Search Results (57,162)

The release of ChatGPT in November 2022 transformed second language (L2) writing instruction and led to rapid growth in research on large language model (LLM)-generated feedback; however, no synthesis has mapped this literature in terms of feedback quality, learner uptake, and pedagogical integration. This scoping review examines 185 empirical studies published between November 2022 and March 2026 that were identified through a Scopus search (n = 283 screened) and analysed using a systematic keyword-based charting framework applied to full abstracts, with full-text analysis of 35 studies. The review identifies four major patterns: (1) comparative AI–human feedback research dominates the literature (27.6%); (2) content-level feedback remains underexplored (13.5% of studies); (3) learner uptake is rarely measured as a primary outcome; and (4) LLM feedback is broadly comparable to teacher feedback for surface-level errors but weaker for content and argumentation, while learner perceptions often exceed demonstrated performance outcomes. Hybrid AI–teacher models show promising but underexamined potential, accounting for only 12.4% of the literature. The field shows a focus on perceptions rather than learning outcomes, an apparent tendency toward positive-results reporting, and no clear teaching models. This study proposes a typology of LLM feedback functions and outlines a research agenda focused on uptake, longitudinal outcomes, and hybrid AI–teacher integration. Full article

Cyanobacterial blooms occur frequently in lakes worldwide, disrupting aquatic ecosystem balance and directly threatening drinking water safety and fisheries production. Establishing a reliable bloom early-warning system has therefore become an urgent priority for lake water management. This study adopts a structured narrative review approach to synthesize the major early-warning methods, including indicator threshold methods, statistical and empirical models, mechanistic models, machine learning, and remote sensing monitoring. These methods are compared in terms of their fundamental principles, data requirements, predictive capabilities, applicability, interpretability, and computational and maintenance requirements. Emerging trends in multi-source data fusion, multi-model integration, and the development of integrated early-warning systems are also summarized. The findings indicate that each method has distinct strengths and limitations with respect to forecasting lead time, spatial coverage, process interpretation, and operational costs, and that no single method can simultaneously meet the requirements of multiscale bloom monitoring and forecasting. Integrating multi-source data from in situ monitoring, remote sensing observations, and meteorological and hydrological measurements, while coordinating statistical models, mechanistic models, and artificial intelligence algorithms according to specific forecasting objectives, represents an important pathway for improving the robustness and operational applicability of early-warning systems. Given the pronounced seasonal ice cover, substantial hydrological variability, limited monitoring data, and marked regional heterogeneity of some cold and arid region lakes, future research should strengthen high-frequency monitoring during critical periods, promote coordination between remote sensing and in situ observations, and conduct local calibration of early-warning thresholds and model parameters. Season-specific models should also be developed to account for environmental differences among ice-covered, ice-off transition, and open-water periods. Overall, early warning of cyanobacterial blooms in lakes is evolving from the application of individual methods toward the integration of multi-source monitoring, multi-model integration, and decision support, thereby providing a reference for bloom risk prevention and water environment management across different types of lakes. Full article

The development of the tourism sector is one of the main objectives of the Vision 2030 reform package introduced by Saudi Arabia in 2016 to develop a resilient, knowledge-based and competitive economy. However, the determinants of tourism development from the perspective of reduced climate vulnerability and visa liberalization are rarely researched in the case of Saudi Arabia. Accordingly, this research paper focuses on investigating the impacts of reduced climate vulnerability and visa liberalization policy on tourism development using data from Saudi Arabia for the period 1995–2024. This study measures climate vulnerability using the ND-GAIN Index, which captures both climate vulnerability and climate readiness, while the visa liberalization policy is captured through a dummy variable. For the estimation of the model, this study uses the Autoregressive Distributed Lag (ARDL) cointegration approach, which simultaneously produces both the long-run and short-run relationships between the independent variables and the dependent variable. This study’s findings demonstrate that reduced climate vulnerability or greater national readiness for climate change is positively linked with increased international tourist arrivals both in the long-run and short-run horizons. Similarly, the results also prove the importance of the visa liberalization policy on the development of the tourism sector in the long run. Moreover, the results indicate that trade openness and income level are the main driving forces behind the development of the tourism sector in the long run. Furthermore, this study’s results further show that the rate of inflation is detrimental to the development of the tourism sector, as it directly enhances the cost of traveling. This research study contributes to the sustainability discourse by visualizing how policy-driven interventions can safeguard tourism growth in climate-vulnerable economies. Finally, this study offers important policy implications related to sustainable tourism planning and economic diversification under the framework of Vision 2030 in Saudi Arabia. Full article

