Search Results (987)

Grounded in a human–land coupling framework, this study takes Purple Mountain, a representative composite urban mountain heritage site, as the research object. It integrates historical archives, field survey data, and multi-source geospatial data, and adopts a set of GIS-based spatial statistical methods, including the nearest neighbour index, kernel density estimation, standard deviational ellipse, coupling coordination degree model, and Geodetector. This paper systematically explores the spatial differentiation, spatiotemporal evolution, human–land coupling patterns, and multidimensional driving mechanisms of four heritage types: geomorphological relics, ritual architecture, modern commemorative heritage, and eco-scenic heritage. The results show that: (1) Heritage resources across Purple Mountain display statistically significant clustering, with ritual architectural heritage exhibiting the highest agglomeration degree; heritage sites form an east–west high-density corridor along the southern foothills, presenting a consistent northeast–southwest spatial orientation. (2) Purple Mountain heritage has undergone multi-stage diachronic evolution. Jointly driven by topographic constraints and socio-cultural forces, its heritage quantity, spatial coverage, and functions fluctuated across dynasties, with an overall expanding trend. (3) A total of 63.07% of the study area’s grid units are in a near-dissonant human–land coupling state, while highly coordinated units are concentrated in the southern core corridor of the Ming Xiaoling Mausoleum, Sun Yat-sen Mausoleum, and Linggu Temple, with remarkable disparities among heritage types in coupling patterns among the four heritage categories. (4) Historical and cultural aggregation density dominates heritage spatial differentiation, while topographic factors show weak explanatory power, and all influencing factor interactions present prominent non-linear enhancement effects. This study establishes a three-level quantitative framework of “spatial pattern—coupling coordination—driving mechanism”, enriching the theoretical framework of human–land coupling for urban mountain heritage, and provides scientific support for refined coordinated governance of mountain heritage embedded in high-density urban environments. Full article

Artificial intelligence (AI) is increasingly embedded across hotel service journeys, but limited research explain how guests’ perceptions of the overall AI-enabled service configuration are associated with their subsequent behavioral intentions toward the hotel. This study develops and tests an AI-enabled value realization model linking perceived AI service intensity with technology appraisal, service personalization, value co-creation, expectation confirmation, and behavioral intentions. A cross-sectional online survey was completed by 1733 guests who had encountered AI-enabled services in three-, four-, or five-star hotels located across 12 cities in mainland China. The model was examined using structural equation modeling, bias-corrected bootstrapping of indirect associations, and exploratory multi-group path comparisons. Perceived AI service intensity was positively associated with perceived usefulness, perceived ease of use, and behavioral intentions. The total indirect association was significant, and the largest reported specific indirect pathways involved service personalization. Ten of the eleven structural paths did not differ significantly across hotel tiers; the personalization–behavioral intentions association was nominally stronger in three-star than in five-star hotels. The findings position perceived AI service intensity as a journey-level service configuration and identify personalization as a prominent connection between technology appraisal and value realization, while recognizing the associational and exploratory boundaries of the evidence. Full article

Function-level source-platform attribution asks which known repository a code fragment originated from; it does not infer vulnerabilities, defects, exploitability, or other security properties. This study benchmarks lexical, frozen semantic, and multi-view pipelines for five open-source UAV flight-control repositories. UAV-SAFENet (a project-specific name; the “SAFE” label does not denote safety or security assurance) is used as a controlled comparator combining character-level TF-IDF, frozen CodeBERT embeddings, and 20 lightweight count-based indicators. Exact-hash cleaning yields 170,761 functions, and a path-keyword-selected (PKS) subsystem subset contains 42,808 functions. Under the random function-level split, 32,334 of 34,153 full-dataset test functions (94.7%) and 8018 of 8562 PKS test functions (93.6%) originate from files also represented in training; the scores may therefore partly reflect file- or module-specific memorization and do not establish unseen-file generalization. Class-weighted LinearSVM remains strongest at 0.9580 Accuracy/0.9276 Macro-F1 on the full dataset and 0.9547/0.9112 on PKS. Across seeds 42, 43, and 44, UAV-SAFENet obtains 0.9349 ± 0.0036/0.8946 ± 0.0045 and 0.9342 ± 0.0030/0.8846 ± 0.0026, respectively; frozen CodeBERT + MLP obtains 0.9277 ± 0.0034/0.8820 ± 0.0059 and 0.9249 ± 0.0033/0.8691 ± 0.0032. Single-seed diagnostics show that concatenation and equal-mean fusion outperform the learned gate on PKS, that removing supervised contrastive learning does not reduce performance, and that the indicator branch receives mean gate weights near 10⁻⁵. The principal contribution is therefore the traceable benchmark, artifact-audited comparison, and diagnosis of lexical shortcut dependence rather than a claim of a new best model or a security detector. Full article

