Search Results (27,181)

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

Accurate stress measurement is essential to evaluating structural integrity and plays a pivotal role in the health monitoring and predicting the service life of steel infrastructures. This study proposes a deep learning approach for stress prediction based on longitudinal critically refracted (LCR) ultrasonic waves. The model integrates gated recurrent units (GRU), attention mechanisms, and one-dimensional convolutional neural networks (1D-CNN), enabling direct stress prediction from raw ultrasonic signals without the need for manual feature extraction or explicit physical modeling. To validate the approach, LCR signals were acquired using a custom-built piezoelectric ultrasonic system from 20# steel specimens subjected to uniaxial stresses ranging from 0 to 200 MPa. A dataset comprising 4200 samples was augmented to enhance training efficiency. The proposed model achieved a mean absolute error of 1.94 MPa. Generalization tests demonstrated high accuracy across diverse stress levels, with average errors below 3 MPa, highlighting the model’s robustness. This research presents an accurate, intelligent, and calibration-free ultrasonic method for stress evaluation, providing practical support for stress evaluation in steel structures under actual operating conditions. Full article

Alpha-mangostin (AM), a xanthone from Garcinia mangostana, has shown promising nephroprotective properties, but its mechanisms in acute kidney injury (AKI) remain incompletely defined. In this study, we applied an integrative network pharmacology pipeline combined with molecular docking to clarify AM’s multi-target mechanisms in AKI. We identified 128 predicted AM targets and intersected them with AKI-related genes, yielding 122 shared targets. Protein–protein interaction analysis identified ten hub genes—TNF, AKT1, IL6, SRC, CTNNB1, HSP90AA1, NFKB1, HIF1A, PPARG, and PTGS2—implicating inflammatory, hypoxia, and cell-survival pathways. KEGG enrichment highlighted HIF-1 signaling, PI3K–Akt signaling, chemokine signaling, AGE–RAGE signaling, and pathways related to cellular senescence and oxidative stress, while GO terms emphasized responses to chemical/oxygen-containing compounds, kinase activity, signal transduction, and apoptosis. Molecular docking against the ten hub proteins showed favorable binding energies across multiple targets. The strongest predicted affinities were observed for PTGS2 (−11.13 kcal/mol), TNF (−9.74 kcal/mol), and AKT1 (−9.48 kcal/mol). Docking positioned AM within the COX-2 catalytic pocket, engaging key catalytic and hydrophobic residues similar to known inhibitors. MD simulation interaction analysis confirmed that AM maintained stable contacts with key human PTGS2 residues, characterized by dominant hydrogen bonds and water-bridge interactions with SER353, TYR355, ARG513, and SER530, along with consistent hydrophobic contacts, and persistent interactions sustained throughout the 200 ns trajectory. Collectively, these results suggest that AM modulates interconnected inflammatory, hypoxic, and survival pathways relevant to AKI, acting as a multi-target ligand with notable interaction involving COX-2, TNF, and AKT1. Further experimental validation and formulation strategies to improve bioavailability are recommended for the advancement of AM toward therapeutic evaluation in AKI. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

