Search Results (3,427)

Conventional building materials predominantly rely on non-renewable resources, while the exploration of high-performance and renewable alternatives exhibits the potential for sustainability. Bamboo offers excellent renewability, mechanical properties, and eco-friendliness; however, the susceptibility to cracking impedes its application, especially for long-term structural requirements. The cracking primarily occurs when tangential tensile stresses on inner/outer surfaces exceed the transverse tensile strength of bamboo. This study addresses the issue of transverse cracks in Moso bamboo (Phyllostachys edulis) by proposing and validating a tangential stress prediction model based on the theoretical model of transverse stress in standard circular rings. A correction factor K was determined through finite element analysis to account for the non-standard circular ring shape of bamboo and the presence of the bamboo culm base. Using Moso bamboo samples aged 1 to 7 years, experiments were conducted under varying temperatures (35 °C, 45 °C, and 55 °C) and humidity levels (30%, 50%, and 60%) to measure the initiation and propagation of cracks under tangential stress. Based on experimental data, a functional relationship was established between the internal and external surface strains of bamboo and factors such as bamboo age, geometric dimensions of the bamboo ring, temperature, and humidity. This model can calculate the tangential stress of bamboo based on bamboo age, geometric dimensions of the bamboo ring, temperature, humidity, tangential/radial elastic modulus ratio, and water loss time. It provides a theoretical foundation and engineering reference for predicting and preventing cracking in Moso bamboo. Full article

Graduate education has entered the era of big data, and systematic analysis of dissertation evaluations has become crucial for quality monitoring. However, the complexity and subjectivity inherent in peer-review texts pose significant challenges for automated analysis. While natural language processing (NLP) offers potential solutions, most existing methods fail to adequately capture nuanced disciplinary criteria or provide interpretable inferences for educators. Inspired by soft-sensor, this study employs a BERT-based model enhanced with additional attention mechanisms to quantify latent evaluation dimensions from dissertation reviews. The framework integrates Shapley Additive exPlanations (SHAP) to ensure the interpretability of model predictions, combining deep semantic modeling with SHAP to quantify characteristic importance in academic evaluation. The experimental results demonstrate that the implemented model outperforms baseline methods in accuracy, precision, recall, and F1-score. Furthermore, its interpretability mechanism reveals key evaluation dimensions experts prioritize during the paper assessment. This analytical framework establishes an interpretable soft-sensor paradigm that bridges NLP with substantive review principles, providing actionable insights for enhancing dissertation improvement strategies. Full article

Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can lead to catastrophic yield loss, challenging traditional physics-based control methods. In response, the industry has increasingly adopted regression analysis and predictive modeling as essential analytical frameworks. Classical regression, long used to support design of experiments (DOE), process optimization, and yield analysis, has evolved to enable multivariate modeling, virtual metrology, and fault detection. Predictive modeling extends these capabilities through machine learning and AI, leveraging massive sensor and metrology data streams for real-time process monitoring, yield forecasting, and predictive maintenance. These data-driven tools are now tightly integrated into advanced process control (APC), digital twins, and automated decision-making systems, transforming fabs into agile, intelligent manufacturing environments. This review synthesizes foundational and emerging methods, industry applications, and case studies, emphasizing their role in advancing Industry 4.0 initiatives. Future directions include hybrid physics–ML models, explainable AI, and autonomous manufacturing. Together, regression and predictive modeling provide semiconductor fabs with a robust ecosystem for optimizing performance, minimizing costs, and accelerating innovation in an increasingly competitive, high-stakes industry. Full article

