Search Results (1,994)

In recent years, China has vigorously promoted the EPC mode in the construction industry. However, under the weak trust environment of China’s construction industry, both owners and general contractors are involved in the design stage of EPC projects. Owing to conflicting interests in the design stage, there is a two-stage design interest paradox between the owners and general contractors of Chinese EPC projects, and this causes significant difficulties and challenges for project implementation. To resolve this paradox, this study proposes the “DART-PDCA” design management model by integrating value co-creation theory with the PDCA cycle. Applied to the Yuzhou High-speed Rail Station Square and Related Infrastructure PPP Project and the extended case, the model demonstrates how it resolves the paradox by (1) establishing structured dialogue platforms for aligning evolving design intentions, (2) enhancing information access and transparency through agreed protocols, and (3) facilitating dynamic risk assessment and allocation mechanisms. The results confirm that (1) the two-stage design interest paradox negatively impacts design management quality in China’s low-trust environment; and (2) the “DART-PDCA” design management model effectively resolves this paradox, leading to demonstrable improvements in design management quality, efficiency, and stakeholder alignment. This research forges novel interdisciplinary linkages among owner–general contractor relationships, design management, and EPC projects, providing critical insights into managing multi-organizational dynamics in complex EPC project environments. Full article

This study investigates the influence of adaptive leadership on crisis management effectiveness in complex construction engineering projects in Saudi Arabia. Adaptive leadership was conceptualized through six core dimensions: Flexibility in Decision-Making, Emotional Intelligence, Leader-Follower Communication, Problem-Solving Adaptability, Resilience in Leadership, and Fostering Collaboration. The study aimed to evaluate the impact of these leadership dimensions on crisis response effectiveness and safety outcomes within the high-risk, dynamic environment of the Saudi construction sector. A quantitative cross-sectional survey was conducted among managerial and supervisory personnel across major engineering and construction firms in Saudi Arabia. A total of 183 valid responses were obtained using a non-probability convenience sampling technique. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicated that five adaptive leadership dimensions—Flexibility in Decision-Making, Emotional Intelligence, Problem-Solving Adaptability, Resilience in Leadership, and Fostering Collaboration—had significant positive effects on crisis management effectiveness. However, Leader-Follower Communication did not demonstrate a statistically significant relationship with crisis outcomes. The findings contribute theoretical value by validating an adaptive leadership framework tailored to engineering project crises. Practically, the study underscores the importance of enhancing leadership flexibility, emotional intelligence, and collaborative engagement to strengthen crisis responsiveness and project continuity in Saudi construction firms. Recommendations include the development of targeted leadership training programs and the integration of digital technologies to support adaptive decision-making in real-time crisis conditions, resulting in better Safety and Crisis Management. Although, study limitations include reliance on self-reported data and the context-specific focus on the Saudi construction sector, which may affect generalizability, the findings are contextualized through comparison with international literature to support broader relevance. Full article

Background/Objectives: Glioblastomas are a part of adult-type diffuse gliomas, the most common and most aggressive primary brain tumors in adults (glioblastoma, IDH-wildtype). The identification of the genetic factors associated with glioblastoma could be an important contribution to the diagnosis and early prevention of this disease. We compiled data from the global literature and analyzed clinically relevant variants implicated in glioblastoma risk. Methods: PubMed, Web of Science, and Scopus were used as databases. Associations between the SNPs and glioblastoma risk were calculated as a measure of pooled odds ratios (ORs) and 95% confidence intervals. Pearson’s analysis was used for epidemiological correlation (only p-values less than 0.05 were statistically significant), and data were obtained from the World Health Organization platform and the 1000 Genomes Project. Statistical analysis was performed using Review Manager (RevMan) 5.4 and BioEstat 5.0. Results: CCDC26 rs891835 G/T, G/G, and G/T-G/G genotypes were analyzed and determined to increase glioblastoma risk (G/T OR = 1.96, 95% CI: 1.38–2.77, p = 0.0002, I² = 0%; G/G OR = 1.33, 95% CI: 0.46–3.85, p = 0.60, I² = 0%; G/T − G/G OR = 1.96, 95% CI: 1.39–2.76, p = 0.0001, I² = 0%). Epidemiological correlation also demonstrated that the higher the frequency of the CCDC26 rs891835 variant, the higher the incidence of that variant in the European population. Conclusions: CCDC26 rs891835 may serve as a predictive biomarker for glioblastoma, IDH-wildtype risk and may influence higher glioblastoma incidence rates in the European population. Full article

