Search Results (881)

Urban inefficient land redevelopment (UILR) is crucial for sustainable urban development, yet its progress is driven by the interplay of multiple factors. To systematically uncover the driving mechanisms and dynamic patterns of these factors, an integrated analytical approach combining Structural Equation Modeling (SEM) and Fuzzy Cognitive Map (FCM) is developed in this study. Based on 222 valid survey responses from professionals across eight cities in China’s Yangtze River Delta region, five key factors are identified within the “drivers–pressure–enablers” conceptual framework: economic incentives, environmental objectives, social needs, policy guidance, and implementation conditions. SEM is first employed to examine static causal relationships, and the quantified pathway effects are subsequently incorporated into an FCM model to simulate the long-term evolution. The results reveal the following: (i) All five factors exert significant direct effects, with economic incentives, environmental objectives, and policy guidance also demonstrating notable indirect effects. (ii) The factors exhibit distinct temporal characteristics: policy guidance acts as a “fast variable” enabling short-term breakthroughs; economic incentives serve as a “strong variable” driving medium-term progress; and social needs function as a “slow variable” with long-term benefits. (iii) Policy guidance is essential, as its absence leads to persistently low effectiveness, while its synergy with implementation conditions can achieve satisfactory performance even without economic incentives. The combined SEM–FCM approach validates static hypotheses and simulates dynamic scenarios, offering a new perspective for analyzing complex driving mechanisms of UILR and providing practical insights for targeted redevelopment strategy design. Full article

Humanity faces converging crises of climate change, biodiversity loss, inequality, and social fragmentation. These challenges are usually treated as technical or policy problems, yet their persistence suggests deeper causes in the paradigms through which human beings understand themselves and act in the world. Systems thinking highlights that paradigms shape perception, motivation, and institutions, but it does not specify which paradigms best support sustainability. This article develops a conceptual framework to examine how paradigms of human nature have shifted historically and how these shifts influence sustainability outcomes. Using a comparative synthesis of wisdom traditions (Greek, Islamic, Christian, Jewish, Hindu, Confucian, and Daoist) together with modern and late-modern frameworks, the study identifies key differences in how human faculties and values are ordered, and how these differences manifest in ecological and social outcomes. A paradigm–perception–intention–action–impact feedback model is introduced to explain how worldviews propagate into institutions and outcomes, and how inversions contribute to ecological overshoot, inequality, and dislocation. The article contributes a synthesized map of paradigms across traditions, a causal schema linking paradigm shifts to sustainability outcomes, practice-oriented design principles, and a research agenda for testing the framework in sustainability transitions. Re-examining paradigms is argued to be a critical leverage point for durable sustainability. Full article

Modelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for representing utility networks; however, most existing implementations rely on relational databases, where connectivity must be reconstructed through joins rather than represented as explicit relationships. This creates challenges when managing densely connected network structures. This study introduces the UNADE–Labelled Property Graph (UNADE-LPG) model, a graph-based representation that maps the classes, relationships, and constraints defined in the UNADE Unified Modelling Language (UML) schema into nodes, edges, and properties. A conversion pipeline is developed to generate UNADE-LPG instances directly from CityGML UNADE datasets encoded in GML, enabling the population of graph databases while maintaining semantic alignment with the original schema. The approach is demonstrated through two case studies: a schematic network and a real-world water system from Frankston, Melbourne. Validation procedures, covering structural checks, topological continuity, classification behaviour, and descriptive graph statistics, confirm that the resulting graph preserves the semantic structure of the UNADE schema and accurately represents the physical connectivity of the network. An analytical path-finding query is also implemented to illustrate how the UNADE-LPG structure supports practical network-analysis tasks, such as identifying connected pipeline sequences. Overall, the findings show that the UNADE-LPG model provides a clear, standards-aligned, and operationally practical foundation for representing utility networks within graph environments, supporting future integration into digital-twin and network-analytics applications. Full article

