Search Results (2,543)

Whether machine-learning models extract predictive signal from ETF returns across markets at different efficiency levels and whether statistical accuracy translates into trading value, remain contested. We examine this for iShares MSCI Brazil (EWZ) and iShares Core S&P 500 (IVV) from January 2010 to July 2026, training through December 2022 and testing thereafter. Random Forest, XGBoost with random search, XGBoost with Bayesian optimization, LSTM, GRU and an LSTM + XGBoost ensemble, are compared against historical mean, random walk, and AR(1) benchmarks at one-day (h = 1), five-day (h = 5) and monthly (h = 21) horizons using ten technical indicators. Every model is also evaluated against the classifier that predicts the majority class, and risk-adjusted performance is reported with bootstrap intervals. No model exceeds that trivial classifier in any combination examined. The two markets fail by distinct mechanisms: collapse onto the majority class in the developed market, and dispersed but unprofitable signals in the emerging one. Under Diebold–Mariano tests with autocorrelation-consistent variance and false-discovery control, no model is superior to the historical mean. No strategy outperforms Buy-and-Hold, and in the emerging market, no Sharpe ratio is distinguishable from zero. Where directional significance does appear, at the monthly horizon in the emerging market, it delivers no economic value. Together, these results argue for evaluating financial forecasting models simultaneously on regression metrics, economic performance, and regime stability rather than on any single criterion. Full article

Most predictive approaches in educational data mining rely on complex models whose opacity limits practical adoption by classroom teachers, creating a gap between model sophistication and classroom usability. This gap is particularly acute at the class-group level, where institutional gradebook data are routinely aggregated for teacher-level planning but rarely modelled with an explicit account of when model complexity is actually justified. This paper addresses that gap: its novelty is to provide a structural explanation, grounded in group-level academic dynamics, for why linear models are highly competitive, rather than merely adequate, for this type of data, and to test this account empirically. An eight-year longitudinal dataset (2013/2014–2020/2021) from a Spanish secondary school—1070 class-group records across 32 subjects—was used to compare linear regression and Random Forest for final grade prediction, a Random Forest classifier against an XGBoost classifier for academic risk detection, and SHAP (SHapley Additive exPlanations)-based explainability, validated through Leave-One-Course-Out (LOCO) cross-validation. Within this dataset, linear regression consistently matches or outperforms Random Forest in both scenarios (R² = 0.857 with two assessments; R² = 0.740 with one), explained by stable cohort dynamics—baseline grades, teaching continuity, group composition—that produce a linear temporal structure (Spearman ρ > 0.81) leaving little predictive return for ensemble complexity in this setting. For the passing class, the Random Forest classifier achieves F1 = 0.972 with high inter-cohort stability (LOCO F1 ∈ [0.944, 0.984]); for the minority at-risk class, it outperforms XGBoost (F1 = 0.69 vs. 0.57), a gap consistent with the benefit of explicit class-imbalance handling, though fully disentangling this from a possible ensemble-family effect is left for future work. The 2019/2020 cohort is statistically anomalous (Mann–Whitney U, p < 0.001), reflecting an exogenous shift in the grade-generating process under emergency evaluation rather than evidence against the linearity account under normal conditions. Simple, transparent models operating on routinely collected gradebook data deliver actionable early-warning signals within the digital competence of most practising teachers; group-level prediction additionally protects student identity by ensuring no individual is labelled at-risk, combining predictive utility with ethical design. Full article

