Search Results (1,058)

With the rapid development of cross-sea infrastructure, the immersed tube method has been increasingly applied to deep-water immersed-tube tunnel construction. However, when the construction depth reaches the scale of one hundred meters, issues such as high hydrostatic pressure, complex hydrological conditions, and limited construction windows significantly elevate project risks. Against this backdrop, this study systematically reviews relevant domestic and international research findings in the context of 100-m-deep water environments and constructs a comprehensive risk index system covering the construction processes of the WBS breakdown system based on the WBS-RBS decomposition method within the HSE framework. A risk index weighting analysis combines quantitative and qualitative analysis, categorizing the indicators into qualitative and quantitative categories. Quantitative analysis employs threshold determination and the LEC method; qualitative analysis utilizes expert surveys and the G1 method. Ultimately, a model that combines multiple methods for a 100-m-deep water environment, integrating subjective expertise and objective data, is developed. On this basis, multi-level prevention and control measures are proposed for hundred-meter-deep water-immersed tube construction. The results demonstrate that the proposed system can effectively identify key risk sources under deep-water conditions and provide practical countermeasures, offering significant guidance for ensuring construction safety and engineering quality in hundred-meter immersed-tube tunnel projects. Full article

Construction price indices play a critical role in shaping construction activity and determining the economic success of building projects in Germany, where they can serve as central inputs to cost planning and to updating trade-level project budgets over the planning and construction horizon. This paper develops a forecasting framework for 35 sub-construction price indices for office buildings, providing granular inputs for cost escalation and risk assessment. We employ regularized vector autoregressive models with exogenous variables (VARX) implemented via the BigVAR package and estimate them in a model-vintage design for an unbalanced panel. These high-dimensional models are benchmarked against compact VARX and vector error-correction models (VECM) that jointly forecast each target index with a small macroeconomic block consisting of the gross domestic product (GDP) and the three-month interbank rate. Candidate specifications are evaluated using mean absolute percentage error (MAPE) and out-of-sample root mean square error (RMSE), and the final forecasting model for each index is selected based on ex post MAPE. The results show that regularized VARX models capture dynamic interdependencies among the sub-indices and, for most series, outperform the VARX and VECM benchmarks. The resulting forecasts provide practitioners with trade-specific escalation factors that can support budgeting, contract design, and the mitigation of cost risk in office-building projects. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

This paper introduces GECO, a real-time, computer vision-assisted gesture controller for IoT-based smart home systems. The platform uses a markerless MediaPipe interface that combines gesture-driven navigation and command execution, enabling intuitive control of multiple domestic devices. The system integrates binary and analog gestures, such as continuous light dimming based on thumb–index angles, while operating on-device through a private MQTT network. Technical evaluations across multiple Android devices have demonstrated ultra-low latency times (<50 ms), enabling real-time responsiveness. A user experience study with seventeen participants reported high intuitiveness (9.5/10), gesture accuracy (9.2/10), and perceived inclusivity, mainly for individuals with speech impairments and low technological literacy. These results position GECO as a lightweight, accessible, and privacy-preserving interaction framework, advancing the integration of artificial intelligence and IoT within smart home environments. Full article

Current resource and processing constraints on conventional chitin production call for novel sources and more sustainable methods for its production. Herein, domestic cricket (Acheta domesticus L.) meal obtained from supercritical CO₂ oil extraction was investigated as a viable source of chitin via a one-pot approach using acidic (choline chloride: glycerol, CCG) and alkaline (potassium carbonate: glycerol, KG) deep eutectic solvents (DESs). The chitin samples obtained were compared with those obtained using conventional acid-alkaline extraction (CE) and commercial crab shell chitin (CS chitin) by robust characterization of their composition and physicochemical properties employing color, FTIR, XRD, XPS, and SEM analysis. The results showed that KG DES and recovered KG DES exhibited high demineralization and deproteinization capacity, producing chitin with high purity, α-chitin form, high acetylation degree (>77%), crystallinity (crystallinity index > 81%), and micro-fibrous morphology closely similar to those of CE chitin and CS chitin. Whereas CCG DES demonstrated excellent demineralization, it was less effective at deproteinization, leading to chitin with lower purity and crystalline properties. Together, the results demonstrated that cricket meal could be an alternative source of chitin, while KG DES one-pot extraction holds strong potential as a sustainable and eco-friendly approach for obtaining commercial-grade chitin. Full article

