Search

There is a need to reduce human–wildlife conflicts in the area around Liupanshan Nature Reserve in Ningxia Hui Autonomous Region, China. This study investigated the financial losses caused by wild boar and their causes. A questionnaire investigation (n = 135) and a field test were conducted, which included 108 sample lines and 97 infrared cameras. A principal component analysis and generalised linear model was used to analyse the importance of the effect of the factors on wild boar damage. Based on an estimate of 17,049 wild boars in the study area, we found that in the agricultural land owned by the residents, the boar density of each county and distance from the village to the nature reserve were the most significant factors that affected crop damage. Then, financial losses in spring, summer, and autumn had a moderate effect on financial loss, and the crop type had the lowest effect. We recommend reducing the wild boar population by increasing leisure hunting and the number of leopards. Additionally, a focus on farmland protection is a practical way to prevent wild boar invasions. Meanwhile, it is also necessary to conduct long-term monitoring of wild boar population status and manage the relationship between the government, research teams, and local people to more efficiently and comprehensively reduce conflicts between humans and wild boars.

Abstract Human interventions have a great potential of spatially cornering and limiting species, therefore investigating the species distribution is one of the most crucial issues for managing wildlife populations and suggesting robust conservation strategies. Water deer (Hydropotes inermis) are widespread in China throughout history and are endemic to East Asia. However, they disappeared from Northeast China for years. We rediscovered the water deer in a previous study in Jilin Province, China. Then, we conducted further research in Northeast China to determine their distribution status, supplying fundamental data for the recovery and expansion of their population. An interview survey, line transect survey and infrared camera monitoring were carried out in some counties/cities in Northeast China from June to December 2021. The results showed that the water deer were distributed in Wuchang city of Heilongjiang Province, Changbai Korean Autonomous County, Baishan Municipal District, Ji’an city, Hunchun city, Huadian city, Antu County and Helong County of Jilin Province, Benxi Manchu Autonomous County, Huanren Manchu Autonomous County, Kuandian Manchu Autonomous County, Fengcheng city and Donggang city of Liaoning Province. The ensemble species distribution model constructed by sdm within the TSS of various models that were set as weight revealed that the potential distribution area of the water deer in the study area was 8764.66 km2 (28.77% of the study area). Combining recent studies concerning the distribution of water deer and the current study, we updated the distribution of wild water deer in Northeast China, which is vital for their conservation worldwide.

This study introduces a multicriteria tradeoff analysis method based on Dempster-Shafer (DS) theory in conjunction with the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) and the e-STEP method, termed DSM-eSTEP method, to assist transportation agencies in making optimal investment decisions under alternative budget allocation scenarios. Specifically, the DS evidence theory is employed to develop belief functions from preferences of multiple transportation performance criteria revealed by transportation decision-makers. The belief functions are transformed to probability functions using the pignistic transformation technique, leading to a set of initial weights that could reflect the priorities of decision-makers on the performance criteria. To enhance the robustness of relative weights assigned, the MACONT technique and the entropy measure are employed to derive refined weights via context-dependent adjustments. Having established the refined weights of transportation performance measures, the multi-objective budget allocation formulation according to various tradeoff scenarios could be converted to a linear programming model readily solvable for optimality. An empirical study is conducted for optimal budget allocation of 233 highway bridge and pavement preservation projects proposed for a U.S. state-maintained urban Interstate highway network. Cross comparisons of budget allocation results are made between the proposed method and the widely used compromise programming (CP) method. It shows that the proposed DSM-eSTEP method could generate efficient investment outcomes and slightly outperform the CP method.

This article presents the data utilized in a study focused on identifying an optimal bus dispatching strategy in light of epidemic impacts. The study specifically examines the Xi'an Xiaozhai central business district (CBD) street network, which consists of 33 major signalized intersections and 112 bus stops associated with 12 bus routes. The dataset includes details of intersection and bus stop geospatial data, street segment and intersection design, intersection signal timing plans, bus route operational properties such as dispatching frequencies, fleet sizes, loading bay capacities, and bus-specific parameters. It also encompasses data on passenger boarding and alighting counts, as well as travelers’ origin and destination (O-D) locations, routes, and departure times during three time periods: 10:00-11:00 PM, 1:00-2:00 PM, and 7:00-8:00 PM on Monday, June 7, 2021. These times represent off-peak (10:00 PM–1:00 AM the next day), adjacent-to-peak (9:00–11:00 AM, 1:00–4:00 PM, and 8:00–10:00 PM), and peak (7:00–9:00 AM, 11:00 AM–1:00 PM, and 4:00–8:00 PM) periods, respectively. Data collection involves searching government and organizational records, utilizing Alibaba Cloud's Amap platform, conducting onsite measurements, and performing a field survey. The dataset is a valuable resource for studying the integrated operations of various urban mass transit services, including buses, bus rapid transit (BRT), and fixed guideway transit, under both normal and epidemic-affected travel conditions. Additionally, it can be used to investigate multimodal integrated urban passenger services offered by automobiles, transit, ridesharing, and active transportation modes.

