Psychological Problems resulted from COVID-19 and its relation to E-Learning and E-Assessment Stress in a sample of College of Education Students, Sultan Qaboos University

هدفت الدراسة إلى تحديد أهم المشكلات النفسية المترتبة على فيروس كورونا المستجد وتحديد نسبة شيوعها، ومعرفة دلالة الفرق بين الذكور والإناث في إدراكهم تلك المشكلات، والكشف عن طبيعة العلاقة الارتباطية بين المشكلات النفسية المترتبة على ذلك الوباء وبين كل من: ضغوط التعلم والتقييم الإلكتروني. ولتحقيق تلك الأهداف، تم بناء وتقنين مقياس المشكلات النفسية المترتبة على COVID-19، واستبانة ضغوط التعلم والتقييم الإلكتروني، وقد تم جمع البيانات بطريقة إلكترونية من خلال Google Form من 125 طالبًا وطالبة بكلية التربية، جامعة السلطان قابوس. وأسفرت النتائج عن أن المشكلات النفسية المترتبة على فيروس كورونا (مشكلات انفعالية، ومشكلات سلوكية وجسدية، والخوف من العدوى، والعزلة الاجتماعية، ونقص الدافعية) تنتشر بين أفراد العينة بدرجة متوسطة، وَوُجِدَت علاقة ارتباطية موجبة متوسطة دالة إحصائيًّا بين المشكلات النفسية وبين كل من: ضغوط التعلم والتقييم الإلكتروني، ولم يكن ثمة فرقٌ دالٌّ إحصائيًّا بين الجنسين في كل من: المشكلات النفسية، وضغوط التعلم الإلكتروني، وضغوط التقييم الإلكتروني.The present study aimed at identifying the psychological problems resulted from the COVID-19 outbreak.  Additionally, it explored the frequency, and gender differences in those problems. The paper also investigated the correlation relationship between COVID-19 associated psychological problems and e-learning and e-assessment stress.  To achieve the objectives of the study, a COVID-19 psychological problems scale (CPPS) and an E-learning and E-assessment stress questionnaire were developed and validated. Data were collected through Google form from 125 students enrolled in the College of Education, Sultan Qaboos University. Results indicated that COVID-19 associated psychological problems (Emotional problems, behavioral and health problems, fear of infection, social isolation, and lack of motivation) occupied a moderate rank. A significant positive correlation relationship was detected between COVID-19 associated psychological problems and e-learning stress r= 0.492, P > 0.01 and e-assessment stress r= 0.331, P > 0.01. No significant differences were found between both genders in COVID-19 associated psychological problems, e-learning stress, and e-assessment stress

The new distribution (Topp Leone Marshall Olkin-Weibull) properties with an application

The few standard probability distributions available are insufficient for modelling naturally occurring events. Most especially, normal distribution is not ideal for modelling asymmetrical data. Generalizing a new distribution is an important area in probability theory. It is known that we can improve the performance of the distributions by adding a new parameters. So, in this paper, we introduce Topp Leone Marshall Olkin-Weibull distribution as new method with four parameters based on recently proposed family (Topp Leone Marshall-Olkin of distributions). Accordingly, mathematical properties such as quantile function, moments generating functions, entropy, and order statistics have been investigated. The estimation of four parameters by maximum likelihood method was implemented. Application for real data based on the proposed method can be employed to show the best fitting as compared with the other models

An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets

© 2019, Springer Nature Switzerland AG. This paper illustrates the utilise of various kind of machine learning approaches based on support vector machines for classifying Sickle Cell Disease data set. It has demonstrated that support vector machines generate an essential enhancement when applied for the pre-processing of clinical time-series data set. In this aspect, the objective of this study is to present discoveries for a number of classes of approaches for therapeutically associated problems in the purpose of acquiring high accuracy and performance. The primary case in this study includes classifying the dosage necessary for each patient individually. We applied a number of support vector machines to examine sickle cell data set based on the performance evaluation metrics. The result collected from a number of models have indicated that, support vector Classifier demonstrated inferior outcomes in comparison to Radial Basis Support Vector Classifier. For our Sickle cell data sets, it was found that the Parzen Kernel Support Vector Classifier produced the highest levels of performance and accuracy during training procedure accuracy 0.89733, AUC 0.94267. Where the testing set process, accuracy 0.81778, the area under the curve with 0.86556

Design and Implementation of Iris Pattern Rec-ognition Using Wireless Network System

Abstract The goal of this paper is to propose a fast and accurate iris pattern recognition system by using wireless network system. This paper consists of three parts: the first part includes two methods of the iris pattern recognition system: Libor Masek and genetic algorithms, the second part includes the compression-decompression process of iris image using Principal Component Analysis (PCA) as a data reduction method, in order to reduce image size, and the third part talks about wireless network. In this work, an iris image is transferred across wireless network which contains two independent-parallel lines connected to the central Personal Computer (PC) in order to be recognized at the end of each line, then the results of recognition are sent back to the central PC. The proposed genetic algorithm, which is used in this paper is more accurate than Masek algorithm and has low computational time and complexity, which makes this method better than Masek method in recognizing iris patterns

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models

Assessing cow health condition by using the recent Cowdition Smartphone App and its correlation with vital clinical parameters

