Characterisation of Diesel Engine Cylinder Liner Deposits by Surface Measurements

This paper briefly reviews cylinder liner deposits and describes their effect on lubricating oil consumption and surface topography. Cylinder liner deposits may be invisible to the naked eye, but can be identified by surface topography measurement. A simulation of a cylinder liner with deposits is used to identify which of the available surface characteristics is best suited as a measure of deposit severity. Surface measurements are used to detect deposits in a case study. The deposits are found to be concentrated in a belt around the mid-stroke region of the cylinder liner. The comparatively low position of the deposit may explain the slow response of a fuel additive treatment in restoring oil consumption control, as the additive treatment in this case is seen to be most effective in the uppermost part of the cylinder liner.     

Genomic Regions Associated with Fusarium Wilt Resistance in Flax

Modern flax cultivars are susceptible to many diseases; arguably, the most economically damaging of these is the Fusarium wilt fungal disease. Over the past decades international flax breeding initiatives resulted in the development of resistant cultivars. However, much remains to be learned about the mechanisms of resistance to Fusarium infection in flax. As a first step to uncover the genetic factors associated with resistance to Fusarium wilt disease, we performed a genome-wide association study (GWAS) using 297 accessions from the collection of the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. These genotypes were infected with a highly pathogenic Fusarium oxysporum f.sp. lini MI39 strain; the wilt symptoms were documented in the course of three successive years. Six different single-locus models implemented in GAPIT3 R package were applied to a selected subset of 72,526 SNPs. A total of 15 QTNs (Quantitative Trait Nucleotides) were detected during at least two years of observation, while eight QTNs were found during all three years of the experiment. Of these, ten QTNs occupied a region of 640 Kb at the start of chromosome 1, while the remaining QTNs mapped to chromosomes 8, 11 and 13. All stable QTNs demonstrate a statistically significant allelic effect across 3 years of the experiment. Importantly, several QTNs spanned regions that harbored genes involved in the pathogen recognition and plant immunity response, including the KIP1-like protein (Lus10025717) and NBS-LRR protein (Lus10025852). Our results provide novel insights into the genetic architecture of flax resistance to Fusarium wilt and pinpoint potential candidate genes for further in-depth studies.     

基于多元线路特征因素融合的电压暂降严重程度评估

现有电压暂降严重程度评估方法未充分考虑多元线路特征因素对输电线路故障概率的影响,从而导致评估结果出现较大误差。由此,本文提出了基于多元线路特征因素融合的电压暂降严重程度评估方法。首先,基于线路历史故障数据,采用关联规则量化多元线路特征因素对线路故障的影响程度。然后,通过改进D-S证据理论融合多元线路特征因素建立准确的线路年故障概率模型;并采用基于最大熵模型的故障点法评估节点的电压暂降水平。最后,提出了一种同时考虑系统侧电压暂降严重程度和用户敏感设备耐受特性的综合电压暂降严重程度指标用于评估节点电压暂降严重程度。通过与某区域电网实际电能质量监测数据对比,本文所提方法的准确率可达到90%以上。同时通过与未充分考虑线路特征因素的评估案例对比,所提方法的准确率明显提高。因此,在进行电压暂降严重程度评估时,综合考虑线路特征因素可以有效的提高评估结果的准确率。     

Assessing the relative risk of severe injury in automotive crashes for older female occupants

A logistic regression model was used in the prediction of injury severity for individuals who are involved in a vehicular crash. The model identified females and older occupants (segmented by age 55-74, and 75 and older) as having a significantly higher risk of severe injuries in a crash. Further, interactions of older females with other factors, such as occupant seat position, crash type, and environmental factors were also shown to significantly impact the relative risk of a severe injury. This study revealed that females 75 years and older had the lowest odds of injury among all female occupants studied (OR=1.16) while females between 55 and 74 years old have higher risk of severe injuries (OR=1.74). All older females (55 and older) were at greater risk for head-on, side-impact and rear-end collisions. Seatbelt use reduced severe injuries for females in this age group, but not to the same extent as the rest of the population studied. Additionally, crashes in severe weather, which were less likely to result in severe injuries for the general population, increased the risk of severe injuries to females that were 55 and older. Among occupants of light trucks, sport utility vehicles and vans, older females were less likely than others to be severely injured. In this case, older females appear better off in vehicles which are larger and protect better in severe crashes. This research demonstrates that circumstances surrounding a crash greatly impact the severity of injuries sustained by older female occupants.     

