Distance Threshold Research Articles

Performance of point charge embedding schemes for excited states in molecular organic crystals.

Modeling excited state processes in molecular crystals is relevant for several applications. A popular approach for studying excited state molecular crystals is to use cluster models embedded in point charges. In this paper, we compare the performance of several embedding models in predicting excited states and S1-S0 optical gaps for a set of crystals from the X23 molecular crystal database. The performance of atomic charges based on ground or excited states was examined for cluster models, Ewald embedding, and self-consistent approaches. We investigated the impact of various factors, such as the level of theory, basis sets, embedding models, and the level of localization of the excitation. We consider different levels of theory, including time-dependent density functional theory and Tamm-Dancoff approximation (TDA) (DFT functionals: ωB97X-D and PBE0), CC2, complete active space self-consistent field, and CASPT2. We also explore the impact of selection of the QM region, charge leakage, and level of theory for the description of different kinds of excited states. We implemented three schemes based on distance thresholds to overcome overpolarization and charge leakage in molecular crystals. Our findings are compared against experimental data, G0W0-BSE, periodic TDA, and optimally tuned screened range-separated functionals.

Polarized training intensity distribution in distance running

In this case study, we analysed the online available one-year training diary of a long-distance runner participating in the 2021 Olympic Games in terms of training volume and training intensity distribution during the year and in different phases to track periodization. Based on the literature, we categorized the distances covered in relation to the athlete's race speed into three zones: Z1 <80% RP; Z2 80-95% RP; Z3 >95% RP. The training intensity distribution was calculated using the Polarization Index: PI = log10 (Z1/Z2 x Z3*100). The athlete's average weekly training volume during the 52-week season was 141.77 ± 27.27 km/week (571.94 ± 106 min/week), completed in 10.4 ± 1.24 training sessions. Throughout the season (Z1: 89.95%; Z2: 4.58%; Z3: 5.43%) and also during the different preparation phases, the training intensity distribution showed a polarized pattern (PI >2.00 a.U.). In a typical interval form, the athlete performed intense training (HIT) at and above the anaerobic threshold twice a week (vLT2). Most (>80%) of the high average weekly training volume was sustained running at low intensities. High-intensity interval training (HIIT) twice weekly in the base period typically took the form of long partial distances at and above the anaerobic threshold (~90% vVO2max) and short partial (<800m) distances close to race speed. The polarization rate increased as the racing season approached, and more extended interval training at race speeds was used. In conclusion, in addition to the Pyramid distribution, a Polarized training intensity distribution can also be observed during elite distance runners' training.

A method for accurately extracting power lines and identifying potential intrusion risks from urban laser scanning data

Timely and accurate monitoring of power line safety is a key step in ensuring urban production and daily life. The rapid development of vehicle-borne mobile laser scanning (MLS) technology has provided an effective solution for intelligent maintenance of power grids. This paper proposes a comprehensive method for accurate extraction, missing completion, and intrusion risk detection of urban power lines. Firstly, local linear geometric features are described using principal component analysis (PCA) covariance matrix, and outliers are accurately separated using a joint Bezier curve distance threshold. Secondly, the statistical inference function estimation method is employed to accurately simulate the complete topology of power lines. Finally, a fast intrusion risk detection method based on the separating axis theorem (SAT) for boundary box neighboring objects is developed. Experimental results demonstrate that our method achieves a recall rate and precision rate of 98.28 % and 98.71 %, respectively, for power line extraction. The root mean square error (RMSE) accuracy and coordinate residual of catenary line fitting are both below 0.04 m, indicating strong noise resistance and robustness. Compared to mainstream geometric extraction methods, our approach exhibits superior performance.

Weather whiplash events in Europe and North Atlantic assessed as continental-scale atmospheric regime shifts

The term “weather whiplash” describes abrupt transitions from one persistent weather regime to another substantially different one, such as from a frigid cold spell to anomalous warmth. Weather whiplash events (WWEs) are often highly disruptive to agriculture, ecosystems, infrastructure, and human activities. While no consistent definition exists, we identify WWEs based on substantial shifts in the continental-scale, upper-atmosphere circulation. As first demonstrated in our earlier study focused on the NE Pacific/North American region, a WWE is detected when one persistent, large-scale pattern in 500 hPa height anomalies shifts to another distinctly different one. Patterns are identified using self-organizing maps (SOMs), which create a matrix of representative patterns in the data. In the present study, we apply this approach to identify WWEs in the North Atlantic/European sector. We analyze the occurrence of WWEs originating with long-duration events (four or more days) in each pattern. A WWE is detected when the pattern two days following a long-duration event is substantially different, measured with distance thresholds internal to the matrix. Changes in WWE frequency, past and future, are assessed objectively based on reanalysis output and climate model simulations. We find that future changes under RCP 8.5 forcing exhibit distinct trends, especially in summer months, while those based on reanalysis are less clear. Patterns featuring positive height anomalies in high latitudes are projected to produce more WWEs in the future, while patterns exhibiting negative anomalies produce fewer. Shifts in temperature and precipitation extremes associated with these WWEs are diagnosed.