This study provides an integrated synthesis of Artificial Intelligence (AI) applications in Biomedical 3D Printing, mapping the conceptual and structural evolution of this rapidly emerging field. The bibliometric analysis, based on 229 publications indexed in the Web of Science Core Collection (2018–2025) and visualised in CiteSpace, identifies three interconnected research domains: AI-driven design and process optimisation, data-assisted bioprinting for tissue engineering, and the development of smart and adaptive materials enabling 4D functionalities. The results highlight a clear progression from algorithmic control of additive manufacturing parameters toward predictive modelling, deep learning, and autonomous fabrication systems. Leading contributors include China, India, and the USA, while journals such as Applied Sciences, Polymers, and Advanced Materials act as major dissemination platforms. Emerging clusters around “4D printing”, “deep learning”, and “shape memory polymers” indicate a shift toward intelligent, sustainable, and personalised biomanufacturing. In addition, a qualitative synthesis of the most influential papers complements the bibliometric mapping, providing interpretative depth on the scientific core driving this interdisciplinary evolution. Overall, the study reveals the consolidation of a multidisciplinary research ecosystem in which computational intelligence and biomedical engineering converge to advance the next generation of adaptive medical fabrication technologies. Full article

This paper examines the changes in the level of informal and shadow economy, mapping their evolution within the EU and measuring their implications on economic growth. The study also addresses the issue of conceptual differences in the methodology for measuring these phenomena. We used a two-dimensional methodological approach, combining theoretical and empirical analysis. Initially, the bibliometric analysis—conducted exclusively on the Web of Science Core Collection to ensure methodological rigour, international comparability, and high-quality, standardised data—reveals the evolution of the subject and the inconsistencies in the conceptualisation and measurement of phenomena associated with the shadow economy. Subsequently, the normative analysis highlighted the most relevant norms, directives, and projects developed and applied at the European Union level to prevent and combat tax evasion activities. Finally, the empirical dimension of this study was conducted through structural equation modelling and fixed and random effects regressions, using data from the EU 27 member states for the period 2000–2022. Our results reveal a potential relationship between the level of scientific research and the prevalence of the shadow economy within EU countries and indicate a negative effect of the informal economy on economic growth, as undeclared work produces goods and services that are consumed in the informal economy and hinders economic growth. Since the level of the shadow economy has not decreased proportionally with the increase in the GDP per capita, we conclude that the efforts to combat the shadow economy are insufficient, and tax administration needs to be more drastic and efficient. Full article

This study conducts an Academic Literature Analysis of 75 maturity models to clarify how Industry 4.0 and Industry 5.0 are being conceptualized and assessed. We map model scope, level structures, evaluated dimensions, and enabling technologies and complement descriptive statistics with exploratory non-parametric tests on the relationship between level structure and dimensional breadth. Results show a persistent dominance of Industry 4.0 models (≈92%), alongside a recent but steady emergence of Industry 5.0 and hybrid approaches in the latest models. Structurally, five-level schemes prevail, balancing diagnostic granularity and comparability. Content-wise, Technology and Digitalization, Processes and Operations, and Management and Strategy remain core, while People and Competencies and Innovation gain relevance; Sustainability and Social Responsibility and Human–Machine Interaction appear with the rise of Industry 5.0. We contribute (i) an operational definition of “hybrid” maturity models to make the I4.0→I5.0 transition measurable, (ii) a meta-typology of maturity levels explaining the five-level preference, and (iii) an evidence-based technology cartography across models. The findings suggest that future designs should retain the digital backbone of I4.0 while integrating explicit indicators for human-centricity, sustainability, and resilience with transparent weighting and scenario-based validation. Full article

