Search Results (1,407)

Due to the evident increase in tick-borne diseases worldwide, it is necessary to constantly update information on the distribution and zoonotic potential of hard ticks. We studied diversity, population structure, and seasonal dynamics of hard tick fauna, faunal similarity and the climate impact on tick occurrence in natural habitats (NHs) (forest communities) and anthropogenic habitats (AHs) (orchards, grasslands, degraded forests) in eastern and central parts of Continental Croatia. Host-seeking hard ticks were sampled by the flag-dragging method in lowland AHs (Bansko Hill (BH); 2023–2024 yr.) and in mountainous NHs (Medvednica Mountain (MM); 2019–2021, 2024 yr.). Overall, 2726 specimens belonging to eight hard tick species (Ixodes ricinus, I. frontalis, I. hexagonus, I. kaiseri, Haemaphysalis inermis, H. concinna, Dermacentor marginatus, D. reticulatus) were identified in AHs, while in NHs 1543 hard ticks, belonging to three species (I. ricinus, I. frontalis, D. reticulatus), were collected. The most abundant species in both habitat types (47.83% in AHs, 99.80% in NHs) was I. ricinus, showing unimodal seasonal activity within studied NHs and bimodal activity at AHs. Comparison of hard tick fauna in different habitats using the Sørenson index on BH and MM showed a high percentage of similarity (50.0–88.8). At AHs, a significant (p < 0.05) negative correlation was determined between the abundance (N) and the mean monthly air temperatures (°C) for H. inermis (r = −0.5931; p = 0.0421) and D. reticulatus (r = −0.6289; p = 0.0285), while their numbers positively correlated (r = 0.5551; p = −0.2667; r = 0.4430; p = 0.1492) with air humidity (%). In contrast, the number of sampled host-seeking I. ricinus ticks at natural forest habitats on MM was positively associated with air temperature and negatively with air humidity at elevations from 200 to 1000 m a.s.l. (r = −0.7684; p = 0.0259; at 200 m a.s.l.). Collected specimens of I. frontalis mark the first record for Osijek–Baranja County, while the sampled D. reticulatus on MM represents the first catch at 1000 m a.s.l. in Croatia. This new data on the distribution and seasonality of medically important hard tick species in Continental Croatia contributes to identifying tick-risk foci and high-risk periods. Full article

Background/Objectives: Laboratory tests are a cornerstone of modern medicine, and their interpretation depends on reference intervals (RIs) that define expected values in healthy populations. Standard RIs are obtained in cohort studies that are costly and time-consuming and typically do not account for demographic factors such as age, sex, and ethnicity that strongly influence biomarker distributions. This study establishes a data-driven approach for deriving RIs directly from routinely collected laboratory results. Methods: Multidimensional joint distributions of lipid biomarkers were estimated from large-scale real-world laboratory data from the Puerto Rican population using a Gaussian Mixture Model (GMM). GMM and additional statistical analyses were used to enable separation of healthy and pathological subpopulations and exclude the influence of comorbidities all without the use of diagnostic codes. Selective mortality patterns were examined to explain counterintuitive age trends in lipid values while comorbidity implication networks were constructed to characterize interdependencies between conditions. Results: The approach yielded sex- and age-stratified RIs for lipid panel biomarkers estimated from the inferred distributions (total cholesterol, LDL, HDL, triglycerides). Apparent improvements in biomarker profiles after midlife were explained by selective survival. Comorbidities exerted pronounced effects on the 95% ranges, with their broader influence captured through network analysis. Beyond fixed limits, the method yields full distributions, allowing each individual result to be mapped to a percentile and interpreted as a continuous measure of risk. Conclusions: Population-specific and sex- and age-segmented RIs can be derived from real-world laboratory data without recruiting healthy cohorts. Incorporating selective mortality effects and comorbidity networks provides additional insight into population health dynamics. Full article

