Search Results (18,139)

This study examines the impact of ESG disclosure, leverage, and profitability on firm value, measured by Tobin’s Q, among 67 non-financial Tadawul-listed companies in Saudi Arabia over the period 2015–2024. ESG disclosure is captured through a manual content-analysis index that scores the proportion of expected environmental, social, and governance items reported by each firm. The study further investigates whether board independence moderates these relationships while controlling for liquidity, firm size, current ratio, capital expenditure, and board size. Methodologically, the study employs the two-step system generalized method of moments (system GMM) estimator, which addresses dynamic persistence, endogeneity, and unobserved heterogeneity. The findings reveal that ESG disclosure has a positive and significant effect on firm value, indicating that the Saudi market increasingly rewards firms that provide broader sustainability-related information. Profitability also exerts a positive influence on Tobin’s Q, while leverage has a negative and significant effect, suggesting that higher debt weakens market valuation. Among the moderating effects, board independence significantly reduces the negative impact of leverage on firm value, although it does not significantly strengthen the positive ESG disclosure–firm value relationship. The results also show that liquidity, firm size, capital expenditure, and board size positively influence firm value. The study’s novelty lies in being the first, to our knowledge, to integrate ESG disclosure, financial structure, profitability, and board independence within a single dynamic firm-value framework over a decade-long panel that brackets the Saudi Exchange’s 2021 ESG disclosure guideline. In doing so, it advances emerging-market ESG research by showing that, under Saudi Arabia’s largely voluntary disclosure regime and concentrated-ownership structure, board independence operates primarily as a risk-monitoring mechanism rather than as an amplifier of disclosure value. The findings imply that regulators should strengthen and progressively mandate ESG reporting frameworks, that investors should treat ESG transparency as value-relevant information, and that firms should view ESG transparency and prudent governance as strategic tools for enhancing market value in line with Vision 2030. Full article

Stilbene-type compounds are vital plant secondary metabolites that are classified under polyphenols and generally exhibit significant biological activities, as well as potential health benefits. These compounds, prevalent in food sources and medicinal plants, are recognized for their complex structures and their roles in plant defense mechanisms against environmental stressors. Despite their beneficial properties, the natural stilbenes face limitations related to their bioavailability and solubility, highlighting the need for chemical modifications to enhance their therapeutic efficacy. Studies have focused on structural modifications of the stilbene scaffold, including the introduction of carbon-based fragments, aiming to improve the compounds’ stability, selectivity, and overall biological activities. The development of stilbene analogues through chemical modifications not only expands the library of valuable stilbene-type compounds but also holds promise for new therapeutic applications in combating chronic diseases. This review summarizes current knowledge on the sources, biological activities, and chemical modifications of stilbene compounds, emphasizing their potential in healthcare and nutrition. Full article

FDM-printed polylactic acid (PLA) composites containing 5 and 10 wt% obsidian powder sourced from the Kars region of Eastern Anatolia (Turkey) were produced via twin-screw masterbatch extrusion and subsequent single-screw filament dilution. Mechanical (tensile, three-point flexure, notched Charpy impact, Shore D), physical (density), thermal (simultaneous TGA/DSC) and microstructural (macroscopic fractography and SEM at 100×–1000×) characterizations were performed on FDM-printed specimens. Young’s modulus rose monotonically by +9.0% at 5 wt% and +18.2% at 10 wt%, while ultimate tensile strength decreased by 12.4% and 17.3%, respectively. The flexural modulus increased by +15.2% at 5 wt% and plateaued at 10 wt% (+16.7%), whereas the flexural strength decreased by only 3.5% at 10 wt%, indicating that flexure-mode loading is markedly more tolerant of obsidian filler than axial tension. Shore D hardness rose by +2.11 points from 0 to 5 wt% with saturation thereafter. TGA showed a dual thermal effect: T₅ and T₁₀ dropped by 5–6 °C from 5 to 10 wt%, while the main decomposition rate decreased by ~46% and the decomposition interval widened from 9.7 to 23.5 °C, indicating a barrier/heat-shielding effect of dispersed silicate particles. SEM revealed a continuous ductile → transitional → brittle progression with increasing obsidian content; extended interfacial debonding lines at 10 wt% identified weak unmodified filler/matrix coupling as the principal performance-ceiling factor. Density measurements indicated a ~3–6% residual void fraction consistent with the inter-bead voids observed by SEM. To the authors’ knowledge, this is the first systematic study of obsidian as a reinforcing filler in PLA; the 5 wt% composition is identified as a strong candidate for esthetic, flexure-dominant, and low-load structural applications. Full article

