Search Results (14,853)

Dynamic modulus is a key stiffness parameter in the mechanistic–empirical design of asphalt pavements. Traditional laboratory tests are time-consuming and costly, while conventional empirical models fail to characterize the nonlinear viscoelasticity introduced by fibers, and existing machine learning methods suffer from premature hyperparameter convergence and limited interpretability. To address these issues, this study employs an improved black-winged kite algorithm (IBKA) to optimize eXtreme Gradient Boosting (XGBoost) for establishing a dynamic modulus prediction model. Gaussian chaotic mapping, guided pool strategy, and adaptive step size are introduced to enhance global hyperparameter optimization capability. A dataset of 288 samples involving temperature, frequency, strain, and fiber categories is compiled from multi-condition tests. Nested cross-validation and an independent test set are adopted for internal optimization and generalization assessment, with permutation testing (1000 Monte Carlo, p < 0.001) confirming the statistical reliability of the model. The results demonstrate that IBKA–XGBoost delivers excellent accuracy and robustness, achieving an RMSE of 355.1248 MPa and an R² of 0.9966 in NCV and 373.5450 MPa and 0.9955 on the independent test set. It outperforms BKA–XGBoost, four metaheuristic algorithms, and three conventional tuning strategies across nine evaluation metrics; compared with BKA–XGBoost, RMSE decreases by 23.9% and prediction uncertainty U₉₅ narrows by 23.7%. SHAP and PDP analyses identify temperature as the dominant factor, reveal fiber-type differentiation governed by modulus matching and interfacial compatibility, and confirm asymmetric temperature–frequency interactions consistent with the time–temperature superposition principle. The proposed framework facilitates fiber screening and the intelligent refined design of pavement materials. Full article

Emergency and operating procedures in nuclear power plants usually require operators to search for information, judge system states, and make control decisions across several linked interfaces. Conventional TACOM assessment quantifies the structural complexity of such procedure-guided tasks, but it does not directly show how operators visually process the task during execution. This study examined whether eye-tracking features can provide preliminary process-level evidence for TACOM-based assessment, rather than replace the TACOM framework. We extracted 25 eye-tracking features from 21 nuclear engineering graduate students while they completed 17 SGTR/SLOCA procedure fragments in an M310 full-scope simulator. A partial least-squares regression model predicted task-level TACOM scores, with RMSE = 0.357, MAE = 0.300, and R² = 0.538 under leave-one-task-out cross-validation. The RMSE corresponded to 18.3% of the observed TACOM range (2.070–4.017), and prediction was more strongly associated with observed task ranking (Spearman’s rho = 0.775) than with exact linear calibration (Pearson’s r = 0.750). Feature analyses suggested that fixation–duration variability, fixation dwell, pupil response, gaze dynamics, and spatial sampling jointly carried TACOM-related information. Exploratory subdimension analyses further indicated that task scope was most consistently associated with fixation dwell and fixation-time proportion, whereas task uncertainty was more closely associated with pupil variability and spatial entropy. These findings suggest that eye tracking may complement TACOM by describing execution-process demands, although the evidence remains correlational and limited by the small task set, graduate student sample, and task interface variability. Future studies should validate the signatures with licensed operators, larger multi-scenario task sets, independent TACOM scoring, and step-level AOI analyses. Full article

Balancing the learning experience for players with diverse skill levels, particularly in multi-user learning environments, remains a challenge. Many game-based learning systems rely on static difficulty settings that do not adapt to individual abilities, leading to frustration or disengagement. Dynamic difficulty adjustment (DDA) aims to create a more personalized experience by dynamically adjusting the task’s difficulty to match the player’s evolving skill level, while also accommodating players with lower performance capabilities. This paper explores the potential of a DDA system in a multiplayer learning environment that includes block-based puzzles to teach programming concepts. We used a rating algorithm to evaluate the player’s performance and dynamically adjust in-game objectives. To explore the player experience, we conducted an exploratory quasi-experimental between-group study comparing the adaptive version with a fixed-difficulty implementation. The largest observed between-group difference concerned perceived competence, with higher scores in the DDA group (DDA: AVG = 2.31, SD = 0.76; Non-DDA: AVG = 1.62, SD = 1.07). However, this difference did not remain statistically significant after adjustment. No significant differences were found in perceived levels of challenge, workload, flow, or team involvement. Full article