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

In karst regions (KRs), unique surface morphology and irrational human exploitation have led to increasingly prominent issues such as land fragmentation and rocky desertification. Understanding the spatiotemporal evolution of cultivated land (CL) in these areas is of great significance for supporting regional socioeconomic development, food security, and ecological sustainability. This study focuses on Guilin, combining GIS spatial analysis with methods including kernel density analysis, dynamic degree, spatial transfer matrix, and a Geodetector to examine the spatiotemporal distribution characteristics, evolution trends, and driving factors of land use based on five-phase of land use data from 2000 to 2020. The results show that: (1) over the past two decades, land use in Guilin has been dominated by CL and forest land, with CL exhibiting a spatial pattern of more in the east and south, and less in the west and north; (2) the CL transfer-out rate exceeded the transfer-in rate, mainly shifting to construction land and forest land; (3) the overall density of CL showed a declining trend, with a relatively stable spatial pattern; and (4) driving factor analysis indicates that the spatiotemporal changes in CL are jointly influenced by multiple factors, with natural factors exerting a stronger influence than socio-economic factors. Among them, the interaction between elevation and temperature had the greatest impact and served as the dominant factor. Although GDP and population were not dominant individually, their explanatory power and sensitivity increased significantly when interacting with other factors, making them key sensitive factors. The results can provide a scientific reference for the protection and rational utilization of CL resources in KR. Full article

This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, a three-layer cooperative search framework to achieve a stronger balance between exploration and exploitation, and a dynamic adaptive mechanism with t-distribution re-exploration to reinforce both global escaping and local refinement. On the CEC2017 benchmark suite, IWMA demonstrates clear superiority over seven representative algorithms, delivering the best results on 27 out of 29 functions by best, 25 by mean, and 23 by standard deviation in 30 dimensions, and on 25, 18, and 18 functions, respectively, in 50 dimensions. Compared with other migration-based optimizers, its average rank improves by more than 30 percent, while runtime analysis shows only a small additional overhead of 7 to 12 percent. These outcomes, supported by convergence curves, boxplots, radar charts, and Wilcoxon tests, confirm the effectiveness of the proposed improvements. In six multi-UAV path planning scenarios, IWMA reduces the average cost by 14.5 percent compared with WMA and achieves up to 32.1 percent reduction in the most complex case. Overall, its average cost decreases by 27.4 percent across seven competitors, with a 23.6 percent improvement in the best solutions. These results demonstrate that the proposed modifications are effective, enabling IWMA to transfer its performance gains from benchmark tests to practical multi-UAV cooperative mission planning, where it consistently produces safer and smoother trajectories under complex constraints. Full article

This study investigated the distribution of pine wood nematodes (PWNs, Bursaphelenchus xylophilus) and their co-occurrence with B. mucronatus in recently dead pine trees across coastal and inland regions while monitoring the seasonal emergence patterns of Monochamus alternatus from 2021 to 2023. Nematodes were extracted from felled trees and beetle bodies using the Baermann funnel method. Aggregation pheromone traps were used to monitor vector activity and to assess temperature-dependent emergence. The results showed a negative correlation between PWN and B. mucronatus density (r = −0.73, p < 0.01), which prompted tests on interspecific interactions. M. alternatus emergence was positively associated with average temperature (r = 0.74–0.78), supporting the temperature-informed surveillance timing in this dataset. These findings highlight the role of climate-driven dynamics in shaping vector behavior and nematode population structures. This study supports the development of sustainable temperature-responsive management strategies for controlling pine wilt disease. These strategies provide a foundation for climate-resilient forest health and long-term ecosystem sustainability. Full article

In recent years, under the dual pressures of climate change and human activities, wetlands in Liaoning Province, China, are increasingly threatened, raising concerns about regional ecological security. To better understand these changes, we developed a vulnerability assessment framework integrating a 30 m wetland dataset (2000–2020) with multi-source environmental and socio-economic data. Using the XGBoost–SHAP model, we analyzed wetland spatiotemporal evolution, driving mechanisms, and ecological vulnerability. Results show the following: (1) ecosystem service functions exhibited significant spatiotemporal differentiation; carbon storage has generally increased, water conservation capacity has significantly improved in the northern region, while wind erosion control and soil retention functions have declined due to urban expansion and agricultural development; (2) driving factors had evolved dynamically, shifting from population density in the early period to increasing influences of precipitation, vegetation index, GDP, and wetland area in later years; (3) ecologically vulnerable areas demonstrated a pattern of fragmented patches coexisting with zonal distribution, forming a three-level spatial gradient of ecological vulnerability—high in the north, moderate in the central region, and low in the southeast. These findings demonstrate the cascading effects of natural and human drivers on wetland ecosystems, and provide a sound scientific basis for targeted conservation, ecological restoration, and adaptive management in Liaoning Province. Full article

