Search Results (446)

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

China’s nuclear power plants traditionally operate to meet baseload needs, with minimal involvement in peak load regulation. However, as the share of renewable energy generation rapidly increases, the volatility of the power system and the demand for peak load regulation have significantly risen, necessitating greater nuclear power flexibility to meet the new power system’s requirements. Our study forecasts the energy structure and load demand for the Province of Liaoning in Northeastern China in 2035. Under this vision, it analyzes the flexibility challenges faced by nuclear generation units. A joint clearing model for spot electricity and ancillary services, along with an energy storage revenue model, was established. Based on this, this study analyzed the clearing results for various typical scenarios in the Province of Liaoning in 2035. The simulation results demonstrate that nuclear units will participate in peak shaving by the target year. This study demonstrates the feasibility of solid-state thermal storage in improving both flexibility and economic efficiency of nuclear generation. Based on these findings, policy recommendations are proposed, including improving regulation compensation mechanisms and promoting multi-energy coupling, providing crucial theoretical and practical support for the role transformation of nuclear generation entities in the new power system. This study establishes a full lifecycle economic assessment model for combined heat and power revenue versus thermal storage investment costs, considering integrated nuclear power–solid thermal energy storage heating systems as the primary technical pathway. Taking a configuration plan with a 715 MW heating capacity and a 6000 MWh thermal storage capacity as an example under Liaoning Province’s 2035 long-term scenario, the simulation results indicate that introducing solid thermal energy storage can significantly improve the revenue structure of nuclear units while meeting deep peak shaving demands, reducing the project’s static payback period to under 11 years. Full article

This paper tested the use of the surplus efficient frontier (a minimum tracking error portfolio selection method) to select the optimal hedging portfolio that replicates the best Mexican #4 lemon price in a t + 1 and t + 4 week hedging scenario. Using data on the nine most traded agricultural futures in the US from January 2000 to February 2025, we tested hedging effectiveness across 502 futures portfolios in a weekly backtest. The results suggest that a corn and wheat portfolio increases the hedging effectiveness of the lemon price by 0.7033 or 70.33%. A result that, including the impact of trading fees and taxes, leads to a reduction in income risk to a lemon seller in a t + 1 week hedging horizon. The results suggest that a public or private financial institution could take a short position in such a portfolio to provide a hedge at a price that finances the spot/future price difference at minimum cost to Mexican taxpayers. Full article

More than a century ago, bacteriophages (phages) were discovered as entities that could both replicate and dramatically reduce bacterial culture turbidities. By the late 1940s, phage impact on broth turbidity was being studied using electronic detectors. This review examines such turbidimetric, also known as colorimetric or optical density means of studying phage biology. The focus is especially on relatively rapid and higher throughput phenotypic phage characterization versus methods that rely instead on phage plaques, spots, or genotype determinations. Topics covered include (i) the most probable number method along with Appelmans’ approach, (ii) estimation of phage growth parameters including especially that of phage lysis timing, (iii) consideration of lysis inhibition as a complicating factor, (iv) phage titering based on degrees of optical density change, (v) detection of both lysis from without and resistance to lysis from without, (vi) phage host-range determination, and (vii) study of post-lysis culture grow back, that is, of bacterial evolution of phage resistance. Based on over 30 years of experience using and studying optical density approaches to the exploration of broth-culture phage biology, the author takes a critical look at both the benefits and limitations of this increasingly common approach to phage biological characterization. Full article

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production. Full article

To solve the spatial water resources shortage, lots of water diversion projects have been constructed for sustaining development. As the water resource utilization efficiency (WRUE) is assumed not to decrease after the operation of water diversion projects, it is necessary to analyze the WRUE and its driving factors in a water-receiving area. Taking the Tao River Diversion Project as a case study, a Super-SBM (Super Slack-Based Measure) model and the Malmquist–Luenberger index are applied in estimating the WRUE values in the seven counties or districts in the water-receiving area of the Tao River Diversion Project. Spatial autocorrelation and a geographical detector are applied to explore the patterns and influencing factors. The results show that there is significant spatial variation in WRUE across the water-receiving areas from 2010 to 2019. High-efficiency areas maintain or improve their efficiencies, while low-efficiency areas show a stagnant or declining trend. The nondecreasing premise of WRUE is not fully satisfied in any area and at any time. The water diversion project is found to be a key driver for the shifting spatial patterns of WRUE from a cold spot dominance to a stronger hot spot agglomeration. The influencing factors on WRUE’s spatial differentiation are also dynamic with the operation of the water diversion project. Therefore, our study will not only help to assess the benefits of the Tao River Diversion Project, but can also provide many valuable insights for water resource planning. Full article