Surface electromyography (sEMG)-based gesture recognition has attracted considerable attention in intelligent prosthesis control, human–computer interaction, and rehabilitation assistance. However, practical deployment remains challenging because new users can usually provide only a few labeled samples for calibration. Under cross-subject and posture-varying conditions, this setting further aggravates distribution shifts and can destabilize target-domain adaptation. To address these issues, this paper proposes a cross-subject sEMG gesture-recognition framework integrating training-time augmentation, two-stage meta-transfer learning, and matched feature regularization. Model-agnostic meta-learning (MAML) is first used on source-subject data to learn an initialization for rapid transfer, after which target-subject fine-tuning is performed with an auxiliary class-consistent feature constraint. Experiments were conducted on a self-collected 11-subject multi-posture dataset using a leave-one-subject-out (LOSO) protocol under FT-1_full, which uses one complete calibration repetition, and FT-2_k10, which uses 10 windows per gesture per posture. The proposed MAML + FT + MATCHED method achieved higher average Accuracy and Macro-F1 than Source-pretrain + FT. Under FT-2_k10, the average improvements were 9.20 and 9.70 percentage points, respectively. Nevertheless, the primary subject-level paired Wilcoxon comparisons did not reach statistical significance (Accuracy: p = 0.2402; Macro-F1: p = 0.2402; Holm-adjusted p = 1.0000 for both). The results therefore indicate favorable average trends and positive effect sizes, while pairwise statistical superiority was not established in the present 11-subject cohort. Full article

Objective: To compare electroencephalography (EEG)-based subject-identification and open-set rejection performance across experimental paradigms while examining model robustness and cross-session generalization. Methods: The M3CV database was analyzed across resting state, transient sensory stimulation, steady-state sensory stimulation, P300 Oddball, and motor execution. Closed-set identification was evaluated across feature representations, conventional machine-learning models, and raw-EEG deep-learning baselines. Open-set performance was further assessed using an identity-first leakage-free nested procedure with 60 enrolled identities, 15 development unknown identities, and 20 final-test unknown identities. Results: Under the P4 within-session protocol, transient sensory stimulation achieved the highest Rank-1 accuracy (98.10%). Differential entropy and shrinkage linear discriminant analysis achieved mean Rank-1 accuracies of 98.01% and 98.29%, respectively. Under leakage-free nested open-set evaluation, transient sensory stimulation achieved DIR@FPIR = 5%, DIR@FPIR = 1%, and AU-OSCR values of 91.75%, 77.32%, and 97.36%, respectively. Deep-learning analyses confirmed strong within-session identity discrimination but showed model-dependent open-set paradigm rankings. In contrast, strict cross-session transfer produced marked degradation across all model families, with Rank-1 accuracy falling to approximately 1.9–5.4% and verification AUC to approximately 0.53–0.56; unsupervised target normalization provided little recovery. Conclusions: EEG paradigms exhibited strong but largely session-dependent identity discriminability. Transient sensory stimulation provided the most favorable within-session open-set performance under the primary conventional framework, whereas cross-session variability dominated paradigm-dependent differences under zero-shot transfer. Full article

