Search Results (179)

Urban flood disaster management is an interdisciplinary field that integrates hydrology, geology, engineering, and urban planning, with prediction, assessment, and optimization serving as its core components. However, a comprehensive and systematic synthesis of recent developments in this domain remains limited, constraining both theoretical understanding and practical advancement. To address this gap, this study conducts an in-depth analysis of urban flood management research as a systematic review, with a particular focus on advances in prediction, assessment, and optimization. Utilizing a multistep holistic review, combining bibliometric and scientometric analysis with structured literature categorization, the research critically examines and synthesizes relevant findings. This study analyzed 166 research papers related to urban flood management within the Web of Science database. Through co-citation and keyword co-occurrence analyses, five dominant research dimensions are identified: physics-based simulation methods, data-driven approaches, risk assessment tasks, optimization strategies, and miscellaneous emerging topics. Based on these insights, we propose a task-oriented framework that systematically integrates prediction, assessment and optimization across the four phases of disaster management: mitigation, prevention, emergency response and recovery. This framework aids scholars and practitioners in understanding and implementing effective techniques and strategies. The study’s findings shed light on key trends and potential future directions, providing a roadmap for further exploration of urban flood management and guiding professionals in related fields. Full article

Organizational learning refers to the systematic development, exchange and dissemination of knowledge throughout the organization. Organizational learning processes in construction are disrupted by the decentralized flow of information and the temporary, short-term nature of project teams. The emergence of Building Information Modelling (BIM) has significantly enhanced the ability to capture and disseminate construction project knowledge within the architecture, engineering, construction, and facilities management (AEC-FM) sector. Despite this progress, existing research has predominantly focused on the technical aspects of BIM, with limited evidence on its effects on organizational learning capabilities. This study addresses this gap by examining how BIM shapes organizational learning mechanisms within AEC-FM contexts. Employing a systematic literature review (SLR) approach, 104 articles from the Scopus database were analyzed using scientometric and thematic analyses. The systematic review of the literature was carried out following the PRISMA guidelines. The SLR provided a comprehensive examination of BIM’s contribution to strengthening the three core organizational learning mechanisms: experience accumulation, knowledge articulation, and knowledge codification. The thematic analysis revealed seven BIM-enabled organizational learning factors that are expected to strengthen learning mechanisms in AEC-FM organizations: agility of thinking and reasoning skills; enhanced decision-making; interconnected stakeholders’ relationships; integrated business processes; BIM-facilitated project knowledge sharing; BIM-supported project knowledge retention; and BIM-supported project knowledge extraction. Findings suggest that BIM significantly facilitates learning mechanisms within AEC-FM firms. A conceptual model of BIM-supported learning mechanisms was developed to highlight opportunities for enhancing organizational learning capabilities in the BIM environment. Full article

University rankings based on scientometric indicators frequently rely on compensatory aggregation models that allow extreme values to dominate the evaluation, while also remaining sensitive to outliers and unstable weighting procedures. These issues reduce the reliability and interpretability of the resulting rankings. This study proposes a hybrid BWM–GRA–PROMETHEE (BGP) framework that combines judgement-based weighting Best-Worst Method (BWM), outlier-resistant normalization Grey Relational Analysis (GRA), and a non-compensatory outranking method Preference Ranking Organization Methods for Enrichment Evaluation (PROMETHEE II). The framework is applied to an expert-validated set of scientometric indicators to generate more stable and behaviorally grounded rankings. The results show that the proposed method maintains stability under weight and threshold variations and preserves ranking consistency even under outlier-contaminated scenarios. Comparative experiments further demonstrate that BGP is more robust than Additive Ratio Assesment (ARAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), achieving the highest Spearman. This study contributes a unified evaluation framework that jointly addresses three major methodological challenges in scientometric ranking, outlier sensitivity, compensatory effects, and instability from data-dependent weighting. By resolving these issues within a single integrated model, the proposed BGP approach offers a more reliable and methodologically rigorous foundation for researchers and policymakers seeking to evaluate and enhance research performance. Full article

Natural disasters pose significant risks to engineering projects, necessitating a systematic analysis of their risk factors. This study focuses on identifying and mapping these factors using a mixed-methods approach that integrates a qualitative literature review with scientometric analysis via Social Network Analysis (SNA). Through a meta-analysis of 81 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect, the qualitative review establishes a comprehensive list and classification of 48 natural disaster risk factors, categorized into geological, climatic, hydrological, topographic, and biological groups, while providing a theoretical foundation. SNA complements this by quantifying co-occurrence frequencies, centrality metrics (degree, betweenness, and eigenvector), and network structures, revealing dynamic interactions, key influential factors, and research gaps—particularly in under-explored areas like hydrological hazards, extreme temperatures, lightning storms, and temperature variations—that qualitative methods alone might miss. This multi-perspective integration highlights discrepancies between theoretical discussions and practical applications, underscoring overlooked cascading effects. Findings emphasize the absence of an integrated model for all 48 factors, urging the development of a holistic predictive framework to bolster disaster resilience. Theoretically, the study offers a novel SNA-based quantification of factor importance and interrelations, addressing literature fragmentation. Practically, it guides project managers in prioritizing risks for optimized design, resource allocation, and prevention strategies. Future research should incorporate real-time data sources to refine this framework for enhanced risk management in engineering projects. Full article