As a meta-heuristic inspired by human student learning behaviors, the original Student Psychology-Based Optimization (SPBO) suffers from insufficient exploitation of historical population records, simplistic individual interaction patterns and high risk of falling into local optima. This work develops an enhanced SPBO (ESPBO) embedding three dedicated learning mechanisms. The adaptive knowledge-accumulation learning component imports personal historical best, global elite and population-mean information into position update formulas to sustain coherent search trajectories and enhance convergence precision. The multi-level peer collaborative learning module categorizes agents into excellent, intermediate and under-performing groups based on fitness values. Customized learning rules are configured for each group to enable diverse information sharing: elite individuals expand promising search regions, medium-level agents learn from counterparts, and inferior individuals move toward high-quality candidates. The progressive examination feedback component dynamically modulates search intensity by measuring the fitness improvement of each individual, so as to better balance global exploration and local exploitation. Comparative numerical experiments are carried out on CEC2017 and CEC2022 benchmark test suites against multiple advanced meta-heuristic algorithms. Results indicate that ESPBO exhibits outstanding accuracy and robustness on unimodal, multimodal, hybrid and composite test functions. To explore its real-world applicability, ESPBO is adopted for mobile-robot path-planning simulations under multi-scale grid maps. Simulation results from 20 × 20, 40 × 40 and 60 × 60 environments illustrate that ESPBO stably produces collision-free trajectories, outperforming comparative algorithms in path length, smoothness and safety performance. It is demonstrated that the three embedded learning mechanisms substantially strengthen the optimization capacity of vanilla SPBO, and ESPBO possesses considerable application potential for numerical optimization as well as mobile robot path-planning scenarios. Full article

This study proposes an explainable artificial intelligence (XAI) engineering workflow for provider-level healthcare claim audit prioritization using SHAP-guided hybrid retrieval-augmented generation (RAG). The framework integrates provider-level claim aggregation, tree-based risk screening, SHAP explanation, exploratory group-level SHAP clustering, policy concept retrieval, and constrained large language model audit narrative generation. Experiments on a public Medicare provider fraud dataset use the dataset-provided PotentialFraud label as a weak audit prioritization label rather than a legal determination of fraud. The results show that reimbursement exposure, utilization duration, claim repetition, deductible patterns, and beneficiary case mix contribute to provider-level risk scores. Additional cross-validation, calibration, threshold, and scale-confounding analyses indicate that provider size and financial exposure are important confounders, while non-scale and contextual features also retain predictive information. Beyond prediction, the framework organizes local SHAP drivers into exploratory provider archetypes and maps explanation patterns to audit-relevant policy concepts. Compared with pure embedding retrieval, the SHAP-guided hybrid retriever increases policy concept diversity and explanation alignment, although these retrieval metrics do not replace independent expert audit validation. Because the public dataset does not include referral pathways, inter-facility transfers, provider–network relationships, or care-coordination records, the framework cannot determine whether utilization patterns are explained by clinically appropriate referrals, regional access constraints, or multi-level care pathways. Its current applicability is therefore limited to provider-level audit prioritization using the available claims and beneficiary variables. The proposed system is positioned as a reproducible engineering prototype for cautious, human-reviewed audit support rather than a comprehensive or automated fraud determination system. Full article

