Search Results (5,090)

Background: The convergence of machine learning (ML), gamification, and critical thinking assessment within adaptive educational platforms has accelerated since 2020, driven by large language models (LLMs) and graph neural networks (GNNs). No prior systematic review has jointly addressed all three dimensions, and Central Asian educational contexts remain underrepresented. Methods: Following PRISMA 2020 guidelines, we searched Scopus (n  =  4396) and OpenAlex (n  =  4152) for publications from 2016 to 2026. Quality assessment used the Mixed Methods Appraisal Tool (MMAT 2018; threshold ≥  2), yielding 82 papers. Five research questions addressed ML personalization (RQ1), gamification and engagement (RQ2), critical thinking assessment tools (RQ3), recommendation algorithms (RQ4), and regional applicability in Kazakhstan and Central Asia (RQ5). Results: Transformer-based and GNN models dominate the recent literature (52% of corpus from 2025), with an accuracy of 91–97% for dropout prediction and learning path recommendation under single-institution conditions. Gamification studies report up to 90% student satisfaction; LLM-based critical thinking assessment shows promise but faces validity concerns. Thirteen papers address Central Asian contexts. Conclusions: Significant gaps persist: no integrated gamification–critical thinking framework exists, recommendation systems lack explainability, and Kazakh-language datasets are severely underrepresented. Future research should prioritize multilingual adaptive systems, explainable algorithms, and privacy-preserving federated learning for low-resource contexts. Full article

If concentrating coal is difficult or impossible using gravity methods (such as jigs, shaking spirals, dense-media drum, and cyclone), which are among the cheapest and simplest options, flotation becomes an alternative. This is due to the differences in surface chemistry properties between the relatively hydrophobic coal and the gangue minerals. On the other hand, flotation methods are far more complex than gravity methods and involve many more parameters that influence concentrate, such as coal particle size, amounts of reagents dosages (e.g., collectors, activators, depressants, and frothers), conditioning times, pulp mixing speeds, flotation times, and pH levels of the pulp medium. In flotation methods with so many variables, determining the combustible recovery (CR) and ash content (AC) of clean coal concentrate that can be obtained may require many experiments. To facilitate these challenging processes, understand the effects of parameters influencing concentration on the flotation method, and estimate the resulting clean coal recovery and ash content, it is necessary to utilize various statistical regression methods. In this study, the effects of six parameters on the flotation of a lignite coal sample with 40% ash content were used to estimate the CR and AC of coal concentrate using multivariate linear regression (MLR) and artificial neural network (ANN) models. As a result, the ANN model demonstrated superior estimate accuracy, with correlation coefficients of 0.988 and 0.963, compared with the MLR models (R² = 0.575 and 0.540) for estimating the ash content (AC, %) and combustible recovery (CR, %) of coal concentrate, respectively. Full article

Rapid and reliable flood segmentation utilizing optical remote-sensing imagery is critical for effective flood disaster response and risk assessment. Nevertheless, current models frequently struggle with imprecise boundary delineation and fragmented predictions in complex environments, especially where floodwater displays high spectral variability and closely resembles shadows, dark pavements, or wet soil. To overcome these challenges, we introduce FloodSeg, an innovative Mamba-CNN encoder–decoder network incorporating two lightweight yet highly effective components: a Shift module and a sequence-shuffle module. The spatial Shift module leverages spatially shifted feature aggregation to fortify boundary-aware representations, thereby ensuring the continuity of inundation contours even under varying illumination and cluttered backgrounds. Meanwhile, the sequence-shuffle module reorganizes multi-scale features via sequence-wise mixing and cross-regional interaction, significantly enhancing long-range dependency modeling. This facilitates the generation of globally consistent flood masks while mitigating local overfitting to dataset-specific textures. Evaluated on the Kaggle and FloodNet benchmark datasets, FloodSeg achieves outstanding mIoU scores of 81.85% and 91.21%, respectively. By outperforming various state-of-the-art CNN-, Transformer-, and Mamba-based baselines, our model demonstrates a superior accuracy-efficiency trade-off. These results substantiate that FloodSeg significantly advances boundary recognition and overall segmentation completeness, establishing it as a robust and practical solution for real-world remote-sensing flood mapping applications. Full article