Over the past fifteen years, ship manoeuvring has evolved from a highly specialised branch of marine hydrodynamics into a key enabler within multidisciplinary research, integrating seakeeping and intact stability, and paving the way for digital twins and autonomous maritime systems. The scope of this review is to examine the existing literature in a way that paves the way forward for integration with robotics, aerial and surface drones, digital-twin (DT) ecosystems, and other interconnected autonomous platforms. This paper reviews the published articles during this period, tracing the field’s progression from classical hydrodynamic models to intelligent, data-centric, and regulation-aware maritime systems. Drawing on a structured bibliometric dataset covering 2010–2025, this study organises the literature into interconnected themes spanning physics-based manoeuvring models, adaptive and predictive control, machine learning and digital-twin (DT) technologies, collision-avoidance and regulatory reasoning, environmental performance, and cooperative autonomy. The analysis reveals the transition from static empirical modelling toward hybrid physics, artificial intelligence (AI) frameworks capable of capturing nonlinear dynamics, uncertainty, and multi-vessel interactions. At the same time, this review highlights the growing influence of Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the Second-Generation Intact Stability Criteria, and emissions-reduction targets in shaping technical developments. While learning-enabled prediction, model predictive control (MPC)-based regulatory compliance, and real-time DT synchronisation show increasing maturity, this study identifies unresolved challenges, including domain shift, model interpretability, certification barriers, multi-agent safety guarantees, and DT divergence under sparse data. By mapping both demonstrated capabilities and conceptual frontiers, this review presents manoeuvring as a central pillar of future Maritime Autonomous Surface Ships (MASS) operations and sustainable shipping. The findings outline a research agenda toward integrated, explainable, and environmentally aligned manoeuvring intelligence that can support safe, efficient, and regulation-compliant autonomous maritime systems. Full article

The article aims to determine how ergonomic measures support the achievement of ESG goals and how ergonomics as a discipline can be used in sustainability reporting. The study was designed as a mixed-method approach, started with a systematic review of the literature conducted according to the PRISMA protocol, and was followed by a qualitative analysis of the identified literature. The search strategy was based on a combination of keywords in the areas of ergonomics and environmental management. The results of the review identify the main trends in combination of ergonomics with ESG: Ergoecology, Green ergonomics, Environmental ergonomics, and Immaterial ergonomics, as well as indicating areas of objectives particularly reinforced by ergonomic interventions and documenting examples of good practices valuable for ESG reporting. The main results of the study are as follows: (1) organizing research trends in ergonomics for sustainable development; (2) a systematizing approach to green ergonomic practice; (3) a set of ergonomic practices for sustainability that are most frequently described in the literature; and (4) a conceptual model termed the Sustainability through Ergonomic Excellence Model (StEEM). The proposed framework organizes a range of practices into seven areas of excellence and assigns the collected green ergonomic practices to them, showing their contribution to implementing ESG metrics. The research carried out indicates that the role of ergonomics is still underestimated in current reporting standards. The proposed mapping and StEEM frameworks provide a framework to facilitate the systematic integration of ergonomics into ESG strategies and reporting and to provide a structured foundation for future empirical and evaluative research. Full article

This study undertakes a systematic literature review combined with bibliometric analysis to examine how abnormal returns are studied in relation to environmental, social, and governance (ESG) factors, investor sentiment, and dividend policy. Using RStudio version 2025.09.0+387 and VOSviewer version 1.6.20, we conduct a bibliometric study that integrates performance analysis, science mapping, and network analysis. The dataset consists of 532 publications published between 2000 and 2025 and indexed in the Web of Science and Scopus databases. Our results show that scholarly work on abnormal returns is organised around three main thematic areas. First, investor sentiment is closely linked with event study applications, behavioural finance explanations, and sentiment analysis, which underscores the importance of psychological influences in understanding market anomalies. Second, prior studies on dividend policy continue to rely heavily on event study designs to evaluate how markets react to dividend announcements. Third, investor sentiment and dividend policy are connected through their common focus on abnormal returns, which operate as a central conceptual link between these strands of literature. Although interest in behavioural and policy-related determinants of abnormal returns has grown over time, work that explicitly incorporates ESG considerations remains relatively marginal. This peripheral position points to an important gap, suggesting that the dynamic relationships among ESG performance, investor sentiment, dividend decisions, and abnormal returns are still not fully explored. The contribution of this study lies in bringing these elements together by mapping research on event studies while treating ESG performance as a potential market signal that may shape both investor sentiment and corporate financial policy. Full article

