Search Results (32,711)

Chronic kidney disease (CKD) leads to metabolic and cardiovascular complications, and the dysregulation of key biomolecules, namely fibroblast growth factor 23 (FGF23) and Klotho, plays a central role. This study investigated the effects of high-intensity interval training (HIIT) and moderate aerobic training (AT) on FGF23, Klotho, mineral metabolism, apoptosis markers (BAX, Bcl2), and atrial fibrillation (AF) in a rat CKD model. The study used 35 Wistar rats randomly assigned to control (CTL), sham (SH), CKD, CKD + HIIT, and CKD + AT groups. CKD was induced by 5/6 nephrectomy surgery. Exercise interventions consisted of eight weeks of HIIT (80–100% of maximum speed, 24–54 min/week) or AT (45–55% of maximum speed, 40–60 min/week), conducted three times weekly on a treadmill. We measured heart weight, blood levels of FGF23, Klotho, and mineral metabolism markers, as well as the heart expression of apoptosis proteins (i.e., BAX, Bcl2) and atrial fibrillation (AF). Both exercise types reduced the heart weight and heart/body weight ratio; attenuated CKD-induced elevations in FGF23 and reductions in Klotho; improved blood levels of phosphate, PTH, and vitamin D; and modulated apoptotic markers by decreasing BAX and increasing Bcl2 levels. Exercise improved cardiac function and reduced the AF duration. These findings emphasize that exercise could be a helpful non-pharmacological intervention to ameliorate CKD-induced cardiovascular and metabolic disturbances through the modulation of the FGF23 and Klotho pathways. Full article

Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification, insufficient novel-event reasoning accuracy, and weak domain adaptability. To address these issues, this study proposes a semantics-enhanced model (LLM-DSaR) integrating Large Language Models (LLMs), temporal attention networks, and optimized contrastive learning. Specifically, a two-stage LLM semantic enhancement (LLM1 + LLM2) framework first generates structured semantic analysis reports via adaptive prompt engineering, and then extracts domain-specific semantic embeddings from the last-layer hidden states through pooling and linear projection, which are further fused with TransE-based structural embeddings; meanwhile, LLM2 mitigates data sparsity in novel-event reasoning; a dynamic weight fusion (DWF) framework adaptively assigns feature weights to achieve deep feature synergy; an LLM-enhanced contrastive-learning module strengthens event clustering and discrimination. Experiments on five public datasets and a self-constructed Robotics Temporal Knowledge Graph (RTKG) show LLM-DSaR outperforms 16 baselines: on RTKG, its MRR is 10.35 percentage points higher than GCR, and Hits@10 reaches 88.87%. Ablation experiments validate core modules’ effectiveness, confirming LLM-DSaR adapts to professional scenarios like robot maintenance prediction, providing a novel technical paradigm for complex-domain TKG reasoning. Full article

Aiming at the problems of a fragile ecological environment, water shortage and system uncertainty in inland arid irrigation districts in Xinjiang, this study takes sustainable development as the guide, selects the Tailan River Irrigation District in Xinjiang as an example, and constructs a multi-objective optimal allocation model of agricultural water and soil resources in irrigation districts driven by water–carbon–economy synergy. The model aims to minimise irrigation water shortage, maximise crop carbon absorption and maximise economic benefits. By comparing six multi-objective algorithms such as APSEA, CMEGL, DCNSGA-III, DRLOS-EMCMO, MOEA/D-CMT and θ-DEA-CPBI, the optimal is selected based on the hypervolume (HV) index. The surface water, groundwater and crop-planting structure of five decision-making units in the irrigation district from 2021 to 2024 were optimised. Further, combined with the entropy weight–TOPSIS coupling-coordination comprehensive-evaluation model, the scheme evaluation system is constructed to screen the optimal configuration scheme of each year and unit. The results show that the MOEA/D-CMT algorithm has the highest HV value in each unit model over the years, which is the best solution algorithm for the model in this paper. The comprehensive evaluation value and coupling coordination degree of the optimal scheme of each unit fluctuate between years, and the difference between units is significant. Compared with the original planting and water source allocation scheme of the irrigation district from 2021 to 2024, the overall planting area of the optimised irrigation district is moderately reduced, forming an optimised pattern of ‘cotton pressure, grain expansion, economic increase and strong forest’; after optimization, the overall water shortage in the irrigation district is reduced by 1.4~11 million m³; the total amount of crop carbon absorption increased by 90.3~128.8 million kg; the net economic benefits increased by CNY 21.5~68.2 million. The research can provide decision support for the optimisation of the water and soil resource system in arid irrigation districts and has a scientific reference value for promoting the sustainable development and modernisation of agriculture in the inland irrigation districts of Northwest China. Full article

