Search Results (21,869)

This research investigates the relationships between the parameters of the GR4J hydrological model and a set of morphometric descriptors, climatic indices, land-cover characteristics, and soil properties across the Caquetá River Basin (Colombia). Twelve limnimetric–limnographic gauges with consistent records for the period 2001–2022 were selected for model calibration and validation. The corresponding sub-watersheds were delineated and characterized in terms of geomorphometry, vegetation cover, and soil permeability. According to that, the morphometric assessment focused on estimating key geomorphometric parameters, while land-cover descriptions utilized NDVI data. Soil type identification was based on the average approximate permeability across each analyzed sub-watershed. Model calibration was performed using the Differential Evolution Markov Chain (DE-MC) algorithm with 8000 simulations, forced by CHIRPS satellite precipitation and ERA5 potential evaporation data. Relationships between GR4J parameters and watershed attributes were assessed using Spearman’s rank correlation and curve-fitting analyses. The results reveal strong and consistent relationships between GR4J parameters (X1–X4) and key morphometric variables, including basin perimeter, circularity ratio, main channel length, and channel slope. Coefficients of determination ranged from 0.80 to 0.98, highlighting the potential for parameter regionalization based on physiographic and environmental descriptors. Full article

The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article

Despite recent advances in automatic speech recognition (ASR), performance remains limited for Korean-accented English due to the limited availability of accent-specific speech data, including pronunciation and prosodic variations. To address this limitation, we propose a synthetic data generation framework for improving Whisper-based ASR performance. Synthetic speech is generated by converting English text into Hangul-based phonetic transcriptions using an intermediate IPA representation to reflect the phonological characteristics of Korean-accented English. The ASR model is fine-tuned using Low-Rank Adaptation with a mixture of synthetic and authentic speech data. Experimental results demonstrate relative reductions of up to 16.40% in the character error rate, 14.93% in the word error rate, and 14.81% in the phoneme error rate compared to the pretrained baseline. Full article

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. Full article

The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods. Full article

The Analytic Hierarchy Process (AHP) is widely used in Multi-Criteria Decision Analysis (MCDA), yet its strong reliance on expert judgment constrains its scalability and may introduce variability in weighting outcomes, particularly in high-stakes applications such as wildfire risk assessment. In this study, we investigate how Large Language Models (LLMs) can function as decision-support agents in an AHP-style hierarchical evaluation task derived from validated wildfire literature. Based on this structure, four representative LLM-assisted strategies are examined: Direct LLM Scoring (DLS), Multi-Model Debate Scoring (MDS), Full-Document Prompting (FDP), and Indicator-Guided Prompting (IGP). To evaluate their effectiveness, we benchmark LLM-generated rankings against expert-defined ground truth across 16 sub-criteria. Using the mean correlation coefficient R as the key evaluation metric, with reported values expressed as mean ± standard deviation across models: DLS shows no correlation with expert rankings (R = 0.009 ± 0.070), MDS yields marginal gains (R = 0.181), and FDP remains unstable (R = 0.081 ± 0.189). By contrast, IGP, which incorporates retrieval-informed structured prompting, shows the highest agreement with the expert reference among the four compared strategies (R = 0.598 ± 0.065), suggesting that structured contextual guidance may improve the performance of LLM-assisted weighting within the evaluated benchmark. This study suggests that, within the evaluated wildfire benchmark and the tested set of hosted LLMs, LLMs may serve as useful decision-support tools in MCDA tasks when guided by structured inputs or coordinated through multi-agent mechanisms. The proposed framework provides an interpretable basis for exploring LLM-assisted risk evaluation in the present wildfire benchmark, while further validation is needed before extending it to other environmental or safety-critical contexts. Full article

