Analysis of the Operating Costs of Passenger Cars Declared by Vehicles' Manufacturers and Obtained by Users

Every year, there are more and more cars on the roads that cause pollution. To reduce it, conventional vehicles have started to be replaced by electric and hybrid vehicles. Therefore, the average fuel/energy consumption of electric, hybrid, spark ignition and self-ignition vehicles over a test distance of 100 km was investigated. The test results were then compared to the manufacturer's data and the average difference between the manufacturer's data and the test data is shown. The largest average difference in fuel consumption between the manufacturer's data and the test data was observed for hybrid vehicles (over 230 %) and the smallest for electric vehicles (less than 10 %) and spark ignition vehicles (almost 18 %). Considering costs, the largest difference between manufacturer's data and test data is observed, as in the previous case, in electric vehicles (0.25 €) and the largest in hybrid vehicles (almost 6 € per 100 kilometers driven).

S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification

Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time. Traditional OSC methods usually train discriminative or generative models with the owned in-class data, and then utilize the pre-trained models to classify test data directly. However, these methods always suffer from the embedding confusion problem, i.e., partial out-of-class instances are mixed with in-class ones of similar semantics, making it difficult to classify. To solve this problem, we unify semi-supervised learning to develop a novel OSC algorithm, S2OSC, which incorporates out-of-class instances filtering and model re-training in a transductive manner. In detail, given a pool of newly coming test data, S2OSC firstly filters the mostly distinct out-of-class instances using the pre-trained model, and annotates super-class for them. Then, S2OSC trains a holistic classification model by combing in-class and out-of-class labeled data with the remaining unlabeled test data in a semi-supervised paradigm. Furthermore, considering that data are usually in the streaming form in real applications, we extend S2OSC into an incremental update framework (I-S2OSC), and adopt a knowledge memory regularization to mitigate the catastrophic forgetting problem in incremental update. Despite the simplicity of proposed models, the experimental results show that S2OSC achieves state-of-the-art performance across a variety of OSC tasks, including 85.4% of F1 on CIFAR-10 with only 300 pseudo-labels. We also demonstrate how S2OSC can be expanded to incremental OSC setting effectively with streaming data.

Application of Decision Tree Intelligent Algorithm in Data Analysis of Physical Health Test

With the continuous development of China’s cultural industry, people’s health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained.

Model-Based Analyses for the Causal Relationship Between Post-stroke Impairments and Functional Brain Connectivity Regarding the Effects of Kinesthetic Illusion Therapy Combined With Conventional Exercise

Aims: Therapy with kinesthetic illusion of segmental body part induced by visual stimulation (KINVIS) may allow the treatment of severe upper limb motor deficits in post-stroke patients. Herein, we investigated: (1) whether the effects of KINVIS therapy with therapeutic exercise (TherEx) on motor functions were induced through improved spasticity, (2) the relationship between resting-state functional connectivity (rs-FC) and motor functions before therapy, and (3) the baseline characteristics of rs-FC in patients with the possibility of improving their motor functions.Methods: Using data from a previous clinical trial, three path analyses in structural equation modeling were performed: (1) a mediation model in which the indirect effects of the KINVIS therapy with TherEx on motor functions through spasticity were drawn, (2) a multiple regression model with pre-test data in which spurious correlations between rs-FC and motor functions were controlled, and (3) a multiple regression model with motor function score improvements between pre- and post-test in which the pre-test rs-FC associated with motor function improvements was explored.Results: The mediation model illustrated that although KINVIS therapy with TherEx did not directly improve motor function, it improved spasticity, which led to ameliorated motor functions. The multiple regression model with pre-test data suggested that rs-FC of bilateral parietal regions is associated with finger motor functions, and that rs-FC of unaffected parietal and premotor areas is involved in shoulder/elbow motor functions. Moreover, the multiple regression model with motor function score improvements suggested that the weaker the rs-FC of bilateral parietal regions or that of the supramarginal gyrus in an affected hemisphere and the cerebellar vermis, the greater the improvement in finger motor function.Conclusion: The effects of KINVIS therapy with TherEx on upper limb motor function may be mediated by spasticity. The rs-FC, especially that of bilateral parietal regions, might reflect potentials to improve post-stroke impairments in using KINVIS therapy with TherEx.

Fuzzy Cluster Analysis and Prediction of Psychiatric Health Data Based on BPNN

PMH (psychiatry/mental health) is affected by many factors, among which there are numerous connections, so the prediction of PMH is a nonlinear problem. In this paper, BPNN (Back Propagation Neural Network) is applied to fuzzy clustering analysis and prediction of PMH data, and the rules and characteristics of PMH and behavioral characteristics of people with mental disorders are analyzed, and various internal relations among psychological test data are mined, thus providing scientific basis for establishing and perfecting early prevention and intervention of mental disorders in colleges and universities. Artificial neural network is a mathematical model of information processing, which is composed of synapses similar to the structure of brain neurons. The fuzzy clustering analysis and data prediction ability of optimized PMH data are obviously improved. Applying BPNN to the fuzzy clustering analysis and prediction of PMH data, analyzing the rules and characteristics of PMH and the behavioral characteristics of patients with mental disorders, can explore various internal relations among psychological test data, and provide scientific basis for establishing early prevention and intervention of mental disorders.

Motorcycle Accident Reconstruction

Accident reconstruction utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash, to determine how and why the crash occurred, how the crash could have been avoided, or to determine whose description of the crash is most accurate. This process draws together aspects of mathematics, physics, engineering, materials science, human factors, and psychology, and combines analytical models with empirical test data. Different types of crashes produce different types of evidence and call for different analysis methods. Still, the basic philosophical approach of the reconstructionist is the same from crash type to crash type, as are the physical principles that are brought to bear on the analysis. This book covers a basic approach to accident reconstruction, including the underlying physical principles that are used, then details how this approach and the principles are applied when reconstructing motorcycle crashes. This second edition of Motorcycle Accident Reconstruction presents a thorough, systematic, and scientific overview of the available methods for reconstructing motorcycle crashes. This new edition contains: Additional theoretical models, examples, case studies, and test data. An updated bibliography incorporating the newest studies in the field. Expanded coverage of the braking capabilities of motorcyclists. Updated, refined, and expanded discussion of the decelerations of motorcycles sliding on the ground. A thoroughly rewritten and expanded discussion of motorcycle impacts with passenger vehicles. Updated coefficients of restitution for collisions between motorcycles and cars. A new and expanded discussion of using passenger car EDR data in motorcycle accident reconstruction. A new section covering recently published research on post-collision frozen speedometer readings on motorcycles. A new section on motorcycle interactions with potholes, roadway deterioration, and debris and expanded coverage of motorcycle falls. This second edition of Motorcycle Accident Reconstruction is a must-have title for accident reconstructionists, forensic engineers, and all interested in understanding why and how motorcycle crashes occur.