Background/Objectives: The right inferior frontal gyrus (rIFG) is a key node of the neural network underlying inhibitory control and a promising target for non-invasive brain stimulation. Here, we investigated whether an accelerated anodal transcranial direct current stimulation (tDCS) protocol over the rIFG could produce cumulative improvements in response inhibition. This issue is particularly relevant because cumulative effects of accelerated stimulation cannot be taken for granted, and repeated administrations may lead to progressive improvement, saturation, or no measurable offline persistence. In addition, sham-controlled accelerated studies remain limited. Methods: Twenty-two healthy participants underwent three stimulation sessions within the same day. Inhibitory control was assessed using the Stop-Signal task, while Stop-Signal Reaction Time (SSRT) and mean error rate were analyzed by means of linear mixed-effects models. Results: Results showed a significant effect of time and a significant stimulation-by-time interaction across the three sessions, indicating a progressive reduction in SSRT during the accelerated protocol. Thus, inhibitory control performance improved as stimulation sessions increased. Conclusions: These findings suggest that accelerated anodal tDCS over the rIFG can induce cumulative short-term improvements in inhibitory control. The results support the relevance of the rIFG as a neuromodulation target, while highlighting the importance of interindividual variability and the need for more translationally oriented protocols. Full article

Three-dimensional (3D) endothelium–stromal co-cultures were established using human endometrial cells from biopsy of healthy women (n = 13) and serum samples from both healthy and endometriotic women (n = 5). For 3D construction, stromal cells were mixed with extracellular matrix components, followed by endothelial cell seeding. Morphological analysis confirmed the organization of tissue-like structures. Immunofluorescence and flow cytometry verified the expression of specific stromal and endothelial markers (Cytokeratin, Vimentin, Insulin-like growth factor-binding protein 1, and von Willebrand factor). Cell viability and proliferation increased over time, with minimal cell death. To test functional responsiveness, these co-cultures were exposed to inflammatory serum from endometriotic patients. After 48 h, cytometric bead array showed elevated levels of IL-1β, IL-6, and IL-8 in cultures treated with inflammatory serum, indicating preserved functional activity and responsiveness. By allowing detailed investigation of functional endometrial states within a physiologically relevant cellular network, this approach provides a valuable organoid-like tool to explore conditions such as implantation failure and infertility and to study the cellular interactions underlying reproductive pathologies. Full article

The aim of this study was to evaluate the possibility of using rapeseed protein–fiber concentrate (RPFC) as a functional ingredient for wheat pasta fortification, with emphasis on dough rheology, gel-like network formation, microstructure, and cooking quality. For this purpose, five formulations of rigatoni pasta were produced by partially substituting wheat flour with 0, 5, 10, 15, and 20% RPFC. Dough rheological behavior was assessed by frequency sweep and creep–recovery tests, while mixing and pasting behavior was evaluated using the Mixolab device. Microstructure was analyzed by scanning electron microscopy (SEM), and pasta technological and chemical parameters were determined using standard methods. All dough systems exhibited viscoelastic, gel-like behavior characterized by the dominance of the storage modulus (G’) over the loss modulus (G”), confirming the formation of a structured gluten-based network. Moderate RPFC incorporation (5–15%) enhanced G′, indicating reinforcement of the continuous protein–starch gel matrix and improved structural integrity and deformation resistance. Mixolab results showed a significant increase in water absorption and dough stability with RPFC addition, reflecting improved hydration and strengthening of the gel-forming protein network. SEM observations confirmed the development of a more compact and continuous starch–protein gel system, associated with reduced stickiness and improved structural cohesion. However, higher RPFC levels (15–20%) disrupted the continuity of the gel network, leading to increased cooking losses (8.8–10.4%), higher fracturability, and reduced firmness of cooked pasta. According to the data obtained, RPFC represents a promising functional protein ingredient for gel-like food systems such as cereal-based products, particularly pasta. These findings offer feasible formulation strategies and support its use as a sustainable, high-quality plant protein ingredient in pasta production. Full article

Mixed ground conditions cause sudden fluctuations in the tunnel boring machine (TBM) advance rate (AR). Accurate forecasting is necessary for tunneling safety. Existing data-driven models, however, often treat the excavation process as an isolated event. They ignore the physical memory effect of rock–machine interactions. They also lack the ability to diagnose abnormal AR drops. To address these issues, an interpretable forecasting framework is proposed. First, a Selection–Processing (SP) system is established to standardize data handling and quantify geological heterogeneity. Second, a Time-Series Structure (TSS) network is developed to construct a one-ring-ahead input block using the current completed-ring state and CCF/PACF-guided historical windows. The framework is validated on the Shenzhen–Dayawan Intercity Line. The optimized GWO-LSTM model achieves high accuracy (R² = 0.977, MAE = 2.15, RMSE = 3.07). Compared with the no-TSS reference scheme, the MAE and RMSE decrease from 2.7081 and 3.6045 to 2.1496 and 3.0724, respectively. Furthermore, Shapley Additive Explanations (SHAP) are applied for ring-by-ring anomaly diagnosis. Local SHAP analysis indicates that both current-state variables and selected lagged variables provide diagnostic information for AR fluctuations. The identified lags are interpreted as project-specific memory indicators rather than universal physical delay constants. This method provides model-based diagnostic clues for associating sudden AR drops with specific operational or geological factors. The proposed framework provides a transparent and practical tool for TBM performance prediction and field diagnosis. Full article