Floating Car Data (FCD) are increasingly used to analyse mobility patterns and support transport-system management, but the evidence remains fragmented across heterogeneous applications, sensing technologies, and data-processing methods, particularly regarding road-infrastructure monitoring. This study presents a systematic literature review and structured evidence map of FCD research published between 2010 and 2025. Following PRISMA methodology, Scopus and Google Scholar were searched using the exact expression “floating car data”. The search retrieved 2127 records; after bibliographic harmonisation, duplicate removal, title-and-abstract screening, full-text retrieval, and eligibility assessment, 165 publications were included. The studies were classified through a top-down framework covering application domain, sensing technology, processing approach, validation method, geographical region, deployment scale, and integration with Pavement Management Systems (PMSs). Traffic-state estimation and mobility-planning applications covered 100 publications (60.6%), whereas infrastructure monitoring was the primary domain in 20 studies (12.1%). GPS or GNSS data were used in 128 publications (77.6%), while accelerometers, gyroscopes, or inertial measurement units were reported in 29 studies (17.6%). Only 15 publications (9.1%) described operational or real-time deployment, and explicit PMS-oriented integration was identified in only 9 studies (5.5%). The findings show that FCD research is methodologically mature for traffic and mobility applications but remains comparatively fragmented for pavement-condition assessment. The review therefore proposes an operational pathway linking accelerometric data acquisition, preprocessing, normalization, fleet-level aggregation, validation, data fusion, and PMS decision-making. These results highlight that the principal research gap concerns not sensor availability, but the development of standardized, transferable, and operationally validated frameworks for network-wide pavement monitoring. Full article

This paper presents a study conducted with a view to analysing the sensitivity of measures determined based on the characteristics of the multifractal spectrum when detecting damage in rolling bearings. The research was conducted using vibration signals of a real rotating machine shaft supported by bearings, each of which was in a different technical condition. The effects of damage-induced changes in vibration signals on the calculated waveforms of multifractal spectra and the measures determined from them were analysed. In addition, qualitative and quantitative analyses of the effect of additional signal interference on changes in the characteristics of the spectra were carried out. The study shows that the application of vibration signal processing using a multifractal spectrum enables fast and unambiguous demonstration of changes occurring in the signals that can be linked to damage of bearings. Irrespective of the type of bearing damage symptoms present in the vibration signals—whether high-energy quasi-correlated impulse or medium and low-energy stochastic—an increase in measures determined from the multifractal spectrum was identified. The study shows that, regardless of the level of vibration signal interference, the calculated quantitative measures of the spectra were indicative of damage to rolling bearings. Full article

Manipulative materials are widely considered valuable resources in geometry education because they may support visualization, spatial reasoning, and conceptual understanding when implemented within appropriate instructional contexts. However, many existing resources focus on isolated concepts and provide limited opportunities for connecting geometric relationships through transformation and decomposition. This study presents Geomaniguras, a manipulative material designed for upper primary and lower secondary education. The research adopts a design and development approach focused on pedagogical design and expert-based validation rather than measuring student learning outcomes. The study combined prototype construction, expert evaluation, and iterative refinement. Twelve specialists in mathematics education assessed the material using a structured validation guide addressing pedagogical usefulness, conceptual coherence, usability, visual design, and classroom applicability. Results indicate that experts perceived Geomaniguras as a potentially valuable resource for exploring polygon classification, area relationships, geometric decomposition, circumference, and the Pythagorean theorem. The findings provide preliminary evidence regarding the pedagogical plausibility and classroom applicability of the material, although no direct conclusions can yet be drawn about instructional effectiveness. The originality of the proposal lies in its integrated conceptual framework, which connects multiple geometric learning experiences within a single manipulative system. Full article