This study investigates the readiness of future teachers in Romania to meet the requirements of inclusive education, with a specific focus on curricular deficits and student teachers’ perceptions of competence. Respecting the right to education for students with Special Educational Needs (SEN) is a central policy commitment. Yet, the capacity of initial teacher education (ITE) programs to operationalize this mandate remains uncertain. Using a convergent parallel mixed-methods design, the research combines a systematic documentary analysis of national regulations and psycho-pedagogical curricula (Orders No. 4139/2022 and 4524/2020; Law No. 199/2023) with a survey of 327 student teachers across eight universities. Systematic Content Analysis, based on a three-level depth protocol, reveals a structural curricular deficit: Level 1 outcomes (theoretical awareness of SEN and inclusion) appear in approximately 40% of compulsory subjects, whereas Level 2 outcomes (operational competence, such as designing adapted lessons or differentiated assessments) are almost completely absent from the mandatory core and are confined to electives. Survey results mirror this gap: although 81% of respondents anticipate working with pupils with SEN, 29.9% feel poorly or very poorly prepared, 25.5% report a lack of basic knowledge of SEN, and only 14.6% report high confidence in designing adapted activities. Analysis of Covariance (ANCOVA) shows that training level has a statistically significant but small effect on technical inclusive skills (p = 0.043; η²_p = 0.013), while inclusive attitudes are mainly associated with age. The study concludes with a roadmap for reforming ITE through mandatory SEN-focused practicum placements and transversal integration of inclusive pedagogy. Full article

Adipose tissue remodeling is a dynamic process essential for metabolic homeostasis, enabling tissue expansion, extracellular matrix (ECM) turnover, angiogenesis, and coordinated immune adaptation. In obesity, however, maladaptive remodeling characterized by fibrosis, chronic low-grade inflammation, and hypoxia disrupts adipose plasticity and promotes systemic insulin resistance. Central to these processes is the tightly regulated homeostasis between proteases and their inhibitors, in which the serine protease inhibitor (serpin) superfamily represents an important yet underappreciated regulatory axis. Beyond their classical roles in coagulation and fibrinolysis, serpins regulate ECM remodeling, macrophage recruitment and polarization, cytokine signaling, angiogenic responses, adipokine activity, and insulin sensitivity, thereby orchestrating immune–metabolic crosstalk within adipose depots. Emerging evidence indicates that individual serpins exert distinct and context-dependent effects, with some promoting fibrosis, inflammation, and metabolic dysfunction, whereas others preserve adipose tissue homeostasis and metabolic function. This review synthesizes current knowledge on the structural and functional diversity of the serpin superfamily and examines their mechanistic roles in adipose tissue remodeling during obesity, with particular emphasis on how adipose-associated serpins regulate adipose tissue homeostasis, depot-specific remodeling, and immune–metabolic crosstalk. The review further discusses the experimental and translational applications of emerging single-cell and spatial transcriptomics, multi-omics, and computational approaches that may advance the understanding of serpin biology, improve the investigation of human adipose tissue, and accelerate the identification of clinically relevant serpin-related biomarkers and therapeutic targets for obesity and related metabolic disorders. By positioning serpins as key regulators of adipose tissue remodeling and immune–metabolic integration, this review highlights protease–antiprotease balance as a central determinant of metabolic health and identifies serpins as promising biomarkers and therapeutic targets for obesity and related metabolic disorders. Full article

