Search Results (2,316)

This study investigates the development and application of the GDELT (Global Database of Events, Language, and Tone) news database. Through experiments, we conducted a quantitative statistical analysis of the GDELT event database to evaluate its practical characteristics. The results indicate that although the database achieves comprehensive coverage across all countries and regions and includes most major global media outlets, the accuracy rate of its key fields is only approximately 55%, with a data redundancy as high as 20%. Based on these findings, while the GDELT data demonstrates good coverage and data integrity, data correction and deduplication are recommended before its use in research contexts and industrial applications. Subsequently, a survey of the existing literature reveals that current studies using GDELT primarily focused on event-related metrics, such as event quantity, tone, and GoldsteinScale, for application in international relations analysis, crisis event prediction, policy effectiveness testing, and public opinion impact analysis. Nevertheless, news constitutes a fundamental channel of information dissemination in media networks, and the propagation of news events through these networks represents a critical area of study for information recommendation, public opinion guidance, and crisis intervention. Existing research has employed the Event, GKG, and Mentions tables to construct cross-national news flow network models. However, the informational correlations across different data table fields have not been fully leveraged in preliminary data selection, leading to substantial computational overhead. To advance research in this field, this study employs chained list queries on the Event and Mentions tables within GDELT. Using social network analysis, we constructed a media co-occurrence network of event reports, through which core hubs and associative relationships within the event dissemination network are identified. Full article

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R²_CV up to 0.68; RMSE_CV ≈ 1.3 t ha⁻¹), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions. Full article

Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advantage of directly providing more objective and less susceptible-to-manipulation results compared to traditional polygraph methods. To this end, this study proposes a novel multi-scale entropy approach by fusing fuzzy entropy (FE), time-shifted multi-scale fuzzy entropy (TSMFE), and hierarchical multi-band fuzzy entropy (HMFE), which enables the multidimensional characterization of EEG signals. Subsequently, using machine learning classifiers, the fused feature vector is applied to lie detection, with a focus on channel selection to investigate distinguished neural signatures across brain regions. Experiments utilize a publicly benchmarked LieWaves dataset, and two parts are performed. One is a subject-dependent experiment to identify representative channels for lie detection. Another is a cross-subject experiment to assess the generalizability of the proposed approach. In the subject-dependent experiment, linear discriminant analysis (LDA) achieves impressive accuracies of 82.74% under leave-one-out cross-validation (LOOCV) and 82.00% under 10-fold cross-validation. The cross-subject experiment yields an accuracy of 64.07% using a radial basis function (RBF) kernel support vector machine (SVM) under leave-one-subject-out cross-validation (LOSOCV). Furthermore, regarding the channel selection results, PZ (parietal midline) and T7 (left temporal) are considered the representative channels for lie detection, as they exhibit the most prominent occurrences among subjects. These findings demonstrate that the PZ and T7 play vital roles in the cognitive processes associated with lying, offering a solution for designing portable EEG-based lie detection devices with fewer channels, which also provides insights into neural dynamics by analyzing variations in multi-scale entropy. Full article

This paper introduces the Knowledge-Guided Genetic Algorithm (KGGA), a hybrid metaheuristic that reimagines crossover as a form of genetic engineering rather than random recombination. By embedding knowledge-guided exploitation principles directly into the crossover operator, KGGA selectively amplifies high-quality genetic material, intensifying the search around promising regions of the solution space. Experimental results on a large scale DRC-FJSSP benchmark show that KGGA outperforms state-of-the-art alternatives—including the Classic Genetic Algorithm (GA), Knowledge-Guided Fruit Fly Optimization Algorithm (KGFOA), and Hybrid Artificial Bee Colony Algorithm (HABCA)—consistently achieving superior solution quality. Full article

