Search Results (1,137)

One of the most common problems in drilling operations is lost circulation, which can significantly increase well costs and lead to issues such as pipe sticking, blowouts, and even well closures. Identifying thief zones using analytical models is especially difficult, and there are no robust equations available in the literature due to a wide range of influential parameters, both controllable and uncontrollable. These parameters include operational factors, as well as the physical properties of the rock and drilling fluid. This study presents an artificial intelligence-based model designed to predict lost circulation zones. It investigates the underexplored potential of WV-curves for feature selection. Traditionally used to represent the spectral characteristics of training data, their role in feature selection has not been widely examined in the literature. The presentation of WV-curves is modified, and their effectiveness in identifying the optimal number of input and hidden neurons is evaluated. In this research study, a total of 15,000 data points were used and collected from oil wells in the Middle East. The artificial neural network (ANN) model exhibited a remarkable ability to accurately predict the locations of lost circulation zones based on the collected data, achieving an impressive accuracy of 94.5%. This is a significant achievement when compared to existing ANN models in the literature. The results highlight the strength of the ANN model in predicting lost circulation locations across a wide range of data collected from various wells in the Middle East. In addition, this model takes into account a diverse set of drilling operational parameters, as well as rock characteristics and fluid properties, offering a broader approach compared to other available ANN models. This advancement will also greatly facilitate future studies, enabling the prediction of lost circulation zones, and enabling advanced planning of appropriate prevention and remediation methods during the well planning phase to reduce the risk of lost circulation. Nevertheless, it should be noted that one limitation of the proposed methodology relates to data availability, as comprehensive formation parameters were not fully accessible; the inclusion of additional formation data may offer opportunities for further improvement in future studies. Full article

Generative artificial intelligence (AI) is increasingly present in medical education, yet its indiscriminate use risks impairing the acquisition of foundational clinical competencies, including clinical reasoning, hypothesis generation, and patient-centered communication, through processes of never-skilling, mis-skilling, and deskilling. This paper presents M3RGE-AI (Responsible, Reliable, and Reflexive use of Generative AI in Medical Education), a conceptual framework for the purposeful integration of AI as a cognitive scaffold in medical training. Drawing on established learning theories, zone of proximal development, deliberate practice, and peer learning, the framework assigns progressively expanding AI functions across training stages, prioritizes Socratic over directive interactions, requires transparent and verifiable sourcing of AI-generated content, and incorporates peer moderation and AI-off assessment checkpoints to mitigate over-reliance. The framework is operationalized through alternating AI-on and AI-off cycles, governance processes, and educator training protocols. Applied within these constraints, AI can shorten feedback loops and broaden clinical exposure while preserving independent reasoning and authentic patient communication. M3RGE-AI offers a theoretically grounded and institutionally implementable model for integrating generative AI into medical curricula without sacrificing the essential human competencies that underpin safe clinical practice. Full article

The Yilgarn Craton hosts some of the world’s largest orogenic gold deposits, yet discovery rates have declined sharply as near-surface resources approach exhaustion. Exploring deeper, covered terrains demands new predictive tools that transcend the limitations of conventional mineral prospectivity mapping (MPM). Here we integrate convolutional neural networks (CNNs) and Vision Transformers to construct a data-driven MPM framework trained on 6028 gold occurrences across 16 map sheets in the Yilgarn Craton. The CNN achieves 79.3% classification accuracy by capturing local structural features; the Vision Transformer attains 74.0% but identifies prospective zones in data-sparse regions that the CNN misses. An empirical test was conducted in the untrained Sandstone Greenstone Belt to verify the model’s generalization ability. The results reveal that most known gold deposits lie within the high metallogenic potential zones defined by the model. Meanwhile, three prospective targets are newly delineated in this area based on model prediction, including northwest-trending ultramafic units, a basalt-sediment transition zone and NW-SE trending amphibolite units along the Edale Shear Zone. These targets are hardly identifiable by conventional exploration techniques and merit further field investigation. These results demonstrate that CNN–Transformer integration provides a robust, complementary framework for orogenic gold exploration in covered terrains. Full article