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

With a large number of intrusion detection datasets and high feature dimensionality, the emergent nature of new attack types makes it impossible to collect network traffic data all at once. The modified three-way attribute reduction method is combined with a Self-Organizing Incremental learning Neural Network (SOINN) algorithm to propose a self-organizing incremental neural network intrusion detection model based on three-way attribute reduction. Attribute importance is used to perform attribute reduction, and the data after attribute reduction are fed into a self-organized incremental learning neural network algorithm, which generalizes the topology of the original data through self-organized competitive learning. When the streaming data are transferred into the model, the inter-class insertion or node fusion operation is performed by comparing the inter-node distance and similarity threshold to achieve incremental learning of the model streaming data. The inter-node distance value is introduced into the weight update formulation to replace the traditional learning rate and to optimize the topological structure adjustment operation. The experimental results show that T-SOINN achieves high precision and recall when processing intrusion detection data.

A Direction-Preserved Vessel Trajectory Compression Algorithm Based on Open Window

Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a certain amount of pressure on the relevant research. This paper proposes a direction-preserved vessel trajectory compression method based on Open Window, which can effectively retain the direction change feature points while ensuring the position error. In addition, the method can work in both offline and online modes. Also, the velocity threshold and low-speed redundancy points problems are considered. In order to verify the effect of the proposed method and the adaptability of the method to different feature data, a parallel experiment was performed on port water and coastal water datasets. The results show that our method can compress vessel trajectories while retaining the feature points. Meanwhile, by comparing it with the compression method that is based on distance thresholds, the vessel trajectory compress time is drastically reduced by up to 87.3% in the port water data. The research in this paper provides a new method through which to compress vessel trajectories for research in marine-related fields on vessel trajectory.

Identification and analysis of spatial access disparities related to primary healthcare in Batna City, Algeria.

The issue of reducing spatial disparities is one of the most pressing concerns for policymakers and planners, which consider a crucial focus in planning and public service, especially accessibility to healthcare. Accessibility and proximity are the principal keys to providing good public service. Therefore, a healthcare system that meets the requirements of availability and affordability will be useless if spatial accessibility is not provided equally to all demands (population). Many technics and methods exist to quantify accessibility, including the two-step floating catchment area (2SFCA) method, its widely used to measure healthcare accessibility based on the travel distance threshold. This research paper aims to use the 2SFCA method to measure the spatial healthcare accessibility in Batna City because the 2SFCA method offers to measure accessibility on both spatial and functional levels. The spatial level will consider the threshold distances between the health demand (population) and the health provider location (healthcare facilities); moreover, functional accessibility is measured based on facility to population ratio that will give a better overview of Batna's healthcare provider. As a result, the optimal threshold distance that offers balanced results between the spatial accessibility score and other WHO ratios will be a distance between 1000- and 1500-meters travel distance. In addition, the central census districts have a higher access score than the rest of the city's districts; most census districts that do not have accessibility (12% of the population) to healthcare facilities are concentrated in the southwest of Batna city.

RESEARCH ON SELF-CROSS-TRANSFORMER MODEL OF POINT CLOUD CHANGE DETECTION

Abstract. With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based on traditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network.

Deep learning-based automated detection of the dental crown finish line: An accuracy study