An effective housing policy must ensure affordability for individuals across all income levels by integrating advanced technological innovations with comprehensive socioeconomic strategies. Affordable housing fosters social inclusion, whereas sustainability supports long-term environmental protection and economic stability. The success and long-term sustainability of affordable housing initiatives are heavily influenced by current socioeconomic conditions, emphasizing the need for context-specific, inclusive, and sustainable housing solutions. Benchmarks are crucial in affordable housing to determine if it is climate-positive, aligning with the goals of the United Nations’ Sustainable Development Goal 11.1, which seeks to provide affordable and sustainable housing for everyone by 2030. This study uses the Scopus database to perform a scientometric analysis of 595 publications (2015–2024) on sustainability and affordability in housing. Using R-Studio 2025.05.1 + 513.pro3 and VOSviewer 1.6.20, it examines bibliographic trends, research gaps, and collaboration patterns across countries and journals. This study highlights performance thresholds related to economic, environmental, energy, territorial, and climatic factors. However, cost and ecological objectives can cause conflict with each other practically, and hence a balanced approach including green practices, efficient materials, and subsidies is crucial. There is a need for policymakers to address market gaps to prevent socially exclusive or environmentally harmful outcomes, maintain long-term urban resilience, and ensure sustained urban resilience and equitable access to affordable, sustainable housing by 2030. Integrating sustainable materials, circular and climate-resilient design, smart technologies, inclusive governance, and evidence-based policies is crucial for advancing affordable, equitable, and resilient housing. This approach guides future research and policy toward long-term social, economic, and environmental benefits. The findings and recommendations promote sustainable, affordable housing, emphasizing the need for further research on climate-resilient, energy-efficient, and cost-effective building solutions. Full article

Photovoltaic (PV) power forecasting combined with energy storage systems (ESS) is critical for grid stability and renewable energy optimization. Machine learning (ML) techniques have shown promise in improving PV forecast accuracy and ESS operation. However, the intersection of PV forecasting and ESS control remains underexplored, warranting a systematic review of recent advances and evaluation of ML effectiveness in PV–ESS integration. To assess research trends in ML-based PV forecasting with ESS (scientometric analysis), synthesize state-of-the-art ML approaches for PV–ESS forecasting (systematic review), and quantify their overall predictive performance via meta-analysis of the coefficient of determination (R²). A comprehensive search of Scopus (2010–2025) was conducted following PRISMA 2020 guidelines. Studies focusing on ML-based PV power forecasting integrated with ESS were included. Multiple reviewers screened the records and extracted data. Study quality was appraised using Joanna Briggs Institute checklists. A random-effects meta-analysis of R² was performed to aggregate model performance across studies. The search identified 227 records; 50 studies were included in the review and 5 in the meta-analysis. Publications grew rapidly after 2018, indicating increased interest in PV–ESS forecasting. Deep learning models and hybrid architectures were the most frequently studied and outperformed traditional methods, while integrating PV forecasts with ESS control consistently improved operational outcomes. Common methodological limitations were noted, such as limited external validation and non-standardized evaluation metrics. The meta-analysis found a pooled R² ~0.95 (95% CI) with no heterogeneity (I² = 0), and no evidence of publication bias. ML-based forecasting significantly improves PV–ESS performance, underscoring AI as a key enabler for effective PV–ESS integration. Future research should address remaining gaps and explore advanced approaches to further enhance PV–ESS outcomes. Full article