Mosquito-borne viruses, including dengue, Zika and chikungunya viruses, place a substantial burden on diagnostic services, especially where molecular laboratories are inaccessible or slow to return results. Point-of-care biosensors could reduce turnaround times and bring testing closer to patients in primary care, outbreak response, and field settings. This review critically examines nucleic acid amplification, CRISPR-assisted assays, lateral-flow platforms, microfluidic systems, electrochemical and optical biosensors, paper-based devices, and smartphone-enabled readouts. These technologies are evaluated in terms of sample preparation, analytical sensitivity and specificity, matrix interference, multiplexing, workflow integration, cost, and clinical validation. Overall, nucleic-acid-amplification and CRISPR-assisted platforms often achieve low reported detection limits under controlled conditions; lateral-flow and paper-based devices offer relatively simple and minimally instrumented workflows; and microfluidic, electrochemical, and smartphone-enabled systems support increasing levels of workflow integration, quantitative readout, and connectivity. However, few platforms currently integrate these advantages into a fully integrated and clinically validated “sample-to-result” workflow. Due to sample heterogeneity, viral strains, reference methods, assay conditions, and disparities in reporting practices, conducting meaningful cross-study comparisons remains challenging. Limited comparisons and insufficient prospective clinical and field validation further restrict the assessment of practical diagnostic utility. Therefore, strong analytical performance alone should not be interpreted as evidence of clinical validity. Priority directions include unified definitions of performance and reporting units, standardized validation protocols and external quality assessment, prospective multi-site evaluation using representative populations and specimens, and earlier consideration of manufacturing scalability, reagent stability, quality systems, and applicable regulatory requirements. Future platforms should integrate simplified sample preparation, multiplex detection, objective digital or AI-assisted interpretation, and secure connectivity while demonstrating measurable benefits for patient management and outbreak surveillance. Full article

Commercial operations across trading, import/export, logistics, and technology distribution are being reshaped by the convergence of artificial intelligence (AI), machine learning, multi-agent robotics, and Extended Reality (XR). Small and Medium-sized Enterprises (SMEs) feel this shift acutely: they face the same pressures as their larger competitors; labor shortages, high-SKU inventories that resist tidy categorization, narrow margins, and customer expectations set by Amazon-grade fulfilment, but rarely command the capital or the structured warehouse environments that make industrial automation straightforward. Existing frameworks for AI-driven automation and digital twins have been developed primarily for large-scale industrial settings and do not account for the capital, infrastructure, and organizational constraints specific to SMEs, leaving a gap in SME-scoped integration models. This research addresses that gap by asking how AI-driven robotics and immersive technologies can be integrated to optimize commercial workflows in SMEs operating in dynamic logistics and trading environments. The proposed framework is grounded in Sociotechnical Systems Theory, which treats technology and organizational workflows as jointly designed and mutually adapting, and follows a Design Science orientation in which the architecture itself is constructed as an evaluable artifact rather than a purely descriptive model. Methodologically, the study conducts a narrative synthesis of literature on embodied AI, computer vision, digital twins, VR training, and AR-assisted operations, combined with workflow analysis to identify where SMEs lose the most time and money. These are translated into a four-layer system architecture (perception, cognition, execution, integration) deployed through a four-phase implementation model: needs assessment, digital-twin and VR pre-training, controlled hardware pilot, and AR-supported scaling. The contribution of the study is twofold: conceptually, it brings together several technologies that are often discussed separately in the literature, while focusing specifically on the needs and constraints of SMEs while practically, it proposes a phased roadmap that can help SMEs adopt these technologies gradually, reducing both financial and operational risks. The approach also emphasizes human–robot collaboration rather than replacing human workers. The study does not include experimental validation, it presents a conceptual architecture and implementation roadmap consistent with a Design Science artifact-construction stage that can serve as a basis for empirical testing in real commercial environments. Full article

We introduce a fuzzy aura topological space$(X,\tilde{\tau },\tilde{a})$: a Chang-type fuzzy topological space carrying a fuzzy scope function $\tilde{a}:X\to \tilde{\tau }$ with $\tilde{a}\left(x\right)\left(x\right)=1$, generalizing the crisp aura spaces. The scope function is a graded, per-point observation window; narrowing it turns the induced rough approximation into a variable-resolution model whose boundary shrinks as the window sharpens (refinement monotonicity), illustrated by closed-form multi-resolution segmentation examples on the line and in the plane. The fuzzy aura-closure ${\mathrm{cl}}_{\tilde{a}}$ is a fuzzy additive Čech closure operator; it need not be idempotent, but its countable iteration is a fuzzy Kuratowski closure with the same fixed points, so both operators induce one fuzzy aura topology ${\tilde{\tau }}_{\tilde{a}}$, which, unlike in the crisp theory, need not be contained in $\tilde{\tau }$. We introduce five classes of generalized fuzzy open sets forming a strict hierarchy, study fuzzy $\tilde{a}$-continuity and its decompositions, and define separation axioms fuzzy $\tilde{a}$-$T_i$ ($i=0,1,2$) and fuzzy $\tilde{a}$-regularity that depend on the scope function rather than the topology, together with a graded level variant of $T_1$ suited to continuous scope functions. The approximation operators coincide with the Dubois–Prade pair of the associated reflexive fuzzy relation and recover the Pawlak and crisp aura models as special cases. Full article

