Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8^(+)T cell immune infiltration and immune suppression.We constructed a CD8^(+)T cells related risk score model to predic...Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8^(+)T cell immune infiltration and immune suppression.We constructed a CD8^(+)T cells related risk score model to predict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8^(+)T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS 2,and TNFRSF1B was constructed.The risk score model was well validated through an independent external validation cohort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P<0.0001)and lower CD8^(+)T cells infiltration(P<0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P<0.01).Drug sensitivity analysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene model was verified by immunohistochemistry.In summary,the establishment and validation of a CD8^(+)T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.展开更多
Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77...Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.展开更多
The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and...The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index （VI） and neural network （NN） methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance （SWIR） bands are stronger than the positive correlations between FPAR and near-infrared band re- flectance （NIR）. For the six VIs, the normalized difference vegetation index （NDVI） and simple ratio （SR） performed best for estimating corn FPAR （the maximum R2 of 0.8849 and 0.8852, respectively）. However, the NN method esti- mated results （the maximum Rz is 0.9417） were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation （APAR）, and makes further impact on corn-canopy FPAR.展开更多
Seahorses have evolved many unique biological traits,including a male brood pouch,the absence of caudal and pelvic fins,and the lack of spleen and gut-associated lymphatic tissue.The mitogenactivated protein kinases(M...Seahorses have evolved many unique biological traits,including a male brood pouch,the absence of caudal and pelvic fins,and the lack of spleen and gut-associated lymphatic tissue.The mitogenactivated protein kinases(MAPKs)are known to be involved in various important biological processes including growth,differentiation,immunity,and stress responses.Therefore,we hypothesized that the adaptive evolution and expression of the MAPK gene family in seahorse may differ from those of other teleost species.We identified positive selection sites in the erk2,erk5,jnk1,and p38αMAPK genes of the lined seahorse Hippocampus erectus and tiger-tailed seahorse Hippocampus comes.A novel expression profile of MAPK cascade genes was found in seahorse larvae during the first day after birth based on the RNA-seq data of H.erectus,which refl ected vital signs of immune response to its parental immune system.The expression patterns of the four positively selected MAPK genes were analyzed following the bacterial challenge of Vibrio fortis,revealing their upregulation pattern in brood pouch and other immune tissues.This study enriched our knowledge of the evolution of the H.erectus MAPK subfamilies,and could help better understanding the functional role of MAPKs in teleosts.展开更多
We used the Integrated Biological Responses version 2(IBRv2)method to evaluate the biological eff ects of heavy metals in the sediments in Laizhou Bay,China on the benthic goby Acanthogobius ommaturus.In December 2018...We used the Integrated Biological Responses version 2(IBRv2)method to evaluate the biological eff ects of heavy metals in the sediments in Laizhou Bay,China on the benthic goby Acanthogobius ommaturus.In December 2018,gobies and sediments were collected from 15 stations.We measured the activities of defense enzymes and the contents of malondialdehyde(MDA)and metallothionein(MT)in the goby liver as well as the levels of heavy metals in the sediments and goby muscle tissue.Most of the heavy metal concentrations in sediment at each station were below the Class I criteria set by Chinese Standards for Marine Sediment Quality,and the Håkanson ecological risk index suggested low risk for the heavy metals.We found that A.ommaturus could eff ectively accumulate mercury,cadmium,arsenic,and zinc and that the contents of MT and MDA and the activities of glutathione peroxidase and glutathione reductase were suitable biomarkers of heavy metal pollution in this species.The IBRv2 method integrated these four biomarkers and discriminated stations according to heavy metal pollution.Higher IBRv2 values suggested more adverse eff ects in gobies,corroborating more serious heavy metal contamination.The stations with high IBRv2 values and high contents of heavy metals were mainly distributed in the west and northeast parts of the bay.These results show that the IBRv2 approach is a feasible strategy for assessing heavy metal pollution through biological response and biological status and that it can be implemented for environmental monitoring in Laizhou Bay.展开更多
In the wake of the era of big data,the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry.The sintering process ...In the wake of the era of big data,the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry.The sintering process is an extremely complex industrial scene.As the main process of the blast furnace ironmaking industry,it has great economic value and environmental protection significance for iron and steel enterprises.It is also one of the fields where deep learning is still in the exploration stage.In order to explore the application prospects of deep learning techniques in iron ore sintering,a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures.Firstly,the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail,and then,the development of iron ore sintering simulation techniques was introduced.Secondly,deep learning techniques were introduced,including commonly used models of deep learning and their applications.Thirdly,the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction,controlling,and optimisation of key parameters.Generally speaking,deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.展开更多
