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Abstract The tolerance of pedestrians to red light signals is a crucial factor in urban residents’ travel experiences and road traffic safety. To address the current lack of research on methods for measuring pedestrian red light tolerance time at crossings, this paper proposes a stacking model for predicting the red light tolerance time of individual pedestrians. The model employs the XGBoost (XGB), random forest (RF), support vector machine regression (SVR), and multilayer perceptron (MLP) models as primary models, with a multiple linear regression model as the secondary-layer meta-model, as well as Bayesian hyperparameter tuning. Using random survival forest (RSF) and K-means clustering methods, the dataset was divided into three categories: the low, medium, and high tolerance groups. The feature variables influencing the red light tolerance time were grouped accordingly. Stacking models were established for each tolerance and feature group. The experimental results demonstrated that the feature group voted on by multiple machine learning models performed the best in all three tolerance groups. In the low tolerance group, the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 6.58, 1.91, and 19.78%, respectively. For the medium tolerant group, the MSE, MAE, and MAPE were 4.82, 1.53, and 7.63%, respectively. For the high tolerance group, the MSE, MAE, and MAPE were 33.32, 3.89, and 10.14%, respectively. Individual XGB, RF, SVR, MLP, and ungrouped stacking models were established for comparative analysis of the test set. The results indicated that the proposed grouped stacking model outperformed the other models overall. The method provides a means of obtaining the probability distribution function of the length of the waiting crowd’s tolerance time for each red light range and thus determining the duration of the red light at the crossing signal for different personnel compositions.

Abstract Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

Begonia ferox C.I Peng & Yan Liu (2013) was rated as endangered according to Red List of Chinese Plants. In this study, we report the complete chloroplast genome of B. ferox. The chloroplast genome is 169,114 bp in length as the circular, with the GC content of 35.5%, composed by a large single-copy (LSC) region of 75,887 bp, a small single-copy (SSC) region of 18,105 bp, and two inverted repeat regions (IRs) of 37,561 bp in each. The genome comprises 174 encoded genes in total, including 114 protein-coding genes, eight ribosomal RNA genes, and 52 transfer RNA genes. Phylogenetic analysis indicated that B. ferox is genetically closest to B. gulongshanensis.

Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.

Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to comprehensively review the latest issues and research progress in decision-making and planning for autonomous vehicles in intersection environments. This paper reviews the research progress in the behavioral prediction of traffic participants in terms of machine learning-based behavioral prediction, probabilistic model behavioral prediction, and mixed-method behavioral prediction. Then, behavioral decision-making is divided into reactive decision-making, learning decision-making, and interactive decision-making, each of which is analyzed. Finally, a comparative analysis of motion planning and its applications is performed from a methodological viewpoint, including search, sampling, and numerical methods. First, key issues and major research progress related to end-to-end decision-making and path planning are summarized and analyzed. Second, the impact of decision-making and path planning on the intelligence level of autonomous vehicles in intersecting environments is discussed. Finally, future development trends and technical challenges are outlined.

Ardisia humilis is an excellent wild ornamental plant. For the purpose of fully utilization of this elite germplasm, the flowering biology and breeding system of this plant were explored in this study. We conducted field investigation in the flowering phenology, flower-visiting insect and their visiting behaviors on A. humilis individuals introduced in homogeneous garden. Furthermore, pollen viability and stigma receptivity were detected by pollen germination in vitro and benzidine-hydrogen peroxide method, respectively. Combined with artificial pollination, the self-compatibility and the self-pollination of A. humilis were also analyzed in this study. The results were as follows: (1) The florescence of A. humilis was from mid-late May to early June, and the full flowering period was at the end of May. The flowering period of the population, single plant and single flower were approximately 25 d, 17-20 d and 1 d, respectively. (2) A. humilis developed protogynic bisexual flowers, the pistil ripened first with partial overlaps on mature stage of pistil and that of stamen, while stigma remained receptivity until 1 d after stamens fall. (3) With the out-crossing index (OCI) value of 4 and pollen / ovule value of 5.61×103, the breeding system of A. humilis was considered to be outcrossing with partial self-compatibility, and the pollinator insects were essential. (4) The seed set of natural pollination ranked the highest level (average 52.96%), followed by that of autogamy (52.29%) and of artificial-cross pollination (50.33%), with no significant differences among the three (P>0.05), but significantly higher than that of artificial geitonogamy (28.67%) (P