The Pinglu Canal, the first river-to-sea canal project since the founding of the People’s Republic of China, reshapes basin-scale ecosystem services by altering land use/land cover, landscape patterns, and soil–water processes. This study assesses spatiotemporal variations in net primary production (NPP), soil conservation, water yield, and nitrogen/phosphorus output in the Pinglu Canal Basin from 2000 to 2024. Hotspot analysis, the interannual fluctuation index, Random Forest and Shapley additive explanations, climate–NPP residual analysis, and Geodetector were integrated. The results indicate that: (1) Over the past 25 years, ecosystem services have generally improved, with NPP increasing significantly and nitrogen/phosphorus outputs declining; northern hilly and low-mountain forests formed stable service supply areas, whereas water yield was more prominent in southern plains and river valleys. Water yield showed the strongest interannual fluctuation, while soil conservation remained relatively stable. (2) Hotspot stability was mainly regulated by population density, normalized difference vegetation index (NDVI), elevation, and precipitation, with evident nonlinear threshold effects. When the NDVI reaches about 0.63, the NPP and nutrient retention capacity are significantly enhanced, indicating that vegetation coverage and community structure can exert their ecological regulation functions effectively after reaching a certain level. (3) During construction, negative NPP disturbances expanded along the canal. The Geodetector results showed that while the explanatory power of population density was enhanced during the construction stage of the Pinglu Canal, precipitation, NDVI, and topographic/hydrothermal conditions remain foundational constraints for ecosystem services spatial differentiation. Furthermore, interactions, such as population–NDVI and population–precipitation, generally manifest as bivariate enhancement. This study supports ecological monitoring, risk warning, and zoned restoration for large linear infrastructure projects. Full article

Jujube is an important fruit tree and one of the five major economic forest tree species native to China, possessing high nutritional and medicinal value. It plays a key supporting role in the efficient utilization of marginal land resources—such as mountainous, sandy, saline-alkali, and drought-prone areas—as well as in the revitalization of rural industries. Molecular marker technology, with its advantages of stability, accuracy, and efficiency, has become an important tool in jujube genetic breeding research and is widely applied. This article summarizes molecular markers based on three generations of technological intergenerational systems: the first generation of hybridization-based markers (Restriction Fragment Length Polymorphism, RFLP), the second generation of PCR-based markers (represented by Simple Sequence Repeat, SSR), and the third generation of high-throughput sequencing markers (Single Nucleotide Polymorphism, SNP, Genotyping-by-Sequencing, GBS, etc.). A comprehensive classification system based on technical principles, polymorphism sources, and other dimensions is constructed to clearly explain the driving forces and development trends of molecular marker system evolution in jujube tree research, and to clarify the selection criteria and adaptation strategies of molecular markers. Research has found that the second-generation molecular marker SSR is the fundamental core tool for standardized identification of jujube germplasm resources and genetic analysis of low-budget populations. The third-generation molecular markers represented by SNPs and InDel are high-density maps, Genome-Wide Association Study(GWAS), Genomic selection, and other modern precision breeding core carriers, forming a layered complementary technology system, and are gradually becoming the mainstream of research. This paper summarizes the application progress of molecular markers in the precise identification of jujube germplasm resources, analysis of genetic diversity, determination of genetic relationships, construction of high-density genetic maps, genome-wide association analysis, functional gene mapping, and tracing of domestication and evolution. This article integrates all existing research using the unified scientific framework of “technology defect driven tagging iteration”, analyzes the internal logic of the evolution and replacement of different tagging systems, the inherent limitations of early tagging, the existing problems in current research, and discusses future research directions, aiming to provide a reference for the scientific and efficient application of molecular markers in jujube and to promote the improvement and upgrading of the jujube molecular marker-assisted breeding technology system. Full article

Background: Autoimmune hepatitis (AIH) is a chronic immune-mediated liver disease that may progress to cirrhosis. This study aimed to develop and externally validate interpretable machine-learning models for the classification of prevalent cirrhosis in patients with AIH. Methods: The development cohort included 55 patients with biopsy-confirmed AIH. Cirrhosis was defined histologically as F4, whereas F0–F3 was classified as non-cirrhosis. Logistic Regression with L2 regularization, Random Forest, and XGBoost were evaluated. The original stratified 70/30 hold-out analysis was retained, and repeated stratified five-fold cross-validation with 20 repeats was additionally performed to assess internal stability. Primary external validation was performed in an independent histology-matched cohort of 42 patients. Models were applied without refitting, recalibration, or threshold optimization. Discrimination, probabilistic accuracy, calibration, and threshold-dependent classification metrics were evaluated. Results: In the original held-out test set, AUROC was 0.900 for Logistic Regression with L2 regularization, 0.830 for Random Forest, and 0.890 for XGBoost. In repeated cross-validation, mean AUROC was 0.878 ± 0.021, 0.914 ± 0.015, and 0.896 ± 0.015, respectively. In the primary external validation cohort, Random Forest showed the highest numerical discrimination (AUROC 0.810; 95% CI, 0.653–0.933), followed by XGBoost (0.728; 95% CI, 0.566–0.878) and Logistic Regression with L2 regularization (0.716; 95% CI, 0.545–0.875). Random Forest also had the lowest Brier score (0.188). However, confidence intervals were wide and overlapping. Elastography stage alone achieved an AUROC of 0.745, and the numerical improvement of the full Random Forest model was not statistically clear. Conclusions: Machine-learning models integrating routinely available clinical, biochemical, immunological, and elastography-related variables showed preliminary external transportability for the classification of prevalent cirrhosis in AIH. However, the small development and validation cohorts, uncertainty in calibration, and lack of a clearly demonstrated incremental advantage over elastography alone indicate that larger prospective multicenter studies are required before clinical implementation. Full article

