Search Results (6,173)

Historic earthen architecture—particularly rammed earth—is underrepresented in digital heritage initiatives despite its widespread historical use and vulnerability to degradation. This paper presents a novel methodology for integrating semantic, geometric, and geospatial information from earthen heritage into a unified digital environment, bridging Heritage Building Information Modeling (HBIM) and Geographic Information Systems (GIS) through an object-relational database. The proposed workflow enables automated and bidirectional data exchange between Revit (via Dynamo scripts) and open-source GIS tools (QGIS and PostgreSQL/PostGIS), supporting semantic alignment and spatial coherence. The method was tested on seven fortified rammed-earth sites in the southwestern Iberian Peninsula, chosen for their typological and territorial diversity. Results demonstrate the feasibility of multiscale documentation and analysis, supported by a structured database populated with geometric, semantic, diagnostic, and environmental information, enabling enriched interpretations of construction techniques, material variability, and conservation status. The approach also facilitates the integration of HBIM datasets into broader territorial management frameworks. This work contributes to the development of scalable, open-source digital tools tailored to vernacular heritage, offering a replicable strategy for bridging the gap between building-scale and landscape-scale documentation in cultural heritage management. Full article

Almost every cell of a multicellular organism is in contact with the extracellular matrix (ECM), which provides the shape and mechanic stability of tissue, organs and the entire body. At the molecular level, cells contact the ECM via integrins. Integrins are transmembrane cell adhesion molecules that connect the ECM to the cytoskeleton, which they bind with their extracellular and intracellular domains. Cysteine residues are abundant in both integrin subunits α and β. If pairwise oxidized into disulfide bridges, they stabilize the folding and molecular structure of the integrin. However, despite the oxidative environment of the extracellular space, not all pairs of cysteines in the extracellular integrin domains are permanently engaged in disulfide bridges. Rather, the reversible and temporary linkage of cystine bridges of these cysteine pairs by oxidation or their reductive cleavage can cause major conformational changes within the integrin, thereby changing ligand binding affinity and altering cellular functions such as adhesion and migration. During recent years, several oxidoreductases and thiol isomerases have been characterized which target such allosteric disulfide bridges. This outlines much better, albeit not comprehensively, the role that such thiol switches play in the redox regulation of integrins. The platelet integrin αIIbβ3 is the best examined example so far. Mostly referring to this integrin, this review will provide insights into the thiol switch-based redox regulation of integrins and the known effects of their allosteric disulfide bridges on conformational changes and cell functions, as well as on the machinery of redox-modifying enzymes that contribute to the redox regulation of cell contacts with the ECM. Full article

Sludge dewatering is a key step in the overall process of sludge treatment and disposal. In this study, ferric tannate was synthesized by chemically complexing tannic acid with Fe₂(SO₄)₃ under various conditions and then was innovatively employed to enhance electrochemical conditioning (ECC) for municipal sludge dewatering. The optimal preparation conditions of ferric tannate were determined as a tannic acid to iron ion molar ratio of 0.8:10, pH of 10, and reaction time of 2 h. Subsequently, ferric tannate-enhanced ECC was investigated under different dosages and operating parameters. The optimal conditions were identified as ferric tannate dosage of 20% total solid, voltage of 50 V, and reaction time of 30 min, under which capillary suction time, specific resistance to filtration, and water content of dewatered sludge cake decreased by 84.3%, 84.2%, and 17.6%, respectively. Results of the mechanism analysis indicated that ferric tannate effectively reduced sludge viscosity, increased zeta potential, and neutralized the negative surface charges via charge neutralization, hydrophobic interactions, and hydrogen bonding. Meanwhile, adsorption bridging promoted floc aggregation and particle growth. Compared with the ECC process alone, the addition of ferric tannate in the ferric tannate-enhanced ECC process generated more ^•OH, promoting the extracellular polymeric substance degradation and protein removal, thereby improving sludge hydrophobicity. Furthermore, the floc structure was reconstructed into a more compact and smooth morphology, facilitating the release of bound water during filtration. These findings provide new technical and theoretical support for the development of eco-friendly and efficient sludge conditioning and dewatering processes. Full article

This study employs a fluid–structure interaction (FSI) collision-modeling approach to investigate the hydrodynamic effects on impact forces during collisions involving ships and bridges. The influences of the collision speed, the mass of the ship, and the water-flow velocity on the impact force are investigated. The constant added-mass (CAM) method is a widely employed technique in relevant studies to account for water influence due to its efficiency in conserving computational resources and reducing analysis time. This method is also employed in numerical simulations for comparative analysis. The impact force and dynamical response of a container ship using the FSI and CAM methods are investigated to determine whether the CAM method is suitable for considering the influence of the water surrounding the ship. The impact forces assessed by numerical simulations are also compared with the existing formulae. It is found that the water flow significantly affects the collision force, which must be taken into account in high-energy collision situations. Full article

