Search Results (293)

The conservation of architectural heritage (AH) in regions threatened by natural and human-induced factors requires interdisciplinary approaches that integrate physical documentation with semantic modeling. This study introduces a comprehensive framework combining Historic Building Information Modeling (HBIM) with ontology-based modeling aligned with the CIDOC Conceptual Reference Model (CIDOC CRM). Focusing on the Big Gate and adjacent curtain walls in Ibb, Yemen, where the gate is entirely lost, the study reconstructs the structure using historical photographs, eyewitness accounts, and analogical references. The methodology incorporates UAV and terrestrial photogrammetry surveys, point cloud generation, and semantic enrichment using Autodesk Revit V. 2024 and Protégé V. 5.5. Decay phenomena such as cracks, efflorescence, and disintegration were ontologically classified and spatially linked to the HBIM model, revealing deterioration patterns concerning historical phases and environmental exposure. The resulting system enables dynamic documentation, facilitates strategic conservation planning, and enhances data interoperability across heritage platforms. The proposed framework is transferable to other heritage sites, supporting both the conservation of extant structures and the reconstruction of lost ones. Full article

The construction industry is a cornerstone of New Zealand (NZ)’s economic growth, yet strategic infrastructure planning is constrained by fragmented and inconsistent pipeline data. Despite the increasing availability of construction pipeline datasets in NZ, their limited clarity, interoperability, and standardisation impede effective forecasting, policy development, and investment alignment. These challenges are compounded by disparate data structures, inconsistent reporting formats, and semantic discrepancies across sources, undermining cross-agency coordination and long-term infrastructure governance. To address this issue, the study begins by assessing the quality of four prominent pipeline datasets using Wang and Strong’s multidimensional data quality framework. This evaluation provides a necessary foundation for identifying the structural and semantic barriers that limit data integration and informed decision-making. The analysis examines four dimensions of data quality: accessibility, intrinsic quality, contextual relevance, and representational clarity. The findings reveal considerable inconsistencies in data fields, classification systems, and levels of detail across the datasets. Building on these insights, this study also develops a conceptual minimum dataset (MDS) framework comprising three core thematic categories: project identification, project characteristics, and project budget and timing. The proposed conceptual MDS includes unified data definitions, standardised reporting formats, and semantic alignment to enhance cross-platform usability and data confidence. This framework applies to the New Zealand context and is designed for replication in other jurisdictions, supporting the global push toward open, high-quality infrastructure data. The study contributes to the construction informatics and infrastructure planning by offering a practical solution to a critical data governance issue and introducing a transferable methodology for developing minimum data standards in the built environment to enable more informed, coordinated, and evidence-based decision-making. Full article

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote sustainable farming practices. An integrated geospatial approach was adopted, combining Remote Sensing (RS), Geographic Information Systems (GIS), and Multi-Criteria Decision Analysis (MCDA). Key thematic layers, including soil type, land use/land cover, slope, and drainage density were processed in a GIS environment to model runoff potential. The Soil Conservation Service Curve Number (SCS-CN) method was used to estimate surface runoff. Criteria were weighted using the Analytical Hierarchy Process (AHP), enabling a structured and consistent evaluation of site suitability. The resulting suitability map classifies the region into four categories: very high suitability (10.2%), high (26.6%), moderate (40.4%), and low (22.8%). The integration of RS, GIS, AHP, and MCDA proved effective for strategic RWH site selection, supporting cost-efficient, sustainable, and data-driven agricultural planning in water-stressed environments. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

Tourism development in coastal zones is often guided by marketing strategies focused on promotion, without real integration with the ecological, identity, and planning challenges facing these territories. This disconnection compromises environmental resilience, dilutes local cultural identity, and hinders adaptive governance in contexts of increasing tourism pressure and climate change. In response to this problem, the article presents the concept of Blue Marketing, a place-based, sustainability-oriented approach designed to guide communication, product development, and governance in marine and coastal destinations. Drawing on socio-environmental marketing and inspired by Integrated Coastal Zone Management (ICZM), the study proposes a Blue Marketing Decalogue (BMD), structured into three thematic blocks: (1) Ecosystem-focused sustainability, (2) cultural identity and territorial uniqueness, and (3) strategic planning and adaptive governance. Methodologically, the decalogue is empirically grounded in a territorial diagnosis of the Barbate–Vejer coastal corridor (Cádiz, Spain), developed through Geographic Information Systems (GIS), local planning documents, and field observations. This case study provides a detailed analysis of ecological vulnerabilities, cultural resources, and tourism dynamics, offering strategic insights transferable to other coastal contexts. The BMD incorporates both strategic and normative instruments that support the design of responsible tourism communication strategies, aligned with environmental preservation, community identity, and long-term planning. This contribution enriches current debates on sustainable tourism governance and provides practical tools for coastal destinations aiming to balance competitiveness with ecological responsibility. Ultimately, Blue Marketing is proposed as a vector for transformation, capable of reconnecting tourism promotion with the sustainability challenges and opportunities of coastal regions. Full article

