A Model of Inlet Circumferential Fluctuation in Compressor Cascades

Through-flow methods are widely used in turbomachinery design and performance prediction due to its fast calculation. In the past, circumferentially-averaging through-flow methods tended to ignore circumferential fluctuation source term (CFST) in the governing equations, leading to an inaccurate result when blades showed a strong three-dimensional feature. To solve this problem, CFST needs to be properly modeled. In this paper, three-dimensional numerical simulation is conducted to find the area where CFST dominates. The influence of inlet Mach number, incidence, camber angles and sweep angle on the CFST in this area is analyzed. A model for CFST is constructed. Three-dimensional verification of this model suggests that within the common scope of use of aerospace compressor designing, this model shows a good accuracy.

Coexisting illness and COVID-19

Background: Coronavirus disease 2019 (COVID-19) has rapidly spread throughout worldwide. Hypertension, diabetes, cardiovascular disease, and cerebrovascular disease were the most common coexisting illness among patients with severe SARS-CoV-2 infection. We aim to analyze the effect of them on the result of laboratory finding in patients with severe or critical SARS-CoV-2 infection.Methods: The date of a total of 49 patients who met the inclusion criteria from January 12 to March 20, 2020, from the first affiliated hospital of Harbin medical university were analyzed in our study.Results: Compared with patients without any coexisting illness, we found that PT levels were decreased in patients with cerebrovascular disease, hypertension or cardiovascular disease, and D-Dimer levels were decreased in patients with cerebrovascular disease, hypertension or diabetes. Similarly, LDH and ALT levels were lower in patients with cerebrovascular disease than that without any coexisting illness.Conclusions: Hypertension, diabetes, cardiovascular disease, and cerebrovascular disease are associated with an increased disease severity and risk of death in patients with COVID-19. Recently study also reported that the levels of PT, D-dimer, and LDH were increased and predicted the deterioration of disease in severe patients with SARS-CoV-2 infection. Interestingly, our results demonstrate that the levels of laboratory indicators such as PT, D-dimer, LDH and ALT were decreased in patients with coexisting illness than without any coexisting illness. It may give us the inaccurate result when we use those laboratory indicators to predict the deterioration of the patients and we need to pay more attention to it.

Investigation of white blood cell biomaker model for acute lymphoblastic leukemia detection based on convolutional neural network

Acute Lymphoblastic Leukemia (ALL) is a disease that is defined by uncontrollable growth of malignant and immature White Blood Cells (WBCs) which is called lymphoblast. Traditionally, lymphoblast analysis is done manually and highly dependent on the pathologist’s skill and experience which sometimes yields inaccurate result. For that reason, in this project an algorithm to automatically detect WBC and subsequently examine ALL disease using Convolutional Neural Network (CNN) is proposed. Several pretrained CNN models which are VGG, GoogleNet and Alexnet were analaysed to compare its performance for differentiating lymphoblast and non-lymphoblast cells from IDB database. The tuning is done by experimenting the convolution layer, pooling layer and fully connected layer. Technically, 70% of the images are used for training and another 30% for testing. From the experiments, it is found that the best pretrained models are VGG and GoogleNet compared to AlexNet by achieving 100% accuracy for training. As for testing, VGG obtained the highest performance which is 99.13% accuracy. Apart from that, VGG also proven to have better result based on the training graph which is more stable and contains less error compared to the other two models.

Computer aided system for lymphoblast classification to detect acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a disease that is detected by the presence of lymphoblast cell. Basically, lymphoblast cell is the abnormal cell of lymphocyte which is one of the White Blood Cell (WBC) types. Early prevention is suggested as this disease can be fatal and caused death. Traditionally, ALL is detected by using manual analysis which is challenging and time consuming. It can also yield inaccurate result as it is highly dependent on the pathologist’s skills. Industry has come out with hematology counter which is fast, accurate and automated. However, these machines are costly and cannot be afforded by some countries. For that reason, Computer Aided System (CAS) will be a great help to the pathologist for assisting purposes and it also can act as second opinion for the pathologist. This system contains six main steps which are color space correction, WBC segmentation, post processing, clumped area extraction, feature extraction and lymphoblast classification. Firstly, color space correction is apply by using l*a*b* color space to standardize the image’s intensity. Next, WBC segmentation is made to prune out WBC region using color space analysis with Otsu thresholding. However, segmented image contains noises that need to be eliminated and it is accomplished by applying morphological filter with Connected Component Labelling (CCL). There is an overlapping WBC which need to be separated by using Watershed method to extract the individual cells. Next, feature extraction is made to collect the cell’s data to be fed into the classifier. Classifier used in this system to classify lymphoblast is Support Vector Machine (SVM) and this system is able to achieve 96.69% of accuracy.

