After-Stroke Arm Paresis Detection using Kinematic Data

This paper presents an approach for detecting unilateral arm paralysis/weakness using kinematic data. Our method employs temporal convolution networks and recurrent neural networks, guided by knowledge distillation, where we use inertial measurement units attached to the body to capture kinematic information such as acceleration, rotation, and flexion of body joints during an action. This information is then analyzed to recognize body actions and patterns. Our proposed network achieves a high paretic detection accuracy of 97.99\%, with an action classification accuracy of 77.69\%, through knowledge sharing. Furthermore, by incorporating causal reasoning, we can gain additional insights into the patient's condition, such as their Fugl-Meyer assessment score or impairment level based on the machine learning result. Overall, our approach demonstrates the potential of using kinematic data and machine learning for detecting arm paralysis/weakness. The results suggest that our method could be a useful tool for clinicians and healthcare professionals working with patients with this condition.Comment: submitted to IEEE Symposium Series on Computational Intelligenc

The Degree of Applying the Criteria of Excellence Management in the Light of the EFQM Excellence Model by the Leadership of King Khalid University from the Point of View of Faculty Members

The current research aims at identifying the degree of applying the standards of excellence management in the light of the EFQM Excellence Model by the leadership of King Khalid University from faculty members\u27 point of view and at identifying whether there are statistically significant differences in the mean scores of application according to the variables of scientific rank, specialization, and number of experience years. To collect data, the researcher used a questionnaire composed of (60) indicators distributed among (9) criteria for excellence: leadership, policies and strategies, human resources, relationships and material resources, administrative processes, results of beneficiary satisfaction, results of employee satisfaction, results of community service, and the main results of performance. After verifying the validity and reliability of the instrument, it was administered to (350) faculty members during the academic year 1438-1439.The results revealed that the mean score of application of the standards of excellence at King Khalid University was (3.03) which is a medium degree and which expresses an acceptable degree of application. While (6) criteria reached the minimum limit of acceptable degree of application, (3) criteria failed. The results also showed that there are statistically significant differences in the degree of application in favor of human specializations and according to the variable of scientific rank in favor of the rank of associate professor. However, the results showed that there are no significant differences according to the variable of number of experience years. Keywords: EFQM Excellence Model, leadership of King Khalid University, excellence Managemen

The Degree of Applying the Criteria of Excellence Management in the Light of the EFQM Excellence Model by the Leadership of King Khalid University from the Point of View of Faculty Members

The current research aims at identifying the degree of applying the standards of excellence management in the light of the EFQM Excellence Model by the leadership of King Khalid University from faculty members\u27 point of view and at identifying whether there are statistically significant differences in the mean scores of application according to the variables of scientific rank, specialization, and number of experience years. To collect data, the researcher used a questionnaire composed of (60) indicators distributed among (9) criteria for excellence: leadership, policies and strategies, human resources, relationships and material resources, administrative processes, results of beneficiary satisfaction, results of employee satisfaction, results of community service, and the main results of performance. After verifying the validity and reliability of the instrument, it was administered to (350) faculty members during the academic year 1438-1439.The results revealed that the mean score of application of the standards of excellence at King Khalid University was (3.03) which is a medium degree and which expresses an acceptable degree of application. While (6) criteria reached the minimum limit of acceptable degree of application, (3) criteria failed. The results also showed that there are statistically significant differences in the degree of application in favor of human specializations and according to the variable of scientific rank in favor of the rank of associate professor. However, the results showed that there are no significant differences according to the variable of number of experience years. Keywords: EFQM Excellence Model, leadership of King Khalid University, excellence Managemen

Cloud Computing Awareness among Practitioners in Yemeni Universities: An Exploratory Study

الحوسبة السحابية هي نموذج جديد لتكنولوجيا المعلومات اُعتمد في العديد من مؤسسات التعليم العالي للحصول على ميزة تنافسية. تهدف هذه الدراسة إلى استكشاف الوعي بتكنولوجيا الحوسبة السحابية في مؤسسات التعليم العالي في اليمن. تم اجراء البحث على عينة من الأكاديميين والإداريين، وطُلب من المشاركين إظهار مستوى وعيهم بهذه التكنولوجيا الناشئة، ومدى استخدامهم لهذه التكنولوجيا، وإبداء آرائهم حول المزايا والتحديات والعوائق التي تحول دون استخدام هذه التكنولوجيا. أظهرت النتائج مستوى عال من الوعي بالحوسبة السحابية بين المستجوبين. إلى جانب ذلك، فإن الجامعات على استعداد لتبني هذه التكنولوجيا عندما تتمكن من التغلب على معظم التحديات التي أهمها التكلفة، سرعة الإنترنت، الخصوصية، وقلة المعرفة بكيفية تطبيق هذه التكنولوجيا.Cloud computing is a new IT model adopted by many higher education institutions to gain competitive advantage. This study aims to explore the awareness of cloud computing technology among higher education institutions in Yemen. Using academic and administrative staff as the sample, the participants were asked to show their level of awareness of this emerging technology, the extent they utilize the technology, and to give their opinions about the advantages, challenges, and barriers of using this technology. The findings show high level awareness of cloud computing importance for higher education institutions. Besides, the universities are ready to adopt this technology when they can overcome the most challenges which are cost, Internet speed, privacy, and lack of knowledge on how to apply this technology

Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages

In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU. For Amharic, we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets. We followed previous research in mapping labels for all datasets to just two classes, positive and negative. Thus we can compare performance on different languages directly, and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for the three models were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each pair: AmharicGerman, AmharicEnglish, and AmharicUrdu. Results with Amharic as target suggested that using English or German as source will give the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percent greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training a SER classifier when resources for a language are scarce.Comment: 16 pages, 9 tables, 5 figure

Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing.

Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et al., 2019. We carry out experiments on various data splits, to match those used by other researchers. We also pay close attention to Arabic preprocessing and include novel steps not reported in other works. In an ablation study, we investigate the effect of two steps in particular, the processing of emoticons and the use of a custom stoplist. On the 4-class task, these can make a difference of up to 4.27% and 5.48%, respectively. On the 2-class task, the maximum improvements are 2.95% and 3.87%

A deep CNN architecture with novel pooling layer applied to two Sudanese Arabic sentiment data sets

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5)

Presentation and outcome of Middle East respiratory syndrome in Saudi intensive care unit patients.

BACKGROUND: Middle East respiratory syndrome coronavirus infection is associated with high mortality rates but limited clinical data have been reported. We describe the clinical features and outcomes of patients admitted to an intensive care unit (ICU) with Middle East respiratory syndrome coronavirus (MERS-CoV) infection. METHODS: Retrospective analysis of data from all adult (>18 years old) patients admitted to our 20-bed mixed ICU with Middle East respiratory syndrome coronavirus infection between October 1, 2012 and May 31, 2014. Diagnosis was confirmed in all patients using real-time reverse transcription polymerase chain reaction on respiratory samples. RESULTS: During the observation period, 31 patients were admitted with MERS-CoV infection (mean age 59 ± 20 years, 22 [71 %] males). Cough and tachypnea were reported in all patients; 22 (77.4 %) patients had bilateral pulmonary infiltrates. Invasive mechanical ventilation was applied in 27 (87.1 %) and vasopressor therapy in 25 (80.6 %) patients during the intensive care unit stay. Twenty-three (74.2 %) patients died in the ICU. Nonsurvivors were older, had greater APACHE II and SOFA scores on admission, and were more likely to have received invasive mechanical ventilation and vasopressor therapy. After adjustment for the severity of illness and the degree of organ dysfunction, the need for vasopressors was an independent risk factor for death in the ICU (odds ratio = 18.33, 95 % confidence interval: 1.11-302.1, P = 0.04). CONCLUSIONS: MERS-CoV infection requiring admission to the ICU is associated with high morbidity and mortality. The need for vasopressor therapy is the main risk factor for death in these patients

Risk Factors Associated with Periodontal Diseases among Yemeni Adult Patients

Objectives: This study aimed to investigate the possible risk factors associated with periodontal diseases among adult dental patients in Yemen. Methods: The study population comprised of 805 patients attending the teaching dental hospital in Dhamar city, Yemen. The first part of investigation was interview including age, gender, systemic diseases, pregnancy (for women), as well as tooth brushing, smoking, and Qat chewing habits and dentist interval visits. The second part was clinical examination including the periodontal parameters. Data were analyzed and presented in terms frequencies and percentages or means and standard deviations, as appropriate. Chi-squared test was used for associations and Mann-Whitney U test was used for differences. Regression analysis was utilized for the determinants of the periodontal disease. A P-value < 0.05 was considered significant. Results: The bivariate analyses revealed significant differences (P< 0.05) between the independent variables (proposed risk factors) groups in relation to the periodontal parameters except for systemic disease in relation to GI and BI, visiting dentist in relation to CI, BI, and GR, and pregnancy in relation to PI, GI, CI, and BI (P> 0.05). The regression analyses revealed that the age, smoking, and brushing teeth are significant (P< 0.05) determinants for all periodontal parameters. Whereas, systemic disease was a significant determinant for GR, and sex for BI. Conclusions: Age, brushing teeth and smoking are the significant determinants of periodontal health. Education about the side effects of bad habits as well as maintaining good oral hygiene should be implemented

Rapid alteplase administration improves functional outcomes in patients with stroke due to large vessel occlusions

Background and Purpose: We report the relation of onset-to-treatment time and door-to-needle time with functional outcomes and mortality among patients with ischemic stroke with imaging-proven large vessel occlusion treated with intravenous alteplase. Methods: Individual patient-level data from the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration were pooled from 7 trials that randomized patients to mechanical thrombectomy added to best medical therapy versus best medical therapy alone. Analysis was restricted to patients who received alteplase directly at the endovascular hospital. The primary outcome was disability defined on the modified Rankin Scale at 3 months. Results: Among 601 patients, mean age was 66.0 years (SD, 13.9), 50% were women, and median National Institutes of Health Stroke Scale score was 17. Onset-to-treatment time was median 125 minutes (interquartile range, 90–170). Door-to-treatment time was median 38 minutes (interquartile range, 26–55). Each 60-minute onset-to-treatment time delay was associated with greater disability at 90 days; the odds of functional independence (modified Rankin Scale, 0–2) at 90 days was 0.82 (95% CI, 0.66–1.03). With each 60-minute delay in door-to-needle time; the odds of functional independence was 0.55 (95% CI, 0.37–0.81) at 90 days. The absolute decline in the rate of excellent outcome (modified Rankin Scale, 0–1 at 90 days) was 20.3 per 1000 patients treated per 15-minute delay in door-to-needle time. The adjusted absolute risk difference for a door-to-needle time <30 minutes versus 30 to 60 minutes was 19.3% for independent outcome (number-needed-to-treat ≈5 to gain 1 additional good outcome). Symptomatic intracranial hemorrhage occurred in 3.4% of patients, without a significant time dependency: odds ratio, 0.74 (95% CI, 0.43–1.28). Conclusions: Faster intravenous thrombolysis delivery is associated with less disability at 3 months among patients with large vessel occlusion