Industrial water treatment plants are increasingly dependent on cyber–physical systems (CPS) and automated control processes for their operational safety and efficiency. However, the embedding of digital control networks exposes these critical infrastructures to sophisticated cyber–physical attacks, including malicious tampering with chemical dosing units and physical actuators. This paper proposes a robust, AI-enhanced anomaly detection framework designed to identify multi-stage malicious activities in water treatment systems using real-world industrial datasets. The proposed system is developed and validated on the Secure Water Treatment (SWaT) dataset, which contains multivariate sensor and actuator time-series data collected from a fully operational physical testbed under both normal operations and targeted cyber–physical attacks. First, high-frequency sensor noise is filtered, and cross-channel measurement reliability is maximized using a Kalman filter-based sensor fusion module. Subsequently, the fused-state vector is analyzed using an unsupervised Isolation Forest algorithm optimized for high-dimensional boundary isolation. To eliminate false negatives caused by stealthy, low-amplitude data injections that bypass purely statistical models, a deterministic, rule-based verification layer derived from physical process control logic is integrated. By integrating a discrete linear Kalman filter with an unsupervised Isolation Forest and deterministic physical rules, the framework effectively suppresses high-frequency sensor noise, achieving a 67.8% reduction in root mean square error (RMSE), while maintaining high detection accuracy across complex industrial attack scenarios. Experimental results demonstrate that the proposed hybrid framework yields superior detection capability, achieving a Precision of ≈95%, a Recall of ≈93%, a scenario-level F1-score of 94.1 % (alongside a sample-level F1-score of 21.5 %) and an edge inference latency of 0.6 ms, effectively demonstrating its suitability for deployment within simulated real-time industrial edge computing environments. The findings further confirm that combining statistical machine learning, state-space sensor fusion, and invariant physical process logic provides a resilient defense paradigm for securing critical industrial infrastructure against modern cyber–physical threats. Full article

Accreditation and quality assurance in technical and vocational education and training (TVET) remain largely dependent on manual review and expert judgment. This study presents AccreditNet, an explainable tabular AI model embedded in a broader accreditation decision support framework. The experimentally evaluated component combines per-feature tokenization, multi-head self-attention, hierarchical criterion fusion, class-balanced focal loss, integrated gradients, and descriptive performance gap scoring. Evaluation is conducted against a structured NAAC benchmark that includes criterion scores and institutional attributes. Because the official NAAC grade is derived from criterion-level scoring, the benchmark is interpreted as a test of how well the model represents the criterion-to-grade mapping rather than as evidence of an independent predictive relationship. On the reported held-out split, AccreditNet achieved 93.72% accuracy, 92.84% macro F1-score, and 0.9706 AUC-ROC. OCR-based ingestion, NLP/LLM-assisted evidence mapping, automated reporting, and Saudi deployment are proposed operational extensions and were not evaluated in the present experiments. The results support further study of explainable accreditation analytics and decision support, subject to validation on local data. Full article

This study conceptualizes RE100 not merely as a voluntary corporate campaign, but as an evolving socio-technical system that intersects with South Korea’s path-dependent energy regime. Applying BERTopic to a comprehensive corpus of 21,901 raw news articles (reduced to 13,507 analytically relevant articles after noise filtering; January 2019–October 2025), we trace the systemic evolution and multi-layered governance of RE100. Grounded in social constructionism and institutional systems theory, the research reveals how the global sustainability norm becomes structurally embedded in South Korea’s industrial and economic systems through a partially sequential evolutionary process of externalization, objectivation, and internalization. BERTopic identified 39 analytically meaningful topics, subsequently organized into four interpretive clusters: (1) local government-centered construction, (2) spatial reconfiguration and industrial-policy formation, (3) corporate-led market institutionalization, and (4) ESG-driven corporate governance. Collectively, these clusters demonstrate that RE100 has evolved from a peripheral international initiative into a multilayered governance norm embedded across local governance, industrial infrastructure, corporate strategy, and ESG systems. The findings hold theoretical and policy implications for energy-transition governance and climate communication. This progression is recursive rather than strictly linear: corporate-level internalization (Cluster 4) is conceptually linked to, and may interact with, regional and industrial-policy externalization (Clusters 1–2), consistent with a non-linear reading of institutionalization in which corporate site-selection criteria shape local-government infrastructure competition. Cluster 1’s prominence reflects local governments’ discursive visibility rather than substantive policy authority. Notably, RE100’s “normalization” denotes routinized corporate compliance practices, not political consensus on its legitimacy relative to alternatives such as carbon-free 100% or nuclear power. Full article