The role of international financial resource inflows, foreign direct investment (FDI), development foreign assistance (DFA), and personal remittance earnings (PRE) in decisive advancement toward environmental sustainability (SDG13) and economic development is increasingly recognized. However, depending on the situation, their effects on environmental outcomes vary in degree and direction, and are still subject to debate. This research examines how the three main international financial resources impact environmental sustainability, which is measured by the bio-capacity index, with a specific focus on the moderating role of corruption. The system panel generalized method of moments with balanced panel data (2001–2023) was used to attain the objectives of this study. This study focused on 28 developing Organization of Islamic Cooperation member countries because of their significant reliance on these financial inflows, regional/economic variety, and diverse levels of governance, which offer a crucial setting for evaluating the corruption moderation hypothesis. The findings reveal a comprehensive scenario of SDG synergies and trade-offs. In the base model, FDI directly improves the situation, whereas DFA and PRE are initially negligible. When considering internal economic factors, FDI and PRE greatly advance sustainability, whereas domestic financial measures such as domestic credit and fixed capital formation show adverse effects, underscoring a tension between environmental objectives and national financial systems. Importantly, the moderation analysis shows that while the advantages of FDI and PRE continue to be robust, corruption severely reduces the efficacy of DFA. To assure environmental effectiveness, these findings call for distinct policies that encourage green FDI, leverage remittances for green investments at the family level, and above all, fasten development assistance to strict governance changes. Full article

To clarify the spatio-temporal changes of water quality, pollution factors, and sources in Chaohu Lake Basin over the past five years, this study focused on the Chaohu Lake and its tributaries as the research zone. By analyzing the water quality parameters, the pollution factors of Chaohu Lake were clarified and the water quality was comprehensively evaluated using the water quality index (WQI). Correlation analysis, cluster analysis and principal component analysis (PCA) were conducted in SPSS 26.0 to identify pollutant sources. The results showed that the water of Chaohu Lake and its tributaries were, to some extent, polluted during the dry and wet seasons over the past five years. The primary excessive pollutants were COD, nitrogen, and phosphorus. The average total nitrogen (TN) and total phosphorus (TP) concentrations of Chaohu Lake were 1.74 and 0.09 mg/L in dry seasons and 1.39 and 0.08 mg/L in wet seasons, respectively, which belonged to Class IV water quality standard regulated by GB 3838-2002. Notably, in the Nanfei, Paihe, and Baishishan Rivers, TN concentrations consistently exceeded the Class V water quality standard threshold in both seasons, while TP levels exceeded the Class IV standard. Water quality assessment revealed that the water quality state was classified as “good” in Chaohu Lake, but “moderate” in its tributaries, especially in wet seasons. This finding indicated that while eutrophication remained the primary pollution issue in the lake, the overall physicochemical condition of the water body has not undergone comprehensive deterioration and still maintains a certain degree of ecological health. Source analysis indicated that domestic and industrial wastewater, and agricultural activities were the primary sources of pollution in Chaohu Lake and its tributaries. Therefore, integrated management strategies are required, including enhanced monitoring and control of nitrogen and phosphorus inputs from inflow rivers, rational industrial restructuring, and optimization of agricultural and industrial water use. Full article