This study focuses on the tight reservoirs of the Jingzigou Formation in the Satan 1 block of the Jinan Sag, Junggar Basin. By integrating analyses of sedimentary microfacies, reservoir characteristics, and fracture distribution, it innovatively applies machine learning algorithms for the quantitative identification and prediction of “sweet spots”. The results indicate that subaqueous distributary channels within the braided river delta front are the dominant sedimentary microfacies. The reservoir exhibits typical tight oil characteristics, with porosity primarily below 10% and permeability generally less than 0.01 mD. Sedimentary microfacies significantly control reservoir quality, with the subaqueous distributary channels exhibiting the best physical properties. Mid- to high-angle structural fractures effectively enhance reservoir permeability and show a strong positive correlation with oil saturation. This research employs machine learning techniques—including Decision Trees, Random Forest, and Support Vector Machines—to establish a comprehensive sweet spot classification model by integrating pore-throat structure, petrophysical parameters, reservoir thickness, and fracture development intensity. Among these, the Random Forest algorithm demonstrated optimal performance across all evaluation metrics. Prediction results reveal that Class I and Class II sweet spots are predominantly distributed in the northern slope area, while Class III sweet spots are located in the central trough and southern nose-like structural zone. These classification results show a high consistency with actual production data, confirming the effectiveness and applicability of machine learning for sweet spot prediction in this study area. The research outcomes provide reliable geological guidance for well placement optimization and reserve development in the Satan 1 block, offering significant reference value for the prediction and development of sweet spots in similar heterogeneous tight oil reservoirs. Full article

To accurately analyze the dynamic response and driving mechanism of forest carbon sequestration in the core area of the Loess Plateau’s Returning Farmland to Forestry Project, this study takes the Beiluo River Basin as the research area. Using spatial autocorrelation, gravity model, a geodetector, and spatiotemporal geographically weighted regression models, it analyzes the spatiotemporal evolution of forest carbon sequestration and the spatial heterogeneity of its influencing factors based on 2000–2023 data. The results show the following: (1) Forest carbon sequestration in the basin increased by 13.55% from 2000 to 2023; its spatial pattern shifted from “middle reaches concentration” to “stable middle reaches core plus significant upper reaches growth”, with the gravity center moving “southeast then northwest”. (2) Forest carbon sequestration had significant positive spatial correlation, with hotspots in soil–rock mountain forest areas and cold spots in ecologically fragile or high-human-activity areas. (3) Natural ecological factors dominated forest carbon sequestration evolution, socioeconomic factors enhanced synergy, and evapotranspiration and NDVI had significant impacts. (4) Factor impacts had spatiotemporal heterogeneity, such as the decaying positive effect of precipitation and the “positive-negative-equilibrium” change in forestry value-added. This study provides scientific guidance for basin and Loess Plateau ecological restoration and “double carbon” goal achievement. Full article

Amid the rapid evolution of the digital economy reshaping global competitiveness, China has advanced regional coordination through the Digital China initiative and the “Data Elements ×” Three-Year Action Plan (2024–2026). To further integrate digital transformation with high-quality growth in the urban agglomerations of the middle and lower Yellow River, this study aims to strengthen regional competitiveness, expand digital industries, foster new productivity, refine the development pathway, and safeguard balanced economic, social, and ecological progress. Taking the Yellow River urban clusters as the research object, a comprehensive assessment framework encompassing seven subsystems is established. By employing a mixed-weighting approach, entropy-based TOPSIS, hotspot analysis, coupling coordination models, spatial gravity shift techniques, and grey relational methods, this study investigates the spatiotemporal dynamics between the digital economy and high-quality development. The findings reveal that: (1) temporally, the coupling–coordination process evolves through three distinct phases—initial fluctuation and divergence (1990–2005), synergy consolidation (2005–2015), and high-level stabilization (2015–2022)—with the average coordination index rising from 0.21 to 0.41; (2) spatially, a persistent “core–periphery” structure emerges, while subsystem coupling consistently surpasses coordination levels, reflecting a pattern of “high coupling but insufficient coordination”; (3) hot–cold spot analysis identifies sharp east–west contrasts, with the gravity center shift and ellipse trajectory showing weaker directional stability but greater dispersion; and (4) grey correlation results indicate that key drivers have transitioned from economic scale and infrastructure inputs to green innovation performance and data resource allocation. Overall, this study interprets the empirical results in both temporal and spatial dimensions, offering insights for policymakers seeking to narrow the digital divide and advance sustainable, high-quality development in the Yellow River region. Full article