Background: Tumor heterogeneity arises from complex interactions among diverse biological factors, posing a major challenge for the development of robust multi-omics data integration methods. While the existing Tumor Multi-Omics pre-trained Network (TMO-Net) enables the fusion of multi-omics features into unified representations, its practical utility is constrained by issues such as missing modalities, incomplete within-omics data, and high-dimensional noise. To overcome these limitations, we propose TMO-Net+, an enhanced architecture specifically designed to improve the robustness and reliability of multi-omics modeling. Methods: TMO-Net+ introduces several coordinated architectural enhancements. First, a feature attention encoder is applied to each omics data type to reduce the influence of modality-dependent input variation. Second, we combine a gated Mixture-of-Experts (MoE) module with a Product-of Experts (PoE) mechanism to capture sample-specific contributions and enable robust inference even when partial omics data are available. Additionally, a supervised deep classification head with a tailored loss function is incorporated to enhance the separability of learned embeddings in the latent space. Results: Extensive experiments on pan-cancer datasets demonstrate that TMO-Net+ consistently outperforms the original TMO-Net, as measured by LogME scores. Furthermore, in various downstream tasks (e.g., pan-cancer classification, primary/metastatic site prediction, and prognostic modeling), TMO-Net+ achieves superior performance under partial-omics settings, which proves that it enhances the robustness and cross-cancer transferability of the multi-omics representations. Conclusions: The proposed TMO-Net+ improves the robustness and cross-cancer transferability of multi-omics representations within the evaluated TCGA cohorts. Biological interpretability analyses further show that TMO-Net+ prioritizes established cancer-driver genes, preserves cancer-dependent molecular-state information, and adaptively redistributes relative modality contributions across molecular states. By addressing modality-level missingness and modality-dependent input variation, it offers a reliable framework for integrative tumor analysis within the evaluated TCGA cohorts. Full article

To address the challenges in multi-modal trajectory prediction for multi-aircraft interactions within non-towered terminal airspace, including the insufficient extraction of long-range temporal dependencies, neglect of physical separation constraints, and barriers to integrating flight intentions and multi-source environmental context, this paper develops a trajectory prediction model integrated with long-range temporal modeling, physics-aware spatial interaction, and intention context enhancement. A parameter-shared ST-Transformer temporal encoder is established to capture long-term motion patterns of aircraft via global multi-head self-attention, and temporal attention pooling is adopted to mitigate error accumulation in long-term prediction. A physical distance-aware ST-GAT module is designed, which embeds the spatial distance prior between aircraft into the attention calculation and leverages distance masks to reduce interference from distant irrelevant aircraft. Furthermore, an intention-aware context enhancement module (IACEM) is proposed. It identifies the distribution of flight phases and adaptively incorporates meteorological information to construct enhanced features embedded with high-level semantics and environmental priors. Finally, a CVAE-based framework is utilized to generate multiple candidate trajectories satisfying kinematic constraints. Multiple verification experiments are carried out on the TrajAir dataset. The experimental results demonstrate that the proposed model outperforms various baseline models in terms of ADE and FDE. Ablation studies and visual analysis verify that the three core modules produce synergistic improvements. The model achieves higher prediction accuracy under scenarios involving 2D complex maneuvers, 3D climbing turns, and dense multi-aircraft interactions, which proves the effectiveness and superiority of the proposed algorithm for trajectory prediction in non-towered terminal airspace. Full article