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly affects the scientific basis of decision-making for rocky desertification control and ecological conservation. This study selected the Beipanjiang River Basin in Guizhou Province, a typical karst region, as the study area. The study selected the SOS, LOS, OM, and EOS indices from the 2001–2020 MODIS MCD12Q2 phenological dataset, combined with topographic zoning data. This study developed a sample spatial optimization scheme for complex karst terrain by integrating Spearman’s correlation analysis, SKATER spatially constrained clustering, statistical tests, adaptive stratified sampling, and Random Forest classification. The scheme was designed to test a phenology–landform joint stratification strategy for spatial sample allocation. The results indicate that (1) the study area was divided into six phenological pattern subregions, with significant spatial differentiation observed among them; (2) the “phenology–landform joint stratification + dual-weighted sample allocation” method was associated with improved sample representativeness and greater internal homogeneity within sample strata under the current experimental setting; and (3) compared to simple random sampling, the remote sensing phenological pattern-driven spatial optimization scheme improved overall accuracy from 71.33% to 77.55% and increased the Kappa coefficient from 0.43 to 0.62. These results suggest that, under the current study-area, sample-size, and validation settings, the phenology–landform joint stratification and dual-weighted allocation scheme can improve the spatial organization of training samples and classification performance over complex karst terrain, although weakly vegetated or bare classes remain difficult to separate. Full article

Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ($3\times 3\times 3$) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon $p<{10}^{-7}$, Cohen’s $d=4.91$); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ($p=0.470$). Ablation confirmed both PSO and ACO are individually necessary ($\mathsf{\Delta }\ge 2.7$ pp, $p<0.001$). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization. Full article

Background: High-level badminton performance requires rapid perceptual processing, visuomotor coordination, and precise movement responses under continuously changing spatial conditions. Although wearable sports vision interventions have shown potential for enhancing perceptual–motor performance, evidence regarding their longitudinal effects and transfer to sport-specific outcomes remains limited. Trial design: A single-center, exploratory randomized controlled trial using a parallel-group structure. Simple randomization without blocking or stratification resulted in a final allocation ratio of 16:10 (approximately 1.6:1) between the training and control groups. Methods: Twenty-six collegiate badminton athletes aged 18–25 were randomized into a wearable sports vision training group (n = 16) or a control group (n = 10). The intervention group completed wearable sports vision training using Automatic Dual Rotational Risley Prisms (ADRRPs) for 15 min twice weekly over 4 weeks. Results: Baseline-adjusted ANCOVA demonstrated significant between-group effects for reaction time (p = 0.003) and target-zone accurate hits (p = 0.004), whereas binocular visual function outcomes did not show statistically significant between-group differences. No adverse events were reported. Conclusions: Four weeks of wearable sports vision training may be associated with improvements in selected visuomotor outcomes, particularly reaction performance and target-zone hitting accuracy, in collegiate badminton players. Larger trials are needed to evaluate long-term retention and broader sport-specific applicability. Trial registration: ClinicalTrials.gov Identifier: NCT07105462, registered 29 July 2025. Full article

The intertidal beach profile provides a fundamental representation of beach morphology and serves as a key indicator of shoreline morphodynamics. To enable frequent and accurate mapping of intertidal beach profiles, this study proposes an automated reconstruction framework that integrates single-pixel image columns with a stacked bidirectional long short-term memory (Bi-LSTM) network. Time-exposure imagery, commonly referred to as Timex imagery, acquired from a shore-based video monitoring station at Xisha Bay, China, is used as the primary data source, while wave records obtained from a wave buoy are incorporated to assign elevations to the detected waterline breakpoints, thereby enabling automatic beach profile reconstruction. The stacked Bi-LSTM network is trained for land–sea segmentation and waterline breakpoint localization. achieving the best performance among the tested methods, with precision, recall, accuracy, and F1 score values of 0.951, 0.894, 0.978, and 0.903, respectively, and a mean breakpoint localization error of 2.23 pixels. Breakpoint elevations were then estimated using a local slope–wave setup attribution model. Validation against field-measured topographic data from four fixed profiles and three survey periods showed good agreement between the reconstructed and measured profiles, with a period-based root mean square error (RMSE) of 0.212 ± 0.080 m. When all validation points were combined, the reconstructed elevations showed strong agreement with the measured elevations, with a coefficient of determination (R²) of 0.988 and an overall RMSE of 0.24 m. The profile comparisons further showed that the reconstructed profiles generally captured the overall profile shape and cross-shore morphological pattern of the measured profiles, although reconstruction accuracy varied among the four fixed profiles. These differences demonstrate that camera viewing angle, field-of-view position, camera-to-profile distance, and image quality are important factors influencing video-derived beach profile reconstruction. These results indicate that the proposed method can directly reconstruct fixed intertidal beach profiles from shore-based Timex imagery without generating a digital elevation model of the entire intertidal zone. It provides a practical tool for high-frequency monitoring of intertidal profile morphology and supports the quantitative analysis of beach erosion–accretion dynamics. Full article