Begonia is the fifth largest genus of angiosperms in the world, and Begonia handelii is a member of the Begonia(Begoniaceae), and is one of the few species with floral fragrance in this genus. However, the chloroplast genome structure and phylogenetic relationship of this species is still unclear. In this study, the chloroplast genome of B. handelii was sequenced by Illumina HiSeq X platform, and the phylogenetic relationship of this species in Begonia was analyzed with related species. The whole chloroplast genome of B. handelii is 169,406 bp in size, which consist one large single-copy region (LSC) with 95,403 bp, one small single-copy region (SSC) with 20,089 bp, and two inverted repeat regions (IR) with 26,957 bp. The GC content of this chloroplast genome is 35.6%. Moreover, 140 genes were found in the chloroplast of B. handelii, including 90 protein-coding genes, 8 rRNA genes, 42 tRNA genes. Phylogenetic analysis showed that B. handelii is closed to B. coptidifolia and B. pulchrifolia. This study lays the foundation for further research on the chloroplast genome evolution of B. handelii chloroplasts.

To reduce the probability of secondary accidents caused by the direction deviation, body rollover, and excessive deceleration of runaway vehicles during braking on a truck escape ramp (TER), the safety of the occupants and the vehicles must be ensured. Based on the momentum theorem, an intelligent control method for a TER is proposed. In the method, a slope aggregate with a small rolling resistance coefficient is used on the original escape lane, and the information acquisition, braking device control, and braking modules are established. Through these modules, the operation parameters and control parameters of the out-of-control vehicle are obtained in advance, to control the operation state of the runaway vehicle. Achieve the purpose of reducing the braking effect of the slope bed aggregate and ensuring maximum utilization of the braking ramp. Finally, the proposed control method is simulated on the MATLAB/Simulink simulation platform. The results show that the control method can realize safe braking of an out-of-control vehicle at various speeds and different mass conditions, and it can play a highly significant role in the braking of high-speed and heavy-duty vehicles.

Electric buses have been vigorously promoted and implemented in major countries worldwide and have generated a huge demand for charging stations. Optimizing the daily charging experience of electric buses, adapting the daily operation scheduling, improving the utilization rate of charging stations, reducing the load on the power grid, and improving the operation efficiency of electric bus line networks require the reasonable layout of the charging stations. In this study, public transportation charging station layout and siting is the research object. We summarize the progress of analysis methods from the charging station and vehicle sides; introduce related research on the planning and layout of charging stations based on optimization models, including cost analysis and siting and layout for electric bus systems; summarize the data-driven station planning and siting research; and provide an overview of the current charging demand estimation, accuracy, and charging efficiency. Finally, we address the problems of the charging demand estimation accuracy, the mismatch between the charging station layouts for electric buses, and the charging demand on a long time scale. We suggest that research be conducted on data fusion for the temporal and spatial refinement of charging demand prediction in the context of the electrification of public transportation systems and the big data of telematics.

An effective way to solve the problem of urban traffic congestion is to predict the road traffic status accurately and take effective traffic control measures in time. Considering the impact of visibility on traffic, the pavement status and time characteristics were finely divided, and a regression decision tree was used to establish the traffic flow velocity prediction model with pavement status, time characteristics, and working day characteristics as characteristic parameters. Furthermore, based on the perspective of avoiding using velocity as a single parameter to classify the road traffic status levels, the Kmeans clustering algorithm was used to obtain the classification label results. Moreover, the traffic flow velocity and pavement status were used as characteristic parameters of the classification decision tree to establish the multi-parameter road traffic status prediction model. The experimental result showed that the prediction accuracy of the proposed road traffic status prediction model was 81.31%, and this method has good applicability and certain application value for road traffic status prediction.

Prioritizing the development of public transit and enhancing its attractiveness is an important way to solve the problem of urban traffic congestion and achieve sustainable development. To improve the service quality and overall opera-tional efficiency of urban public transit, an evaluation index system related to the comprehensive experience of pas-sengers, service supply quality of public transit enterprises, and supervision of management departments was intro-duced from both the demand and the supply of public transit travel services. Based on the data distribution characteris-tics of the boxplot in statistics, the evaluation level and corresponding value range of each index were determined, and the comprehensive weight of the index was determined using the linear weighting method combining the analytic hierarchy process and the entropy weight method, so as to reduce the influence of single weighting method on the evaluation results of comprehensive service quality of public transit. An evaluation method of public transit compre-hensive service quality based on the extension cloud model was established. The evaluation results of the model were obtained by calculating the cloud affiliation and comprehensive certainty, and a reliability factor was used to test the evaluation results, which solved the problem of randomness and fuzziness in the process of comprehensive service quality evaluation of public transportation and made the evaluation results closer to the reality. Finally, the estab-lished comprehensive evaluation model was applied to a city for example analysis, and the corresponding evaluation level was obtained as good. The value of the reliability factor in the model was less than 0.01, indicating that the model has good applicability and a certain application value for the comprehensive service quality evaluation of public transit. The evaluation method fully considered a variety of evaluation indicators, specified the evaluation level of comprehensive service quality of public transit, and the evaluation results provide a theoretical basis for public transport enterprise to make targeted improvement measures.