Background: The metastatic dissemination of melanoma involves adhesion of circulating tumor cells within organ-specific vascular beds; however, the relative contribution of the endothelial environment versus that of the melanoma-intrinsic molecular state remains unclear. Materials and Methods: We quantified the in vitro adhesion of primary (n = 5) and metastatic (n = 3) melanoma cell lines to human hepatic, brain, and pulmonary endothelial cells under co-culture conditions, and we profiled the expression of 86 adhesion- and extracellular-matrix-related genes in melanoma and endothelial cells. Results: Adhesion was highest for the hepatic endothelium, intermediate for the pulmonary endothelium, and lowest for the brain endothelium. This endothelial preference was conserved in both primary and metastatic melanoma cells, though metastatic cells exhibited higher absolute adhesion. The linear mixed-effect models revealed that the effects of adhesion state on melanoma gene expression were modest and varied by endothelial type, whereas melanoma origin had more widespread and larger effects (mean absolute standardized coefficients of 0.32–0.47 versus 0.60–0.87, respectively). The expression of three genes (SPP1, ITGA11, and MMP2) was associated with melanoma origin in all endothelial types. Spearman’s co-expression analysis revealed endothelial-type-specific gene networks, and within-sample permutation confirmed the non-random coordination in all three endothelial types. Conclusions: Our findings support a model in which endothelial organ specificity contributes to melanoma cell adhesion behavior and associated transcriptional patterns, highlighting the importance of the vascular interface as a biologically active mediator of early metastatic cell–endothelium interactions. Full article

Person re-identification (re-ID) aims to match pedestrian images across disjoint camera views. Existing multi-source unsupervised domain adaptation (UDA) re-ID methods still face two critical issues: they fail to effectively balance domain-invariant feature learning and domain-specific style preservation and cannot adequately model the implicit correlations among diverse source domains, resulting in limited cross-domain generalization performance. To address these challenges, this paper proposes a novel multi-source UDA re-ID framework equipped with a Mixture of Experts feature extraction (MEFE) network and a Graph-Based Relation (GBR) module. Specifically, the MEFE network integrates mixed Instance and Batch Normalization (MIBN) to extract robust domain-invariant features, while the embedded domain-specific style information (DSI) module compensates for lost domain-specific style details at the feature level. Furthermore, the cascaded Graph Attention and Graph Convolution Networks (GATs/GCNs) in the GBR module adaptively explore implicit feature correlations and achieve effective multi-source feature fusion. Center maximum mean discrepancy loss is adopted to further reduce cross-domain distribution discrepancies. Extensive experiments on large-scale datasets demonstrate that the proposed method achieves state-of-the-art performance and substantially outperforms mainstream UDA re-ID approaches. Full article

Optimal coordination of directional overcurrent relays (DOCRs) in medium-to-large power systems constitutes a computationally demanding, mixed-integer, nonlinear optimisation problem whose complexity escalates rapidly with system size, making the simultaneous minimisation of relay operating time and computational cost a critical open challenge. This study presents a two-level hierarchical framework in which Type-2 Fuzzy C-Means (T2FCM) clustering partitions 226 fault scenarios into subproblems at the upper level, while the Hybrid Fractional Entropy Evolution (HFEE) algorithm independently optimises relay settings for each cluster at the lower level. HFEE integrates fractional-order velocity updates—derived from the Grünwald–Letnikov formulation—with a Shannon entropy diversity-control mechanism to prevent premature convergence. T2FCM captures inherent fault-current uncertainty through interval-valued type-2 fuzzy memberships, yielding more robust cluster assignments near protection-zone boundaries than crisp partitioning methods. The framework is validated on the extended IEEE 30-bus system. An ablation study demonstrates that standalone HFEE achieves a 29.19% improvement in $T_{\mathrm{op}}$ over the prior best-reported result; however, a comprehensive parameter sweep over cluster counts $K\in \{2,\dots ,8\}$ and fractional orders $\alpha \in \{0.1,\dots ,0.9\}$ across 50 independent runs per configuration shows that the proposed clustering-decomposed method achieves 3.68–66.67% lower wall-clock computation time while maintaining zero CTI violations across all active relay pairs. The communicationless, entirely offline framework demonstrates scalability for simultaneous sub-transmission and distribution protection coordination and offers a practically deployable strategy for modern power networks. Full article

The complexity of authentic assessment within Deep Learning frameworks often hinders teacher adoption. This study analyses the diffusion process of such an innovation at SMP Taruna Bakti Bandung using an Explanatory Sequential Mixed Methods design. Through Social Network Analysis (SNA) of the entire teacher population and in-depth interviews, this study maps communication patterns and the roles of key actors. SNA results reveal a network structure with moderate density and subject-based clustering patterns. Qualitative findings confirm that adoption success relies heavily on opinion leaders acting as “pedagogical translators” to simplify the technical complexities of assessment. Through collaborative strategies, innovation barriers are reduced by enhancing aspects of trialability and observability. The study concludes that the adoption of authentic assessment requires synergy between formal institutional support and technical validation fostered within interpersonal trust networks, rather than relying solely on managerial instruction. Full article