The spread of the invasive mosquitoes Aedes albopictus, Aedes aegypti, and Aedes japonicus in Spain represents an increasing public health risk due to their capacity to transmit arboviruses such as dengue, Zika, and chikungunya, among others. Traditional field entomological surveillance remains essential for tracking their spread, but it faces limitations in terms of cost, scalability, and labor intensity. Since 2014, the Mosquito Alert citizen-science project has enabled public participation in surveillance through the submission of geolocated images via a mobile app, which are identified using AI in combination with expert validation. While field surveillance provides high accuracy, citizen science offers low-cost, large-scale, real-time data collection aligned with open data management principles. It is particularly useful for detecting long-distance dispersal events and has contributed up to one-third of the municipal detections of invasive mosquito species since 2014. This study assesses the value of integrating both surveillance systems to capitalize on their complementary strengths while compensating for their weaknesses in the areas of taxonomic accuracy, scalability, spatial detection patterns, data curation and validation systems, geographic precision, interoperability, and real-time output. We present the listing of municipal detections of these species from 2004 to 2024, integrating data from both sources. Spain’s integrated approach demonstrates a pioneering model for cost-effective, scalable vector surveillance tailored to the dynamics of invasive species and emerging epidemiological threats. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

In software development, software requirement engineering (SRE) is an essential stage that guarantees requirements are clear and unambiguous. However, incomplete inconsistency, and ambiguity in requirement documents often occur, which can cause project delay, cost escalation, or total failure. In response to these challenges, this paper introduces a machine learning method to automatically identify the risk levels of software requirements according to ensemble classification methods. The labeled textual requirement dataset was preprocessed utilizing conventional preprocessing techniques, label encoding, and oversampling with the synthetic minority oversampling technique (SMOTE) to handle class imbalance. Various ensemble and baseline models such as extra trees, random forest, bagging with decision trees, XGBoost, LightGBM, gradient boosting, decision trees, support vector machine, and multi-layer perceptron were trained and compared. Five-fold cross-validation was used to provide stable performance evaluation on accuracy, area under the ROC curve (AUC), F1-score, precision, recall, root mean square error (RMSE), and error rate. The bagging (DT) classifier achieved the best overall performance, with an accuracy of 99.55%, AUC of 0.9971 and an F1-score of 97.23%, while maintaining a low RMSE of 0.03 and error rate of 0.45%. These results demonstrate the effectiveness of ensemble-based classifiers, especially bagging (DT) classifiers, in accurately predicting high-risk software requirements. The proposed method enables early detection and mitigation of requirement risks, aiding project managers and software engineers in improving resource planning, reducing rework, and enhancing overall software quality. Full article

This study investigates the application of two complementary risk analysis methods—risk matrix and Monte Carlo simulation—in the context of a large-scale office building construction project. The paper explores the theoretical and practical aspects of construction risk, focusing on how probabilistic and qualitative tools can support informed decision-making. Twelve key risks, including both threats and opportunities, were identified and quantified using expert judgment and historical data. The risk matrix provided an initial prioritization of risk severity and likelihood, while Monte Carlo simulations allowed for the modeling of uncertainty in cost outcomes across a probabilistic spectrum. The results indicate a high level of consistency between the methods, with both identifying value engineering as a dominant opportunity and network documentation errors as critical threats. Monte Carlo simulations further revealed that under proper risk management, the project is likely to avoid additional cost overruns with 60% certainty. This integrated approach provides practical insights for contractors and project managers seeking to enhance the robustness of risk assessment in complex construction environments. Full article

Roads are important infrastructure for a country’s economic and social development. The stability of subgrade slopes, as one of the most prominent engineering issues in roads, is of great significance for the assessment and analysis of the stability and prevention of subgrade slopes. In order to further promote the development of roadbed slope stability and prevention engineering, improve the design level of road engineering slope engineering disaster management projects, and reduce the cost of engineering investment, this paper starts from the category of roadbed slope damage form, respectively, for the research method of slope stability, slope stability influencing factors, and slope damage prevention and control measures to carry out a review, based on the current status of the research and the future direction of research. It was found that the damage forms of roadbed slopes are roughly divided into four categories: landslide, avalanche, slump, and spalling. A field test is the most accurate and practical means of evaluating slope stability research. The current analysis of the factors affecting slope stability is too independent, and there is a need to analyze the coupling relationship between the various influencing factors as a whole. Furthermore, the paper outlines a range of preventive measures and control strategies, including slope reinforcement techniques, vegetation cover, and drainage management, which have proven effective in reducing slope failure risks. These results provide critical insights into the design of more resilient roadbed slopes, with potential implications for reducing engineering costs and improving the safety and sustainability of road infrastructure. Full article