Three equal field plots were cultivated with respectively wheat, field pea and faba bean. The common conventional production technology, including the use of chemical fertilizers was applied in wheat, but no fertilize...Three equal field plots were cultivated with respectively wheat, field pea and faba bean. The common conventional production technology, including the use of chemical fertilizers was applied in wheat, but no fertilizers at all were used in faba bean and field pea plots. Atter legume harvesting, forty day old broccoli and cauliflower seedlings were transplanted to each of them according to three replications randomized block design. The transplanting was conducted at equal planting density, and common organic production practices were applied in entire production cycle. The legume crops improved soil fertility by increasing total soil N （Nitrogen） and improving P （Phosphorus） and K （Potassium） availability to the subsequent crops. As a result, an enhanced vegetative growth, improved curd setting and increased average curd weight was found in broccoli and cauliflower. However, there were significant differences between legume crops themselves regarding the proved benefits to the subsequent crops, confirming a clear advantage of faba bean versus field pea. A significantly higher above ground biomass was recorded in cauliflower plants followed faba bean, compared with field pea and wheat, but no difference was found regarding the biomass production in broccoli. The higher percentage of plants set curds （either broccoli or cauliflower） was obtained in the variants followed faba bean and then field pea. The same was true regarding total curd yield and the average curd weight for both： broccoli and cauliflower.展开更多
Traffic barriers are in widespread all around the USA as safety countermeasures for reducing the severity of run-off-road crashes. The effect of traffic barriers’ dimension had been ignored in past real-world crash s...Traffic barriers are in widespread all around the USA as safety countermeasures for reducing the severity of run-off-road crashes. The effect of traffic barriers’ dimension had been ignored in past real-world crash studies due to the considerable cost and time needed for collecting field data. This paper presented two new analytical models to investigate the effect of different variables on the severity of crashes involving traffic barriers, and end treatments. For this reason, a field survey was conducted on over 1.3 million linear feet of traffic barriers (approximately 4,176 miles road) in Wyoming to measure traffic barriers’ geometric features like height, length, offset, and slope rate. The collected data included 55% of all non-interstate roads of Wyoming. Based on results, the crashes involving box beam barriers were less severe than the crashes involved with W-beam or concrete barriers. The traffic barriers with a height between 28 and 31 in. were found safer than the traffic barriers shorter than 28 in., while there was no significant difference between the traffic barriers taller than 31 in. to those shorter than 28 in. in terms of crash severity. The end treatments located nearer to the traffic lane had lower crash severity.展开更多
Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection cra...Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.展开更多
In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.Howe...In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.展开更多
The purpose of this study was to analyse polymorphisms of the CAPN1, CAST and MSTN genes and their association with the microstructure of the Musculus longissimus thoracis (MLT) and textural parameters in bulls of the...The purpose of this study was to analyse polymorphisms of the CAPN1, CAST and MSTN genes and their association with the microstructure of the Musculus longissimus thoracis (MLT) and textural parameters in bulls of the Holstein-Friesian breeds, black-and-white variety. The polymorphisms at the three loci: in position 6536 of the 3’UTR region of the CAPN1 gene, in position 230 of intron 5 in CAST gene, and in position 371 of the promoter region of the MSTN gene were analysed. Given the inconsequential genetic diversity at the analysed CAPN1 and MSTN loci in the animal sample, it was considered unreasonable to perform further statistical analyses aimed at determining associations between polymorphisms in these positions and meat characteristics. Based on an analysis of the CAST gene polymorphism, a significant association with certain histological and textural parameters was identified.展开更多
Background:Physical activity,sedentary behavior(SB),and sleep duration are associated with brain health.Effects of those on developing Parkinson’s disease(PD)are poorly investigated.This study aimed to examine the in...Background:Physical activity,sedentary behavior(SB),and sleep duration are associated with brain health.Effects of those on developing Parkinson’s disease(PD)are poorly investigated.This study aimed to examine the independent and joint associations of physical activity,SB,sleep with PD risk.Methods:We analyzed data on 401,697 participants from the UK Biobank cohort,which was enrolled in 2006–2010.Physical activities were measured based on a questionnaire.Sleep and SB time were defined through self-reported total number of hours.Models fitted with restricted cubic spline were conducted to test for linear and non-linear shapes of each association.Cox proportional hazards regression models were used to estimate the association of three modifiable