Search Results (2,083)

Human Capital Agility is increasingly recognized as a critical capability for achieving sustainable project success in the highly dynamic construction sector, yet an original and empirically testable Human Capital Agility model rooted in Human Capital theory is still lacking. This study aims to develop and validate a Human Capital Agility framework that integrates Leadership Agility and Knowledge Management and to construct a hierarchical roadmap for the gradual development of Human Capital Agility. Using a multi-method design, survey data from 141 construction professionals were analyzed with Partial Least Squares Structural Equation Modeling to test the structural relationships among Knowledge Management, Leadership Agility, Human Capital Agility, Sustainable Project Success, and the moderating role of Firm Size, while expert judgments from nine practitioners were modeled using Modified Total Interpretive Structural Modeling to derive the internal hierarchy of Human Capital Agility components. The results show that Leadership Agility is a dominant driver of Human Capital Agility and that Human Capital Agility significantly enhances Sustainable Project Success, whereas the direct effect of tacit knowledge on Leadership Agility is not supported. The hierarchical model maps nine key components of Human Capital Agility into six levels, separating foundational drivers such as attitudes and predisposition from higher-level outcome capabilities such as generative behavior, responsiveness, adaptability, and resilience. These findings provide an integrated and empirically grounded Human Capital Agility model that offers both a causal explanation and a practical roadmap for strengthening human capital capabilities in construction projects. Full article

This study investigates the impact of Governance, Risk management, and Compliance (GRC) practices on operational efficiency and corporate reputation. Drawing on the Resource-Based View (RBV), Stakeholder Theory, and the signaling perspective, it conceptualizes GRC as a set of organizational capabilities that enhance operational efficiency and company reputation. It also examines the mediating role of operational efficiency in the GRC–reputation relationship, particularly within technologically advanced and regulated sectors. Data were collected through a structured questionnaire distributed to 126 professionals across various Saudi technology-driven organizations, and the analyses combined descriptive statistics, hierarchical regression, and bootstrapped mediation testing using PROCESS to assess direct and indirect effects. The results indicate that operational efficiency partially mediates the effects of governance and compliance on reputation, supporting the argument that strengthened internal processes enhance external stakeholder evaluations; meanwhile, no mediation was found for risk management. Although the study offers meaningful insights, its sample size and sectoral focus limit the generalizability of conclusions, suggesting the need for broader or longitudinal research. This study contributes by advancing the conceptualization of GRC as organizational capabilities and empirically demonstrating their roles in strengthening both efficiency and reputation within technology-driven firms where digital governance and compliance capabilities are increasingly central. Full article

Traditional retrieval-augmented generation (RAG) methods struggle with hierarchical documents, often causing semantic fragmentation, structural loss, and inefficient retrieval due to fixed strategies. To address these challenges, this paper proposes HiSem-RAG, a hierarchical semantic-driven RAG method. It comprises three key modules: (1) hierarchical semantic indexing, which preserves boundaries and relationships between sections and paragraphs to reconstruct document context; (2) a bidirectional semantic enhancement mechanism that incorporates titles and summaries to facilitate two-way information flow; and (3) a distribution-aware adaptive threshold strategy that dynamically adjusts retrieval scope based on similarity distributions, balancing accuracy with computational efficiency. On the domain-specific EleQA dataset, HiSem-RAG achieves 82.00% accuracy, outperforming HyDE and RAPTOR by 5.04% and 3.98%, respectively, with reduced computational costs. On the LongQA dataset, it attains a ROUGE-L score of 0.599 and a BERT_F1 score of 0.839. Ablation studies confirm the complementarity of these modules, particularly in long-document scenarios. Full article