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

Deep-mining operations are increasingly challenged by severe thermal hazards, which have become a critical bottleneck for achieving safe, efficient, and sustainable mineral extraction. While research on deep-mine cooling and heat hazard mitigation has proliferated, the field lacks a systematic, critical review that explicitly examines these advances through the lens of sustainability science. To address this gap, this study conducted a comprehensive bibliometric analysis of 432 publications (1994–2024) retrieved from the Web of Science Core Collection. The methodology employs Bibliometrix, Vosviewer, and CiteSpace to map the intellectual landscape, research hotspots, and evolving frontiers of the field. The results reveal a clear three-stage development trajectory and identify China, the USA, South Africa, and Canada as leading contributors, with national research emphases on ventilation, energy conservation, and refrigeration, respectively. Crucially, keyword clustering and burst detection uncover a notable paradigm shift: the focus has moved from isolated cooling techniques toward integrated, multi-objective strategies—including geothermal energy co-exploitation, phase-change material applications, and system-level energy optimization—signaling a growing alignment with resource efficiency and low-carbon mining principles. However, a critical finding is that the literature remains predominantly techno-centric, overwhelmingly evaluating performance through operational energy savings while largely neglecting life-cycle environmental impacts, holistic sustainability assessment metrics, and the influence of policy drivers. This review thus not only provides a structured overview of the domain, but, more importantly, exposes these critical knowledge gaps. We argue that future research must pivot toward a multi-dimensional sustainability framework that integrates technical, economic, and environmental dimensions, thereby guiding the next generation of research toward truly sustainable deep-mining practices. Full article

Understanding the drivers and barriers of wine consumption is of substantial importance for both market development and sensory science research, and this is particularly salient in rapidly changing non-Western markets. Young, highly educated Chinese consumers represent one of the fastest-growing segments in the global wine market, yet large-scale studies of their consumption preferences and rejection patterns remain limited. This study aimed to characterize the conditional dependence structure of wine-consumption behavior in this population and to examine the associations between common consumption barriers and sociodemographic variables. A nationwide cross-sectional online survey collected 4823 valid responses. Non-parametric tests were used to compare sociodemographic groups, and a regularized Gaussian graphical model (GGM) was estimated to characterize the conditional associations among 15 consumption-behavior variables. The sample was dominated by young respondents (18–24 years) and individuals with higher education. The three most frequently endorsed barriers were taste aversion (51.1%), price sensitivity (38.7%), and lack of knowledge (19.6%). Age and education were the most central sociodemographic variables in the network. The knowledge barrier showed a moderate negative conditional association with education (partial r ≈ −0.171), whereas taste aversion—although the most frequently endorsed barrier—did not show clear conditional associations with sociodemographic variables in the network. Gender was not conditionally associated with any other variable in the network. These observations suggest that the three consumption barriers may operate through different network pathways and may therefore have different implications for intervention design, a possibility that warrants further confirmatory and longitudinal research. Full article

Voltage-gated ion channels (VGICs) are traditionally associated with electrically excitable cells; however, increasing evidence indicates that they are also expressed in non-excitable cells, including vascular endothelial cells. This review aims to summarize the current knowledge on the expression, regulation, and functional role of VGICs in the vascular endothelium, and to highlight their potential contribution to endothelial signaling. We examined the molecular structure, biophysical properties, and functional roles of voltage-gated Na⁺ (Na_V), Ca²⁺ (Ca_V), and K⁺ (K_V) channels in vascular endothelial cells. Particular attention was given to studies investigating VGIC activity in native endothelium and to emerging mechanisms regulating their activation. Endothelial cells express multiple VGIC subtypes at low densities, which are insufficient to generate action potentials but can modulate membrane potential (V_M) and Ca²⁺-dependent signaling. The dynamic regulation of the endothelial V_M, through the interplay between hyperpolarizing and depolarizing conductances, emerges as a key determinant of VGIC availability and activation. VGICs contribute to essential endothelial functions, including angiogenesis, vasomotor responses, blood–brain barrier permeability, and inflammation. Dysregulated VGIC expression and/or activity may be implicated in several pathological conditions, such as atherosclerosis, calcific aortic stenosis, and tumor vascularization. VGICs represent an unexpected but functionally relevant component of endothelial signaling. Elucidating their role in native vascular beds and disease contexts may uncover novel mechanisms of endothelial regulation and identify new therapeutic targets in cardiovascular and cancer biology. Full article

