Search Results (2,089)

Accurately estimating total accident costs is essential for managing construction safety budgets. While direct costs are well-documented, indirect costs—such as productivity loss, material damage, and legal expenses—are difficult to predict and often overlooked. Traditional ratio-based methods lack accuracy due to variability across projects and accident types. This study introduces a two-tiered machine learning framework for real-time indirect cost estimation. In the first tier, classification models (decision tree, random forest, k-nearest neighbor, and XGBoost) predict total cost categories; in the second, regression models (decision tree, random forest, gradient boosting, and light-gradient boosting machine) estimate indirect costs. Using a dataset of 1036 construction accidents collected over two years, the model achieved accuracies above 87% in classification and an R² of 0.95 with a training MSE of 0.21 in regression. Compared to conventional statistical and single-step models, it demonstrated superior predictive performance, reducing average deviations to $362.63 and sometimes achieving zero deviation. This framework enables more precise, real-time estimation of hidden costs, promoting better safety investment, reduced financial risk, and adaptive learning through retraining. When integrated with a national accident cost database, it supports ongoing improvement and informed risk management for construction stakeholders. Full article

By 2045, approximately 783.2 million people are projected to be diagnosed with type 2 diabetes mellitus (T2DM). In addition, obesity is expected to affect up to 22% of the world’s population or one in four people. The diabesity epidemic, a worrying trend in which T2DM and obesity co-occur, is becoming increasingly evident and could be the most significant epidemic of non-communicable chronic diseases in human history. The salutogenic pattern of health, which emphasises well-being and resistance resources, could be a promising solution to address this alarming worldwide problem. The salutogenic pattern of health has numerous positive effects on the health of persons with T2DM. These include reducing the risk of it, lowering some biomarkers and laboratory parameters related to its control, and promoting a better lifestyle, ultimately improving the overall quality of life. The salutogenic pattern of health offers an effective and evidence-based approach to address the growing global problem of chronic non-communicable diseases such as T2DM. Integrating this theory into standard modern medical practice has the potential to significantly improve health outcomes and overall patient well-being, making it an important direction for modern medicine. Accordingly, the aim is to explore and analyse the salutogenic pattern of health associated with T2DM in order to prevent it, but also the better management of it. Full article

Organizations increasingly face challenges in aligning project management and supply chain management, as project success relies on reliable supply chains while supply chain resilience hinges on effective project coordination. Despite the growing recognition of this interdependence, research remains fragmented, with most studies treating PM and SCM in isolation, limiting systematic theorization and practical guidance for integration. Addressing this gap, this review examines how optimization methods can facilitate PM–SCM integration. Through a comprehensive bibliometric analysis, incorporating co-citation, keyword co-occurrence, and cluster analysis, the study maps the intellectual structure, thematic evolution, and diverse applications of optimization within both domains. The findings uncover key trends, showing that optimization provides a methodological foundation for managing complexity and uncertainty across diverse integration scenarios, including project scheduling, resource allocation, and supply chain coordination. It further reveals that emerging technologies extend these optimization approaches by enabling real-time prediction, improved transparency, and adaptive decision-making. Theoretically, the study reframes PM and SCM as interdependent components of an adaptive system, offering a concrete and analytically tractable framework for operationalizing integration. Practically, it outlines strategies for strengthening cross-domain coordination and risk management through optimization-enabled solutions. By consolidating fragmented research, this review not only synthesizes the evolution of optimization in PM–SCM contexts but also identifies critical future opportunities, emphasizing the development of scenario-specific models, technology-driven integration mechanisms, and resilience-oriented strategies to enhance performance in project-intensive settings. Full article

As global climate change intensifies, understanding drought mechanisms is crucial for managing water resources and agriculture. This study employs the Standardized Precipitation–Actual Evapotranspiration Index (SPAEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI) to analyze meteorological, hydrological, and agricultural droughts in the lower Songhua River basin. The PLUS model was used to predict future land types, with model accuracy validated using four evaluation metrics. The projected land cover was integrated with CMIP6 data into the SWAT model to simulate future runoff, which was used to calculate future SRI. Drought events were extracted using run theory, while drought occurrence probability and return period were calculated via a Copula-based joint distribution model. Bayesian conditional probability was employed to explore propagation mechanisms. The results indicate a significant increase in multidimensional drought risk, particularly when the cumulative frequency of univariate droughts reaches 25%, 50%, or 75%. Although increased duration and intensity enhance the likelihood of combined droughts, extremely high values cause a decline in joint probability under “OR” and “AND” conditions. Under different climate scenarios, the recurrence intervals of meteorological, hydrological, and agricultural droughts in the lower reaches of the Songhua River exhibit increased sensitivity with severity, demonstrating consistent propagation patterns across the meteorological–hydrological–agricultural system. Meteorological drought was found to propagate to hydrological and agricultural drought within ~6.00 months and ~3.67 months, respectively, with severity amplifying this effect. Propagation thresholds between drought types decreased with increasing intensity. This study combined SWAT and CMIP6 models with PLUS-based land-use scenarios, highlighting that land-use changes significantly influence spatiotemporal drought patterns. Model validation (Kappa = 0.83, OA = 0.92) confirmed robust predictive accuracy. Overall, this study proposes a multidimensional drought risk model integrating Copula and Bayesian networks, offering valuable insights for drought management under climate change. Full article