Background/Objectives: Ulcerative colitis (UC) involves inflammatory response, oxidative stress, changes in metabolites, and the gut microbiota. Jingangteng capsule (JGTC) has been utilized clinically for the treatment of inflammatory diseases for many years. However, the efficacy of JGTC in ameliorating UC remains unclear, and the underlying mechanisms have not yet been elucidated. This study aims to investigate the effect and mechanism of JGTC on UC. Methods: The chemical compositions of JGTC were examined using ultra-high-performance liquid chromatography with quadrupole time-of-fight mass spectrometry. The anti-UC effect of JGTC was evaluated by assessing the disease activity index (DAI), colon length, intestinal barrier recovery, and inflammatory factors in a dextran sulfate sodium (DSS)-induced colitis model. Mechanisms were investigated through fecal 16S rDNA sequencing, metabolomics analysis, enzyme-linked immunosorbent assay (ELISA), Western blotting, and network pharmacology analysis. Results: JGTC significantly reduced the DAI scores in UC mice, increased their body weight and colon length (p < 0.001), repairing damaged intestinal tissue. It decreased the levels of inflammatory cytokines TNF-α, IL-6, IL-1β, and LPS (p < 0.01, p < 0.001), alleviating intestinal inflammation. It also raised the expression of tight junction proteins ZO-1, Claudin-1, and Occludin (p < 0.05, p < 0.001), thereby enhancing intestinal barrier function. Fecal metabolomic analysis revealed that the favorable alterations in amino acid and lipid metabolites were more pronounced. Heat maps showed strong correlations between pharmacological indicators and gut microbiota, as well as between the main differential metabolites and gut microbial communities. UPLC-QTOF-MS detection yielded 33 components of JGTC, and network pharmacology analysis based on these components predicted pathways of action of JGTC in UC. Functional pathways closely associated with significantly differential metabolites and metabolic pathways were also investigated. The PI3K-AKT-mTOR pathway was one of them, which is consistent with the conclusions drawn from network pharmacology. JGTC significantly modulated key factors in this pathway, inhibiting the expression of PI3K, Akt, PDK1, and mTOR, while augmenting the expression of PTEN (p < 0.05, p < 0.01, p < 0.001). It also mitigated the levels of related oxidative stress factors MDA, MPO, and D-LA, and raised SOD levels (p < 0.01, p < 0.001). Conclusions: JGTC improved the excessive inflammatory response in UC by regulating intestinal flora and metabolic disorders, affecting the PI3K-AKT-mTOR signaling pathway, restoring intestinal tissue damage and intestinal barrier, and inhibiting inflammatory and oxidative stress factors. Full article

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

Brassica rapa is widely cultivated in alpine and cold mountainous regions due to its strong cold tolerance. However, lodging severely limits its yield and quality. This study integrated agronomic traits, stem microstructure, and transcriptomic profiles to explore the mechanism of lodging resistance by comparing a resistant cultivar (Ganyou 3064, GY) and a susceptible cultivar (Tianyou 2022, TY) across four developmental stages (full flowering, final flowering, podding, and maturity). At the four growth stages, the stem breaking strength of GY was 1.71, 1.93, 1.88, and 1.88 times that of TY, respectively. Compared with TY, the gravity center height of GY was decreased by 25.04%, 16.6%, 11.18%, and 8.98% at these four stages, respectively. Similarly, the lodging index of GY was decreased by 65.94%, 55.08%, 56.06%, and 55.63% compared with TY, respectively. Biochemical and anatomical analyses revealed that compared with TY, the lignin content of GY increased by 1.93%, 2.7%, 3.05%, and 3.42% at the four stages, while the cellulose content increased by 92.75%, 45.32%, 44.4%, and 49.92%, respectively. Meanwhile, the epidermal thickness, cortical thickness, vascular bundle length, vascular bundle area, and vascular bundle density of GY were also significantly increased. Transcriptomic and KEGG pathway analyses revealed a predictive defense mechanism of GY. At the final flowering stage, GY showed pre-activation of hormone and MAPK signal transduction, as well as phenylpropanoid biosynthesis; it shifted to energy supply and sustained cell wall reinforcement at the podding stage. In addition, upregulated genes in phenylpropanoid biosynthesis (such as PAL3, CCoAOMT, and CAD9) indicated that enhanced stem lignification is a key molecular determinant of lodging resistance. In summary, GY enhances its lodging resistance through coordinated morphological and transcriptional regulation. This study is the first to integrate the lodging characteristics of Brassica rapa, offering valuable candidate genes and phenotypic markers for molecular breeding. Full article

Cyclodextrins (CDs) have gained increasing attention as versatile platforms for enhancing drug delivery to the central nervous system, particularly in overcoming the restrictive properties of the blood–brain barrier (BBB). Owing to their unique cyclic oligosaccharide structure, CDs are capable of forming inclusion complexes with a wide range of therapeutic agents, thereby improving their solubility, stability, and bioavailability. In addition to their role as excipients, growing evidence indicates that CDs can actively modulate biological processes, including membrane fluidity and cholesterol homeostasis, which are critical factors in neurological disorders. This review explores the application of CDs in facilitating drug transport across the BBB through multiple mechanisms, including carrier-mediated transport, receptor-mediated transcytosis, and nanoparticle-based delivery systems. Special emphasis is placed on their use in the treatment of neurodegenerative and neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, Niemann–Pick type C disease, and other central nervous system disorders. In these contexts, CD-based formulations have demonstrated the ability to enhance brain targeting, reduce pathological protein aggregation, and improve therapeutic outcomes in preclinical models. This review uniquely integrates cyclodextrin’s physicochemical properties with specific blood–brain barrier transport mechanisms, proposing a structure–transport–therapy framework that enables a more predictive understanding of brain-targeted drug delivery. Full article

