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Abstract Although the relationship between hypertension and hyperuricemia is widely recognized, there is still a relative lack of research on prehypertensive individuals and the individual associations of systolic and diastolic blood pressure with the risk of hyperuricemia. From 2011 to 2016, we conducted a study on 53,323 individuals at Wuhu City Hospital in China. Based on initial blood pressure readings, participants were categorized into normal, prehypertension, or hypertension groups. We used Cox regression to analyze the associations with baseline factors. In subgroup analyses, systolic and diastolic pressures were treated as continuous variables, and their relationship with the risk of hyperuricemia was examined using restricted cubic spline analysis. The risk increased in the prehypertension and hypertension groups compared to the normal blood pressure group, with hazard ratios of 1.192 and 1.350, respectively. For each unit increase in blood pressure, the risk of hyperuricemia rose by 0.8% (systolic) and 0.9% (diastolic), especially when blood pressure levels exceeded 115/78 mmHg. Additionally, we observed that factors such as gender, alcohol consumption habits, obesity, and dyslipidemia might further influence this association. These findings emphasize the importance of early risk assessment and intervention in these patient populations in clinical practice.

Wide-band gap semiconductor devices based on GaN materials, such as high electron mobility transistors (HEMT), are gradually replacing traditional Si devices in industrial applications owing to their excellent electrothermal properties. Nonetheless, reliability concerns and accurate junction temperature estimation remain critical in the deployment of these devices. This paper proposes an enhanced Cauer thermal network model accounting for bidirectional heat transfer. The improved model takes into account the path from the junction via the top case to the ambient. Additionally, it utilizes the top case temperature rise to identify thermal parameters and establishes the relationship between the top case temperature time constant and the Cauer model’s thermal parameters. This method eliminates the need for measuring power losses or invasively modifying the original devices, nor does it require detailed structural and material parameters of the packaged device. The feasibility and reliability of the obtained thermal network parameters are verified through simulation and validated through experimentation.

Combining first-principles calculations and classical nucleation theory, the results of elemental doping on the structure, electrons and stability of niobium carbides are investigated. Ti, Cr and Mn doping altered the physical characteristics of the carbides, resulting in lower mismatch strains and interfacial energies between the carbide and the substrate. Elemental doping reduced the critical nucleation size of carbides and contributed to the promotion of carbide nucleation. However, excessive doping with Cr and Mn increases the formation energy of carbides and unfavorable to carbide stabilization. Therefore, Cr and Mn are involved in the early elemental aggregation of carbides, while Ti and Nb are the main carbide-forming elements. The nano-precipitated phases of various compositions produced in niobium microalloyed steel were experimentally validated utilizing transmission electron microscopy (TEM) and three-dimensional atom probe tomography (3D-APT). The carbides primarily composed of Nb, Ti and C elements, with the presence of Cr and Mn in the core region leading to the formation of a core-shell structure, which is consistent with theoretical calculations.

The stochastic search algorithms are an important optimization technique used to solve complex global optimization problems. The Slime Mould Algorithm (SMA) is one of stochastic search algorithm inspired by the observed behaviors and morphological changes in the foraging process of slime moulds. SMA has the advantage of having few parameters and a simple structure, making it applicable to various real-world optimization problems. However, it also has some drawbacks, such as high randomness during the search process and a tendency to converge to local optima, resulting in decreased accuracy. Therefore, we propose an effective method for global optimization - improved slime mould algorithm combine multiple strategy, called EISMA. In EISMA, we introduce a method of exploration that combines the average position of the population with Levy flights to enhance the algorithm's search capability in the previous phase. Then, a novel information-exchange hybrid elite learning operator is proposed to improve the guidance ability of the best search agent. Finally, a dual differential mutation search method that combines global and local optimization is introduced to maintain the diversity of the population by updating the search agents obtained in each iteration. These operations facilitate the algorithm's ability to escape local optima and ensure continuous optimization. To validate the applicability of EISMA, we numerically test it on 39 benchmark functions from CEC2013 and CEC2017 and compare its performance with SMA, as well as 7 modified, 5 standard and 4 classic stochastic search algorithms. Experimental results demonstrate that EISMA outperforms other versions in terms of optimization search performance. Furthermore, EISMA has achieved promising outcomes in testing problems related to path planning for three-dimensional unmanned aerial vehicles, pressure vessel design and robot gripper problem.