As local risk assessments are fundamental for risk management and mitigation strategies, this work introduces a methodology for assessing multi-hazard scenarios of extreme compounded events and their duration using daily time series of surface variables from high-resolution climate simulations during historical and future periods under RCP8.5. The aim was to investigate the return level extremes of 20- and 50-year periods of hazards occurring within specific durations and concurrent extreme values of other surface variables, for selected locations in Greece. In addition, future changes in the temporal occurrence of compounded hazards involving precipitation and wind with temperature extremes were performed based on temperature extreme percentiles. The assessment revealed the geographical dependence in the projected occurrence, intensity, and duration of compounded multi-hazard extremes, emphasising the need for high spatial resolution climate data for their investigation. The highlights of the findings include a significant increasing trend of compounded multi-hazard extremes, e.g., hot days and tropical nights, milder winter minimum temperatures with lower rainfall extremes, hotter and windier events of shorter duration, and longer precipitation extremes with increased extreme temperatures. The projections showcased the impact of climate change on extreme compounds with a multitude of interesting findings associated with significant changes in their duration, intensity, and temporal occurrence. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

With intensifying watershed pollution pressures and growing ecological vulnerability, scientifically revealing and enhancing the water environmental carrying capacity is crucial for ensuring the long-term health of the basin and the sustainable socioeconomic development of the region. However, the dynamic regulatory mechanisms linking narrow-sense and broad-sense water environmental carrying capacity remain poorly understood, limiting the development of integrated management strategies. This study systematically investigated the changing trends of both the narrow-sense and broad-sense water environmental carrying capacity in the Harbin section of the Songhua River basin through model calculations, along with the regulatory mechanisms of its key influence indicators. The results of the study on the carrying capacity of the water environment in the narrow sense show that permanganate, total phosphorus, and ammonia nitrogen exhibited partial carrying capacity across water periods, while dissolved oxygen decreased during flat and dry periods, with only limited capacity remaining at the Ash River estuary and in the Hulan River. The biochemical oxygen demand in the Ash River was consistently overloaded, and total nitrogen showed insufficient capacity except during the abundant water period. Broad-sense analysis indicated that improving urbanization quality, water supply infrastructure, and drinking water safety could effectively reduce future overload risks, with projections suggesting a transition from critical to loadable levels by 2030, though latent threats persist. Correlation analysis between narrow- and broad-sense indicators informed targeted control strategies, including stricter regulation of nitrogen- and phosphorus-rich industrial discharges, restoration of aquatic vegetation, and periodic dredging of riverbed sediments. This work is the first to dynamically integrate pollutant and socio-economic indicators through a hybrid modelling framework, providing a scientific basis and actionable strategies for improving water quality and achieving sustainable management in the Songhua River Basin. Full article

This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they focused on climate adaptation, flood protection, or other goals) and how they tracked performance. We are hoping to bring some clarity to a very scattered field, helping us to see patterns in what is actually being carried out in terms of landscape interventions and increasing sea levels. We are hoping to provide a practical reference for making better, more climate-responsive design decisions. Coastal cities face escalating climate-driven threats from increasing sea levels and storm surges to urban heat islands. These threats are driving increased interest in nature-based solutions (NbSs) as green adaptive alternatives to traditional gray infrastructure. Despite an abundance of individual case studies, there have been few systematic syntheses aimed at landscape designers and managers linking design typologies, project framing, and performance outcomes. This study addresses this gap through a meta-synthesis of 123 implemented coastal landscape interventions aimed directly at landscape-oriented research and professions. Flood risk reduction was the dominant framing strategy (30.9%), followed by climate resilience (24.4%). Critical evidence gaps emerged—only 1.6% employed integrated monitoring approaches, 30.1% provided ambiguous performance documentation, and mean monitoring quality scored 0.89 out of 5.0. While 95.9% of the projects acknowledged SLR as a driver, only 4.1% explicitly integrated climate projections into design parameters. Community monitoring approaches demonstrated significantly higher ecosystem service integration, particularly cultural services (36.4% vs. 6.9%, $p<0.001$), and enhanced monitoring quality (mean score 1.64 vs. 0.76, $p<0.001$). Implementation barriers spanned technical constraints, institutional fragmentation, and data limitations, each affecting 20.3% of projects. Geographic analysis revealed evidence generation inequities, with systematic underrepresentation of high-risk regions (Africa: 4.1%; Latin America: 2.4%) versus concentration in well-resourced areas (North America: 27.6%; Europe: 17.1%). Full article