The high-throughput sequencing method was used for the first time to determined complete mitochondrial genome of Orgyia postica. The complete mitochondrial genome was a circular molecule with 15,258 bp in full-length, including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs) and 2 ribosomal RNA genes (rRNAs). The nucleotide composition of O. postica mitogenome was A: 37.6%, C: 7.9%, G: 14.8%, T: 39.7%. In addition, the results of the phylogenetic analysis indicate that O. postica had the closest relationship with Laelia suffusa. Our study will contribute to further research on the evolution of Lymantriidae and the identification of larva species.

A traffic survey using an unmanned aerial vehicle on the Haixia road in Chongqing, China found a significant correlation between the velocity of the vehicle and the distance of the vehicle when moving forward and laterally. To mitigate driving disruptions owing to vehicle intrusion at a desired distance, personal space (PS) is introduced to analyze the car-following behavior of continuous mixed traffic flow formed by human-driven (HD) vehicles and cooperative adaptive cruise control (CACC) vehicles. PS is a virtual boundary that refers to the space where psychological tension is caused by the invasion of others. Further, an intelligent driver model (IDM) was used to establish a mixed traffic car-following PS-IDM. The stability of the disturbance transfer function analytical model in homogeneous and heterogeneous traffic flows was used to calculate the traffic flow stability region under different CACC vehicle permutations. The results show that the PS-IDM-based car-following model effectively improves the stability fitting effect of mixed traffic flow, and the driving comfort is increased by up to 20.7% when compared with that of the single car-following model. In addition, there is a negative correlation between the PS and the unstable velocity range of the traffic flow. Compared with the homogeneous HD traffic flow, the average intrusion rate of homogeneous CACC traffic flow is reduced by 4.6%, and the driving comfort of vehicles is improved by approximately 65%.

Bus guidance information supplied by advanced traveler information systems provide a reference for route selection by bus passengers. This study analyzes the interaction between bus passengers’ route-choice behavior and the relationship in a game. The game revenue of the passengers waiting on the bus platform is quantified by parameters such as the number of passengers waiting on the platform, the number of passengers on the bus, and the passenger capacity of the bus. Based on the characteristics of passenger-bounded rationality, an evolutionary game model of bus choice behavior under information-induced conditions is established. Through the evolutionary game model, the distribution of the proportion of passengers receiving the guidance strategy is obtained when the evolution is stable. Finally, according to the distribution results, the passenger guidance strategy is discussed to achieve a win-win situation between passengers and bus operators.

In order to help select high-quality electric buses, we established a performance index system for pure electric buses based on an extensible cloud model. With the rapid development of electric buses, choosing a suitable pure electric bus considering its applicability is challenging. Based on the analysis of the characteristics of the passenger car industry, a preliminary evaluation index system for pure electric passenger cars was constructed. The preliminary indicator system was formed based on the optimization of the main points of current laws and regulations, and divided into four aspects: safety assistance system, comfort, convenience, and economy. Then, the index system was determined from multiple perspectives, and the analytic hierarchy process and the entropy weight method were applied to determine the comprehensive weight. Meanwhile, the evaluation level of the index system of pure electric buses was calculated by the extensible cloud model. At last, six electric buses were selected from Chinese electric bus companies as examples to determine the relevant level. The results show that the method has satisfactory feasibility and applicability in the comprehensive evaluation and that it provides a reference for pure electric bus selection based on application performance.

In the study, the complete mitochondrial genome of Pyrops candelaria (Linnaeus) was determined for the first time. The genome was a circular molecule with 16,253 bp in full-length, consists of 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), two ribosomal RNA genes (rRNAs), and one control region. The nucleotide composition of Pyrops candelaria (Linnaeus) mitogenome was A: 47.13%, C: 15.9%, G: 10.79%, and T: 26.18%. Phylogenetic analysis showed that P. candelaria had a close relationship with to Lycorma delicatula.

In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.

