The knowledge of e-courses quality standards among faculty members at the University of Najran

Many individuals and institutions have sought to adopt specific criteria to judge the quality of e-courses that are still absent from many faculty members, although they need to use these e-courses in their curriculum. The current research aim to explore the knowledge of e-courses quality standards among faculty members at the University of Najran. The researcher used descriptive method. The research sample is 70 faculty members from various faculties of Najran University. The researcher relied on the use of a questionnaire as a data collection tool based on Quality Matters standards that consisted of 8 main standards and (43) sub-standards for e-courses. The research findings that the faculty members at the University of Najran has high level of knowledge of e-courses quality standards. Key Words: E-courses - Quality standards – Quality Matters - University of Najra

The effect of interactive e-book on students' achievement at Najran University in computer in education course

The current study aims to examine the effect of interactive e-book on students' achievement at Najran University in computer in education course. Quasi-experimental study designs is used in the study and to collect data the researchers built achievement test to measure the dependent variable represented in the achievement affected by experimental treatment. The study sample is (60) students from the fifth level at Najran University in Saudi Arabia who registered in computers in education course, which has been divided into two groups, each with a strength (30) student, experimental group students study the computer in education course via e-book, and the control group students study the computer in education course via printed-book. The finding of the study illustrate there is a significant differences between the study groups in academic achievement favor to experimental group students whose study with e-book. Keywords: e-book, e-textbook, higher education, technology diffusion, computer in education, Najran Universit

Higher-order tensor decompositions for muscle synergy analysis

This doctoral thesis outlines several methodological advances in the application of higher-order tensor decomposition for muscle synergy analysis estimated from surface Electromyogram (EMG). This entails both assessing current muscle synergy extraction methods and a novel direct approach to estimate useful muscle synergies using higher-order tensor decomposition. The underlying hypothesis is that higher-order tensor decompositions provide advantages in the estimation of temporal profiles and muscle synergies thanks to the consideration of other domains such as spectral, task or repetition information. Moreover, we implement these advances to inspect potential applications of tensor synergies in biomechanical analysis and myoelectric control. Firstly, we provide an overview of the current mathematical models for the concept of muscle synergies and compare the common matrix factorisation methods for muscle synergy extraction, in addition to second-order blind identification (SOBI), a technique which has not been used for muscle synergy estimation previously. Synthetic and real EMG datasets related to wrist movements from the publicly available Ninapro dataset were used in this evaluation. Results suggest that a sparse synergy model and a higher number of channels would result in better-estimated synergies. SOBI has better performance when a limited number of electrodes is available, but its performance is still poor in that case. Overall, non-negative matrix factorisation (NMF) is the most appropriate method for synergy extraction and, therefore, it is considered as a benchmark in the rest of the thesis. We then show the benefits of higher-order tensor decompositions of EMG data for muscle synergy analysis, discussing possible 3rd and 4th-order tensors models for EMG data. We explore muscle synergy estimation from 4th-order EMG tensors by taking the spectral profile into account and utilise this model for classification between the wrist’s movements in comparison with NMF. The results provide a proof-of-concept for higher-order tensor decomposition as classification accuracy is slightly improved using tensor decomposition over NMF. However, the addition of spectral mode -with time-frequency analysis- increases the computational cost for tensor synergy estimation. After the previous proof of concept, we focus on the 3rd -order tensor model for efficient and reliable extraction of meaningful muscle synergies. The most prominent tensor decomposition models (Tucker and PARAFAC) are compared under different constraints. We notice that unconstrained Tucker decomposition cannot extract unique and consistent muscle synergies as it converges into different local minima, while PARAFAC model cannot deal with a higher number of synergies or tasks as the decomposition deviates from the trilinear model. As a result, we introduce a constrained Tucker decomposition model as a framework for muscle synergy analysis. The advantages of this method over NMF are highlighted in the biomechanical application of identifying shared and task-specific muscle synergies. This benefits from the natural multi-way form of the EMG data, which makes higher-order tensor decompositions a better option than applying matrix factorisation repetitively. The constrained Tucker decomposition can successfully identify shared and task-specific synergies and is robust to disarrangement regarding task-repetition information, unlike NMF. The constrained Tucker model is then used as a framework to extract synergistic information that could be applied to proportional upper limb myoelectric control. The consistency of extracted muscle synergies with the increase of the wrist’s task dimensionality into 3 degrees of freedom (DoF) is investigated in comparison with NMF. In the literature, NMF approaches for synergy-based proportional myoelectric control were viable only with a task dimension of 2 DoF. In contrast, the results show that a constrained Tucker model identifies consistent muscle synergies from 3-DoFs dataset directly. Moreover, a tensor-based approach for proportional myoelectric control is introduced and compared against NMF and sparse NMF as state of the art benchmarks. To sum up, higher-order tensor decomposition had not been utilised in EMG analysis despite the substantial attention it received in biomedical signal processing applications in recent years. This thesis explores higher-order tensor decompositions for synergy extraction to account for the natural multi-way structure of EMG data. We hope that it will pave the way for the development of muscle activity analysis methods based on higher-order techniques in broader applications

Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

In the recent years, muscle synergies have been utilised to provide simultaneous and proportional myoelectric control systems. All of the proposed synergy-based systems relies on matrix factorisation methods to extract the muscle synergies which is limited in terms of task-dimensionality. Here, we seek to demonstrate and discuss the potential of higher-order tensor decompositions as a framework to estimate muscle synergies for proportional myoelectric control. We proposed synergy-based myoelectric control model by utilising muscle synergies extracted by a novel \ac{ctd} technique. Our approach is compared with \ac{NMF} \ac{SNMF}, the current state-of-the-art matrix factorisation models for synergy-based myoelectric control systems. Synergies extracted from three techniques where used to estimate control signals for wrist's \ac{dof} through regression. The reconstructed control signals where evaluated by real glove data that capture the wrist's kinematics. The proposed \ac{ctd} model results was slightly better than matrix factorisation methods. The three models where compared against random generated synergies and all of them were able to reject the null hypothesis. This study provides demonstrate the use of higher-order tensor decomposition in proportional myoelectric control and highlight the potential applications and advantages of using higher-order tensor decomposition in muscle synergy extraction

Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control

In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matrix factorisation, NMF), which requires identification of synergies to tasks or movements. In addition, NMF methods were viable only with a task dimension of 2 degrees of freedoms(DoFs). Here, the potential use of a higher-order tensor model for myoelectric control is explored. We assess the ability of a constrained Tucker tensor decomposition to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3rd-order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via constrained Tucker decomposition were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions

Muscle Activity Analysis using Higher-Order Tensor Decomposition: Application to Muscle Synergy Extraction

Higher-order tensor decompositions have hardly been used in muscle activity analysis despite multichannel electromyography (EMG) datasets naturally occurring as multi-way structures. Here, we seek to demonstrate and discuss the potential of tensor decompositions as a framework to estimate muscle synergies from $3^{rd}$-order EMG tensors built by stacking repetitions of multi-channel EMG for several tasks. We compare the two most widespread tensor decomposition models -- Parallel Factor Analysis (PARAFAC) and Tucker -- in muscle synergy analysis of the wrist's three main Degree of Freedoms (DoFs) using the public first Ninapro database. Furthermore, we proposed a constrained Tucker decomposition (consTD) method for efficient synergy extraction building on the power of tensor decompositions. This method is proposed as a direct novel approach for shared and task-specific synergy estimation from two biomechanically related tasks. Our approach is compared with the current standard approach of repetitively applying non-negative matrix factorisation (NMF) to a series of movements. The results show that the consTD method is suitable for synergy extraction compared to PARAFAC and Tucker. Moreover, exploiting the multi-way structure of muscle activity, the proposed methods successfully identified shared and task-specific synergies for all three DoFs tensors. These were found to be robust to disarrangement with regard to task-repetition information, unlike the commonly used NMF. In summary, we demonstrate how to use tensors to characterise muscle activity and develop a new consTD method for muscle synergy extraction that could be used for shared and task-specific synergies identification. We expect that this study will pave the way for the development of novel muscle activity analysis methods based on higher-order techniques.Comment: Accepted February 5, 201

The relation between of insulin like growth factor II and endometrial carcinoma

Background: Endometrial cancer is the most common gynecologic malignancy in the United States and the fourth most common cancer in women. The need of a soft marker that can be used with CA-125 tumor marker for early detection of endometrial cancer and to predict late stages and advanced histopathological grades and to specify the cases who will be managed by complete surgical staging including para aortic and pelvic lymphadenectomy is of great importance. The aim of the study was to evaluate the role of insulin like growth factor 2 in endometrial carcinoma and to correlate it with different histopathological grades of the disease.Methods: This study was applied on sixty patients with abnormal uterine bleeding and were divided into two groups, Group A included 30 cases of endometrial carcinoma, while Group B included 30 cases complaining of abnormal vaginal bleeding due to other causes as a control group. Serum samples were taken from all patients and estimation of IGF-2 serum levels using ElISA technique was done. Comparison of IGF-2 serum level between both groups and correlation of its levels with different histopathological grades of endometrial cancer group were done.Results: As regard comparison between both groups and ILGF2 serum level, study results demonstrated that ILGF2 levels ranged between 600.0-1440.0 ng/ml and 40.0-560.0 ng/ml with the mean of 781.33 ng/ml±196.45 and 336.0 ng/ml±212.86 for cases Group A and control Group B respectively. There was a statistically significant difference between the two studied groups regarding ILGF2 serum level (p<0.001). As regards correlation between histopathological grades and ILGF-2 serum level in cases Group A, the study revealed a strong positive correlation.Conclusions: ILGF-2 can be used as a serum marker for endometroid adenocarcinoma of the body of the uterus and to predict its higher histopathological grades