behaviors.Results:Our analytic sample included 401,697 participants with 3030 identified cases of PD(mean age,63 years;62.9%male).PD risk was 18%lower in the high total physical activity group(95%CI,0.75–0.90),22%lower in the high leisure-time physical activity(LTPA)group(95%CI,0.71–0.86)compared with the low level and 14%higher in the high sleep duration group(95%CI,1.05–1.24)compared to moderate group.Total SB time was irrelevant with PD risk,while high TV viewing showed a 12%increase of PD risk compared to the low group(95%CI,1.02–1.22).Low computer use(0 h/day)was associated with a 14%higher risk compared to 1 h/day use(95%CI,1.04–1.26).Those associations were independent.A combination of 7 h/day sleep,moderate-to-high computer use,and moderate-to-vigorous intensity of LTPA showed lowest PD risk(HR,0.70;95%CI,0.57–0.85).Conclusions:Physical activity,SB,and sleep were associated with PD risks separately.Our findings emphasize the possibility for changing these three daily activities concurrently to lower the risk of PD.These findings may promote an active lifestyle for PD prevention.展开更多
Roadside safety is one of the important components of highway systems due to its considerable rate of high-severity crashes.Traffic barriers play a key role in reducing the crash severity and saving more lives on road...Roadside safety is one of the important components of highway systems due to its considerable rate of high-severity crashes.Traffic barriers play a key role in reducing the crash severity and saving more lives on roadsides but choosing an inappropriate traffic barrier could decrease traffic barriers’efficiency.This paper investigated the variables affecting crash severity in different traffic barrier types(cable,guardrail,and rigid)and vehicle types(truck,and non-truck).For this purpose,an ordinal logistic regression approach was conducted on the crash data collected between 2007 and 2016 in Wyoming.According to the results,different traffic barrier types show different relationships to crash severity based on posted speed limit at the site.In guardrail segments,a high-severity crash is more likely when the posted speed limit is more than 55 mph;however,cable barrier crashes were less severe in high-speed limit(>55 mph)areas.While light vehicles were more vulnerable to crashes hitting rigid barriers,no significant difference was found between performances of cable and rigid barriers in terms of truck crash severity.The analytical models proposed in this study would make a clear view for designers and decision-makers in selecting the most appropriate traffic barrier.Also,the effective factors introduced in each model could be used to rank the risk posed by existing traffic barrier segments based on traffic barrier type,truck volume percentage,and other highway characteristics.展开更多
Road deaths,injuries and property damage places a huge burden on the economy of most nations.Wyoming has one of the highest truck-related fatality rates among the states in the US.The high crash rates observed in the ...Road deaths,injuries and property damage places a huge burden on the economy of most nations.Wyoming has one of the highest truck-related fatality rates among the states in the US.The high crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state,which places extra burden on truck drivers in terms of requiring higher levels of alertness and driving skills.The difficult geometry of roads characteristic of mountainous terrain in terms of steep grade lengths adds extra risks of fatalities or injuries occurring as a result of a crash.These risks are more pronounced for truck-related crashes due to their weight and sizes.As part of the measures to reduce the incidence of truck-related crashes on mountainous areas,the Wyoming Department of Transportation(WYDOT)initiated a study to investigate causes of truck crashes on downgrade areas of Wyoming.Several studies have investigated the contributory factors to severe injury crashes but the focus has mostly been on level sections.This study analyzed the contributory geometric factors of truck crashes on downgrades by estimating three crash prediction negative binomial models.These models took into account the injury severity of the crashes.The results indicate that downgrade length,shoulder width,horizontal curve length,number of lanes,number of access points and truck traffic on the highway all impact truck-related crashes and injury frequencies ondowngrades in Wyoming.The results of this study will be helpful to future downgrade road design policy aimed at reducing downgrade truck related crashes.展开更多
Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical knowledge.This study sought to identify predictors among a co...Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical knowledge.This study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction model.Methods From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years old.We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD.The model was validated through a leave-one center-out cross-validation.Results During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of smoking.Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±0.006.Further,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.展开更多
Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted ...Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted that drivers under influence,type of vehicle,location of crashes,estimated speed of vehicles,driving over the recommended speed are some of factors contributing to the severity of crashes.In this study,we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values.However,it has been argued that this assumption might be unrealistic,especially at the presence of extreme values.Thus,the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes,or proportional odds effects.In addition,we accounted for the spread rate,or scale,of the model’s latent distribution of pedestrian crashes.The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model.Comparisons were also made across standard normal,simple partial ordinal model,and partial ordinal accounting for scale heterogeneity.In addition,various potential threshold structures such as symmetric and flexible were considered,but similar goodness of fits were observed across all those models.Extensive discussion has been made regarding the formulation of the implemented methodology,and its implications.展开更多