Climate change poses significant threats to urban tree health and survival worldwide. This study evaluates climate suitability risks for 12 common tree species in Wuhan, a Chinese metropolis facing escalating climate challenges. We analyzed risk dynamics and interspecific variations across three periods, the baseline (1981–2022), near future (2023–2050), and distant future (2051–2100), quantifying climate risk as differences between local climate conditions and species’ climatic niches. We further examined how species’ geographic distribution and functional traits influence these climate risks. The results revealed significant warming trends in Wuhan during the baseline period (p < 0.05), with projected increases in temperature and precipitation under future scenarios (p < 0.05). The most prominent risk factors included the precipitation of the driest month (PDM), annual mean temperature (AMT), and maximum temperature of the warmest month (MTWM), indicating intensifying drought–heat stress in this region. Among the studied species, Cedrus deodara (Roxb.) G. Don, Platanus acerifolia (Aiton) Willd., Metasequoia glyptostroboides Hu & W.C.Cheng, and Ginkgo biloba L. faced significantly higher hydrothermal risks (p < 0.05), whereas Koelreuteria bipinnata Franch. and Osmanthus fragrans (Thunb.) Lour. exhibited lower current risks but notable future risk increases (p < 0.05). Regarding the factors driving these interspecific variation patterns, the latitude of species’ distribution centroids showed significant negative correlations with the risk values of the minimum temperature of the coldest month (MTCM) (p < 0.05). Among functional traits, the wood density (WD) and xylem vulnerability threshold (P50) were negatively correlated with precipitation-related risks (p < 0.05), while the leaf dry matter content (LDMC) and specific leaf area (SLA) were positively associated with temperature-related risks (p < 0.05). These findings provide scientific foundations for developing climate-adaptive species selection and management strategies that enhance urban forest resilience under climate change in central China. Full article

Preventive conservation (PC) of built heritage has proved to be one of the most efficient and sustainable approaches to ensure its long-term preservation. Nevertheless, the management of all the areas involved in a PC project is complex, often resulting in poor interaction between them. This research proposes a GIS-based methodology for integrating data from different PC areas into a centralised digital model, establishing a Common Data Environment (CDE) to optimise PC strategies for heritage systems in complex contexts. Applying this method to the pavilions of the 1929 Ibero-American Exhibition in Seville (Spain), the study addresses five key PC areas: active follow-up, damage detection and assessment, risk analysis, maintenance, and dissemination and valorisation. The approach involved designing a robust relational database structure—using PostgreSQL—tailored for heritage management, defining several data standardisation criteria, and testing semi-automated procedures for generating multi-scale 2D and 3D GIS (LOD2 and LOD4) entities using remote sensing data sources. The proposed spatial database has been designed to function seamlessly with major GIS platforms (QGIS and ArcGIS Pro), demonstrating successful integration and interoperability for data management, analysis, and decision-making. Geographic web services derived from the database content were created and uploaded to a WebGIS platform. While limitations exist, this research demonstrates that simplified GIS models are sufficient for managing PC data across various working scales, offering a resource-efficient alternative compared to more demanding existing methods. Full article

The Euphrates–Tigris Basin is experiencing significant environmental transformations due to climate change, Land Use and Land Cover Change (LULCC), and anthropogenic pressures. This study employs Earth Map, an open-access remote sensing platform, to comprehensively assess climate trends, vegetation dynamics, water resource variability, and land degradation across the basin. Key findings reveal a geographic shift toward aridity, with declining precipitation in high-altitude headwater regions and rising temperatures exacerbating water scarcity. While cropland expansion and localized improvements in land productivity were observed, large areas—particularly in hyperarid and steppe zones—show early signs of degradation, increasing the risk of dust source expansion. LULCC analysis highlights substantial wetland loss, irreversible urban growth, and agricultural encroachment into fragile ecosystems, with Iraq experiencing the most pronounced transformations. Climate projections under the SSP245 and SSP585 scenarios indicate intensified warming and aridity, threatening hydrological stability. This study underscores the urgent need for integrated water management, Land Degradation Neutrality (LDN), and climate-resilient policies to safeguard the basin’s ecological and socioeconomic resilience. Earth Map is a vital tool for monitoring environmental changes, offering rapid insights for policymakers and stakeholders in this data-scarce region. Future research should include higher-resolution datasets and localized socioeconomic data to improve adaptive strategies. Full article