基于GIC无功损耗严重度指标的电网地磁暴易损区识别方法

首先分析了电网在不同感应地电场强度和方向条件下各变电站无功损耗的变化规律,定义了变电站地磁感应电流(GIC)无功损耗严重度指标,指标本身仅对实际的无功损耗做归一化处理,未改变各变电站之间无功损耗大小的相对关系。基于该指标提出了给定系统中易受地磁暴灾害区域的识别方法,可以为地磁暴影响下的电网规划和系统调度人员的操作提供依据。最后以750 kV规划电网为例,验证了所提指标和方法的可行性。     

Optimization design to study the effect of acid-catalyzed hydrolysis of corn stalk using severity and statistical model for high solid and liquid phase recovery

In this research, the effects of combined severity parameters (CSP), response surface methodology (RSM), and separation factor (SF) on the recovery and/or removal rate of main fraction for acid-catalyzed hydrolysis of corn stalk were defined and investigated. The results demonstrated that the maximum total sugar yield (44.31 wt%) was obtained from corn stalk after hydrolysis with 2.05 wt% of acid catalysis at 113.17 °C for 61.69 min (CSP₀ = 1.80) while the high glucan recovery (88.02%), de-pentosane rate (97.21%), and SF of solid phase fraction were obtained with CSP₀ range of 1.14–2.18 (CSP₁ range of 1.80–2.82). The findings suggest that CSP₁ can be used to more efficiently evaluate the separation degree of glucan with lignin (SF_G-L) than CSP₀. The acid-catalyzed hydrolysis of corn stalk has an advantage of high solid–liquid phase recovery and CSP can be used to describe the effect of acid hydrolysis.     

Initial Empirical Testing of Potential Factors Contributing to Patient Use of Secure Medical Teleconferencing

Providing timely and cost-effective healthcare appears to be significantly desired. Factors such as computer skills and comprehension of instructions impact people’s perception of the accessibility of such alternatives, especially for medical professionals. There are limited studies examining the aforementioned factors in information systems (IS) literature in the context of medical teleconferencing (ehealth) from the patients’ perspective. Thus, the purpose of this research study was to examine the underlying structure of four factors, namely IS usage (ISU), computer self-efficacy (CSE), ethical severity of misusing IS (ESMIS), and the resistance to use IS (RESUIS). Based on data gathered from 140 participants, confirmatory factor analysis (CFA) was conducted, using the partial least squares (PLS) technique. Results indicated a very strong validity and reliability of the constructs, however, with a relatively low overall model predictability. Furthermore, the constructs of CSE and RESUIS appear to demonstrate significant contribution toward ISU.     

Short communication: Model for metritis severity predicts that disease misclassification underestimates projected milk production losses

The objective of this research was to determine the effect of disease misclassification on the estimated effect of metritis on milk production. Misclassification introduces bias that usually results in an underestimation of the association between exposure (disease) and the outcome of interest (milk production). This distorted measure of association results from the comparison of an affected population (some of which may not truly be affected) to a nonaffected population (which often includes affected subjects that are unidentified). A convenience sample of DairyComp305 (Valley Agricultural Software, Tulare, CA) data representing 1 yr of calvings (n = 3,277) from 1 Midwestern Holstein herd was used. This herd was chosen because of its ongoing efforts to consistently and completely record all clinical diseases, including the incidence of both mild and severe metritis cases. Metritis was defined as the presence of a flaccid uterus containing fetid fluids or a foul watery discharge within 14 d of calving. Cows that appeared clinically normal other than the discharge were considered mild and those with systemic signs of disease were classified as severe. The original data set included metritis recorded as mild, severe, or not recorded (NR), where no metritis was observed, and was considered to contain the metritis true severity (TrS). First, to evaluate the effect of misclassification bias, we retrospectively randomized 45% of mild metritis to be classified as NR to simulate inconsistent disease recording (IR); then, in a separate model, all mild metritis cases were changed to NR to simulate a situation of very poor disease recording (PR), where only the most severe cases are recorded. The TrS, IR, and PR data sets were analyzed separately in JMP (SAS Institute Inc., Cary, NC). An ANOVA was conducted for second test 305-d mature-equivalent milk projection (2nd305ME), and nonsignificant variables were removed, but the variable metritis was forced into all models. Based upon the TrS model, adjusting for effects of lactation group, month of calving, dystocia, twins, retained placenta, early-lactation mastitis, displaced abomasum, and significant interactions, a case of mild metritis was associated with 384 kg less 2nd305ME and a case of severe metritis was associated with 847 kg less 2nd305ME compared with no metritis. For the IR model, a case of mild metritis was associated with 315 kg less 2nd305ME and a case of severe metritis was associated with 758 kg less 2nd305ME compared with no metritis. For the PR model, severe metritis was associated with 680 kg less 2nd305ME compared with NR. The IR and PR models underestimated 2nd305ME loss for severe metritis cases by 89 and 166 kg/cow, and resulted in 180,441 and 330,256 kg of total milk loss unaccounted for at the herd level, respectively, compared with TrS. Overall, misclassification of metritis cases results in greater bias and largely underestimates the true association between metritis and the consequence costs of the disease.     

Production of oligosaccharides from coconut meal by subcritical water treatment

Coconut meal, a by‐product from coconut milk production, was hydrolysed via subcritical water treatment at various maximum temperatures in the range of 100–300 °C using a batch‐type vessel under nonisothermal conditions. The effect of the treatment temperature and time was evaluated using the severity factor (R₀) as a parameter. The highest yield of mono‐ and oligosaccharides (28.3 g/100 g dry coconut meal) was obtained at lnR₀ = 10.4 (maximum temperature of 250 °C within the treatment time of 14 min). At higher temperature, degradation of the saccharides was observed and a large quantity of 5‐hydroxymethyl‐2‐furaldehyde was detected. The hydrolysates contained mannose, glucose and manno‐oligosaccharides with various degrees of polymerisation. The yield and ratio of saccharide components were affected by variation of the ratio of coconut meal to water used in the subcritical water treatment.     