This paper developed a traffic safety management system （TSMS） for improving safety on county paved roads in Wyoming. TSMS is a strategic and systematic process to improve safety of roadway network. When funding is ...This paper developed a traffic safety management system （TSMS） for improving safety on county paved roads in Wyoming. TSMS is a strategic and systematic process to improve safety of roadway network. When funding is limited, it is important to identify the best combination of safety improvement projects to provide the most benefits to society in terms of crash reduction. The factors included in the proposed optimization model are annual safety budget, roadway inventory, roadway functional classification, historical crashes, safety improvement countermeasures, cost and crash reduction factors （CRFs） associated with safety improvement countermeasures, and average daily traffics （ADTs）. This paper demonstrated how the proposed model can identify the best combination of safety improvement projects to maximize the safety benefits in terms of reducing overall crash frequency. Although the proposed methodology was implemented on the county paved road network of Wyoming, it could be easily modified for potential implementation on the Wyoming state highway system. Other states can also benefit by implementing a similar program within their jurisdictions.展开更多
One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships b...One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features,traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries.Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally,the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.展开更多
Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate miti...Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries.In this study,a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children’s injuries resulting from crashes while accounting for possible intra-class correlation effects(those of unobserved factors common to children involved in the same crash).A strong correlation among crashes justified the use of the hierarchical Bayesian logit model.As per the modeling results,the children’s ages,safety restraint types,vehicle types,drivers’ages,alcohol/drug involvement,drivers’seat belt use habits,drivers’actions,manners of collision and environmental conditions contributed to child injury risk.The child’s age was found to be inversely related to the risk of injury.Similarly,among safety restraint types,rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes.When it comes to the drivers’characteristics,the probability of incurring injuries among the child population increased in the presence of young,unbuckled and impaired drivers.Furthermore,improper driving actions,such as running off the road,raised the risk of incurring injuries to children.The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.展开更多
The ability to identify risk factors associated with crashes is critical to determine appropriate countermeasures for improving roadway safety. Many studies have identified risk factors for urban systems and intersect...The ability to identify risk factors associated with crashes is critical to determine appropriate countermeasures for improving roadway safety. Many studies have identified risk factors for urban systems and intersections, but few have addressed crashes on rural roadways, and none have analyzed crashes on Indian Reservations. This study analyzes crash severity for rural highway systems in Wyoming. These rural systems include interstates, state highways, rural county local roads, and the roadway system on the Wind River Indian Reservation （WRIR）. In alignment with the Wyoming strategic highway safety goal of reducing critical crashes {fatal and serious injury}, crash severity was treated as a binary response in which crashes were classified as severe or not severe. Multiple logistic regression models were developed for each of the highway systems. Five effects were prevalent on all systems including animals, driver impairment, motorcycles, mean speed, and safety equipment use. With the exception of animal crashes, all of these effects increased the probability that a crash would be severe. Based upon these results, DOTs can pursue effective policies and targeted design decisions to reduce the severity of crashes on rural highways.展开更多
基金国家自然科学基金项目(No.81902513)山西省应用基础研究计划项目(No.202303021211114 and 202103021224228)山西省高等教育百亿工程“科技引导”专项(No.BYJL047)资助。
文摘Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8^(+)T cell immune infiltration and immune suppression.We constructed a CD8^(+)T cells related risk score model to predict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8^(+)T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS 2,and TNFRSF1B was constructed.The risk score model was well validated through an independent external validation cohort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P<0.0001)and lower CD8^(+)T cells infiltration(P<0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P<0.01).Drug sensitivity analysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene model was verified by immunohistochemistry.In summary,the establishment and validation of a CD8^(+)T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.
