Search Results (713)

Graph neural networks remain challenging to apply to traffic forecasting, often failing to consistently outperform simple baselines. We observe that the strongest baseline is regime-dependent: last-value persistence dominates at short horizons on smooth freeway data, while time-of-day climatology prevails at longer horizons and on bursty arterial networks. Rather than treating this as a limitation, we propose a learnable approach that automatically selects the optimal baseline. Our method anchors predictions to a learned, per-horizon convex blend of persistence and climatology, introducing only twelve scalar parameters. This learned anchor recovers whichever baseline is locally most effective, allowing the model to focus on capturing residual variations that neither baseline captures. We evaluate on six public benchmarks (METR-LA, PEMS-BAY, PEMS03/04/07/08) spanning traffic speed and flow data under standard 70/10/20 chronological splits with masked evaluation metrics and holiday-aware climatology. Our anchored temporal models consistently beat both baseline methods on the 12-step average across all datasets, and outperform at every horizon on five of the six benchmarks. When integrated into two strong architectures (STID and Graph WaveNet), the anchor yields substantial gains at long horizons where climatology is most informative. Notably, within our lightweight framework, learned spatial graph components do not improve accuracy and can slightly degrade performance, a finding we analyze and discuss. Full article

‘Wellness-at-sea’ is an integral part of the cruise holiday service bundle. Marketed within an image of experiential diversity within a safe, controlled environment, it features medical centers, spa facilities and restoration-focused itineraries. Nevertheless, epidemiologically speaking, cruise ships rank amongst the riskiest transmission environments in tourism. This perspective paper proposes a typology of wellness cruising along two axes (i.e., offer scope: themed voyages versus onboard amenities and promotion focus: borrowed credibility versus narrative ‘storyscaping’) and uses it to develop the ‘wellness-risk paradox’, a boundary case for health-tourism literature. Three testable propositions emerge: reputational risk exposure increases with the offer’s wellness centrality; narrative-based promotion is more susceptible than credibility-based promotion and a quadrant-adjusted health-assurance scheme is more effective than current inspection standards. The argument draws on the 2026 Andes hantavirus outbreak aboard the MV Hondius (three deaths among thirteen cases), the 2020 Diamond Princess outbreak, and a decade of official outbreak surveillance, reporting an increase in gastrointestinal outbreaks from 0.34 to 0.62 per million passengers between 2019 and 2025 (rate ratio 1.84, 95% CI 0.87–3.86), which reverses the pre-pandemic downward trend although annual counts remain too small for the change to reach conventional statistical significance. Sanitation-inspection compliance cannot reliably predict outbreaks, nor does it facilitate trust. Four domains of action are proposed: continuous air-quality monitoring, wastewater surveillance, behavioral risk modeling and jurisdictional coordination. Full article

This study examines the effect of institutional quality on tax revenue mobilization in 10 selected Sub-Saharan African (SSA) countries spanning from 2002 to 2023. The period was chosen because it captures a significant era of fiscal and institutional reforms across Sub-Saharan Africa. The data explored in this study originate from the World Development Indicators (WDI) dataset. The data includes tax revenue mobilization, institutional quality components such as regulatory quality (REQ), voice and accountability (VOA), control of corruption (COC), rule of law (ROL) and government effectiveness (GOE) and political stability (POS). The study also explored some other control variables such as GDP Growth, macroeconomic stability (Inflation) and international economic integration (FDI Inflows and Trade Openness). The study explored total tax revenue as a percentage of GDP to proxy tax revenue mobilization. Utilizing a Pooled Mean Group (PMG) Autoregressive Distributed Lag (ARDL) estimation technique, the study analyzes the distinct short-run and long-run dynamics of fiscal capacity. The empirical results reveal a robust long-run cointegrating relationship, evidenced by a statistically significant and negative Error Correction Term (ECT) of −0.1858, which suggests that an 18.6% annual deviation from equilibrium is corrected within the following year. The long-run estimates indicate that institutional quality is a pivotal catalyst for tax. Additionally, inflation and trade openness exhibit significant and positive long-run effects, while Foreign Direct Investment (FDI) exerts a significant damping effect on tax revenue, likely due to aggressive tax incentives. Conversely, the short-run results revealed a significant effect of institutional quality, which suggests that stricter regulations and administrative overhauls may cause immediate transition costs and compliance shocks. Robustness checks using disaggregated institutional quality indicators, which include control of corruption, rule of law and government effectiveness, consistently validate the primary findings. The study concludes that while institutional reforms may disrupt revenue collection in the short term, they are indispensable for building a sustainable long-term social contract and expanding the formal base. Policymakers should prioritize institutional transparency and trade integration while rationalizing FDI-related tax holidays. Full article