The accurate prediction of motor progression in Parkinson’s disease (PD) remains a major clinical challenge that limits personalized treatment planning and efficient clinical trial design. In this study, we developed and validated a machine-learning framework integrating a targeted panel of plasma proteins measured by Olink proximity extension assays with clinical variables to stratify patients according to their progression risk. We analyzed baseline plasma samples from 211 early-stage PD patients enrolled in the Parkinson’s Progression Markers Initiative (PPMI) cohort using four targeted Olink panels, from which 28 circulating proteins were retained after quality-control filtering. Patients were classified as rapid or slow progressors based on their annualized change in MDS-UPDRS Part III scores. Among the algorithms tested, Random Forest achieved the highest discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.751 (95% CI: 0.684–0.811), which exceeded that of clinical predictors alone (AUC 0.666). The integration of targeted proteomic and clinical features further improved model performance (AUC 0.773; p = 0.009). Nested cross-validation confirmed minimal optimistic bias (AUC 0.743). To enhance clinical interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, which identified interleukin-6 (IL-6), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor A (VEGF-A) as the most influential predictors. SHAP feature rankings were highly stable across cross-validation folds (mean Spearman ρ = 0.91). The robustness of these findings was confirmed through sensitivity analyses using extreme quartile comparisons (AUC 0.823), treatment-naïve subgroup analysis (AUC 0.738), and a clinically anchored outcome definition based on the minimal clinically important difference (AUC 0.739). A decision curve analysis demonstrated a net clinical benefit across threshold probabilities of 0.25–0.70. Our results establish targeted plasma protein profiling combined with interpretable machine learning as a promising tool for PD motor progression risk stratification, with potential applications in individualized patient counseling regarding motor prognosis and the selection of candidates for disease-modifying trials. Full article

We present a Retrieval-Augmented Generation (RAG)-based question-answering system for nuclear energy science communication, characterizing retrieval quality in generated responses. The system introduces a dual-similarity analysis that jointly measures (i) question-to-context (Q→C) and (ii) answer-to-context (A→C) semantic consistency, serving as “retrieval-side semantic alignment signal” and “post-generation semantic alignment indicator” respectively. Built with LangChain, FAISS retrieval, and a large language model, our pipeline separates offline indexing from online inference and is grounded on authoritative Taiwanese Nuclear Safety Commission documents. We evaluate two settings: (a) in-domain prompts derived from the corpus and (b) out-of-domain, randomly generated nuclear energy questions. Results show that generated answers are, on average, more semantically similar to retrieved contexts than the original questions under the present setup, while the overall association between retrieval-side and answer-side signals remains stronger in the in-domain setting. Out-of-domain questions show weaker but still observable answer-to-context alignment patterns, contingent on corpus overlap. These findings suggest that combining RAG with dual-similarity analysis offers a practical and audit-oriented approach for educational Q&A, and we discuss potential improvements in versioned regulations, re-ranking, and abstention strategies. In this study, the RAG technique and dual-similarity analysis are combined together to promote nuclear energy knowledge. The research flow chat of this study can be applied to many other fields of scientific knowledge. Full article

Soil enzyme activities are sensitive biochemical indicators that could benefit soil health assessments, especially in coarse-textured soils. Current protocols are inconsistent for fluorimetric assays and an optimized assay would facilitate comparisons of activities across climates and soils. A factorial experiment was conducted to evaluate how assay conditions affect the activity of three enzymes (acid phosphatase, β-glucosidase, and N-acetyl-β-glucosaminidase) across seven Florida mineral soils (>89% sand) by crossing two temperatures, four pH values, and two reaction termination reagents. Results between microplate fluorimetry and benchtop colorimetry and between air-dried and frozen (−80 °C) soils were also compared. For these soils, a pH of 4.5 with sodium hydroxide termination and a temperature of 25 °C were deemed “optimal” for maximizing activities and maintaining consistent trends. Activities measured with benchtop colorimetry and microplate fluorimetry were related for each enzyme (R² range: 0.58–0.83) and activities from air-dried soils were 50–90% of those from frozen soils (R² range: 0.75–0.91). Enzyme activities were positively correlated with other indicators (total C, nutrients), supporting their use in soil health assessments. As the rankings of soil samples by highest enzyme activities were similar regardless of protocol variations, this suggests that inherent soil properties were the dominant drivers of enzymatic activity. Full article