Medical-image retrieval systems must balance semantic relevance with storage and search cost while remaining robust to errors introduced by hierarchical routing. This work presents an anatomy-aware hierarchical contrastive hashing framework for radiograph classification and retrieval. A calibrated ConvNeXt-Tiny classifier first estimates anatomical-region probabilities, after which a shared Swin-Tiny encoder and lightweight anatomy-specific heads produce fine-grained predictions and compact binary codes. The hashing objective combines embedding-level supervised contrastive learning with hash-space semantic supervision, route-specific binary prototypes, a sign-margin constraint, quantization, and route-wise bit balance. Confidence-adaptive multi-route database indexing and top-r query routing are used to reduce irrecoverable failures caused by hard Stage-1 assignment. Experimental results on IRMA and MURA datasets reveal that the proposed framework improves retrieval performance and efficiency over competing deep-feature and hashing-based approaches. The Stage-1 classifier on IRMA achieved 97.05% sample-level accuracy, 93.15% macro recall, and 95.33% macro-F1, whereas Stage 2 reported 96% accuracy and 93.2% macro-F1. 128-bit hash codes achieved Precision@20 of 0.96, and mAP of 0.871. On MURA, Stage 1 achieved 96.93% anatomy-classification accuracy, 96.44% macro-F1, and a calibration error of 0.0126. The 14-class Stage 2 model achieved 75.13% accuracy and 74.84% macro-F1. Joint anatomy–abnormality retrieval showed a code-length-dependent trade-off: 32-bit codes obtained the highest mAP of 0.646, while 256-bit codes achieved the best early-rank performance with Precision@10 of 0.631 and nDCG@10 of 0.626. Study-level normal/abnormal prediction achieved an AUROC of 0.860, AUPRC of 0.857, and accuracy of 80.46%. These results support the use of anatomy-aware routing and compact semantic hashing for efficient radiograph retrieval, while also showing that abnormality-level discrimination remains substantially more challenging than anatomical categorization. Full article

The inherent uncertainty of concrete dam systems, together with the complex influence of environmental loads and measurement noise, makes it difficult for traditional deterministic point prediction models to provide reliable deformation forecasts. In particular, the prediction performance of conventional models is highly dependent on parameter settings, while the uncertainty and potential deviation of future displacement responses are often not fully quantified. To address these limitations, this study proposes an intelligent data-driven prediction framework for dam displacement based on the integration of convolutional neural networks and long short-term memory networks. In the proposed framework, convolutional neural networks are used to extract local feature information from monitoring data, while long short-term memory networks are employed to capture temporal dependencies in displacement sequences. The Black-winged Kite Algorithm is introduced to optimize the key parameters of the integrated multi-level network, thereby improving the accuracy and robustness of point prediction. Furthermore, quantile regression is embedded into the optimized learning framework to construct an interval prediction model for dam deformation, enabling the conditional predictive uncertainty associated with displacement evolution to be quantitatively characterized. The engineering case study and comparative analyses with other models demonstrate that the proposed model achieves improved prediction performance for the investigated monitoring point. The interval prediction results further show that, for the investigated dam and monitoring point, the proposed framework can effectively characterize conditional predictive uncertainty and provide additional information for deformation interpretation and safety assessment. Further studies involving additional monitoring points and dams are required to assess its broader applicability. Full article