Cyberattacks increasingly generate systemic economic and geopolitical effects in an era of dense global interdependence. While prior research emphasises geopolitical rivalry, institutional capacity, and technological sophistication as determinants of national cyber risk, less attention has been given to structural vulnerabilities arising from countries’ positions within global economic networks. This study advances a relational theory of national cyber risk, arguing that structurally central countries provide greater systemic leverage to attackers because disruptions to highly accessible nodes can propagate widely across interconnected trade systems. Using annual bilateral trade data from 2010 to 2020, we construct directed, weighted global trade networks and derive centrality measures capturing accessibility, brokerage, and embeddedness. These indicators are linked to country-level cyber incident data to evaluate both the probability and intensity of cyberattacks. Logistic and negative binomial models with lagged network metrics show that countries occupying more accessible positions face significantly higher cyberattack risk. The findings demonstrate that national cyber vulnerability emerges from relational exposure within interconnected economic systems, underscoring the importance of systems-based cybersecurity risk assessment. Full article

With the continuous integration of a large amount of renewable energy sources such as wind and solar power into the power system, the economic and secure scheduling of the power grid, as a crucial carrier for electricity transmission, becomes of paramount importance. However, issues such as voltage fluctuations at grid nodes, low renewable energy consumption rates, and increased active power losses, caused by the widespread integration of high proportions of renewable energy, urgently need to be addressed. To effectively solve these problems, this paper proposes a multi-objective coordinated optimization scheduling method for the economy and security of source–grid–load–storage based on an effective scenario-screening approach. Firstly, an iterative self-organizing data analysis algorithm based on density noise application spatial clustering is designed to efficiently generate typical output scenarios for renewable energy sources such as wind and solar power. Meanwhile, to achieve low-carbon scheduling objectives, green certificate and carbon trading mechanisms are introduced. A multi-objective coordinated scheduling and trading model for the economy and security of large power grids, sources, loads, and storage is constructed with the goal of enhancing renewable energy consumption, and it is solved using the weight assignment method and an improved particle swarm optimization algorithm. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated based on an improved IEEE standard node test system. Full article

Background: The recognition of autism spectrum disorder (ASD) has been a challenge due to the heterogeneity in symptoms and complex variations in brain function. Resting-state functional magnetic resonance imaging (rs-fMRI) has become instrumental in studying these disorders by accessing underlying abnormal neural activity and connectivity. Recently, deep learning approaches have shifted the analysis of brain networks by capturing spatiotemporal information from fMRI sequences. Nonetheless, most existing studies are limited by relying on a single representational scale, typically restricting analysis to either voxel-level spatiotemporal patterns or static connectivity matrices. Additionally, the dynamic reconfiguration of functional coupling and its variations across different anatomical parcellations are often ignored, which obscures neurobiologically meaningful dynamics. Methods: In this regard, we propose a multi-atlas dynamic connectivity transformer fused with 4D spatiotemporal modeling for ASD recognition (MADCT-4D). Specifically, the framework comprises two complementary branches. The 4D spatiotemporal branch encodes raw rs-fMRI volumes to learn hierarchical representations of evolving neural activity, while the dynamic-connectivity branch models time-resolved functional connectivity sequences constructed from multiple atlases, enabling the network to capture dynamic reconfiguration at the connectome level under different parcellation granularities. Moreover, we perform late fusion by combining the branch-specific decision scores with a learnable gate, allowing the model to adaptively weight voxel-level dynamics and multi-atlas connectivity evidence for each subject. Results: Extensive experiments on the publicly available ABIDE dataset demonstrate that the proposed method achieves 90.2% accuracy for ASD recognition, outperforming multiple competitive baselines. Conclusions: The proposed framework yields interpretable biomarkers based on learned dynamic connectivity patterns that are consistent with altered functional coupling in ASD. Full article

