Search Results (703)

Surface subsidence caused by coal mining activities generates diverse wetland ecosystems. These newly formed wetlands exhibit distinct environmental characteristics due to variations in subsidence age, resulting in divergent biological communities. While species adapt to environmental changes through specific functional trait combinations, the response of aquatic community functional diversity to environmental gradients across chronosequences of mining subsidence wetlands remains unclear. This study investigated 13 coal mining subsidence wetlands (1–18 years) of macrozoobenthos in Jining, China. Through seasonal monitoring, we analyzed functional traits along with taxonomic and functional diversity patterns. Initial-stage wetlands were dominated by medium-sized (63.9%) and tegument-respiring taxa, whereas late-stage wetlands exhibited a shift toward large-sized (43.9%) and gill-respiring groups. Both species richness and functional richness declined over time, with taxonomic diversity demonstrating greater sensitivity to subsidence age. Seasonal community variability was more pronounced in initial-stage wetlands (1–4 years post-subsidence). Despite increasing habitat heterogeneity with subsidence age, functional redundancy maintains ecosystem stability. The shared origin and developmental trajectory of these wetlands may constrain functional divergence. Current research predominantly relies on traditional taxonomic metrics, whereas our findings emphasize functional trait analysis’s importance for ecosystem assessment, which provides a theoretical framework for ecological restoration and biodiversity conservation in post-subsidence wetlands. Full article

The on-orbit geometric calibration accuracy of high-resolution space cameras directly affects the application value of Earth observation data. Conventional on-orbit geometric calibration methods primarily rely on ground calibration fields, making it difficult to simultaneously achieve high precision and real-time monitoring. To address this limitation, we, in collaboration with Tsinghua University, propose a high-precision, real-time, on-orbit geometric calibration system based on active optical monitoring. The proposed system employs reference lasers to integrate the space camera and the star tracker into a unified optical system, enabling real-time monitoring and correction of the camera’s exterior orientation parameters. However, during on-orbit operation, the space camera is subjected to a complex thermal environment, which induces thermal deformation of optical elements and their supporting structures, thereby degrading the measurement accuracy of the geometric calibration system. To address this issue, this article analyzes the impact of temperature fluctuations on the focal plane, the reference laser unit, and the laser relay folding unit and proposes athermalization design optimization schemes. Through the implementation of a thermal-compensated design for the collimation optical system, the pointing stability and divergence angle control of the reference laser are effectively enhanced. To address the thermal sensitivity of the laser relay folding unit, a right-angle cone mirror scheme is proposed, and its structural materials are optimized through thermo–mechanical–optical coupling analysis. Finite element analysis is conducted to evaluate the thermal stability of the on-orbit geometric calibration system, and the impact of temperature variations on measurement accuracy is quantified using an optical error assessment method. The results show that, under temperature fluctuations of 5 °C for the focal plane and the reference laser unit, 1 °C for the laser relay folding unit, and 2 °C for the star tracker, the maximum deviation of the system’s measurement reference does not exceed 0.57″ (3σ). This enables long-term, stable, high-precision monitoring of exterior orientation parameter variations and improves image positioning accuracy. Full article

Background: Chinese Red Steppe cattle (CRS) combine indigenous environmental resilience with moderate dairy performance, whereas Holstein cattle (HOL), despite their high milk yield, suffer reduced genetic diversity and compromised adaptation. A comparative analysis of their population genetic architecture and selection signatures can reveal valuable targets for CRS dairy improvement. Methods: We genotyped 61 CRS and 392 HOL individuals using the Illumina GGP Bovine 100K SNP array and performed stringent quality control. Population structure was assessed via principal component analysis, neighbor-joining trees, and sparse nonnegative matrix factorization. Historical effective population size (Ne) and divergence time were inferred with SMC++. Genome-wide selection scans combined Fixation Index (FST) and Cross-Population Composite Likelihood Ratio test (XP-CLR); overlapping high-confidence regions were annotated and subjected to GO and KEGG enrichment analyses. Results: CRS and HOL were clearly separated along PC1 (explaining 57.48% of variance), with CRS exhibiting high internal homogeneity and weak substructure, versus greater diversity and complex substructure in HOL. SMC++ indicated a split approximately 3500 years ago (700 generations) and a pronounced recent decline in Ne for both breeds. Joint selection mapping identified 767 candidate genes; notably, the ACSM1/2B/3/4 cluster on chromosome 25—key to butanoate metabolism—showed the strongest signal. Enrichment analyses highlighted roles for proteasome function, endoplasmic reticulum stress response, ion homeostasis, and RNA processing in regulating milk fat synthesis and protein secretion. Conclusion: This study delineates the genetic divergence and demographic history of CRS and HOL, and pinpoints core genes and pathways—particularly those governing butanoate metabolism and protein quality control—underlying dairy traits. These findings furnish molecular markers and theoretical guidance for precision breeding and sustainable utilization of Chinese Red Steppe cattle. Full article