Today’s increasingly brittle, anxious, nonlinear, incomprehensible world of work calls for a socially and emotionally competent workforce. However, there is a clear gap in higher education settings regarding the assessment and promotion of students’ social and emotional competence (SEC). Our study aims to address the pressing need to evaluate and develop higher education students’ SEC by providing a tool to assess these skills, enabling researchers and practitioners to intervene and actively promote them. A sample of 767 higher education students (62.8% female, M = 22.88 years, SD = 7.30) enrolled in the study. Structural, discriminant and concurrent criterion validity, and reliability of the measure were assessed. A multiple hierarchical regression analysis tested the relation of SEC and well-being. Confirmatory Factor Analysis supported the hypothesized factorial structures. Coefficient omegas indicated adequate internal consistency. The results also supported the measure’s discriminant and criterion validities in relation to external measures. Multi-group invariance across gender and academic fields was attained. We found evidence of the predictive role of intrapersonal skills on students’ personal and academic well-being. This study bridges a gap in research and practice by introducing a psychometrically sound yet parsimonious instrument for assessing higher education students’ SEC. It also highlights the supportive role of SEC in promoting students’ well-being. Full article

Apolipoprotein A (ApoA) proteins, ApoA-I, ApoA-II, ApoA-IV, and ApoA-V, play critical roles in lipid metabolism, neuroinflammation, and blood–brain barrier integrity, making them pivotal in neurological diseases such as Alzheimer’s disease (AD), stroke, Parkinson’s disease (PD), and multiple sclerosis (MS). This review synthesizes current evidence on their structural and functional contributions to neuroprotection, highlighting their dual roles as biomarkers and therapeutic targets. ApoA-I, the most extensively studied, exhibits anti-inflammatory, antioxidant, and amyloid-clearing properties, with reduced levels associated with AD progression and cognitive decline. ApoA-II modulates HDL metabolism and stroke risk, while ApoA-IV influences neuroinflammation and amyloid processing. ApoA-V, although less explored, is implicated in stroke susceptibility through its regulation of triglycerides. Genetic polymorphisms (e.g., APOA1 rs670, APOA5 rs662799) further complicate disease risk, showing population-specific associations with stroke and neurodegeneration. Therapeutic strategies targeting ApoA proteins, including reconstituted HDL, mimetic peptides, and gene-based approaches, show promise in preclinical models but face translational challenges in human trials. Clinical trials, such as those with CSL112, highlight the need for neuro-specific optimization. Further research should prioritize human-relevant models, advanced neuroimaging techniques, and functional assays to elucidate ApoA mechanisms inside the central nervous system. The integration of genetic, lipidomic, and clinical data offers potential for enhancing precision medicine in neurological illnesses by facilitating the generation of ApoA-targeted treatments and bridging current deficiencies in disease comprehension and therapy. Full article

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda. Full article

This study investigates how Environmental, Social, and Governance (ESG) principles can be effectively integrated into the Balanced Scorecard (BSc) framework within local government organizations (LGOs) to enhance strategic planning and sustainability performance. Addressing a gap in the literature on ESG–BSc integration in the public sector, particularly in the Greek context, the study employs a dual-method approach. First, a bibliometric analysis of 3053 academic publications (1993–2025) was conducted using Scopus data to assess the evolution and thematic focus of ESG and BSc research. Second, a structured questionnaire—comprising both closed- and open-ended questions—was administered to 17 administrative staff members of a Greek LGO in 2024. This expert sample provided insights into strategic planning practices, ESG awareness, and performance management barriers. The findings reveal low levels of ESG–BSc application, a limited strategic capacity, and institutional resistance. In response, the study proposes a novel, context-sensitive ESG-integrated BSc model tailored for small municipalities, emphasizing stakeholder participation, operational simplicity, and the alignment with national sustainability policies. The model serves as a practical tool to support public sector performance measurement, bridging the gap between sustainability goals and local governance strategy. Full article