Reliable estimation of winter wheat yield is critical to food system stability and farmland management. Integrating multi-spectral remote sensing data with agronomic parameters represents a primary strategy for improving yield estimation accuracy. However, existing research often overlooks parameters reflecting crop population structure and fails to account for dynamic shifts in the contributions of multidimensional agronomic variables across growth stages, thereby limiting prediction accuracy and model stability. To address these limitations, a winter wheat yield estimation model was developed. This model integrates multi-source and multi-temporal data, incorporates stem tiller density, a key population structure parameter, and accounts for dynamic variation across growth stages. Unmanned aerial vehicle multi-spectral images were collected at four key growth stages: jointing (stem elongation with detectable nodes), booting (flag leaf sheath swelling preceding heading), heading (spike emergence) and filling (grain filling with dry matter accumulation). Three growth indicators, stem tiller density, leaf area index and above-ground biomass, were measured. Two comprehensive growth indicators were derived using the coefficient of variation and the CRITIC weighting methods (CGI_CR). Correlation and feature importance analyses were used to identify sensitive vegetation indices (VIs), which were subsequently integrated with the comprehensive growth indicators. Single-stage, multi-source feature fusion and multi-temporal yield estimation models were established using the Kernel Extreme Learning Machine and its optimised algorithm using the Crested Porcupine Optimizer. The results showed the following: (1) among the individual growth stages, features from the filling stage achieved the highest prediction accuracy; (2) the fusion of multi-source features (VIs + CGI_CR) enhanced the prediction accuracy of the model, achieving a validation set R² of 0.884 and a relative prediction deviation of 2.916 at the filling stage; and (3) the multi-temporal model further improved predictive performance, with the validation R² reaching 0.920, indicating that information from different growth stages contributed complementarily to yield prediction and improved overall model performance. By contrast, the model exhibited relatively weak predictive capability at the early growth stages and was better-suited to early risk identification. Meanwhile, its generalisation ability under cross-regional and inter-annual conditions still requires further validation. Overall, integrating multi-source and multi-temporal data can enhance the precision and stability of predicting winter wheat yield, thereby facilitating precision agriculture management. Full article

Background/Objectives: Pressure pain thresholds (PPTs) are widely used to quantify hyperalgesia in tension-type headache (TTH). However, previous diagnostic accuracy research has mainly focused on muscular or segmental sites, while the discriminative performance of PPTs measured over peripheral nerves remains largely unexplored. This study primarily aimed to compare PPTs measured across nerve-related and muscular anatomical sites between women with frequent episodic TTH and headache-free controls. As a secondary exploratory objective, site-specific ROC analyses were performed to examine within-sample discrimination and derive sample-specific exploratory cut-offs. Methods: A cross-sectional exploratory study with a case–control sampling design evaluated 31 women with frequent episodic TTH (mean age: 19.6 ± 4.3 years) and 32 headache-free women (mean age: 22.1 ± 4.9 years). The groups differed significantly in age (p = 0.039). PPTs were recorded over peripheral nerves (greater occipital, median, ulnar, radial, tibial, common fibular), muscles (temporalis, tibialis anterior), 2nd–3rd interdigital hand space and the C5/C6 zygapophyseal joints. Results: Women with TTH showed lower PPTs than controls across all evaluated locations. However, after adjustment for age and Holm correction across the 20 anatomical sites, only the right greater occipital nerve remained statistically significant (p = 0.043). The right greater occipital nerve also yielded the highest area under the curve (AUC = 0.706; 95% CI 0.576–0.836) and was the only location whose AUC remained significantly different from 0.50 after correction for multiple testing (p = 0.040). Its sample-derived exploratory cut-off of 1.75 kg/cm² showed low sensitivity (0.419; 95% CI 0.245–0.609) but high specificity (0.969; 95% CI 0.838–0.999). Although the corresponding LR+ was 13.419, its confidence interval was extremely wide (95% CI 1.866–96.525), indicating substantial imprecision. The LR− was 0.599 (95% CI 0.442–0.814). Overall, the operating characteristics varied considerably across locations and should be interpreted as exploratory. Conclusions: Women with frequent episodic TTH showed generally lower PPTs across nerve and muscle locations. However, after adjustment for age and multiple testing, the between-group difference was statistically supported only at the right greater occipital nerve. Although this location showed the highest AUC and high specificity at the sample-derived cut-off, its low sensitivity and the considerable imprecision of the LR+ estimate limit its interpretation. These findings reflect discrimination between selected participants with established TTH and headache-free controls, and should not be interpreted as evidence of clinical differential-diagnostic accuracy. Full article

High-power semiconductor laser diode arrays (LDAs) are pivotal for applications such as optical pumping, industrial manufacturing, and precision measurement, yet they face inherent bottlenecks in balancing high output power, superior beam quality, and stable phase synchronization. The Talbot cavity, leveraging the Talbot self-imaging effect, has emerged as a core external cavity technology to address these challenges, enabling global coupling and passive phase locking of LDAs. This paper systematically reviews the research progress of Talbot cavities in phase-locked LDAs under global coupling. It elaborates on the fundamental principle of Talbot-effect-based phase locking, along with the structural characteristics and working mechanisms of three typical Talbot cavity configurations: conventional Talbot cavities, V-shaped Littrow–Talbot cavities, and monolithic integrated Talbot cavities. Furthermore, it summarizes key experimental advancements of LDAs, covering diverse laser media (e.g., near-infrared, blue, terahertz, and mid-infrared antimonide lasers) and array scales ranging from a few to thousands of emitters, with representative performance metrics including far-field visibility up to 99%, narrowed spectral linewidths achieving 20–50 pm for blue LDA, and output power exceeding 200 W. Numerical simulation progress on supermodel stability and parameter optimization is also discussed. Finally, the current challenges, such as thermal crosstalk and integration complexity, are analyzed, and future prospects involving intelligent control and novel physical mechanisms are outlined. This review aims to provide a comprehensive reference for the further development and practical application of high-brightness phase-locked laser sources. Full article