Quercus petraea subsp. pinnatiloba is a narrowly distributed oak taxon in southeastern Türkiye, and its taxonomic position has long remained uncertain. This study aims to clarify its distinctiveness by integrating morphological, molecular, and biogeographical evidence. Principal Component Analysis (PCA) and Stepwise Discriminant Analysis (SDA) of 14 leaf traits revealed that subsp. pinnatiloba constitutes a morphologically stable and distinctly differentiated group from other Q. petraea subspecies and closely related taxa, characterized by key diagnostic traits such as petiole length (PL), lamina length (LL), length of leaf blade at its broadest point (WP), and lobe width at the tip of the widest lobe (LW). Phylogenetic analyses based on nuclear ITS and plastid markers (rbcL, psbA-trnH) confirmed its placement within sect. Quercus, yet consistently distinguished it genetically from other subspecies for the first time. Molecular dating (BEAST) suggested divergence in the Miocene (11 Mya with 95% HPD 3.01, 20.95) while RASP biogeographical analysis indicated an origin in the Euro-Siberian region with later dispersal into the Mediterranean. These integrative results support its recognition at species rank as Quercus pinnatiloba, clarifying its phylogenetic placement and underscoring the conservation importance of this lineage. Full article

Citrus tristeza virus (CTV) is an important pathogen threatening the global citrus industry, but its evolution and transmission mechanism in wild citrus has not been clarified. Most of the existing studies are based on CTV-specific gene fragments, lacking genome-wide analysis. There is especially a lack of understanding of CTV transmission dynamics in wild citrus, which needs further investigation. In this study, wild citrus samples from three provinces of China were collected, virus genome data were obtained by high-throughput sequencing (HTS) technology and combined with public database data, and Bayesian phylogeographic inference was used to analyze virus composition characteristics in wild citrus, as well as the population genetic structure, temporal dynamic evolution, and spatial transmission mode of CTV. The results showed that Yunnan wild citrus samples contained the most abundant virus components, including CTV, Citrus Exocortis Viroid (CEVd), Citrus associated Ampelovirus 1 (CaAV-1), and Citrus Virus B (CiVB), while Jiangxi and Hunan samples only contained CTV and CEVd, with all samples showing mixed infection. Phylogenetic analysis showed that nine wild citrus CTV isolates were scattered in different evolutionary clades, and only 9.27% of genetic variation existed between the populations, while 90.72% of genetic variation existed within the populations, indicating little effect of geographic isolation on gene flow. The time to the most recent common ancestor (tMRCA) of CTV was estimated at 1360 CE, with subsequent divergence into two lineages, with population size stabilizing after a rapid increase in 1980–1990. Asia has been identified as the central source of CTV’s global spread, with key migration events including Asia to North America (1746), Asia to Oceania (1829), and Asia to South America (1965), coinciding with global maritime trade and the expansion of the citrus industry. Full article

A wavelet-based noise reduction method for time series is proposed. Traditional denoising techniques often adopt a “trial-and-error” approach, which can prove inefficient and may result in suboptimal filtering outcomes. In contrast, our method systematically selects the most suitable wavelet function from a predefined set, along with its associated tuning parameters, to ensure an optimal denoising process. The denoised series produced by this approach maximizes a suitable objective function based on information-theoretic divergence. This is particularly significant in economic time series, which are frequently characterized by non-linear dynamics and erratic patterns, often influenced by measurement errors and various external disturbances. The method’s performance is evaluated using time series data derived from the Business Confidence Climate Survey, which is freely and publicly accessible via the World Wide Web through the Italian National Institute of Statistics. The results of our empirical analysis demonstrate the effectiveness of the proposed method in delivering robust filtering capabilities, adeptly distinguishing informative signals from noise, and successfully eliminating uninformative components from the time series. This capability not only enhances the clarity of the data, but also significantly improves the overall reliability of subsequent analyses, such as forecasting. Full article