Nitrate in groundwater (GW) poses a public-health concern in semi-urban northeastern Saudi Arabia, where households rely on untreated wells. We measured nitrate in 45 wells spanning treated/untreated commercial stations, private domestic wells, and agricultural wells, and linked contamination severity to age-specific risks using the Nitrate Pollution Index (NPI), Chronic Daily Intake (CDI), and Hazard Quotient (HQ). Nitrate ranged from 12 to 380 mg·L⁻¹ (35% > 50 mg·L⁻¹ World Health Organization (WHO) guideline), with untreated private and agricultural wells most affected. Based on NPI, 65% of wells were “clean”, while 18% showed significant to very significant pollution. Infants and children had the highest exposure: CDI frequently exceeded the oral reference dose (1.6 mg·kg⁻¹·d⁻¹), and HQ > 1 occurred in 56% (infants) and 51% (children) of samples from untreated sources. Treated stations consistently achieved lower nitrate and HQ < 1. Sensitivity analysis identified nitrate concentration as the dominant risk driver, followed by ingestion rate, with body weight mitigating the dose. The findings suggest that monitoring based solely on compliance may underestimate risks in sensitive age groups, thereby advocating for immediate actions such as fertilizer management, septic system upgrades, extension of treatment to vulnerable households, and community monitoring. The integrated NPI–CDI–HQ framework provides a replicable methodology for associating groundwater contamination with demographic-specific health risks in arid, water-stressed regions. Full article

This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance. Full article

With the growing financialization of energy markets, financial and energy security have become critical global concerns. This study overcomes the limitations of traditional methods in analyzing extreme events by adopting a conditional quantile spillover index approach. Using China’s energy market prices and financial sub-market pressure indices, it constructs Quantile Vector Autoregressive (QVAR) models for both traditional and new energy-finance systems to examine their time-varying risk spillovers. Key findings are: (1) A significant risk spillover effect exists within China’s energy-finance system. The energy market acts as the primary risk transmitter, driven by both industrial policy and market demand, while capital and foreign exchange markets are the main risk absorbers. (2) The system exhibits significant tail spillover and asymmetry. The traditional energy market is more sensitive to upside extreme risks, whereas the new energy market is more sensitive to downside extremes. (3) Uncertainties like supply demand imbalances, policy shifts, and changing domestic/international conditions are major volatility drivers. Supply demand issues primarily affect the traditional energy market, while policy adjustments trigger chain reactions in the new energy sector. Based on these insights, the paper proposes recommendations to prevent systemic risks and potential energy crises. Full article

Purple maize and canary beans are economically important crops in Peru, grown for domestic and international markets. However, postharvest storage losses are a major challenge along the value chain due to insect pests. This study evaluated the efficacy of the Purdue Improved Crop Storage (PICS) and polypropylene (PP) bags in storing both crops in the Arequipa region, Peru. Oxygen and CO₂ levels were monitored every month. Insect pests, germination rates, sugar content, peroxide index, moisture content, and colony-forming units (CFU) were measured at the start and nine months after storage to determine the level of grain deterioration. PICS maintained a hypoxic environment that suppressed insect development and limited grain damage and weight loss to below 1%. In contrast, PP bags allowed infestations to escalate, resulting in weight losses of about 20% for each crop. Germination declined markedly in PP for both crops; in PICS, it remained high for purple maize (87.5%) but fell substantially for canary beans (20.8%). Moisture rose modestly in PICS, yet deterioration markers, including sugar content and peroxide index, were lower than in PP bags. Although Fusarium, Aspergillus, and Penicillium were detected in purple maize across both systems, mold loads were far lower in PICS. Overall, hermetic storage in PICS bags is effective in reducing postharvest losses and preserving important quality characteristics of beans and maize in Peru. Full article

Nurses in Africa are exposed to musculoskeletal disorders at work (WMSDs). They are multifactorial and may be related to demographic, economic, and quality-of-life factors. The aim of this study was to investigate the effect of nurses’ age, experience, body mass index (BMI), and macroscopic indices such as nurse-to-bed ratio, Human Development Index (HDI), and Gross Domestic Product (GDP) on the overall prevalence and prevalence by body area. A systematic review and a meta-analysis were conducted during September 2025. ScienceDirect, PubMed/Medline, Google Scholar, Science.gov, and Mendeley were scanned without a date limit. The article selection, review, critical appraisal, and data extraction were performed by two authors independently. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used for reporting the search results. Among the 4305 identified records, 18 studies included for a total of 4266. The overall prevalence was pooled at 77.4%. Subgroup analyses demonstrated a decrease in WMSDs with age, and nurse-to-bed ratio, and an increase in lower limb WMSDs with experience, BMI, GDP, and HDI (for most of body areas). Future work could examine the effect of the nurse-to-bed ratio by considering the resources of a facility and conducting more in-depth analyses by subgroup. The development of ergonomic programs remains essential to the well-being at work of African nurses. Full article