Global climate change, habitat fragmentation, and human interference have resulted in a significant, ongoing decline in the population of goitered gazelles. Effective conservation strategies require an understanding of resource requirements of threatened species, such as dietary needs. Therefore, we aimed to elucidate the food composition and seasonal dietary changes of goitered gazelles through microhistological analyses of fresh feces. Fabaceae (11.5%), Gramineae (9.4%), Chenopodiaceae (20.2%), Asteraceae (10.1%), and Rosaceae (19.5%) formed the primary dietary components of goitered gazelle. Additionally, Krascheninnikovia arborescens (13.4%) and Prunus sibirica (16.3%) were identified as the key forage plants. Forbs (50.4%) were the predominant plants for grazing throughout the year, particularly in the spring (72.9%). The proportion of trees in the diet was highest in the autumn (36.7%) and comparatively lower in other seasons. Furthermore, the proportions of shrubs (22.0%) and graminoids (14.8%) both reached their peaks in the winter. Our findings indicate that goitered gazelles strategically forage seasonally to cope with resource bottlenecks, enhancing their adaptability to arid and semi-arid habitats. Our study provides essential ecological information for the conservation of goitered gazelles and emphasizes the importance of dietary studies of species of ecological significance in environmentally sensitive areas.

The high-speed collision issue brought on by railway vehicles’ intensive and high-speed operation has become a critical concern for operational safety. Therefore, it is necessary to research the high-speed collision problem of trains. Firstly, a train collision dynamic model suitable for higher collision velocities is established. This model includes a 38-degree-of-freedom (DOF) three-dimensional vehicle model and a three-layered vibrating fixed-track model. Corresponding mathematical models are developed for nonlinear factors such as wheel–rail contact, car–end contact, and coupler overload representation in the model. Then, the Train Collision State Score (TCSS) is proposed, enabling quantitative safety evaluation under different collision conditions. Finally, the influence of the energy-absorbing device’s initial attitude and leading car parameters on collisions is studied. The results indicate that initial vertical height difference and pitching angle significantly increase TCSS. As the force level of the energy-absorbing device of the leading car increases, the effects of two intermediate coupler overload states on TCSS show opposite trends.

Gut microbiota are essential to maintain host health and fitness largely through its influence with behavior, development and reproduction of host, particular among the amniotic ectothermic reptiles. Global warming effects on the composition of the host's gut microbiota in some taxa are generally known. Previous studies on reptiles in both natural habitats and lab-reared individuals have demonstrated that temperature changes can alter the composition and function of the host's gut microbiota. However, these effects on wild-caught reptiles are not well-understood. This study investigated changes in the composition of gut microbiota of wild-caught lizards (Eremias argus), which were experimentally exposed to two different temperatures. The results showed that the increase in temperature altered the gut microbiota; both the groups showed an altered and destabilized composition of gut microbiota in response to their adaptive states. The warming did not significantly alter the relative abundances of the main gut microbial communities in both the lizard genders, but increased those of predicted pathogenic bacterial genera, including Acinetobacter, Anaerotruncus, and Dehalobacterium. These results provided insights into the ecological adaptations of Eremias argus, but the body health and fitness of wild populations, concerning temperature changes, warrant further investigations.

