Objective Evaluation of Drivability in Passenger Cars with Dual-Clutch Transmission: A Case Study of Static Gearshift Condition

This paper is aimed at the problem that the subjective drivability evaluation by experienced test drivers is limited in time efficiency and is of high cost and poor repeatability. In this article, an intelligent drivability objective evaluation tool (I-DOET) for passenger cars with dual-clutch transmission (DCT) is developed and verified by real vehicle testing. First, the signal denoising method and its key parameters, which are suitable for drivability evaluation, are selected based on analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). Besides, combined with the uncertainty characteristics of subjective judgment, a mathematical model of the objective drivability evaluation FARODE (fuzzy AHP-RS based on objective drivability evaluation) is proposed by using the fuzzy comprehensive assessment (FCA) method. The AHP and rough set (RS) method are used to calculate the subjective and objective weights of the drivability evaluation, respectively, and the proportion of subjective and objective weights is determined by the principle of minimum relative information entropy. The fuzzy matrix is built by membership function of the evaluation indexes. Finally, the static gearshift condition focused on by the subjective evaluation experts is taken as a case study. The predictability score is obtained by combining the drivability quantization lever vector, comprehensive weight, and fuzzy matrix. The experimental results indicate that the proposed method is applicable for objective drivability evaluation in passenger cars with DCT.

Prevalence of acute kidney injury and use of renal replacement therapy in intensive care unit patients in Indonesia

Background : Currently, there is limited epidemiology data on acute kidney injury (AKI) from Southeast Asia, especially from Indonesia which is one of the biggest countries in Southeast Asia. Therefore, we assessed the prevalence of AKI and the utilization of renal replacement therapy (RRT) in Indonesia. Methods : Demographic and clinical data were collected from 952 ICU participants. The participants were categorized into AKI and non-AKI groups. The participants were further classified according to the 3 different stages of AKI as per the Kidney Disease Improving Global Outcome (KDIGO) criteria. We then assessed the Acute Physiology and Chronic Health Evaluation (APACHE) II score of AKI and non-AKI participants. RRT modalities were listed according to the number of times the procedures were carried out. Results : Overall incidence of AKI was 43%. The participants were divided into three groups based on the AKI stages: 18.5 % had stage 1, 33% had stage 2, and 48.5 % had stage 3. The use of mechanical ventilation was higher among the participants with AKI compared to the non-AKI participants. Also, AKI participants had higher average APACHE score compared to the non-AKI participants (16.5 vs 9.9). Among the AKI participants, 24.6% required RRT. The most common RRT modalities were intermittent hemodialysis (69.4%), followed by slow low-efficiency dialysis (22.1%), continuous renal replacement therapy (4.2%), and peritoneal dialysis (1.1%). Conclusions : This study showed that AKI is a common problem in the Indonesian ICU and had a high mortality rate. We strongly believe that identification of the risk factors associated with AKI will help us to develop a predictability score for AKI so we can prevent and improve AKI outcome in the future.

Exploration of the Evolution of Airport Ground Delay Programs

This study introduces a novel method of merging disparate but complementary datasets and applying machine learning techniques to ground delay program (GDP) data. More specifically, it aims to characterize GDPs with respect to changing weather forecasts, GDP plan parameters, and operational performance. The analysis aims to gain insights into GDP usage patterns (implementation and revisions), with respect to these key dimensions. It also aims to gain insights into how GDP cancelations and revisions correlate with operational efficiency and predictability. The results could be used to help traffic managers and air carriers understand complex patterns in the evolution of GDPs, so that they might, for example, better anticipate or even plan a response to a change in weather conditions. The focus is on GDPs at Newark Liberty International Airport (EWR), from 2010 through 2014. A master dataset was generated by merging several datasets on GDPs, weather forecasts, and individual flight information. Several scenarios of GDP evolution were then identified by reducing the dimensionality of the master GDP dataset, then applying cluster analysis on the lower dimensional data. It was found that GDPs at EWR can be categorized into 10 types based on weather forecasts, realized weather, GDP scope, arrival rates, and duration. The characteristics of these 10 GDP clusters were further explored by examining the relationships between GDP scenarios and their performance. It was found that GDPs under stable, low-severity weather and with large scope may score higher on the efficiency metric than expected. When GDPs called in the same weather conditions have high program rates, medium durations, and narrow scopes, capacity utilization was higher than expected—less affected flights lead to fewer cancelations and more arrivals (albeit delayed), and therefore, higher capacity utilization. Results also suggest that program rates are set more conservatively than needed for some poor weather conditions that end earlier than expected. GDPs with fewer revisions were associated with a higher predictability score but lower efficiency score. These findings can provide greater insights and knowledge about GDPs for future planning purposes. More specifically, the findings could, for example, be used to support discussion around, or even future guidance regarding, how to set and adjust GDP program rates. In future work additional data could be utilized to provide a more comprehensive operational picture of GDPs, and a wider range of performance metrics could be considered. It is also recommended that the patterns of how GDPs evolve over their lifetimes be further explored using other machine learning techniques that may provide new and useful insights.

Intrapatient Variability of Efavirenz Concentrations as a Predictor of Virologic Response to Antiretroviral Therapy

ABSTRACT Intrapatient variability of drug concentrations over time has not been evaluated as a predictor of drug response but may provide information on the onset and maintenance of response and a patient's adherence to therapy. Our objective was to develop a pharmacologically based measure of intrapatient variability of concentrations and investigate its association with a patient's response to antiretroviral therapy. Efavirenz concentrations were obtained for 50 children enrolled in Pediatric AIDS Clinical Trials Group study 382, a concentration-controlled trial of efavirenz plus nelfinavir and at least one nucleoside reverse transcriptase inhibitor. Efavirenz pharmacokinetic parameters were determined from 24-h concentration-time profiles at weeks 2 and 6 and used to predict trough concentrations obtained during 1 year of therapy. A concentration predictability score, defined as the fraction of measured trough concentrations that fell within a ±50% range of the predicted concentration, was used to place subjects into high and low concentration predictability groups. Relationships between this score and human immunodeficiency virus RNA levels in plasma were investigated. Eight of 33 children (24%) in the high-predictability group experienced viral rebound, compared with 9 of 17 children (53%) in the low-predictability group (P = 0.042). Children with low predictability scores exhibited a significantly shorter time to their first viral rebounds and were significantly more likely to experience viral rebound; the latter finding persisted after adjustment for baseline viral load and efavirenz exposure at week 6. This novel method for the quantitation of intrapatient concentration variability was independently predictive of virologic rebound. This measure may allow interventions to minimize therapeutic failure and is applicable to other drugs.