Research on selection of abrasive grain size and cutting parameters when grinding of interrupted surface using aluminum oxide grinding wheel with ceramic binder

In this article, a study on intermittent surface grinding using aluminum oxide grinding wheel with ceramic binder is presented. The testing material is 20XH3A steel (GOST standard – Russian Federation). The testing sample has been sawn 6 grooves, with the width of each groove of 10 mm, the grooves are evenly distributed on the circumference of sample. The testing sample resembles a splined shaft. An experimental matrix of nine experiments has been built by Taguchi method, in which abrasive grain size, workpiece speed, feed rate and depth of cut were selected as input variables. At each experiment, surface roughness (Ra) and roundness error (RE) have been measured. Experimental results show that the aluminum oxide and ceramic binder grinding wheels are perfectly suitable for grinding intermittent surface of 20XH3A steel. Data Envelopment Analysis based Ranking (DEAR) method has been used to solve the multi-objective optimization problem. The results also showed that in order to simultaneously ensure minimum surface roughness and RE, abrasive grain size is 80 mesh, workpiece speed is 910 rpm, feed rate is 0.05 mm/rev and depth of cut is 0.01 mm. If evaluating the grinding process through two criteria including surface roughness and RE, depth of cut is the parameter having the greatest effect on the grinding process, followed by the influence of feed rate, workpiece speed, and abrasive grain is the parameter having the least effect on the grinding process. In addition, the effect of each input parameter on each output parameter has also been analyzed, and orientations for further works have also been recommended in this article

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model.

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.

Identification of Risk Factors Associated With Mortality Among Patients With COVID-19 Using Random Forest Model: A Historical Cohort Study

There is conflicting evidence about factors associated with Clinical course and risk factors for mortality of adult inpatients. We aimed to identify the demographic, clinical, treatment, and laboratory data factors associated with mortality in the Khoy district. We performed a retrospective cohort study including COVID-19 infected patients who were admitted to Qamar-Bani Hashim hospital from 2 November 2020 to 4 December 2020. We used random forest methods to explore the risk factors associated with death. The applied method was evaluated using sensitivity, specificity, accuracy, and the area under the curve. Age, pulmonary symptoms, patients need a ventilator, brain symptoms, nasal airway, job were the most important risk factors for mortality of COVID-19 in the random forest (RF) method. The RF method showed the highest accuracy, 82.9 and 79.3, for training and testing samples, respectively. However, this method resulted in the highest specificity (89.5% for training and 95.7% for testing sample) and the highest sensitivity (91.9% for training and 94.5% for testing sample). The potential risk factors consisting of older age, pulmonary symptoms, the use of a ventilator, brain symptoms, nasal airway, and the job could help clinicians to identify patients with poor prognosis at an early stage.

A New Bearing Fault Diagnosis Method Based on Refined Composite Multiscale Global Fuzzy Entropy and Self-Organizing Fuzzy Logic Classifier

In this paper, a new feature extraction method called refined composite multiscale global fuzzy entropy (RCMGFE) is proposed. Based on the proposed RCMGFE and self-organizing fuzzy logic classifier (SOF), a new method for bearing fault diagnosis is proposed. Firstly, the fault features of the original bearing signal are extracted by using the proposed refined composite multiscale global fuzzy entropy, and the fault feature set of RCMGFE is constructed on this basis. Secondly, the extracted RCMGFE fault feature set is divided into an offline training sample set, an online training sample set, and a testing sample set. The offline training sample set and the online training sample set are, respectively, input into the offline training stage and the online training stage of the SOF for selecting representative samples and constructing fuzzy rules. Then, the testing sample set is input to the testing stage of the SOF for classification. Finally, the data of drive end bearing and fan end bearing provided by Case Western Reserve University are used to verify the validity of the proposed fault diagnosis method. The experimental results show that, compared with other methods, the proposed fault diagnosis method has a higher classification effect.