Stanford type A aortic dissection (TAAD) is a life-threatening cardiovascular condition associated with high mortality. Ferroptosis has been implicated in TAAD pathogenesis, but comprehensive analyses and experimental validation of ferroptosis-related driver genes (FRDGs) remain limited. This study systematically investigated FRDGs in TAAD using bioinformatics and experimental approaches. Differentially expressed ferroptosis-related driver genes (DEFRDGs) were identified by integrating the GSE153434 dataset with the FerrDb database. Functional enrichment analysis was subsequently performed, followed by the construction of a protein–protein interaction (PPI) network, assessment of immune cell infiltration, and prediction of potential miRNA interactions. Candidate hub genes were then validated using an independent cohort (GSE52093) and clinical tissue samples, with their diagnostic value evaluated via receiver operating characteristic (ROC) curve analysis and their protein expression confirmed by immunohistochemistry. We identified 25 DEFRDGs (17 upregulated, 8 downregulated) enriched in oxidative stress, iron binding, and ferroptosis/HIF-1 signaling pathways. Six hub genes (HIF1A, IL6, TIMP1, SAT1, HMOX1, LPCAT3) were significantly upregulated in validation cohorts, five genes (HIF1A, TIMP1, SAT1, HMOX1, LPCAT3) achieved an area under the curve (AUC) of 1.000, while IL6 also exhibited high diagnostic accuracy (AUC = 0.914). Fibroblast infiltration was elevated in TAAD tissues. Further miRNA interaction prediction revealed the potential involvement of miRNAs, such as miR-138-5p, miR-18b-5p, miR-199a-5p, miR-185-5p, miR-506-3p and miR-4644. Immunohistochemistry confirmed increased protein expression of HIF1A, SAT1, and LPCAT3. These three genes emerge as key ferroptosis-related drivers in TAAD. Their consistent upregulation and strong diagnostic performance support ferroptosis as a potential therapeutic target and provide a basis for mechanism-focused interventions. Full article

Geohazards such as landslides, earthquakes, debris flows, and floods are governed by complex interactions among geological, hydrological, and human processes. Traditional data-driven models have improved hazard prediction but often lack interpretability and adaptability. This review examines the evolution of knowledge-guided approaches in geohazard research, highlighting how knowledge representation and artificial intelligence have progressively converged to enhance understanding, reasoning, and model transparency. A bibliometric analysis of 1410 publications indexed in the Web of Science reveals an evolution from early ontology-based knowledge engineering for expert reasoning to knowledge graphs (KG), frameworks enabling multi-source data integration and relational inference, and more recently, to large language model (LLM), augmented systems for automated knowledge extraction and cognitive geoscience. This review synthesizes advances in knowledge representation, knowledge graphs, and LLM-based reasoning, demonstrating how hybrid models that embed physical laws and expert knowledge can improve the interpretability and generalization of machine learning. These developments enable new forms of knowledge-driven geohazard intelligence and support applications in hazard monitoring, early warning, and risk communication. There are challenges we still face, including semantic fragmentation, limited causal reasoning, and sparse data for extreme events. Future directions require unified knowledge–data–mechanism architectures, causality-aware modeling, and interoperable standards to advance trustworthy and explainable geohazard intelligence. Full article

Retinal detachments following blunt ocular trauma are challenging to predict due to the complex and transient biomechanical responses of the globe. This study combines an in vitro weight-drop experiment and finite element analysis (FEA) to evaluate the mechanical pathways leading to traumatic retinal detachment and to predict the spatial likelihood of injury. In the in vitro model, a cylindrical weight was impacted onto freshly enucleated mouse eyes (16 weeks old) supported on a rigid metal plate. Following impact, the eyes were sectioned and stained using hematoxylin and eosin (H&E) for histological assessment. A finite element model of a mouse eye, including the cornea, sclera, lens, zonule, vitreous body, aqueous humor, and retina, was reconstructed from the histological section and used to simulate the whole sequence of compression and rebound following the blunt impact. The simulation demonstrated that the lens retained a high momentum. It generated an alternating compressive (up to −6.57 × 10⁻³ MPa) and tensile (up to 1.62 × 10⁻³ MPa) radial stress at the posterior pole and sustained compressive stress at the peripheral region (up to −3.12 × 10⁻³ MPa) and tensile-compressive stress variation at the equatorial region of the retina. In addition, the regions experiencing tensile stress overlapped with the region exhibiting retinal detachment in the in vitro experiment. These findings highlight the spatiotemporal mapping of biomechanical stress to predict traumatic retinal detachment following blunt impact and provide an understanding of early biomechanical response following ocular trauma. Full article