Statement of problemThe marginal fit of dental prostheses is a clinically significant issue, and dental computer-aided design software programs use automated methods to expedite the extraction of finish lines. The accuracy of these automated methods should be evaluated. PurposeThe purpose of this study was to compare the accuracy of a new hybrid method with existing software programs that extract finish lines using fully automated and semiautomated methods. Material and methodsA total of 182 jaw scans containing at least 1 natural tooth abutment were collected and divided into 2 groups depending on how the digital data were created. Group DS used desktop scanners to scan casts trimmed for improved finish line visibility, while Group IS used intraoral scans. The method from Dentbird was compared using 3 software packages from 3Shape, exocad, and MEDIT. The Hausdorff and Chamfer distances were used in this study. Three dental laboratory technicians experienced in the digital workflow evaluated clinical finish line acceptance and its Hausdorff and Chamfer distances. For statistical analysis, t tests were performed after the outliers had been removed using the Tukey interquartile range method (α=.05). ResultsOutliers identified by using the Tukey interquartile range method were more numerous in the semiautomatic methods than in the automatic methods. When considering data without outliers, the software performance was found to be similar for desktop scans of the trimmed casts. However, the method from Dentbird demonstrated statistically better results (P<.05) for the posterior tooth with finish lines in concave regions than the 3Shape, exocad, and MEDIT software programs. Furthermore, thresholds coherent with clinical acceptance were determined for the Hausdorff and Chamfer distances. The Hausdorff distance threshold was 0.366 mm for desktop scans and 0.566 mm for intraoral scans. For the Chamfer distance, the threshold was 0.026 for desktop scans and 0.100 for intraoral scans. ConclusionsThe method from Dentbird demonstrated a comparable or better performance than the other software solutions, particularly excelling in finish line extraction for intraoral scans. Using a hybrid method combining deep learning and computer-aided design approaches enables the robust and accurate extraction of finish lines.

Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks

As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks.

Characteristics of the different HIV-1 risk populations based on the genetic transmission network of the newly diagnosed HIV cases in Jiangsu, Eastern China

IntroductionThe HIV-1 prevalence has been steadily increasing in Jiangsu, China. HIV-1 genetic transmission network can be used to explore the transmission kinetics and precision intervention in high-risk populations. Thus, we generated an HIV-1 genetic transmission network, explored key risk populations based on different risk factors and found out the risk factors for HIV-1 prevention and control among the newly-diagnosed HIV-1 cases from 2017 to 2018. MethodWe amplified the HIV-1 pol sequences from the plasma samples of the newly-diagnosed HIV-1 cases from 2017 to 2018 and obtained the infection data from The National HIV/AIDS Surveillance System. HIV-Trace and Cytoscape Software were both used to construct the HIV-1 genetic network with a gene distance of <0.005. The R software was used to analyze the risk factors for inclusion into the network. ResultsWe obtained 3362 sequences with the pol gene region, of which 3316 contained detailed individual information. CRF01_AE accounted for 42.3 % of the HIV-1 subtypes in the samples. The median CD4+T lymphocyte count was 329 cells/μL in 2017 and 313 cells/μL in 2018. At the gene distance threshold of 0.005, 481 sequences were incorporated into the HIV-1 gene network, constructing 202 clusters. Age over 60 years old, heterosexual transmission route, subtype (CRF105_0107, CRF55_01 B, and CRF67_01 B) and CD4+T lymphocyte count (>200) were the risk factors influencing inclusion into the HIV-1 gene network. Moreover, south Jiangsu cities had higher inclusion in the network. Thus, key risk populations in the clusters with different transmission routes, new emerging subtypes, drug resistance nodes, and individuals above 60 years of age in the network represented the critical risk populations that should be focused more on for intervention. ConclusionThe HIV-1 genetic transmission network is adept at discovering undiagnosed HIV-infected cases and linking all diagnosed cases for determination of risk infections. Therefore we should pay more attention to these risk infections with further investigation and intervention, helping to achieve the goal of 95 % use combination prevention from the World Health Organization, and push to end AIDS epidemic.

Crosswalk Detection from Satellite Imagery for Pedestrian Network Completion

Pedestrian network creation and completion for various tasks in transportation planning engineering generally requires laborious manual labeling of sidewalks and crosswalks. In this study, we employed the deep learning-based object detection model YOLO v5 for detecting crosswalks using Google Maps satellite imagery data of four North American cities. We also tested for two additional scenarios involving two image-processing techniques to exploit the special properties of crosswalks. Observations showed that the original images performed better than with binary thresholding and nearly the same even when noisy regions outside the right-of-way were removed. We proposed an algorithm to assign the detected crosswalks to intersection legs. We demonstrated the effectiveness of this technique for Washington, D.C. and Los Angeles, CA, showing classification accuracy rates of 71% and 89%, respectively. We also showed the influence of increasing distance threshold, a tolerance radius of prediction accuracy, in degrading the classification performance. This method, when combined with existing methods for sidewalk detection from aerial and street view images, could be reasonably used to help complete urban pedestrian networks in cities where high-resolution satellite images are available.

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.

Assessing Regional Development Balance Based on Zipf’s Law: The Case of Chinese Urban Agglomerations

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China’s population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf’s law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies.

Use of the Rasch Model for Fit Statistics and Rating Scale Diagnosis for the Student Perception Appraisal.