Reliable prediction and interpretation of photosynthetic rate are important for precision environmental management in greenhouse tomato production, but model stability is often limited by variable redundancy, measurement-session effects, and environmental heterogeneity. This study developed an integrated gas-exchange-data-based framework for key-factor selection, photosynthetic-rate reconstruction, cross-session validation, environmental correction, and physiology-informed limitation screening. Core predictors were first identified from high-dimensional gas-exchange variables using K-means clustering and random-forest importance analysis. A CatBoost–ExtraTrees–RBF-SVR stacked ensemble was then constructed to reconstruct the leaf photosynthetic rate from selected gas-exchange variables, and its cross-session generalization was evaluated using nested cross-validation and leave-one-file-out (LOFO) extrapolation. Environmental correction was further applied to improve cross-session comparability, and rule-based limitation screening was used to classify potential environmental constraints associated with reduced photosynthesis. The stacked model achieved an RMSE of 0.806 and an $R^2$ of 0.9918 under nested cross-validation, and an RMSE of 0.814 and an $R^2$ of 0.9917 under LOFO extrapolation. After environmental correction, the cross-session variability of the environmentally corrected photosynthetic indicator was reduced by 96.0%, reflecting improved cross-session comparability. Under deployable sensor inputs, performance decreased markedly (A1: RMSE = 4.578, $R^2$ = 0.735; A2: RMSE = 4.610, $R^2$ = 0.731), compared with the gas-exchange baseline (RMSE = 0.833, $R^2$ = 0.991). Thus, routine greenhouse sensor models should be regarded as approximate rather than high-accuracy substitutes for the gas-exchange-based model. Full article

Gene expression datasets provide valuable information for disease classification and biomarker discovery; however, their high dimensionality and limited sample size may limit classification performance and reduce biological interpretability. This study proposes GeDiRep, a prior knowledge-guided framework for identifying compact and informative gene subsets. The method first organizes filtered genes into disease-associated groups using curated gene–disease associations from DisGeNET. Each group is then scored according to its predictive contribution, and representative genes are selected using Random Forest-based feature importance. Representative genes from the top-ranked groups are progressively accumulated, and the resulting gene subsets are used to assess classification performance on the test set. Experiments on eight microarray datasets showed that GeDiRep reduced the average number of selected features from 40.3 to 8.3 compared with G-S-M while improving the average AUC from 0.83 to 0.87. In comparison with traditional feature selection methods using the same number of genes, GeDiRep also achieved competitive AUC values. Biological analyses, including term–gene network, hub gene, and heatmap analyses, supported the functional relevance and stability of several selected genes. Overall, GeDiRep provides a structured and interpretable framework for high-dimensional gene expression analysis by selecting reduced yet discriminative and biologically meaningful gene subsets. Full article

