Search Results (5,324)

Globally, Central Asian wild fruit forests are critical repositories of wild fruit germplasm resources and provide essential ecosystem services. However, their habitats are facing escalating degradation risks driven by climate warming, shifting precipitation regimes, and intensifying anthropogenic disturbances. Accurately quantifying climate-driven spatiotemporal variations in habitat suitability for keystone wild fruit tree species is therefore an essential prerequisite for formulating targeted conservation and management strategies in arid and semi-arid landscapes. In this study, we applied the maximum entropy (MaxEnt) model to simulate the current (2000–2020 baseline) and future (2030s, 2050s, 2070s) potential suitable habitats of two dominant wild fruit tree species, Malus sieversii (Ledeb.) M.Roem. and Prunus cerasifera Ehrh., in the Ili Valley, a core distribution area of Central Asian wild fruit forests in northwestern China. We integrated rigorously screened species occurrence records with key environmental predictors and characterized future climate conditions using three Shared Socioeconomic Pathways (SSPs; SSP126, SSP245, and SSP585) spanning low to high radiative forcing levels. The model exhibited excellent predictive performance (AUC > 0.85), confirming the robustness and reliability of our habitat suitability simulations. Elevation and annual precipitation were identified as the dominant environmental variables governing habitat suitability for both species, highlighting the critical role of terrain–hydroclimate interactions in maintaining viable dryland refugia for wild fruit forests. Under the baseline climate scenario, the total area of suitable habitats reached 24.014 × 10³ km² for Malus sieversii and 18.990 × 10³ km² for Prunus cerasifera. Future climate projections revealed a consistent and significant contraction trend in suitable habitats for both species, with the magnitude of habitat loss escalating with increasing radiative forcing and longer projection time horizons. Specifically, under the high-emission SSP585 scenario by the 2070s, the suitable habitat area is projected to decline by 7.579 × 10³ km² for Malus sieversii and 9.883 × 10³ km² for Prunus cerasifera relative to the baseline. Our findings delineate climate-vulnerable hotspots of wild fruit forests and provide a robust spatial scientific basis for prioritizing in situ conservation, targeted habitat restoration, and anthropogenic disturbance regulation to support the long-term persistence of these irreplaceable wild fruit germplasm resources under accelerating global climate change. Full article

When a line short-circuit fault occurs in a DC distribution network, the fault current rises quickly and affects a wide range, jeopardizing the safe operation of the system. In order to locate the fault quickly and accurately, this study proposes a fault localization method based on the Variational Mode Decomposition (VMD) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. First, the nonlinear relationship between the intrinsic principal frequency and fault distance is analyzed; then, the intrinsic principal frequency of the faulty traveling wave is extracted by using VMD, and the nonlinear relationship between the spectral energy of the principal frequency of the intrinsic frequency and the fault distance is fitted by training the Bi-LSTM network incorporating the attention mechanism. Finally, in response to the issue that a small amount of fault data in practical engineering is difficult to support the amount of data required for deep learning, a transfer learning method is used to locate the fault in the target domain. A small sample test of the target domain is carried out using the migration learning method. The experimental results show that the proposed method has high localization accuracy and good resistance to over-resistance and noise; compared with the traditional network training, the localization error based on migration learning is smaller, and the network convergence effect is better. Full article

Granary systems formed a core institutional foundation of state governance, famine relief, and social stabilization in premodern China. Using the complete corpus of the Twenty-Six Dynastic Histories, this study employs digital humanities methods—including text preprocessing, word-frequency analysis, collocation analysis, time-series comparison, and geographic co-occurrence analysis—to examine the long-term evolution and institutional structure of three major granary types: ever-normal granaries (ChangpingCang), charitable granaries (Yicang), and community granaries (Shecang). The results reveal significant temporal and spatial variation closely associated with dynastic stability, fiscal capacity, and disaster conditions. Ever-normal granaries evolved from early formation in the Western Han to institutional consolidation in the Tang, peak expansion in the Song, and functional diversification thereafter, operating as a centralized mechanism integrating price regulation, fiscal management, and famine relief. Charitable and community granaries, by contrast, display increasingly differentiated roles, reflecting a shift toward localized and socially embedded relief in later periods. Spatial analysis further demonstrates a hierarchical deployment pattern centered on political and agrarian cores and extended through transport corridors and frontier zones. Overall, the study highlights a multilayered relief system combining state authority and social participation, offering a data-driven reinterpretation of Chinese charity and governance. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

