Search Results (904)

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

Introduction: AQUArestore is a three-year project focused on promoting adaptive ecological restoration strategies for river ecosystems in the vulnerable cross-border region of Portugal. The project responds to pressing environmental challenges across the territory, including severe habitat degradation, climate vulnerability, declining water security, and biodiversity loss, with particular concern for freshwater fish communities, making river restoration essential to preserve native species and freshwater ecosystem services. Objective: The project aims to develop a replicable framework for restoration of Mediterranean transboundary riverine habitats, supporting the objectives of the EU Nature Restoration Law (NRL, Regulation 2024/1991). The consortium AQUArestore will develop (1) robust restoration indicators, (2) implement living labs for restoration experimentation, and (3) establish capacity-building and training programs for technicians and citizens. Methodology: The project kick-off meeting was used to operationalize project tasks, detail the implementation calendar and milestones, and clarify responsibilities of each project member and partner institutions within the different work tasks. The meeting gathered consortium members from the coordinating institution CEF-ISA (researchers at the Instituto Superior de Agronomia) and partners WWF Portugal (an environmental NGO) and Mushmore Cooperative, each one contributing according to their respective expertise and institutional objectives. Results: The AQUArestore project kick-off meeting took place in January 2026 at ISA, Lisbon, and included a presentation of the NRL and a detailed discussion of project task development. In detail, the activities will begin with the compilation of information on previously restored sites (Task 1). This will support the development and validation of environmental and biodiversity indicators of restoration outcomes, including those linked to freshwater fish assemblages and riparian vegetation (Task 2). The project will then establish two living labs as platforms to test nature-based solutions in collaboration with stakeholders and local communities (Task 3). In parallel, AQUArestore will strengthen technical capacity through training for practitioners and public authorities (Task 4). Finally, dissemination will be supported through citizen science, communication activities, and stakeholder engagement, fostering a broader impact (Task 5). Together, these tasks provide an integrated, science-based, and participatory framework aiming to support adaptive river restoration under climate and environmental changes. Conclusions: By integrating ecological restoration, biodiversity and environmental monitoring, and stakeholder engagement, AQUArestore is expected to contribute to the recovery of Mediterranean freshwater ecosystems and improve habitat quality and connectivity for native fish communities, enhancing resilience to climate change and other anthropogenic pressures. Full article

Surface electromyography (sEMG) gesture recognition degrades across recording days under domain shift, increasing calibration burden for myoelectric interfaces. Many cross-day adaptation pipelines retrain the deployed recognizer or require labeled target-session data, which can be impractical in assistive-device settings where classifier versions may need to remain locked for traceability and regulatory compliance. We study unsupervised cross-day adaptation under two constraints: the task classifier remains frozen and holdout-day labels are not used when training the adaptor. We propose the Source-Reprojection Module (SRM), a plug-in front end that combines conditional adversarial feature learning with a residual signal-space projector guided by the frozen classifier’s gradients, identity regularization, and latent-space distribution matching, using labeled source days and unlabeled adaptation days only. On a multi-day protocol with four healthy participants (at least five calendar-day sessions per participant, split 3:1:1 into source, adaptation, and holdout domains) and three random seeds per participant (12 runs), mean holdout accuracy increases from 70.9% for the frozen classifier alone to 72.8% with SRM ($+1.98\pm 0.91$ percentage points averaged across subjects). SRM outperforms the frozen baseline in 10 of 12 subject–seed runs. The gain is modest and the cohort is small, so the result supports proof-of-mechanism under the stated protocol rather than population-level clinical generalization. Full article