In this paper we investigate the short-run and long-run determinants of the informal economy in Romania using Dynamic General Equilibrium (DGE)-based estimates of informal output as the dependent variable. An ARDL model is used to analyze macroeconomic and institutional variables for Romania during the period of 1995–2023, including inflation (INF), primary net lending/borrowing (NLB), the political stability index (PSI), interest payments (INTPAY), gross domestic product per capita (GDP), and self-employment (SEMP). The findings show that inflation, fiscal balance, political stability, interest payments, and GDP per capita have a short- and long-run impact on informal output. In the long run, a 1% increase in inflation raises informal output by 0.03%, while a 1% rise in GDP per capita reduces it by 0.29%. The error correction term suggests a rapid adjustment speed of 79% toward the long-run equilibrium. These findings suggest that institutional reforms, sustained economic growth, and stable macroeconomic policies play an important role in reducing informality and promoting sustainable economic resilience in Romania. Full article

Exploring the spatiotemporal pattern of water quality and identifying pollution sources is crucial for achieving precise management of reservoir watersheds. This study is based on monthly water quality data from 9 monitoring stations in the upstream watershed of Guanting Reservoir in 2024, combined with an improved water quality index method (WQI) and multivariate statistical analysis (clustering, discrimination, principal component and factor analysis), to reveal the spatiotemporal variation characteristics of water quality and pollution sources. The results show (1) significant spatiotemporal heterogeneity. In terms of time, the water quality is worst during the summer rainy season (June August), indicating that the pollution load input from surface runoff exceeds the dilution effect of rainfall. In terms of space, the water quality deteriorates significantly downstream along the river network, with the most prominent pollution occurring in the entrance area. (2) The results also show clear identification of key indicators and dominant pollution sources. Discriminant analysis shows that BOD₅ and DO are key indicators for distinguishing rainy and dry seasons, while TN, TP, COD, COD_Mn, and F can effectively distinguish spatial clusters. Factor analysis further revealed that organic pollution (originating from domestic and industrial wastewater) and nutrient pollution (mainly from agricultural non-point sources) are the dominant factors. This study confirms that pollution input during rainfall is the primary driving factor for water quality deterioration, and human activities have led to the cumulative effect of pollutants along the river network. Based on this suggestion, differentiated and precise governance strategies should be implemented according to the spatiotemporal differentiation characteristics to improve the water environment quality of the upstream watershed of Guanting Reservoir. Full article

This paper assesses whether national level digitalization is associated with lower greenhouse gas emissions intensity, and under which conditions the association holds, using an unbalanced panel for twenty-seven European countries from 2014 to 2022. Digitalization is measured through a principal component index constructed from enterprise information technology adoption and fixed broadband subscriptions per capita. The outcome is the logarithm of emissions per unit of gross domestic product. Identification is implemented with two-way fixed effects and Driscoll–Kraay standard errors to exploit within country variation, and dynamic bias and potential endogeneity are probed through parsimonious difference GMM designs. Robustness is assessed through alternative outcomes, country specific linear trends, clustered inference, and leave one out checks. Evidence for an unconditional reduction in emissions intensity associated with digitalization is not found. It is predicted that the relationship between digitalization and e-commerce will be positive and statistically significant, thus meaning that at the median and the upper quartile of e-commerce a one standard deviation increase in digitalization relates to increases in intensity of about 2.4% and 3.3%, respectively. More renewable energy shares come with lower intensity and mitigate the digitalization effect. The contribution here attempts to quantify such conditional associations within a multi-country setting and thereby infer design and energy contexts wherein environmental gains from digital expansion would most likely take place. Full article