Optimal reshaping and stress controlling of double-layer spherical structures under vertical loadings

Architectural structures’ nodal coordinates are significant to shape appearance; vertical overloading causes displacement of the joints resulting in shape distortion. This research aims to reshape the distorted shape of a double-layer spherical numerical model under vertical loadings; meanwhile, the stress in members is kept within the elastic range. Furthermore, an algorithm is designed using the fmincon function to implement as few possible actuators as possible to alter the length of the most active bars. Fmincon function relies on four optimization algorithms: trust-region reflective, active set, Sequential quadratic progra mming (SQP), and interior-point. The fmincon function is subjected to the adjustment technique to search for the minimum number of actuators and optimum actuation. The algorithm excludes inactive actuators in several iterations. In this research, the 21st iteration gave optimum results, using 802 actuators and a total actuation of 1493 mm. MATLAB analyzes the structure before and after adjustment and finds the optimum actuator set. In addition, the optimal actuation found in MATLAB is applied to the modeled structure in MATLAB and SAP2000 to verify MATLAB results

Using Minimum Actuators to Control Shape and Stress of a Double Layer Spherical Model Under Gravity and Lateral Loadings

Spherical domes are picturesque structures built in developed countries to attract tourists. Due to horizontal and vertical overloading, the structures’ attractive shapes may be disturbed, and some members' stress may exceed the elastic level. In this paper, the shape and stress of a deformed double-layer spherical numerical model due to simultaneous lateral and vertical loadings are controlled, meanwhile, the number of actuators to alter the length of active members is minimized. The nodal displacements of the outer shape of the numerical model of the double-layer spherical structure are nullified. In addition, the stress of the members of the structure was monitored to stay within the elastic level. Moreover, the number of used actuators was minimized. These objectives are done by subjecting controlling formulations to a function that finds the minimum of constrained nonlinear multivariable which is called fmincon. The defined function in MATLAB uses one of the optimization algorithms (sequential quadratic programming, interior point, trust-region reflective, and active set). The algorithms search for active members that have a significant influence in controlling the targeted joints and members. Furthermore, the algorithms exclude the inactive actuators in several loops. The results obtained from MATLAB program are validated by SAP2000 software

The Impact of Social Media on the Interaction Between Students and Teachers at the University of Halabja

The aim of this article is to investigate the role of web 2.0 tools and social media applications in the relationship between undergraduate students and university lecturers. The researchers used the quantitative approach to design the methodology of the research. The sample of the study was 85 students from the departments of (Social Sciences, Arabic Language, and English Language) in the second year, third year, and fourth year at the college of basic education at the University of Halabja for the academic year 2020-2021. The questionnaire was used to collect the data from the participants. The result of the study demonstrated that there was a significant influence on the relationship between the students and the lecturers in using social media applications. Students showed that they use social media every day and it has a positive impact on their interaction with the lecturers

COVID-19 Vaccination Among Diverse Population Groups in the Northern Governorates of Iraq

Objectives: The present study was carried out to investigate COVID-19 vaccination coverage among populations of internally displaced persons (IDPs), refugees, and host communities in northern Iraq and the related underlying factors.Methods: Through a cross-sectional study conducted in five governorates in April–May 2022, 4,564 individuals were surveyed. Data were collected through an adapted questionnaire designed to gather data on participants.Results: 4,564 subjects were included (59.55% were 19–45 years old; 54.51% male). 50.48% of the participants (51.49% of host communities, 48.83% of IDPs, and 45.87% of refugees) had been vaccinated with at least one dose of COVID-19 vaccine. 40.84% of participants (42.28% of host communities, 35.75% of IDPs, and 36.14% of refugees) had been vaccinated by two doses, and 1.56% (1.65% of host communities, 0.93% of IDPs, and 1.46% of refugees) were vaccinated with three doses.Conclusion: Sociodemographic factors including age, gender, education, occupation, and nationality could affect vaccination coverage. Moreover, higher acceptance rate of vaccination is associated with belief in vaccine safety and effectiveness and trust in the ability of the vaccine to prevent complications

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91% accuracy