The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impact remain limited. This study applies a scientometric approach to 135 documents indexed in Scopus and Web of Science between January 2017 and June 2025, using Bibliometrix Bibliometrix (R package version 4.5.2), VOSviewer version 1.6.20, and Voyant Tools to examine publication trends, leading contributors, collaboration patterns, thematic structures, and reported economic outcomes. The analysis shows a strong upward trajectory with an estimated 66.48% annual increase in publications, identifying Xiukang Wang and Shaowen Wang as leading contributors among 791 authors from diverse institutions. Thematic analysis reveals three interconnected clusters: (i) precision agriculture and remote sensing as the sensing backbone; (ii) prediction and soil analysis as data-driven decision-support mechanisms; and (iii) vegetation indexes and productivity as measurement tools linking spectral information to yield and input use. Economic evidence includes high disease-detection accuracy (up to 95%), notable pesticide-use reductions (around 40%), improved autonomous-navigation precision (<6 cm error), and crop-detection performance exceeding 99%. However, adoption challenges persist, including technological heterogeneity, high implementation costs, limited model transferability, and varying levels of digital readiness across regions. Overall, the findings indicate that optical sensors and deep learning are transitioning from experimental applications to technologies with measurable economic impact, offering guidance for researchers, policymakers, technology developers, and agricultural producers seeking economically viable precision-agriculture solutions. Full article

Digital servitization represents a relatively nascent field of research, whose conceptual and terminological apparatus is still under development. This necessitates its systematization and organization to facilitate a clear understanding of the processes inherent to digital servitization and the underlying patterns in achieving the objectives of sustainable economic development. The purpose of this study is to formulate and structure the conceptual framework for research in the field of digital industrial servitization within the context of addressing sustainable economic development challenges. The research methodology is based on the application of scientometric analysis using VOSviewer software to identify and map the interrelationships of keywords. The selection of scientific publications for analysis was conducted following the PRISMA methodology. The results of the study demonstrate that the domain of digital servitization has emerged from the integration of concepts from servitization, digitalization, circularization, and sustainable development, thereby incorporating their fundamental terms and concepts. The paper presents a structured overview of the research field concerning digital industrial servitization in the context of sustainable development, organized along distinct research avenues. This structured approach enables the development of strategies and business models for digital industrial servitization that are aligned with the overarching logic of sustainable development. These models are founded on integrated solutions that combine the principles of digitalization and circularization. Full article

The cultivated carrot (Daucus carota L. subsp. sativus) is a globally important crop valued for its high content of beta-carotene, vitamins, and other bioactive compounds. Advances in molecular genetics and genomics have driven improvements in yield, stress tolerance, disease resistance, pigment accumulation, and nutritional quality. Here we present a comprehensive scientometric analysis of 398 peer-reviewed articles on carrot genetics (1964–2023), integrating keyword co-occurrence mapping and co-authorship network analysis to identify research lines, methodological approaches, and international collaboration. The focus of research was predominantly on Plant Biology (67.59%), followed by Conservation (15.58%) and Plant Breeding (10.55%). The integration of omics technologies has yielded new insights into abiotic stress tolerance, carotenoid and anthocyanin biosynthesis, and genetic diversity; however, significant challenges remain in translating genomic resources into routine breeding practice and conservation of genetic resources. We recommend prioritizing genome-assisted breeding strategies, functional genomics and interdisciplinary, multi-omics approaches to accelerate the development of resilient and high-quality carrot cultivars under climate change. Full article

Windows play a crucial role in maintaining indoor thermal comfort, influenced by occupant behavior, passive design strategies, and advanced technologies that contribute to sustainable building practices. Despite advancements in adaptive and occupant-centric design, critical gaps remain unresolved in understanding of multi-climate adaptability, the complex interrelation between window operation and occupant behavior, and the integration of occupant roles into energy-related strategies under emerging technologies. This scoping review synthesizes peer-reviewed studies to assess the importance of window design (geometry, glazing, shading), operational strategies (manual control to AI-driven systems), and technological approaches (passive to smart systems) on thermal comfort, energy performance, and occupant behavior. Using bibliometric and scientometric analyses, the review focuses on four primary research clusters: thermal comfort and occupant behavior, window operation strategies, their impact on energy performance, and sustainability, with an emphasis on emerging trends. The findings highlight that glazing technologies, shading systems, and operational choices have a significant impact on both comfort and energy efficiency. The study develops a framework linking thermal comfort to window operation, occupant behavior, and climate context while conceptualizing a comprehensive design matrix and outlining future research directions aligned with the Sustainable Development Goals (SDG 3: health and well-being, SDG 7: clean energy, and SDG 11: sustainable cities and communities). Full article

Prefabricated Construction (PC) Technology is recognized for its advantages in reducing carbon emissions, lowering energy consumption, conserving materials, and improving waste management. Despite significant research efforts, few systematic analyses have been conducted to consolidate the current understanding of carbon emissions in PC. To address this gap, the present study undertakes a comprehensive review using a synergistic approach that integrates scientometric and rigorous qualitative analyses. The aim is to synthesize state-of-the-art research on carbon emissions in PC and provide insightful directions for future academic work in this field. A database of 114 relevant journal articles was compiled through a meticulous data collection process, followed by scientometric analysis to map influential journals, key articles, active countries, and emerging research trends. The qualitative analysis identifies prevailing research domains, highlights critical research gaps, and anticipates future needs. This study contributes to enriching the existing knowledge base and offers both theoretical insights and practical guidance for advancing low-carbon construction, optimizing assessment frameworks, and promoting interdisciplinary collaboration and informed policymaking. Full article