Prediction Model as Sustainability Tool for Assessing Financial Status of Non-Profit Organizations in the Slovak Republic

Non-profit organizations (NPOs) play an important role in society. Nowadays, many companies apply the phenomenon—corporate social responsibility (CSR) which supports sustainable development and cooperation between the for-profit and non-profit sector. These companies are careful to cooperate with organizations and make decisions based on many factors, such as financial stability and independence of non-profit organizations. These attributes are assessed by predictive models. The models are a common tool in the for-profit sector compared to the non-profit sector. In our case, the main aim of the research is to propose a prediction model to estimate financial status of Slovak non-profit organizations using discriminant analysis. The overall sample consists of 351 NPOs dividing into training and testing sub-samples. We find that model classifies correctly almost 91% of NPOs in the training sample, respectively less than 80% in the testing sample. However, the results show that all vulnerable NPOs are correctly classified based on the testing sample.

Fuzzy enhancement and deep hash layer based neural network to detect Covid-19

This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.

Instrument Test Development Of Mathematics Skill On Elementary School

The aims of this research are producing the test instrument of mathematics skill on elementary school which is valid and reliable. The instrument test development in this research use the development model of Wilson, Oriondo and Antonio which is modified. The number of testing sample in this research is 160 of students in each class. This research result: 1) the validity index of aiken v is 0.979 in grade IV and 0.988 in grade V. The coefficient of instrument skill in class IV and V are 0.883 and 0.954. The case indicates that the overall item is accepted and reliable to be used for measuring the development of mathematics skill of elementary school students.

Saliva as a testing sample for SARS-CoV-2 detection by RT-PCR in low prevalence community settings

There is an urgency for the rapid and simple SARS-CoV-2 detection method. Our study aimed to demonstrate that saliva can be used as a specimen for SARS-CoV-2 detection notably for the screening of extensive population groups via pooling. We collected paired nasopharyngeal/oropharyngeal swabs (NPS) and saliva and performed 36 serial measurements of 8 SARS-CoV-2 positive saliva samples to confirm the stability of the specimen. We also completed 37 pools by adding one positive saliva specimen per pool. A field study including 3,660 participants was performed between September 29 and October 1, 2020. Saliva specimens were stable for testing for up to 24 hours. Overall, 1.2% of the saliva samples tested positive for SARS-CoV-2. The results of saliva samples were consistent with those obtained from NPS with 90% sensitivity (95% CI 68.3%-98.7%) and 100% specificity during the first two weeks after the onset of symptoms. All pools showed 100% positivity in different pooling proportions. Our findings demonstrate that saliva is an appropriate specimen for pooling and SARS-CoV-2 screening with accurate diagnostic performance. Patient-performed specimen collection allows testing an extensive number of people rapidly, obtaining results of the spread of SARS-CoV-2 and allowing authorities to take timely measures.

Toward better understanding developmental reflection differences for use in management development research and practice

PurposeWe introduce a new approach to developmental reflection in which the focus is on differences in how people reflect. When reflecting on challenging experiences, people achieve better development when they tend to look for causes of what happened within changeable personal characteristics, and they subsequently focus on the improvement of those personal characteristics.Design/methodology/approachSupervisors and subordinates with leadership responsibilities in diverse jobs in varied industries provided survey data (444 individuals in a psychometric testing sample, and 419 paired subordinate/supervisor dyads in a model-testing sample).FindingsThe reflection difference construct had the expected factor structure, reliability, and was distinguishable from eight conceptually related variables in the literature. Reflection differences were predicted by the theoretically relevant job, person, and situational variables and were associated with development and performance outcomes.Practical implicationsThe reflection construct might be used for prediction to identify the individuals who are likely to get the most from challenging experiences and improve. Further, by identifying predictors of reflection, ideas for enhancing reflection are provided. Also, by uncovering specific underlying dimensionality of reflection, this offers specific targets for interventions beyond generally encouraging people to reflect.Originality/valueThis study establishes support for: (1) the new theoretical framing of reflection differences, (2) a new preliminary model of antecedents and outcomes, and (3) an initial scale for future research and practice that can be more explicit about understanding and addressing underlying differences in how people reflect.