Aphids remain exceptional models for symbiosis research due to their unique experimental advantages that extend beyond documenting symbiont-mediated phenotypes. Nine commonly occurring facultative bacterial symbionts provide well-characterized benefits, including defense against parasitoids, pathogens, and thermal stress. Yet the system’s greatest value lies in enabling diverse research applications across biological disciplines through experimental tractability combined with ecological realism. Researchers can create controlled experimental lines through symbiont manipulation, maintain clonal host populations indefinitely, and cultivate symbionts independently. This experimental power is complemented by extensive knowledge of symbiont dynamics in natural populations, including temporal and geographic distribution patterns—features generally unavailable in other insect-microbe systems. These advantages facilitate investigation of key processes in symbiosis, including transmission dynamics, mechanisms, strain-level functional diversity, multi-partner infections, and transitions from facultative to co-obligate relationships. Integration across biological scales—from genomics to field ecology—enables research on symbiont community assembly, ecological networks, coevolutionary arms races, and agricultural applications. This combination of experimental flexibility, comprehensive natural history knowledge, and applied relevance positions aphids as invaluable for advancing symbiosis theory while addressing practical challenges in agriculture and invasion biology. Full article

Given that the increasing non-agricultural conversion of cultivated land (NACCL) endangers food security, studying the spatial and temporal variation characteristics and driving mechanisms of NACCL in Sichuan Province can offer a scientific foundation for developing local farmland preservation measures and controlling further conversion. Guided by the theoretical framework of land use transition, this study utilizes land use datasets spanning multiple periods between 2000 and 2023. Comprehensively considering population scale factors, natural geographical factors, and socioeconomic factors, the county-level annual NACCL rate is calculated. Following this, the dynamic evolution and underlying driving forces of NACCL across 183 counties in Sichuan Province are examined through temporal and spatial dimensions, utilizing analytical tools including Nonparametric Kernel Density Estimation (KDE) and the Geographical Detector model with Optimal Parameters (OPGD). The study finds that: (1) Overall, NACCL in Sichuan Province exhibits phased temporal fluctuations characterized by “expansion—contraction—re-expansion—strict control,” with cultivated land mainly being converted into urban land, and the differences among counties gradually narrowing. (2) In Sichuan Province, the spatial configuration of NACCL is characterized by the expansion of high-value agglomerations alongside the dispersed and stable distribution of low-value areas. (3) Analysis through the OPGD model indicates that urban construction land dominates the NACCL process in Sichuan Province, and the driving dimension evolves from single to synergistic. The findings of this study offer a systematic examination of the spatiotemporal evolution and underlying drivers of NACCL in Sichuan Province. This analysis provides a scientific basis for formulating region-specific farmland protection policies and supports the optimization of territorial spatial planning systems. The results hold significant practical relevance for promoting the sustainable use of cultivated land resources. Full article

Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel in static settings, issues like keyframe redundancy and optimization inefficiencies further hinder their practical deployment on robotic platforms. To address these challenges, we propose DKB-SLAM, a real-time RGB-D visual SLAM system specifically designed to enhance robotic autonomy in complex dynamic scenes. DKB-SLAM integrates optical flow with Gaussian-based depth distribution analysis within YOLO detection frames to efficiently filter dynamic points, crucial for maintaining accurate pose estimates for the robot. An adaptive keyframe selection strategy balances map density and information integrity using a sliding window, considering the robot’s motion dynamics through parallax, visibility, and matching quality. Furthermore, a heterogeneously weighted local bundle adjustment (BA) method leverages map point geometry, assigning higher weights to stable edge points to refine the robot’s trajectory. Evaluations on the TUM RGB-D benchmark and, crucially, on a mobile robot platform in real-world dynamic scenarios, demonstrate that DKB-SLAM outperforms state-of-the-art methods, providing a robust and efficient solution for high-precision robot localization and mapping in dynamic environments. Full article

Increasing demand for land and resources in Himalayan catchments is altering hydrological processes and threatening freshwater ecosystems. Sediment mobilization and nutrient fluxes, especially during monsoon rainfall events, are intensifying the degradation of water bodies. This study investigates land cover change and its effects on nutrient dynamics in the Phewa Lake catchment, Nepal. Landsat imagery from 1990 to 2021, processed through Google Earth Engine, was used to map land changes. Nutrient loading for the two time periods was estimated with the InVEST model. Surface soils were sampled across the catchment to analyze nitrogen and phosphorus distribution, while their particle-bound transport to the lake was assessed through riverbed sediments and the suspended sediments collected during monsoon rainfalls. Pre-monsoon water quality was examined to evaluate eutrophication levels across different lake zones. Results reveal forest recovery in the upper catchment, but agricultural land in the lower catchment is being rapidly converted to urban areas. While forest recovery has enhanced sediment retention, nutrient inputs to the lake, particularly nitrogen and phosphorus, have increased. Fertilizer leaching and untreated sewage emerge as key sources in rural and urban areas, respectively. Seasonal constraints of the dataset may underestimate the overall extent of water quality deterioration, as indicated by high nutrient loads in monsoon suspended sediments. Overall, this study highlights the dual effect of land cover change: forest regrowth coincides with rising nutrient discharge. Without timely interventions, growing urban populations in the region may face worsening water quality challenges. Full article