Sustainability-driven products rely heavily on market success and customer awareness to achieve their intended impact. While knowledge-based enterprises (KBEs) are committed to enhancing the sustainability of their products and services, they often face challenges in building brand equity and raising awareness of what makes their products sustainable, particularly within social media engagement. AI agents transfer social media and enable KBEs to promote sustainable products by enhancing brand equity. However, this requires an effective strategy to enhance brand equity among the new social media generation. This study highlights critical factors identified through a rigorous literature review that influence the enhancement of brand equity in KBEs, with a focus on sustainable development and brand innovation. This research employs a mixed-method approach using VOSviewer and NVivo software. A scientometric review of articles published in the Scopus database and a critical thematic analysis were conducted. Furthermore, Google Trends data were utilized to complement the analysis and propose a set of key factors and future directions. Out of 1552 articles extracted from scientific databases from 1994 until 2024, 33 articles were selected and thoroughly analyzed after a rigorous screening and evaluation process. The review demonstrates that social media enhances brand equity for KBEs by increasing brand awareness, improving their efforts in sustainable development, strengthening brand image, strengthening customer loyalty, and facilitating effective interactions. Moreover, leveraging brand innovation and focusing on sustainable development through social media amplifies these positive impacts, further influencing the brand’s perceived quality. This research uniquely identifies the value of sustainability in the brand equity model and the moderating roles of technological complexity and customer environmental sensitivity in this process. The research offers significant insights for managers of science and technology parks, guiding them to adopt effective social media strategies that create sustainable competitive differentiation and strengthen brand positioning in competitive markets. This study also establishes a strong foundation for future studies focusing on AI-powered tools for brand equity within the digital environment by proposing a set of factors that can be used by researchers to develop an initial model. Full article

Building maintenance decisions are complex and often influenced by various factors. Multi-criteria decision-making (MCDM) methods have been widely applied to address this complexity, yet guidance on selecting the most appropriate method for specific problems remains limited. Considering these, the purpose of this study is to provide a guidance for the nexus of MCDM methods and facilities management (FM) and building maintenance with the aim of supporting the selection of the most appropriate MCDM method for a specific problem. To achieve this, the study first offers a comprehensive overview of MCDM applications in FM and building maintenance through a systematic literature review guided by the PRISMA framework combined with scientometric analysis. This approach identifies key trends, reviews the methods most frequently employed, and outlines future research directions. From an initial pool of 4291 records retrieved from Scopus and Web of Science between 2000 and 2024, 107 studies were further analyzed. Using VOSviewer and Bibliometrix, the review maps the application of MCDM methods in FM and building maintenance over this period. As a major outcome of the study, a contextual MCDM Method Selection Matrix is developed, linking specific FM and maintenance problems to the most suitable MCDM methods. The findings reveal growing adoption of hybrid MCDM methods and highlight persistent challenges, including subjectivity, uncertainty, expert qualifications, methodological gaps, and technology integration in the decision-making process. By providing structured guidance on method selection, the contextual MCDM Method Selection Matrix supports researchers and practitioners in achieving consistent, data-driven, and context-sensitive decision-making, ultimately enhancing the longevity, efficiency, and sustainability of the built environment. Full article

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems. Full article

Background: Digital Pathology (DP) and Artificial Intelligence (AI) have strongly developed in recent years, especially in pathology, with a high interest in dermatopathology. Accelerated by the COVID-19 pandemic, DP and AI are now integrated in pathology, research and education, bringing value to histopathological diagnoses, telepathology and personalized medicine. This narrative review presents a comprehensive literature review by defining three research directions, using scientometric analysis, of the current state of DP and AI in pathology and dermatopathology. Methods: The research was conducted through the Pubmed and Web of Science databases, within the research period of January 2019–July 2025: a two-phase methodology. Four independent pathologists selected the articles in accordance with the inclusion and exclusion criteria, and the synthesis of the articles was based on three research directions. Results: The research shows that CNN (Convolutional Neural Network), AI powered diagnostic platforms and telepathology strongly contribute to increasing the speed and accuracy of diagnostics, especially on cutaneous malignant skin tumors. There are still several challenges and limitations in terms of validation, interoperability, initial high implementation costs, ethics and transparency in AI and equity in healthcare. Conclusions: DP and AI are essential pillars of modern dermatopathology, with a high necessity of standardization, regulation and a multidisciplinary approach. Full article