Lobo radish yield and quality depend heavily on summer sowing dates. A three-year field trial (2022–2024) in the Central Forest-Steppe of Ukraine evaluated four sowing dates–D1 (first decade of July), D2 (second decade of July), D3 (third decade of July), and D4 (first decade of August)–for cultivars Troiandova and Lebidka. Sowing in late July (D3) was optimal, securing maximum marketable yield (42.2 t·ha⁻¹ for Troiandova, 38.9 t·ha⁻¹ for Lebidka), peak marketability (70.5–72.0%), and minimal bolting (2.1–2.8%). Early sowing dates (D1–D2) increased bolting (up to 20.0%) and pest damage by cabbage root fly (Delia radicum L., 25.1–27.3%), reducing marketability to 45.4–52.0%. August sowing (D4) reduced root weight (204.6–224.3 g) but increased dry matter (8.6–9.8%) and soluble sugars (4.8–5.5%). AMMI and GGE biplots revealed higher yield stability for cultivar Lebidka (notably at D4), whereas Troiandova was highly responsive to optimal conditions (Troy-D3). Overall, sowing in the third decade of July (D3) is recommended for commercial production. Full article

Studying the relationships between understory plant diversity and soil physicochemical properties aids in understanding the sustainable development of plantation forests. Vegetation restoration in latosol gully erosion areas plays a key role in preventing soil erosion. Variations in understory plant diversity and soil physicochemical properties in areas that have undergone vegetation restoration subsequent to gully erosion are not well understood. In this study, we investigated the understory species composition, importance values, plant diversity, and soil physicochemical properties and explored their correlations following vegetation restoration using four forest types (Acacia mangium forest, Eucalyptus robusta forest, A. mangium–E. robusta mixed forest, and A. mangium–E. robusta–Schizostachyum pseudolima mixed forest) in the Mahuangling Watershed, Hainan Province. A total of 49 plant species belonging to 47 genera and 20 families were recorded. The E. robusta forest (31 species) and A. mangium–E. robusta mixed forest (27 species) had higher species richness and more complex community structures. The dominant shrub species were Rhodomyrtus tomentosa, Breynia fruticosa, Aporosa dioica, and Dodonaea viscosa, while the dominant herbaceous species were Ageratum conyzoides, Chromolaena odorata, Spermacoce alata, and Erigeron sumatrensis. In all four vegetation restoration forest types, the richness index in the herbaceous layer was higher than in the shrub layer, while the diversity index showed no significant difference between the shrub and herbaceous layers (p > 0.05). The soil bulk density ranged from 1.57 g·cm⁻³ to 1.63 g·cm⁻³, with the A. mangium forest having better soil total porosity (38.77%) and water-holding capacity (190.37 t·hm⁻²) than the other forests. NH₄⁺-N and NO₃⁻-N were lower in the E. robusta forest, while the A. mangium forest had significantly higher organic matter content (13.71 g·kg⁻¹). Correlation and redundancy analyses showed that soil water content, pH, and soil organic matter were key factors affecting herbaceous-layer and shrub-layer plant diversity. On the whole, we suggest that the planting of pure and mixed forests of A. mangium should be considered in future ecological restoration projects in the gully erosion area of Mahuangling in order to maintain the stability of understory plant diversity and improve latosol soil fertility. Full article

Wind erosion poses a serious threat to land stability and agricultural sustainability across global arid and semi-arid zones. This study was conducted in Tuoksun County, China’s sole county situated below sea level; its unique low-elevation landform generates distinctive wind–sand movement processes, forming a representative research platform for elucidating shelterbelt functional mechanisms under extreme arid environments. In this work, we systematically monitored wind field characteristics, shelterbelt windbreak performance, and soil wind erodibility and quantitatively analyzed the regulatory effects of different shelterbelt configurations on sand-fixing efficiency and soil anti-wind erosion capacity. The results revealed an obvious decoupling between wind velocity and wind direction frequency within the study area: the maximum wind speed (≈5.5 m s⁻¹) occurred in the NNW direction, whereas the W and WSW directions exhibited the highest wind occurrence frequency. Shelterbelt height showed an extremely significant positive correlation with wind speed reduction efficiency (r = 0.817 ***), while ambient wind speed was significantly negatively correlated with windproof benefit (r = −0.690 ***). Among forest types, F2 and PF achieved significantly higher efficiency and lower wind speeds than F1. Intact shelterbelts reached >85% efficiency, while degraded belts fell below 40%. Soil texture acted as the dominant factor controlling soil erodibility: clay, fine sand, and very fine sand increased soil wind erodibility, while coarse sand and gravel suppressed erodibility. Hierarchical clustering analysis of vertical wind speed profiles confirmed that shelterbelts can substantially reduce near-ground wind velocity; compared with shelterbelt types, spatial position (windward side, leeward side, and central zone) exerted a stronger influence on wind field regulation. This study elucidates the internal correlations among shelterbelt spatial configuration, sand-fixing efficiency, and soil wind erodibility, providing scientific support for the optimization of shelterbelt layout in low-elevation arid ecological regions. Full article