This revised manuscript presents a real-calendar-anchored SEIR simulation benchmark for comparing manifold-constrained Gaussian process inference (MAGI) and physics-informed neural networks (PINNs). The study is explicitly positioned as an empirical benchmarking and reproducibility contribution rather than a new epidemic model or a new inference algorithm. A deterministic proportional SEIR system defines the mechanistic truth, while municipal surveillance records motivate the calendar and observation context. We compare full observation of $E,I,R$ with a missing–exposed regime in which only $I,R$ are observed. A parametric bootstrap with independent log-normal measurement noise generates an expanded ensemble ($B=80$); this ensemble supports bootstrap medians, interquartile ranges, outlier assessment, and sensitivity analysis under the declared measurement-error model. The revision clarifies the role of the PINN data–physics weight $\lambda$, the oracle MAGI hyperparameter stabilization used in the missing-E experiment, the distinction between MAP estimates and Bayesian posterior uncertainty, and the operational role of PELT changepoint-guided sparse sampling. The results support a balanced conclusion: MAGI is stable in the fully observed setting, whereas PINNs can be competitive under appropriate $\lambda$ choices; the missing–exposed case remains ill-posed and requires cautious interpretation. Full article

The transition toward sustainable power systems requires load forecasting methods that can support renewable integration under increasing uncertainty. However, many deep learning models mix historical load, temporal priors, and external drivers in black-box structures, and often assume that true future driver values are available. To address these issues, this study proposes ADDF (Automatic Driver Discovery and Fusion), a semi-explicit self-driven framework for multi-step load interval forecasting. ADDF organizes historical load, calendar priors, and external drivers into three functional branches to distinguish load inertia, temporal regularity, and external forcing. The Driver Branch estimates future driver states under practical information constraints and uses dynamic gating to screen useful driving information. The three branch representations are adaptively integrated through Three-Way Fusion, followed by bounded residual correction to generate multi-step quantile forecasts. Experiments on the Panama electricity load dataset and ETTh1 dataset under one-step and 24-step settings show that ADDF achieves competitive point accuracy and interval prediction performance. Mechanism analyses indicate that the proposed branch-level structure provides clearer interpretability than post-hoc black-box explanations. The framework offers uncertainty-aware forecasting support for sustainable power system operation, including day-ahead scheduling, reserve planning, and energy management. Full article

Background: This scoping review examines how schedule-related load affects athletic and team performance in professional sport, an issue that has received less systematic attention than training and competition load despite its clear implications for recovery, injury risk, and performance. Methods: Following PRISMA-ScR guidelines, Web of Science, SPORTDiscus, Scopus, and MEDLINE were searched for relevant publications (1993–2025) examining schedule-related load in professional sport. Five theoretical frameworks (Fitness-Fatigue, Circadian Disruption, Allostatic Load, Training-Injury Prevention, and Conservation of Resources) were used to interpret underlying mechanisms. Results: Seventy-two sources were included. At the athlete level, schedule-related load degrades physical performance, impairs sleep and recovery, increases injury risk, and disrupts circadian function. At the team level, it deteriorates game outcomes, alters offensive and defensive strength, and constrains lineup management. Six research gaps were identified involving measurement, interaction effects, advanced metrics, player heterogeneity, integration with training load, and longitudinal analysis. Conclusions: The findings position schedule design as a measurable performance variable and highlight the need for more rigorous sport analytics research to support evidence-based optimization of competition calendars and workload management. Full article

Milk titratable acidity is a key indicator of raw milk freshness and quality, but its variation across different dairy animal species remains incompletely characterized. Based on 16,984 raw milk samples from eight dairy animal species (Holstein cow, goat, buffalo, camel, sheep, yak, donkey, and horse) collected within a retrospective raw milk quality monitoring framework in China from 2016 to 2024, this study provides a large-scale descriptive comparison of milk titratable acidity across species. Distinct titratable acidity profiles were observed among species, with camel and yak milk showing relatively high values, sheep, Holstein, and buffalo milk exhibiting intermediate values, and donkey and horse milk presenting markedly low values. Calendar-season-associated patterns also differed among species. Correlations between titratable acidity and milk components varied by species, with relatively stronger positive associations with protein and solids-not-fat (SNF) in several ruminant milks, suggesting that milk composition may contribute to differences in titratable acidity. However, because this study was based on an unbalanced observational dataset with limited animal-level, farm-level, feeding, management, physiological, and environmental metadata, these observations should be interpreted as descriptive and exploratory patterns rather than causal biological mechanisms. This dataset provides preliminary reference information for future studies on species-associated variation in raw milk titratable acidity and for discussions on species-specific raw milk quality evaluation. Full article

