Search Results (343)

Freshwater scarcity remains a pressing global issue, exacerbated by inefficiencies in stormwater management during rainy seasons. Strategic stormwater harvesting offers a sustainable solution through runoff utilization for irrigation and livestock support. However, challenges such as limited farmer knowledge, difficult terrain, financial constraints, unpredictable weather, and scarce meteorological data hinder the accuracy of optimum stormwater harvesting sites. This study employs a GIS-based SCS-CN hydrological approach to address these issues, identifying suitable stormwater harvesting locations, estimating runoff volumes, and recommending site-specific storage structures. Using spatial datasets of daily rainfall (20 years), land use/land cover (LULC), digital elevation models (DEM), and soil data, the study evaluated 80 watersheds in Uganda’s cattle corridor. Annual runoff estimates within watersheds ranged from 62 million to 557 million m³, with 56 watersheds (70%) identified for multiple interventions such as farm ponds, check dams, and gully plugs. These structures are designed to enhance stormwater harvesting and utilization, improving water availability for livestock and crop production in a region characterized by water scarcity and erratic rainfall. The findings provide practical solutions for sustainable water management in drought-prone areas with limited meteorological data. This approach can be scaled to similar regions to enhance resilience in water-scarce landscapes. By offering actionable insights, this research supports farmers and water authorities in effectively allocating stormwater resources and implementing tailored harvesting strategies to bolster agriculture and livestock production in Uganda’s cattle corridor. Full article

The intelligent soft open point (SOP) has powerful power flow regulation capabilities in the distribution network. If applied to the distribution network, it can flexibly cope with the output uncertainty of unmanageable distributed energy sources. However, considering the investment, operation, and maintenance costs, as well as the assistance of reactive power equipment, the site selection and capacity determination of SOP have become an urgent problem to be solved. This article proposes the optimal configuration strategy of SOP in a flexible interconnected distribution network, taking into account the features of distributed generation and reactive power sources. Firstly, based on the unpredictability of DG output, this paper uses improved sensitivity analysis to determine the optimal SOP installation location. Subsequently, with the optimization objective of minimizing the annual cost of the distribution network, this paper considers the characteristics of DGs, CBs, and OLCTs and uses second-order cone programming to optimize and solve SOP capacity under constraints such as trends. Finally, in the enhanced IEEE 33-node distribution system model, the effects of different scenarios on node voltage, reactive power components, and SOP location and capacity are compared. Full article

Background: In Germany, over 16 million pre-anesthesia consultations (PAC) are conducted annually, which is associated with a significant investment of time and high costs. However, some PACs do not lead to surgery, which is inefficient and results in wasted resources. This study evaluates the costs and time loss associated with PACs that did not result in anesthesia-required surgery or diagnostic procedures and identifies the predictors of these cancellations. Methods: A total of 1357 PACs conducted in September 2023 at the University Hospital Aachen were retrospectively analyzed. The study groups included patients whose PACs resulted in anesthesia-required surgery or diagnostic procedures (SURG group) and those whose PACs did not (NoSURG group). The primary outcomes were costs in EUR and the hours lost due to PACs not resulting in anesthesia for patients in the NoSURG group, and the secondary outcomes included the predictors of surgery cancellations, the frequency of missing test results, necessary pre-anesthesia re-consultations due to missing tests, and hospital length of stay for NoSURG patients. Results: In September 2023, 7.3% (99/1357) of PACs did not result in anesthesia-required procedures. ASA scores were higher in the NoSURG group, with almost two-thirds classified as ASA III or higher (p = 0.001). The NoSURG group had more planned postoperative IMC stays (16.2% vs. 9.3%; p = 0.027) and fewer medical report letters available (50.5% vs. 97.1%; p < 0.001). The reasons for surgery cancellation were often undetermined (47.5%). Other reasons included surgeons opting for a conservative approach (19.2%), patient decisions (9.1%), surgery no longer indicated (8.1%), hospital capacity constraints (5.1%), patient transfers (3.0%), and high surgical risk (8.1%). The annual projected cost for the NoSURG group was EUR 29,182, with 888 h of time loss. The median hospital length of stay for the NoSURG group was 5 (2; 15) days. Conclusions: PACs that were carried out but were not followed by anesthesiology services led to substantial costs and time loss. Improving medical report availability and assessing procedure necessity beforehand might help to reduce these expenses and time losses. Full article