Background: Predicting functional recovery after selective nerve transfer remains challenging. Intraoperative nerve action potential (NAP) recording is widely used to confirm axonal continuity in peripheral nerve surgery; however, its quantitative prognostic value in selective nerve transfer has not been clearly established. This study evaluated whether intraoperative donor fascicle NAP amplitude predicts functional recovery following selective ulnar-to-musculocutaneous nerve transfer (Oberlin procedure) for restoration of elbow flexion. Methods: This retrospective exploratory observational study included 20 patients who underwent selective ulnar-to-musculocutaneous nerve transfer (Oberlin procedure) with standardized intraoperative neurophysiological mapping and quantitative donor fascicle NAP recording. Functional outcome specific to elbow flexion was assessed at last follow-up using the Medical Research Council (MRC) grading system. Time to first electromyographic evidence of biceps reinnervation was recorded. Associations between intraoperative NAP amplitude and functional, temporal, and clinical variables were analyzed using Spearman’s rank correlation coefficient and non-parametric tests. Results: Donor NAP amplitude demonstrated substantial interindividual variability (range 60–400 µV; median 137.5 µV, IQR 87.5–200 µV). No significant associations were observed between NAP amplitude and final MRC grade (ρ = −0.103; p = 0.666), time to electromyographic reinnervation (days: ρ = −0.123; p = 0.617), patient age, or time from injury to surgery. A moderate negative correlation between NAP amplitude and lesion severity was observed but did not reach statistical significance in this small cohort (ρ = −0.419; p = 0.0659). In contrast, shorter time to electromyographic reinnervation was significantly associated with improved final functional outcome (ρ = −0.559; p = 0.013). No patient reported postoperative hand weakness. Conclusions: In this exploratory cohort, intraoperative donor NAP amplitude was not associated with time to electromyographic reinnervation or final elbow flexion strength following selective ulnar-to-musculocutaneous nerve transfer. Although intraoperative NAP mapping remains essential to confirm axonal continuity and conduction viability of the donor fascicle, NAP amplitude did not demonstrate prognostic value in this cohort and should be interpreted cautiously as an isolated predictor of functional recovery, particularly given the limited sample size and exploratory design. These findings suggest that recovery after selective nerve transfer may be influenced by broader biological determinants, including regenerative timing, rather than by isolated intraoperative amplitude metrics. Full article

Objectives: While the previous literature has established that meteorological conditions are associated with COVID-19 mortality fluctuations, the relative effect of each of these highly correlated factors remains unclear. This study aims to conduct a comparative analysis to determine which of three main meteorological variables—Ambient Temperature, Ultraviolet (UV) Index, and Sunlight Duration—have the strongest negative association with COVID-19 mortality. The objective is to quantify and rank their impact over a 7-to-21-day biological exposure window. Methods: We conducted retrospective spatiotemporal analyses in the form of panel Poisson Distributed Lag Models (PDLMs) regression using daily data from 21 January 2020 to 10 January 2023, spanning 129 distinct geographical regions worldwide. To ensure a direct and fair comparison of effect sizes, all meteorological and environmental variables were Z-score standardized. We estimated three independent PDLMs—each focusing separately on UV Index, Ambient Temperature, and Sunlight Duration—with lags ranging from 7 to 21 days. These models controlled for overarching time trends and utilized a categorical variable to account for Region Fixed Effects modeling time-invariant regional health and socioeconomic determinants (e.g., obesity, age demographics, healthcare capacity). Furthermore, distributed lags of daily PM2.5 (air pollution) and relative humidity were explicitly included in each model as dynamic confounders. Results: The comparison of PDLM results reveals that the UV Index has the strongest negative association with COVID-19 mortality. A one standard deviation increase in the UV Index corresponds to a massive, highly significant cumulative reduction in deaths observed 1 to 3 weeks later (p < 0.001). Sunlight Duration is the second-strongest protective meteorological factor, whereas Ambient Temperature has the weakest effect. The distributed lags of particulate matter (PM2.5) and relative humidity were found to be statistically insignificant when modeled alongside the meteorological variables. Conclusions: After standardizing variables and controlling for dynamic environmental confounders like air pollution and humidity, the study findings provide robust empirical evidence that meteorological conditions have a strong significant association with COVID-19 mortality fluctuation with a temporal delay, overcoming the confounding effects of merely dry or clear-air conditions. Full article