基金by the National Natural Science Foundation of China(No.52174114)the State Key Laboratory of Hydroscience and Engineering of Tsinghua University(No.61010101218).
文摘Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.
基金Under the auspices of National Key Research Program of Global Change Research (No.2010CB951302)National Natural Science Fundation of China (No.40771146)China Postdoctoral Science Foundation Funded Project (No.07Z7601MZ1)
文摘The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index （VI） and neural network （NN） methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance （SWIR） bands are stronger than the positive correlations between FPAR and near-infrared band re- flectance （NIR）. For the six VIs, the normalized difference vegetation index （NDVI） and simple ratio （SR） performed best for estimating corn FPAR （the maximum R2 of 0.8849 and 0.8852, respectively）. However, the NN method esti- mated results （the maximum Rz is 0.9417） were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation （APAR）, and makes further impact on corn-canopy FPAR.
基金Supported by the Shandong Province Science and Technology Support Program for Outstanding Youth of Colleges and Universities(No.2020KJF007)the Shandong Province Science and Technology Research Program for Colleges and Universities(No.J18KA146)+3 种基金the Yantai Foundation for Development of Science and Technology(Nos.2020LJRC120,2019CXJJ040)the Weihai Foundation for Development of Science and Technology(No.2017GNS10)the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2019ZD0407)the Guangdong Basic and Applied Basic Research Foundation(No.2019A1515110199)。
文摘Seahorses have evolved many unique biological traits,including a male brood pouch,the absence of caudal and pelvic fins,and the lack of spleen and gut-associated lymphatic tissue.The mitogenactivated protein kinases(MAPKs)are known to be involved in various important biological processes including growth,differentiation,immunity,and stress responses.Therefore,we hypothesized that the adaptive evolution and expression of the MAPK gene family in seahorse may differ from those of other teleost species.We identified positive selection sites in the erk2,erk5,jnk1,and p38αMAPK genes of the lined seahorse Hippocampus erectus and tiger-tailed seahorse Hippocampus comes.A novel expression profile of MAPK cascade genes was found in seahorse larvae during the first day after birth based on the RNA-seq data of H.erectus,which refl ected vital signs of immune response to its parental immune system.The expression patterns of the four positively selected MAPK genes were analyzed following the bacterial challenge of Vibrio fortis,revealing their upregulation pattern in brood pouch and other immune tissues.This study enriched our knowledge of the evolution of the H.erectus MAPK subfamilies,and could help better understanding the functional role of MAPKs in teleosts.
基金Supported by the National Key Research and Development Program of China(No.2019YFD0900704)the Yantai Key Research and Development Program(No.2019XDHZ097)+2 种基金the National Natural Science Foundation of China(No.42076137)the Natural Science Foundation of Shandong Province(No.ZR2020QD003)the Shandong Key Laboratory of Coastal Environmental Processes,YICCAS(No.2019SDHADKFJJ16)。
文摘We used the Integrated Biological Responses version 2(IBRv2)method to evaluate the biological eff ects of heavy metals in the sediments in Laizhou Bay,China on the benthic goby Acanthogobius ommaturus.In December 2018,gobies and sediments were collected from 15 stations.We measured the activities of defense enzymes and the contents of malondialdehyde(MDA)and metallothionein(MT)in the goby liver as well as the levels of heavy metals in the sediments and goby muscle tissue.Most of the heavy metal concentrations in sediment at each station were below the Class I criteria set by Chinese Standards for Marine Sediment Quality,and the Håkanson ecological risk index suggested low risk for the heavy metals.We found that A.ommaturus could eff ectively accumulate mercury,cadmium,arsenic,and zinc and that the contents of MT and MDA and the activities of glutathione peroxidase and glutathione reductase were suitable biomarkers of heavy metal pollution in this species.The IBRv2 method integrated these four biomarkers and discriminated stations according to heavy metal pollution.Higher IBRv2 values suggested more adverse eff ects in gobies,corroborating more serious heavy metal contamination.The stations with high IBRv2 values and high contents of heavy metals were mainly distributed in the west and northeast parts of the bay.These results show that the IBRv2 approach is a feasible strategy for assessing heavy metal pollution through biological response and biological status and that it can be implemented for environmental monitoring in Laizhou Bay.
