method can suppress background noise significantly. Consequently, the excitation power can be brought down to about 100 pW with the concentration of N₂O being 10 ppm. As a demonstration of application, the presented system is also used for N₂O sensing in an open-path geometry, highlighting the potential for stand-off leak detection. Our proposal offers promising applications to monitor trace gases over long distances with weak excitation powers. Full article

Background: Podiatrists are crucial for managing lower limb pathologies, and effective appointment scheduling is vital for allocating adequate consultation time based on patient conditions. While occasional late patient arrivals may not significantly impact services, frequent lateness can disrupt patient flow and quality of care. This study explored the impact of patient lateness on podiatry practices worldwide, where no countries of origin were excluded. This study assessed current strategies to manage patient lateness, evaluated their effectiveness and reported recommendations for improvement. Methods: An international cross-sectional online survey was conducted between January and March 2024. Results: The survey, which garnered 201 responses from podiatrists, revealed that over 90% of podiatrists experienced disruptions in their clinic workflow due to late patients. Common reasons for lateness included traffic issues and difficulties with parking. SMS reminders emerged as the most effective tool for reducing tardiness. Over half (59.3%) of podiatrists implemented a 10-minute grace period before rescheduling late appointments, which effectively reduced lateness by 50%. However, some podiatrists refrained from rescheduling to avoid worsening patients' conditions or dealing with complaints. Additionally, many podiatrists reported a lack of managerial support in handling late patients. Conclusion: The frequency of late arrivals in podiatry is similar to other health professions and negatively impacts clinic workflow and staff morale. Enhanced managerial support is needed to better manage late patients, allowing podiatrists to concentrate on their clinical responsibilities. Full article

Fishways play a critical role in restoring river connectivity and conserving fishery resources, yet their efficiency is often limited by mismatches between hydraulic conditions and species-specific behavioral traits. To quantify the upstream migration behavior of fish under the combined influence of flow heterogeneity and schooling effects, this study examined the endangered species L. elongata in the Yangtze River Basin. Volitional swimming behavior was tested in an open-channel flume under three spatially heterogeneous flow regimes (I: Low–Moderate–High; II: High–Moderate–Low; III: Moderate–High–Low). A video monitoring system recorded the upstream movement of solitary fish and three-individual schools. Swimming trajectories, upstream migration time, preferred flow velocities, and schooling metrics—including nearest neighbor distance (NND) and mean pairwise distance (MPD)—were analyzed. Linear mixed-effects models were employed to account for repeated measures and individual variability. Results showed that schooling behavior significantly enhanced upstream migration efficiency: schooling fish arrived at the target area on average 8.93 s earlier than solitary individuals (p < 0.01), while flow condition alone had no detectable effect on arrival time. L. elongata consistently preferred low-velocity zones (0.20–0.50 m/s) and avoided high-velocity regions (0.75–1.25 m/s), with meandering upstream trajectories predominating. NND showed no significant differences across flow conditions (p > 0.05), indicating stable schooling cohesion. However, MPD increased significantly under Flow III compared to Flows I and II (p < 0.01), suggesting that higher flow heterogeneity leads to more dispersed group spacing while overall cohesion is maintained. Distinct movement strategies were observed: solitary fish predominantly utilized boundary regions as hydraulic refuges (wall-following: 63.8–80.5%), whereas schools exhibited greater spatial exploration and reduced wall-following. These findings demonstrate that schooling enhances migration efficiency while preserving a cohesive group structure and that flow heterogeneity influences within-group spatial organization. To optimize fishway performance for L. elongata, we recommend maintaining flow velocities within 0.20–0.50 m/s. This study provides scientific guidance for hydraulic regulation in fishway design and habitat restoration, emphasizing the combined effects of flow heterogeneity and schooling behavior on migration performance. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

