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Although some methods have identified the physical urban area to a certain extent, the driving factors for the identification threshold have not been studied deeply. In this paper, vector building data and road intersection data are used for comparative validation based on the urban expansion curve method to identify the physical urban area using the meso-city scale. The geographical detector technique is used to detect how and to what extent the urban spatial structure factors, geographical environment factors and social economic factors affect the optimal distance threshold of 22 administrative districts in the Chongqing municipality. The results based on the vector buildings are more precise and show the characteristics of the physical urban area of core-periphery distribution and the distribution along the water corridor. From the results of quantitative attribution, it was found that the road network density, building density, urbanization rate and urban population density, and their interaction with regional GDP, play a critical role in the optimal distance threshold, with the index value of influence degree ≥0.79. Under the influence of different factors, the optimal distance thresholds of 22 administrative districts show adaptive characteristics. Looking forward to the future, this study provides ideas for further research on the morphological characteristics and distribution laws of multi-spatial scale cities.

Urban planning is essential for managing the diverse impacts of urban green spaces, such as public access, stormwater control, urban life quality, and landscape aesthetics, promoting sustainable urban development and urban residents’ well-being by integrating green space considerations into city planning. The aim of this study is to use graph-based metrics to calculate the connectivity of UGS across the main municipal zones of Ardabil city over consecutive periods under different population growth rates. Another objective of this study is to compare the connectivity values of UGS in the four municipal zones and to evaluate changes in the connectivity indices at various distance thresholds of UGS patches. After identifying UGS in different periods, the changes in graph-based connectivity indices at various distance thresholds of UGS patches were analyzed. Additionally, the changes in connectivity indices over different periods and across various municipal zones were compared and analyzed. The findings reveal that UGS areas were larger in the past but have recently had smaller patch sizes. Connectivity between UGS nodes (dNL) decreased at various distances over the study years, showing a declining trend in different connectivity indices. UGS connectivity decreased in municipal zones 1, 2, and 3 but increased in recent years after a decline until 2012 across all four zones of Ardabil city. Zone 4 had the highest UGS connectivity due to newly developed urban areas and well-allocated UGSs. Integrating the ecological impacts of UGS connectivity in urban development and design will enhance trade-offs between conservation, public health, and social equity. New urban areas should allocate sufficient land for UGS and parks, ensuring accessibility to support health and leisure through municipal planning. The study highlights the need for sustainable urban development policies that prioritize the allocation and maintenance of UGSs.

Robot navigation in confined spaces has gained popularity in recent years, but offline planning assumes static obstacles, which limits its application to online path-planning. Several methods have been introduced to perform an efficient robot navigation process. However, various existing methods mainly depend on pre-defined maps and struggle in a dynamic environment. Also, diminishing the moving costs and detour percentages is important for real-world scenarios of robot navigation systems. Thus, this study proposes a novel perceptron-Q learning fusion (PQLF) model for Robot Navigation to address the aforementioned difficulties. The proposed model is a combination of perceptron learning and Q-learning for enhancing the robot navigation process. The robot uses the sensors to dynamically determine the distances of nearby, intermediate, and distant obstacles during local path-planning. These details are sent to the robot’s PQLF Model-based navigation controller, which acts as an agent in a Markov Decision Process (MDP) and makes effective decisions making. Thus, it is possible to express the Dynamic Robot Navigation in a Confined Indoor Environment as an MDP. The simulation results show that the proposed work outperforms other existing methods by attaining a reduced moving cost of 1.1 and a detour percentage of 7.8%. This demonstrates the superiority of the proposed model in robot navigation systems.

With the rapid development of urbanization, natural habitats in many parts of the world have been seriously damaged by urban expansion. However, urban expansion is a complex process, and the impacts of different levels of urban expansion on habitats at regional scales and their distance thresholds are still unclear. We conducted a study in Hubei Province, China to evaluate the impacts of the expansion of prefecture-level cities and county towns on the quantity, area, and quality of natural habitats and the critical threshold distances affecting habitats. The results show that, at a regional scale, habitat degradation was driven primarily by the expansion of large numbers of county towns, but the expansion of prefecture-level cities affected habitat degradation over greater distances. Specifically, the impact of county town expansion on habitat first increased and then decreased with greater distance from built-up areas, the threshold distances for habitat quantity and quality being approximately 8 km and 80 km, respectively. The impact of expanding prefecture-level cities on habitat showed a similar nonlinear change with greater distance, but the distance thresholds for habitat quantity and quality rose to approximately 40 km and 130 km, respectively. These findings not only reverse the conventional view that the expansion of large cities dominates habitat degradation, but also draws more attention to the influence of the expansion of numerous small county and towns on habitat, when measured at the regional scale. Understanding the distance threshold of particular spatial impacts can be help to inform spatial decision-making with regards to habitat conservation.