Zinc dendrite growth and side reactions remain critical challenges hindering the advancement of aqueous zinc-ion batteries (AZIBs). This study proposes a gel-based strategy for designing high-performance separators by regulating the crystal morphology of the metal–organic framework UiO-66 within a cellulose nanofiber (CNF) gel matrix. The resulting gel-derived separators exhibit distinctive structural and interfacial properties that significantly enhance battery performance. Compared with hierarchical porous structures (H-UiO-66), the octahedral morphology (O-UiO-66) disperses more uniformly in the CNF gel network, forming well-defined ion transport channels through its integrated gel architecture. The fabricated O-UiO-66/CNF gel separator demonstrates exceptional hydrophilicity (contact angle 21°), high porosity (73.2%), and significantly improved zinc ion migration number (0.72). Electrochemical tests reveal that this gel-based separator effectively guides uniform zinc deposition while suppressing dendrite growth. Zn/Zn symmetric cells using the O-UiO-66/CNF gel separator achieve a cycle life exceeding 800 h at 1 mA cm⁻². The Zn/MnO₂ full cell maintains 98.1% capacity retention after 100 cycles at 1 A g⁻¹. This work establishes a structure–performance relationship between MOF morphology and gel separator properties, providing new insights for designing advanced gel-based materials for AZIBs. Full article

The growing demand for sustainable protein sources has increased interest in algae and aquatic plants as alternatives to animal-derived proteins. These resources are rich in protein, amino acids, and umami compounds but require suitable extraction methods to maximize yield and quality. This study compared three green extraction techniques—maceration (MAE, 80 °C, 2 h), ultrasound-assisted extraction (UAE, 750 W, 20 kHz, 50% amplitude, 35 °C, pH 12, 1 h), and enzyme-assisted extraction (EAE, 5% β-glucanase/flavourzyme, 55 °C, pH 6.5, 1 h)—on five raw materials: wakame (commercial seaweed), hair seaweed, sea lettuce, water silk algae, and Wolffia. The result revealed that both raw materials and extraction methods significantly (p < 0.05) affected protein yield, amino acid, physicochemical properties, and taste detection with e-tongue. Wolffia extracted by MAE yielded the highest protein overall, followed by UAE and EAE methods, when compared with commercial seaweed. The relationship between amino acid profiles and taste detection was investigated by principal component analysis (PCA) and hierarchical cluster analysis (HCA); the samples with higher glutamic and aspartic acids were linked with umami taste, while histidine contributed to bitter taste. Overall, the findings highlighted that extraction efficiency was influenced more by the extraction method–material compatibility than the raw material alone. Full article

Visual Relationship Prediction (VRP) is crucial for advancing structured scene understanding, yet existing methods struggle with ineffective multimodal fusion, static relationship representations, and a lack of logical consistency. To address these limitations, this paper proposes a Hierarchical CLIP model (H-CLIP) for structured scene parsing. Our approach leverages a pre-trained CLIP backbone to extract aligned visual, textual, and spatial features for entities and their union regions. A multi-head self-attention mechanism then performs deep, dynamic multimodal fusion. The core innovation is a consistency and reversibility verification mechanism, which imposes algebraic constraints as a regularization loss to enforce logical coherence in the learned relation space. Extensive experiments on the Visual Genome dataset demonstrate the superiority of the proposed method. H-CLIP significantly outperforms state-of-the-art baselines on the predicate classification task, achieving a Recall@50 score of 64.31% and a Mean Recall@50 of 36.02%, thereby validating its effectiveness in generating accurate and logically consistent scene graphs even under long-tailed distributions. Full article

Cultivating a green innovation-oriented work climate exerts a positive effect on employees’ participation in green knowledge sharing and other co-creation behaviors. Previous studies analyzed the influential factors of green value co-creation from the perspective of green motivation and green dynamic capabilities, but there is a lack of research on the antecedents of green value co-creation from the perspective of green innovation climate. Therefore, based on the social information processing theory, this paper make an in-depth research on the impact mechanism of green innovation climate on employee green value co-creation, through perception of corporate social responsibility and employees’ sense of belonging. A questionnaire survey was conducted on Chinese enterprises implementing green innovation, and 337 valid questionnaires were collected. The effect mechanism of green innovation climate on employees’ green value co-creation was analyzed by the hierarchical regression analysis method. Process regression analysis was used to explore the moderating effect of inclusive leadership. The result of the research shows that green innovation climate has a significant relation to employees’ sense of belonging, perception of corporate social responsibility and employees’ sense of belonging. Perception of corporate social responsibility and employees’ sense of belonging have mediating effects on the relations between green innovation climate and employees’ green value co-creation. Inclusive leadership can moderate the relationship between perception of corporate social responsibility and employees’ green value co-creation. In theory, from the perspectives of green innovation climate and inclusive leadership, it further enriches the research on the driving factors of green value co-creation. In practice, It provides a theoretical reference for enterprises to utilize the strategy of green innovation climate and inclusive leadership to promote green value co-creation of enterprises effectively. Full article