Rapid identification of fish disease lesions is essential for disease monitoring and early intervention in aquaculture, yet existing detection methods rely heavily on extensive lesion annotations and fixed category definitions. To address the challenges of limited annotated data and heterogeneous supervision, this study proposes PaliGemma2-FishGrounding, a generative vision-language grounding framework for few-shot fish disease lesion localization. The framework reformulates lesion detection as an open-vocabulary grounding task and unifies lesion localization, disease recognition, health-status classification, and symptom understanding within a single instruction-learning paradigm. By integrating heterogeneous supervision from multiple fish disease datasets, the proposed method enables lesion localization through structured vision-language generation rather than conventional closed-set detection. Experimental results on an Epizootic Ulcerative Syndrome (EUS) lesion dataset demonstrate that the proposed framework outperforms YOLOv8-based baselines, achieving an AP50 of 0.3659 and an AR@10 of 0.5378 while maintaining a zero invalid-box rate. Ablation studies further confirm the effectiveness of the instruction-learning strategy and grounding-based design. The results indicate that generative vision-language models can effectively leverage limited lesion annotations and auxiliary disease knowledge for fish disease analysis. This framework provides a practical solution for low-annotation disease monitoring and offers a promising direction for intelligent aquaculture applications under data-scarce conditions. Full article

Background: In comparative anatomy, muscles of the gluteal and hamstring groups are categorized and described inconsistently between, but also within, different species. Due to insufficient information on the exact topography of the individual muscles and a lack of illustrations, it is partly difficult to compare the data provided. Particularly for the albino rat, there is only limited information available for these muscles. The aim of this study was therefore to investigate the presence and morphology of the gluteal and hamstring muscles in the albino rat, and to compare them with other species, including humans. Methods: Both hind limbs of 30 formalin-embalmed male albino rats were carefully dissected. The individual muscles were identified based on their position in relation to nerves and to each other. Therefore, previous descriptions of other species were considered. Results: The gluteus superficialis and tensor fasciae latae muscles formed always a continuous musculoaponeurotic plate. The femorococcygeus and gluteus accessorius muscles were constant structures. The gluteus medius, piriformis and gluteus profundus muscles could always be identified. Within the hamstring group, the two-headed semitendinosus, the one-headed biceps femoris, the semimembranosus, and the caudofemoralis muscles were always present. Conclusions: In this study, a detailed description and dissection guide of the systematic anatomy and topography of the gluteal and hamstring muscles of the albino rat is provided for the first time. The outcomes are intended to help improve knowledge of the anatomy of these muscle groups and to serve as a basis for future studies using rats as an animal model. Full article

Sustainable management of sugar agro-industrial value chains requires a multidimensional approach that integrates economic, environmental, and social criteria. Current literature addresses risk management, circularity, and artificial intelligence in isolation, without an integrated framework that generates synergistic value. The objective of this research is to validate an integrated framework for the sustainable management of sugar agro-industrial value chains. A mixed-methods, qualitative-quantitative, descriptive-retrospective study was conducted on the Cuban sugar agro-industry during 2023–2025. The procedure was structured into five phases and 10 stages; Petri net simulation was used to validate its logical consistency. Material, economic-financial, and knowledge flows were mapped; 16 stakeholder groups and their influence–dependence relationships were analyzed; 41 risks were identified, of which six were classified as critical. Simulation-based scenario modeling, which integrates risk, circularity, and AI interventions, projects an average potential reduction of 33.4% in total chain lead time, pending empirical validation. Petri nets confirmed the absence of connectivity errors, free-choice violations, and flow noise, formally validating the logical consistency of the procedure. The research supports the hypothesis that an integrated framework combining risk management, circularity, and AI, validated using Petri nets for logical consistency, projects improvements in the efficiency and sustainability of the value chain. Full article

