Search Results (2,949)

Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multilingual natural language understanding system designed to perform both semantic parsing and clinical intervention classification from emergency dialogues. The system is built on a federated learning architecture to ensure data privacy and adaptability across regions and is trained using TriageX, a synthetic, clinically grounded dataset covering five languages (English, Spanish, Romanian, Arabic, and Mandarin). TriagE-NLU integrates fine-tuned multilingual transformers with a hybrid rules-and-policy decision engine, enabling it to parse structured medical information (symptoms, risk factors, temporal markers) and recommend appropriate interventions based on recognized patterns. Evaluation against strong multilingual baselines, including mT5, mBART, and XLM-RoBERTa, demonstrates superior performance by TriagE-NLU, achieving F1 scores of 0.91 for semantic parsing and 0.89 for intervention classification, along with 0.92 accuracy and a BLEU score of 0.87. These results validate the system’s robustness in multilingual emergency telehealth and its ability to generalize across diverse input scenarios. This paper establishes a new direction for privacy-preserving, AI-assisted triage systems. Full article

A novel multi-scale copula-based model predictive control (MPC) method is proposed to address the core regulation challenges of runoff hydropower and distributed photovoltaic systems within high-penetration renewable energy grids. Complex spatio-temporal complementarity under ecological constraints and the limitations of conventional methods were critically analyzed. The core innovation lies in integrating copula theory with MPC, enabling adaptive spatio-temporal optimization and weight adjustment to significantly enhance the efficiency of complementary regulation and overcome traditional performance bottlenecks. Key nonlinear dependencies of water–solar resources were investigated, and mainstream techniques (copula analysis, MPC, rolling optimization, adaptive weighting) were evaluated for their applicability. Future directions for improving modeling precision and intelligent adaptive control are outlined. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Silk fibroin (SF)-based intelligent wearable systems represent a frontier research direction in artificial intelligence and precision medicine. Their core efficacy stems from the inherent advantages of silk fibroin, including excellent mechanical properties, interfacial compatibility, and tunable structure. This article systematically reviews conductive modification strategies for silk fibroin and its research progress in the smart wearable field. It elaborates on the molecular structural basis of silk fibroin for use in smart wearable devices, critically analyzes five conductive functionalization strategies, compares the advantages, disadvantages, and applicable domains of different modification approaches, and summarizes research achievements in areas such as bioelectrical signal sensing, energy conversion and harvesting, and flexible energy storage. Concurrently, an assessment was conducted focusing on the priority performance characteristics of the materials across diverse application scenarios. Specific emphasis was placed on addressing the long-term functional performance (temporal efficacy) and degradation stability of silk fibroin-based conductive materials exhibiting high biocompatibility in implantable settings. Additionally, the compatibility issues arising between externally applied coatings and the native substrate matrix during conductive modification processes were critically examined. The article also identifies challenges that silk fibroin-based smart wearable devices currently face and suggests potential future development directions, providing theoretical guidance and a technical framework for the functional integration and performance optimization of silk fibroin-based smart wearable devices. Full article

The thermal environment problem is one of the main focuses of current urban environment research. At present, there are various methods used in urban space thermal environment (USTE) research. As a simulation method to quantify the USTE, the urban weather generator (UWG) has undergone great development and achieved many progressive results. It is necessary to establish and review its current research status by synthesizing UWG multi-scale applications. This review adopts a literature review approach, leveraging the Web of Science Core Collection to obtain previous relevant publications from 2010 to 2025 using “urban weather generator” and “thermal environment” as keywords. The literature is categorized by research themes, including model development, parameter optimization, and application cases. Through innovative analyses of spatio-temporal-scale classification, parameter optimization, the integration of anthropogenic heat emissions, and the multi-domain simulation potential of the UWG, this review synthesizes the application outcomes of the UWG model in multi-scale research, addressing gaps in current urban climate studies. The paper aims to elaborate and analyze the model’s current research status considering the following six aspects. First, the basic parameters in UWG simulation are introduced, including the data and parameter determination settings used in such simulations. Secondly, we introduce the simulation model and its basic principles, the simulation process, and the main steps of this process. Third, we classify and define UWG simulations of spatial thermal environments at different time scales and spatial scales. Fourth, regarding how to improve the accuracy of the UWG model, the deterministic parameters and uncertainty parameters settings are analyzed, respectively. Then, the impacts of anthropogenic heat during the simulation process are also discussed. Fifth, the applications of the UWG model in some major fields and its possible future development directions are addressed. Finally, the existing problems are summarized, the future development trends are prospected, and research on possible expected mitigation measures for the USTE is described. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