Biaxially stretched polytetrafluoroethylene (PTFE) membranes are promising media for high-efficiency air filtration because of their stable node–fiber microstructure and environmental durability. To clarify how resin properties and microstructure govern filtration behavior, ten PTFE resins with different average molecular weights (Mn) and particle size characteristics were processed into membranes under essentially identical biaxial stretching and sintering conditions. Resin particle size, fiber diameter and pore size distributions were quantified, and coefficients of variation (CVs), together with Spearman rank correlations, were used to analyze material–structure–performance links. Filtration efficiency, pressure drop and quality factor (QF) were measured according to ISO 29463-3 using 0.1–0.3 μm aerosols. Higher Mn combined with lower particle-size dispersion favored finer fibers and narrower pores, yielding efficiencies close to 100%, but increased pressure drop and slightly reduced QF, indicating a trade-off between efficiency and flow resistance. The sample with the lowest Mn in its group and a high machine-direction draw ratio (12×), showed pronounced fibril breakage, node coalescence, broadened pore-size distribution and degraded QF, illustrating the sensitivity of structure and performance to resin-process mismatch. Overall, the study establishes a hierarchical material–fiber–pore–performance relationship that can guide resin selection, structural tuning and process optimization of biaxially stretched PTFE membranes. Full article

Background/Objectives: Autism spectrum disorder (ASD) exhibits remarkable genetic heterogeneity involving hundreds of risk genes; however, the mechanism by which these genes organize within biological networks to contribute to disease pathogenesis remains incompletely understood. This study aims to elucidate these organizational principles and identify critical network bottlenecks using a novel integrative computational framework. Methods: We analyzed 893 SFARI genes using a three-pronged computational approach: (1) a Machine Learning Dynamic Perturbation Propagation algorithm; (2) a hypergraph construction method explicitly modeling multi-gene complexes by integrating protein–protein interactions, co-expression modules, and curated pathways; and (3) Hypergraph Neural Network embeddings for gene clustering. Validation was performed using hub-independent features to address potential circularity, followed by a druggability assessment to prioritize therapeutic targets. Results: The hypergraph construction captured 3847 multi-way relationships, representing a 45% increase in biological relationships compared to pairwise networks. The perturbation algorithm achieved a 51% higher correlation with TADA genetic evidence than random walk methods. Analysis revealed a hierarchical organization where 179 hub genes exhibited a 3.22-fold increase in degree centrality and a 4.71-fold increase in perturbation scores relative to non-hub genes. Hypergraph Neural Network clustering identified five distinct gene clusters, including a “super-hub” cluster of 10 genes enriched in synaptic signaling (4.2-fold) and chromatin remodeling (3.9-fold). Validation confirmed that 8 of these 10 genes co-cluster even without topological information. Finally, we identified high-priority therapeutic targets, including ARID1A, POLR2A, and CACNB1. Conclusions: These findings establish hierarchical network organization principles in ASD, demonstrating that hub genes maintain substantially elevated perturbation states. The identification of critical network bottlenecks and pharmacologically tractable targets provides a foundation for understanding autism pathogenesis and developing precision medicine approaches. Full article

Background/Objectives: Neck pain is highly prevalent among dentists and has been linked to occupational exposure and cervical degeneration. However, the relationship between cervical MRI findings and symptoms remains inconsistent. This study aimed to explore MRI-based cervical structural profiles in active dentists and examine their associations with neck pain, disability, and participant characteristics. Methods: A cross-sectional study was conducted in 57 practicing dentists. Participants reported neck pain and completed the Numeric Pain Rating Scale and the Neck Disability Index (NDI). Cervical MRI scans were assessed by an experienced musculoskeletal radiologist. An exploratory hierarchical cluster analysis (complete linkage, Euclidean distance) was applied using MRI degenerative variables to identify structural profiles, followed by bivariate comparisons with clinical and occupational factors. Results: Degenerative MRI findings were common (disc bulging, 66.7%; disc herniation, 54.4%). Two MRI-based profiles were identified, one characterized by a higher burden of degenerative findings (including disc and facet changes) (70.2%), and another with fewer/milder degenerative features (29.8%). Neck pain and NDI scores ≥ 20 were more frequent in the higher-degeneration profile (p = 0.001 and p = 0.004, respectively). Age showed a non-linear pattern, with younger dentists reporting pain despite milder MRI changes, whereas older dentists showed more degeneration with fewer symptoms. Conclusions: In this exploratory study, individual MRI findings were not independently associated with neck pain, while a higher overall burden of degenerative changes tended to co-occur with greater symptom reporting and disability. These findings should be interpreted as hypothesis-generating and warrant confirmation in larger, longitudinal studies. Full article