Intermediate crops, such as Brassica carinata and Camelina sativa, offer a promising pathway for expanding sustainable feedstock supply for advanced biofuels in Europe without competing with food and feed production. This study applies a competitive priority framework to assess the performance of intermediate crops in Italy and Spain, integrating agronomic, environmental, and regulatory dimensions. Using Member State-specific agroecological conditions, cost structures, and land-use profiles, the analysis identifies key challenges across land use and biomass-production stages and links them to measurable indicators and targeted optimisation strategies. Evidence from both experimental studies and modelling indicates that camelina can be seamlessly integrated into existing cropping systems without compromising crop yields or triggering soil carbon losses. These findings highlight the potential of intermediate crops to enhance soil health, to reduce erosion, and to stabilise yields under climate variability. This study also examines the policy conditions required to enable deployment, emphasising the need for region-specific crop calendars, digital traceability systems, and coherent implementation of RED III, CAP, ESCA, and CRCF frameworks. The distinction between volumetric and GHG-based targets is shown to be critical: intermediate crops perform strongly under GHG-based intensity reduction frameworks that reward soil carbon gains and sustainable cultivation. National instruments in Italy and Spain—including the Piano Strategico della PAC, Decreto Biocarburanti, Plan Estratégico de la PAC, and Real Decreto 376/2022—provide mechanisms for operationalising these strategies. Overall, the results demonstrate that intermediate crops can contribute meaningfully to both national and EU renewable energy, soil restoration, and climate mitigation objectives when supported by coherent agronomic and policy frameworks. Full article

Accurate substation-level Short-Term Load Forecasting (STLF) is essential for secure day-ahead power-system operation, yet localized demand is often affected by meteorological variation and discrete calendar shifts such as statutory holidays and makeup workdays. At this spatial scale, end-to-end forecasting models may over-smooth abrupt local changes and fail to represent peaks and valleys accurately. To address this issue, this study proposes a Calendar-Aware Frequency-Decoupled Framework (CA-FDF) for 24 h ahead substation load forecasting. The load series is decomposed by the Discrete Wavelet Transform (DWT), and the low-frequency component is tracked by a regime-aware Recursive Least Squares (RLS) filter. The residuals are then refined through explicit calendar-state encoding and day-ahead weather forecasts. A Multi-Layer Perceptron (MLP) learns latent weather representations, while SHapley Additive exPlanations (SHAP) interpret calendar- and weather-related effects. Experiments on hourly operational data from 29 anonymized substations in East China show that CA-FDF achieves a Mean Absolute Percentage Error (MAPE) of 1.92% and outperforms representative baselines under the same day-ahead setting. The results indicate that frequency-decoupled residual refinement improves localized load forecasting, with calendar-aware correction contributing the largest gain. Full article

Background: The reliability of large language models (LLMs) in complex oncologic decision-making remains inadequately validated. This study evaluated the concordance of ChatGPT-5 with multidisciplinary tumor board (MDT) decisions in gynecologic oncology, assessing accuracy, reproducibility, and domains of discordance. Methods: We analyzed 242 gynecologic cancer cases (endometrial n = 102, ovarian n = 85, cervical n = 40, rare n = 15) discussed at the Çukurova University Gynecologic Oncology MDT (2024–2025). Standardized clinical summaries were input into ChatGPT-5 using a structured prompt template. Each case was queried three times within a single calendar day using independent conversations. Recommendations were evaluated by two blinded medical oncologists using a 5-point Likert scale. A composite performance score (CPS) was calculated as (mean Likert/5) × 100. Concordance was analyzed using Cohen’s kappa (κ). Results: Inter-rater reliability was substantial to almost perfect for both MDT (κ = 0.761) and AI (κ = 0.814) evaluations (both p < 0.001). MDT–AI concordance was fair (Rater 1: κ = 0.258; Rater 2: κ = 0.334). CPS were significantly higher for MDT versus AI (Rater 1: 93.8% ± 5.2 vs. 89.4% ± 6.7; Rater 2: 93.4% ± 5.5 vs. 89.7% ± 6.4; both p < 0.001). Full consistency across three queries was achieved in only 37.2% of cases (90/242). AI performance was significantly inferior in advanced-stage disease (p = 0.008), genetic testing (p = 0.006), fertility-sparing (p = 0.018), and novel therapeutics (p = 0.003). Conclusions: ChatGPT-5 demonstrates potential as a clinical decision support tool but lacks sufficient reliability for independent use. Key limitations include inconsistency in 62.8% of cases, suboptimal performance in advanced-stage disease, and deficiencies in precision oncology domains. These findings suggest that human expertise remains indispensable for the individualized management of complex gynecologic malignancies. Full article