This study develops the Information Systems–Organizational Fit Alignment Model (ISOFAM) to evaluate information systems (IS) alignment during the pre-combination phase of mergers and acquisitions (M&A)—a critical yet underexplored stage in integration planning. Through constructivist grounded theory and the reanalysis of qualitative data from two anonymized M&A cases—one domestic (Slovenian) and one cross-border (European)—this study identifies four diagnostic dimensions: Technical Compatibility, Functional Complementarity, Cultural and Governance Fit, and Planning Maturity. ISOFAM is operationalized through visual tools, including the Risk–Opportunity Diagnostic Matrix, IS Misalignment Escalation Flowchart, and Temporal Integration Framework, which facilitate early alignment and strategic foresight. These contributions position IS as a strategic pre-combination priority, enhancing both theoretical and practical outcomes in digital M&A. Full article

Bagheria, located on the northern coast of Sicily, is home to one of the Mediterranean’s most remarkable ensembles of Baroque villas, constructed between the 17th and 18th centuries by the aristocracy of Palermo. Originally situated within a highly structured rural landscape of citrus groves, gardens, and visual axes, these monumental residences have undergone substantial degradation due to uncontrolled urban expansion throughout the 20th century. This study presents a diachronic spatial analysis of Bagheria’s territorial transformation from 1850 to 2018, integrating historical cartography, aerial photography, satellite imagery, and Geographic Information System (GIS) tools. A total of 33 villas were identified, georeferenced, and assessed based on their spatial integrity, architectural condition, and relationship with the evolving urban fabric. The results reveal a progressive marginalization of the villa system, with many heritage assets now embedded within dense residential development, severed from their original landscape context and deprived of their formal gardens and visual prominence. Comparative insights drawn from analogous Mediterranean heritage landscapes, such as Ortigia (Siracusa), the Appian Way (Rome), and Athens, highlight the urgency of adopting integrated conservation frameworks that reconcile urban development with cultural and ecological continuity. As a strategic response, the study proposes the creation of a thematic cultural route, La città delle ville, to enhance the visibility, accessibility, and socio-economic relevance of Bagheria’s heritage system. This initiative, supported by adaptive reuse policies, smart heritage technologies, and participatory planning, offers a replicable model for sustainable territorial regeneration and heritage-led urban resilience. Full article

Decentralized desalination systems driven by renewable energy sources have surfaced as a feasible way to alleviate water scarcity in arid and rural areas. This bibliometric study aims to clarify the research trends, conceptual frameworks, and cooperative dynamics in the scientific literature on decentralized renewable-powered desalination techniques. Using a thorough search approach, 1354 papers were found. Duplicates, thematically unrelated works, and entries with poor information were removed using the PRISMA 2020 framework. A selected 832 relevant papers from a filtered dataset were chosen for in-depth analysis. Quantitative measures were obtained by means of Bibliometrix; network visualisation was obtained by means of VOSviewer (version 1.6.19) and covered co-authorship, keyword co-occurrence, and citation structures. Over the previous 20 years, the data show a steady rise in academic production, especially in the fields of environmental science, renewable energy engineering, and water treatment technologies. Author keyword co-occurrence mapping revealed strong theme clusters centred on solar stills, thermoelectric modules, reverse osmosis, and off-grid systems. Emphasizing current research paths and emerging subject borders, this paper clarifies the intellectual and social structure of the field. The outcomes are expected to help policy creation, cooperative projects, and strategic planning meant to hasten innovation in sustainable and decentralized water desalination. Full article

This paper investigates integrating a sensory data model for managing an existing 50-year-old building. A primary challenge in retrofitting older structures is the optimal deployment of high-quality sensors, systematic data acquisition, and subsequent data management. To address this, the study implemented a network of over 50 sensors connected via 270 m of wired infrastructure, deliberately avoiding wireless transmission to ensure data reliability. This configuration generates 5568 data points daily, which are archived on a dedicated server. The data is planned for integration into the Campus Geographical Information System (GIS), enabling private and public access. A methodology was employed, involving the strategic placement of sensors based on building use patterns, continuous data monitoring, and iterative sensor performance evaluation. The findings from the study indicate that integrating sensory data through this structured approach significantly enhances building management capabilities. Specifically, the results demonstrate improved energy efficiency and environmental performance, which is particularly relevant for public and educational facilities. The research highlights that a data-driven, monitoring-based management system can optimize operational functions and inform future retrofitting strategies for aging buildings. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