IntroductionPrevious coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19–related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach.MethodsA Least Absolute Shrinkage and Selection Operator–regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19–related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined.ResultsOur results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only.ConclusionOur findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.

Constructing a resilient ecological network (EN) and identifying critical strategic nodes and corridors within the EN, stands as a pivotal approach toward achieving a harmonious equilibrium between regional development and ecological conservation. It is imperative to expand the perspective to encompass the holistic resilience of the EN, thus identifying and safeguarding ecologically strategic nodes and corridors that play pivotal roles throughout the entire network. Therefore, this study takes Nanjing City as a case study and establishes a comprehensive EN resilience assessment framework based on resilience theory and complex network theory, grounded in a foundation of multidimensional indicators. Building upon this, the research develops the ‘regional network simulation - ecological spatial analysis - strategic spatial identification’ investigative framework. The results show that a core EN structure centered around green spaces, rivers, lakes, and similar features has taken shape, encompassing a total of 39 ecological nodes and 69 ecological corridors. Examining from six perspectives, connectivity, integration, complexity, centrality, efficiency, and substitutability, and their integrated assessment, this study analyzed the impact of single and sequential failures in different EN component spaces on the overall resilience of the EN. The study aimed to identify the component space that significantly contributes to the network's resilience. As a result, the strategic space of the EN was divided into three levels. The ecological source areas 1, 2, 7, and 34, along with ecological corridors 1–34, 1–4, 4–11, 19–34, 18–19, and 27–39, emerge as the primary strategic spaces within the study region. Previous research primarily emphasized the individual characteristics of ecological spaces. However, this study adopts a systemic perspective rooted in resilience theory and complex network theory to meticulously explore the pivotal impact of specific ecological spaces on the overall resilience of the ecological system network. Moreover, it amalgamates considerations of both the significance and scale of ecological spaces, conducting a comprehensive assessment of their combined influence on the overall EN resilience. This approach not only aids in identifying and preserving strategic ecological nodes and corridors but also contributes to evaluating, planning, and restoring regional EN resilience. This progression plays a crucial role in establishing an equilibrium between regional advancement and ecological conservation.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease with sporadic occurrence and high mortality. Herein, we report an example of the in-hospital transmission of SFTS virus (SFTSV) infections with familial and nosocomial clustering in Zhejiang Province, eastern China, from March to April 2023. The epidemiological investigation and genomic analysis revealed that at least eight suspected cases of SFTS occurred in this cluster, including one death and one asymptomatic case. Our report reemphasizes the risk of familial and nosocomial SFTSV infections in healthcare settings and the urgent need for the long-term systematic surveillance of SFTSV evolution in humans and animals in the eastern coastal regions of China.

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes.

The quality of urban green space has an impact on the health and well-being of populations. Previous studies have shown that consideration of crowd activity characteristics is the key premise of landscape space design and planning. However, there is limited research on the correlation between features of the spatial layout of Mini Urban Green Spaces (MUGS) and the behavior of people, and it is difficult to take into account the possible distribution of people and their activity characteristics during the design phase of MUGS. This study aims to construct a technical workflow utilizing the AnyLogic platform and agent-based simulation methods for analyzing the characteristics of landscape spatial layouts considering dynamic human behavior. One MUGS, named 511 Park in Nanjing, China, was selected as the case for the application of the method and exploration of the impacts of spatial elements and layout on crowd activity types and density. We investigated the impact of four types of spatial elements—paths, facilities, nodes, and entrances—on human activities in MUGS. The results showed that path layout emerged as the most significant influencing factor. Changes in nodes and the number of facilities have a relatively minor impact on people’s activities. There was an apparent impact of changes in path orientation around nodes on the dynamics of the flow of people. This study could provide valuable insights for landscape designers, aiding informed decision-making during the construction, renovation, and management of MUGS.