This work is framed as an application of static and small exponential random graph models for complex networks in multiple layers. This document revisits the small network and exhibits its potential. Examining the bibliography reveals considerable interest in large and dynamic complex networks. This research examines the application of small networks (50,000 population) for analyzing global commerce, conducting a comparative graph structure of the tariffs, and importing multilayer networks. The authors created and described the scenario where the readers can compare the graph models visually, at a glance. The proposed methodology represents a significant contribution, providing detailed descriptions and instructions, thereby ensuring the operational effectiveness of the application. The method is organized into five distinct blocks (Bn) and an accompanying appendix containing reproduction notes. Each block encompasses a primary task and associated sub-tasks, articulated through a hierarchical series of steps. The most challenging mathematical aspects of a small network analysis pertain to modeling and sample selection (sel_p). This document describes several modeling tasks that confirm that sel_p = 10 is the best option, including modeling the edges and the convergence and covariance model parameters, modeling the node factor by vertex names, Pearson residual distributions, goodness of fit, and more. This method establishes a foundation for addressing the intricate questions derived from the established hypotheses. It provides eight model specifications and a detailed description. Given the scope of this investigation, a historical examination of the relationships between different network actors is deemed essential, providing context for the study of actors engaged in global trade. Various analytical perspectives (six), encompassing degree analyses, diameter and edges, hubs and authority, co-citation and cliques in mutual and collapse approaches, k-core, and clustering, facilitate the identification of the specific roles played by actors within the importation network in comparison to the tariff network. This study focuses on the Latin American and Caribbean region. Full article

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging. Full article

In maritime target search missions, UAV swarm task scheduling faces several challenges. These include uncertainties in target states, the high-dimensional multimodal characteristic of the solution space, and dynamic constraints on swarm collaboration. In terms of target position estimation, existing methods ignore the spatiotemporal correlation of target movement. At the level of optimization algorithms, existing algorithms struggle to balance global exploration and local exploitation, and they tend to fall into local optima. To address the above shortcomings, this paper constructs a technical system of “state perception-strategy optimization-collaborative execution”. First, a Serial Memory Iterative Method (GMMIM) integrated with the Gaussian–Markov model is proposed. This method recursively corrects the probability distribution of target positions using historical state data, thereby providing accurate situational support for decision-making. As a result, task scheduling efficiency is improved by 5.36%. Second, the sliding window technique is introduced to improve the Grey Wolf Optimizer (GWO). Based on the convergence of the population’s optimal fitness, the decay rate of the convergence factor is dynamically and adaptively adjusted. This balances the capabilities of global exploration and local exploitation to ensure swarm scheduling efficiency. Simulations demonstrate that the optimization performance of the proposed FSW-GWO algorithm is 16.95% higher than that of the IPSO method. Finally, a dynamic task weight update mechanism is designed. By combining resource load and task timeliness requirements, this mechanism achieves complementary adaptation between swarm resources and tasks. Full article

We propose a general and experimentally accessible framework to quantify transition timing in discrete quantum systems via the time-of-flow (TF) distribution. Defined from the rate of population change in a target state, the TF distribution can be reconstructed through repeated projective measurements at discrete times on independently prepared systems, thus avoiding Zeno inhibition. In monotonic regimes, it admits a clear interpretation as a time-of-arrival (TOA) or time-of-departure (TOD) distribution. We apply this approach to optimize time-dependent Hamiltonians, analyze shortcut-to-adiabaticity (STA) protocols, study non-adiabatic features in the dynamics of a three-level time-dependent detuning model, and derive a transition-based quantum speed limit (TF-QSL) for both closed and open quantum systems. We also establish a lower bound on temporal uncertainty and examine decoherence effects, demonstrating the versatility of the TF framework for quantum control and diagnostics. This method provides both a conceptual tool and an experimental protocol for probing and engineering quantum dynamics in discrete-state platforms. Full article