Background and Purpose: Nursing student retention is essential to meet workforce demands. Jeffreys' Nursing Student Retention Student Perception Appraisal-Revised (SPA-R1) has been used extensively to understand factors that impact retention. Psychometric testing of the SPA-R1 contributes to greater confidence in the instrument's reliability and validity. Methods: Item response theory and specifically, the single parameter polytomous Rasch model was used as a framework for fit statistic testing and rating scale diagnostics of the SPA-R1. This was a secondary analysis of a convenience sample of undergraduate prelicensure nursing students. The setting for the previous study was virtual, and the study period was 2022. Results: The model item characteristic curves for the 27 items of the SPA-R1 have similar shapes and are clustered in proximity. Overall, there are three clusters of items evident in the Rasch standardized residual contrast. The Rasch scale diagnostics indicated that the scale appropriately monotonically increases. However, there is a greater than 5 logit distance between does not apply and severely restricts, between severely restricts and moderately restricts, and between does not restrict or support and moderately supports. These large threshold distances indicate that additional steps in the scale may be warranted. The items cover the mid-range of the amount of retention perceptions; however, there are no items that represent the highest magnitude of the perceived amount of influence on retention. Conclusions: This study contributes further evidence to support the validity and reliability of the SPA-R1. We recommend adding steps to the scale, removing the does not apply response option, and considering scoring by three domains or clusters.

Assessment and simulation of thermal environments in Taiyuan urban built-up area, China

The urban heat island (UHI) effect has intensified with increases in impervious surface areas and population densities due to urbanization, which affects the quality of urban life and ecological services. Here, the Moran’s I and hot spot analysis (Getis-Ord Gi*) are used to explore spatial autocorrelation of land surface temperature (LST) in Taiyuan built-up area. Then, the built-up area is divided to 41 sub-areas to accurately explore the urban LST differences caused by different LULC types. Moreover, geographically weighted regression (GWR) is used to analysis the spatial heterogeneity of LST. Finally, we simulate the LST changes using the CA-Markov model in the study area in the year 2030. Our results showed that 1) average LST was 22.76°C in Taiyuan built-up area in 2018. The Highest-temperature areas were distributed in heavy-industry intensive areas in the north, north central, and southeast, whereas the Lowest-temperature areas mainly corresponded to rivers, lakes, urban forests, and green spaces. 2) The Moran’s I gradually decreased from 0.8635 to 0.2097 with an increase in the spatial distance threshold. The optimal recognition effect was obtained at a 400 × 400-m scale. The Getis-Ord Gi* analysis indicated that the cold area was 1248.32 km2 (12.24% of the study area) and the hot area was 43.84 km2 (11.11% of the study area) in 2018. 3) The GWR analysis showed significant spatial non-stationarity in the influence of LULC types on LST. The GWR model was calculated with reference to the observation values of the adjacent areas, so as to better reveal the spatial relationship between artificial surface, woodland, water, grassland, and bare land and LST. 4) The UHI distribution was more concentrated in 2030 than in 2021. The statistics of the proportion and transfer matrix of LST indicated that the proportion of the Highest and Lowest-temperature areas in 2030 decreased and the UHI effect will further intensify. This study could be used to guide sustainable development in cities and provides theoretical support for adjusting the urban spatial structure.

The effects of peeling on finite element method -based EEG source reconstruction

The problem of reconstructing brain activity from electric potential measurements performed on the surface of a human head is not an easy task: not only is the inverse problem fundamentally ill-posed, but the methods utilized in constructing a synthetic forward solution themselves contain many inaccuracies. As an example the usual method of modelling primary currents in the human head via dipoles brings about at least two modelling errors: one from the singularity introduced by the dipole, and one from placing such dipoles near conductivity discontinuities in the active brain layer boundaries.In this article, we observe how the removal of possible source locations from the vicinity of active layer surfaces affects the localisation accuracy of two inverse methods, Standardized Low-Resolution Tomography (sLORETA) and Dipole Scan, at different signal-to-noise ratios (SNR), when the H(div) source model is used. This source location restriction is achieved by setting a peeling depth or a threshold distance dp, that acceptable source positions must have from active gray matter boundaries. Our aim is to understand the full effect and potential of peeling with a multi-compartment and high-resolution head model, in which the cortical activity is limited into a thin and strongly folded layer of gray matter.Our results suggest that peeling can significantly improve the overall regularity of the reconstructed distributions, when a suitable peeling depth and a low enough noise level are selected. The applied inverse algorithm and brain compartment under observation also affect the accuracy.