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical character of the atmosphere, the relationships between co-emitted species, the balance of photochemical processing, and the combustion fingerprint of emission sources. This study introduces a framework that identifies and diagnoses such evolutions within the pollutants of the atmosphere. A chemistry-aware Variational Autoencoder is trained on 19 multivariate pollution features (7 raw concentrations, 5 chemical ratios, 7 temporal gradients) at London Marylebone Road (urban roadside) and North Kensington (urban background) from 2015 to 2019, and tested on 2022–2025. A four-method ensemble framework (VAE reconstruction error, reconstruction probability, Isolation Forest, and statistical Z-score) requires ≥3 agreement to identify high-confidence departed pollution states. Per-feature decomposition of the reconstruction probability diagnoses the chemical character of each departure. At the roadside site, 14.5% of post-COVID hours fall within departed states, dominated by the CO/NO_x combustion ratio (513.2) and the photostationary state proxy (391.4), chemical relationships rather than individual concentrations. This indicates that at the point of emission, London’s fleet modernisation and Ultra Low Emission Zone (ULEZ) have changed the combustion fingerprint and photochemical equilibrium. The same structural indicators are carried over during the COVID-19 lockdown; however, O₃ rises 3.2× during the pandemic period, reflecting suppressed NO titration. Conversely, at the urban background site, where the departures are driven by concentrations and boundary-layer trapping ($r=-0.659$), the combustion fingerprint of the atmosphere is invisible to detect (CO/NO_x$=-45.0$). These findings indicate that London’s emission landscape has undergone fundamental transformations over the past decade, and the consequences of ULEZ and similar interventions or greater impacts of pandemic-related events are non-homogeneously distributed across the relevant region. Full article

Water Resource Carrying Capacity (WRCC) is a crucial measure for assessing the balance between regional water availability, socioeconomic development, and ecological needs, especially in arid and semi-arid regions. This study evaluates the spatiotemporal evolution of WRCC across 14 prefecture-level units in Gansu Province, China, from 2000 to 2023. A multi-dimensional evaluation system comprising 29 indicators across water resources, ecological environment, economy, society, and coordination subsystems was established. The Entropy Weight Method was applied to determine indicator weights and calculate a comprehensive index (CI) to quantify carrying pressure. A Random Forest model identified dominant influencing factors, and an autoregressive integrated moving average model projected trends from 2024 to 2028. The results show the provincial mean CI increased from 0.49 to 0.91, indicating intensifying pressure and a shift toward mild overload. Spatially, pressure exhibits a stable west–east gradient, with the highest levels persistently in western prefectures like Jiuquan, Jinchang, and Baiyin. In contrast, Gannan and Longnan in the south maintain lower pressure but show high interannual variability, indicating ecological sensitivity. The Random Forest model demonstrated strong performance, with training R² values exceeding 0.88 across all regions and mean absolute error mostly below 0.10. Projections suggest continued high pressure from 2024 to 2028 in the west, while central and southern regions show stable or slightly decreasing trends. These findings provide a quantitative basis for establishing differentiated, zoned water resource management and sustainable demand-side regulation strategies in water-limited regions. Full article