基金supported by the Department of Education of Hebei Province,China(QN2019026).
文摘In the wake of the era of big data,the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry.The sintering process is an extremely complex industrial scene.As the main process of the blast furnace ironmaking industry,it has great economic value and environmental protection significance for iron and steel enterprises.It is also one of the fields where deep learning is still in the exploration stage.In order to explore the application prospects of deep learning techniques in iron ore sintering,a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures.Firstly,the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail,and then,the development of iron ore sintering simulation techniques was introduced.Secondly,deep learning techniques were introduced,including commonly used models of deep learning and their applications.Thirdly,the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction,controlling,and optimisation of key parameters.Generally speaking,deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.
文摘Three equal field plots were cultivated with respectively wheat, field pea and faba bean. The common conventional production technology, including the use of chemical fertilizers was applied in wheat, but no fertilizers at all were used in faba bean and field pea plots. Atter legume harvesting, forty day old broccoli and cauliflower seedlings were transplanted to each of them according to three replications randomized block design. The transplanting was conducted at equal planting density, and common organic production practices were applied in entire production cycle. The legume crops improved soil fertility by increasing total soil N （Nitrogen） and improving P （Phosphorus） and K （Potassium） availability to the subsequent crops. As a result, an enhanced vegetative growth, improved curd setting and increased average curd weight was found in broccoli and cauliflower. However, there were significant differences between legume crops themselves regarding the proved benefits to the subsequent crops, confirming a clear advantage of faba bean versus field pea. A significantly higher above ground biomass was recorded in cauliflower plants followed faba bean, compared with field pea and wheat, but no difference was found regarding the biomass production in broccoli. The higher percentage of plants set curds （either broccoli or cauliflower） was obtained in the variants followed faba bean and then field pea. The same was true regarding total curd yield and the average curd weight for both： broccoli and cauliflower.
基金part of project#RS03218 funded by the Wyoming Department of Transportation(WYDOT)
文摘Traffic barriers are in widespread all around the USA as safety countermeasures for reducing the severity of run-off-road crashes. The effect of traffic barriers’ dimension had been ignored in past real-world crash studies due to the considerable cost and time needed for collecting field data. This paper presented two new analytical models to investigate the effect of different variables on the severity of crashes involving traffic barriers, and end treatments. For this reason, a field survey was conducted on over 1.3 million linear feet of traffic barriers (approximately 4,176 miles road) in Wyoming to measure traffic barriers’ geometric features like height, length, offset, and slope rate. The collected data included 55% of all non-interstate roads of Wyoming. Based on results, the crashes involving box beam barriers were less severe than the crashes involved with W-beam or concrete barriers. The traffic barriers with a height between 28 and 31 in. were found safer than the traffic barriers shorter than 28 in., while there was no significant difference between the traffic barriers taller than 31 in. to those shorter than 28 in. in terms of crash severity. The end treatments located nearer to the traffic lane had lower crash severity.
文摘Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.
文摘In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.
文摘The purpose of this study was to analyse polymorphisms of the CAPN1, CAST and MSTN genes and their association with the microstructure of the Musculus longissimus thoracis (MLT) and textural parameters in bulls of the Holstein-Friesian breeds, black-and-white variety. The polymorphisms at the three loci: in position 6536 of the 3’UTR region of the CAPN1 gene, in position 230 of intron 5 in CAST gene, and in position 371 of the promoter region of the MSTN gene were analysed. Given the inconsequential genetic diversity at the analysed CAPN1 and MSTN loci in the animal sample, it was considered unreasonable to perform further statistical analyses aimed at determining associations between polymorphisms in these positions and meat characteristics. Based on an analysis of the CAST gene polymorphism, a significant association with certain histological and textural parameters was identified.
基金supported by grants from the Science and Technology Innovation 2030 Major Projects(No.2022ZD0211600)National Key R&D Program of China(No.2021YFC2502200)+5 种基金the National Natural Science Foundation of China(Nos.82071201,81971032)Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01)Research Start-up Fund of Huashan Hospital(No.2022QD002)Excellence 2025 Talent Cultivation Program at Fudan University(No.3030277001)supported by grants from the National Natural Sciences Foundation of China(No.82071997)the Shanghai Rising-Star Program(No.21QA1408700).