The visual attributes of urban green spaces influence people’s perceptions, preferences, and behavioural activities. While many studies have established correlations between landscape perception and visual attributes, they often focus on specific timeframes and overlook dynamic changes in the spatial form of urban green spaces. This study aims to explore the long-term changes in the visual attributes of urban green spaces. We propose a method to quantitatively analyse changes in visual attributes using point clouds to simulate visual interfaces. Using an unmanned aerial vehicle, we conducted a five-axis tilt photography survey of Qinglvyuan Park in Nanjing, China, in August 2018 and September 2023. Point cloud models were generated for the two periods, and five visual attribute indicators, openness (OP), depth variance (DV), green view ratio (GVR), sky view ratio (SVR), and skyline complexity (SC), were analysed for long-term changes. The results indicate that OP, DV, and SVR decreased after five years, while GVR increased. The maximum increase in GVR was 26.6%, and the maximum decrease in OP was 12.8%. There is a positive correlation between GVR and its change (d_GVR). Conversely, there are negative correlations between SC and its change (d_SC), as well as between SVR and d_GVR. Tree growth emerged as a primary factor influencing changes in the visual attributes of urban green spaces. This study highlights the importance of adopting a long-term and dynamic perspective in visual landscape studies, as well as in landscape design and maintenance practices. Future research on predicting long-term changes in the visual attributes of urban green spaces should focus on understanding the relationships between tree properties and environmental conditions.

(1) Background: Sequential landscape changes give people experiences of dynamic beauty, and the key to creating spatial sequences lies in the organization of spatial changes. The purpose of this study is to use a 3D point cloud model to achieve a refined description of spatial sequences’ organizational characteristics from information acquisition to description and explore the quantitative interpretation methods of typical sequence organizational characteristics. (2) Methods: The proposed model contains three main steps: data acquisition and extraction, characteristic index system construction and data processing, and quantitative characterization analysis. A typology research method that combines quantitative induction with qualitative statistical verification is proposed. (3) Results: Seventy-two spatial sequence point cloud models of study cases are obtained; 4 indicators are established; 3 typical organization types are summarized, namely fluctuating, reversal and moderate type; and the characterization factors and threshold intervals for each sequence organization type are analyzed to validate the type classification result. (4) Conclusions: This research improves the accuracy of spatial data, the comprehensiveness of sequence organization characterization factors, and the reliability of classification results. It supplements the existing spatial sequence theoretical knowledge system and provides parameters that can be referred to in practical design.

The present investigation endeavors to explore the influence of rare earth elements on the strength and plasticity characteristics of low-carbon microalloyed steel under tensile loading conditions. The findings from the conducted tensile tests indicate that the incorporation of rare earths leads to a notable enhancement in the yield strength, ultimate tensile strength, and ductility properties of the steel. A comparative analysis of the microstructures reveals that the presence of rare earths significantly refines and optimizes the microstructure of the microalloyed steel. This optimization is manifested through a reduction in grain size, diminution of inclusion sizes, and a concomitant rise in their number density. Moreover, the addition of rare earths is observed to foster an increase in the volumetric fraction of carbides within the steel matrix. These multifaceted microstructural alterations collectively contribute to a substantial strengthening of the microalloyed steel. Furthermore, it is elucidated that the synergistic interaction between rare earth elements and both carbon (C) and niobium (Nb) in the steel matrix augments the extent of the Lüders strain region during the tensile deformation of specimens. This phenomenon is accompanied by the effective modification of inclusions by the rare earths, which serves to mitigate stress concentrations at the interfaces between the inclusions and the surrounding matrix. This article systematically evaluates the modification mechanism of rare earth microalloying, which provides a basis for broadening the application of rare earth microalloying in microalloyed steel.