Lipid metabolites in maize kernels determine grain quality by influencing nutritional value, oxidative stability, and post-harvest deterioration, making their profiling essential for quality improvement and breeding. This study applies hyperspectral imaging (HSI) with a spectral range of 900–1700 nm to detect maize lipid metabolites. Six lipid metabolites—9-Octadecynoic acid (stearolic acid), pinolenic acid (Δ5,9,12 18:3), N-Acylethanolamine (16:0), N-Acylethanolamine (18:0), N-Acylethanolamine (18:1), and propionic acid—were selected due to their strong relevance to maize kernel quality and favorable spectral response. First, two-trace two-dimensional (2T2D) correlation spectroscopy with heterogeneous preprocessing is employed to capture both synchronous and asynchronous correlations across different preprocessing spectra. A convolutional autoencoder (CAE) was subsequently used to extract low-dimensional latent features from heterogeneous 2T2D-COS representations, followed by regression modeling using random forest (RF), support vector regression (SVR), gradient boosting (GB), and partial least squares regression (PLSR). A total of 82 maize seed varieties were employed for experimental validation. Compared with one-dimensional spectral, homogeneous preprocessing, and PCA-based feature extraction, the proposed approach provided improved predictive performance across the six lipid metabolites, with the optimal CAE-based models achieving R_P² values of 0.629–0.887, RMSEP values of 0.241–0.565, and RPD values of 1.656–2.069. Overall, this approach provides a rough screening solution for metabolite prediction in maize crop. Full article

Understanding the distribution of Emys orbicularis in Andalusia is essential for assessing its conservation status within one of the most fragmented sectors of its Iberian range. In this study, we applied a modelling framework integrating environmental favourability, spatial structure, and their intersection to identify the factors determining the species’ presence across the Andalusian territory. All presence records were obtained from field surveys and long-term monitoring programmes conducted by the authors, while environmental and spatial predictors were compiled from publicly available databases and processed following the modelling methodology developed by Santoro et al. A consistent pattern emerged across all models: E. orbicularis is strongly associated with forested riparian systems, stable aquatic habitats, and low levels of disturbance, while its distribution is constrained by a marked north–south gradient associated with the Sierra Morena mountain range. The species also shows a historical and continuous presence in Huelva province, including coastal areas, and traditionally occupied parts of the western Betic systems and the Cádiz coastline. Integrating environmental and spatial components allowed us to capture both habitat suitability and the spatial processes limiting the species’ presence. The intersection model, which synthesises the results of both analyses, provided the most reliable representation of the species’ potential distribution by reducing false positives and highlighting core areas of persistence associated with hydrological stability and landscape connectivity. We also observed a marked reduction in the area of occupancy derived from the records, consistent with the initial hypothesis regarding the species’ current status. Full article

The diversity of today’s malware and the conflicting criteria for predictive performance, computational efficiency, dependability, and interpretability have made the choice of a suitable malware detection model more complicated. Existing research focuses predominantly on predictive performance, while the multidimensional decision process required for practical model selection remains insufficiently addressed. To address this gap, this study provides an explainability-aware hybrid multi-criteria decision-making (MCDM) framework that systematically evaluates and ranks malware detection models across heterogeneous malware datasets. The methodology incorporates predictive performance, computational efficiency, false positive rate, and a composite Explainability Index into a single decision procedure. The Explainability Index integrates explanation stability, sparsity, and expert relevance, enabling interpretability to be explicitly considered in the model-selection process. The hybrid criteria weights are obtained by combining the Analytic Hierarchy Process (AHP) with the entropy weighting method, thereby integrating expert-driven criterion importance with data-driven variability. The final ranking is obtained using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Four candidate detection models, including Random Forest, XGBoost, Convolutional Neural Network, and Long Short-Term Memory, are independently used to validate the framework on the CIC-MalMem-2022 and CICMalDroid2020 datasets. Under a harmonized evaluation protocol without assuming direct cross-dataset predictive transfer, the experimental results rank XGBoost first with a TOPSIS closeness score of 0.670, followed by Random Forest with 0.624. Sensitivity and ablation analyses further show that the model ranking remains stable while changes in criterion weighting and framework components produce measurable variations in the multi-criteria preference structure. Overall, the framework provides a transparent and multidimensional alternative to conventional performance-centered malware model evaluation. Full article