Clarifying SOC sequestration via physical and microbial processes is key for improving farmland fertility, yet the relative contributions of agronomic practices to carbon fractions and aggregate sizes remain unclear. This study (2010–2019, rice–wheat rotation, Funiu Mountain eastern plain, central China) examined tillage and organic amendment effects on SOC dynamics and underlying mechanisms across aggregate sizes under six treatments (conventional/reduced tillage with no fertilizer, chemical fertilizer, or organic manure). SOC, particulate organic carbon (POC), and mineral-incorporated organic carbon (MOC) were measured in bulk soil and water-stable aggregates (>2000, 250–2000, 53–250, <53 μm), and physical fractionation and phospholipid fatty acid (PLFA) analysis were conducted to assess interactions among aggregates, carbon quality, and microbial communities. Results showed that, compared with conventional tillage without fertilization, both conventional tillage and reduced tillage with organic manure significantly increased bulk SOC by 92–122% and macroaggregate (>250 μm) mass by 15–110%. The combined application of organic manure and reduced tillage redirected SOC from micro- to macroaggregates. Moreover, POC and MOC were the primary contributors to bulk SOC, with POC showing a strong direct effect on SOC accumulation. Furthermore, a positive priming effect was detected exclusively in macroaggregates, identifying them as key sites for SOC turnover and confirming that optimized tillage with manure shifts aggregates to larger sizes and boosts SOC through physical protection. The micro-to-macro cascade offers a robust framework for SOC dynamics, and its persistence under diverse climates warrants future research for sustainable management. Full article

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous inflammatory disease with a complex pathogenesis that significantly affects patients’ quality of life. Type 2 inflammation plays a dominant role in its course and is associated with the activation of immune pathways involving interleukins IL-4, IL-5 and IL-13, eosinophils, and immunoglobulin E. Standard treatment methods, including corticosteroids and surgical interventions, despite their proven efficacy, often fail to provide sustained disease control and are associated with a high rate of recurrence. The aim of this review is not only to summarize the available evidence on biological therapies in CRSwNP, but also to critically evaluate their current position within treatment algorithms, with particular emphasis on patient selection, integration with endoscopic sinus surgery, comparison of available biologic mechanisms, and remaining challenges in personalized treatment strategies. The paper discusses available monoclonal antibodies, such as dupilumab, omalizumab, mepolizumab, and benralizumab, which act by selectively inhibiting key mediators of type 2 inflammation. Analysis of clinical trial results indicates that biological therapies lead to a significant reduction in nasal polyp size, improvement in nasal patency, restoration of olfactory function, and enhancement of quality of life as measured by the SNOT-22 scale. Furthermore, they demonstrate a favourable safety profile and may represent an effective therapeutic option for patients with severe, treatment-resistant disease, particularly in cases with coexisting eosinophilic asthma. Biological therapies represent a breakthrough in the treatment of CRSwNP and align with the concept of personalised medicine. Their role in clinical practice continues to expand; however, further research is required to optimise patient selection and assess long-term treatment outcomes. Full article

This paper proposes a manner of modeling properties of stationary and tracking photovoltaic installations. A SPICE-based model of such photovoltaic installations is proposed. With the use of this model, the properties of two photovoltaic installations located adjacent to each other are compared. The first is stationary, while the second is equipped with a solar-tracking system. The design of both installations is described. The results of measurements and computations for both installations are presented and compared. These results illustrate the daily changes in the generated power. These measurements and computations were conducted for selected days across four seasons. The energy produced by each installation in each month was also determined. The obtained results are discussed. It was demonstrated that the use of a solar tracker can significantly increase annual energy production, especially during months with a high solar trajectory. The presented research results demonstrate that the developed model provides accurate predictions of the performance of both photovoltaic installations and that their efficiency is strongly influenced by seasonal meteorological conditions. Full article

