Search Results (138)

Due to their large scale, long duration, complex geological conditions, and multiple stakeholders, water conservancy engineering projects are subject to diverse, interrelated, and uncertain risk factors that affect the construction timeline. Traditional critical chain buffer calculation methods, such as the cut-and-paste method and the root variance method, typically assume the independence of risks, which limits their effectiveness in addressing schedule delays caused by correlated risk events. To overcome this limitation, this paper proposes a novel critical chain buffer calculation approach that explicitly incorporates risk correlation analysis. A fuzzy DEMATEL-ISM-BN model is employed to systematically identify the interrelationships and influence pathways among schedule risk factors. Bayesian network inference is then used to quantify the overall occurrence probability while accounting for risk correlations. By integrating critical chain management theory, risk impact coefficients are introduced to improve the traditional root variance method, resulting in a buffer calculation model that captures interdependencies among schedule risks. The effectiveness of the proposed model is validated through a case study of the X Pumped Storage Power Station. The results indicate that, compared with conventional methods, the proposed approach significantly enhances the robustness of project schedule planning under correlated risk conditions while appropriately increasing buffer sizes. Consequently, the adaptability and reliability of schedule control are improved. This study provides novel theoretical tools and practical insights for schedule risk management in complex engineering projects. Full article

Construction industry in Afghanistan is crucial for economic and social advancement, particularly after years of instability. However, the construction industry has been already confronting huge time overruns, affecting all stakeholders. This research aims to identify the various risks associated with time overruns in construction projects within Afghanistan and to explore effective risk management strategies to mitigate these challenges. To address time overruns, this study employed Monte Carlo simulations using RiskPert to assess time overruns by combining expert judgment with historical data. This study assesses construction project historical data from 2002 to 2023, emphasizing the political and economic circumstances of that period using a literature review and an examination of 74 construction project reports, in addition to semi-structured interviews with industry experts to determine schedule-related risks and their frequent causes. This research found 29 distinct risk indicators classified into eight categories, facilitating a methodical integration of risks into the simulation model. The Monte Carlo Simulations conducted with @RISK software (version 8.0, Palisade Corporation, New York, NY, USA) assessed the influence of these risks on project performance over 10,000 iterations, demonstrating a robust association with actual project results and a standard deviation of ±15% in durations. Time overruns in projects are linked to socio-political, organizational, and financial risks. The findings emphasize the significance of these factors on project outcomes and recommend strategies for their mitigation to improve decision-making and ensure project management success. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

This study tests the connectionbetween corporate governance structures and firm value, incorporating financial distress as a mediating mechanism among construction companies listed in ASEAN markets. Utilizing a sample of 58 firms drawn from an initial population of 169 companies over the 2018–2021 period, this study measures governance mechanisms through managerial ownership, institutional ownership, independent commissioners, audit committees, and litigation risk. Firm value is proxied by Tobin’s Q, while financial distress is assessed utilizing the Altman Z-Score. Panel data regression is employed to test the direct connections, and the Sobel test is used to evaluate the mediating role of financial distress. The outcome describes that managerial ownership and audit committees have a favorable effect on firm value, whereas independent commissioners and litigation risk exert a negative influence. Institutional ownership shows no significant association with firm value. Moreover, institutional ownership significantly affects financial distress, whereas the other governance mechanisms show no significant association with financial distress, although financial distress itself has a detrimental impact on firm value. The mediation analysis describes that financial distress mediates only the connection between institutional ownership and firm value. These outcomes help clarify prior inconsistencies in the literature and underscore the importance of strengthening managerial ownership and audit committees, optimizing the role of independent commissioners, and mitigating litigation risk to sustain firm value. Full article

This study evaluates the early impact of Central Bank Digital Currencies (CBDCs) on key financial indicators in The Bahamas, focusing on the introduction of the SandDollar—the world’s first fully implemented retail CBDC. Using the Synthetic Control Method (SCM), the analysis constructs counterfactual scenarios to assess the effects of CBDCs on three dependent variables: outstanding loans from commercial banks as a percentage of GDP, outstanding deposits as a percentage of GDP, and the number of deposit accounts per 1000 adults. Three separate SCM models were estimated for the period 2014–2024, incorporating a broad set of control variables reflecting financial infrastructure, economic performance, demographic characteristics, and digital readiness. The findings consistently show that the SandDollar’s implementation is associated with reductions in loan issuance, deposit levels, and deposit account ownership compared to their synthetic counterparts. These results support the hypothesis that direct CBDC models may amplify “deposit substitution” and increase liquidity risks by shifting financial activity away from commercial banks. Although the SCM provides a structured causal framework, the short post-treatment period and potential pandemic-related disruptions limit the scope of a long-term understanding. The study underscores the importance of careful CBDC design, particularly the role of intermediated models in mitigating unintended financial stability risks. Full article