One of the crucial types of information needed to guarantee the secure operation of power systems is their voltage stability condition. This is particularly true for power systems operating at peak hours or under abnormal conditions, such as contingencies. The literature shows several methods for voltage stability assessment; however, they are either accurate and computationally burdensome or less accurate and computationally efficient. The main goal of this research work is to propose methods that are both accurate and fast, features that are especially important in strict real-time operating conditions. Two new methods for computing the maximum loadability and the voltage stability margin of power systems are proposed. Both methods use a powerful, second-order, and non-divergent power flow with an optimally computed step size; however, each of them is initialized differently. Very high-quality initializations are obtained by using a linear voltage stability index and sensitivity analysis factors. This combination leads to a fast, robust, and accurate method, suited for strict real-time power system operation. The proposed methods require 90% fewer power flow runs compared with conventional methods, such as the continuation method for small systems, and tend to require even fewer power flow runs for larger systems. Computer simulations of the proposed methods use small benchmarks to large realistic power systems, showing that the requirements for real-time use—namely accuracy, robustness, and computational efficiency—are met. Full article

This study applies a multicriteria decision analysis to explore how experts from different backgrounds assess traditional and emerging criteria for urban cycling. A hierarchical model with 7 main criteria and 31 subcriteria was evaluated by 30 specialists from academic, technical, and user-focused groups. Using pairwise comparisons and aggregated judgments, this study reveals points of agreement and divergence among expert priorities. Safety and infrastructure were rated as the most important factors. In contrast, contextual and technological aspects, such as Multimodality, Environmental Quality, Shared Systems, and Digital Solutions, received moderate to lower weights, with differences linked to expert profiles. These results highlight how different disciplinary perspectives influence the understanding of bikeability-related factors. Conceptually, the findings support a broader view of cycling conditions that incorporates both established and emerging criteria. Methodologically, this study demonstrates the value of the Analytic Hierarchy Process (AHP) as a participatory and transparent tool to integrate diverse stakeholder opinions into a structured evaluation model. This approach can support cycling mobility planning and policymaking. Future applications may include case studies in specific cities, combining expert-based priorities with local spatial data, as well as longitudinal research to track changes in cycling conditions over time. Full article

Background. Antimicrobial resistance (AMR) poses a significant global health threat, necessitating a deeper understanding of bacterial adaptation mechanisms. Introduction. This study investigates the genotypic and phenotypic evolutionary trajectories of Escherichia coli under meropenem and gentamicin selection, and it benchmarks these findings against florfenicol-evolved strains. Methodology. Utilizing a downsized, three-layer acrylic modified “Microbial Evolution and Growth Arena (MEGA-plate) system”—scaled to 40 × 50 cm for sterile handling and uniform 37 °C incubation—we tracked adaptation over 9–13 days, enabling real-time visualization of movement across antibiotic gradients. Results. Meropenem exposure elicited pronounced genetic heterogeneity and morphological remodeling (filamentous and circular forms), characteristic of SOS-mediated division arrest and DNA-damage response. In contrast, gentamicin exposure produced a uniform resistance gene profile and minimal shape changes, suggesting reliance on conserved defenses without major morphological adaptation. Comprehensive genomic analysis revealed a core resistome of 22 chromosomal loci shared across all three antibiotics, highlighting potential cross-resistance and the central roles of baeR, gadX, and marA in coordinating adaptive responses. Gene ontology enrichment underscored the positive regulation of gene expression and intracellular signaling as key themes in resistance evolution. Discussion. Our findings illustrate the multifaceted strategies E. coli employs—combining metabolic flexibility with sophisticated regulatory networks—to withstand diverse antibiotic pressures. This study underscores the utility of the MEGA-plate system in dissecting spatiotemporal AMR dynamics in a controlled yet ecologically relevant context. Conclusions. The divergent responses to meropenem and gentamicin highlight the complexity of resistance development and reinforce the need for integrated, One Health strategies. Targeting shared regulatory hubs may open new avenues for antimicrobial intervention and help preserve the efficacy of existing drugs. Full article

Soft nanocomposites were prepared by dispersing lipophilic carbon quantum dots (CQDs) in the liquid crystal compound 8CB. The quality of the dispersion was evaluated using fluorescence microscopy, while the microstructure of the samples was examined via polarized optical microscopy. We investigated the influence of CQDs on the orientational order parameter S as a function of temperature and sample composition by measuring birefringence. Additionally, the Fréedericksz transition threshold, along with the characteristic response and relaxation times, was measured for each sample as a function of temperature and applied voltage amplitude. The extracted rotational viscosity ${\gamma }_1$ exhibits a pretransitional divergence upon cooling toward the smectic-A phase. Its temperature dependence was analyzed using established models from the literature, and the corresponding activation energy was determined. Notably, our analysis suggests that the presence of CQDs alters the power-law dependence of ${\gamma }_1$ on the orientational order parameter S. The influence of CQDs on the elastic constants has been investigated. Full article