The study at the Peggy Guggenheim Collection in Venice highlights critical interactions between indoor air quality, visitor dynamics, and microclimatic conditions, offering insights into preventive conservation of modern artworks. By analyzing pollutants such as ammonia, formaldehyde, and organic acids, alongside visitor density and environmental data, the research identified key patterns and risks. Through three seasonal monitoring campaigns, the concentrations of SO₂ (sulphur dioxide), NO (nitric oxide), NO₂ (nitrogen dioxide), NO_x (nitrogen oxides), HONO (nitrous acid), HNO₃ (nitric acid), O₃ (ozone), NH₃ (ammonia), CH₃COOH (acetic acid), HCOOH (formic acid), and HCHO (formaldehyde) were determined using passive samplers, as well as temperature and relative humidity data loggers. In addition, two specific short-term monitoring campaigns focused on NH₃ were performed to evaluate the influence of visitor presence on indoor concentrations of the above compounds and environmental parameters. NH₃ and HCHO concentrations spiked during high visitor occupancy, with NH₃ levels doubling in crowded periods. Short-term NH₃ campaigns confirmed a direct correlation between visitor numbers and the above indoor concentrations, likely due to human emissions (e.g., sweat, breath) and off-gassing from materials. The indoor/outdoor ratios indicated that several pollutants originated from indoor sources, with ammonia and acetic acid showing the highest indoor concentrations. By measuring the number of visitors and microclimate parameters (temperature and humidity) every 3 s, we were able to precisely estimate the causality and the temporal shift between these quantities, both at small time scale (a few minute delay between peaks) and at medium time scale (daily average conditions due to the continuous inflow and outflow of visitors). Full article

This study investigates the relationship between sovereign credit rating transitions and domestic equity market performance, focusing on Greece from 2004 to 2024. Although credit ratings are central to sovereign risk assessment, their immediate influence on financial markets remains contested. This research adopts a multi-method analytical framework combining algebraic combinatorics and time-series econometrics. The methodology incorporates the construction of a directed credit rating transition graph, the partially ordered set representation of rating hierarchies, rolling-window correlation analysis, Granger causality testing, event study evaluation, and the formulation of a reward matrix with optimal rating path optimization. Empirical results indicate that credit rating announcements in Greece exert only modest short-term effects on the Athens Stock Exchange General Index, implying that markets often anticipate these changes. In contrast, sequential downgrade trajectories elicit more pronounced and persistent market responses. The reward matrix and path optimization approach reveal structured investor behavior that is sensitive to the cumulative pattern of rating changes. These findings offer a more nuanced interpretation of how sovereign credit risk is processed and priced in transparent and fiscally disciplined environments. By bridging network-based algebraic structures and economic data science, the study contributes a novel methodology for understanding systemic financial signals within sovereign credit systems. Full article

In the context of the new developmental philosophy, this study aimed to address the bottleneck of regional sustainable development; it constructs a three-system evaluation indicator system for Industrial Structure Optimization (ISO), Ecological Environment Management (EEM), and Socio-economic Development (SED), based on panel data from 20 cities in the Western Taiwan Straits Economic Zone between 2011 and 2023. To reveal how the synergistic development of the three subsystems in different domains can achieve sustainable development through their interactions and to analyze the dynamic patterns of the three subsystems, this study employed the panel vector autoregression (PVAR) model to examine the interactions between subsystems. Additionally, drawing on the framework of evolutionary economics, the study quantified the temporal evolution and spatial characteristics of the coupling coordination level among the three subsystems based on the results of the degree of coupling coordination model. The results indicate the following: (1) ISO shows a significant upward trend, EEM slightly declines, and SED experiences minor fluctuations before accelerating. (2) ISO, EEM, and SED exhibited self-reinforcing effects. (3) The degree of coupling, coordination, and coupling coordination all exhibit a trend of “fluctuating and increasing initially, followed by steady growth”. The spatial patterns of the degree of coupling, coordination, and coupling coordination have shifted from “decentralized” to “centralized”, with clear signs of synergistic development. (4) The difference in the degree of coupling coordination along the north–south direction remained the primary factor contributing to inter-regional disparities. Regions with the higher degrees of coupling coordination were concentrated in the southeastern coastal areas, while those with the lower degrees of coupling coordination appeared in the northeastern mountainous areas and southwestern coastal areas. (5) The spatial connection in the strength of the degree of coupling coordination has gradually increased, with notable intra-provincial connections and weakened inter-city connections across the province. The study’s results provided decision-making references for the construction of a sustainable development community. Full article

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting. Full article

As a major contributor to energy consumption and carbon emissions, the low-carbon transformation of the construction industry is crucial for China to achieve its established carbon-emission reduction targets. Therefore, a systematic analysis of the spatial and temporal evolution trends and key drivers of carbon emissions in the construction industry is an important reference for the formulation of emission reduction policies in the industry and the promotion of green and low-carbon development. This study first estimated carbon emissions from direct and indirect energy consumption in China’s construction industry. Spatial and temporal variations in emissions were then analyzed using spatial autocorrelation and kernel density methods. Furthermore, an improved logarithmic mean Divisia index decomposition model, tailored to the characteristics of the construction industry, was applied to quantify the key driving factors. The results reveal that total carbon emissions follow an inverted U-shaped trend, with indirect carbon emissions—mainly from the production of cement and steel—being the dominant contributors. Emissions display a spatially uneven pattern: high in the east and south, low in the west and north, with the high-emission zone gradually expanding from the east to the central regions. Marked regional differences also exist in the evolution of emission intensity. Output intensity and energy intensity are identified as primary drivers of emissions, with their impact particularly prominent in the eastern region. These findings provide a quantitative basis and theoretical support for developing region-specific emission reduction policies, advancing the green and high-quality development of China’s construction industry. Full article

