scholarly journals Statistical testing of hypotheses about the form of the factor law of influence by the Kolmogorov criterion

2020 ◽  
Vol 1614 ◽  
pp. 012082
Author(s):  
A Tumanov ◽  
A Sabanaev ◽  
A Solovyov ◽  
V Tumanov
Keyword(s):  
Statistical Testing ◽  
Testing Of Hypotheses ◽  
Kolmogorov Criterion

Hubungan Religiusitas dengan Citra Tubuh pada Wanita Dewasa Awal

2018 ◽  
Vol 1 (1) ◽  
pp. 9-28
Author(s):  
Dessy Sumanty ◽  
Deden Sudirman ◽  
Diah Puspasari
Keyword(s):  
Body Image ◽  
Correlation Coefficient ◽  
Research Design ◽  
Calculation Result ◽  
The Body ◽  
Statistical Testing ◽  
P Value ◽  
Significance Level ◽  
Correlational Research

This research attempts to relate the body image phenomenon with the level of subject religiosity. This research used correlational research design that was involving 332 respondents. The statistical testing which is used to test the hypothesis Rank Spearman. The calculation result with the significance level of trust 95% (a = 0.05) show that the correlation coefficient is 0.083 and p-value is 0.129. It means that Ho is accepted and H1 is rejected. It can be concluded that there is no relationship between religiosity with body image.

A Review on Machine-Learning Based Code Smell Detection Techniquesin Object-Oriented Software System(s)

2020 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur ◽  
Sushma Jain ◽  
Shivani Goel ◽  
Gaurav Dhiman
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Software Quality ◽  
Empirical Studies ◽  
Statistical Testing ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Code Smells ◽  
Detection Techniques ◽  
Code Smell

Context: Code smells are symptoms, that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exists many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like, code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: Majority of empirical studies have proposed machine- learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as, Xerces, Gantt Project and ArgoUml. Furthermore, researchers paid more attention towards Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like, need of code smell detection techniques using hybrid approaches, need of validation employing industrial datasets, etc.

scholarly journals QOLP-03. NEURO-ONCOLOGY PALLIATIVE CARE SURVEY OF PHYSICIANS IN SUB-SAHARAN AFRICA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii175-ii175
Author(s):  
Ramya Tadipatri ◽  
Amir Azadi ◽  
Madison Cowdrey ◽  
Samuel Fongue ◽  
Paul Smith ◽  
...  
Keyword(s):  
Palliative Care ◽  
Care Delivery ◽  
Cultural Beliefs ◽  
Oral Feeding ◽  
Statistical Testing ◽  
Sub Saharan Africa ◽  
Neurological Deficits ◽  
Survey Study ◽  
Prior Training ◽  
Sub Saharan

Abstract BACKGROUND Early access to palliative care is a critical component of treating patients with advanced cancer, particularly for glioblastoma patients who have low rates of survival despite optimal therapies. Additionally, there are unique considerations for primary brain tumor patients given the need for management of headaches, seizures, and focal neurological deficits. METHODS We conducted a survey of 109 physicians in Sub-Saharan Africa in order to determine level of understanding and skill in providing palliative care, types of palliative care therapies provided, role of cultural beliefs, availability of resources, and challenges faced. Demographic data including age, gender, and prior training was collected and analyzed using ANOVA statistical testing. RESULTS Among the participants, 48% felt comfortable in providing palliative care consultations, 62% have not had prior training, 52% believed that palliative care is only appropriate when there is irreversible deterioration, 62% expressed having access to palliative care, 49% do not have access to liquid opioid agents, 50% stated that cultural beliefs held by the patient or family prevented them from receiving, palliative care, and 23% stated that their own beliefs affected palliative care delivery. Older providers (age > 30) had a clearer understanding of palliative care (p = 0.004), were more comfortable providing consultation (p = 0.052), and were more likely to address mental health (p < 0.001). CONCLUSIONS Palliative care delivery to glioblastoma patients in Sub-Saharan Africa is often delayed until late in the disease course. Barriers to adequate palliative care treatment identified in this survey study include lack of training, limited access to liquid opioid agents, and cultural beliefs. Challenges most often identified by participants were pain management and end-of-life communication skills, but also included patient spirituality and psychological support, anxiety and depression, terminal dyspnea, ethics, and intravenous hydration and non-oral feeding.

scholarly journals Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

2021 ◽  
Vol 13 (13) ◽  
pp. 2494
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Point Cloud ◽  
Goodness Of Fit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Statistical Testing ◽  
Computational Time ◽  
Terrestrial Laser Scanner ◽  
Reference Surface ◽  
Local Refinement ◽  
Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

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