This paper presents a systematic methodology for identifying optimal scaling regions in segment-based box-counting fractal dimension calculations through a three-phase algorithmic framework combining grid offset optimization, boundary artifact detection, and sliding window optimization. Unlike traditional pixelated approaches that suffer from rasterization artifacts, the method used directly analyzes geometric line segments, providing superior accuracy for mathematical fractals and other computational applications. The three-phase optimization algorithm automatically determines optimal scaling regions and minimizes discretization bias without manual parameter tuning, achieving significant error reduction compared to traditional methods. Validation across the Koch curve, Sierpinski triangle, Minkowski sausage, Hilbert curve, and Dragon curve demonstrates substantial improvements: excellent accuracy for the Koch curve (0.11% error) and significant error reduction for the Hilbert curve. All optimized results achieve $R^2\ge 0.9988$. Iteration analysis establishes minimum requirements for reliable measurement, with convergence by level 6+ for the Koch curve and level 3+ for the Sierpinski triangle. Each fractal type exhibits optimal iteration ranges where authentic scaling behavior emerges before discretization artifacts dominate, challenging the assumption that higher iteration levels imply more accurate results. Application to a Rayleigh–Taylor instability interface (D = 1.835 ± 0.0037) demonstrates effectiveness for physical fractal systems where theoretical dimensions are unknown. This work provides objective, automated fractal dimension measurement with comprehensive validation establishing practical guidelines for mathematical and real-world fractal analysis. The sliding window approach eliminates subjective scaling region selection through systematic evaluation of all possible linear regression windows, enabling measurements suitable for automated analysis workflows. Full article

Urban growth in hot, arid regions intensifies the urban heat island effect, making green spaces vital for climate mitigation. This research investigates the impact of public gardens on the surrounding urban thermal environment and on the mitigation of the urban heat island (UHI) in a hot arid region. This study selects an important public garden in Biskra, the “5 July 1962” Garden, as a case study of significance at the urban scale. To achieve research objectives, onsite measurement using a digital measurement device (5-in-1 Environmental Meter “Extech EN300”) and satellite remote sensing data from LANDSAT8 are employed, capturing summer measurements of key parameters and indices: Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI). The analysis and correlation of these indices with the LST values allow us to evaluate the zoning and distance impacts of the garden studied. Land surface temperature rises gradually from the garden outward, peaking in the North-East with the strongest heat island effect and remaining lower in the cooler, vegetation-rich South-West. The results reveal that air temperature is the primary driver of land surface temperature (72% impact), while relative humidity (17.3%), vegetation index (7.8%), moisture index (2.9%), and water index (1.7%) contribute to cooling, with vegetation and moisture reducing surface temperatures through shading, transpiration, and latent heat exchange. Full article

This study evaluates the use of seawater and continental water in tailings thickening and copper flotation at laboratory scale, focusing on water reuse in mining operations in arid regions. The tailings had a mean particle size of 10 µm, with 75% < 50 µm, and a specific weight of 2.64 g/cm³. Seawater contained significantly higher ion concentrations Na⁺ 10,741 ppm, Mg²⁺ 1245 ppm, and Ca²⁺ 556 ppm compared with continental water (187, 32, and 127 ppm, respectively), which negatively affected polymer performance. Sedimentation tests showed that the anionic polymer (A3) increased settling rates by 33 times with continental water at 40 g/t, while with seawater the increase was 31 times at 60 g/t. In column thickener tests, discharge solids reached 65% with continental water and 62% with seawater, representing an annual reduction of ~17,000 m³ of recovered water when seawater is used. Consistency tests indicated that achieving slump <20% required 75% solids with continental water and 77.5% with seawater. With dewatering polymers, doses of 200 g/t achieved ~70% solids and slump values near 50%, surpassing column thickener performance. Primary flotation results showed that recirculated and filtered seawater improved copper recovery by 3–5% compared with fresh seawater, due to partial removal of interfering ions. In contrast, recirculated and filtered continental water reduced recovery by 2–4%, likely because of residual polymer effects on mineral surfaces. These findings highlight the importance of polymer selection and dosage optimization to ensure efficient water recovery and sustainable flotation performance under varying water chemistries. Full article