Climate change represents a complex and multifaceted challenge for health systems, particularly in the African region, where the research has predominantly focused on physical health impacts while overlooking critical mental health dimensions. Our central hypothesis is that integrating culturally adapted mental health and psychosocial support (MHPSS) into climate resilience frameworks and disaster response planning will significantly reduce psychological distress (e.g., anxiety, depression, and trauma) and enhance adaptive capacities among both mainstream and migrant communities in disaster-prone African regions. This rapid review methodology systematically explores the intricate relationships between climate change, mental health, and migration by examining the existing literature and identifying significant information gaps. The key findings underscore the urgent need for targeted research and strategic interventions that specifically address mental health vulnerabilities in the context of climate change. This review highlights how extreme weather events, environmental disruptions, and forced migration create profound psychological stressors that extend beyond immediate physical health concerns. This research emphasizes the importance of developing comprehensive adaptation strategies integrating mental health considerations into broader climate response frameworks. Recommendations emerging from this assessment call for immediate and focused attention on developing specialized research, policies, and interventions that recognize the unique mental health challenges posed by climate change in African contexts. We also note the current limitations in the existing national adaptation plans, which frequently overlook mental health dimensions, thereby underscoring the necessity of a more holistic and nuanced approach to understanding climate change’s psychological impacts. In this exploratory study, we intended to provide a crucial preliminary assessment of the complex intersections between climate change, mental health, and migration, offering valuable insights for policymakers, researchers, and healthcare professionals seeking to develop more comprehensive and responsive strategies in an increasingly challenging environmental landscape. Full article

As cities worldwide pursue sustainability, integrating renewable energy has emerged as a strategic priority in urban planning. This research provides a case study investigation into how Dubai South, a distinctive aerotropolis combining aviation, logistics, and residential sectors, can implement a comprehensive renewable energy strategy aligned with the UAE’s clean energy goals. Grounded in the theoretical frameworks of Sustainable Strategic Management (SSM) and Energy Management Systems (EMSs), and informed by global best practices and advanced technological innovations, this study proposes a strategic roadmap tailored to the complex energy demands and urban dynamics of Dubai South. Using the Dubai South HQ solar deployment as a baseline, this research explores technical, regulatory, and economic barriers alongside key enabling factors. Its core contribution is the development of a scalable strategy for renewable energy integration in aerotropolis settings, offering practical insights for policymakers, urban planners, and developers aiming to advance sustainability in rapidly evolving, logistics-based cities. Full article

Walking and cycling, as active modes of transportation, play a vital role in advancing sustainable urban mobility by reducing emissions and improving public health. However, widespread adoption faces challenges such as inadequate infrastructure, safety concerns, socio-cultural barriers, and policy limitations. This study systematically reviewed 56 peer-reviewed articles from 2004 to 2024, across 30 countries across five continents, employing the Capability, Opportunity and Motivation-Behaviour (COM-B) framework to identify the main drivers of walking and cycling behaviours. Findings highlight that the lack of dedicated infrastructure, inadequate enforcement of road safety measures, personal and traffic safety concerns, and social stigmas collectively hinder active mobility. Strategic interventions such as developing integrated cycling networks, financial incentives, urban planning initiatives, and behavioural change programs have promoted increased engagement in walking and cycling. Enhancing urban mobility further requires investment in pedestrian and cycling infrastructure, improved integration with public transportation, the implementation of traffic-calming measures, and public education campaigns. Post-pandemic initiatives to establish new pedestrian and cycling spaces offer a unique opportunity to establish enduring changes that support active transportation. The study suggests expanding protected cycling lanes and integrating pedestrian pathways with public transit systems to strengthen safety and accessibility. Additionally, leveraging digital tools can enhance mobility planning and coordination. Future research is needed to explore the potential of artificial intelligence in enhancing mobility analysis, supporting the development of climate-resilient infrastructure, and informing transport policies that integrate gender perspectives to better understand long-term behavioural changes. Coordinated policy efforts and targeted investments can lead to more equitable transportation access, support sustainability goals, and alleviate urban traffic congestion. Full article

Accurately predicting the success of startup financing is critical for strategic business planning and informed investor decision-making. Traditional financing prediction models typically focus on a company’s financial indicators to explore the impact of factors such as resource allocation and strategic choices on financing success, yet they often overlook the important role of social media as an external source of information in influencing financing performance. To address this gap, this paper focuses on the role of social media sentiment in predicting startup financing success and proposes a decision support system (DSS) framework that integrates multi-source data. Specifically, this study combines financial data from the Crunchbase platform with company-related social media news data from Twitter. The BERTweet model is used to perform sentiment analysis on the social media texts, extracting sentiment features such as polarity and intensity to capture public attitudes and expectations toward the company. Subsequently, financial indicators, social media numerical features, and sentiment features are combined to construct a decision support system for predicting financing success using a deep neural network (DNN). Experimental results show that the decision support system incorporating social media data significantly outperforms traditional decision support systems in prediction accuracy, with sentiment features further enhancing the model’s ability to identify a company’s financing performance. Our study provides strong support for understanding the profound influence of public sentiment, offering practical guidance for startups to optimize financing strategies and for investors to make informed decisions. Full article