Studying the coexistence mechanisms of sympatric wildlife helps to shed light on why the earth has so many different species. When ungulates share ranges, food and habitat requirements may partially or fully overlap. Therefore, the aim of this study was to determine how sympatric ungulates share limited resources. Carcasses of 27 adult blue sheep (Pseudois nayaur) and three adult red deer (Cervus elaphus alxaicus) were collected in the Helan Mountains, China. Nutritive indices of plant species foraged and morphometric measurements of the digestive system of the two sympatric ungulates were determined. In addition, 120 passive, infrared motion-triggered cameras recorded spatial overlap and temporal overlap between the two species. Camera trapping revealed relatively limited spatial overlap and significantly different activity rhythms between blue sheep and red deer. Differences were also observed in stomach weight, surface enlargement factor of the rumen, and intestine length between the two species. However, the combined relative weight of the stomach and intestine was not different between species. The low spatiotemporal overlap decreased opportunities for encounters between sympatric blue sheep and red deer, and significant differences in digestive systems allowed the two species to consume different plant species or different parts of the same species. Thus, the two sympatric ungulates coexist harmoniously in the Helan Mountains because of long-term evolutionary behavioral and physiological adaptations that eliminate negative effects on the survival of the other species.

The complexity and changefulness of inland navigation environment in space and time makes it hard to guarantee the applicability and accuracy of existing ship speed models. In this paper, a novel method for inland ship speed modelling under complex and changeful navigation environment is proposed. Firstly, an unsupervised machine learning algorithm, Density-Based Spatial Clustering of Application with Noise (DBSCAN), is utilized to cluster the environmental data including water level, water speed, wind speed and wind direction, to get the segment division information, which greatly helps reduce the influence of other uncertain environmental factors on the speed model. Then, Generalized Regression Neural Network (GRNN) is tailored and employed to build the ship speed estimation model with multiple input variables. Finally, a detailed case study of a ship sailing in the Yangtze River trunk line is conducted to validate the proposed methods. The results show that the ship speed model established based on machine learning methods works effectively in speed estimation and analysis. Moreover, compared with other regression methods and neural networks, the proposed GRNN model has the best performance in ship speed modelling.

This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.

The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this article, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R2 (R-squared) increased to 0.9843. What's more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.

This study introduces a bi-level model for optimal travelway design of an urban street network by successively executing a lower-level model for traffic assignments and an upper-level model for network travel time minimization. A computational experiment is conducted for optimal travelway design of a 4-square-km urban street network containing 25 signalized intersections, 80 street segments, and 5 bus routes that accommodates 62,640, 43,200, and 33,120 person-trips per hour in AM/PM peak, adjacent-to-peak, and off-peak periods, respectively. Model execution results indicate that adopting a higher number of narrow lanes for auto use only and auto/bus shared use could potentially lead to increases in auto mode share and savings of network total travel time. More narrow lanes for auto use could raise auto speeds, but the auto/bus shared use of narrow travel lanes could slightly fluctuate bus speeds. Further converting narrow lanes for shared use by autos and buses to exclusive bus lanes (EBLs) could enlarge bus mode share, reduce network total travel time, slightly elevate auto speeds, and drastically increase bus speeds. The proposed model could be augmented to incorporate optimization of networkwide intersection signal timing plans, bus signal priorities, and bus dispatching frequencies into optimal travelway design.

Constant adaptation to environmental changes is required by ruminants to allow them to adapt to different ecological niches and feeding habits. In addition, the morphology and function of ruminant digestive systems reveal some adaptive evolutionary characteristics. Blue sheep (Pseudois nayaur) display a variety of morpho-physiological adaptations that are typical of grazers. In this study, we collected 64 adult blue sheep samples (whole animal carcasses) from the Helan Mountains, China, during different seasons. The external morphological parameters, digestive system morphological indexes, and rumen surface enlargement factors were determined. Our results reveal that the rumen and reticulum weights were positively correlated with the body weight (p = 0.004), while the food channel aperture, intestinal length, and weight of the blue sheep digestive tract presented no significant differences between different seasons (p > 0.05) and sexes (p > 0.05). There were significant differences (p < 0.001) in the density, length, and width of mastoids, and the rumen surface enlargement factor was 2.85 ± 1.37, which is typical of roughage feeders. The nutritional and ecological characteristics of blue sheep represent obvious morphological and physiological adaptations to an herbivorous diet. Adopting a quick foraging strategy allows blue sheep to rapidly consume and excrete large amounts of feed, thus obtaining the required energy for their activities and facilitating better adaption to environmental changes.

We determined the whole mtDNA genome of the Common Pochard (Aythya ferina) in the Ningxia Hui Autonomous Region, China. The complete mitochondrial genome is 16,599 bp in length and consists of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loop). The nucleotide composition is 29.34% A, 22.23% T, 15.66% G, and 32.77% C. Phylogenetic analysis results showed close genetic relationship between A. ferina and Aythya americana.