Highly productive milk cows suffer from increasing loss in body condition at early lactation, and are more prone to metabolic disorders. Recent Cowdition smartphone application has the ability to determine animal health situation and it is called body condition scoring (BCS) system. It can apply adequately for proper farming and management the animal performance. BCS is also helping to assure that all stages of annual cow cycle are in a good condition. Consequently, appropriate dietary changes can be done to prevent any deficiencies and metabolic diseases. Routinely, rectal body temperature and pulsation and respiratory rates are measured as suitable vital indicators for evaluation the health of the animals and recognize the clinical abnormalities. Therefore, this study intends to correlate between the animal body condition and vital physiological parameters measurements to assess cow health. A total of 30 cows at different stages of the reproduction period, raised at different farms location in Al Muthanna Governorate/ Iraq was nominated animal material of the present study. For each cow, Bayer smartphone Application/ BCS Cowdition was used to measure the body condition, and at the same time, body temperature and pulse and respiratory rates were also measured. Scores that collected from the Cowdition application system were compared with physiological vital indicators parameters. The overall means of BCS were found as 3.9 ± 0.068 and range from 2.5 to 5 for minimum and maximum values respectively. Moreover, 63.33 % (19 out of 30) cows showed the standard BCS ranged between 3.25-3.75 and revealed typical vital clinical parameters. Also, 30% (9 out of 30) cows showed fat BCS values ranged between 4- 4.25 accompanied with variation in the vital clinical parameters that increase with high BCS values. Only 6.66% (2 out of 30) cows showed extremist BCS values which were 2.5 and 5 for poor (emaciated) and grossly fat cow respectively. Moreover, these cows showed also variations in the vital clinical parameters. In conclusion, this study represented for the first time in Iraq the adoption of smartphone BCS Cowdition system to evaluate the animal health. Besides, to understand the relationship between BCS and physiological vital clinical parameters values (body temperature, pulse and respiratory rates), to evaluate and assess the cow body health that helps in the improving of animal nutrition and avoid the metabolic diseases that commonly occur in the highly productive cow. The authors recommend another future study that uses BCS Cowdition Smartphone Appication and correlates it with the animal’s metabolic diseases

Smart fuzzy logic control of photovoltaic system: Case study Kingdom of Saudi Arabia

Robust controls of photovoltaic (PV) system applications that include modular multilevel inverter (MMI) for interfacing stand-alone and grid-connected operating modes are investigated in this paper to overcome interfacing problems of two-level and three-level inverters. The MMI provides high-quality voltage, current, and power signals without additional filters, which reduces complexity and cost of interface circuits. A new control method of active and reactive power has been introduced for PV systems to get maximum power point (MPP) in various climatic circumstances. The steady-state and dynamic performances of MMI are investigated using MATLAB/Simulink. A fuzzy logic control is proposed to track the MPP utilizing the perturb and observe (P&O) technique. A fair comparison between fuzzy logic control and proportional integral (PI) control was conducted using MATLAB/Simulink. The fuzzy logic controller for obtaining MPPT by P&O method is proposed to get fast and accurate results. The obtained results obvioused that the fuzzy logic controller is quick accessing MPP than PI controller. A simple LC filter can achieve minimum harmonics provided by total harmonic distortion (THD) of MMI within IEEE limit. The PV system for standalone and grid-connected modes is being tested under climatic conditions in the city of Tabuk, Kingdom of Saudi Arabia (KSA). © 2023 Institute of Advanced Engineering and Science. All rights reserved

Sect and House in Syria: History, Architecture, and Bayt Amongst the Druze in Jaramana

This paper explores the connections between the architecture and materiality of houses and the social idiom of bayt (house, family). The ethnographic exploration is located in the Druze village of Jaramana, on the outskirts of the Syrian capital Damascus. It traces the histories, genealogies, and politics of two families, bayt Abud-Haddad and bayt Ouward, through their houses. By exploring the two families and the architecture of their houses, this paper provides a detailed ethnographic account of historical change in modern Syria, internal diversity, and stratification within the intimate social fabric of the Druze neighbourhood at a time of war, and contributes a relational approach to the anthropological understanding of houses

Structure-based design and synthesis of antiparasitic pyrrolopyrimidines targeting pteridine reductase 1

The treatment of Human African Trypanosomiasis remains a major unmet health need in sub-Saharan Africa. Approaches involving new molecular targets are important and pteridine reductase 1 (PTR1), an enzyme that reduces dihydrobiopterin in Trypanosoma spp. has been identified as a candidate target and it has been shown previously that substituted pyrrolo[2,3-d]pyrimidines are inhibitors of PTR1 from T. brucei (J. Med. Chem. 2010, 53, 221-229). In this study, 61 new pyrrolo[2,3-d]pyrimidines have been prepared, designed with input from new crystal structures of 23 of these compounds complexed with PTR1, and evaluated in screens for enzyme inhibitory activity against PTR1 and in vitro antitrypanosomal activity. 8 compounds were sufficiently active in both screens to take forward to in vivo evaluation. Thus although evidence for trypanocidal activity in a stage I disease model in mice was obtained, the compounds were too toxic to mice for further development