Normal and abnormal red blood cell recognition using image processing

<p>In medical field, the recognition of red blood cells (RBC) are used as an indicator to detect the type of diseases such as anaemia, malaria and leukaemia etc. The problems using manual detection of normal and abnormal RBCs under the microscope is tend to give inaccurate result and errors. This paper proposed a method to recognize the normal and abnormal shaped RBCs image by using Form Factor as feature descriptor. Detecting normal cells of RBCs indicate a healthy patient and abnormal cells indicate presence of disease. And is very important in medical field to detect abnormal condition in early stage because it saves and protects human lives. The patients waiting time for blood test is more because the time taking to generate the result of the patient is high due to high demand and less equipment this method is used in order to improve the accuracy of the existing one and 94% accuracy was achieved in the detection.</p>

Effective XQuery keyword using XML query processing

<span>The data has structured is determined using the standard is known as XML whereaslarge amount of data has consumed through internet consist of the both structural data format as well as semi structural data format which gets stored and processed whereas XML allow the data of semi-structured and hierarchical data representation not only consist of concept with individual items from various kind of database but also have relationship among data items. The utilized knowledge bed is provided with concise ideas for both structured and semi structured data files, XML document contents and rapid with exact solutions for the queries required at any time. The user can search their resources with the help of queries. Searching the resources with the help of queries is not a simple task, where inaccurate result and complexity would occur. Hence it is not a better way for searching the resources. This paper proposes the query answering system of Linear search usingwild card searchfor extracting the frequent pattern to maximize your search results in library database on XML document to extract the most relevant feeds from the large file directly. It will help the user to find his resources completely.</span>

Localization of Leader-Follower Robot Using Extended Kalman Filter

Non-holonomic leader-follower robot must be capable to find its own position in order to be able to navigating autonomously in the environment this problem is known as localization. A common way to estimate the robot pose by using odometer. However, odometry measurement may cause inaccurate result due to the wheel slippage or other small noise sources. In this research, the Extended Kalman Filter (EKF) is proposed to minimize the error or the inaccuracy caused by the odometry measurement. The EKF algorithm works by fusing odometry and landmark information to produce a better estimation. A better estimation acknowledged whenever the estimated position lies close to the actual path, which represents a system without noise. Another experiment is conducted to observe the influence of numbers of landmark to the estimated position. The results show that the EKF technique is effective to estimate the leader pose and orientation pose with small error and the follower has the ability traverse close to leader based-on the actual path.

Teaching Translation:

Google Translate as the recent platform which enables people translate from one language to others has been hype nowadays.On the other hand, inaccurate result in translating frequently happens. In accordance with that, Google Translate lets everyone help them in correcting the result of translation through their Google account. They are asked to fix inaccurate words then give corrections or translate from one language to others. This idea has been brought as teaching method in Translation course for EFL learners at Department of English of Universitas Negeri Malang. By giving contribution in translating the words, they will get badges and stats as rewards and become the lecturer’s assessment. Therefore, the researchers are going to analyze the effectiveness of Google Translate based in Translation course. Meanwhile, the significance of this study is to give insight for education practitioners in developing teaching method of Translation. The data is analyzed using qualitative descriptive by distributing questionnaire to twenty seven EFL learners who took the course and interviewing the lecturer who applied this method.

Consideration of Lightning Current along a Tall Structure in the Presence of Horizontal Arrangement Grounding

The evaluation of the behaviour of lightning current along a tall structure is becoming an important issue when considering the lightning protection equipment required along a tall structure. This behaviour is affected by the element of the ground reflection factor which has a correlation between the impedance of the ground and the tower. The impedance of the ground is considered for a horizontal grounding arrangement while the impedance of the tower is assumed to be constant. Hence, the ground reflection factor can be given a variable value, whereas most of the existing literature assumes a constant value. When a constant value is entered into a calculation field, an inaccurate result is computed.In this paper, the lightning current along a tall structure under varying values of ground reflection factor and including the effect of the presence of a horizontal grounding arrangement has been considered and the results discussed accordingly. Modelling of the lightning return stroke current along a tall structure was undertaken by entering the calculated variable value of the ground reflection factor. The results indicated that a variable value of the ground reflection factor causes different peak values of the current and wave shape of the lightning current for different positions along a tall structure.

A Differentiated Registration Method for Complex Surfaces

Different error requirements on different surfaces are necessary in many situations. If the registration between the measured dataset and the nominal surface is performed ignoring these differences, an inaccurate result could occur due to the surfaces which have low precision. An improper registration even results in false verdict. So the differentiated registration is of significance in precision engineering. In this paper, a new registration method taking into consideration of the different error requirements is proposed. Firstly surfaces are classified based on different error requirement. Then, appropriate weight factors are iteratively given to these surfaces. Based on the change relationship of error and weight factors, the algorithm controls the error distribution by optimizing the weighted least squares. Our method has the great flexibility in distributing the error, which is suitable for engineering applications. The experiment demonstrates the validity.