COVID-19 still poses a global public health challenge, exerting pressure on radiology services. Chest X-ray (CXR) imaging is widely used for respiratory assessment due to its accessibility and cost-effectiveness. However, its interpretation is often challenging because of subtle radiographic features and inter-observer variability. Although recent deep learning (DL) approaches have shown strong performance in automated CXR classification, their black-box nature limits interpretability. This study proposes an explainable deep learning framework for COVID-19 detection from chest X-ray images. The framework incorporates anatomically guided preprocessing, including lung-region isolation, contrast-limited adaptive histogram equalization (CLAHE), bone suppression, and feature enhancement. A novel four-channel input representation was constructed by combining lung-isolated soft-tissue images with frequency-domain opacity maps, vessel enhancement maps, and texture-based features. Classification was performed using a modified Xception-based convolutional neural network, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to provide visual explanations and enhance interpretability. The framework was evaluated on the publicly available COVID-19 Radiography Database, achieving an accuracy of 95.3%, an AUC of 0.983, and a Matthews Correlation Coefficient of approximately 0.83. Threshold optimisation improved sensitivity, reducing missed COVID-19 cases while maintaining high overall performance. Explainability analysis showed that model attention was primarily focused on clinically relevant lung regions. Full article

Rapid changes in Arctic summer sea ice exert substantial influences on the polar climate system, maritime navigation, and resource exploitation, while subseasonal-to-seasonal (S2S) prediction of sea ice state remains highly uncertain. Using daily observations and reanalysis data of sea ice concentration (SIC) and thickness (SIT) from 1979 to 2023, together with concurrent atmospheric and oceanic fields, this study develops a multivariate linear Markov model to perform S2S predictions of Arctic summer sea ice. Sensitivity experiments with different variable combinations, weighting strategies, and modal truncation schemes are conducted, and predictive skill is systematically evaluated against persistence and climatological baselines. Results indicate that the model exhibits stable forecast skill without pronounced error accumulation at extended lead times. SIC predictability is primarily governed by its intrinsic spatiotemporal persistence and is significantly modulated by oceanic thermodynamic forcing, particularly sea surface temperature and surface net energy flux, highlighting a pronounced oceanic memory effect. In contrast, local atmospheric dynamic variables provide limited incremental skill. For SIT, predictability is dominated by its own historical state, with SIC contributing marginal short-term improvement and air–sea coupling exerting weak influence. Overall, the proposed framework effectively extracts dominant predictable signals with clear physical interpretability, providing a computationally efficient statistical approach for S2S prediction of Arctic summer sea ice. Full article

Background/Objective: Women with a history of gestational diabetes mellitus (GDM) are at increased risk of type 2 diabetes mellitus (T2DM), dysglycemia, and dyslipidemia. However, the role of postpartum weight change in long-term metabolic outcomes remains unclear. Here, we determined the long-term incidence of dysglycemia and dyslipidemia after GDM and evaluated whether postpartum changes in body mass index (BMI) independently predicted these outcomes. Methods: This single-center prospective cohort study included 205 Japanese women diagnosed with GDM. All participants underwent a 75 g oral glucose tolerance test at 6–12 weeks postpartum. The incidence of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), T2DM, and dyslipidemia was evaluated over a median follow-up of 3.6 years. Cumulative incidence was estimated using the Kaplan–Meier method, and Cox proportional hazards models identified independent risk factors, particularly postpartum BMI change. Results: During follow-up, 42.4%, 6.3%, and 35.6% of women developed IFG or IGT (prediabetes), T2DM, and dyslipidemia, respectively. The estimated cumulative incidence rates at 6 years postpartum were 57.1% and 50% for IFG/IGT and dyslipidemia, respectively, whereas the 5-year incidence of T2DM was 10.3%. Postpartum BMI increase was independently associated with new-onset dysglycemia. No independent predictor of T2DM progression was identified. Dyslipidemia was independently associated with higher pre-pregnancy BMI and multiparity, whereas postpartum BMI change was not independently associated after multivariable adjustment. Conclusions: Postpartum BMI change was independently associated with dysglycemia in women with a history of GDM. These findings suggest that postpartum weight change may help identify women at higher risk of subsequent metabolic abnormalities, particularly dysglycemia, in this high-risk population, although causal relationships cannot be inferred from this observational study. Full article