Natural gas wells operate under complex conditions with frequent environmental disturbances. Fault types vary significantly and often present weak signals, affecting both safety and efficiency. This paper proposes an intelligent fault-diagnosis method based on a CNN-Transformer model using real-time wellsite data. A time series diffusion model is applied to enhance small-sample data by generating synthetic fault samples, and the CNN-Transformer model extracts both local and global features from time series inputs to improve fault recognition in complex scenarios. Validation on a real-world dataset demonstrates that the proposed method achieves a macro F1-Score of 99.52% in multi-class fault diagnosis, significantly outperforming baseline models (1D-CNN: 95.83%, LSTM: 93.54%, GRU: 94.98%). Quantitative analysis confirms the diffusion model’s superiority in data augmentation, with lower Earth Mover’s Distance (0.087), KL Divergence (0.245), and Mean Squared Error (0.298) compared to GAN and VAE variants. Ablation studies show that removing diffusion-based augmentation leads to a 14.96% drop in F1-Score, highlighting its critical role in mitigating class imbalance. Results validate the diffusion model’s effectiveness for data augmentation and the CNN-Transformer’s superior ability to capture complex time series patterns, providing theoretical support and practical tools for intelligent monitoring and maintenance in natural gas well systems. Full article

Infectious bronchitis virus (IBV) remains a major threat to poultry health worldwide due to frequent genetic changes mainly driven by recombination and limited cross-protection between genotypes. In this study, we analyzed IBV strains collected from clinical outbreaks in Chile between 1986 and 2021 to assess the long-term impacts of live-attenuated vaccines (Massachusetts and 4/91) on viral evolution. Phylogenetic analysis of the S1 and N genes revealed four major lineages circulating in Chile—GI-1, GI-13, GI-16, and a novel monophyletic clade we propose as GI-31. The latter, identified in isolates from 1986 to 1988, is highly divergent (22–24%) from other known lineages, representing a previously unreported South American IBV variant. Despite widespread Mass vaccination, genetically distinct field strains circulated during the 1980s, facilitating potential recombination with GI-1 vaccine-derived strains, including evidence of shared ancestry with GI-11, an endemic lineage from Brazil. Non-recombinant GI-16, likely introduced from Asia, was detected in isolates from 2009. Notably, a recombinant strain emerged in 2015, four years after 4/91 vaccine introduction, indicating vaccine–field-strain genetic exchange. By 2017, isolates with >99% identity to the 4/91 strain were recovered, suggesting vaccine-derived variants. In 2021, GI-1 re-emerged, showing recombination signatures between GI-1 and GI-13 (4/91-derived) strains, likely reflecting suboptimal or inconsistent vaccination strategies. Selection analyses showed strong purifying selection across most of the S1 gene, with limited sites under positive selection in the receptor-binding domain. Phylodynamic reconstruction revealed time-structured evolution and multiple introduction events over 35 years, with lineage-specific tMRCA estimates. Collectively, these findings highlight the emergence of a novel lineage in South America and demonstrate that vaccine use, while mitigating disease, has significantly shaped the evolution of IBV in Chile. Our results underscore the importance of continuous genomic surveillance to inform vaccine strategies and limit recombinant emergence. Full article