Soil contamination by heavy metals (HMs) threatens crop productivity, food safety, and ecosystem health, especially in intensively cultivated Mediterranean regions. This study investigated the influence of soil HM contamination on nutrient uptake, photosynthetic traits, and metal bioaccumulation in avocado (Persea americana Mill.) orchards. Twenty orchard sites were sampled, collecting paired soil and mature leaf samples. Soil physicochemical properties and HM concentrations were determined, while leaves were analyzed for macro- and micronutrients, photosynthetic pigments, and metal contents. Bioaccumulation Factors (BAFs) were computed, and multivariate analyses (Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR)) were applied to assess soil–plant relationships, complemented by Monte Carlo simulations to quantify probabilistic contamination risks. Results revealed substantial inter-site variability, with leaf Cd and Pb concentrations reaching 0.92 and 3.54 mg/kg, and BAF values exceeding 1 in several orchards. PLSR models effectively predicted leaf Cd (R² = 0.789) and Pb (R² = 0.772) from soil parameters. Monte Carlo simulations indicated 15–25% exceedance of FAO/WHO safety limits for Cd and Pb. These findings demonstrate that soil metal accumulation substantially alters avocado nutrient balance and photosynthetic efficiency, highlighting the urgent need for site-specific soil monitoring and sustainable remediation strategies in contaminated orchards. Full article

Background/Objectives: Periodontal disease is a condition marked by the destruction of tooth-supporting tissues, driven by an exaggerated immune response to an unbalanced dental biofilm. Conventional treatments struggle due to antimicrobial resistance and the biofilm’s protective extracellular matrix. This study evaluates the potential of bacteriophages as an innovative strategy for managing periodontal disease. Methods: This research employed a qualitative approach using Discursive Textual Analysis, with IRAMUTEQ version 0.8 alpha 7 (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires) software. The search was conducted in the Orbit Intelligence and PubMed databases, for patents and scholarly articles, respectively. The textual data underwent Descending Hierarchical Classification, Correspondence Factor Analysis, and Similarity Analysis to identify core themes and relationships between words. Results: The analysis revealed an increase in research and patent filings concerning phage therapy for periodontal disease since 2017, emphasizing its market potential. The primary centers for intellectual property activity were identified as China and the United States. The study identified five focus areas: Genomic/Structural Characterization, Patent Formulations, Etiology, Therapeutic Efficacy, and Ecology/Phage Interactions. Lytic phages were shown to be effective against prominent pathogens such as Fusobacterium nucleatum and Enterococcus faecalis. Conversely, the lysogenic phages poses a potential risk, as they may transfer resistance and virulence factors, enhancing pathogenicity. Conclusions: Phage therapy is a promising approach to address antimicrobial resistance and biofilm challenges in periodontitis management. Key challenges include the need for the clinical validation of formulations and stable delivery systems for the subgingival area. Future strategies, such as phage genetic engineering and data-driven cocktail design, are crucial for enhancing efficacy and overcoming regulatory hurdles. Full article