To address land space issues in the West Qinling Mountains—including habitat degradation, ecosystem damage, spatial pattern imbalance and unsustainable resource use—this study employed the InVEST habitat quality model and spatial autocorrelation analysis. Based on land use remote sensing data from 1990 to 2020, we simulated and evaluated habitat quality and degradation over this 30-year period to propose scientific recommendations and optimization strategies. The results showed that: (1) The area of grassland and farmland in the West Qinling Mountains decreased significantly, the area of construction land, bare land and forest land increased mainly; (2) The habitat quality of the West Qinling Mountains was generally high, and the average of the habitat quality showed an overall decreasing trend in the period of 1990–2020. The proportion of worst habitat increased from 4.11% to 5.21%. The habitat quality is in the process of polarization, the spatial distribution of habitat quality in West Qinling shows a pattern of “high in the west, low in the north and southeast”; (3) The hot and cold spots of habitat quality in West Qinling are spatially manifested as “hotter in the west and the south; colder in the center and the east”; (4) The spatial clustering of habitat quality in the West Qinling Mountains is obvious, with the area of the high–high area and the low–low area increasing with time, the high–low area decreasing, and the low–high area slightly increasing. (5) The degree of habitat degradation in the West Qinling Mountains is generally low, the average value of degradation from 1990 to 2020 showed an upward trend, habitat degradation is in the process of converging to medium risk. The area of medium habitat degradation expanded by nearly 1.5 times between 1990 and 2020. The spatial distribution of habitat degradation in the West Qinling Mountains generally shows a pattern of low in the west and high in the north and high in the southeast. In future planning and management, the west Qinling Mountains should formulate and carry out scientific ecological restoration plans and projects in terms of improving the quality of habitats, curbing habitat degradation, optimizing the direction of regional land use and reasonably protecting land resources, in an effort to balance urban development and ecological protection, curbing ecological degradation, guaranteeing the sustainable development of the habitats in a benign direction. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article

Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed flood risk by integrating flood hazards based on hydrologic–hydraulic modeling with the Analytic Hierarchy Process-based Multi-Criteria Decision Analysis (AHP-MCDA) approach. Land cover maps for past years were generated using Landsat satellite images, and land use/land cover maps for future years were projected based on machine learning techniques. Flood simulations were conducted using a rainfall runoff inundation model with land cover maps for different flood scales to analyze the impact of urbanization and land cover changes on flood runoff, flood inundation extent, and flood inundation volume. Then, we comprehensively assessed flood risk by integrating hazard conditions simulated under different land cover conditions using a hydrologic–hydraulic model and the AHP-MCDA approach. The results showed that the flood inundation extent and the peak inundation volume for a 200-year flood may increase in the future by 10.66% and 15.04%, respectively, as a result of urbanization. The results also highlighted that urbanization may lead to an expansion of high-risk and very-high-risk areas in the future by 3.2% and 9.4%, respectively, indicating an increase in the valley’s flood vulnerability and greater severity of flood hazards. Full article

The impact of climate change on watershed hydrological processes has become increasingly significant, with the frequent occurrence of extreme flood events posing a severe challenge to the water resource security of the upper Yangtze River and the Three Gorges Reservoir. To enhance the understanding of runoff evolution under future climate scenarios, this study focuses on the upper Yangtze River Basin, integrating CMIP6 climate model data with the WRF-Hydro model to systematically assess the effects of climate change on runoff projections. Firstly, using CMFD data as a benchmark, the systematic biases in CMIP6 simulation results were evaluated and corrected. Precipitation and temperature data accuracy were improved through Local Intensity Correction (LOCI) and Daily Bias Correction (DBC). Secondly, a large-scale WRF-Hydro model suitable for the upper Yangtze River was developed and calibrated. Finally, based on the corrected CMIP6 data, the climate and runoff changes under the SSP2-4.5 and SSP5-8.5 scenarios for the period 2021–2050 were projected. The results show that: (1) the corrected CMIP6 data significantly improved issues of overestimated precipitation and underestimated temperature, providing a more realistic reflection of regional climate characteristics; (2) the sub-basin calibration strategy outperformed the overall calibration strategy at key control sites, with high runoff simulation accuracy during the validation period; (3) future temperatures exhibit a continuous rising trend, while precipitation changes are not significant—however, the magnitude and uncertainty of extreme events increase—and (4) under the SSP5-8.5 scenario, the inflow to the Three Gorges Reservoir during the wet season significantly increases, raising flood risk. The findings provide a scientific basis for understanding the hydrological response mechanisms in the upper Yangtze River Basin under climate change and offer decision-making support for flood control scheduling and water resource management at the Three Gorges Reservoir. Full article