Quantifying CPT cone factors in clays derived from weathered mudstone

The empirical interpretation of cone penetration test (CPT) cone factors ( N k ) can be subject to considerable variability for clays derived from weathered mudstones, leading to significant deviations in the estimation of undrained shear strength ( S u ). This paper presents a comparison of triaxial and CPT data from a site investigation in clays derived from weathered mudstones in central England. Corrected cone factors ( N kt,UU ) were derived from a one-to-one comparison of 94 pairs of unconsolidated, undrained triaxial and CPT data from equivalent depths. The performance of the cone factors was evaluated using a training set (75 pairs) and a test set (19 pairs). A parametric study was used to explore the variability of N kt,UU , quantified using the coefficient of variation (COV Nkt,UU ), for varied separation distance thresholds ( D s ) between individual triaxial and CPT data. The absolute deviation between the laboratory shear strength ( S u(Lab) ) and that predicted from CPT profiles (S u(CPT) ) was not sensitive to N kt,UU values in the range 25 &lt; N kt,UU &lt; 31. The parametric study showed that D s could be increased from 50 to 250 m, to include more data pairs for estimates of N kt,UU , without substantially increasing COV Nkt,UU .

Optimizing an Urban Green Space Ecological Network by Coupling Structural and Functional Connectivity: A Case for Biodiversity Conservation Planning

Strengthening and optimizing the spatial structure and functional connectivity of green space ecological networks can not only relieve the tight urban space and provide biodiversity protection but also promote the virtuous cycle of the urban ecosystem and provide a new method for the resilient development of the urban landscape. In this study, the central area of Chengdu was taken as the study area; Morphological Spatial Pattern Analysis (MSPA) with landscape metrics were combined to determine the optimal distance threshold and identify the ecological sources. Graph theory and circuit theory were applied to construct and optimize the green space ecological network with structural or functional connectivity, respectively. Based on the coupling effect, the optimization of the ecological network was put forward, and the network analysis method was used to evaluate the connectivity of three different types of ecological networks. The results were as follows: (1) The ecological network with structural connectivity was composed of 74 stepping stones, 43 protective sources, and 315 ecological corridors. The connectivity of green space structures gradually decreased from west to east and from periphery to center. (2) In the optimal ecological network with functional connectivity, 176 important ecological corridors were protected, and 40 pinch points and 48 protective sources were identified. The number of important corridors in the east and south was the largest, and the network structure was relatively complex. The barriers were divided into three different levels of ecological restoration areas. (3) The green ecological network with structural and functional connectivity has the best network connectivity. A green space ring network optimization pattern of one center, two belts, multi-points, multi-corridors, and multi-zones connected in a series was proposed. It was suggested to build a multi-level forest ecosystem in Longquan Mountain, develop eco-fruit agriculture and eco-tourism, enrich the biodiversity of the ecological source, and improve its anti-interference ability to the external environment. It is also important to increase ecological strategic points and stepping stones to strengthen the links between different ecological restoration areas, properly handle the use of cultivated land in different regions, strictly observe the red line of cultivated land, and maintain the integrity and diversity of ecological sources. Therefore, the optimization method of the green space ecological network in this study provides technical support for the effective determination of ecological protection areas, the accurate implementation of green space ecological networks, and a scientific planning strategy for decision-makers.

Sensitivity of habitat network models to changes in maximum dispersal distance

Predicting the presence or absence (occurrence-state) of species in a certain area is highly important for conservation. Occurrence-state can be assessed by network models that take suitable habitat patches as nodes, connected by potential dispersal of species. To determine connections, a connectivity threshold is set at the species’ maximum dispersal distance. However, this requires field observations prone to underestimation, so for most animal species there are no trustable maximum dispersal distance estimations. This limits the development of accurate network models to predict species occurrence-state. In this study, we performed a sensitivity analysis of the performance of network models to different settings of maximum dispersal distance. Our approach, applied on six amphibian species in Switzerland, used habitat suitability modelling to define habitat patches, which were linked within a dispersal distance threshold to form habitat networks. We used network topological measures, patch suitability, and patch size to explain species occurrence-state in habitat patches through boosted regression trees. These modelling steps were repeated on each species for different maximum dispersal distances, including a species-specific value from literature. We evaluated mainly the predictive performance and predictor importance among the network models. We found that predictive performance had a positive relation with the distance threshold, and that almost none of the species-specific values from literature yielded the best performance across tested thresholds. With increasing dispersal distance, the importance of the habitat-quality-related variable decreased, whereas that of the topology-related predictors increased. We conclude that the sensitivity of these models to the dispersal distance parameter stems from the very different topologies formed with different movement assumptions. Most reported maximum dispersal distances are underestimated, presumably due to leptokurtic dispersal distribution. Our results imply that caution should be taken when selecting a dispersal distance threshold, considering higher values than those derived from field reports, to account for long-distance dispersers.