Introduction: Cerebrovascular disorders are major contributors to maternal morbidity and mortality during pregnancy. In this nationwide study in Hungary, we evaluated the frequency of cerebrovascular diagnoses during first recorded pregnancies in a 17-year period. Material and Methods: In the framework of the NEUROHUN project utilising nationwide administrative healthcare data, we included women with at least one delivery and with at least one cerebrovascular diagnosis during their first pregnancies recorded between 2004 and 2020. To minimise the number of misclassified first pregnancies due to database limitations appearing towards the beginning of the database, trend analyses using linear regression models were restricted to the 2011–2020 period. Results: During first recorded pregnancies in the 17-year study time frame (n = 952,451), the frequency of ICD-10 cerebrovascular diagnoses was 0.17% (n = 1614), with an estimated overall prevalence rate of 169.4 per 100,000 women (95% CI: 161.4–177.9). Transient ischaemic attack (TIA) was the most prevalent specific diagnosis, with a rate of 72.7 per 100,000 (95% CI: 67.4–78.3). In a multiple linear regression model on the mean age at first recorded birth within 2004–2020, women diagnosed with a cerebrovascular disorder were, on average, 1.935 years older at the time of their first birth compared to those without a diagnosis (mean difference: 1.935 years; 95% CI [1.188–2.682], p < 0.001). This analysis, adjusted for calendar year trends between 2011 and 2020, suggests that higher maternal age is an important factor associated with these events. In a sensitivity analysis of the linear regression using the diagnoses of G45, I60, I61, I63, and I67 we found that the relationship between the presence of diagnosis and mean age remained significant in the case of G45, I63 and I67, but not for I60 and I61. In the logistic regression model, compared to the reference group of women < 25 years, the prevalence for all evaluable cerebrovascular diagnoses was significantly higher in the >34 age group, and was also significantly higher for TIA (G45) and cerebral infarction (I63) diagnoses in the 25–34 age group. The rate of cerebral infarction among cerebrovascular disorders showed an increasing trend towards higher maternal age (<25 years age group: 12%; 25–34 years age group: 16.5%; >34 years age group: 20.0%). Also, when compared to the reference category of diagnosed women < 25 years, the increase in the odds of cerebral infarction was significant at the 5% level among women > 34 years. In contrast, there was no increment in the proportion of intracranial bleedings at older age. Discussion and Conclusion: The prevalence of most cerebrovascular diagnoses increases significantly with higher maternal age. Allowing for the limitations of our study, we found that in a Central-Eastern European population, the prevalence of cerebrovascular diagnoses during first recorded pregnancies between 2004 and 2020 was 169.4 per 100,000 (0.17%), with TIA being the most common diagnosis in approximately one-third of cases. The rate of cerebral infarctions among cerebrovascular diagnoses was almost twice as high in those over 34 years of age compared to those below 25. The frequency of pregnancy-related ischemic strokes and cerebral haemorrhages in the Central-Eastern European population corresponds to published values. Full article

Implied volatility surfaces describe option-implied volatilities across strikes, and maturities and play a central role in derivative pricing and risk management. However, in practice, they are often incomplete due to illiquidity or sparse trading, requiring reliable reconstruction of missing regions. Existing approaches typically rely on parametric assumptions or latent space optimisation methods, which may be restrictive or computationally intensive. This study proposes a data-driven framework based on conditional generative adversarial networks (GANs) to map partially observed surfaces to completed ones in a single forward pass. The approach is evaluated in a controlled setting using synthetic data generated from the Heston stochastic volatility model, with varying levels of missingness (10–96%). The generator objective incorporates penalty terms enforcing the absence of call-spread, butterfly-spread, and calendar-spread arbitrage, together with a smoothness regulariser on the implied risk-neutral density. Compared with a conditional variational autoencoder (VAE), the Bates model, and the stochastic volatility-inspired (SVI) parameterisation, the proposed approach achieves lower reconstruction errors across all levels of missingness, including unseen cases, while preserving the no-arbitrage properties. An ablation study shows that the conditional GAN implicitly learns no-arbitrage behaviour, with density smoothness regularisation being the only constraint that meaningfully improves reconstruction quality. Full article