Global climate change is intensifying extreme rainfall, exacerbating urban flood risks, and undermining the effectiveness of urban stormwater drainage systems (USDS) designed under stationary climate assumptions. While prior studies have identified general trends of increasing flood risk under climate change, they lack actionable connections between climate projections and practical flood risk assessment. Specifically, quantifiable forecasts of extreme rainfall for defined return periods and integrated frameworks linking climate modeling to hydrological simulation at the watershed scale. This study addresses these gaps by developing an integrated framework to assess USDS resilience under future climate scenarios, demonstrated through a case study in Changsha City, China. The framework combines dynamic downscaling of the MRI-CGCM3 global climate model using the Weather Research and Forecasting (WRF) model to generate high-resolution precipitation data, non-stationary frequency analysis via the Generalized Extreme Value (GEV) distribution to project future rainfall intensities (for 2–200-year return periods in the 2040s and 2060s), and a 1D-2D coupled urban flood model built in InfoWorks ICM to evaluate flood risk. Key findings reveal substantial intensification of extreme rainfall, particularly for long-term period events, with the 24 h rainfall depth for 200-year events projected to increase by 32% by the 2060s. Flood simulations show significant escalation in risk: for 100-year events, an area with ponding depth > 500 mm grows from 1.38% (2020s) to 1.62%, (2060s), and the 300–500 mm ponding zone expands by 21%, with long-return-period events (≥20 years) driving most future risk increases. These results directly demonstrate the inadequacy of stationary design approaches for USDS, which carries substantial applied significance for policymakers and stakeholders. Specifically, it underscores the urgent need for these key actors to update engineering standards by adopting non-stationary intensity-duration-frequency (IDF) curves and integrate Sustainable Urban Drainage Systems (SUDS) into formal flood management strategies. Full article

Disaster management requires the development of effective technologies for managing both pre-and post-disaster processes. Therefore, utilizing effective tools and techniques to mitigate the disaster risks or lower the adversarial impacts is essential. Over the last decade, digital twin (DT) applications have found a wider implementation area for varying purposes, but most importantly, they are utilized for simulating disaster impacts. This study aims to develop an open-source digital twin (DT) framework for earthquake disaster management in the Cayirova district of Kocaeli, Türkiye, one of the country’s most seismically active regions. The primary objective is to enhance local resilience by integrating multi-source data into a unified digital environment that supports risk assessment, response planning, and recovery coordination. The digital model developed using QGIS (3.40.9 Bratislava), Autodesk InfraWorks 2025 software for DT modeling integrates various data types, including geospatial, environmental, transportation, utility, and demographic data. As a result, the developed model is expected to be used as a digital database for disaster management, storing both geospatial and building data in a unified structure. The developed model also aims to contribute to sustainable practices in cities, where disaster risks are particularly critical. In this respect, the developed model is expected to create sustainable logistics chains and sustainable targets aiming to reduce the number of people affected by disasters, reducing the direct economic losses caused by disasters. In this framework, the developed model is expected to further assess seismic risk and mitigate risks with DTs. These capabilities enable the project to establish an open-source district-level DT system implemented for the first time in Cayirova, provide an alternative disaster model focused on region-specific earthquakes, and integrate 2D/3D assets into an operational, ready-to-respond digital database. In terms of practical importance, the model provides a digital database (digital backup) that can be used in emergencies, helping decision-makers make faster, data-driven decisions. The significance of this study lies in bridging the gap between urban digitalization and disaster resilience by providing a scalable and transparent tool for local governments. Ultimately, the developed DT contributes to sustainable urban management, enhancing preparedness, adaptive capacity, and post-disaster recovery efficiency. Full article