Non-cohesive earth dams are widely distributed in natural and semi-engineering scenarios, and overtopping-induced breaches are their most catastrophic failure mode. Accurate prediction of the overtopping failure process and breach evolution is critical for risk assessment, emergency management, and dam design optimization. In this study, an improved 3D numerical method is developed to simulate the coupled hydrodynamic–erosion–breach evolution processes of non-cohesive earth dams. The model based on the finite volume method integrates three core modules: a hydrodynamic module based on the Reynolds-Averaged Navier–Stokes equations with the Volume of Fluid method for free surface tracking, a dam material erosion module considering particle entrainment and transport mechanisms of non-cohesive soils, and a breach development module coupling erosion and gravitational collapse. To validate the model, two levels of verification are conducted: first, a classic benchmark dam break case is employed to confirm the feasibility of the hydrodynamic and breach evolution algorithms; second, published flume experimental data of non-cohesive earth dam overtopping failures are adopted to evaluate the model accuracy in predicting breach hydrographs and spatiotemporal evolution of breach geometry. The results demonstrate that the proposed model accurately reproduces the key characteristics of overtopping failure with high fidelity. The predicted breach flow rates and flow depths are in excellent agreement with experimental observations, with relative errors less than 5% for both peak discharge and time to peak. Consequently, this study provides a reliable numerical tool for detailed simulation of non-cohesive earth dam breaches and offers scientific support for emergency management. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

University students face sustained academic, employment, and social pressures. Campus pathways, as central linear spaces in daily routines, hold significant potential to influence well-being, yet existing research has largely overlooked how their environmental characteristics affect multidimensional health. Using Shenyang Jianzhu University as a case, this study identified frequently used pathways through GPS tracking and surveys, and quantitatively analyzed how environmental features affect walking willingness, emotional experience, and social interaction. By comparing high- and low-benefit groups, the key environmental thresholds were identified to inform health-oriented design. Beyond verifying some established understandings (e.g., daily commuting paths prioritize efficiency, while leisure paths focus on experiential quality), the study further revealed several mechanisms through quantitative analysis. For example, “road accessibility”—an indicator of convenience—showed a significant negative correlation with emotional experience. The study established quantifiable prediction models and identified design thresholds for campus pathways. A high aesthetic greenery was key to achieving high overall benefits, while low building enclosure and vegetation complexity promoted social interaction. This achievement transforms health-oriented campus pathway design from qualitative principles into a measurable and optimizable scientific practice, thus providing an empirical basis and practical guidance for the construction of health-supportive campus environments. Full article

Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling and estimation, particularly in the presence of uncertainty in structural systems. Flexible structures in aerospace and robotics require advanced control to mitigate vibrations under model uncertainty. This paper proposes a data-driven strategy leveraging a Gaussian Process (GP) integrated within a Nonlinear Model Predictive Control (NMPC) framework. The core innovation lies in using a Gaussian Process Nonlinear AutoRegressive model with eXogenous input (GP-NARX) as a probabilistic predictor to capture structural dynamics while quantifying uncertainty. The operational mechanism involves a tight coupling where the GP provides multi-step-ahead forecasts that the NMPC optimizer uses to minimize a cost function subject to constraints. Validated through simulations on Duffing oscillators, linear oscillators, and cantilever beams, the GP-NMPC achieved an 88.2% reduction in displacement amplitude compared to uncontrolled systems. Quantitative analysis shows high predictive accuracy, with a Root Mean Square Error (RMSE) of 0.0031 and a Standardized Mean-Squared Error (SMSE) below 0.05. Furthermore, Mean Standardized Log Loss (MSLL) evaluations confirm the reliability of the predictive uncertainty within the control loop. These results demonstrate strong performance in both regulation and tracking tasks, justifying this Bayesian-predictive coupling as a powerful approach for high-performance structural vibration control and a potential foundation for bio-inspired mechanical design. Full article