Analysis of traffic injury severity: an application of non-parametric classification tree techniques

Statistical regression models, such as logit or ordered probit/logit models, have been widely employed to analyze injury severity of traffic accidents. However, most regression models have their own model assumptions and pre-defined underlying relationships between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimations of injury likelihood. The classification and regression tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study uses the 2001 accident data for Taipei, Taiwan. A CART model was developed to establish the relationship between injury severity and driver/vehicle characteristics, highway/environmental variables and accident variables. The results indicate that the most important variable associated with crash severity is the vehicle type. Pedestrians, motorcycle and bicycle riders are identified to have higher risks of being injured than other types of vehicle drivers in traffic accidents.     

Rollover crashes: predicting serious injury based on occupant, vehicle, and crash characteristics

The purpose of this research was to determine occupant, vehicle, and crash characteristics predicting serious injury during rollover crashes. We compared 27 case occupants with serious or greater severity injuries with 606 control occupants without injury or with only minor or moderate injury. Odds ratios (OR) for individual variables and logistic regression were used to identify predictive variables for serious injury associated with rollovers. Cases more often had thorax, spine, or head injury compared to controls that more often had extremity injuries. Intrusion (especially roof rail or B-pillar intrusion) at the occupant's position, the vehicle interior side and roof as sources of injury, and improper safety belt use were significantly associated with serious injury. Even when safety belt use or proper use was controlled for, occupants with greater magnitude of intrusion at their seat position were about 10 times more likely to receive serious injury. Although prevention of rollover crashes is the ultimate goal, it is important to develop safer vehicles and safety systems to better protect occupants who are involved in rollover crashes. This also requires improvement in data collection systems documenting these types of crashes.     

基于WSN的核电设备振动监测节点设计

为了保证核电设备的安全运行,对核电设备的实时状态监测是必要的。设计了基于WSN的核电设备振动监测的原型节点。研制了频域上计算振动烈度和频率的算法,并对其关键参数做了详细分析。该节点前端实时更新振动烈度和频率,通过无线模块传输数据。将WSN应用于核电设备监测,可有效提高设备运行的安全性,为状态维修提供条件。     

Analysis of driver injury severity in rural single-vehicle crashes

Rural roads carry less than fifty percent of the traffic in the United States. However, more than half of the traffic accident fatalities occurred on rural roads. This research focuses on analyzing injury severities involving single-vehicle crashes on rural roads, utilizing a latent class logit (LCL) model. Similar to multinomial logit (MNL) models, the LCL model has the advantage of not restricting the coefficients of each explanatory variable in different severity functions to be the same, making it possible to identify the impacts of the same explanatory variable on different injury outcomes. In addition, its unique model structure allows the LCL model to better address issues pertinent to the independence from irrelevant alternatives (IIA) property. A MNL model is also included as the benchmark simply because of its popularity in injury severity modeling. The model fitting results of the MNL and LCL models are presented and discussed. Key injury severity impact factors are identified for rural single-vehicle crashes. Also, a comparison of the model fitting, analysis marginal effects, and prediction performance of the MNL and LCL models are conducted, suggesting that the LCL model may be another viable modeling alternative for crash-severity analysis.     

Risk and protection factors in fatal accidents

This paper aims at addressing the interest and appropriateness of performing accident severity analyses that are limited to fatal accident data. Two methodological issues are specifically discussed, namely the accident-size factors (the number of vehicles in the accident and their level of occupancy) and the comparability of the baseline risk. It is argued that - although these two issues are generally at play in accident severity analyses - their effects on, e.g., the estimation of survival probability, are exacerbated if the analysis is limited to fatal accident data. As a solution, it is recommended to control for these effects by (1) including accident-size indicators in the model, (2) focusing on different sub-groups of road-users while specifying the type of opponent in the model, so as to ensure that comparable baseline risks are worked with. These recommendations are applied in order to investigate risk and protection factors of car occupants involved in fatal accidents using data from a recently set up European Fatal Accident Investigation database (Reed and Morris, 2009). The results confirm that the estimated survival probability is affected by accident-size factors and by type of opponent. The car occupants’ survival chances are negatively associated with their own age and that of their vehicle. The survival chances are also lower when seatbelt is not used. Front damage, as compared to other damaged car areas, appears to be associated with increased survival probability, but mostly in the case in which the accident opponent was another car. The interest of further investigating accident-size factors and opponent effects in fatal accidents is discussed.     

基于模糊核聚类的乙烯裂解深度DE-LSSVM多模型建模

乙烯裂解深度的建模与控制对于裂解炉的实时优化具有重要意义。针对石脑油原料组分复杂、油品特性波动大等状况,采用模糊核聚类对石脑油数据库进行最优划分,建立最小二乘支持向量机的多模型,对于最小二乘支持向量机中模型的参数选取,利用差分进化算法进行参数寻优,提高了模型的精度和泛化能力。通过对现场数据的建模实验,结果表明：基于模糊核聚类的乙烯裂解深度最小二乘支持向量机多模型跟踪性能良好,预测精度较高。