文摘Background:Physical activity,sedentary behavior(SB),and sleep duration are associated with brain health.Effects of those on developing Parkinson’s disease(PD)are poorly investigated.This study aimed to examine the independent and joint associations of physical activity,SB,sleep with PD risk.Methods:We analyzed data on 401,697 participants from the UK Biobank cohort,which was enrolled in 2006–2010.Physical activities were measured based on a questionnaire.Sleep and SB time were defined through self-reported total number of hours.Models fitted with restricted cubic spline were conducted to test for linear and non-linear shapes of each association.Cox proportional hazards regression models were used to estimate the association of three modifiable behaviors.Results:Our analytic sample included 401,697 participants with 3030 identified cases of PD(mean age,63 years;62.9%male).PD risk was 18%lower in the high total physical activity group(95%CI,0.75–0.90),22%lower in the high leisure-time physical activity(LTPA)group(95%CI,0.71–0.86)compared with the low level and 14%higher in the high sleep duration group(95%CI,1.05–1.24)compared to moderate group.Total SB time was irrelevant with PD risk,while high TV viewing showed a 12%increase of PD risk compared to the low group(95%CI,1.02–1.22).Low computer use(0 h/day)was associated with a 14%higher risk compared to 1 h/day use(95%CI,1.04–1.26).Those associations were independent.A combination of 7 h/day sleep,moderate-to-high computer use,and moderate-to-vigorous intensity of LTPA showed lowest PD risk(HR,0.70;95%CI,0.57–0.85).Conclusions:Physical activity,SB,and sleep were associated with PD risks separately.Our findings emphasize the possibility for changing these three daily activities concurrently to lower the risk of PD.These findings may promote an active lifestyle for PD prevention.
基金part of project#RS03218 funded by the Wyoming Department of Transportation(WYDOT).
文摘Roadside safety is one of the important components of highway systems due to its considerable rate of high-severity crashes.Traffic barriers play a key role in reducing the crash severity and saving more lives on roadsides but choosing an inappropriate traffic barrier could decrease traffic barriers’efficiency.This paper investigated the variables affecting crash severity in different traffic barrier types(cable,guardrail,and rigid)and vehicle types(truck,and non-truck).For this purpose,an ordinal logistic regression approach was conducted on the crash data collected between 2007 and 2016 in Wyoming.According to the results,different traffic barrier types show different relationships to crash severity based on posted speed limit at the site.In guardrail segments,a high-severity crash is more likely when the posted speed limit is more than 55 mph;however,cable barrier crashes were less severe in high-speed limit(>55 mph)areas.While light vehicles were more vulnerable to crashes hitting rigid barriers,no significant difference was found between performances of cable and rigid barriers in terms of truck crash severity.The analytical models proposed in this study would make a clear view for designers and decision-makers in selecting the most appropriate traffic barrier.Also,the effective factors introduced in each model could be used to rank the risk posed by existing traffic barrier segments based on traffic barrier type,truck volume percentage,and other highway characteristics.
文摘Road deaths,injuries and property damage places a huge burden on the economy of most nations.Wyoming has one of the highest truck-related fatality rates among the states in the US.The high crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state,which places extra burden on truck drivers in terms of requiring higher levels of alertness and driving skills.The difficult geometry of roads characteristic of mountainous terrain in terms of steep grade lengths adds extra risks of fatalities or injuries occurring as a result of a crash.These risks are more pronounced for truck-related crashes due to their weight and sizes.As part of the measures to reduce the incidence of truck-related crashes on mountainous areas,the Wyoming Department of Transportation(WYDOT)initiated a study to investigate causes of truck crashes on downgrade areas of Wyoming.Several studies have investigated the contributory factors to severe injury crashes but the focus has mostly been on level sections.This study analyzed the contributory geometric factors of truck crashes on downgrades by estimating three crash prediction negative binomial models.These models took into account the injury severity of the crashes.The results indicate that downgrade length,shoulder width,horizontal curve length,number of lanes,number of access points and truck traffic on the highway all impact truck-related crashes and injury frequencies ondowngrades in Wyoming.The results of this study will be helpful to future downgrade road design policy aimed at reducing downgrade truck related crashes.