Objective: To investigate long-term prognosis and impact factors in children with vasovagal syncope (VVS) receiving metoprolol therapy. Methods: This retrospective study included children with VVS who underwent metoprolol therapy at the Pediatric Syncope Unit of Peking University First Hospital between January 2012 and November 2023. Baseline demographic data, pre-treatment indices, including head-up tilt test (HUTT) and 24 h Holter monitoring, were collected. All participants received standardized metoprolol therapy for a minimum duration of one month. Follow-up was conducted between June and July 2025, with syncope recurrence as the primary endpoint. Multivariable Cox proportional hazards regression analysis was performed to identify independent impact factors of prognosis and to construct a Prognostic Risk Score (PRS) model. The model’s performance was rigorously validated through receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and Bootstrap resampling (1000 iterations). Furthermore, children were stratified into high- and low-risk groups based on median PRS values. Kaplan–Meier survival analysis was then performed to assess the model’s discriminative efficacy. Results: This study included 97 children diagnosed with VVS. The median duration of metoprolol therapy was 2.5 months (interquartile range [IQR]: 2.0–3.0 months), with a median follow-up period of 59 months (IQR: 25.5–72 months). During follow-up, syncope recurrence was observed in 37 patients, while 60 patients remained symptom-free. COX regression analysis showed that time-domain indices of heart rate variability (HRV), including the standard deviation of all NN intervals (SDNN) and the triangular index (TR), as well as the frequency-domain index of HRV very low frequency (VLF), were relative factors of the long-term prognosis in children with VVS treated with metoprolol. Based on the above three identified factors, the PRS model was calculated as: PRS = 0.03 × SDNN − 0.02 × VLF − 0.1 × TR. ROC showed that the area under the curve (AUC) for discriminative power related to long-term prognosis was 0.808 (p < 0.01). The cumulative recurrence rate of symptoms in the high-risk score group was significantly higher than that in the low-risk score group (p < 0.01). The DCA curve demonstrated the clinical applicability of the model. Bootstrap internal verification indicated high stability, with the bias-corrected and accelerated (Bca) confidence interval (CI) of the C index ranging from 0.71 to 0.89. Conclusions: After metoprolol treatment, 38.1% of children with VVS experienced syncope recurrence during a median follow-up period of 59 months. Baseline HRV index, SDNN, TR, and VLF were identified as factors associated with the long-term prognosis of children with VVS treated with metoprolol. The PRS model based on the above indices demonstrated good value in linking to the individual long-term prognosis. Full article

Ensuring workers’ safety is a critical component of social sustainability in the construction industry. Accidents that occur while workers are walking on construction sites constitute a significant portion of overall accidents, yet they are often overlooked in conventional task-oriented safety risk assessments. This study proposes novel Accident-During-Walk (ADW) risk indices, hierarchical and data-driven metrics designed to quantify workers’ accident risk during walks. The indices are built on Association Rule Mining and utilize structured accident data, accounting for both environmental and work-related attributes. By integrating these indices with project-specific work schedules and worker allocation plans, this study establishes an automated method for daily and weekly look-ahead ADW risk monitoring aligned with construction progress. Case studies on two construction projects validate the discriminative power of the proposed method. The results demonstrate that the indices effectively capture risk fluctuations driven by concurrent multi-trade operations and environmental severity. Notably, the analysis reveals counterintuitive patterns where adverse weather conditions paradoxically reduce risk values by constraining worker mobility, a nuance often missed by static assessments. Ultimately, this framework serves as a data-driven decision-support tool, enabling safety managers to transition from uniform inspections to targeted interventions during high-risk periods, thereby fostering a safer and more socially sustainable construction environment. Full article