Signal synchronization is one of the core aspects of communication, ensuring that the receiver accurately decodes the signals transmitted by the sender. However, in the diverse application scenarios and broad spectrum range of 5G new radio (NR), the performance of traditional estimation algorithms often deteriorates as frequency offset increases and noise interference intensifies. This work focuses on the estimation of time offset, cell sector identifier (ID), and frequency offset in 5G mobile communication systems. We leverage the advanced learning capabilities and adaptability of a convolutional neural network (CNN) to optimize the estimation process. Additionally, we incorporate a long short-term memory (LSTM) network to capture the dynamic variations in time-varying channels. The results demonstrate that the proposed neural network exhibits significant advantages in estimation performance. Full article

Early-stage architectural decisions strongly condition long-term energy demand and thermal comfort; however, their combined effects are often evaluated in isolation. This study investigates the interactive influence of mass configuration, orientation and the window-to-wall ratio (WWR) on the energy and thermal comfort performance of school buildings in Erbil, Iraq. Five representative school mass typologies were assessed using a structured two-phase simulation framework based on an Interactive Architectural Approach (IAA). The results reveal that mass configuration functions as a conditioning variable, governing not only absolute energy demand but also responsiveness to design variation. Articulated typologies showed amplified increases in cooling demand, overheating, and mean radiant temperature under a higher WWR, whereas compact forms exhibited comparatively stable behavior. Importantly, orientations minimizing energy demand did not consistently correspond to those minimizing thermal discomfort, revealing typology-dependent divergence between performance objectives. By quantifying interaction-based sensitivity rather than isolated parameter effects, the study advances IAA as a structured early-stage assessment framework for school design. Full article

The maintenance mechanisms underlying community temporal stability represent a pivotal concern in ecology. However, empirical evidence on how multiple mechanisms independently or synergistically stabilize natural communities, and how their importance responds to external factors and evolves over time, remains limited. Leveraging a 12-year (2007–2018) manipulative experiment involving clipping and fertilization in an alpine meadow, we assessed the relative contributions of four mechanisms, namely, species asynchrony (compensatory dynamics among species), the portfolio effect (statistical averaging of species’ fluctuations), the selection effect (dominance of stable species), and interspecific interactions, across treatments and temporal scales. Stability was quantified as the reciprocal of the coefficient of variation in community coverage. Asynchrony was a ubiquitous foundation of stability across all treatments and time periods. The portfolio effect was a critical positive driver in the initial phase but was suppressed by fertilization over time. In contrast, interspecific interactions and the selection effect emerged as central determinants of long-term stability in later stages. Fertilization amplified the portfolio effect and fostered weak interactions while reducing the fluctuation disparity between dominant and non-dominant species. Clipping enhanced stability mechanisms by preserving species richness and asynchrony. Structural equation modelling revealed that treatments indirectly influenced stability by “reprogramming” the causal pathways among these mechanisms. Our study demonstrates that community stability is upheld by multiple coordinated mechanisms, whose relative importance is contingent on treatment and time scale. Grassland management should therefore move beyond a singular focus on species richness and adopt strategies that promote the synergistic functioning of multiple stability mechanisms. Full article