基金the National Natural Science Foundation of China(82071997,82071201)National Key R&D Program of China(2018YFC1312904,2019YFA0709502)+6 种基金Science and Technology Innovation 2030 Major Projects(2022ZD0211600)Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)the 111 Project(B18015)hanghai Rising-Star Program(21QA1408700)Research Start-up Fund of Huashan Hospital(2022QD002)Excellence 2025 Talent Cultivation Program at Fudan University(3030277001)Shanghai Municipal Health Commission New Interdisciplinary Research Project(2022JC014).
文摘Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical knowledge.This study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction model.Methods From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years old.We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD.The model was validated through a leave-one center-out cross-validation.Results During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of smoking.Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±0.006.Further,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.
文摘Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted that drivers under influence,type of vehicle,location of crashes,estimated speed of vehicles,driving over the recommended speed are some of factors contributing to the severity of crashes.In this study,we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values.However,it has been argued that this assumption might be unrealistic,especially at the presence of extreme values.Thus,the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes,or proportional odds effects.In addition,we accounted for the spread rate,or scale,of the model’s latent distribution of pedestrian crashes.The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model.Comparisons were also made across standard normal,simple partial ordinal model,and partial ordinal accounting for scale heterogeneity.In addition,various potential threshold structures such as symmetric and flexible were considered,but similar goodness of fits were observed across all those models.Extensive discussion has been made regarding the formulation of the implemented methodology,and its implications.
基金the Wyoming LTAP Center for supporting this research study
文摘This paper developed a traffic safety management system （TSMS） for improving safety on county paved roads in Wyoming. TSMS is a strategic and systematic process to improve safety of roadway network. When funding is limited, it is important to identify the best combination of safety improvement projects to provide the most benefits to society in terms of crash reduction. The factors included in the proposed optimization model are annual safety budget, roadway inventory, roadway functional classification, historical crashes, safety improvement countermeasures, cost and crash reduction factors （CRFs） associated with safety improvement countermeasures, and average daily traffics （ADTs）. This paper demonstrated how the proposed model can identify the best combination of safety improvement projects to maximize the safety benefits in terms of reducing overall crash frequency. Although the proposed methodology was implemented on the county paved road network of Wyoming, it could be easily modified for potential implementation on the Wyoming state highway system. Other states can also benefit by implementing a similar program within their jurisdictions.
文摘One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features,traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries.Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally,the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.
基金funded by the Wyoming Department of Transportation(WyDOT)supported by the Mountain Plains Consortium(Grant Number 69A3551747108(FAST Act))。
文摘Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries.In this study,a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children’s injuries resulting from crashes while accounting for possible intra-class correlation effects(those of unobserved factors common to children involved in the same crash).A strong correlation among crashes justified the use of the hierarchical Bayesian logit model.As per the modeling results,the children’s ages,safety restraint types,vehicle types,drivers’ages,alcohol/drug involvement,drivers’seat belt use habits,drivers’actions,manners of collision and environmental conditions contributed to child injury risk.The child’s age was found to be inversely related to the risk of injury.Similarly,among safety restraint types,rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes.When it comes to the drivers’characteristics,the probability of incurring injuries among the child population increased in the presence of young,unbuckled and impaired drivers.Furthermore,improper driving actions,such as running off the road,raised the risk of incurring injuries to children.The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.
文摘The ability to identify risk factors associated with crashes is critical to determine appropriate countermeasures for improving roadway safety. Many studies have identified risk factors for urban systems and intersections, but few have addressed crashes on rural roadways, and none have analyzed crashes on Indian Reservations. This study analyzes crash severity for rural highway systems in Wyoming. These rural systems include interstates, state highways, rural county local roads, and the roadway system on the Wind River Indian Reservation （WRIR）. In alignment with the Wyoming strategic highway safety goal of reducing critical crashes {fatal and serious injury}, crash severity was treated as a binary response in which crashes were classified as severe or not severe. Multiple logistic regression models were developed for each of the highway systems. Five effects were prevalent on all systems including animals, driver impairment, motorcycles, mean speed, and safety equipment use. With the exception of animal crashes, all of these effects increased the probability that a crash would be severe. Based upon these results, DOTs can pursue effective policies and targeted design decisions to reduce the severity of crashes on rural highways.