Autonomous driving technology is vital for intelligent transportation systems. Vehicle driving behavior prediction is the foundation and core of autonomous driving. A detailed review of the existing research on vehicle driving behavior prediction can improve the understanding of the current progress of research on autonomous driving and provide references for follow-up researchers. This paper primarily reviews and analyzes the control models of autonomous driving, prejudgment methods, on-road and intersection traffic decision-making, and shortcomings of the research about the prediction of individual intelligent vehicle driving behavior, the prediction on movements of vehicles connected via the Internet, and prediction of driving behavior in a mixed traffic environment. The deficiencies in the research on vehicle driving behavior prediction are as follows: (1) there are numerous limitations in the intelligent application scenarios of individual intelligent vehicles; (2) although the Internet of Vehicles is a significant developmental trend, the training and test datasets are not rich enough; and (3) as the research of mixed traffic flow is still in the initial stages, the comfort brought by autonomous driving in hybrid driving environments is not being considered. In addition to the above analyses and comments, the future research prospects of vehicle driving behavior prediction are discussed as well.

Neurofibrillary tangles are aggregates of hyperphosphorylated tau that are one of the pathological hallmarks of Alzheimer's disease (AD). Tau phosphorylation is regulated by a balance of kinase and phosphatase activities. Our previous study has demonstrated that ginsenoside Rd, one of the principal active ingredients of Pana notoginseng, inhibits okadaic acid-induced tau phosphorylation in vivo and in vitro, but the underlying mechanism(s) is unknown. In this study, we showed that ginsenoside Rd pretreatment inhibited tau phosphorylation at multiple sites in beta-amyloid (Aβ)-treated cultured cortical neurons, and in vivo in both a rat and transgenic mouse model. Ginsenoside Rd not only reduced Aβ-induced increased expression of glycogen synthase kinase 3beta (GSK-3β), the most important kinase involved in tau phosphorylation, but also inhibited its activity by enhancing and attenuating its phosphorylation at Ser9 and Tyr216, respectively. Moreover, ginsenoside Rd enhanced the activity of protein phosphatase 2A (PP-2A), a key phosphatase involved in tau dephosphorylation. Finally, an in vitro biochemical assay revealed that ginsenoside Rd directly affected GSK-3β and PP-2A activities. Thus, our findings provide the first evidence that ginsenoside Rd attenuates Aβ-induced pathological tau phosphorylation by altering the functional balance of GSK-3β and PP-2A.

BackgroundPersistent human papillomavirus (HPV) infection remains a key risk factor for cervical cancer. HPV-based primary screening is widely recommended in clinical guidelines, and further longitudinal studies are needed to optimize strategies for detecting high-grade cervical lesions compared to cytology.MethodsFrom November 2015 to December 2023, 31,942 participants were included in the real-world observational study. Among those, 4,219 participants underwent at least two rounds of HPV tests, and 397 completed three rounds of HPV tests. All participants were tested for high-risk types of HPV 16/18/31/33/35/39/45/51/52/56/58/59/66/68 (hrHPV) and low-risk types of HPV6/11 genotyping. Some participants also received cytology or colposcopy with pathology.ResultsIn the cross-sectional cohort, the prevalence of hrHPV and all HPV subtypes was 6.6% (2,108/31,942) and 6.8% (2,177/31,942), respectively. The three top hrHPV genotypes were HPV52 (1.9%), HPV58 (0.9%), and HPV16 (0.9%). Age distributions showed two peaks at 45–49 and 60–65 years. For the primary screening cohort, the hrHPV prevalence rate increased from 4.8% in 2015–2017 to 7.0% in 2020–2020 and finally reached 7.2% in 2023. For the longitudinal cohort study, the hrHPV prevalence rates in the repeated population (3.9, 5.3, and 6.0%) were lower than the primary hrHPV screening rates (6.6%), which indicated that repeated screening might decrease the prevalence rate. Methodologically, the hrHPV (89.5%) and the screening group of 16 subtypes (92.3%) demonstrated superior sensitivity than the cytology group (54.4%). Moreover, the longitudinal study indicated that the persistent hrHPV subgroup had a significantly higher (p = 0.04) incidence of high-grade squamous intraepithelial lesions and more histology progression events (7/17 vs. 0/5) than the reinfection group.ConclusionThe study indicates a rising high-risk HPV prevalence in Dongguan, with repeated screening reducing this trend. The findings support HPV-based primary screening and might guide HPV vaccination and cervical cancer prevention in South China.