Background/Objectives: Emergency department (ED) overcrowding burdens rural and remote areas where geographic isolation limits timely care. The Compact Emergency Unit (CEU)—a 24 h facility with remote physician oversight—has been proposed but lacks an empirical foundation. We aimed to (1) quantify CEU-eligible (final KTAS 4–5) patients in a multicenter ED cohort; (2) compare their operational metrics with non-eligible patients; (3) characterize hourly demand for facility planning; and (4) develop machine-learning models for non-discharge prediction within this low-acuity stratum. Methods: Retrospective analysis of 12 months (January–December 2025) of NEDIS data from two Korean university-affiliated EDs. Effect sizes (Cliff’s δ, Cramér’s V) were reported alongside p-values. Three classifiers (logistic regression, random forest, and XGBoost) were developed with patient-level cross-validation, comparing a 16-feature baseline and a 22-feature set augmented with arrival vital signs. Calibration and decision curve analysis were performed. Results: Of 34,544 valid triage visits (27,743 unique patients), 9871 (28.6%) were CEU-eligible. They had shorter LOS (92 vs. 171 min; Cliff’s δ = −0.51), 98.8% symptomatic home discharge, and a median of 0 specialty consultations. Nighttime visits comprised 43.7% of CEU-eligible encounters, peaking at 20:00 (1.76 visits/h/day). The non-discharge rate was 1.20% (118/9871). The vital-augmented random forest reached AUROC 0.794 (95% CI 0.758–0.829); XGBoost calibration was near-perfect (ECE 0.020). A combined ML-or-vital-sign screening rule raised non-discharge sensitivity to 94.1%. Conclusions: Approximately 29% of ED visits could be CEU-suitable. Single-modality machine learning is insufficient for safety-critical triage, but a layered ML-plus-vitals screening approach achieves operationally relevant sensitivity. Prospective implementation studies are required before clinical deployment. Full article

Timely monitoring of tourism demand is essential for destination management, yet official monthly arrival statistics are often released with delays and can be difficult to use for near-real-time decision-making, particularly under structural shocks such as coronavirus disease 2019 (COVID-19). This study develops a fully reproducible, open-data nowcasting pipeline for Ecuador’s international tourist arrivals using a Python workflow. The framework integrates (i) the official monthly arrivals series published by Ecuador’s Ministry of Tourism (MINTUR), (ii) open online attention proxies from Wikipedia pageviews retrieved via the Wikimedia REST application programming interface (API), and (iii) open climate covariates derived from the ERA5-Land land reanalysis. Multiple forecasting models are evaluated under a rolling-origin, one-step-ahead backtest, with a mandatory seasonal naïve benchmark and a regime-aware assessment that separates a stress-test window (2019–2021) from an operational post-COVID window (2022–2025). Forecast accuracy is summarized using root mean squared error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), and statistical significance of performance differences is assessed using the Diebold–Mariano (DM) test. Results show that a ridge-regularized autoregressive model (ridge_ar) achieves the best overall accuracy, reducing RMSE by approximately 79% relative to the seasonal naïve baseline over the full evaluation window. Windowed results confirm robust performance during the shock period and sustained improvements in the post-2022 operational regime, while the incremental benefit of broader exogenous signals is heterogeneous across windows, underscoring the importance of regularization and regime-aware reporting. The proposed approach provides a transparent, low-cost blueprint for reproducible tourism monitoring that is transferable to other destinations using open data and standard computational tools. Full article

Signals of opportunity (SoO) enable emission-free passive sensing, but low Earth orbit (LEO) satellite illumination with unmanned aerial vehicle (UAV) array receivers exhibits rapid geometry variation. As a result, the received phase evolves in a space–time coupled manner, and the array snapshots become nonstationary even within one coherent processing interval (CPI), degrading conventional stationary-snapshot direction-of-arrival (DOA) estimators. This paper proposes a decomposition-based sparse reconstruction with successive interference cancellation (D-SR-SIC) framework for dynamic DOA estimation in LEO SoO UAV passive sensing. The proposed estimator leverages a sparse-reconstruction signal model and is implemented via a computationally efficient decomposition-based search-and-cancel procedure. A short-CPI parameterized space–time phase model captures the common motion-induced phase history and the time-varying steering drift; the coupled multi-parameter estimation is decomposed into two low-dimensional correlation searches followed by least-squares amplitude estimation and multi-target peeling. Optional local refinement and multi-beam pre-screening improve robustness to off-grid mismatch, near–far interference, and wide field-of-view operation. Simulations show that the proposed method achieves about 0.11° DOA root-mean-square error (RMSE) at −20 dB signal-to-noise ratio (SNR) in a representative highly dynamic setting. Full article