Volcanic reservoirs exhibit strong lithological heterogeneity and complex seismic responses, making lithofacies prediction between wells challenging, particularly in the Yingcheng Formation of the Songliao Basin. To address this issue, this study proposes an integrated workflow for volcanic lithofacies prediction based on Principal Component Analysis (PCA)-optimized multi-attribute seismic clustering. Seismic facies are first identified from reflection configuration and external geometry, and three types—chaotic, layered, and shield-like facies—are established and calibrated using well logs and core data. PCA is then applied to reduce attribute redundancy and optimize the attribute set. The selected attributes, including root mean square (RMS) amplitude, energy half-life, and gradient magnitude, are used for multi-attribute clustering. The results indicate that eruption facies dominate the study area and correspond to favorable reservoirs, accounting for the majority of high-quality reservoir zones. In contrast, overflow and volcanic sedimentary facies show comparatively lower reservoir potential. The predicted lithofacies distribution shows strong spatial consistency with well observations, demonstrating the method’s reliability. Overall, the proposed workflow improves lithofacies prediction accuracy and provides an effective tool for reservoir characterization and well deployment in complex volcanic settings. Full article

Deep-water gravity-flow depositional systems are characterized by intricate internal architectural configurations and pronounced heterogeneity. Traditional outcrop studies rely heavily on qualitative observations and often fail to capture the sub-seismic 3D spatial heterogeneity required for precise hydrocarbon exploration. This study leverages an integrated multi-geophysical framework to achieve a high-resolution, quantitative characterization of internal structures within gravity-flow fan units in field outcrops. Focusing on the Upper Ordovician Lashizhong Formation at the Beishan outcrop in Wuhai, northwestern Ordos Basin, the research integrates Terrestrial Laser Scanning (LiDAR) to construct a 3D topographic framework and uses Ground-Penetrating Radar (GPR) to probe subsurface architecture. Furthermore, Full-Waveform Inversion (FWI) is employed for the high-resolution reconstruction of relative permittivity, culminating in the generation of a 3D geological model via Sequential Gaussian Simulation (SGS). Six primary lithofacies were identified: (1) massive-to-graded medium-to-fine-grained sandstone, (2) graded fine- to silty sandstone, (3) parallel-laminated fine sandstone, (4) climbing ripple-laminated siltstone, (5) wave ripple-laminated siltstone, and (6) horizontally bedded mudstone. Geometrically, six channel–levee architectural stages and one sheet-lobe stage within a mid-fan setting. Quantitative analysis shows that channel axes maintain a high net-to-gross (N/G) ratio of 0.82 with 92% connectivity, whereas levee and lobe margins exhibit a significantly reduced N/G of 0.28 and 42% connectivity. The overall cross-validation accuracy of the 3D model reached 86.4%. This research provides an objective, quantitative technical framework for 3D spatial characterization and reservoir modeling of complex gravity-flow systems. Full article