Enhanced Priming for Trauma-Related Material in Posttraumatic Stress Disorder.

Intrusive reexperiencing in posttraumatic stress disorder (PTSD) has been linked to perceptual priming for trauma-related material. A prospective longitudinal study (N = 69) investigated perceptual priming for trauma-related, general threat, and neutral words in assault survivors with and without PTSD, using a new version of the word-stem completion task. Survivors with PTSD showed enhanced priming for trauma-related words. Furthermore, priming for trauma-related words measured soon after the trauma was associated with subsequent PTSD severity at 3, 6, and 9 months. The enhanced priming effect was specific to trauma-related words. Enhanced perceptual priming for traumatic material appears to be one of the cognitive processes operating in PTSD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Analysis of driver injury severity in wrong-way driving crashes on controlled-access highways

For more than five decades, wrong-way driving (WWD) has been notorious as a traffic safety issue for controlled-access highways. Numerous studies and efforts have tried to identify factors that contribute to WWD occurrences at these sites in order to delineate between WWD and non-WWD crashes. However, none of the studies investigate the effect of various confounding variables on the injury severity being sustained by the at-fault drivers in a WWD crash. This study tries to fill this gap in the existing literature by considering possible variables and taking into account the ordinal nature of injury severity using three different ordered-response models: ordered logit or proportional odds (PO), generalized ordered logit (GOL), and partial proportional odds (PPO) model. The findings of this study reveal that a set of variables, including driver’s age, condition (i.e., intoxication), seatbelt use, time of day, airbag deployment, type of setting, surface condition, lighting condition, and type of crash, has a significant effect on the severity of a WWD crash. Additionally, a comparison was made between the three proposed methods. The results corroborate that the PPO model outperforms the other two models in terms of modeling injury severity using our database. Based on the findings, several countermeasures at the engineering, education, and enforcement levels are recommended.     

Fuzzy inference model for assessing occupational risks in construction sites

Occupational risk assessment is a key measure to reach safety in construction industries. The assessment process is involved with many parameters which are difficult to assess, due to inadequate data or imprecise information. So, traditional quantitative approaches fail, frequently, to assess risk levels and to identify adequate preventive measures. A Takagi-Sugeno type fuzzy inference system is developed in this article to overcome these lacunas. In the model formulation process, the risk factors and controlling factors for accidental injuries are considered as input parameters. Safety levels of each type of injury prone body parts are evaluated by using analytical hierarchy process. Subtractive clustering technique is used to reduce the number of rules and thereby an initial fuzzy inference system is generated. Finally, the initial model is updated by tuning all the parameters corresponding to the input variables using a hybrid learning process. The developed methodology has been applied to few selected construction sites in India. The derived results validate the applicability of the developed model for assessing risks in construction sites and also identifies the pertinent progress of existing safety strategies.     

Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Multi-temporal change detection is commonly used in the detection of changes to ecosystems. Differencing single band indices derived from multispectral pre- and post-fire images is one of the most frequently used change detection algorithms. In this paper we examine a commonly used index used in mapping fire effects due to wildland fire. Subtracting a post-fire from a pre-fire image derived index produces a measure of absolute change which then can be used to estimate total carbon release, biomass loss, smoke production, etc. Measuring absolute change however, may be inappropriate when assessing ecological impacts. In a pixel with a sparse tree canopy for example, differencing a vegetation index will measure a small change due stand-replacing fire. Similarly, differencing will produce a large change value in a pixel experiencing stand-replacing fire that had a dense pre-fire tree canopy. If all stand-replacing fire is defined as severe fire, then thresholding an absolute change image derived through image differencing to produce a categorical classification of burn severity can result in misclassification of low vegetated pixels. Misclassification of low vegetated pixels also happens when classifying severity in different vegetation types within the same fire perimeter with one set of thresholds. Comparisons of classifications derived from thresholds of dNBR and relative dNBR data for individual fires may result in similar classification accuracies. However, classifications of relative dNBR data can produce higher accuracies on average for the high burn severity category than dNBR classifications derived from a universal set of thresholds applied across multiple fires. This is important when mapping historic fires where precise field based severity data may not be available to aid in classification. Implementation of a relative index will also allow a more direct comparison of severity between fires across space and time which is important for landscape level analysis. In this paper we present a relative version of dNBR based upon field data from 14 fires in the Sierra Nevada mountain range of California, USA. The methods presented may have application to other types of disturbance events.     

Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area

More than 56% of motorcycles in Korea are used for the purpose of delivering parcels and food. Since such delivery requires quick service, most motorcyclists commit traffic violations while delivering, such as crossing the centerline, speeding, running a red light, and driving in the opposite direction down one-way streets. In addition, the fatality rate for motorcycle crashes is about 12% of the fatality rate for road traffic crashes, which is considered to be high, although motorcycle crashes account for only 5% of road traffic crashes in South Korea. Therefore, the objective of this study is to analyze the injury severity of vehicle-to-motorcycle crashes that have occurred during delivery. To examine the risk of different injury levels sustained under all crash types of vehicle-to-motorcycle, this study applied an ordered probit model. Based on the results, this study proposes policy implications to reduce the injury severity of vehicle-to-motorcycle crashes during delivery.