This study predicts expressway asphalt pavement maintenance decisions using machine learning to overcome the information loss inherent in traditional composite indices like PQI and PCI. Using ten years of inspection data from the G3012 Expressway in Xinjiang, an interpretable Random Forest (RF) model was developed. The methodology integrates permutation-based feature selection, three imbalance mitigation strategies (Balanced Weighting, SMOTE, and Cost-Sensitive Learning), and a rigorous time-aware validation framework. Results indicate that raw distress features—specifically strip repairs, block cracking, transverse and longitudinal cracking—are the most influential predictors, significantly outperforming aggregated indices. The optimized model, using Balanced Weighting and mean imputation, achieved an accuracy of 0.826 and ROC-AUC of 0.853 under strict temporal validation, effectively identifying the minority “repair” class. This research demonstrates that leveraging raw distress data through an interpretable ensemble framework provides a robust, data-driven alternative to threshold-based planning, supporting the transition from reactive to preventive maintenance in complex infrastructure management. Full article

Accurate prediction of user mobility patterns is essential for location-based services and intelligent transportation systems. In this study, we propose a sequence modeling framework that utilizes Gated Recurrent Units (GRUs) to predict future geographic coordinates (latitude and longitude) from user trajectory data stored in CSV format. By constructing input sequences of past GPS positions and training the GRU network to estimate the next position, we achieve robust trajectory forecasting performance. Experimental evaluation demonstrates that the GRU-based approach consistently yields higher prediction accuracy than the conventional Long Short-Term Memory (LSTM) model under the same conditions. The results highlight the effectiveness of GRUs in handling sequential spatial data with reduced computational complexity, suggesting their suitability for real-time and resource-constrained location prediction tasks. The models are evaluated on real-world GPS trajectory data consisting of over 800 sequential location samples, using distance-based metrics including MAE, RMSE, Average Displacement Error (ADE), and Final Displacement Error (FDE) to assess prediction accuracy in meters. This study proposes an enhanced GRU model, representing a key innovation and the main contribution of our work. The primary contribution of this study lies not merely in comparing GRU and LSTM models, but in proposing an enhanced GRU architecture that integrates motion features and an attention mechanism for improved GPS trajectory prediction. Unlike prior studies focusing solely on model comparison, our approach demonstrates methodological advancements through attention-based feature weighting and validated performance in real-world autonomous vehicle experiments. Full article

Seasonal influenza A evolves quickly through mutations in haemagglutinin (HA) and neuraminidase (NA), which can reduce vaccine match and lower protection. Many sequence-only models do not link codon-level mutations to three-dimensional (3D) protein context and long-term evolutionary signals within one scoring framework. This study presents TRIAD-Influenza (TRIAD: Token–Residue–Integrated Architecture for Drift), a multi-view transformer that combines (i) codon- and residue-level sequence representations, (ii) structure-derived residue interaction features from predicted HA/NA models, and (iii) an embedding-space phylogeny that captures cluster and drift context. The pipeline curates more than $3\times {10}^5$ paired HA/NA coding sequences from the NCBI Virus resource (2010–2024) using strict quality control and codon-aware alignment and predicts 3D structures for nearly all unique HA and NA proteins to build contact graphs and surface/stability descriptors. TRIAD-Influenza outputs a continuous, structure-aware risk score for each HA/NA pair and produces interpretable mutation hotspot maps using gradient saliency and a contact-weighted mutation risk index (CMRI). On rolling-origin temporal cross-validation and for a temporally held-out internal test window with strong class imbalance (∼3.4% high-risk), the model shows strong ranking performance (AUROC $\approx 0.89$; AUPRC $\approx 0.44$; Brier score $=0.069$) while operating at surveillance speed (median latency $\approx 1.6$ ms per HA/NA pair). External validation on independent GISAID/Nextstrain cohorts (2023–2024; 5000 isolates) preserves discrimination (AUROC $\approx 0.85$–$0.86$). Predicted risk scores correlate with experimental haemagglutination inhibition (HI) antigenic distances (Spearman $\rho$ up to ≈0.82 at the virus-aggregated level), and CMRI hotspots enrich known epitope and deep mutational scanning escape residues (odds ratios $\approx 2.7$–$3.6$). Overall, token–residue–phylogeny coupling enables rapid, structure-aware prioritisation of emerging influenza A HA/NA sequences and delivers compact hotspot maps for expert review and targeted experiments. Full article