This paper presents a short-term traction-load forecasting method that fuses optimization-driven dual-scale decomposition and multiscale information fusion (ODE-MMF) with TimeXer-Mamba to address non-stationary prediction difficulties caused by intermittent and volatile traction loads. A correlation analysis module is first constructed for adjacent feeding sections, where mutual information quantifies cross-arm load transfer induced by train operations and extracts key spatial features. In the ODE-MMF signal processing module, an improved whale migration algorithm searches for the optimal parameters of optimization-driven dual-scale decomposition, enabling multiscale decomposition of load features. Multiscale transfer entropy is then used to measure information flow among decomposed components, and highly redundant components are adaptively merged into complementary feature subsequences. In the TimeXer-Mamba prediction module, TimeXer enhances exogenous variables such as holidays, whereas Mamba captures long-range dependencies through the selective state-space model. A gated fusion mechanism integrates the two representations, after which the merged subsequences are predicted in parallel and reconstructed to obtain the final forecast. Experiments conducted on real-world traction-load data demonstrate that the proposed model consistently outperforms all evaluated baselines. Relative to the best-performing baseline, LSTM-Transformer, it achieves reductions of 9.61%, 9.32%, and 9.81% in mean absolute error, root mean square error, and mean absolute percentage error, respectively, while maintaining high computational efficiency and demonstrating strong potential for practical deployment in railway power supply systems. Full article

Sheep- and goatpox are highly contagious, transboundary viral diseases caused by capripoxviruses (CaPV), severely impacting small ruminant production and resulting in significant economic losses. Our study aimed to analyze the spatiotemporal SGP distribution using the available genome sequence data and to evaluate the recombination occurrence. For this, the WAHIS, WOAH and FAO databases were utilized for the epidemiological analysis of SGP outbreaks. The cross-correlation was calculated to assess the impact of massive animal movements associated with the Islamic holiday of Eid al-Adha on the SGP epizootic situation, and phylodynamic and phylogeographic analysis, as well as a recombination analysis were performed. A total of 1629 SGP outbreaks were reported to WAHIS during 2010–2024, with the majority occurring in Mongolia, the Balkan countries, Russia and the Middle East. Over 60% of all SGP outbreaks were associated with croplands or grasslands, with the highest proportion corresponding to animal densities of 10–50 head/km². Statistically significant positive cross-correlation (p < 0.05) was identified between the month of the Eid al-Adha celebration and the number of SGP outbreaks in Russia, Mongolia, Bulgaria and Tajikistan, while in Greece and China no significant correlation was found. The inferred goat pox virus (GTPV) transmission pathways from China to Vietnam and from India to Bangladesh; for the sheeppox virus SPPV, the routes between Kazakhstan and Russia, Kazakhstan and India, as well as between Russia and China, had the strongest Bayes factor support. Intra-specific recombination events were not detected for the SSPV and GTPV datasets. However, inter-specific CaPV recombination analysis identified a single recombination event in GTPV. Therefore, the use of molecular epidemiological tools, along with the time-calibrated phylodynamic and phylogeographic analyses, has significant applications in the local and international surveillance of the occurrence of SGP outbreaks and for identification of potential recombination events. Full article

Public fireworks shows are widely used for national holiday celebrations, religious ceremonies, and sports events. However, fireworks produce short-lived atmospheric particulate matter (PM) peaks, posing a risk to spectators’ health. Every year, public fireworks shows expose over 140 million Americans to episodic transient PM2.5 peaks that exceed air quality standards by up to 10 times in the United States alone. Yet the magnitude and timing of this representativeness gap have not been measured within an event. This paper investigates the Independence Day fireworks display over Lake Union, Seattle, WA, USA, using a low-cost PM network deployed up to 2 km from the launch site. We evaluated the network observations against routine monitoring data from regulatory stations within 10 km of the launch site and generated a backward–forward Lagrangian stochastic dispersion model, calibrated with the network. Spectator zone PM2.5 concentrations varied by over threefold across sensors within 1.5 km. Duration above the World Health Organization (WHO) 24 h PM2.5 guideline concentration level at network sensors varied from 1 min to nearly 30 min, and regulatory hourly averaging retained 57% of the near-field peak signal on average and only 22% in the worst case. Peak detection at some regulatory stations was delayed by one to two hours compared with the network. The dispersion model, using only network data and public regional wind data, captured the launch site location to within ~100 m and provided minute-scale estimates of PM2.5 and PM1 concentrations across the spectator zone. The findings demonstrate and quantify, for a single event, the representativeness gap expected when transient near-field fireworks plumes are evaluated using spatially sparse and hourly averaged regulatory observations. Full article