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between deaf individuals and the hearing population. However, achieving subject-independent SLR remains a significant challenge due to variations in signing styles, hand shapes, and movement patterns among users. Traditional Markov Chain-based models struggle with generalizing across different signers, often leading to reduced recognition accuracy and increased uncertainty. These limitations arise from the inability of conventional models to effectively capture diverse gesture dynamics while maintaining robustness to inter-user variability. To address these challenges, this study proposes an adaptive SLR framework that integrates Markov Chains with a Niching Genetic Algorithm (NGA). The NGA optimizes the transition probabilities and structural parameters of the Markov Chain model, enabling it to learn diverse signing patterns while avoiding premature convergence to suboptimal solutions. In the proposed SLR framework, GA is employed to determine the optimal transition probabilities for the Markov Chain components operating across multiple signing contexts. To enhance the diversity of the initial population and improve the model’s adaptability to signer variations, a niche model is integrated using a Context-Based Clearing (CBC) technique. This approach mitigates premature convergence by promoting genetic diversity, ensuring that the population maintains a wide range of potential solutions. By minimizing gene association within chromosomes, the CBC technique enhances the model’s ability to learn diverse gesture transitions and movement dynamics across different users. This optimization process enables the Markov Chain to better generalize subject-independent sign language recognition, leading to improved classification accuracy, robustness against signer variability, and reduced misclassification rates. Experimental evaluations demonstrate a significant improvement in recognition performance, reduced error rates, and enhanced generalization across unseen signers, validating the effectiveness of the proposed approach. Full article

Innovative and reliable structural health monitoring (SHM) is indispensable for ensuring the safety, dependability, and longevity of urban infrastructure. However, conventional methods lack full efficiency, remain labor-intensive, and are susceptible to errors, particularly in detecting subtle structural anomalies such as micro-cracks. To address this issue, this study proposes a novel deep-learning framework based on a modified Detection Transformer (DETR) architecture. The framework is enhanced by integrating a Vision Transformer (ViT) backbone and a specially designed Local Feature Extractor (LFE) module. The proposed ViT-based DETR model leverages ViT’s capability to capture global contextual information through its self-attention mechanism. The introduced LFE module significantly enhances the extraction and clarification of complex local spatial features in images. The LFE employs convolutional layers with residual connections and non-linear activations, facilitating efficient gradient propagation and reliable identification of micro-level defects. Thorough experimental validation conducted on the benchmark SDNET2018 dataset and a custom dataset of damaged bridge images demonstrates that the proposed Vision-Local Feature Detector (ViLFD) model outperforms existing approaches, including DETR variants and YOLO-based models (versions 5–9), thereby establishing a new state-of-the-art performance. The proposed model achieves superior accuracy (95.0%), precision (0.94), recall (0.93), F1-score (0.93), and mean Average Precision (mAP@0.5 = 0.89), confirming its capability to accurately and reliably detect subtle structural defects. The introduced architecture represents a significant advancement toward automated, precise, and reliable SHM solutions applicable in complex urban environments. Full article

Industrial systems operating under severe mission environment are frequently confronted with intricate failure behaviors arising from system internal degradation and extrinsic stresses, posing an elevating challenge to system survivability and mission reliability. Mission termination strategies are attracting increasing attention as an intuitive and effective means to mitigating catastrophic mission-induced risk. However, how to manage coupled risk arising from competing fault processes, particularly when these modes are interdependent, has been rarely reported in existing works. To bridge this gap, this study delves into a dynamic risk control policy for continuously degrading systems operating under a random shock environment, which yields competing and dependent fault processes. An optimal mission termination policy is developed to minimize risk-centered losses throughout the mission execution, whose optimization problem constitutes a finite-time Markov decision process. Some critical structural properties associated with the optimal policy are derived, and by leveraging these structures, the alerting threshold for implementing mission termination procedure is formally established. Alternative risk control policies are introduced for comparison, and experimental evaluations substantiate the superior model capacity in risk mitigation. Full article

The GLABROUS1 Enhancer Binding Protein (GeBP) gene family plays a crucial role in plant growth, development, and stress responses. In this study, 28 GeBP genes were identified in Brassica oleracea using HMMER and validated through multiple conserved domain databases. A phylogenetic tree was constructed based on the GeBP protein sequences from B. oleracea, Arabidopsis thaliana, Brassica rapa, and Brassica napus, dividing them into four evolutionary clades (A–D), which revealed a close evolutionary relationship within the genus Brassica. Conserved motif and gene structure analyses showed clade-specific features, while physicochemical property analysis indicated that most BoGeBP proteins are hydrophilic, nuclear-localized, and structurally diverse. Gene duplication and chromosomal localization analyses suggested that both segmental and tandem duplication events have contributed to the expansion of this gene family. Promoter cis-element analysis revealed a dominance of light-responsive and hormone-responsive elements, implying potential roles in photomorphogenesis and stress signaling pathways. Notably, the protein encoded by BolC01g019630.2J possesses both a transmembrane domain and characteristics of the Major Facilitator Superfamily (MFS) transporter family, and it is predicted to localize to the plasma membrane. This suggests that it may act as a molecular bridge between environmental signal perception and transcriptional regulation, potentially representing a novel signaling mechanism within the GeBP family. This unique feature implies its involvement in transmembrane signal perception and downstream transcriptional regulation under environmental stimuli, providing valuable insights for further investigation of its role in stress responses and metabolic regulation. Overall, this study provides a theoretical foundation for understanding the evolutionary patterns and functional diversity of the GeBP gene family in B. oleracea and lays a basis for future functional validation and breeding applications. Full article