Urban park green spaces (UPGSs) play a critical role in enhancing residents’ quality of life, particularly for older adults. However, inequities in accessibility and resource distribution remain persistent challenges in aging urban areas. To address this issue, this study takes Gulou District, Nanjing City, as an example and proposes a comprehensive framework to evaluate the overall quality of UPGSs. Furthermore, an enhanced Gaussian two-step floating catchment area (2SFCA) method is introduced that incorporates (1) a multidimensional park quality score derived from an objective evaluation system encompassing ecological conditions, service quality, age-friendly facilities, and basic infrastructure; and (2) a Gaussian distance decay function calibrated to reflect the walking and public transit mobility patterns of the older adults in the study area. The improved method calculates the accessibility values of UPGSs for older adults living in residential communities under the walking and public transportation scenarios. Finally, factors influencing the social equity of UPGSs are analyzed using Pearson correlation coefficients. The experimental results demonstrate that (1) high-accessibility service areas exhibit clustered distributions, with significant differences in accessibility levels across the transportation modes and clear spatial gradient disparities. Specifically, traditional residential neighborhoods often present accessibility blind spots under the walking scenario, accounting for 50.8%, which leads to insufficient accessibility to public green spaces. (2) Structural imbalance and inequities in public service provision have resulted in barriers to UPGS utilization for older adults in certain communities. On this basis, targeted improvement strategies based on accessibility characteristics under different transportation modes are proposed, including the establishment of multi-tiered networked UPGSs and the upgrading of slow-moving transportation infrastructure. The research findings can enhance service efficiency through evidence-based spatial resource reallocation, offering actionable insights for optimizing the spatial layout of UPGSs and advancing the equitable distribution of public services in urban core areas. Full article

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable areal unit problem (MAUP) on UPGS accessibility and equity. An improved multi-modal Gaussian two-step floating catchment area (G2SFCA) method was employed to measure UPGS accessibility, while the Gini coefficient and Lorenz curve were used to analyze its equity. The results show that (1) UPGS presents a dual-core agglomeration feature, with accessibility blind spots surrounding the edge of the study area and relatively low equity in the western and southern regions; (2) changes in temporal thresholds and spatial scales have a significant impact on UPGS accessibility (p < 0.001), whereas their impact on equity is minor; and (3) UPGS distribution suffers from spatial imbalance, with a huge disparity in resource allocation. This study overcomes the limitations of traditional evaluation methods that rely on a single mode or ignore scale effects and provides a more scientific analytical framework for accurately identifying the spatial heterogeneity of UPGS accessibility and the imbalance between supply and demand. Full article

Hakka traditional vernacular dwellings embody regionally specific climatic adaptation strategies. This study takes the Meizhou Guangludi enclosed house as a case study to evaluate its climate adaptability with longevity and passive survivability factors of the Hakka three-hall enclosed house under subtropical climatic conditions. A mixed research method is employed, integrating visualized parametric modeling analysis and on-site measurement comparisons to quantify wind, temperature, solar radiation/illuminance, and humidity, along with human comfort zone limits and building environment. The results reveal that nature erosion in the Guangludi enclosed house is the most pronounced during winter and spring, particularly on exterior walls below 2.8 m. Key issues include bulging, spalling, molding, and fractured purlins caused by wind-driven rain, exacerbated by low wind speeds and limited solar exposure, especially at test spots like the E8–E10 and N1–N16 southeast and southern walls below 1.5 m. Fungal growth and plant intrusion are severe where surrounding trees and fengshui forests restrict wind flow and lighting. In terms of passive survivability, the Guangludi enclosed house has strong thermal insulation and buffering, aided by the Huatai mound; however, humidity and day illuminance deficiencies persist in the interstitial spaces between lateral rooms and the central hall. To address these issues, this study proposes strategies such as adding ventilation shafts and flexible partitions, optimizing patio dimensions and window-to-wall ratios, retaining the spatial layout and Fengshui pond to enhance wind airflow, and reinforcing the identified easily eroded spots with waterproofing, antimicrobial coatings, and extended eaves. Through parametric simulation and empirical validation, this study presents a climate-responsive retrofit framework that supports the sustainability and conservation of the subtropical Hakka enclosed house. Full article