Hyperspectral imaging (HSI) captures the same scene across multiple spectral bands, providing richer spectral characteristics of materials than conventional RGB images. The spectral reconstruction task seeks to map RGB images into hyperspectral images, enabling high-quality HSI data acquisition without additional hardware investment. Traditional methods based on linear models or sparse representations struggle to effectively model the nonlinear characteristics of hyperspectral data. Although deep learning approaches have made significant progress, issues such as detail loss and insufficient modeling of spatial–spectral relationships persist. To address these challenges, this paper proposes the Cascaded Multi-Attention Feature Recurrent Enhancement Network (CMFREN). This method achieves targeted breakthroughs over existing approaches through a cascaded architecture of feature purification, spectral balancing and progressive enhancement. This network comprises two core modules: (1) the Hierarchical Residual Attention (HRA) module, which suppresses artifacts in illumination transition regions through residual connections and multi-scale contextual feature fusion, and (2) the Cascaded Multi-Attention (CMA) module, which incorporates a Spatial–Spectral Balanced Feature Extraction (SSBFE) module and a Spectral Enhancement Module (SEM). The SSBFE combines Multi-Scale Residual Feature Enhancement (MSRFE) with Spectral-wise Multi-head Self-Attention (S-MSA) to achieve dynamic optimization of spatial–spectral features, while the SEM synergistically utilizes attention and convolution to progressively enhance spectral details and mitigate spectral aliasing in low-resolution scenes. Experiments across multiple public datasets demonstrate that CMFREN achieves state-of-the-art (SOTA) performance on metrics including RMSE, PSNR, SAM, and MRAE, validating its superiority under complex illumination conditions and detail-degraded scenarios. Full article

Digital twins (DTs) have seen widespread application across industries, enabling deep integration of cyber–physical systems. However, previous research has largely focused on domain-specific DTs and lacks a universal, cross-industry modeling framework, resulting in high development costs and low reusability. To address these challenges, this study proposes a DT modeling method based on hierarchical decoupling and topological connections. First, the system is decomposed top–down into three levels—system, subsystem, and component—through hierarchical functional decoupling, reducing system complexity and supporting independent component development. Second, a method for constructing component-level DTs using standardized information sets is introduced, employing the JSON-LD language to uniformly describe and encapsulate component information. Finally, a topological connection mechanism abstracts the relationships between components into an adjacency matrix and assembles components and subsystems bottom–up using graph theory, ultimately forming the system-level DT. The effectiveness of the proposed method was validated using a typical surface water purification system as a case study, where the system was decomposed into four functional subsystems and 12 types of components. Experimental results demonstrate that the method efficiently enables automated integration of DTs from standardized components to subsystems and the complete system. Compared with conventional monolithic modeling approaches, it significantly reduces system complexity, supports efficient component development, and accelerates system integration. For example, when the number of components exceeds 300, the proposed method generates topology connections 44.69% faster than direct information set traversal. Consequently, this approach provides a novel and effective solution to the challenges of low reusability and limited generality in DT models, laying a theoretical foundation and offering technical support for establishing a universal cross-industry DT modeling framework. Full article

This study empirically examines how organizational learning influences problem-solving competency and organizational effectiveness in the context of online travel agencies (OTAs) and tests the moderating role of digital empowerment. Using agency lists registered under Korea’s Tourism Promotion Act, we employed stratified sampling by region and simple random sampling within strata. Data collection was commissioned by the Tourism/Leisure HRD Council. A survey was carried out from 2 to 19 June 2025; of the 210 questionnaires returned, 204 valid responses were analyzed. Measures were adapted from prior studies on a five-point Likert scale. Analyses conducted in SPSS 27.0 included descriptive statistics, exploratory factor analysis (EFA), reliability testing (Cronbach’s α), correlation analysis, and simple and hierarchical regressions. The results indicate that (1) organizational learning has a significant positive effect on problem-solving competency (β = 0.541, p < 0.001, R² = 0.293); (2) organizational learning positively affects organizational effectiveness (β = 0.436, p < 0.001, R² = 0.190); and (3) problem-solving competency positively influences organizational effectiveness (β = 0.624, p < 0.001, R² = 0.389). Regarding moderation, digital empowerment did not significantly moderate the organizational learning → problem-solving link but did significantly moderate the organizational learning → organizational effectiveness relationship (p < 0.05), suggesting that digital empowerment enhances the conversion efficiency of learning into performance. Theoretically, this study substantiates the learning–problem-solving–performance mechanism in a service/tourism setting and identifies digital empowerment as a catalytic moderator that strengthens the translation of learning into organizational outcomes. Practically, the findings imply that OTAs can amplify organizational effectiveness by building digital empowerment structures—data-driven decision systems, process automation, and real-time customer-response capabilities—which enable learned knowledge to materialize into performance. Future research should incorporate digital maturity, leadership, customer orientation, and related variables into extended models. Full article