Blockchain networks have become a major target for fraudulent activities, including phishing and scamming attacks, due to the irreversible and pseudonymous nature of cryptocurrency transactions. Existing Ethereum fraud detection studies commonly formulate the problem as a binary classification task, which limits their ability to distinguish among different malicious behaviors. This paper proposes an explainable Graph Neural Network-based framework for multi-class Ethereum address classification using an enhanced version of the Benchmark Labeled Transactions Ethereum (BLTE) dataset. The original binary BLTE dataset is extended into a multi-class dataset by integrating external attack-category information and labeling addresses as Normal, Phishing, or Scamming. The resulting BLTE multi-class dataset contains 73,033 Ethereum addresses, 23 behavioral node features, and 6588 directed transaction edges. Three GNN architectures, namely Graph Attention Network (GAT), Graph Convolutional Network (GCN), and GraphSAGE, are evaluated under identical experimental settings. Across ten stratified random seeds, GraphSAGE achieves the best overall performance among the implemented GNNs, with $98.84\pm 0.06\%$ accuracy, $80.42\pm 2.67\%$ macro recall, $51.08\pm 1.54\%$ macro F1-score, and $99.27\pm 0.03\%$ weighted F1-score. To improve model transparency, GNNExplainer is used to identify influential transaction subgraphs and important node features that contribute to each prediction. The results indicate that neighborhood-aware graph learning is useful for multi-class Ethereum address classification, while also showing that minority-class precision remains challenging under severe class imbalance. A feature-only multilayer perceptron is found to be a strong baseline whose macro F1-score is statistically indistinguishable from GraphSAGE after multiple-comparison correction; the improvement attributable to graph structure is therefore modest and concentrated on the small connected portion of the network, and external validation on independent datasets is still required before the framework is used in real investigations. Full article

Background/Objectives: Functional tissue units (FTUs), including tertiary lymphoid structures (TLSs), blood vessels, and glands, encode localized immune, vascular, and epithelial organization in histopathology. Accurate quantification of these structures is important for studying tissue architecture and disease-associated tissue organization. However, FTUs are frequently sparse, heterogeneous, and surrounded by large amounts of morphologically similar background tissue, making automated segmentation in whole-slide images (WSIs) challenging. We therefore developed FTU-Seek, a pathology foundation model-guided framework that treats morphology-aware negative-patch selection as a key component of sparse FTU segmentation. Methods: FTU-Seek uses frozen multi-depth features from the UNI pathology foundation model to train a patch-level classifier that distinguishes FTU-containing from FTU-absent tissue. Target-absent patches are subsequently ranked according to their predicted target-containing probabilities, and the highest-scoring hard negatives are selected through a static TopK strategy to construct compact segmentation training sets. The framework was evaluated using five-fold cross-validation and internal test cohorts across TLS, blood-vessel, and gland segmentation tasks, with an additional independent 30-WSI held-out cohort for TLS. Positive-only, all-tissue, random-negative, and matched random TopK sampling strategies served as comparators. Segmentation-derived phenotypes were further explored in external TCGA cohorts. Results: The patch-level classifiers achieved mean validation AUCs of 95.92%, 90.11%, and 98.16% for TLS, blood vessel, and gland classification, respectively. For TLS segmentation, the pre-specified Top1000 configuration retained 27.6% of the all-tissue training workload and achieved a slide-level Dice of 76.69 ± 11.89% on the independent 30-WSI held-out cohort. Compared with matched random Top1000 sampling, it improved Dice by 3.72 percentage points (95% CI, 2.05–5.38). Blood vessel and gland segmentation achieved performance approaching all-tissue training while reducing the retained training workload by approximately one-half and one-third, respectively. Compared with matched random sampling, classifier-guided hard-negative selection produced the greatest improvements for sparse and morphologically ambiguous FTUs. Exploratory TCGA analyses further showed associations of TLS phenotypes with overall survival, vascular phenotypes with overall survival and microvascular invasion, and glandular phenotypes with clinicopathological characteristics. Conclusions: FTU-Seek demonstrates that pathology foundation models can support sparse FTU segmentation not only through feature representation but also through morphology-aware construction of segmentation training sets. By prioritizing informative hard negatives, the framework reduces redundant segmentation-training workload while maintaining competitive segmentation performance and supporting quantitative tissue phenotyping from routine histopathology. Full article