Traditional urban building energy modeling often overlooks the complexity of spatial configurations and mutual shading effects, thereby limiting its accuracy. This study proposes a novel, interpretable, data-driven framework based on heterogeneous graph neural networks (GNNs) to uncover and characterize the complex interrelationships between building morphology and urban topology. Using a parametric platform, this study generated a graph dataset of 285 residential blocks in Wuhan, structured as a dual-level graph: Building Zone Graphs (BZGs) and Building Layout Graphs (BLGs). Four GNN models were trained based on the dataset, and the evaluated results demonstrate that GraphTransformer outperforms GCN, GAT, and GraphSAGE in capturing long-range spatial relationships―particularly those arising from shading and solar access interactions. On a validation set, GraphTransformer achieved superior predictive accuracy, with R² scores exceeding 0.85 and 0.90 for cooling and heating energy predictions, respectively. After that, post hoc interpretability analysis by GNNExplainer identified three important morphology features influencing building energy consumption. Critically, the model found that shading relationships encoded as graph edges―especially those between southern and western façades―had statistically significant influence on building energy consumption. Finally, this work establishes an efficient, interpretable surrogate modeling framework for urban-scale energy analysis, delivering quantifiable, design-actionable insights to support sustainable urban development. Full article

Background: The aim of the study was to compare the effort intensity levels between various futsal training drills designed according to the non-linear pedagogy (NLP) approach and official female academic league matches. Methods: Nine female players representing a university futsal team participated in this study. The analysis involved four official league matches (OM), evaluated across both the first and second halves (H1 and H2), as well as eleven training drills. The drills were conducted using contemporary NLP methods and were classified as: CSD (drills without active opponents), STG (small tactical games with reduced complexity based on the constraints-led approach), and FG (drills based on the full futsal format). The recorded variables included the percentage of peak heart rate (%HRpeak) and average heart rate (HRavg) across five distinct intensity zones. To account for the repeated-measures design, data were aggregated and averaged for each participant within each drill category prior to the main analysis. Results: The overall pairwise comparisons regarding global activity-period intensity failed to reach statistical significance. Although differences in absolute mean values were observed between the training tasks and official match conditions, these variations were not statistically significant. Conclusions: The NLP approach in female academic futsal sessions elicited a comparable cumulative physiological load (expressed via HR metrics and time spent in different intensity zones) to match conditions. However, due to the small sample size and corresponding wide confidence intervals, this lack of significant differences must be interpreted cautiously as exploratory trends rather than definitive evidence of physiological equivalence. Future research with larger cohorts is warranted to evaluate the motor learning potential of these constraints. Full article

Effective diabetes management heavily relies on appropriate insulin administration, which strongly depends on the correct administration strategy. In this sense, insulin administration plays a fundamental role, as its use depends on the patient’s clinical condition and diabetes type. Traditional syringe-based methods require proper training to ensure that insulin is successfully delivered into the subcutaneous tissue, where it can be absorbed and metabolized; however, it is desirable to develop an insulin applicator that does not require training for its appropriate use. Aiming to provide support solutions that help patients to develop a correct administration technique, a biodesign-based methodology, coupled with biomimetic concepts, is employed to design a device that assists the user in creating a stable skin fold and guiding needle orientation during injection without requiring exhaustive training for device usage. A three-step approach is employed for the design, where computational fluid dynamics (CFD) and finite element analysis (FEA) methods are employed to ensure that the device produces a laminar insulin flow and the device strength is tested. It should be pointed out both methods are required since complications produced by sudden flows must be avoided, with CFD allowing assessment of the device mechanical properties in terms of the device strength. Initial functional evaluation indicates that the proposed approach does not require extensive training or complex operational procedures, facilitating its integration into everyday use. The device design is validated from the results obtained for the CFD analysis, as no turbulent flow is produced, whereas the FEA indicates that the geometrical form can handle the stresses produced by the folding generation without generating excessive deformations. Moreover, an infrared thermography analysis is also carried out to find out if the folding force generation is located in the zone of interest, the results of which indicate that the device operates in the desired physical zone. Full article