Background: Periodontitis is a common inflammatory disease characterized by progressive loss of alveolar bone. Cone-beam computed tomography (CBCT) can visualize 3D periodontal bone defects, but its interpretation is time-consuming and examiner-dependent. Deep learning may support standardized CBCT assessment if performance and biological relevance are adequately characterized. Methods: We used the publicly available MMDental dataset (403 CBCT volumes from 403 patients) to train a 3D ResNet-18 classifier for binary discrimination between periodontitis and healthy status based on volumetric CBCT scans. Volumes were split by subject into training (n = 282), validation (n = 60), and test (n = 61) sets. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), and calibration metrics with 95% bootstrap confidence intervals. Grad-CAM saliency maps were used to visualize the anatomical regions driving predictions. To explore phenotype-level biological concordance, we analyzed an independent gingival transcriptomic cohort (GSE10334, n ≈ 220 arrays after quality control) and an independent oral microbiome cohort based on 16S rRNA amplicon sequencing, using unsupervised clustering, differential expression/abundance testing, and pathway-level summaries. Results: On the held-out CBCT test set, the model achieved an AUROC of 0.729 (95% CI: 0.599–0.850) and an AUPRC of 0.551 (95% CI: 0.404–0.727). At a high-sensitivity operating point (sensitivity 0.95), specificity was 0.48, yielding an overall accuracy of 0.62. Grad-CAM maps consistently highlighted the alveolar crest and furcation regions in periodontitis cases, in line with expected patterns of bone loss. In the transcriptomic cohort, inferred periodontitis samples showed up-regulation of inflammatory and osteoclast-differentiation pathways and down-regulation of extracellular-matrix and mitochondrial programs. In the microbiome cohort, disease-associated samples displayed a dysbiotic shift with enrichment of classic periodontal pathogens and depletion of health-associated commensals. These omics patterns are consistent with an inflammatory–osteolytic phenotype that conceptually aligns with the CBCT-defined disease class. Conclusions: This study presents a proof-of-concept 3D deep learning model for CBCT-based periodontal disease classification that achieves moderate discriminative performance and anatomically plausible saliency patterns. Independent transcriptomic and microbiome analyses support phenotype-level biological concordance with the imaging-defined disease class, but do not constitute subject-level multimodal validation. Given the modest specificity, single-center imaging source, and inferred labels in the omics cohorts, our findings should be interpreted as exploratory and hypothesis-generating. Larger, multi-center CBCT datasets and prospectively collected paired imaging–omics cohorts are needed before clinical implementation can be considered. Full article

Deficiencies in road anomaly detection systems precipitate multifaceted risks, including elevated collision probabilities from unidentified hazards, compromised traffic flow efficiency, and exponential maintenance costs. Contemporary methods struggle with complex road environments, dynamic viewing perspectives, and limited datasets. We present AVD-YOLO, an enhanced YOLO variant that synergistically integrates Active Vision-Driven (AVD) multi-scale feature extraction with Position Modulated Attention (PMA) mechanisms. PMA addresses diminished target-background discriminability under variable illumination and weather conditions by capturing long range spatial dependencies, enhancing weak-feature target detection. The AVD technique mitigates missed detections caused by real-time viewing distance variations through adaptive multi-receptive field mechanisms, maintaining conceptual target fixation while dynamically adjusting feature scales. To address data scarcity, a comprehensive Multi-Class Road Anomaly Dataset (MCRAD) comprising 14,208 annotated images across nine anomaly categories is constructed. Experiments demonstrate that AVD-YOLO improves detection accuracy, achieving a 1.6% gain in mAP@0.5 and a 2.9% improvement in F1-score over baseline. These performance gains indicate both more precise localization of abnormal objects and a better balance between precision and recall, thereby enhancing the overall robustness of the detection model. Full article