With the advancement of battery technology and the promotion of the “dual carbon” policy, electric vehicles (EVs) have been widely used in industrial, commercial, and civil fields, and the charging infrastructure of highway service areas across the country has also shown a rapid development trend. However, the charging load of electric vehicles in highway scenarios exhibits strong randomness and uncertainty. It is affected by multiple factors such as traffic flow, state of charge (SOC), and user charging behavior, and it is difficult to accurately model it through traditional mathematical models. This paper proposes a hierarchical Markov chain Monte Carlo (HMMC) simulation method to construct a charging load prediction model with spatiotemporal coupling characteristics. The model hierarchically models features such as traffic flow, SOC, and charging behavior through a hierarchical structure to reduce interference between dimensions; by constructing a Markov chain that converges to the target distribution and an inter-layer transfer mechanism, the load change process is deduced layer by layer, thereby achieving a more accurate charging load prediction. Comparative experiments with mainstream methods such as ARIMA, BP neural networks, random forests, and LSTM show that the HMMC model has higher prediction accuracy in highway scenarios, significantly reduces prediction errors, and improves model stability and interpretability. Full article

The global low-carbon energy transition relies on the orderly integration of a high proportion renewable energy. As an important carrier of demand-side energy systems, parks are responsible for local balancing and the accommodation of distributed renewable energy. However, the energy systems of parks exhibit the integrated characteristics of heterogeneous energy sources, including electricity, heat, and gas. It also encompasses the entire source–network–load–storage process, which renders it huge and complex. For this reason, as a systematic review article, this paper aims to summarize the overall application of artificial intelligence technology in China’s park-level comprehensive energy system. First, the current status of technology applications in the corresponding scenarios is analyzed based on three dimensions: prediction, scheduling, and security. Subsequently, key challenges in applying AI technologies to these scenarios are identified, including multi-temporal and spatial synergy issues in source–load forecasting, multi-agent equilibrium problems in dispatch optimization, and cross-modal matching challenges in security operation and maintenance (O&M). Thereafter, the feasible directions to solve these bottlenecks will be discussed comprehensively in light of the latest research advancements. Finally, we propose a phased roadmap for technological development and to identify the key gaps in this research field, such as the lack of publicly available benchmark datasets, data exchange standards, and cross-campus validation frameworks. This article aims to provide a systematic theoretical reference and development framework for the in-depth empowerment of AI technology in the integrated energy system of industrial parks. Full article

This study examines Chinese university students’ adoption of generative artificial intelligence (GenAI) tools by integrating the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and AI literacy dimensions into a hybrid framework. Survey data from 1006 students across various majors and regions are analyzed using partial least squares structural equation modeling. Notably, AI literacy (i.e., students’ AI ethics, evaluation, and awareness) positively affect their attitudes, subjective norms, and perceived behavioral control, although the influence patterns vary according to the literacy dimension. Perceived privacy risks reduce AI trust, which mediates adoption behavior. Overall, core TPB pathways are validated, with behavioral intentions significantly predicting students’ actual use. Gender and regional differences moderate the key relationships. The results of this study suggest that enhancing students’ ethical and evaluative competencies, building user trust, and addressing privacy concerns could promote generative AI integration in education. Full article

To overcome the limitations imposed by the anisotropy and heterogeneity of natural loess, this study establishes a novel quantitative macro–micro correlation framework for investigating the deformation mechanisms of artificial structural loess (ASL). ASL samples were prepared by mixing remolded loess with cement (0–4%) and NaCl (0–16%), followed by static compaction (95% degree) and 28-day curing (20 ± 2 °C, >90% RH) to replicate the structural properties of natural loess under controlled conditions. An integrated experimental methodology was employed, incorporating consolidation/collapsibility tests, particle size analysis, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). A three-dimensional nonlinear model was proposed. The findings show that intergranular cementation, particle size distribution, and pore architecture are the main factors influencing loess’s compressibility and collapsibility. A critical transition from medium to low compressibility was observed at cement content ≥1% and moisture content ≤16%. A strong correlation (Pearson |r| > 0.96) was identified between the mesopore volume ratio and the collapsibility coefficient. The innovation of this study lies in the establishment of a three-dimensional nonlinear model that quantitatively correlates key microstructural parameters (fractal dimension value (D), clay mineral ratio (C), and large and medium porosity (n)) with macroscopic deformation indicators (porosity ratio (e) and collapsibility coefficient (δ_s)). The measured data and the model’s output agree quite well, with a determination coefficient (R²) of 0.893 for porosity and 0.746 for collapsibility, verifying the reliability of the model. This study provides a novel quantitative tool for loess deformation prediction, offering significant value for engineering settlement assessment in controlled cementation and moisture conditions, though its application to natural loess requires further validation. Full article