The features of patients with multiple urothelial tumors remain to be elucidated. We intend to differentiate primary upper tract urothelial carcinoma with synchronous urothelial bladder cancer (UTUC + sUBC) and UTUC with metachronous UBC (UTUC + mUBC) cases to determine whether these temporal patterns reflect biologically distinct processes. A subgroup analysis of a retrospective cohort of UTUC (n = 114) was performed comparing UTUC + sUBC (n = 14) with UTUC + mUBC (n = 29). IHC expression of cytokeratin 5/6 (CK5/6), CK20, GATA3, and p53 was evaluated to assess relevant subtypes. Genetic characterization comprised TERTp, FGFR3, RAS, and TP53 status. Kaplan–Meier analyses estimated the progression-free survival (PFS) and overall survival (OS) of both UTUC subgroups, and the log-rank test was used to assess differences between subgroups. Our study reveals no significant differences in phenotype or genomic profile between synchronous and metachronous UTUC-UBC cases (p > 0.05). Nevertheless, patients with synchronous UBC revealed significantly worse outcomes in PFS (2y-PFS 23.1% vs. 52.1%, p = 0.029) and OS (2y-OS 40.4% vs. 84.4%, p = 0.016) than those with metachronous disease. These discrepancies could arise from as yet-uncharacterized molecular features or microenvironmental influences. Full article

Risk-based management of water quality in drinking water supply systems requires decision-support tools that extend beyond parameter-level hazard assessment and enable prioritization at the level of physical system objects. In this context, hazard assessment refers specifically to drinking water quality parameters and their possible operational and health-related implications, particularly in facilities serving sensitive user groups. This study proposes a class-based extension of the FPOR (Fuzzy Priority of Objects at Risk) framework to support object-level operational prioritization under conditions of limited data availability. Hazard importance is adopted from prior hazard prioritization using the Fuzzy Priority Index (FPI), while priority premises (PP) are represented as object classes reflecting typical functional and operational characteristics. Class-based profiles of local hazard relevance and object vulnerability are defined using expert-informed fuzzy representations and aggregated into FPOR scores to produce a relative ranking of priority premises classes. The results demonstrate how hazard prioritization can be systematically propagated to object-level decision units without reliance on site-specific monitoring data. The proposed framework provides a transparent and scalable basis for early-stage risk-based planning and supports the operational implementation of object-oriented management strategies in drinking water systems, while maintaining a clear conceptual separation from health risk assessment addressed in subsequent studies. Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

This paper proposes a unified approach based on maximal consistent blocks (MCBs) to address the problem of incomplete single-label and multi-label dimensional reduction. The matrix computation method for maximal consistent blocks is improved by introducing a dynamic multi-row detection mechanism and optimizing the block size determination criteria. The complete set of maximal consistent blocks can be efficiently obtained via matrix intersection operations. For incomplete single-label decision information systems, an attribute reduction algorithm is designed based on maximal consistent blocks. Redundant attributes are eliminated by preserving the upper and lower approximation distributions of decision classes. In the multi-label scenario, a complementary decision reduct method integrating coarse and fine decision functions is proposed, and a unified solution paradigm is adopted to accomplish multi-label dimensional reduction. The effectiveness in classification (F1-score, Ranking Loss, Hamming Loss), reduction performance, and runtime efficiency is validated via statistical tests, scalability studies, structured missingness studies, and comparisons with four representative baselines on Birds, Scene, and Yeast datasets (5%/10%/15% missing rates). Full article