Predicting electrical demand in distribution systems is a fundamental problem for the efficient operation of smart grids, especially under scenarios of high temporal variability. This study compares two multi-step forecasting strategies for 24-h horizons: a direct 24→24 strategy and a recursive strategy based on sequential 24→1 predictions. Four machine learning and deep learning architectures are evaluated: LSTM, N-HiTS, U-Net, and LightGBM, using real data from electrical feeders belonging to distribution systems in the equatorial region of Ecuador. The methodology includes constructing time windows, non-overlapping train/validation/test partitioning for evaluation, consistent normalization, and comparative analysis using MAE, RMSE, and MAPE metrics. The results show that the direct 24→24 strategy achieves the best overall performance, with LSTM standing out with an approximate MAPE of 4.12%. However, the recursive strategies exhibit greater stability in the face of atypical patterns observed during holidays and weekends. Furthermore, U-Net demonstrates competitive performance in both accuracy and temporal robustness, while LightGBM stands out for its computational efficiency. It is concluded that the selection of a forecasting strategy depends on the required balance between overall accuracy, temporal stability, and computational cost. Full article

Background: Sun exposure guidance advises minimisation to reduce skin cancer risk, but advice is geared for white-skinned people. Melanin reduces skin synthesis of vitamin D from solar ultraviolet radiation (UVR), and adolescence is a key life-stage for bone mass acquisition. Methods: This prospective cohort study investigated year-round vitamin D status and its determinants in 143 South Asian adolescents (12–15 y; brown skin) living in Greater Manchester, UK. Serum 25OHD, personal UVR doses, time outdoors, and photoprotective measures were assessed across four seasons, and the findings were compared with those of 131 white Caucasian adolescents studied using identical protocols. Results: Severe vitamin D deficiency (25OHD < 25 nmol/L) was prevalent in South Asians, affecting 79% of girls and 69% of boys in winter, persisting year-round in 59% of girls and 23% of boys, and accompanied by winter PTH levels > the upper reference range in 59% of boys and 45% of girls. Personal UVR doses and time outdoors were lower on summer weekend days (boys 53 min, girls 60 min) vs. schooldays (boys 105 min, girls 108 min) in South Asians (p < 0.01). The findings contrasted with those in white Caucasians, where time outdoors was similar on summer weekend days and schooldays (weekend days: boys 131 min, girls 101 min, p < 0.01 vs. South Asians). Only 26% of South Asians vs. 86% of white Caucasians took a holiday abroad during the year, and days abroad significantly contributed to vitamin D status. Both groups had low oral vitamin D intake. Conclusions: Raising vitamin D status is crucial to prevent secondary hyperparathyroidism in South Asian adolescents and skin type-related sun exposure guidance is required. Full article

Expressway travel demand climbs steeply during public holidays, and the severe congestion that follows has become a principal constraint on both network operating efficiency and the quality of travel services. Dependable forecasting of holiday congestion, coupled with the practical realization of proactive management, accordingly carries considerable theoretical and practical weight for improving how expressways are operated and administered and for safeguarding efficient and safe public travel. Organized around the central theme of congestion prediction and proactive management for expressways over holiday periods, this paper reviews the progress of research across five interrelated areas: traffic speed and flow prediction methods; congestion state identification and forecasting; holiday travel characteristic analysis together with demand prediction; multi-source data fusion and congestion propagation mechanisms; and expressway traffic control together with traveler behavior guidance. Having surveyed the theoretical underpinnings, core technologies, and representative methods of each area, the paper concentrates in particular on the shortcomings and difficulties that existing studies encounter in coping with the hallmark features of holiday traffic, namely demand surges of short duration combined with pronounced spatiotemporal heterogeneity, and proceeds to outline promising avenues for subsequent research. Overall, the review seeks to provide wide-ranging literature support and a theoretical reference for developing technologies that predict and proactively manage congestion on expressways during public holidays. Full article