In the context of urban regeneration, identifying the nonlinear and interactive effects of the built environment on commercial vitality is essential for targeted spatial improvement. Using Xi’an’s central urban area as a case study, this study integrated multi-source data, including POI, AOI, street-view imagery, and mobile phone signaling data, to delineate commercial spaces via kernel density analysis. With actual service population density as the vitality indicator, a built-environment framework was constructed using 14 indicators across four dimensions: transport accessibility, functional diversity, street quality, and environmental capacity. Random forest regression and SHAP-based interpretable machine learning were employed to examine factor importance, nonlinear thresholds, and interactions. Results show that environmental capacity and transport accessibility are the dominant dimensions, with building density, road network density, and employment density contributing most. Built-environment variables generally exhibit nonlinear threshold effects; key thresholds include road network density > 8 km/km², building density > 40%, functional mix > 4.5, and sky view factor around 40%. Interactions involving building density are most pronounced, and its positive effect is significantly amplified under higher accessibility or employment density. These findings suggest prioritizing road network optimization and building coverage, while balancing functional mix and spatial scale in commercial space regeneration. Full article

In the fabric warehouse, order picking operations face high labor intensity and rising operational costs, requiring urgent optimization. This study investigates the order scheduling and task assignment problem within an elastic staffing framework, where temporary labor recruitment and real-time task allocation need to be adjusted dynamically in response to fluctuations in order volumes. Nevertheless, conventional approaches often suffer from severe computational bottlenecks under such highly dynamic conditions, and struggle to maintain optimal solutions when demand undergoes large and frequent fluctuations. To address these challenges, this study proposes a Graph Transformer Policy Network with Proximal Policy Optimization (GTP-PPO), which combines graph structure features with a global attention mechanism. First, the return picking strategy and the S-shaped picking strategy are compared and analyzed in the fabric warehouse scenario. The results reveal that the return strategy is more suitable for the studied warehouse layout. Subsequently, a mixed-integer programming (MIP) model and a GTP-PPO model are established for optimizing order dispatching and scheduling. Finally, an empirical analysis is carried out based on the peak order day of the year in the fabric warehouse. The results demonstrate that the proposed GTP-PPO model not only achieves near-global optimal solutions (gap < 4%) comparable to the MIP model, but also exhibits robust real-time decision-making capabilities under dynamically increasing order volumes and unexpected disruptions. Compared to the MIP model, the GTP-PPO approach reduces unskilled labor hours by 84.80% and decreases operational volatility by 27.60%, with only a 3.52% increase in operational costs. Full article

Accurate and efficient detection of cyanobacteria in microscopic images is important for automated water-quality monitoring, but remains challenging because of complex aquatic backgrounds, large scale variation, and uneven sample quality. This study proposes an adaptive multi-scale fusion enhanced RT-DETR framework for cyanobacteria detection. The baseline RT-DETR-R18 is improved by incorporating the SeFaster module for efficient feature extraction, the high-level screening-feature fusion pyramid network for semantic-guided multi-scale fusion, and the Wise-IoU loss for more stable localization learning under mixed-quality samples. Experiments on the reorganized EMDS-7 dataset show that the proposed method achieved 79.05% mAP@0.5, 66.03% mAP@0.5:0.95, 16.31 M parameters, 54.6 G FLOPs, and 70.85 FPS. The proposed model also obtained the highest mAP@0.5 across the seven cyanobacteria categories. Moreover, cross-dataset evaluations further suggest the stability and transferability of the model. These results indicate that the proposed framework demonstrates potential for effective cyanobacteria detection in microscopic images with a good balance between detection accuracy and computational efficiency. Full article

Most delinquent behavior occurs within age-homogeneous peer groups. Using incident-level data from the National Incident-Based Reporting System (NIBRS), this study reassesses the extent to which contemporary juvenile group offending reflects peer-only networks versus cross-age collaboration. Results show that while juvenile-only groups remain the dominant pattern, approximately one-third of co-offending incidents involve adult participants. Mixed-age groups are associated with group size, offense type, and situational context, and are especially common in serious offenses such as homicide, aggravated assault, and drug crimes. Mixed-age co-offending is also associated with greater offense severity, particularly higher odds of victim physical injury. These findings have important implications for the criminal justice system’s response to juvenile crime. While most juvenile offending diversion programs currently focus on interventions that counter peer influence and reduce the time spent with peers engaging in antisocial behavior, intervention strategies that also address the facilitating role of adult co-offenders may also be necessary. Full article