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In service, the periodic clashes of wheel flat against the rail result in large wheel/rail impact force and high-frequency vibration, leading to severe damage on the wheelset, rail and track structure. This study aims to analyze characteristics and dynamic impact law of wheel and rail caused by wheel flat of high-speed trains.

A full-scale high-speed wheel/rail interface test rig was used for the test of the dynamic impact of wheel/rail caused by wheel flat of high-speed train. With wheel flats of different lengths, widths and depths manually set around the rolling circle of the wheel tread, and wheel/rail dynamic impact tests to the flats in the speed range of 0–400 km/h on the rig were conducted.

As the speed goes up, the flat induced the maximum of the wheel/rail dynamic impact force increases rapidly before it reaches its limit at the speed of around 35 km/h. It then goes down gradually as the speed continues to grow. The impact of flat wheel on rail leads to 100–500 Hz middle-frequency vibration, and around 2,000 Hz and 6,000 Hz high-frequency vibration. In case of any wheel flat found during operation, the train speed shall be controlled according to the status of the flat and avoid the running speed of 20 km/h–80 km/h as much as possible.

The research can provide a new method to obtain the dynamic impact of wheel/rail caused by wheel flat by a full-scale high-speed wheel/rail interface test rig. The relations among the flat size, the running speed and the dynamic impact are hopefully of reference to the building of speed limits for HSR wheel flat of different degrees.

The increase in ownership and use of mobile-based devices among college students creates unique opportunities for faculty to develop highly engaging learning environments. With many educational institutions offering campus-wide Wi-Fi, students have the ability to use their mobile devices, including cell phones, tablets, and laptops for engaging with curriculum, specifically with leadership concepts. One method of engaging students is through the use of mobile-based polling, as an audience response system (ARS). Although most studies on the use of ARS in educational settings include traditional response system methods (e.g., clickers), emerging technologies have fueled interest in mobile-based polling. The purpose of this study was to examine the effects that mobile-based polling has among students of leadership when used as an audience response system. A survey regarding perceptions of mobile-based polling was administered to students enrolled in undergraduate leadership courses with the purpose of understanding its importance on various aspects of student engagement. Results regarding their polling experiences indicated that students became highly engaged on three levels— behaviorally, emotionally, and cognitively. Additionally, survey responses suggested that students viewed mobile-based polling as viable for purposes outside of the classroom. Suggestions for using mobile-based polling for learning leadership concepts are also presented. The authors of this study not only present mobile-based polling as an emerging technology with advantages over traditional clickers, but as a pedagogical approach for increasing student engagement and as a tool for enhancing leadership skills.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.