As highway tunnel operations continue over time, structural defects, particularly cracks, have been observed to increase annually. Coupled with the rapid expansion of tunnel networks, traditional manual inspection methods have proven inadequate to meet current demands. In recent years, machine vision and deep learning technologies have gained significant attention in civil engineering for the detection and analysis of structural defects. However, rapid and accurate defect identification in highway tunnels presents challenges due to complex background conditions, numerous interfering factors, and the relatively low proportion of cracks within the structure. Additionally, the intensive labor requirements and limited efficiency in labeling training datasets for deep learning pose significant constraints on the deployment of intelligent crack segmentation algorithms. To address these limitations, this study proposes an automatic labeling and optimization algorithm for crack sample sets, utilizing crack features and the watershed algorithm to enable efficient automated segmentation with minimal human input. Furthermore, the deep learning-based crack segmentation network was optimized through comparative analysis of various network depths and residual structure configurations to achieve the best possible model performance. Enhanced accuracy was attained by incorporating axis extraction and watershed filling algorithms to refine segmentation outcomes. Under diverse lining surface conditions and multiple interference factors, the proposed approach achieved a crack segmentation accuracy of 98.78%, with an Intersection over Union (IoU) of 72.41%, providing a robust solution for crack segmentation in tunnels with complex backgrounds. Full article

The optimization of urban multi-source water supply systems is essential for addressing the growing challenges of water allocation, cost management, and system resilience in modern cities. This study introduces a graph-theory-based optimization model to analyze the structural and operational dynamics of urban water supply systems, incorporating constraints such as water quality, pressure, and system connectivity. Using Lishui City as a case study, the model evaluates three water allocation plans to meet the projected 2030 water demand. Advanced algorithms, including Floyd’s shortest path algorithm and the GA-COA-SA hybrid optimization algorithm, were employed to address constraints such as pipeline pressure, water quality attenuation, and nonlinear flow dynamics. Results indicate a 1.4% improvement in cost-effectiveness compared to the current allocation strategy, highlighting the model’s capability to enhance efficiency. Among the evaluated options, Plan 2 emerges as the most cost-effective solution, achieving a supply capacity of 4.5920 × 10⁵ m³/d with the lowest annual cost of 5.7015 × 10⁷ yuan, highlighting the model’s capability to improve both efficiency and resilience. This study prioritizes cost-efficiency tailored to regional challenges, distinguishing itself from prior research that emphasized redundancy and water quality analysis. The findings demonstrate the potential of graph-theoretic approaches combined with advanced optimization techniques to enhance decision-making for sustainable urban water management. Full article

Infectious diseases impose a significant burden on global health systems due to high morbidity and mortality rates. According to the World Health Organization, millions die from infectious diseases annually, often due to delays in accurate diagnosis. Traditional diagnostic methods in clinical microbiology, primarily culture-based techniques, are time-consuming and may fail with hard-to-culture pathogens. Molecular biology advancements, notably the polymerase chain reaction (PCR), have revolutionized infectious disease diagnostics by allowing rapid and sensitive detection of pathogens’ genetic material. PCR has become the gold standard for many infections, particularly highlighted during the COVID-19 pandemic. Following PCR, next-generation sequencing (NGS) has emerged, enabling comprehensive genomic analysis of pathogens, thus facilitating the detection of new strains and antibiotic resistance tracking. Innovative approaches like CRISPR technology are also enhancing diagnostic precision by identifying specific DNA/RNA sequences. However, the implementation of these methods faces challenges, particularly in low- and middle-income countries due to infrastructural and financial constraints. This review will explore the role of molecular diagnostic methods in infectious disease diagnosis, comparing their advantages and limitations, with a focus on PCR and NGS technologies and their future potential. Full article

Parsnips (Pastinaca sativa) are a speciality UK crop with an economic value of at least 31M GBP annually. Currently, the major constraints to production are losses associated with root canker disease due to a range of fungal pathogens, among which Itersonilia pastinacae is of most concern to growers. With limited research conducted on this species, this work aimed to provide a much-needed characterisation of isolates from across the UK, continental Europe, and New Zealand. Previously, up to four separate Itersonilia species have been proposed based on the formation of chlamydospores and host specificity: I. pastinacae, I. perplexans, I. pyriformans, and I. pannonica. However, Itersonilia spp. isolates principally from parsnip, but also from a range of other hosts, which were found to infect both parsnip roots and leaves in pathogenicity tests. In growth rate assays, isolates were found to grow at temperatures of 0–25 °C and produce both chlamydospores and ballistospores across the same range of temperatures, although chlamydospore production was found to decrease as temperature increased. Following whole genome sequencing, specific primers were designed for the molecular characterisation of the isolates using six housekeeping genes and three highly variable functional genes. Phylogenetic analysis separated isolates into two and six clades, respectively, but the grouping was not associated with hosts or locations. Based on the results of this research, there was no evidence to support more than a single species of Itersonilia among the isolates studied. Full article