DeFi perpetual futures have expanded from crypto-native instruments to tokenised equities and commodities, yet the economics of these instruments remain poorly understood. We study 17 assets—5 crypto coins, 8 tokenised equities, and 4 tokenised commodities—on three DeFi perpetual platforms (Hyperliquid, EdgeX, Lighter) over July 2025 to February 2026. Applying a rolling 3-day t-test to identify abnormal trading volume without a predetermined event calendar, we document 1797 statistically significant volume anomalies. DeFi perpetual volume is driven primarily by macroeconomic and policy shocks (ADA $t=+628$ on the U.S. Crypto Strategic Reserve announcement; 15 of 17 assets simultaneously anomalous during January 2026 mega-cap earnings), asset-class-specific catalysts, and a recurring 24/7 market-structure effect tied to weekends and U.S. holidays. Price tracking accuracy reveals a sharp maturity gradient: crypto coin perpetuals exhibit near-perfect price tracking ($\rho \ge 0.999$) and strong TradFi volume co-movement ($\rho \left(0\right)\in [0.72,0.83]$), while equity perpetuals show weaker integration and commodity perpetuals range from adequate (oil, gold) to unreliable (natural gas). We conclude that crypto DeFi perpetuals constitute credible synthetic economic claims on underlying assets, while equity and commodity perpetuals remain at an early developmental stage. Integration with traditional financial markets is well-established for crypto coin perpetuals; for equity and commodity perpetuals, the evidence is preliminary, given short observation windows, and further research with longer time series is needed before definitive conclusions can be drawn. Full article

Festive fireworks can substantially affect air quality by causing short-term increases in particulate matter (PM) concentrations. This study analysed spatial and temporal PM pollution patterns in three Slovenian municipalities—Novo mesto, Hrastnik, and Jesenice—during the 2025/2026 Christmas–New Year holiday period. Using descriptive statistics, threshold-based peak detection, temporal segmentation, Pearson correlation analysis, and normalized descriptive indicators, the study evaluated PM_2.5, PM₁₀, relative humidity, and CO₂ levels from late December to mid-January. Results revealed pronounced short-term pollution episodes, with PM_2.5 peaking at 109 µg/m³ in Novo mesto, 128 µg/m³ in Hrastnik, and 133 µg/m³ in Jesenice. Most peaks occurred during late-night and early-morning hours, although Jesenice showed a more dispersed peak pattern. Fine particles represented the dominant PM fraction, with mean PM_2.5/PM₁₀ ratios ranging from 0.91 to 0.93. Normalized indicators showed that Jesenice had the highest relative variability and peak-to-mean ratios despite the lowest average PM concentrations. These findings show that holiday-period air-quality assessment should consider not only average concentrations, but also short-term peak intensity, timing, and local pollution profiles. Full article

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU. Full article

The paper investigates tourists’ willingness to pay (WTP) an increased tourist tax to support the development of accessible tourism. To achieve the research objective, a structured survey was conducted among 452 tourists who spent their holidays in the Ukrainian Carpathians. It has been confirmed that the WTP a higher tourist tax that varies significantly depending on the possible direction of its use. It has been established that in the context of a humanitarian crisis, social inclusion is a more powerful factor of tax loyalty than a traditional environmental programme. A statistically significant relationship was found between WTP an increased tourist tax for the development of accessible tourism and several factors, including respondents’ level of education, income level, frequency of tourist trips over the past five years, current trip expenditure, and perceived accessibility of infrastructure. Consumers’ willingness to voluntarily pay a tourist tax for a specific purpose may suggest that higher tax rates would be publicly acceptable, potentially generating financial resources to support the development of accessible tourism. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

Air quality forecasting and environmental health research at urban and regional scales depend on the combination of measurements from heterogeneous sensor networks, yet the construction of integrated multi-source datasets is rarely described or released as a self-contained deliverable. This paper presents an open dataset that combines four sensor-derived sources covering the whole of Spain over the period from 2022 to 2024: hourly air quality observations from the 588 stations of the national network operated by the Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO), daily meteorological records from the Agencia Estatal de Meteorología (AEMET), daily mobility indicators derived from anonymised mobile telephony events published by the Ministerio de Transportes y Movilidad Sostenible (MITMA) at the municipality level, and a calendar of national and Autonomous Community public holidays. The processing pipeline harmonises sources that differ in temporal resolution, spatial codification and quality regime into a tidy hourly table indexed by station and timestamp, with a fixed feature schema of 56 variables per record. Air quality stations are paired with their nearest AEMET station through a three-tier distance rule, and the daily exogenous features are aligned to the air quality time axis through a two-variant temporal-alignment scheme (lag-and-expand to the hourly grid for the hourly release, same-calendar-day join for the daily release). A complementary daily resolution variant of the dataset is also released, with 72 columns and the same feature schema except for the air quality block, which is aggregated to daily mean, minimum and maximum. The integrated dataset contains approximately 15 million hourly records across the 588 stations and is released on Zenodo (DOI 10.5281/zenodo.20196221) under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. It is intended as a substrate for research on air quality forecasting, environmental epidemiology and multi-source data fusion at the nationwide scale. Full article