Digital platforms for intangible cultural heritage (ICH) function as vibrant electronic marketplaces, yet they grapple with content overload, high search costs, and under-leveraged social networks of heritage custodians. To address these electronic-commerce challenges, we present GCHS, a custodian-aware, graph-based deep learning model that enhances ICH recommendation by uniting three critical signals: custodians’ social relationships, user interest profiles, and content metadata. Leveraging an attention mechanism, GCHS dynamically prioritizes influential custodians and resharing behaviors to streamline user discovery and engagement. We first characterize ICH-specific propagation patterns, e.g., custodians’ social influence, heterogeneous user interests, and content co-consumption and then encode these factors within a collaborative graph framework. Evaluation on a real-world ICH dataset demonstrates that GCHS delivers improvements in Top-N recommendation accuracy over leading benchmarks and significantly outperforms in terms of next-N sequence prediction. By integrating social, cultural, and transactional dimensions, our approach not only drives more effective digital commerce interactions around heritage content but also supports sustainable cultural dissemination and stakeholder participation. This work advances electronic-commerce research by illustrating how graph-based deep learning can optimize content discovery, personalize user experience, and reinforce community networks in digital heritage ecosystems. Full article

A new tunnel undercrossing an existing tunnel not only affects the deformation and stress response of the existing tunnel but also triggers ground settlement due to secondary excavation disturbances. By combining the equivalent layer method with the mirror method and incorporating corrections from numerical simulations based on actual intersection projects, a novel prediction model is developed to consider the impact of the existing tunnel on estimating ground settlement caused by a new tunnel that undercrosses it in an orthogonal manner. The influence of geological conditions, tunnel dimensions, and spatial layout on ground settlement patterns was investigated. The elastic moduli of smaller strata correlate with greater surface settlement. Larger existing tunnel diameters result in reduced settlement within a 15 m area near the new tunnel axis. Conversely, new larger tunnel diameters yield more pronounced settlement. A consistency assessment method was introduced to quantitatively measure the consistency between the prediction model and numerical simulations. The results indicate that the prediction model exhibits high consistency (CI > 0.9) under various conditions. Based on an actual engineering case, indoor similarity model tests were designed. When the new tunnel is directly located beneath the existing tunnel, ground settlement begins, with a maximum settlement of 0.17 mm. After the new tunnel traversed the existing one, ground settlement continued to increase within approximately 50 m on both sides of the new tunnel’s axis, ultimately reaching a value of about 0.765 mm. The CI between the predictive model and the model test results reached 0.8, confirming the model’s robust predictive capability. Full article