The European wildcat exhibits considerable plasticity in its habitat requirements across its distribution, with differences increasing along a continental-scale latitudinal gradient. While wildcats often favor deciduous and mixed forests with dense cover and prey, studies show these preferences vary across their expansion. Range-wide conservation efforts will benefit from incorporating knowledge generated by robust regional ecological models. We used data from a large camera trap grid (n = 292 stations), spanning across eight wildcat-associated habitats, within its range in northern Greece, to understand the regional ecological parameters affecting the species’ habitat selection. We analyzed the data using single-season density-induced detection heterogeneity occupancy models (Royle–Nichols), considering 12 environmental and anthropogenic parameters. The global model’s GoF was high (p = 0.9). Elevation and percent forest cover were both significantly negatively related to wildcat occupancy (as derived from the modeled “relative abundance index” N). Likewise, there was a negative, but moderate, relation between distance to freshwater bodies and human settlements with wildcat occupancy. We used the model-average coefficients to generate a predictive map of wildcat relative abundance across northern Greece, which identified 47,930 km² of potential wildcat habitat. Assuming a range of densities between 0.05 and 0.3 ind/km² in areas with predicted low, medium, and high relative abundance, we speculate the putative wildcat population in northern Greece to be between 3535 and 7070 individuals. The findings, which vary from ecological models of the species in northern Europe, show the need for regional models and the importance of Greece, and the Balkan peninsula, for the species. Full article

This study aims to establish an energy assessment system and provide low-carbon design strategies for block-scale science and technology industrial parks in the Yangtze River Delta region of China. To investigate low-carbon design strategies for these parks, the impact of solar energy utilization potential and heat island effect on the energy consumption of buildings is taken as the entry point. Through an analysis of the spatial characteristics of twenty block-scale science and technology industrial parks in the Yangtze River Delta region of China, two types of idealized park models comprising a total of eighteen variations were established. The simulation process involved six key morphological parameters to describe the specific shape of the parks quantitatively. The Ladybug Tools 1.6.0, Radiance 5.4a, and URBANopt v0.9.2 software were used to simulate the potential for photovoltaic power generation and the energy consumption of the parks. Net Energy Use Intensity (NEUI) and Potential Utilization Ratio of Renewable Energy (PURRE) were selected as the final evaluation indexes to represent the integrated energy performance of the park. The results show that for the park with a circular layout, the optimal integrated energy performance is achieved when the building density is between 35% and 40%; the average building height is designed with lower values within the range of 20 m to 24 m, and the height-to-depth ratio is around 0.3. Finally, based on the results of the analysis, four major low-carbon design strategies were proposed: high-density development, courtyard layout, supporting-function centralized layout, and carbon sink enhancement. Full article

Evapotranspiration (ET) is essential to the terrestrial water and energy cycle. Accurate evapotranspiration estimates are crucial for understanding global and regional climate change and effective water management. This research uses meteorological observations to provide insights into the spatial and temporal trend patterns of potential evapotranspiration (PET) and evapotranspiration in Xinjiang. A comparative analysis was conducted on six remote sensing-based, land surface model-based, and reanalysis-based products across multiple temporal scales (yearly, seasonally, and monthly) and point-to-point spatial dimensions and impacts of different land cover types was explored. The results show that: (1) The annual PET in Xinjiang showed a significant increasing trend, but showed a significant decreasing trend in summer and autumn. The actual evapotranspiration increased significantly in autumn. (2) The simulation of ET products in Xinjiang exhibits pronounced spatial heterogeneity and seasonal dependency. The datasets demonstrated a superior ability to simulate evapotranspiration in the northern part of Xinjiang compared to the southern part. Product performance varied extremely widely in desert areas but was stable in oasis areas. (3) Significant discrepancies exist across the multiple datasets, with the reanalysis-based products demonstrating superior comprehensive performance. This study offers critical insights for the suitable selection of evapotranspiration products and model optimization in the hydro-meteorological research of Xinjiang. Full article