Extreme precipitation constitutes one of the most devastating climatic resulting from global climate change. Jilin Province, a significant commodities grain base in China by a temperate monsoon climate, is particularly susceptible to flood disasters caused by extreme precipitation, usually occurring from late July to early August. The 2010 flood impacted moreover 5.12 million individuals and resulted in direct economic damages amounting to 45.1 billion CNY. However, research on the spatiotemporal characteristics and future trends of extreme precipitation in Jilin Province is still quite inadequate. This study examined the spatiotemporal distribution and future forecasts of extreme precipitation utilizing daily meteorological data from 31 stations (1960–2019) and three CMIP6 models (CanESM5, MPI-ESM1-2-HR, FGOALS-g3) under SSP2-4.5 and SSP5-8.5 scenarios. Eleven extreme precipitation indices, as specified by the WMO, were analyzed utilizing linear regression, the Mann–Kendall test, wavelet analysis, and inverse distance weighting interpolation. The findings indicated that from 1960 to 2019, extreme precipitation demonstrated traits of “increased frequency and total amount, decreased intensity”, with a significant decline in CDD (−2.184 d·(10a)⁻¹, p < 0.001), a notable increase in PRCPTOT (1.493 mm·(10a)⁻¹, p < 0.05), and a significant reduction in SD II (−0.016 mm·d⁻¹·(10a)⁻¹, p < 0.01). The majority of indicators had a predominant cycle of 30 to 50 years. A significant northwest-to-southeast gradient characterized most indicators, with PRCPTOT varying from 327.5 mm in Baicheng to 824.3 mm in Tonghua. Future projections (2025–2100) suggested scenario-dependent intensification. Under SSP5-8.5, all three models forecast substantial increases in precipitation amount indices (PRCPTOT: 2.071–2.457 mm·(10a)⁻¹) and SD II (0.010–0.013 mm·d⁻¹·(10a)⁻¹), reversing the past downward trend in intensity. The anticipated alterations exhibited a northwest-to-southeast gradient, with PRCPTOT increases above 230 mm in the central and southeastern regions. These findings offer a scientific basis for flood management and climate change adaptation in Jilin Province and analogous areas. Full article

Efficiently converting natural capital into economic assets is a critical challenge in urban–rural transformation, yet the interactive mechanism between institutional land reform and physical spatial restructuring remains underexplored. While traditional frameworks emphasize institutional design, this study identifies a “Spatial-First” mechanism where physical reconfiguration serves as a spatial mediator to catalyze property rights breakthroughs. Using an entropy-weighted coupling coordination model, we analyzed policy dynamics in Nanhai District, China, a unique “dual-pilot” zone, from 2020 to 2024. The results indicate a nonlinear leap in the Coupling Coordination Degree (D) from 0.100 to 0.978. We interpret this surge as a policy-driven shock during the intensive pilot phase, where substantive spatial integration (0.719) effectively bypassed high transaction costs inherent in collective tenure, outpacing institutional progress (0.281). However, an Ecological Lag was observed; the disproportionately low weighting of the ecological carrier index (7.09%) suggests that current gains are primarily driven by green industrialization rather than the expansion of absolute ecological stock. This study concludes that while spatial tools can effectively unlock natural capital value in the short term, long-term sustainability necessitates a strategic shift from administrative-led economic efficiency to market-based ecological restoration. Full article

This study introduces an innovative hybrid framework for the sustainable design of meditation centers by integrating entropy-weighted fuzzy comprehensive evaluation (FCE) with principles of cultural sustainability. While conventional sustainable design assessment methods predominantly emphasize technical environmental performance, they remain insufficient for meditation center design, where contemplative experience, cultural continuity, and spatial meaning are equally essential. In response to this gap, this research reinterprets the Mogao Caves as an exemplar of “deep sustainability,” where environmental, social, and cultural dimensions are integrated in a mutually reinforcing manner. Through a systematic analysis of the spatial and artifactual heritage of the Mogao Caves, a robust and quantifiable evaluation system consisting of 27 indicators was developed, spanning architectural design, spatial organization, seating iconography depicted in the murals, and decorative elements. The novelty of this study lies in establishing a heritage-informed and data-driven framework that translates historical spatial wisdom into a contemporary sustainable design assessment model. By applying the entropy-weighting method, the study identifies Functional Diversity (0.087) and Symbolic Representation (0.071) as indicators with comparatively greater discriminative contribution within the present sample, highlighting the importance of programmatic adaptability and cultural expression in meditation center design. The FCE model was applied to 156 valid questionnaire responses, enabling a multi-criteria evaluation of 11 meditation centers worldwide, among which the Fujian Longyan Dahe Meditation Center achieved the highest score (73.032). The findings indicate that the proposed framework offers a more balanced basis for evaluating meditation center design by integrating functional performance with cultural continuity and spatial meaning. Full article