Arsenic (As)-enriched soils in abandoned mining areas pose persistent environmental risks, yet the temporal evolution of remediation is rarely evaluated. In this study, a two-year field monitoring program was conducted at a severely As-contaminated abandoned gold mine in Guangdong Province, China, to examine the temporal dynamics of soil properties and As behavior under different remediation strategies. Three representative slopes were investigated: slope A (slope reshaping and revegetation), slope B (terraced engineering interception), and slope C (an area influenced by acidic water bodies). The results showed that both total and available As at slopes A and B exhibited a similar pattern of initial increase followed by decline and stabilization, indicating a clear temporal scale for remediation effects. Slope A exhibited greater spatial variability, whereas slope B showed relatively minor fluctuations, suggesting that terraced engineering measures contributed to enhanced As stability. In contrast, slope C had lower total As but a higher proportion of available As prior to remediation due to the acidic conditions. Following remediation, both total and available As at slope C decreased markedly and remained stable for about six months; however, a rebound trend was observed after approximately 1.5 years, indicating the time-limited effectiveness of passivation treatments. Specifically, total As at slope C decreased from 22,916 to 4011 mg·kg⁻¹, accompanied by a 65–85% reduction in available As. Meanwhile, soil pH, soil organic matter, and cation exchange capacity exhibited pronounced non-linear variations, with an overall tendency to recover toward pre-remediation conditions. These findings underscore the importance of long-term monitoring for evaluating remediation effectiveness and periodic assessments (e.g., semiannual monitoring of soil As and nutrient status) to support adaptive environmental management and optimization of remediation strategies. Full article

The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students’ future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, overlook temporal factors, and lack further interpretability of the prediction results. To address these shortcomings, we propose Temporal Variational Autoencoder-Generative Adversarial Network (TVAE-GAN), a temporal variational autoencoder-generative adversarial network model aimed at providing early warnings for high-risk students in basic education, with in-depth interpretability analysis of the prediction results to suit the unique context of basic education. TVAE-GAN extracts features from real samples and introduces a Long Short-Term Memory (LSTM) network to capture dynamic features in time series, helping the model better understand temporal dependencies in the data, remember the sequential causal information of students’ online learning, and achieve better data generation performance. Using these features, the generative model generates new samples, and the discriminator model evaluates their quality, producing outputs that closely resemble real samples through training. The effectiveness of the TVAE-GAN model is validated on a collected online basic education dataset while also advancing the timing of interventions in predictions. The performance differences between the proposed method and classic resampling methods, as well as their impact in the educational field, are analyzed, highlighting that misclassification increases teacher workload and affects students’ emotions. Key influencing factors are identified using a decision-tree surrogate model, providing teachers with multidimensional references for academic assessment. Full article

Improving the energy efficiency of historic field stone masonry buildings often requires internal insulation, as external insulation is frequently restricted by heritage and architectural constraints. Internal insulation, however, alters the hygrothermal behavior of massive masonry walls and may increase moisture-related risks. This study assesses the hygrothermal performance of an internally insulated historic field stone masonry wall under past and projected future climatic conditions using long-term transient simulations. Coupled heat and moisture transfer simulations were performed with the DELPHIN software for an uninsulated reference wall and an internally insulated configuration. The analyses accounted for wind-driven rain, masonry heterogeneity, and variations in inner core composition. Past conditions were represented by a continuous 20-year measured climate dataset, while future conditions were evaluated using regional late-century climate projections (RCP2.6 and RCP8.5). Hygrothermal performance was evaluated based on moisture mass density, freeze–thaw exposure, and mold-relevant temperature–relative humidity conditions at predefined evaluation points within the wall. The results show that moisture accumulation develops gradually and cannot be reliably captured by short simulation periods. Internal insulation redistributes moisture-related risks within the wall rather than fundamentally altering the seasonal moisture regime. Freeze–thaw exposure occurs under all investigated climates, while mold-relevant humidity conditions persist at interior-adjacent locations. The findings demonstrate the importance of multi-year hygrothermal analyses when assessing moisture-related risks in internally insulated historic masonry walls. Full article