Background: Glycated hemoglobin (HbA1c) is a parameter commonly used in clinical practice to assess glycemic control in patients with diagnosed diabetes. Hyperglycemia is a strong risk factor for developing cardiovascular (CV) disease. Although there is some evidence that this parameter could also help assess CV health in patients without known carbohydrate metabolism disorders, this is not entirely clear. The purpose of this study was to investigate the relationship between HbA1c and selected echocardiographic parameters in patients without diabetes. Methods: This study was a retrospective analysis of data from 59 patients (females: 72.88%) with a mean age of 54.82 ± 17.34 years without any features of acute illness or exacerbation of chronic diseases hospitalized in the Department of Internal Medicine, Angiology and Physical Medicine of the Medical University of Silesia in Katowice (Poland) in the period between June 2022 and May 2024. Only individuals with HbA1c levels and who have undergone transthoracic echocardiography were included in the analysis. Spearman’s rank correlation test was used for statistical analysis, and a multivariate analysis model was then constructed (adjusted for age, sex, body mass index, low-density lipoprotein cholesterol, systolic blood pressure, hypertension, and smoking). Results: In univariate analysis, HbA1c was found to be significantly correlated with selected parameters relating to left ventricular dimensions and mass, left atrial dimensions, right ventricular systolic function, mitral inflow profile parameters, and tissue Doppler echocardiography. Multivariate analysis did not confirm a significant association between HbA1c and the assessed echocardiographic parameters. Conclusions: Although HbA1c significantly correlates with some echocardiographic parameters, the observed relationships are entirely explained by confounding variables. Full article

Construction of new seawater reverse osmosis desalination (SWRO) plants in the state of California (USA) requires environmental permits containing rather strict conditions. The California Ocean Plan requires the use of subsurface intake systems (SSIs) unless they are deemed to be not feasible. The Governor of California requested that the State Water Resources Control Board (State Board) study the issue of accelerating the desalination plant permitting process and making it more efficient. The State Board formed an independent scientific Panel to study the issue of SSI feasibility and to submit a report. The Panel recommendations included the following: the feasibility assessment (FA) for SSIs should be streamlined for completion within a maximum of three years, and this requirement should be added to the Ocean Plan; applicants need to perform a financial feasibility study before pursuing SSI capacities exceeding 38,000 m³/d (10 MGD) for wells or 100,000 m³/d (25 MGD) for galleries because project financing may be denied for such larger capacity systems; the mitigation options for each site–SSI combination in the screening process should be addressed by both the project proponent and regulatory agencies as early as practicable in the overall permitting process; and the impacts of SSIs on local aquifers and associated wetland systems must be assessed during the analyses conducted during the FA and during post-construction monitoring. The Panel further concluded that the design and evaluation of SSI–site combinations are highly site-specific, involving technically complex issues, which require both the applicant and the reviewing state agencies to have the expertise to design and review the applications. Economic feasibility must consider cost to the consumer and the engineering risk that can preclude project financing. Projected capacities exceeding the above noted limits may not by financed due to risks of failure or could require government guarantees to lenders. The current permitting system in California is likely to preclude construction of large seawater desalination facilities that can provide another source of potable water for coastal communities in California during severe droughts. Without seawater desalination, the potable water supply in California would suffer a greater sustainability and resilience risk during future periods of extended drought. Full article

The construction industry remains one of the most hazardous sectors despite its economic importance, with high-altitude fall accidents being the most prevalent and deadly type of incident. This paper aimed to study and analyze the accident data of the past accident cases in China and find out the key causes and rules of the accidents. This research analyzed 1223 Chinese accident reports (2014–2023) using Latent Dirichlet Allocation topic modeling to identify causal factors, followed by Apriori algorithm correlation analysis to reveal accident causation patterns. This study comprehensively uses topic model, association rules and visualization methods to systematically analyze the causes of high-altitude fall accidents. The research identified 24 distinct accident cause topics across personnel, equipment, management, and environmental dimensions. Key findings revealed that incorrect use of labor protective equipment, inadequate safety inspections, and failure to implement safety management protocols were persistent issues throughout the study period. Notably, the post COVID-19 pandemic introduced new safety challenges, with the intensity of topics related to “subject of responsibility for safety production has not been implemented” showing significant post-pandemic increases. These findings highlight the evolving nature of construction safety challenges and the need for targeted interventions to address persistent and emerging risks. Full article