In the context of climate change, vegetation changes in Laos have attracted widespread attention, especially the profound impact of its greenness changes on ecosystems, water cycles, and climate feedback. However, our understanding of the driving factors of vegetation greenness changes in different latitudes is still limited. This study utilized EVI and climate factor data from 2001 to 2023, employing trend analysis, correlation analysis, and machine learning methods to investigate the spatiotemporal patterns of vegetation greenness changes across Laos and their responses to climate factors. Results revealed an overall increasing trend in vegetation greenness, with 75% of the area exhibiting annual increases, primarily in northern, central, and parts of the southern regions. Conversely, 24.8% of the area experienced declines, concentrated near Vientiane and certain southern regions. Seasonal trends during the wet season largely aligned with annual patterns, although reduced rainfall negatively impacted some areas. The dry season exhibited the most pronounced changes, with 70% of the area showing increased greenness, especially in northern and central regions, despite localized rainfall constraints. Minimum temperature (TMMN) emerged as the most influential factor, with importance values of 0.42 for annual changes and 0.37 for dry season changes, while precipitation impacts varied across space and time. High temperatures affected vegetation more significantly in low-latitude regions, whereas high-latitude areas relied on changes in DSR. This significant finding underscores the differential impact of climate factors on vegetation greenness across latitudes, which is crucial for understanding the complex dynamics of tropical inland ecosystems under climate change and for developing targeted conservation and adaptation strategies. Full article

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea. Full article

The swift expansion of the global population and economy has spurred growing requirements for energy and water in recent decades. Inefficient energy and water consumption, however, has led to an increase in CO₂ emissions. Hence, the socio-economic development of a country must consider the interconnections between energy, water and carbon, as there are mutual dependencies among these three elements. This work considers the nexus between energy, water and carbon in the design of integrated energy–water systems using a new automated targeting modeling (ATM) framework. ATM incorporates the advantages of the insight-based Pinch method and a mathematical programming approach to provide visual understanding for a thorough analysis of the problem while guaranteeing accurate solutions. Minimum targets of power and water based on the integrated network operation were established by the ATM, with corresponding carbon emissions. A specific goal of annual carbon emissions reduction was set as the constraint and the ATM optimized the capacities of the components in the system accordingly to achieve minimum overall cost. The application of ATM on an industrial plant case study shows that a target of 45% reduction in the carbon discharge amount was achieved by shifting to greener fuel in the energy system at a minimum overall cost increase of 0.45% only. The framework can assist users in meeting power and water loads in their plant while planning for the appropriate decarbonization efforts at the minimum possible cost. Full article

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large. Full article

To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical model of synchronization optimal design of pipe layout and pipe diameter selection in a drip irrigation network system with constraints on pipe diameter, flow velocity, and pipe pressure is established. Using an irrigation district in Xinjiang, China, as an example, the Jaya algorithm optimization design program was run independently 50 times, and the relative deviation of each optimization result from the optimal solution was calculated. The results show that the annual cost per unit area o is reduced to 635.99 RMB/hm², a 25.34% reduction compared to the original engineering program, and the investment-saving effect is obvious. The relative deviation is controlled within 3%, which shows that the algorithm has stable convergence performance and can meet the requirements of actual engineering design. The Jaya algorithm eliminates the need for parameter tuning, and it excels in cost savings, algorithm stability, and computational accuracy, making it an effective method for the single-objective optimization design of drip irrigation networks. Full article

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables. Full article

High-quality development is the theme of China’s economic and social development in the new era, and corporate ESG performance is a comprehensive indicator for evaluating the level of corporate environmental responsibility, social responsibility and governance, as well as an important yardstick for identifying the high-quality development of enterprises. This paper takes Chinese non-financial listed companies from 2011 to 2022 as the research sample and empirically examines the impact of corporate nationalism culture on corporate ESG performance and its mechanism by quantifying corporate nationalism culture using the text of corporate annual reports, natural language processing and text analysis methods. The results of the study show that corporate nationalism culture significantly enhances corporate ESG performance. The mechanism analysis suggests that corporate nationalism culture, as an internal informal system, can play a governance role and promote corporate ESG practices by changing attention allocation and mitigating agency problems. The positive effect of corporate nationalism culture on corporate ESG performance is more pronounced in the grouping of firms with lower institutional investor shareholding, fewer analysts’ attention and embedded party organisations. A heterogeneity analysis reveals that the corporate nationalism culture driving effect on corporate ESG performance is more significant in the subsample of firms with weak financing constraints, in the growth period and in the decline period. This study reveals the positive role of soft cultural factors in enhancing corporate ESG performance, providing useful managerial evidence for companies to integrate ESG concepts at the strategic level for high-quality economic development. Full article

The cement industry accounts for 7% of total greenhouse gas emissions, with Pakistan’s industry emitting 8.9 million tons annually. Existing decarbonization efforts are insufficient due to technological and policy constraints. CCS presents several challenges, including high costs and energy requirements, as well as advanced monitoring requirements. Policy challenges include the lack of clear regulatory frameworks and incentives for CCS deployment. This study uses scenario analysis with the Low-Emission Analysis Platform (LEAP) to investigate the viability of CCS in meeting Pakistan’s Nationally Determined Contributions (NDCs) and net-zero targets. According to the results, CCS has the potential to reduce emissions by 18 Mt under the NDC scenario and attain net-zero status by 2050; however, it will require robust policy support, infrastructure, and regulatory frameworks. Full article