Optimizing the layout of major productive forces is key in the advancement of high-quality economic development and will inevitably drive significant changes in the spatial pattern of China’s iron and steel enterprises. This study selects 2005, 2010, 2014, 2020, and 2023 as time nodes during the period from the Tenth to the Fourteenth Five-Year Plan, analyzing the spatial evolution pattern and agglomeration characteristics from multiple scales of China’s iron and steel industry over the past 20 years by adopting various mathematical and theoretical methods. The results show that the distribution characteristics of “gradient” are reduced in the east, middle, and west from the perspective of the belt scale. There are notable differences in the spatial agglomeration of different types of iron and steel member units, except for the trade-type iron and steel member units; for example, on the national scale, iron and steel member units as a whole show a spatial distribution trend of “Northeast–Southwest”. There are a large number of production-type enterprise units displaying obvious relative concentrations and geographies; the movement trend of the regional centre of gravity can first be found in the southwest, moving then to the northeast and finally to the southwest. Based on this study, coastal and existing production bases should further improve environmental regulations, increase structural adjustment, and better play the role of demonstration and drive. Full article

Lighting systems in sports venues have a significant impact on both the user experience and quality of events. However, owing to the large number of luminaires, high individual lamp power, and strict lighting standards, the lighting energy consumption of sports venues is high, accounting for approximately 30% of the total energy use. Therefore, introducing natural light through appropriate means during non-event periods and ensuring adequate lighting via collaborative control between natural light and artificial-lighting systems are crucial for reducing the lighting energy consumption of sports venues. Light pipe systems are a novel form of natural lighting and can effectively supplement artificial lighting. However, no clear methodology for selecting light pipes or designing light pipe systems in high spaces such as sports venues currently exists. Furthermore, developing a method for collaborative control between artificial-lighting systems and light pipe systems under various natural light conditions is an urgent issue in the optimization of the design of sports venue lighting. Therefore, we considered a conventional sports venue as a case study. By conducting HOLIGILM simulation experiments, we first investigated the factors affecting the transmission efficiency of light pipe systems and proposed optimization parameters for system design in terms of the pipe diameter, length, and configuration. Subsequently, using the Chinese Standard for Daylighting Design of Buildings (GB50033-2013) and the construction cost as optimization objectives, we optimized the pipe diameter, length, and placement of the light pipe system by applying non-dominated sorting genetic algorithm II. The simulation results showed that the optimized design of the light pipe system in the sports venue achieved a daylight factor of 1%, which met the standard requirements while reducing the construction cost by approximately 27%. Finally, to meet the indoor Class I (non-tournament) lighting standards stipulated in the Standard for Lighting Design and Test of Sports Venues (JGJ153-2016) and taking energy conservation as the optimization goal, we proposed a strategy for achieving collaborative control between the light pipe system and artificial-lighting system based on a greedy algorithm. The results indicated that under various weather conditions, the collaborative control strategy enabled the lighting of the field of play to meet Class I illuminance standards while reducing the annual lighting energy consumption by 35%. Thus, this study provides a methodological reference for optimizing the design of light pipe systems and achieving collaborative control with artificial-lighting systems in large-scale venues. Although these results were obtained based on meteorological data from Xi’an, China, the research method presented in this study can also be applied to other regions. The study provides a methodological reference for the design and optimization of light pipe systems and associated control systems to operate light pipes alongside artificial lighting systems in sports venues and other large multistory buildings. Full article

Areca palm velarivirus 1 (APV1), the causal agent of yellow leaf disease (YLD), poses a serious threat to the economically important areca palm industry in the Hainan Province, China, yet its evolutionary dynamics remain poorly understood. Here, we performed a large-scale molecular survey by sequencing the coat protein (CP) gene from 364 APV1-infected samples collected across major cultivation regions of Hainan. Population genetic analyses revealed extremely high haplotype diversity (H_d = 0.997) but very low nucleotide diversity (π = 0.017). Neutrality tests (Tajima’s D = −2.266; Fu’s F_S = −23.697) and a unimodal mismatch distribution supported a scenario of recent demographic expansion from a restricted ancestral pool. Evolutionary analyses indicated that the CP gene is subject to strong purifying selection, although eight codons exhibited episodic positive selection, suggesting ongoing viral adaptation. Furthermore, we identified three distinct genetic clusters with significant geographic structuring, indicating that viral dissemination is shaped by local factors. Together, these results reveal a recent explosive invasion of APV1 characterized by rapid island-wide expansion and emerging local differentiation. This work provides novel insights into the evolutionary trajectory of APV1 and establishes a genomic basis for improved surveillance and management of YLD. Full article

