No abstract

The four‐pole parameter matrix of an elliptical chamber with concentric input and output is derived by solving the wave equation in the presence of mean flow. The four‐pole parameter matrix is composed of the product of input, chamber, and output section matrices. The matrix of the chamber is composed of the standing wave component that propagates in the axial direction. The matrices of the input and output sections include terms representing the cross‐sectional area ratio of the section and the chamber, and higher‐order mode components. Based on the results, we investigate the effect of mean flow and compare the experimental results and the estimated results obtained by the proposed method. © 1998 Scripta Technica, Electron Comm Jpn Pt 3, 81(11): 1–10, 1998

Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.

Predicting students' future performance is important for academic stakeholders as the students' success is the objective of the higher educational institutes. The prediction based on past performance and alignment with the curriculum is crucial to support decision-making action effectively for the university with a coherent vertical curriculum. The result of the prediction can be used to intervene and ensure that the student can graduate on time, also preventing the student from dropping out. In this paper, we proposed a methodology for predicting progressively the students' future assessment including feature engineering. Using the ensemble learning techniques, we adapt the existing Ensemble-based Progressive Prediction so it can be applied on students' data that used the Coherent Vertical Curriculum. In this paper, the behavioral data is used instead of domain knowledge-based data. The results show that the algorithm's accuracy has been improved on a real-world student dataset.

SUMMARYPerforated pipes are frequently used to attenuate exhaust noise in contemporary automotive mufflers. Resonances at a particular frequency, however, may exceed the overall acoustic reduction effect of the muffler when the shape and fixing position are inappropriate.