History attempts to make sense of disparate information by trying to create discourse that lays a series of events with crisp cause–effect relationships in a sequence. Epochal shifts, such as the change from Antiquity to the Middle Ages, are especially complex since they involve a large number of economic, political and even religious factors which occur over long periods and that might overlap and interact through reciprocal feedback mechanisms, making this cause–effects sequence difficult to establish. In this research we adopt a data-driven and well-established methodology to identify, with quantifiable statistical precision, the moment when this shift happened, and from there arrive at its possible causes. We will use historical coin hoard data to find out whether such a shift is detected in a peripheral part of the Roman Empire, the Iberian Peninsula. To do so, we will apply different changepoint analysis methods to a time series of trade links created from that data, and conduct a retrospective analysis based on that result, analyzing the structure of the trade networks before and after the link. Thus, we progress from identifying when the shift happened to identifying where it took place, which in turn allows us to get to investigate why it happened, namely, historical events that could have caused it. This methodology can be used to analyze epochal changes in several steps using time-stamped network data, possibly finding disregarded causes or cause–effect links that could have been overlooked by qualitative methods; in this case, we have applied it to a dataset of coin hoards either found in the Iberian Peninsula or including coins minted there, finding a changepoint in the early 5th century, which, through network analysis, has been linked to a loss of trade with the area of Britannia. Full article

This study examines the current account implications of sustainable rural tourism in Türkiye by measuring rural tourism intensity through tourist arrivals in locally embedded and small-scale accommodation structures—including mountain lodges, camping sites, hostels, pensions, motels, village houses, and boutique hotels—collectively referred to as the LESS variable. Using monthly time series data over the period 2000–2025, the trade deficit is modeled as a function of rural accommodation intensity and the real effective exchange rate. The empirical framework employs Johansen cointegration analysis evaluated through the Pantula principle, Vector Error Correction Model-based Granger causality tests, full-sample bootstrap causality tests, and rolling window bootstrap causality analysis to capture time-varying causal dynamics. The findings confirm a long-run cointegration relationship among the variables and reveal that rural tourism intensity exerts a statistically significant causal effect on the trade deficit, with the relationship intensifying during crisis periods such as the 2008 global financial crisis and the COVID-19 shock. Specifically, increases in rural accommodation intensity are found to exert a negative and significant effect on the trade deficit, indicating that locally embedded tourism structures enhance net foreign exchange retention through lower import leakage. These results suggest that tourism structures characterized by stronger local embeddedness and lower import intensity enhance net foreign exchange retention and contribute to external balance stability. Full article

Background: Pediatric out-of-hospital cardiac arrest (OHCA) is rare and associated with poor outcomes. Evidence from physician-staffed EMS systems remains limited. This study aimed to describe the incidence, presenting rhythms, EMS response intervals, and outcomes of pediatric OHCA, and to describe incidence, presenting rhythms, EMS response intervals, and prehospital outcomes in a local physician-staffed EMS system. Methods: We conducted a retrospective study of all pediatric (0–17 years) OHCA cases managed by the ASL Lecce physician-staffed EMS (southern Italy) between 2013 and 2025. Data were abstracted from standardized records. Variables included demographics, initial rhythm, EMS response intervals, temporal patterns, and return of spontaneous circulation (ROSC). The primary outcome was ROSC during prehospital care. Results: Twenty-seven cases were identified, corresponding to a cumulative incidence of 22.9 per 100,000 children over the study period (annualized incidence 1.73 per 100,000 children-year). Mean age was 11.9 ± 5.5 years (median 15); 59% were male. Initial rhythms were asystole in 81% and ventricular fibrillation (VF) in 19%; no pulseless ventricular tachycardia (pVT) or pulseless electrical activity (PEA) were recorded. Five patients had shockable rhythms, with seven shocks delivered overall. Mean time intervals were: event-to-call 1.0 ± 0.6 min, call-to-arrival 10.3 ± 4.1 min, event-to-arrival 11.3 ± 4.4 min. Arrests clustered during daytime (63%) and summer (41%). ROSC occurred in three patients (11%), two with VF and one with asystole; all arrests with ROSC were daytime events. In descriptive comparisons, ROSC cases showed a shorter call-to-arrival interval (T1–T2), whereas no consistent pattern was observed across all prehospital time intervals. Conclusions: Pediatric OHCA in this Italian physician-staffed EMS was infrequent, usually presented with asystole, and rarely achieved ROSC. Shockable rhythms were associated with better outcomes. Given the small sample size, findings related to response times should be interpreted with caution. System preparedness should include pediatric-specific training, early defibrillation access, and multicenter registries to improve care and track outcomes. Full article