Background/Objectives: Professional competencies and personal mastery are key dimensions for the well-being of health professionals and the quality of care. In physiotherapy, where organizational complexity is common, job satisfaction depends on both clinical skills and resilience. While these aspects have been explored in nursing, evidence for physiotherapists is limited. This study aimed to (1) assess perceived competencies and personal mastery in Italian physiotherapists; (2) analyze their relationship with job satisfaction; and (3) examine the factorial structure of the Multidimensional Scale of Competences. Methods: A cross-sectional study was conducted with 481 physiotherapists working in various care settings. Data were collected using the 25-item Multidimensional Scale of Competences, the 7-item Personal Mastery Scale, and a single job satisfaction item, all on a 5-point Likert scale. Analyses included descriptive statistics, Pearson correlations, logistic regression, and exploratory factor analysis (Principal Component Analysis with five components). Results: Participants had a mean age of 31.1 years (SD = 8.3) and 7.3 years of professional experience (SD = 7.7); gender distribution was balanced. Most held a master’s (44.5%) or bachelor’s degree (36.8%). Job satisfaction was high, with 95% reporting moderate to very high satisfaction. Competencies showed a mean of 4.16 (SD = 0.95; α = 0.86), while Personal Mastery averaged 3.52 (SD = 1.29; α = 0.60). Competencies significantly predicted job satisfaction (OR = 8.37, p = 0.003), whereas Personal Mastery did not. Factor analysis identified five domains—technical–clinical, communicative, collaborative, ethical, and educational—explaining 50.3% of variance. Conclusions: Italian physiotherapists report high competencies and moderate personal mastery. Job satisfaction is strongly linked to competencies, highlighting their central role in professional well-being. Results support the importance of continuous professional development and organizational strategies that enhance competencies and resilience. Full article

This study examines the evolution characteristics of ship waves generated by large vessels in shallow waters. A CFD-based numerical wave tank, incorporating Torsvik’s ship wave theory, was developed using the VOF multiphase approach and the RNG k-ε turbulence model to capture free-surface evolution and turbulence effects. Results indicate that wave heights vary significantly near the critical depth-based Froude number ($F_h$). Comparative analyses between CFD results for a Wigley hull and proposed empirical correction formulas show strong agreement in predicting maximum wave heights in transcritical and supercritical regimes, accurately capturing the nonlinear surge of wave amplitude in the transcritical range. Simulations of 2000-ton and 6000-ton class vessels further reveal that wave heights increase with $F_h$, peak in the transcritical regime, and subsequently decay. Lateral wave attenuation was also observed with increasing transverse distance, highlighting the role of vessel dimensions and bulbous bow structures in modulating wave propagation. These findings provide theoretical and practical references for risk assessment and navigational safety in shallow waterways. Full article

Glass fiber reinforced polymer (GFRP) composite profiles produced by pultrusion method are widely used as an alternative to traditional building materials due to their lightness and corrosion resistance. However, these materials are susceptible to crushing type fractures known as “web crippling” especially under local loading due to their anisotropic structure and limited mechanical strength. Understanding web-crippling behavior is crucial for the safe and efficient structural application of pultruded GFRP profiles. This study report narrated the review of experimental, numerical, and analytical investigations of web-crippling behavior of pultruded GFRP profiles. Highlights of the major findings include profile geometry and detailing of the flange–web joint, loading types (end-two-flange (ETF), interior-two-flange (ITF), end bearing with ground (EG), interior bearing with ground (IG)), bearing plate dimensions, presence of web openings, and elevated temperatures. It also considers the limitations of current standards, along with new modeling techniques that incorporate finite element analysis as well as artificial intelligence. Damage types such as web–flange joint fractures, crushing, and buckling were comparatively analyzed; design approaches based on finite element modeling and artificial intelligence-supported prediction models were also included. These insights provide guidance for optimizing profile design and improving predictive models for structural engineering applications. Gaps in current design standards and modeling approaches are highlighted to guide future research. Full article