Demand-responsive transit (DRT) is an innovative public transportation model that dynamically adjusts routes based on passengers’ specific demands. While existing studies offer insights into routing, scheduling, and network design, they remain fragmented, with limited integration of user behavior, policy relevance, and sustainability. To address these gaps, this paper develops a scenario-based evaluation framework that synthesizes bibliometric evidence, operational conditions, modeling approaches, and evaluated outcomes. Using CiteSpace, we conducted keyword co-occurrence and clustering analysis. Thematic clusters such as “routing and scheduling,” “network design,” “stated preference,” “public transport,” and “demand-responsive transit” were mapped to a three-tier analytical structure. Scenarios integrate economic, environmental, and social dimensions, enabling comparative insights across urban, rural, and intercity scenarios. The scenario-based approach offers two key advantages: (1) it captures heterogeneity across operational environments, ensuring that evaluation frameworks are not overly generalized. Research shows that urban scenarios emphasize scheduling precision, rural pilots face cost-efficiency but enhance resilience, and intercity services depend on multimodal synchronization. (2) It facilitates synthesis by linking technical models with real-world outcomes, enhancing policy relevance. This study contributes to sustainable transport research by providing a coherent, empirically validated, and conceptually integrated framework for evaluating DRT systems. Full article

Blue Finance has rapidly emerged as a strategic frontier for channeling capital toward sustainable and resilient ocean economies, connecting financial innovation with environmental governance and climate responsibility. However, its conceptual foundations remain fragmented, hindering theoretical integration and policy application. This study conducts a comprehensive bibliometric and science-mapping analysis of 217 Scopus-indexed publications (2007–2025), using Biblioshiny (Bibliometrix v4.2.2), VOSviewer v1.6.20, and Gephi v0.10.1 to trace the intellectual evolution, thematic configuration, and research agenda of Blue Finance. The analysis reveals a rapidly consolidating field that has evolved through three distinct phases, anchored in sustainability science but constrained by limited financial integration. The field’s cognitive structure is organized around three interlinked pillars: the climate–environmental interface, sustainability integration and governance, and innovative financial mechanisms enhancing economic resilience. Emerging research hotspots in blue bonds, sustainable finance, and blue justice signal a paradigm shift from normative ecological awareness to actionable, market-aligned resilience. The findings outline a forward-looking research agenda that strengthens theoretical consolidation, governance accountability, and sustainable investment frameworks. This study offers strategic guidance for researchers, investors, and policymakers, positioning Blue Finance as a transformative catalyst that unites innovation, resilience, and equity in shaping the future of sustainable finance. Full article

Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI’s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful ‘co-pilots’ for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators. Full article

This article introduces the Olfactory Attribution Circle (OAC), a conceptual tool for integrating olfaction, color and semantic attributes in the design of sensory atmospheres. Developed through a multi-method strategy, the research combined a literature review, semi-structured interviews with academic and industry sources, a case study of Every Human (Algorithmic Perfumery), and AI-assisted exploration. The review revealed a lack of tools operationalizing olfactory design within the built environment. Interviews provided practice-based insights on inclusion, intensity calibration, and feasibility, while the case study demonstrated the potential and limitations of AI-driven personalization. AI was employed to generate mappings between 60 essences, semantic attributes, and chromatic codes, refined through authorial curation. Results highlight systematic crossmodal correspondences between scents, linguistic attributes, and chromatic values, underscoring the importance of crossmodal congruence in designing coherent sensory experiences. The OAC enables congruent, human-centered olfactory design, though cultural variability and semantic ambiguity limit universal application. The study positions the OAC as both a methodological contribution and a foundation for future empirical testing across diverse cultural contexts. Full article