This study systematically explored immune targets in gastric adenocarcinoma (GAC) suitable for mRNA vaccine development. Based on multi-omics data from public databases, we first screened a set of potential tumor-associated antigen genes. Subsequently, using ten machine learning algorithms, we constructed 101 prognostic models and, through optimization and comparison, selected the Random Survival Forest (RSF) method to establish a clinical prognostic model for GAC consisting of seven genes (TYMP, IFGN, ITGAX, GBP5, GBP4, STAT1, CD84). At both the genetic and protein levels, these genes were closely associated with the antigen presentation process, suggesting the potential functional role of this model in antigen presentation. Further analysis of the immune infiltration characteristics in GAC preliminarily revealed its possible immune evasion mechanisms. Building on this, we designed candidate mRNA vaccine templates for GAC using the mRNAdesigner platform. Additionally, this study investigated the potential roles of the above seven genes in GAC progression and screened small-molecule compounds targeting these genes. Molecular dynamics simulations (MD) were performed to verify the binding stability between these compounds and their corresponding proteins. This study comprehensively simulated the tumor microenvironment (TME) and antigen presentation process in GAC, evaluated the clinical translation potential of the neoantigen prognostic model and its predictive value for immunotherapy, and provided a preliminary design scheme for an mRNA vaccine against GAC. The findings offer new evidence for identifying immune therapy targets in GAC and are expected to advance the development of immunotherapy strategies for GAC. Full article

Ebola virus disease (EVD) remains one of the most severe viral hemorrhagic fevers, with recurrent outbreaks challenging global healthcare systems. This narrative review traces the evolving epidemiology of EVD from the first recognized outbreaks in 1976 to the ongoing 2026 Bundibugyo virus public health emergency in the Democratic Republic of the Congo (DRC) and Uganda. Over nearly five decades, the recognized range of Ebola outbreak contexts and amplification mechanisms has broadened considerably: rural zoonotic spillovers remain the predominant mode of emergence, but the scale and reach of subsequent transmission increasingly depend on where introductions occur, on delays in detection, on population mobility, on ecological disruption, on armed conflict, on healthcare-associated transmission, and on viral persistence in survivors. Major advances in molecular epidemiology and genomic surveillance have improved outbreak investigation, enabling real-time transmission reconstruction and detection of survivor-linked resurgence. Vaccination, particularly ring vaccination with rVSV-ZEBOV, has shown high effectiveness against Zaire ebolavirus, yet vaccine equity gaps and the absence of licensed vaccines for Sudan virus and Bundibugyo virus remain critical vulnerabilities, though new candidate vaccines and therapeutics for Bundibugyo virus entered clinical evaluation in mid-2026. Artificial intelligence and digital technologies, including AI-assisted early warning systems, portable sequencing, drones, blockchain, and mobile health platforms, offer promising tools for outbreak preparedness, but robust evidence of their real-world operational impact during filovirus outbreaks remains limited, and their deployment in low-resource settings faces substantial barriers related to infrastructure, literacy, data costs, and governance. Integrated preparedness frameworks that combine ecological surveillance, resilient healthcare systems, community engagement, and international coordination under a One Health umbrella are increasingly viewed as a strategic necessity. The 2026 Bundibugyo outbreak reaffirms that despite decades of lessons, structural weaknesses in surveillance, response timeliness, and community trust continue to recur. Sustainable, multi-year financing, diversified vaccine platforms, regional manufacturing, and local co-design of digital tools are essential to translate lessons into lasting change. Preparedness is best understood as a continuous process rather than a reactive state. Full article

To address insufficient support for innovative methods under the model-based systems engineering (MBSE) framework during the conceptual design phase of nuclear power equipment, this paper proposes an innovative design strategy based on multi-layer problem decomposition. Three core contributions are presented. First, a five-layer hierarchical problem decomposition framework (Layer 0–4) is proposed and integrated into the MBSE architecture, enabling systematic transition from operational requirements to physical solutions. Second, hierarchical matching rules are established to map problem types at each layer to corresponding innovative tools: knowledge stimulation for Layer 1, DSM and FTA for Layer 2, TRIZ technical contradiction matrix for Layer 3, and TRIZ separation principles for Layer 4. Third, a complete solution set is developed for the HPR1000 reactor core detector removal equipment, including a torque-following control strategy, a cylinder spring pressure roller, and a pulley-based clamping mechanism. Using Arcadia and Capella, full-chain MBSE modeling is completed, and prototype tests demonstrate stable forming performance with a mean profile size of 164.20 mm (SD = 0.559 mm), below the 170 mm design limit. The results indicate that this strategy can effectively guide the innovative design of nuclear power equipment under the MBSE framework during the conceptual design phase. Full article