Background: Recovery after hip fragility fracture surgery is heterogeneous. Explainable machine learning (ML) may help identify factors associated with short-term recovery during inpatient rehabilitation. Methods: This retrospective exploratory cohort included 88 patients who underwent surgery at our institution or two outside hospitals and received inpatient rehabilitation at our institution between 2017 and 2019. Improvements at discharge in activities of daily living (ADL), Harris Hip Score (HHS), and numeric rating scale (NRS) pain were defined using clinically established or data-derived thresholds. Admission demographic, clinical, and laboratory variables were analyzed using logistic regression, random forest, and XGBoost with fixed model configurations and class weighting. Model performance was assessed as a secondary feasibility analysis using repeated stratified 5-fold cross-validation (50 splits), corrected resampled t-tests, DeLong’s tests, and McNemar’s tests with Bonferroni correction. TreeSHAP was applied to out-of-fold predictions, and feature stability was evaluated across folds. Results: Discrimination was modest (AUC, 0.52–0.67), without significant between-model differences after correction. Prominent features were blood urea nitrogen, age, hemoglobin, and sodium for ADL; ALT (GPT), cardiovascular comorbidity, blood urea nitrogen, and age for HHS; and DXA, platelet count, sodium, and age for NRS. Stability analyses supported recurring feature associations across cross-validation folds. Conclusions: Explainable ML may be feasible as a hypothesis-generating approach for exploring factors associated with short-term recovery after fragility fracture surgery. Full article

(1) Background: Information diffusion models developed for graph-based platforms such as Reddit and broadcast architectures such as Telegram identify temporal features—particularly the timing of peak spread—as dominant predictors of coverage. Whether these predictors generalise to platforms where content is distributed through algorithmic recommendation rather than social-graph contagion remains an open question. (2) Methods: We analyse the YouNiverse dataset, comprising 133,364 English-language YouTube channels observed weekly from January 2015 to September 2019 (18.9 million observations). We derive channel-level diffusion features—including time-to-peak, post-peak decay rate, diffusion volatility, and upload frequency—and train three machine learning models (Linear Regression, Random Forest, and LightGBM) on two tasks: predicting peak weekly view growth (regression) and identifying viral channels (classification). A single-feature naive baseline (subscriber count alone) establishes the marginal contribution of the broader feature set beyond subscriber count alone, and a temporal split experiment (training on channels peaking before 2018, testing on 2018–2019) assesses cross-temporal stability. Because subscriber count and subscriber rank are measured at the October 2019 crawl, this is a retrospective characterisation rather than a strict real-time forecasting design. (3) Results: LightGBM achieves $R^2=0.776$ (5-fold CV: $0.778\pm 0.003$) compared with $R^2=0.548$ for the naive baseline, a net gain of $+0.228R^2$. Because subscriber rank and subscriber count are near-perfectly collinear, we interpret them jointly as a channel-size dimension (42.2% of total mean absolute SHAP attribution), rather than as independent effects. Time-to-peak ranks fourteenth (1.1%), in contrast to its dominant role on Reddit ($r=0.995$, rank #1). For virality classification, LightGBM achieves ROC-AUC $=0.967$. Under the temporal split, Random Forest ($R^2=0.703$) outperforms LightGBM ($R^2=0.683$), showing greater cross-temporal stability within this retrospective split. (4) Conclusions: Within the 2015–2019 data, the results are consistent with algorithmic recommendation weakening the relationship between temporal diffusion dynamics and coverage magnitude at the channel level. Time-to-peak is weakly informative in this setting, while generalisation to the current recommendation system requires validation on newer data. Full article