Background: Vitamin D status is influenced by season, age, and public health messaging. The COVID-19 pandemic was accompanied by heightened interest in vitamin D, but long-term national data from Central/Eastern Europe remain limited. We aimed to characterize 10-year trends, seasonal variation, and demographic determinants of serum 25-hydroxyvitamin D [25(OH)D] in Slovenia, with particular focus on changes during the COVID-19 period. Methods: We performed a retrospective cross-sectional analysis of all serum 25(OH)D measurements performed at the Slovenian national reference laboratory between January 2014 and December 2023. The core analytic cohort included 106,875 patients with valid 25(OH)D results, aged 0–100 years. Vitamin D status was classified as deficient (<30 nmol/L), insufficient (30–50 nmol/L), adequate (50–75 nmol/L), and optimal (>75 nmol/L). Temporal trends, seasonal patterns, and age- and sex-specific differences were assessed using non-parametric tests and Kendall’s τ. Results: Mean 25(OH)D concentration over the study period was 61.9 ± 34.2 nmol/L; 16.0% of patients were deficient and 22.8% insufficient. Annual mean 25(OH)D increased from 57.0 nmol/L in 2014 to 67.2 nmol/L in 2023, with a significant upward temporal trend and a 14.6% higher mean level during 2020–2023 compared with 2014–2019. Seasonal variation was pronounced (≈20% higher summer–autumn vs. winter–spring), and vitamin D status declined progressively with age, with the highest deficiency prevalence in patients ≥ 70 years. Females had slightly higher 25(OH)D than males, although absolute differences were small. Conclusions: This laboratory-based analysis of tested patients showed higher 25(OH)D concentrations during and after the COVID-19 period, superimposed on persistent seasonal and age-related gradients. These observations identify older adults and winter testing periods as important contexts for vitamin D optimization, but they should be interpreted as descriptive trends among tested individuals rather than as evidence of causal pandemic effects or population-wide prevalence changes. Full article

Soil organic carbon (SOC) stocks in cacao agroecosystems are characterized by accumulating large amounts. They depend on the balance between organic matter inputs (plant residues, roots) and losses (decomposition, erosion), being closely related to climatic conditions, soil nature, vegetation type, topography, and land management practices. The objective of this study was to quantify SOC stocks (0–30 cm) and assess key soil fertility indicators across 107 georeferenced sampling locations in cacao production systems of Guamal (Meta, Colombian Llanos Piedmont). Soil pH varies between extremely acidic and moderately acidic (3.8–6.0; mean 4.57), while available P (Bray II) and exchangeable bases showed low concentrations. Organic carbon concentration averaged 1.18% and bulk density averaged 1.17 g cm⁻³. SOC stocks averaged 41.10 Mg C ha⁻¹, ranging from 7.49 to 81.55 Mg C ha⁻¹, evidencing marked spatial contrasts in carbon storage. Spearman correlations highlighted coupled soil chemical controls, including positive associations of pH with Ca²⁺ and P availability and strong negative associations of pH and P with exchangeable Al³⁺, consistent with acidity-driven fertility constraints. Principal component analysis (PCA) further identified a dominant fertility gradient structured by pH, P availability, and Ca²⁺, and a second axis related to organic carbon and cation retention. Spatial modeling using inverse distance weighting (IDW) in ArcGIS supported the visualization of SOC stock variability across the study area. Overall, the results indicate that SOC stocks in these predominantly sandy soils are strongly influenced by acidity-related constraints and heterogeneous nutrient status, underscoring the need for site-specific management to jointly enhance soil fertility and climate-mitigation potential in cacao systems. Therefore, it would be advisable in the future to address the study of differential variations in soil C storage related to chemical fertilizer application rates, especially in the long term. Full article

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

Stevia rebaudiana Bertoni is a strategically important perennial crop because it is the main botanical source of steviol glycosides, a group of high-intensity, non-caloric sweeteners increasingly demanded by the global food and beverage industry. Despite the rapid expansion of stevia cultivation, commercial production remains strongly dependent on a narrow genetic base, particularly on clonally propagated cultivars such as ‘Morita II’, which has long served as the industrial benchmark because of its favourable rebaudioside A profile and processing consistency. This dependence has raised concerns about limited adaptive capacity, genetic erosion and restricted long-term breeding progress. In this review, we provide an integrated and critical synthesis of current knowledge on the genetic diversity of S. rebaudiana, the biosynthetic and regulatory architecture of steviol glycosides, and the conventional and emerging strategies available for crop improvement. Unlike previous reviews, this article explicitly connects domestication-driven genetic bottlenecks, wild germplasm mobilisation, metabolic pathway regulation, advanced analytical phenotyping and precision breeding into a single systems-oriented framework. We examine the roles of wild germplasm, somaclonal variation, polyploidy, molecular markers, omics-assisted approaches and transgene-free genome editing as complementary tools to broaden the stevia breeding base while preserving industrial quality standards. We finally propose an integrative roadmap for the sustainable genetic improvement of stevia, positioning ‘Morita II’ not as an endpoint, but as a benchmark within a broader diversification strategy. Full article