Recent years, the tremendous number of distributed energy resources, electric vehicles are integrated into the Low-Voltage Distribution Network (LVDN), large amount of data are generated by edge devices in LVDN. The cloud data centers are unable to process these data timely and accurately, making it impossible to meet the demand for fine-grained control of LVDN. To solve the above problems, this paper proposes a flexible orchestration of lightweight artificial intelligence (AI) for edge computing in LVDN. Firstly, the application requirements of LVDN are analysed through feature extraction of its historical data, and a lightweight AI library is constructed to meet its requirements. Secondly, based on the multi-factor priority, a flexible orchestration model is established, to allow the lightweight AI embedded in the edge devices of the LVDN. Finally, the particle swarm optimization algorithm is used to provide the best solution. The simulation results show that the method proposed in this paper can support the deployment of AI at the edge. It can significantly improve the utilization of edge computing resources, and reduce the pressure of cloud computing and the time of application processes.

A distributed control of a Microgrid (MG) depends on the communication network for the exchange of information among the Distributed Generators (DGs). Most of the control schemes consider ideal communication among the DGs; however, in practical systems, delays are inherited in a system that can affect the dynamic performance and even destabilize an MG. Therefore, in this research work, a distributed Secondary Controller (SC) for an islanded MG is designed considering Communication Time Delays (CTDs). Sufficient delay-independent conditions for the robust stability of a buffer-free distributed averaging proportional integral control scheme are proposed. The CTDs are analyzed using the Lyapunov Krasovskii approach. A proposed method synchronizes the frequencies and voltages of the DGs to their respective references while confirming proportionate power sharing in the presence of CTDs. Moreover, different adaptive controllers are designed to update the fixed SC gain parameters to enhance the system’s performance. Finally, the efficacy of the controller is demonstrated through simulation studies that validate its performance against load variation and plug-and-play functionality.

Abstract Surface‐roughened films of biaxially oriented polypropylene (BOPP) are the most common polymeric dielectrics for oil‐immersed capacitors in electrical engineering. How to balance the roughness of film surfaces as well as the mechanical and high‐temperature electrical properties of film ontology remains a challenge. In this study, three grades of electrical specialized isotactic polypropylene resins with different distributions of molecular mass and stereo‐defects are surveyed for crystalline polymorphic behavior. The relationship of the chain microstructure, film surface topography, and mechanical and electrical properties determined through a comprehensive study of the resins’ chain architecture to the morphology of cast sheets and the characteristics of resultant films is elaborated. The chain features of wide molecular mass distribution and uniform stereo‐defects result in excellent β‐crystallization ability, leading to a fine, roughed topography on the film surface. The narrow distribution of molecular mass and the relatively high tacticity play a positive role in the physico‐mechanical properties of the films, especially in the improvement of breakdown strength at high temperatures. An appropriate chain architecture is vital to achieving the balance between surface topography and bulk performance in surface‐roughened BOPP films.

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%− 72.9% and 72.0%− 74.7%, which were 2.1%− 2.5% and 0.3%− 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%− 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17–0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.

Background. Prostate cancer (PCa) harms the male reproductive system, and lncRNA may play an important role in it. Here, we report that the LINC01088/microRNA- (miRNA/miR-) 22/cell division cycle 6 (CDC6) axis regulated through the phosphatidylinositide 3-kinases- (PI3K-) protein kinase B (AKT) signaling pathway controls the development of PCa. Methods. lncRNA/miRNA/mRNA associated with PCa was downloaded and analyzed by Gene Expression Omnibus. The expression and correlation of LINC01088/miR-22/CDC6 in PCa were analyzed and verified by RT-qPCR. Dual-luciferase was used to analyze the binding between miR-22 and LINC01088 or CDC6. Cell Counting Kit-8 and Transwell were used to analyze the effects of LINC01088/miR-22/CDC6 interactions on PCa cell viability or migration/invasion ability. Localization of LINC01088 in cells was analyzed by nuclear cytoplasmic separation. The effect of LINC01088/miR-22/CDC6 interaction on downstream PI3K/AKT signaling was analyzed by Western blot. Results. LINC01088 or CDC6 was upregulated in prostate tumor tissues or cells, whereas miR-22 was downregulated, miR-22 directly targets both LINC01088 and CDC6. si-LINC01088 inhibits the PCa process by suppressing the PI3K/AKT pathway. CDC6 reverses si-linc01088-mediated cell growth inhibition and reduction of PI3K and AKT protein levels. Conclusion. Our results demonstrate that the LINC01088/miR-22/CDC6 axis functions in PCa progression and provide a promising diagnostic and therapeutic target.