Today, on the island of Gran Canaria, conventional photovoltaic installations are being implemented on the ground, with the excuse that electricity production must be decarbonized. This is located on a highly populated island, with a shortage of flat land, and a high dependence on food, in a biodiversity hot spot on the planet. We would like to point out that agrivoltaics could provide a double solution and allow the carbon footprint of this human settlement to be further reduced. In addition, it provides greater resilience to climate change, and by reducing dependence on the outside, it would minimize the effects suffered by pandemics such as SARS-CoV-2. It would also help mitigate water stress in one area facing serious water shortage problems. The reduction of local CO₂ emissions would be achieved in four ways: production of clean electricity, reduction of the transport of fuel for electricity generation, reduction of the transport of food goods from abroad, and the absorption of CO₂ together with the emission of O₂ by the planted crops. It would also lead to greater job creation, a remedy against great soil desertification, stopping agricultural abandonment, and life in rural inland areas. This study analyzes two possible agrivoltaic installation configurations of equal power in a potato field: one with a vertical bifacial (VB) configuration and another with an optimum angle (OA). The monthly production is examined and, specifically, the economic income in the event of pouring all the production into the grid. All this takes into account the reality of the chosen place, the island of Gran Canaria (Spain). Full article

Rural tourism is one of the important measures used to realize the rural revitalization strategy in China. At the same time, the “action of prospering the border and enriching the people” is a crucial link in driving the economic development of border areas. With the continuous improvement of the G331 section of the Jilin Line, the development of rural tourism along the border has been accelerated. Therefore, reducing the livelihood vulnerability of farmers is conducive to promoting the rural revitalization strategy in China and consolidating the achievements of poverty alleviation and difficulties in tackling them. This paper takes Tumen City, Yanbian Korean Autonomous Prefecture, Jilin Province, as the sample area; takes Mapai Village, Bailong Village, Hexi Village, Liangshui Village, and Tingyan Village as the research objects; obtains survey data for 224 households through on-the-spot investigation; constructs an evaluation index system for farmers’ livelihood vulnerability in border rural tourist destinations based on the sustainable livelihood theory and the analysis framework of “exposure–sensitivity–adaptability”; calculates farmers’ livelihood vulnerability in this research area by the entropy method and the comprehensive index method; and classifies different villages’ and farmers’ livelihood vulnerability by the natural breakpoint method. By using the multiple linear regression method, this paper analyzes the factors influencing farmers’ livelihood vulnerability in border rural tourist destinations. The results show the following: (1) The overall livelihood vulnerability of farmers is negative, indicating that farmers have a certain ability to resist external risks, but the livelihood vulnerability of some investigated villages is positive, indicating that farmers’ ability to resist external risks is weak, which is closely related to village geographical environment, tourism market environment, family characteristics, and other factors. (2) The livelihood vulnerability of villages with relatively perfect tourism formats is low, which shows that tourism can effectively reduce the livelihood vulnerability of farmers to a certain extent. (3) The household head’s education level, the total price of agricultural machinery, annual income, the ability to borrow money, family size, the number of disabled people, and other factors have a significant influence on the livelihood vulnerability of farmers. Full article

When a transformer operates under three-phase unbalanced conditions, the location of the winding hot-spot temperature (HST) is no longer fixed on a certain phase. Taking an S13-M-100 kVA/10 kV transformer as the research object, this paper proposes a streamline inversion method for inverting the winding HST based on the analysis of oil flow morphology. The study employs the finite volume method for coupled calculations of a transformer’s thermal fluid field and combines a support vector regression (SVR) model for the HST inversion. An orthogonal experimental method is used to construct the training and testing sample sets, and the grid search method is utilized to optimize the parameters of the SVR model. In response to variations in hot-spot locations under three-phase unbalanced conditions, representative streamlines are reasonably selected, and a genetic algorithm-based dimensionality reduction optimization is performed on the feature quantities. The research results indicate that the established inversion model exhibits high inversion accuracy under three-phase unbalanced conditions, with a maximum temperature difference of 3.71 K, and the robustness check verifies the stability of the model. Full article