Managed aquifer recharge (MAR) can address challenges pertaining to water quality and security, land subsidence, and aquifer degradation. This study has been conducted in the irrigated plains of Indus River Basin (IRB) of Pakistan, where groundwater is being used for drinking, agriculture, industries, and other commercial purposes and where the Punjab Government is implementing the MAR project. The study aims to assess the existing level of heavy metals and trace elements contamination in the groundwater and to set baseline data for the suitability of the site for the MAR project. Groundwater samples from 20 tubewells were collected from an area of 1522 km² to investigate the level of heavy metals concentration in groundwater and to assess its suitability for irrigation and drinking. Samples were analyzed for Aluminum (Al), Arsenic (As), Barium (Ba), Cadmium (Cd), Cobalt (Co), Copper (Cu), Chromium (Cr), Lead (Pb), Manganese (Mn), Molybdenum (Mo), Nickel (Ni), Selenium (Se), Strontium (Sr), and Zinc (Zn). To elucidate the contamination trend of these metals, the Heavy Metal Pollution Index (HPI), Heavy Metal Index (HI), geostatistical description, Pearson correlation analysis, and geospatial mapping were employed. Results showed that groundwater in the study area is not suitable for drinking and may pose serious health risks. It should be, however, generally suitable for irrigation. This concludes that the site is suitable for the implementation of a MAR project where the intended use of groundwater is for irrigation. It has been recommended that the groundwater may not be used for direct human consumption in the study area. It has been recommended, too, that targeted monitoring of identified hotspots and assessment of soil and crop uptake are conducted so that industrial or wastewater discharge into irrigation supplies may be prevented and controlled. For policy decisions, distinguishing irrigation suitability from potable-water safety is essential. Full article

Türkiye is one of the most vulnerable countries in the Mediterranean Basin; the assessment of changes in soil erosion driven by both climate variability and anthropogenic factors is of great importance. This study aims to examine the current state and potential future changes in soil erosion in Sakarya Province, situated in the eastern part of the Mediterranean Basin, by employing the GIS-based RUSLE (Revised Universal Soil Loss Equation) model. Considering the impact of climate change on precipitation regimes, rainfall projections for the 2061–2080 period under the high-emission SSP5-8.5 scenario were evaluated. The analysis revealed that the current average annual soil loss in Sakarya is 2.9 t/ha, with the highest erosion risk occurring on steep slopes, bare surfaces, and agricultural lands. By 2080, under the SSP5-8.5 scenario, the annual average soil loss is projected to be 2.6 t/ha, while slight and very slight erosion levels are expected to increase. These results provide important insights for identifying current risk areas and critical zones for conservation, as well as for projecting future erosion scenarios, thus contributing to sustainable land management policies at the watershed scale. The study suggests that strategies to reduce erosion risk in Sakarya should particularly focus on land management practices such as slope stabilization, afforestation, land cover improvement, and terracing. These approaches are crucial for mitigating land degradation (SDG 15.3) and ensuring sustainable agricultural production (SDG 2.4) within the framework of the Sustainable Development Goals. Full article

Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management. Full article

Background/Objectives: The identification of health risk factors in children should rely not only on body mass index but also on modifiable lifestyle behaviors. Early screening for physical inactivity, poor nutrition, inadequate sleep, and chronic stress is crucial for effective preventive healthcare. The aim of this project was to develop the OWL screening tool, a protocol for the holistic assessment of key lifestyle risk factors in children aged 6–12. Methods/Rationale: The OWL tool was developed by integrating evidence-based recommendations from major health societies (WHO, EFSA, the National Sleep Foundation, and the Pediatric Endocrine Society), incorporating psychological principles, and adapting validated components from existing pediatric screening instruments. Its design prioritizes flexibility for use across various age groups and settings. The development process resulted in the 20-item OWL questionnaire, structured into four lifestyle domains: nutrition, physical activity, sleep, and stress management. Each item is a closed-ended question requiring a dichotomous (yes/no) response. One point is awarded for each health-promoting behavior endorsed, yielding a total possible score of 20. The tool is suitable for self-report by older children, parent-report for younger children, or clinician-administered review. Conclusions: By integrating sleep and stress management with traditional lifestyle factors, the OWL screening tool offers a highly relevant approach to pediatric preventive care. The findings presented here should be interpreted as a proof-of-concept, and the tool is not yet ready for clinical implementation without further rigorous evaluation. Full article

This study investigates spongy moth (Lymantria dispar) infestation patterns in Whirlpool Forest, Ontario, offering a region-specific perspective while largely corroborating existing findings. We analyzed egg mass distribution across 43 sampling plots, relating it to tree characteristics. Results revealed a preference for red oak species, with significant egg-laying above one meter. Positive correlations were found between tree diameter and egg mass quantity (ρ = 0.458, p < 0.001 above 1 m; ρ = 0.218, p = 0.006 below 1 m). Tree health was significantly associated with egg mass presence (χ² = 6.08, p = 0.014). A climate-based regression model (R² = 0.714, p < 0.05) projected substantial increases in outbreak area by 2100, with the most severe scenario predicting 9,927,378.49 hectares at risk. Sensitivity analysis showed a 1 °C temperature increase could expand the outbreak area by 814,100 hectares. These findings underscore complex infestation dynamics, challenging simplified models and emphasizing the need for tailored, adaptive forest management strategies in response to changing environmental conditions and pest behaviors. Full article