Evaluation of Vehicle/Driver Performance Using Genetic Algorithms

Simulation is often used to gain an understanding of vehicle directional response. Furthermore, it is widely agreed that, given an adequate set of parameters that model the vehicle and the surface it drives on, it is reasonable to simulate a particular vehicle with a view toward understanding and perhaps improving its performance. This is not the case with the vehicle/driver system. Rather, in terms of a particular vehicle and driver, simulations provide interesting but not particularly reliable results because of the routine variability of the human part of the system. Genetic algorithms and their derivatives are algorithms with their form drawn from the biological theory of evolution. This paper suggests that genetic algorithms may be useful to evaluate certain important facets of vehicle/driver performance. It supports this suggestion with an example that attempts to answer this question: What is the best a vehicle/driver system could do in the so-called Consumer Union short course?..

Driving Simulation

Recent advances in computing power, computer graphics, and virtual reality systems are leading to important new opportunities for the development and use of driving simulators. These advances in technology are pointing towards a future where human-in-the-loop simulation is increasingly valuable for training, humanfactors research, and virtual prototyping. This paper presents a general literature review of driving simulation, and discusses important components of modern driving simulators. The paper concludes with speculation on the future of driving simulation. INTRODUCTION All driving simulators include four components: . a simulation of the physics of the vehicle model and the road surface . a simulation of the surrounding environment, including other vehicles and their surroundings . a system that enables the operator to interpret the state of the model, e.g., video and audio displays, real or virtual instrument panels, a motion base . control devices, e.g., steering wheel, br..

Screening for Co-Morbidity in 65,397 Obese Pediatric Patients from Germany, Austria and Switzerland: Adherence to Guidelines Improved from the Year 2000 to 2010

Objective: The aim of the study was to analyze the adherence to current guidelines for co-morbidity screening in overweight and obese pediatric patients participating in the Adipositas-Patienten-Verlaufsdokumentation (APV) initiative in three German-speaking countries. Methods: APV database: 181 centers from Germany, Austria and Switzerland, specialized in obesity care, contributed standardized, anonymous data of medical examinations from 65,397 patients performed between 2000 and 2010. Completeness of screening for hypertension, dyslipidemia, and impaired glucose metabolism was analyzed using adjusted means. Results: Mean age of the cohort was 12.5 ± 2.9 years and 46.5% were male. 17.3% were overweight (>90th-97th percentile), 45.1% obese (>97th-99.5th percentile), and 37.7% extremely obese (>99.5th percentile). In 2000, blood pressure was documented for 55.1% of patients, increasing to 88.7% in 2010. The rate of lipid diagnostics also improved from 45.0 to 67.7%, and screening for diabetes rose from 32.7 to 62.3% in the same time period. Blood pressure measurements were performed more often during inpatient care (88.5%) compared to outpatient programs (77.5%). Screening was more complete with increasing age and increasing degree of obesity. In boys screening rate was higher than in girls. Conclusion: During the 11-year period, screening for co-morbidity improved significantly in overweight or obese children and adolescents. However, adherence to guidelines is still insufficient in some institutions. Quality control based on benchmarking may improve obesity care and outcome

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning

Abstract Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts

The plant cell wall decomposing machinery underlies the functional diversity of forest fungi

Brown rot decay removes cellulose and hemicellulose from wood?residual lignin contributing up to 30percent of forest soil carbon?and is derived from an ancestral white rot saprotrophy in which both lignin and cellulose are decomposed. Comparative and functional genomics of the ?dry rot? fungus Serpula lacrymans, derived from forest ancestors, demonstrated that the evolution of both ectomycorrhizal biotrophy and brown rot saprotrophy were accompanied by reductions and losses in specific protein families, suggesting adaptation to an intercellular interaction with plant tissue. Transcriptome and proteome analysis also identified differences in wood decomposition in S. lacrymans relative to the brown rot Postia placenta. Furthermore, fungal nutritional mode diversification suggests that the boreal forest biome originated via genetic coevolution of above- and below-ground biot