El Niño–Southern Oscillation (ENSO) has been identified as a key factor influencing grapevine phenology and harvest timing in South America. Nevertheless, few long-term analyses have explored its varietal impacts in hyper-arid viticultural regions. The goal was to evaluate the effect of ENSO phases on harvest dates and bioclimatic indices in different grapevine varieties cultivated in Northern Chile. The results revealed that Muscat of Alexandria showed little variation in harvest timing across ENSO phases. In contrast, harvest time in Thompson Seedless was delayed under La Niña events, being strongly correlated with the Maximum Spring Temperature Summation (SON_max) Index. Moscatel Rosada and Flame Seedless showed non-statistical significance and high variability on harvest dates. El Niño phases were consistently warmer than La Niña events that showed markedly greater interannual variability on harvest dates and bioclimatic index values. The strength of correlations was improved when the bioclimatic indices were recalculated over adjusted seasonal windows, underscoring the need for phenology-based rather than calendar-based approaches. These results provide new evidence of the heterogeneous responses of table and Pisco grapevine varieties to ENSO events in the hyper-arid regions of Northern Chile, underscoring the varietal differences in sensitivity to early-season climatic anomalies. Full article

The Dominican Constanza de Castilla’s exceptional liturgical book, her Libro de devociones y oficios [Book of Devotions and Offices], served devotional and practical purposes in the convent of Santo Domingo el Real in Madrid. The article begins by examining Dominican nuns’ participation in Corpus Christi processions. It then examines Constanza’s liturgy for the Mass of the Incarnation (25 March). A study of liturgical calendars and offices both prior to and contemporary to Constanza reveals that there are no Castilian offices given the title ‘Incarnation of Our Lord’. Comparison of the contents of the office with the same liturgies reveals none with the exact same antiphons or readings. However, Constanza’s choice of the name ‘office of the Incarnation’ is matched to a similar wording in the account book of Franciscan nuns. They also give the name day of the Incarnation to the feast they celebrate on 25 March. The article also summarizes the theological theme of kenosis, Christ’s self-abasement, apparent elsewhere in the Book of Devotions and Offices and which is touched upon in the office of the Incarnation. Finally, it examines the links between Thomas Aquinas’s thinking on the Virgin Birth and where that is echoed by Constanza. This article concludes that, for this short office, Constanza’s Mass shows no signs of having been copied from existing offices, always with the proviso that what remains of fourteenth and fifteenth century liturgies provides an imperfect picture. Full article

Southern Chinese lion dance (nanshi) in Bangkok moves between temple ritual, community representation, school training, public festival display, and judged competition. This article foregrounds the diasporic and minority position of Teochew lion dance in Thailand. It examines codification as cultural negotiation, asking how a temple-linked Chinese ritual practice is made legible to Thai educational, civic, and sporting publics while remaining tied to community ritual authority and lineage memory. The study draws on public documentary traces from the 2000s–2020s and field-based evidence centred on Bangkok. Sources include temple and association commemorative publications, municipal school records, Thai cultural and competition reporting, heritage registers, transnational rule texts, interviews, observations, and community archival/display materials. The article first shows how huiguan, temples, and schools stabilize calendars, resources, and authority; second, how competition rules translate embodied repertoires into auditable norms of time, team composition, safety, difficulty, and scoring; and third, how trophies, certificates, lion heads, photographs, videos, and anniversary volumes turn evaluated performances into community archives of continuity, merit, and public legitimacy. The Bangkok case shows a diasporic minority practice being reshaped through the combined work of ritual governance, competitive codification, and community archiving within a Teochew-centred institutional ecology. Full article