Under the “double security” goal of achieving both food security and ecological protection, this study explores the green and low-carbon utilization efficiency of cultivated land (GLCUECL) in the Huaihe River Ecological Economic Belt (HREEB). This study identifies the spatiotemporal evolution characteristics and trends, promoting the green, low-carbon, and sustainable utilization of arable land resources in the HREEB, thus contributing to regional and national food and ecological security. Using a global super-efficiency EBM framework that accounts for undesirable outputs, as well as the GML index, the researchers measured and decomposed the GLCUECL in 25 prefecture-level cities of the HREEB from 2005 to 2021. The Theil index and kernel density estimation were applied to analyze regional disparities and changing developmental traits. Spatial convergence and divergence were assessed using the coefficient of variation and spatial convergence models. Key findings include the following: (1) Over time, the GLCUECL in the HREEB exhibited an overall upward trend and a non-equilibrium characteristic, namely the “East Sea-river-lake Linkage Area (ESLA) > Midwest Inland Rising Area (MIRA) > Huaihe River Ecological Economic Belt (HREEB) > North Huaihai Economic Zone (NHEZ)”. The increase in the GML index of the GLCUECL is mainly attributable to a technical progress change. (2) The overall difference in the GLCUECL tends to decline, which is mainly attributable to the intra-regional differences. (3) The overall kernel density curves for the HREEB and its three sub-regions exhibited a “rightward shift” trend. Except for the expansion and polarization of the absolute difference in the GLCUECL in the NHEZ, the absolute difference in GLCUECL in other regions, such as the HREEB, ESLA, and MIRA, exhibited a decreasing trend. (4) Spatial convergence analysis revealed that only the NHEZ lacks σ-convergence, whereas all regions exhibited β-convergence. Moreover, factors such as rural economic development level, cultivated land resource endowment, agricultural subsidy policy, crop planting structure, and technological input exerted a heterogeneous effect on the change in the GLCUECL. Based on these findings, this study offers recommendations for improving GLCUECL in the HREEB. Our recommendations include the implementation of the concept of green new development, optimization of the institution supply, establishing a regional cooperation mechanism for green and low-carbon utilization of cultivated land, and formulation of differentiated paths for improving the green and low-carbon utilization efficiency of cultivated land according to local conditions. Full article

Grassland ecosystems in arid regions increasingly experience resilience loss due to intensifying climatic variability. However, the limited interpretability of conventional machine learning models constrains our understanding of underlying ecological drivers. This study constructs an integrative framework that combines temporal autocorrelation (TAC) metrics with explainable machine learning, employing Random Forest and SHAP (SHapley Additive exPlanations) analysis. Time series of satellite-derived vegetation indices from MODIS (2001–2023), particularly the kernel Normalized Difference Vegetation Index (KNDVI), support the generation of TAC and its trend-based derivative δTAC. The framework assesses ecosystem resilience across seven representative grassland types in Xinjiang, capturing diverse responses to climate variability and vegetation dynamics. Results reveal pronounced spatial heterogeneity: resilience declines in radiation-stressed arid zones, while hydrothermally stable regions maintain stronger recovery capacity. Key drivers include temperature variability and vegetation dynamics, with divergent effects among grassland types. Meadow and Typical Steppe exhibit higher resilience under stable hydrothermal regimes, whereas desert and alpine systems show greater sensitivity to warming and climatic fluctuations. This framework enhances diagnostic transparency and ecological insight, offering a spatially explicit, data-driven tool for resilience monitoring. The findings support the formulation of targeted adaptation strategies and sustainable grassland management in response to ongoing climate change. Full article

The complex systems approach to cognitive–motor processing values multifractal nonlinearity as a key formalism in understanding internal interactions across multiple scales that preserve adequate task-directed behaviors. By using a computer task with increasing difficulty, we focused on the potential link between the difficulty threshold during a task, assessed by the individual’s score ceiling, and the corresponding level of multifractal nonlinearity in movement behavior, assessed based on a time series of cursor displacements. Entropy-based multifractality (MF) and multifractal nonlinearity obtained using a t-test comparison between the original and linearized surrogate series (t_MF) of the time series characterized individual adaptive capacity. A time-varying increase in the score helped in assessing performance when facing increasing difficulty. Twenty-one participants performed a herding task (7 min), which involves keeping three moving sheep near the center of a screen by controlling the mouse pointer as a repelling shepherd dog. The more the score increased, the more the increased herd movement amplitude amplified task difficulty. The time course of the score, score dynamics (score-dyn), markedly diverged across participants, exhibiting a ceiling effect in some during the last third of the task (phase 3). This observation led us to arbitrarily distinguish three phases of the same duration and focus on phase 3, where marked differences in score-dyn emerged. Hierarchical clustering of principal components, starting with principal component analysis, identified three clusters among the participants: cluster 1 was defined by an underrepresentation of score-dyn, MF, and t_MF; cluster 2 was defined by an overrepresentation of MF; and, as a critical outcome, cluster 3 was defined by an overrepresentation of score-dyn and t_MF. Accordingly, participants belonging to cluster 3 had the highest score-dyn and t_MF. Our interpretative hypothesis is that internal interactions that adequately perform the task are reflected in a high degree of multifractal nonlinearity. These findings extend the notion that multifractal nonlinearity is a useful conceptual framework for shedding light on adaptive behavior during complex tasks. Full article