Cycling coaches frequently use training zones and zone distributions based around time in zone (TIZ) to analyse training data and to understand race demands. This study compared the intensity distribution of highly trained cyclists during training and racing using a novel work-in-zone (WIZ) method alongside the traditional TIZ approach. Twelve cyclists recorded their power output during 25 weeks of training and a 7-day stage race. Intensity zones were defined using a three-zone peak-power model, and intensity distribution (TIZ or WIZ) was analysed with compositional data analysis. There were significant main effects for context (training vs. racing) and zone type (TIZ vs. WIZ) on the ILR coordinates. ILR-1, which reflects the balance between Z1 and higher-intensity zones, was higher in training than racing and in TIZ compared with WIZ (p < 0.0001), indicating a relatively greater proportion of Z1 in these conditions. ILR-2, representing the balance within the higher-intensity zones, was significantly lower during racing and higher in TIZ compared with WIZ (p < 0.0001). These findings indicate the cyclists’ training distribution differed substantially from the demands of racing, and that TIZ and WIZ can provide meaningfully different interpretations of intensity distribution. Where TIZ reflects only the time distribution spent within each zone, WIZ incorporates the weighting of intensity. This leads to particularly different results in racing, where intensity is more stochastic and characterised by greater extremes. Combining both methods may enhance understanding of training intensity distribution, race demands, and the difference between these contexts in endurance cyclists. Consequently, WIZ should be used in a complementary manner rather than as a replacement for TIZ. Full article

Accurately simulating fault rupture dynamics is critical for aftershock prediction but remains computationally prohibitive due to the multiscale nature of earthquake processes. While Fourier Neural Operators (FNOs) offer a promising framework for seismic simulation, their direct application to rupture dynamics is hindered by spectral bias from global processing and resolution loss from uniform downsampling. To overcome these limitations, this paper introduces a novel dual-stage FNO architecture explicitly designed for multiscale rupture simulation. The architecture decouples the problem into a first stage for efficient low-frequency wave propagation in the non-fault zone and a second stage for resolving meter-scale nonlinear rupture dynamics within the fault zone. Then, we propose a physics-based sampling strategy that maintains high resolution in the critical fault zone while coarsening the non-fault zone based on wave-propagation criteria, coupled with an interpolation scheme that enforces conservation of mass, momentum, and energy. Evaluated on the SCEC TPV101 benchmark, our integrated framework achieves a 92.4% reduction in model parameters and a 2.34× speedup in training time compared to a baseline FNO approach, while also reducing the NRMSE in fault zones by 80.1%. Furthermore, the model demonstrates robust generalization to unseen geological parameters. Full article

Tree-based machine learning (ML) models offer a powerful classification framework for rapidly screening groundwater for heavy metal contamination and associated health risks. This study applies several tree-based algorithms to classify groundwater samples from the Jamshedpur Township area, Jharkhand, India, as safe or unsafe with respect to selected heavy metals, using physicochemical parameters as predictors and WHO/BIS limits as class thresholds. Groundwater samples collected from shallow and deeper wells were analyzed for pH, EC, TDS, and heavy metals such as As, Pb, Cd, Cr, Ni, Cu, Zn, Fe and Mn, and compared with drinking water standards to define binary class labels. Groundwater samples were classified into safe and unsafe categories based on WHO/BIS standards and health risk thresholds (HI > 1, CR > ${10}^{-4}$). Health risk assessment indicated significant non-carcinogenic and carcinogenic risks, particularly among children. Decision Tree, Random Forest, Gradient Boosting, and an Optimized Forest-type ensemble were trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC, supported by confusion matrices. The Optimized Forest and Random Forest models yielded the highest classification performance, achieving high recall for unsafe samples, which is critical for public health screening, while feature importance analysis highlighted EC, TDS, pH, and specific ions as key predictors. The results indicate that tree-based ML models using routinely measured water quality parameters can serve as efficient decision-support tools for rapid identification of heavy metal risk zones in Jamshedpur Township and similar industrial urban environments. Full article