This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver’s sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process.

The Ferruginous Duck (Aythya nyroca) is a diving duck that is widely distributed in Asia, Africa, and Europe. We determined the complete mitogenome of the Ferruginous Duck gathered at Ningxia, China. The total length of the complete mitochondrial genome is 16,623 bp and it consists of 13 protein-coding, 22 tRNA, 2 rRNA genes, and 1 control region (CR). Only one overlap among the 13 protein-coding genes was found: ND4L/ND4. The CR is 1068 bp in length. The nucleotide composition is 29.66% A, 22.28% T, 15.35% G, 32.71% C. The result of phylogenetic analysis showed that there is close genetic relationship among Aythya nyroca and three ducks in the Genus Aythya.

We determined the whole mtDNA genome of the gray-headed lapwing (Vanellus cinereus) in Ningxia Hui Autonomous Region, China. The complete mitochondrial genome is 17,078 bp in length and consists of 13 protein-coding genes (PCGs), 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loop). The nucleotide composition is 31.65% A, 23.50% T, 13.76% G, and 31.09% C. The result of phylogenetic analysis showed that there was close genetic relationship between V. cinereus and V. vanellus.

Traffic control and safety hardware such as traffic signs, lighting, signals, pavement markings, guardrails, barriers, and crash cushions form an important and inseparable part of highway infrastructure affecting safety performance. Significant progress has been made in recent decades to develop safety performance functions and crash modification factors for site-specific crash predictions. However, the existing models and methods lack rigorous treatments of safety impacts of time-deteriorating conditions of traffic control and safety hardware. This study introduces a refined method for computing the Safety Index (SI) as a means of crash predictions for a highway segment that incorporates traffic control and safety hardware performance functions into the analysis. The proposed method is applied in a computation experiment using five-year data on nearly two hundred rural and urban highway segments. The root-mean square error (RMSE), Chi-square, Spearman’s rank correlation, and Mann-Whitney U tests are employed for validation.

We determined the whole mtDNA genome of the Spotted Nutcracker (Nucifraga caryocatactes) in Tianlong Mountain, Shan’xi Province, China. The complete mitochondrial genome is 16,914 bp in length and consists of 13 protein-coding genes (PCGS), 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loops). The nucleotide composition is 25.08% A, 25.08% T, 24.75% G, and 25.08% C. The result of phylogenetic analysis showed that there was close genetic relationship between N. caryocatactes and N. columbiana. It is expected that the complete mitochondrial genome presented here will contribute to the analysis of species distribution.

We determined the whole mtNDA genome of the Plateau brown frog (Rana kukunoris) in Helan Mountains, Inner Mongolia. The complete mitochondrial genome consists of 13 protein-coding, 22 tRNA, 2 rRNA genes, and 1 control region (CR), and its total length is 16,644 bp. Three overlaps among the 13 protein-coding genes were found: ATP8/ATP6, ND4L/ND4, and ND5/ND6. The CR is 837 bp in length. The nucleotide composition is 27.49% A, 29.06% T, 14.77% G, 28.68% C. The result of phylogenetic analysis showed that there was close genetic relationship between R. kukunoris and Rana chensinensis.

We determined the whole mtDNA genome of the water deer (Hydropotes inermis) in Benxi, Liaoning. The total length of the complete mitochondrial genome is 16,355 bp and it consists of 13 protein-coding, 22 tRNA, rRNA genes, and 1 control region (CR). Two overlaps among the 13 protein-coding genes were found: ND4L/ND4 and ND5/ND6. The CR is 928 bp in length. The nucleotide composition is 30.52% A, % 33.38 T, 22.77% G, and 13.32% C.

This article contains data on research conducted in “A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability” (Liu et al., 2016) [1]. The crash counts were sorted out from comprehensive crash records of over one thousand major signalized intersections in the city of Chicago from 2004 to 2010. For each intersection, vehicular crashes were counted by crash severity levels, including fatal, injury Types A, B, and C for major, moderate, and minor injury levels, property damage only (PDO), and unknown. The crash data was further used to rank intersections by equivalent injury crash frequency. The top 200 intersections with the highest number of crash occurrences identified based on crash frequency- and severity-based scenarios are shared in this brief. The provided data would be a valuable source for research in urban traffic safety analysis and could also be utilized to examine the effectiveness of traffic safety improvement planning and programming, intersection design enhancement, incident and emergency management, and law enforcement strategies.