Additive manufacturing (AM), commonly known as 3D printing, has rapidly transformed modern manufacturing, creating a growing demand for engineers with both theoretical knowledge and practical skills. Despite its increasing relevance, AM is often incorporated into engineering curricula as a supplementary tool rather than a fully integrated subject, limiting students’ understanding of fundamental material–process–performance relationships. This study presents the development, implementation, and assessment of an integrated lecture–laboratory framework for AM education at the New Jersey Institute of Technology (NJIT). Two complementary courses were developed: an undergraduate course (Introduction to 3D Printing, CHE 415) and a graduate course (Additive Manufacturing and Applications, CHE 722). The curriculum integrates instruction in AM technologies, materials, and digital workflows with hands-on design challenges, team-based projects, and structured literature reviews, enabling students to engage in the complete design-to-fabrication process. Student learning outcomes were evaluated over multiple academic years using ABET-aligned assessments, grade distributions, and student self-assessments. Results demonstrate consistently high levels of student proficiency and engagement, with strong performance in design, problem-solving, and communication skills. The courses also attracted students from diverse disciplines, underscoring the interdisciplinary nature of AM education. While limitations remain in providing hands-on exposure to a broader range of AM technologies, ongoing expansion of laboratory infrastructure is expected to address these challenges. Overall, this work demonstrates that an integrated, project-based approach effectively bridges theory and practice and provides a scalable model for incorporating AM into engineering curricula. Full article

Vision Transformer (ViT) has been widely adopted in the computer vision community. However, the standard ViT often contains many parameters, usually performs poorly when trained from scratch on medium-scale datasets, and does not explicitly preserve the local spatial and channel-wise structures within each token. This work proposes a novel model called the Token-Shared Convolutional Projection Vision Transformer (TSCP-ViT). The core idea of TSCP-ViT is to integrate convolutional layers into the multi-head attention mechanism and to apply the same convolutional operation independently to each token, where each token exhibits spatial 2D multi-channel characteristics. In addition, this work introduces a Transformer decoder immediately after each Transformer encoder, enabling the classification tokens to aggregate information from all tokens and be updated using statistical information. Moreover, a trainable Non-Reversing Gate GELU (NRG-GELU) activation is also proposed. Comparative experiments on CIFAR-100, Food-101, and ImageNet100 show that, under comparable parameter counts and without pretraining or knowledge distillation, TSCP-ViT substantially surpasses ViT, outperforms CvT, outperforms ResNet on Food-101, and approaches ResNet on CIFAR-100 and ImageNet100, although with considerably higher FLOPs. Full article

The increasing complexity and volume of technical documentation, including requirements specifications, patents, and engineering reports, create significant challenges for manual analysis and knowledge extraction. This paper includes a systematic review of methods for semantic content analysis of technical documents, with a particular focus on Natural Language Processing (NLP) techniques and Transformer-based models. The study formalizes the task of structured information extraction and provides a mathematical description of Named Entity Recognition (NER) as a core subtask. A practical case study demonstrates an end-to-end NER pipeline for Russian-language technical requirements, leveraging ruRoberta-large via spaCy-transformers. The results highlight both the potential and limitations of current approaches, emphasizing the critical role of annotation consistency and document format normalization. This work contributes to the development of intelligent systems for engineering documentation analysis and outlines key directions for future research. Full article