Objectives: This study evaluated the utility of complex morphometric analyses for predicting radiation pneumonitis (RP) and proposed a quantitative prognostic framework for patients with non-small cell lung cancer (NSCLC) undergoing curative-intent radiotherapy (RT). Imaging biomarkers, including box-counting fractal dimension (BoxFD), lacunarity, and minimum spanning tree fractal dimension (MSTFD), were assessed for their prognostic significance. Materials and Methods: We retrospectively analyzed 166 NSCLC patients who received curative-intent RT and had both pre-treatment and follow-up chest CT scans. Among them, 85 received RT alone and 81 underwent concurrent chemoradiotherapy (CCRT). Fractal features were measured to build a Random Forest model (RFM) predicting RP of grade ≥ 2, and the most important features were used to construct a decision tree model. Results: RP of grade ≥ 2 occurred in 19 patients (22.3%) in the RT alone group and 44 patients (54.3%) in the CCRT group. Lacunarity increased significantly post-RT in both groups, while BoxFD and MSTFD showed no significant changes. In the RFM, pre-RT MSTFD and lung dose parameters (V10 in RT alone; V5–V20 in CCRT) were identified as key predictors. Decision tree models based on these features achieved high predictive performance, with AUROC of 0.83 and 0.85, and F1 scores of 0.92 and 0.76 for RT alone and CCRT groups, respectively. Conclusions: Fractal imaging biomarkers demonstrated promising prognostic value for predicting grade ≥ 2 RP in NSCLC patients. The proposed decision tree model may serve as a practical tool for early identification of high-risk patients, facilitating personalized treatment strategies and informing future research. Full article

This narrative review addresses the complexities of managing the vertical dimension of occlusion (VDO) in restorative dentistry, focusing on predictability in prosthetic reconstructions. Altering VDO impacts biological, biomechanical, esthetic, and functional aspects, making it a controversial topic. While VDO naturally evolves throughout life, interventions require careful consideration due to potential complications. Various techniques guide VDO determination, including facial proportions, physiological methods, phonetics, and cephalometric analysis. Clinicians must understand these principles and adapt them to individual patient needs. Materials and Methods: A narrative literature review was conducted using PubMed, Scopus, Google Scholar, and the Cochrane Library, searching keywords like “vertical dimension of occlusion”, “dental”, “diagnosis”, “management” and “complications”. In addition to the literature review, two case reports with extensive prosthodontic restorations were included to illustrate the diagnostic challenges and treatment considerations in a clinical setting. Results: Increasing VDO aids restorative treatments, re-establishing morphology, and facilitating additive procedures. Minimally invasive approaches, provisional restorations, and fixed restorations with functional contours are favored. Individualized, patient-centered care is critical, recognizing unique anatomical and functional needs. This approach optimizes stomatognathic system rehabilitation while preventing adverse effects on body posture and airway dimensions. Conclusions: To ensure predictable results and minimize risks, changes in VDO should be kept to a minimum to achieve dentofacial aesthetic harmony and secure adequate space for the planned restorations The two case reports presented, with different clinical approaches, underline the importance of understanding the potential risks and benefits of VDO alteration which is crucial for achieving predictable and successful outcomes in complex restorative cases. Full article

Urban streetscapes are among the most frequently encountered spatial environments in daily life, and their restorative visual features have a significant impact on well-being. Although existing studies have revealed the relationship between streetscape environments and perceived restorativeness, there remains a lack of scalable, data-driven methods for quantifying such perception at the street level. This study proposes an interpretable and replicable framework for predicting streetscape restorativeness by integrating semantic segmentation, perceptual evaluation, and machine learning techniques. Taking Liwan District of Guangzhou as a case study, street-view images (SVIs) were collected and processed using the Mask2Former model to extract the following five key visual metrics: greenness, openness, enclosure, walkability, and imageability. Based on the Perceived Restorativeness Scale (PRS), an online questionnaire was designed from four dimensions (fascination, being away, compatibility, and extent) to score a random sample of images. A random forest model was then trained to predict the perceptual levels of the full dataset, followed by K-means clustering to identify spatial distribution patterns. The results revealed that there were significant differences in visual characteristics among high, medium, and low restorativeness street types. The proposed framework enables scalable, data-driven evaluation of perceived restorativeness across diverse urban streetscapes. By embedding perceptual metrics into large-scale urban analysis, the framework offers a replicable and efficient approach for identifying streets with low restorative potential—thus providing urban planners and policymakers with a novel tool for prioritizing street-level renewal, improving public well-being, and supporting perception-oriented urban design without the need for labor-intensive fieldwork. Full article