This study aimed to analyze the characteristics and influencing factors of the access trips of railway users in the Seoul Metropolitan Area, South Korea. A total of 11 metropolitan railway stations and 4 urban railway stations were selected, and data on users’ travel characteristics—including access modes, travel purposes, demographic attributes, and whether they were accompanied by infants—were collected through one-on-one interviews. Based on 1683 collected cases, the data were analyzed using a multivariate analysis of variance (MANOVA). The results showed a statistically significant difference between bus access distances, which were 1.78 km for metropolitan railways and 1.59 km for urban railways. In contrast, the walking access distances were approximately 620 m for both, showing a minimal difference. The further analysis of factors influencing the access distance revealed that apartment ownership, users’ income level, the presence of accompanying travelers, the distance between stations, the number of transfer routes, and whether users were traveling with infants had significant effects.

The object detection algorithm based on vehicle-mounted lidar is a key component of the perception system on autonomous vehicles. It can provide high-precision and highly robust obstacle information for the safe driving of autonomous vehicles. However, most algorithms are often based on a large amount of point cloud data, which makes real-time detection difficult. To solve this problem, this paper proposes a 3D fast object detection method based on three main steps: First, the ground segmentation by discriminant image (GSDI) method is used to convert point cloud data into discriminant images for ground points segmentation, which avoids the direct computing of the point cloud data and improves the efficiency of ground points segmentation. Second, the image detector is used to generate the region of interest of the three-dimensional object, which effectively narrows the search range. Finally, the dynamic distance threshold clustering (DDTC) method is designed for different density of the point cloud data, which improves the detection effect of long-distance objects and avoids the over-segmentation phenomenon generated by the traditional algorithm. Experiments have showed that this algorithm can meet the real-time requirements of autonomous driving while maintaining high accuracy.

Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation–maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes, this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly removing instances with missing values has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations. Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types.

Molecular network analysis based on the genetic similarity of HIV-1 is increasingly used to guide targeted interventions. Nevertheless, there is a lack of experience regarding molecular network inferences and targeted interventions in combination with epidemiological information in areas with diverse epidemic strains of HIV-1.We collected 2,173 pol sequences covering 84% of the total newly diagnosed HIV-1 infections in Shenyang city, Northeast China, between 2016 and 2018. Molecular networks were constructed using the optimized genetic distance threshold for main subtypes obtained using sensitivity analysis of plausible threshold ranges. The transmission rates (TR) of each large cluster were assessed using Bayesian analyses. Molecular clusters with the characteristics of ≥5 newly diagnosed cases in 2018, high TR, injection drug users (IDUs), and transmitted drug resistance (TDR) were defined as priority clusters. Several HIV-1 subtypes were identified, with a predominance of CRF01_AE (71.0%, 1,542/2,173), followed by CRF07_BC (18.1%, 393/2,173), subtype B (4.5%, 97/2,173), other subtypes (2.6%, 56/2,173), and unique recombinant forms (3.9%, 85/2,173). The overall optimal genetic distance thresholds for CRF01_AE and CRF07_BC were both 0.007 subs/site. For subtype B, it was 0.013 subs/site. 861 (42.4%) sequences of the top three subtypes formed 239 clusters (size: 2-77 sequences), including eight large clusters (size ≥ 10 sequences). All the eight large clusters had higher TR (median TR = 52.4/100 person-years) than that of the general HIV infections in Shenyang (10.9/100 person-years). A total of ten clusters including 231 individuals were determined as priority clusters for targeted intervention, including eight large clusters (five clusters with≥5 newly diagnosed cases in 2018, one cluster with IDUs, and two clusters with TDR (K103N, Q58E/V179D), one cluster with≥5 newly diagnosed cases in 2018, and one IDUs cluster. In conclusion, a comprehensive analysis combining in-depth sampling HIV-1 molecular networks construction using subtype-specific optimal genetic distance thresholds, and baseline epidemiological information can help to identify the targets of priority intervention in an area epidemic for non-subtype B.

On the coexistence of genetically modified (GM) and non‐GM maize, the isolation distance plays an important role in controlling the transgenic flow. In this study, maize gene flow model was used to quantify the MTD0.1% and MTD1% in the main maize‐planting regions of China; those were the maximum threshold distance for the gene flow frequency equal to or lower than 1% and 0.1%. The model showed that the extreme MTD1% and MTD0.1% were 187 and 548 m, respectively. The regions of northern China and the coastal plain, including Hainan crop winter‐season multiplication base, showed a significantly high risk for maize gene flow, while the west‐south of China was the largest low‐risk areas. Except for a few sites, the isolation distance of 500 m could yield a seed purity of better than 0.1% and meet the production needs of breeder seeds. The parameters of genetic competitiveness (cp) were introduced to assess the effects of hybrid compatibility between the donor and recipient. The results showed that hybrid incompatibility could minimize the risk. When cp = 0.05, MTD1% and MTD0.1% could be greatly reduced within 19 m and 75 m. These data were helpful to provide scientific data to set the isolation distance between GM and non‐GM maize and select the right place to produce the hybrid maize seeds.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash–Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from −0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.