This study investigates the flexural performance, tensile splitting strength, and fracture behaviour of self-compacting concrete (SCC) reinforced with polypropylene (PP) and basalt (BF) fibres. A total of eleven SCC mixtures with varying fibre types and volume fractions (0.025–0.25%) were tested at 7 and 28 days. In this study, the term high-performance concrete (HPC) refers to SCC mixtures with a 28-day compressive strength exceeding 60 MPa, as commonly accepted in European standards and literature. The control SCC achieved 68.2 MPa at 28 days. While fibre addition enhanced the tensile and flexural properties, it reduced workability, demonstrating the trade-off between mechanical performance and flowability in high-performance SCC. The experimental results demonstrate that both fibre types improve the tensile behaviour of SCC, with distinct performance patterns. PP fibres, owing to their flexibility and crack-bridging capability, were particularly effective at early ages, enhancing the splitting tensile strength by up to 45% and flexural toughness by over 300% at an optimal dosage of 0.125%. In contrast, BF fibres significantly increased the 28-day toughness (up to 15.7 J) and post-cracking resistance due to their superior stiffness and bonding with the matrix. However, high fibre contents adversely affected workability, particularly in BF-reinforced mixes. The findings highlight a dosage-sensitive behaviour, with optimum performance observed at 0.05–0.125% for PP and 0.125–0.25% for BF. While PP fibres improve crack distribution and early-age ductility, BF fibres offer higher stiffness and energy absorption in post-peak regimes. Statistical analysis (ANOVA and Tukey’s test) confirmed significant differences in the mechanical performance among fibre-reinforced mixes. The study provides insights into selecting appropriate fibre types and dosages for SCC structural applications. Further research on hybrid fibre systems and long-term durability is recommended. The results contribute to sustainable concrete design by promoting enhanced performance with low-volume, non-metallic fibres. Full article

Advancements in satellite sensor technology have enabled access to diverse remote sensing (RS) data from multiple platforms. Hyperspectral Image (HSI) data offers rich spectral detail for material identification, while LiDAR captures high-resolution 3D structural information, making the two modalities naturally complementary. By fusing HSI and LiDAR, we can mitigate the limitations of each and improve tasks like land cover classification, vegetation analysis, and terrain mapping through more robust spectral–spatial feature representation. However, traditional multi-scale feature fusion models often struggle with aligning features effectively, which can lead to redundant outputs and diminished spatial clarity. To address these issues, we propose the Cross Attention Bridge for HSI and LiDAR (CAB-HL), a novel dual-path framework that employs a multi-stage cross-attention mechanism to guide the interaction between spectral and spatial features. In CAB-HL, features from each modality are refined across three progressive stages using cross-attention modules, which enhance contextual alignment while preserving the distinctive characteristics of each modality. These fused representations are subsequently integrated and passed through a lightweight classification head. Extensive experiments on three benchmark RS datasets demonstrate that CAB-HL consistently outperforms existing state-of-the-art models, confirm that CAB-HL consistently outperforms in learning deep joint representations for multimodal classification tasks. Full article

Agriculture plays a crucial role in discussions about environmental challenges because of its ecological footprint and high vulnerability to environmental shocks. To better understand the social and behavioral dynamics among food producers and their perceptions of climate change-related risks, this paper draws on forty-one in-depth, semi-structured interviews with farmers and ranchers in Colorado (USA). Leveraging the concept of social capital, the paper extends the concept analytically in a direction missed by previous research highlighting network structures, such as by focusing on its bonding, bridging, and linking characteristics. Instead, focus centers on the inclusiveness and diversity of values, beliefs, worldviews, and cultural orientations within those networks, arguing that these elements can be just as influential, if not more so in certain instances, than structural qualities. The concept of social capital heterogeneity is introduced to describe a network’s level of diversity and inclusivity. The findings do not question the importance of studying network structures when trying to understand how food producers respond to threats like climate change; an approach that remains useful for explaining social learning, technology adoption, and behavioral change. However, this method misses elements captured through a subjective, interpretivist perspective. With social capital heterogeneity, we can use social capital to explore why farmers and ranchers hold specific values and risk perceptions, peering deeper “within” networks, while tools like quantitative social network analysis software help map their structures from the “outside.” Additionally, social capital heterogeneity provides valuable insights into questions about “effective” agro-environmental governance. The paper concludes by discussing practical implications of the findings and reviewing the limitations of the research design. Full article