Climate change has intensified extreme hydrological events in cold regions, threatening the stability of river ecosystems. The traditional monthly frequency method for calculating ecological flow assumes equal guarantee rates across all months, overlooking the complex nonlinear dependencies between interannual and intermonthly flows. This approach may result in flow values for certain months during low-flow years exceeding those of corresponding months in high-flow years, failing to align with actual hydrological patterns. This study integrates Copula functions with the monthly frequency method to establish an improved ecological flow calculation framework, accurately characterizing the statistical correlation between interannual and intermonthly flow variability. The Hailang River basin in Northeast China was selected as the study area. First, the SWAT model was employed to simulate natural runoff processes from 1956 to 1965. The calibration phase demonstrated excellent performance (R² = 0.84, NSE = 0.83), and the validation phase also met standards (R² = 0.82, NSE = 0.81). The improved method selected optimal Copula functions for each month through rigorous statistical tests (AIC, BIC, RMSE, and K-S test), establishing joint probability distributions for annual and monthly average flows. The results indicate that different Copula types better align with monthly hydrological seasonal characteristics: Gaussian Copula suits February, May, and July; t-Copula suits August; Clayton Copula from September to December; Gumbel Copula for January, March, April, and June. Through conditional probability relationships (P(X₀≥x₀, 90%) = 0.9), the monthly guarantee rate range determined by the improved method spans 81.83% to 90.08%, significantly outperforming the uniform 90% guarantee rate employed by traditional methods. Verification using the Tennant method confirmed that ecological flows throughout the year met “excellent” or higher standards. Ecological flows exhibited pronounced seasonal variation, ranging from 6.2 m³/s during winter to spring to 96.93 m³/s during summer to autumn, providing scientific basis for basin-scale ecological water management. This study establishes a reliable methodological framework for ecological flow management in cold-region rivers. Full article

Patio-style dwellings are a highly representative category of traditional dwellings in the Yangtze River Delta region of China. As a crucial climate-adjusting space for traditional dwellings in the hot summer and cold winter zone, patios have long been the focus of practice and research in traditional dwelling renovation. Previous studies have mostly focused on how the shape and scale of patios affect their performance in terms of ventilation, lighting, and thermal environment; however, there is a lack of research on how patio renovation measures influence the thermal comfort of spaces surrounding patios. Based on the two goals of improving the thermal comfort of the general hall space and reducing the overall building energy consumption, this paper takes the patio renovation of Huaigengtang Dwelling as a case study. We use the Design Builder (v7.0.2.006) simulation software to analyze the impact of 10 selected patio measures on thermal comfort and energy consumption and adopt the entropy weight method to conduct a comprehensive evaluation of the indicators for thermal comfort improvement and energy consumption reduction. The quantitative simulation is divided into two scenarios: one where the patio maintains natural ventilation, and the other where the patio is renovated into an enclosed space with split-type air conditioners used for cooling and heating. We select a single patio renovation measure and a combined patio renovation measure based on the values of the comprehensive scores. Regarding the application of the selected optimal measures, and in combination with the dual needs of functional improvement and performance enhancement in traditional dwelling renovation practice, this paper analyzes the corresponding relationships between three types of functional improvement—inheritance-type renovation, optimization-type renovation and replacement-type renovation—and the two performance evaluation indicators, namely thermal comfort improvement and energy consumption reduction, so as to propose the optimal recommendation schemes for different renovation scenarios. Full article