The purpose of this project was to investigate the nutrient density and nutritional value of meat products, seafood products, and plant-derived protein food products using the 2015 Canadian Nutrient File database. Particular emphasis was placed on comparing nutrient density before and after cooking or preparation, as well as evaluating the cost of foods relative to the nutrients they provide using Canadian retail prices collected over a five-month period. Overall, meat and seafood products were consistently rich sources of protein and key micronutrients, including zinc and vitamin B12, whereas many plant-derived protein foods, including kale, broccoli, spinach, lentils, beans, and quinoa, contained substantially lower protein concentrations on both an uncooked (as purchased) and cooked (as prepared) basis. Although the concentrations of nutrients such as fat, iron, phosphorus, and sodium varied among food products, these findings demonstrate meaningful differences in nutrient density and nutrient cost across protein food categories. These results provide a useful framework for consumers, health professionals, and policymakers when evaluating nutrient-rich protein food choices and underscore the importance of considering both nutrient composition and economic value in dietary recommendations. Future research should extend these comparisons by incorporating direct analytical measurements of foods, assessments of protein quality and indispensable amino acid digestibility, mineral bioavailability, and the effects of food processing and preparation on nutrient utilization to better characterize the nutritional contributions of diverse protein food sources. Full article

This study investigates the impact of assimilating Advanced Scatterometer (ASCAT) Level-2 surface soil moisture retrievals into the Application of Research to Operations at Mesoscale (AROME) model, the operational numerical weather prediction system of the Hungarian Meteorological Service. The Level-2 retrievals are geophysical soil moisture estimates derived from satellite radar backscatter observations and represent the uppermost soil layer (approximately 0–5 cm). Data assimilation is performed using a Simplified Extended Kalman Filter (SEKF) within the SURFEX surface modeling platform. In the reference configuration (REF), the same SEKF framework is applied, as used operationally for the assimilation of 2 m temperature and relative humidity observations. A second experiment (ASCAT) extends this configuration by additionally assimilating ASCAT surface soil moisture retrievals. The experimental period covers May–October 2023. The objective of the study is to quantify the added value of ASCAT soil moisture assimilation relative to the REF experiment, which does not assimilate ASCAT retrievals. Results indicate a systematic improvement in root-zone soil moisture and soil temperature, suggesting that the assimilation of surface soil moisture observations propagates beneficially to deeper soil layers. Verification against in situ and model-derived diagnostics shows a positive impact on near-surface atmospheric variables, particularly for 2 m temperature and humidity during nighttime conditions. Furthermore, precipitation verification reveals a measurable improvement, suggesting a beneficial influence of improved land–atmosphere coupling on short-range forecasts. Full article

Coal mining in areas with high groundwater levels often induces land subsidence and water accumulation, leading to the formation of artificial secondary wetlands. Reclaimed wetlands may provide important opportunities for regional biodiversity recovery. Taking Shuoxi Lake Wetland in Huaibei City as a case study, this research aims to reveal the characteristics of waterbird diversity in artificial wetlands after ecological reclamation in a coal mining subsidence area and to identify their key environmental drivers, thereby providing a scientific basis for optimizing the habitat service functions of such wetlands. Based on habitat identification and classification of the reclaimed wetland, waterbird diversity was surveyed, and redundancy analysis (RDA), Mantel tests, and ridge regression models were used to identify the major environmental factors influencing the distribution of different waterbird groups and to quantify their relative contributions. The results showed that after ecological reclamation, a total of 28 waterbird species belonging to 7 families and 6 orders were recorded in the artificial wetland of the coal mining subsidence area. Redundancy analysis (RDA) indicated that wader assemblages were more sensitive to vegetation cover (VC), distance to water bodies (DTW), and distance to buildings (DTB), whereas waterfowl assemblages were mainly affected by distance to buildings (DTB), area (A), and distance to water bodies (DTW), and showed no significant response to vegetation heterogeneity. Mantel tests further confirmed significant spatial correlations between waterbird assemblages and area (A), distance to major roads (DTR), distance to buildings (DTB), water depth (WD), and distance to water bodies (DTW). Ridge regression analysis showed that, under conditions in which anthropogenic disturbance was minimized, vegetation cover (VC) and water depth (WD) were the main positive drivers of wader diversity, whereas perimeter to area ratio (PAR) was the main negative driver. Waterfowl diversity was mainly negatively affected by perimeter to area ratio (PAR) and distance to water bodies (DTW). These findings suggest that appropriately regulating water depth, increasing vegetation cover, and reducing patch fragmentation and anthropogenic disturbance are key measures for enhancing the habitat service functions of artificial secondary wetlands in mining areas. These management strategies provide an important reference for wetland rehabilitation in other coal mining subsidence areas. Full article