Monitoring the concentration of heavy metals in urban soils is of a paramount importance for several reasons. These inorganic pollutants can pose a significant health risk to living organisms, as they are toxic even at low concentrations and can be present in the soil for a long period of time. This study assesses the spatial distribution, concentration levels, and potential anthropogenic and natural sources of eight typical heavy metals (As, Cd, Co, Cr, Cu, Ni, Pb and Zn) occurring in urban surface soils across Debrecen, Hungary. A total of 295 topsoil samples were collected; heavy metal concentrations were determined by energy-dispersive X-ray fluorescence (EDXRF) spectrometry. The results were interpreted using descriptive statistics, correlation analysis, hierarchical clustering, factor analysis, ordinary kriging interpolation, and spatial-discriminant analysis. The dual origin of the metal contaminants was revealed: As, Co, Pb, and Zn showed strong anthropogenic signatures associated with traffic, urban waste, and construction materials, whereas Cr and Ni were associated with natural geogenic sources. Cd reflected both lithogenic and point-source urban pollution. The current evaluation incorporated Hungarian and Dutch regulatory benchmarks to identify exceedances of environmental quality thresholds. It was found that only Cd and Cr exceeded the Hungarian target values, on average. Linear discriminant analysis based on pollution maps highlighted contamination hotspots around traffic corridors and newly industrialized zones. The importance of high-resolution soil monitoring in the rapidly urbanizing city is highlighted. Given its anticipated industrial and transportation developments, accumulations of heavy metals are probably going to be further exacerbated; therefore, the results provide a critical baseline for future environmental assessments and long-term monitoring. Full article

The increased complexity of buildings has led to rigorous performance demands from materials and building envelopes. As markets for low-carbon, renewable construction materials grow, cross-laminated timber and wood fiber insulation have emerged as promising alternatives to meet these rigorous demands. However, an investigation into the performance and interaction of materials within high-performance systems is necessary to determine the durability risks associated with increased complexity and the introduction of new materials. This is important in order to ensure that these materials can meet the required functions of the building while taking advantage of their environmental benefits. To do so, this case study investigated a building constructed of cross-laminated timber and wood fiber insulation in a cold climate (Zone 6A) (Belfast, ME, USA). During construction, the building was instrumented with temperature, relative humidity, and moisture content monitoring instrumentation through the envelope, i.e., wall and roof assemblies. The conditions within the envelope were monitored for a two-year period and used to calibrate a hygrothermal model, along with measured material properties. The calibrated model was used to conduct a 5-year simulation and mold risk assessment. Findings demonstrated that there was no moisture or mold risk throughout the monitoring period or simulation. This supports the integration of cross-laminated timber and wood fiber insulation in sustainable building practices, particularly in cold climates where moisture management is critical. Full article

Objective This study aimed to show the clinicopathological characteristics of large retroperitoneal liposarcoma (RLS) and to develop a customized nomogram model for patients with large RLS. Methods A total of 1735 patients diagnosed with RLS were selected from the public SEER database. Among them, 1113 patients with a maximum tumor diameter greater than 150 mm were included for further analysis. Nomogram models were developed based on Lasso and multivariate Cox regression analyses. A total of 166 patients that presented in the same period at our institution were used for external validations. Results A larger tumor size in RLS was associated with worse survival outcomes. Lasso and Cox regression analyses consistently identified age, TNM stage, occurrence pattern, histology, and surgery as important prognostic factors for OS. The constructed model demonstrated robust predictive performance, with better time-ROC (time-dependent receiver operating characteristic) for 1-year (83.1%), 3-year (83.8%), and 5-year (81.4%) survival in the training cohort. The concordance index (C-index) was approximately 0.80 in both the training and validation cohorts, reflecting excellent discriminatory ability of the model. Survival risk stratification analysis revealed significant differences in survival outcomes of large RLS (HR = 4.12 [3.31–5.12], p < 0.001, in the training cohort). Decision curve analysis (DCA) confirmed that the nomogram provided greater net benefits across a range of threshold probabilities. Conclusion This study identified important prognostic factors for survival in patients with large RLS and developed a reliable nomogram for predicting OS. The model’s strong predictive performance supports its use in personalized treatment strategies, improving prognosis assessment and clinical decision making for these patients. Full article

The analysis of household health financing vulnerability and its impact on health service utilization during the COVID-19 pandemic remains inadequately explored in Kenya. This study was designed to examine the impact of health financing vulnerability on health services utilization during the COVID-19 period. A health financing vulnerability index (HFVI) was constructed to assess the financial risk that individuals faced in accessing essential health services. A pooled panel probit model was estimated to measure the effect of HFVI on service uptake. The study found a significant negative association between HFVI and health service utilization, indicating that a high level of health financing vulnerability is linked to poor health in periods of emergencies. To address this issue, the study recommends implementation of multiple policy measures during crisis periods, including enhancing social health insurance, providing financial support to vulnerable households, and increasing public expenditure on primary healthcare systems across counties, especially on drugs, referral logistics, personnel, medical equipment, and diagnostic technologies. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article