Advances in artificial intelligence, high-performance computing, and generative AI technologies have driven a rapid increase in the memory bandwidth and data throughput required of semiconductor systems, establishing High Bandwidth Memory (HBM)—which vertically stacks multiple DRAM dies—as a key enabling memory technology. However, increasing the stack count and shrinking the interconnect pitch in HBM not only intensify vertical heat accumulation and hotspot formation but also give rise to complex reliability issues, including thermo-mechanical stress arising from coefficient-of-thermal-expansion (CTE) mismatch, package warpage, interfacial delamination, Cu protrusion, void formation, and joint degradation. This review analyzes the heat-generation and heat-transfer mechanisms of HBM packages and examines package-level thermal management strategies based on thermal interface materials, underfill, non-conductive film, epoxy molding compound, heat spreaders, and high-thermal-conductivity composites. It further summarizes the current crowding, electromigration, Cu–dielectric interfacial defects, and thermo-mechanical failure mechanisms that arise at fine-pitch interconnects and hybrid-bonding interfaces, together with the material and process design strategies developed to mitigate them. In addition, structure-based thermal management technologies—thermal TSVs, embedded cooling, and hybrid bonding—are compared. This review emphasizes that the thermal bottlenecks and reliability degradation of HBM are interconnected through interfacial thermal resistance, interfacial adhesion, residual stress, and interfacial defects, and proposes that next-generation, highly stacked HBM requires a multi-scale thermal-reliability co-design that integrally controls the heat-, stress-, and current-transfer pathways across the entire package and interconnect domain, rather than relying on the improvement of individual material properties alone. Full article

Aiming at the problems of large speed synchronization error and prominent speed overshoot existing in conventional PID control algorithms widely adopted for diesel-engine parallel-unit speed-governing systems, this paper proposes a speed control algorithm based on Model Predictive Control (MPC) for dual-diesel-engine parallel operation. A quasi-steady-state method is employed to establish the coupled state-space model for dual-engine parallel operation. Leveraging the prediction-optimization and multi-constraint regulation characteristics of MPC, the fuel-injection outputs of the two diesel engines are regulated respectively by two independent SISO MPC controllers, which share the identical speed reference and are coordinated at the logic level through the Stateflow engagement/disengagement state machine to achieve speed synchronization. Comparative simulations under different operating conditions are carried out on the Matlab/Simulink platform. The simulation results show that the proposed MPC algorithm can effectively suppress speed fluctuations between the two diesel engines under the tested operating conditions compared with the traditional PID control. Furthermore, a hardware-in-the-loop test platform is constructed for validation in the embedded environment. The test results reproduce all operating conditions of offline simulation, achieving zero speed overshoot and restricting the dual-engine synchronization deviation within ±3 rpm, which are consistent with the offline simulation results. The real-time computational capability and engineering feasibility of the proposed algorithm are therefore verified. The proposed method is applicable to stable speed-governing scenarios of marine dual-diesel-engine parallel-unit sets. Full article

Soil fertility in mown-grazed grasslands on the Qinghai–Tibet Plateau reflects hydrothermal conditions, terrain, grassland type, and management disturbance. We developed a soil fertility index (SFI) from 1037 topsoil samples collected at 0–30 cm during 2023–2025. The modelling framework combined static ecological background variables, conventional climate indicators, multi-year seasonal hydrothermal statistics, soil hydraulic attributes, and self-supervised temporal embeddings. Spatial-block, year-held-out, and ecological-zone-held-out validation were used with area-of-applicability (AOA) analysis and spatial-block conformal prediction. Multi-year seasonal hydrothermal statistics supplied the strongest predictive information and increased spatial-block R² by 0.2435 relative to the static-background model. A 10-year seasonal window gave the best empirical balance among explained variance, prediction error and rank consistency. The final deployment model achieved R² = 0.5632, RMSE = 0.1105, Spearman = 0.4473 and AUC = 0.696 under spatial-block validation. These values support regional screening and sampling prioritisation, not local deterministic diagnosis or site-level management prescriptions. Grid prediction identified Zone 2 as the main concentration of relative low-SFI risk. Full-sample AOA schemes covered 82.9%, 81.9% and 85.9% of the system-evaluation grid cells, whereas deployment-grid AOA coverage was lower under the stricter deployment setting. This contrast separates feature-space support from operational grid support. Split conformal prediction achieved 90% coverage close to the nominal level (PICP = 0.899, MPIW = 0.264). Natural-background residuals separated relative low-SFI risk from local deviations below expected natural conditions. The framework provides a reproducible screening tool for regional prioritisation and follow-up field verification in alpine grasslands. Full article