This paper introduces a new methodology for reconstructing vehicle densities of freeway segments by utilizing the limited data collected by traffic-counting sensors and developing a macroscopic traffic stream model formulated as a switched reduced-order state observer design problem with unknown or partially known inputs. Specifically, the traffic network is modeled as a hybrid dynamic system in a state space that incorporates unknown inputs. For freeway segments with traffic-counting sensors installed, vehicle densities are directly computed using field traffic count data. A reduced-order state observer is designed to analyze traffic state transitions for freeway segments without field traffic count data to indirectly estimate the vehicle densities for each freeway segment. A simulation-based experiment is performed applying the methodology and using data of a segment of Beijing Jingtong freeway in Beijing, China. The model execution results are compared with the field data associated with the same freeway segment, and highly consistent results are achieved. The proposed methodology is expected to be adopted by traffic engineers to evaluate freeway operations and develop effective management strategies.

A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average.

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Red light running at signalized intersections is a major safety concern in the United States. Statistics show that approximately 45 percent of crashes at intersections caused by red light running result in severe injuries and fatalities, while only approximately 30 percent of all other types of intersection crashes cause injuries or fatalities. Over the past decade, many US cities and counties have deployed red light running photo enforcement systems for signalized intersections within their jurisdictions to potentially reduce red light running related crashes. This study proposes an empirical Bayesian (EB) before-after analysis method that computes a weighed sum of crashes observed in the field and crashes predicted by safety performance functions (SPFs) to mitigate regression-to-mean biases for analyzing crash reduction effects of red light running enforcement. The analysis explicitly considers red light running related crash types, including head-on, rear-end, angle, turning, sideswipe in the same direction, and sideswipe in the opposite direction; and crash severity levels classified as fatal, injury, and property damage only (PDO). A computational study is conducted to examine the effectiveness of the Chicago program with red light running photo enforcement systems deployed for nearly two hundred signalized intersections. It is revealed that the use of red light running photo enforcement on the whole is positive, as demonstrated by reductions in all types of fatal crashes by 4–48 percent, and injury-related angle crashes by 1 percent. However, it slightly raises PDO-related angle crashes and moderately increases injury and PDO related rear-end crashes. The safety effectiveness of red light running photo enforcement is sensitive to intersection location.

This paper introduces binary recursive partitioning (BRP) as a method for estimating bridge deck deterioration and treats it as a classification and decision problem. The proposed BRP method is applied to the Indiana bridge inventory database containing 25 years of detailed information on approximately 5,500 bridges on state-maintained highways. Classification trees are separately created for 4 and 2 prediction classes and relatively high degrees of success are achieved for deck condition prediction. The significant variables identified as the most influential include current deck condition and deck age. The proposed method offers an alternative nonparametric approach for bridge deck condition prediction and could be used for cross comparisons of models calibrated using the widely applied parametric approaches.

Abstract Parkinson's disease (PD) involves multiple forms of neuronal cell death, but the dominant pathway involved in disease progression remains unclear. This study employed RNA sequencing (RNA-seq) of brain tissue to explore gene expression patterns across different stages of PD. Using the Scaden deep learning algorithm, we predicted neurocyte subtypes and modelled dynamic interactions for five classic cell death pathways to identify the predominant routes of neuronal death during PD progression. Our cell type-specific analysis revealed an increasing shift towards necroptosis, which was strongly correlated with nitric oxide synthase (NOS) expression across most neuronal subtypes. In vitro experiments confirmed that nitric oxide (NO) is a key mediator of necroptosis, leading to nuclear shrinkage and decreased mitochondrial membrane potential via phosphorylation of the PIP1/PIP3/MLKL signalling cascade. Importantly, specific necroptosis inhibitors significantly mitigated neuronal damage in both in vitro and in vivo PD models. Further analysis revealed that NO-mediated necroptosis is prevalent in early-onset Alzheimer's disease (AD) and amyotrophic lateral sclerosis (ALS) and across multiple brain regions but not in brain tumours. Our findings suggest that NO-mediated necroptosis is a critical pathway in PD and other neurodegenerative disorders, providing potential targets for therapeutic intervention.