Background/Objectives: Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns. Methods: A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters. Results: We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p < 0.001), smoking intensity (p < 0.001), geographic distribution (p < 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer. Conclusions: This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine. Full article

Current land surface temperature (LST) products, estimated by sensors on board satellites, show a trade-off between their spatial and temporal resolution. If the spatial resolution is high (i.e., around 100 m), the LST product is delivered every 2 weeks, and for those LST products estimated daily, its spatial resolution is 1 km. Current spatial and temporal resolutions are not adequate for disciplines such as high-precision agriculture, urban decision making, and planning how to mitigate the overheating of cities, for which LST maps at 50–100 m resolution every few days are desirable. This situation has led to the development of disaggregation techniques in order to enhance the spatial resolution of daily LST products. Unfortunately, disaggregation techniques are usually complex since they rely on a number of external inputs and computer resources and are difficult to apply in practice. To our knowledge, there are only two operative downscaled 10 m LST products available to the end user, which are implemented in the Google Earth Engine (GEE) tool. They are the Daily Ten-ST-GEE and LST-downscaling-GEE systems. This study provides a critical benchmark by performing the first direct intercomparison and rigorous in situ validation of these two operative GEE systems. The validation, conducted with reference temperature data from dedicated field campaigns over contrasting agricultural sites in Spain, showed a good correlation of both methods with a R² of 0.74 for Daily Ten-ST-GEE and 0.94 for LST-downscaling-GEE, but the poor results of the first method in a highly heterogeneous site (RMSE of 5.8 K) make the second method the most suitable (RMSE of 3.6 K) for obtaining high-spatiotemporal-resolution LST maps. Full article

Self-calibrating and dual-slope measurements have been used in the field of diffuse optics for robust assessment of absolute values or temporal changes in the optical properties of highly scattering media and biological tissue. These measurements employ optical probes with a minimum of two source positions and a minimum of two detector positions. This work focuses on a quantitative analysis of the impact of errors in these source and detector positions on the assessment of optical properties. We considered linear, trapezoidal, and rectangular optode arrangements and theoretical computations based on diffusion theory for semi-infinite homogeneous media. We found that uncertainties in optodes’ positions may have a greater impact on measurements of absolute scattering versus absorption coefficients. For example, a 4.1% and 19% average error in absolute absorption and scattering, respectively, can be expected by displacing every optode in a linear arrangement by 1 mm in any direction. The impact of optode position errors is typically smaller for measurements of absorption changes. In each geometrical arrangement (linear, trapezoid, rectangular), we identify the direction of the position uncertainty for each optode that has minimal impact on the optical measurements. These results can guide the optimal design of optical probes for self-calibrating and dual-slope measurements. Full article

High-speed railway (HSR) is a key transport mode for achieving carbon reduction targets and promoting sustainable regional economic development due to its fast, efficient, and low-carbon nature. Accurate wind speed forecasting (WSF) is vital for HSR systems, as it provides future wind conditions that are critical for ensuring safe train operations. Numerous WSF schemes based on deep learning have been proposed. However, accurately forecasting strong wind events remains challenging due to the complex and dynamic nature of wind. In this study, we propose a novel hybrid network architecture, MHSETCN-LSTM, for forecasting strong wind. The MHSETCN-LSTM integrates temporal convolutional networks (TCNs) and long short-term memory networks (LSTMs) to capture both short-term fluctuations and long-term trends in wind behavior. The multi-head squeeze-and-excitation (MHSE) attention mechanism dynamically recalibrates the importance of different aspects of the input sequence, allowing the model to focus on critical time steps, particularly when abrupt wind events occur. In addition to wind speed, we introduce wind direction (WD) to characterize wind behavior due to its impact on the aerodynamic forces acting on trains. To maintain the periodicity of WD, we employ a triangular transform to predict the sine and cosine values of WD, improving the reliability of predictions. Massive experiments are conducted to evaluate the effectiveness of the proposed method based on real-world wind data collected from sensors along the Beijing–Baotou railway. Experimental results demonstrated that our model outperforms state-of-the-art solutions for WSF, achieving a mean-squared error (MSE) of 0.0393, a root-mean-squared error (RMSE) of 0.1982, and a coefficient of determination ($R^2$) of 99.59%. These experimental results validate the efficacy of our proposed model in enhancing the resilience and sustainability of railway infrastructure.Furthermore, the model can be utilized in other wind-sensitive sectors, such as highways, ports, and offshore wind operations. This will further promote the achievement of Sustainable Development Goal 9. Full article