The effect of the COVID-19 pandemic crisis on the Jordanian higher education system.

This study investigates the impact of COVID-19 pandemic-induced E-learning in Jordanian higher education. Through a quantitative survey, the study analyzes the independent variables of system use and user satisfaction, finding that information quality and service quality significantly affect these variables and that user satisfaction notably impacts E-learning. System usage moderates these effects. This research comprehensively analyzes the effects of the COVID-19 epidemic on Jordanian higher education, focusing on E-learning. It shows how information, system, and service quality affect system use and user satisfaction. The study also emphasizes these aspects' importance in E-learning platform effectiveness. The study offers actionable insights and recommendations to help Jordan establish more resilient and effective educational policies and practices that can adjust to higher education shocks. The study recommends establishing a specialized department to modify student intention to use E-learning systems, not only during the pandemic crisis but also after-ward, to improve familiarity with E-learning tools. This study provides insights into the pandemic's impact on Jordan's higher education system and suggests future approaches to enhance E-learning platforms. It contributes to the development of effective E-learning systems that can improve higher education standards by pinpointing the key effects of the pandemic on the independent variables and offering workable solutions. The study emphasizes the importance of information and service quality in improving user satisfaction and system usage in E-learning.

Selection of optimal strategy for managing decentralized solar PV systems considering uncertain weather conditions.

Solar power is a promising source of energy that is environmentally friendly, sustainable, and renewable. Solar photovoltaic (PV) panels are the most common and mature technology used to harness solar energy. Unfortunately, these panels are prone to dust accumulation, which can have a significant impact on their efficiency. To maintain their effectiveness, solar photovoltaics s must be cleaned regularly. Eight main techniques are used to clean solar panels: natural, manual, mechanical, robotic, drone, coating, electrical, and acoustic. This study aims to identify the best cleaning method using multiple criteria decision-making (MCDM) techniques. Using the Analytical Hierarchy Process (AHP), Quality Function Deployment (QFD), Fuzzy Technique for Order of Preference by Similarities to Ideal Solution (FTOPSIS), and Preference Selection Index (PSI), this research evaluates all eight cleaning methods based on several criteria that are categorized under cost, performance, resource requirement, and safety in Abu Dhabi. The data are collected from surveys completed by experts in solar and sustainable energy. The AHP, QFD, and PSI results identified natural, manual, and surface coating as the best and most effective cleaning methods. Natural cleaning involves using rainwater primarily to remove dirt and dust; manual cleaning requires cleaning agents and wiping clothes; and surface coatings involve applying a layer of hydrophobic material to the panels to repel dust. Identifying the most effective cleaning method for dust removal from solar panels can ensure optimal efficiency recovery at minimal costs and resources.

The Cost of Frontline Nursing: Investigating Perception of Compensation Inadequacy During the COVID-19 Pandemic.

Background: Nursing professionals experienced greater levels of stress and burnout during the COVID-19 pandemic. Studies examining stress and burnout have found a relationship between compensation and burnout. However, further studies are needed to examine the relationship between the mediating effects of supervisor and community support and coping strategies and the effects of burnout on compensation. Objective: The purpose of this study is to build on previous burnout research by examining the mediation effects of supervisor and community support and coping strategies on the relationship between sources of stress and burnout on feelings of compensation inadequacy, or the desire for more compensation. Methods: Using Qualtrics survey responses from 232 nurses, this study used correlation testing and mediation analyses of indirect, direct, and total effects to explore the relationships between critical factors influencing stress, burnout, nurses' use of coping skills, and the perception of supervisor and community support on perceived compensation inadequacy. Results: This study found that the support domain has a significant and positive direct effect on compensation, with supervisor support increasing the desire for additional compensation. Support was also found to have a significant and positive indirect effect and a significant and positive total effect on the desire for additional compensation. This study's results also found that coping strategies had a significant, direct positive effect on the desire for additional compensation. While problem solving and avoidance increased the desire for additional compensation, transference had no significant relationship. Conclusion: This study found evidence of the mediation effect of coping strategies on the relationship between burnout and compensation.

Analysis and Modeling of Aged SAC-Bi Solder Joints Subjected to Varying Stress Cycling Conditions.

Solder joints are subjected to varied stress cycle circumstances in the electronic packaging service life but are also influenced by aging. There has been limited investigation into the influence of aging and varying cycles on SnAgCu-Bi (SAC-Bi) solder joint fatigue. Cyclic fatigue tests were performed on solder joints of several alloys, including SnAgCu (SAC305), SnAgCu-Bi (SAC-Q), and SnCu-Bi (SAC-R). Individual solder joints were cycled under varying stress levels, alternating between mild and harsh stress levels. At least seven samples were prepared for each alloy by alternating between 25 mild stress (MS) cycles and three harsh stress (HS) cycles until the solder joint broke off. The impact of aging on Bi-doped solder joints fatigue under varied amplitude stress was examined and predicted for 10 and 1000 h under 125 °C. Because of the "Step-up" phenomenon of inelastic work, a new fatigue model was developed based on the common damage accumulation (CDA) model. The experimental results revealed that aging reduced the fatigue life of the tested solder alloys, particularly that of SAC305. According to the CDA model, all solder alloys failed earlier than expected after aging. The proposed model uses the amplification factor to assess inelastic work amplification after switching between the MS and HS cycles under varying stress amplitude conditions. The amplification factor for the SAC-Bi solder alloys increased linearly with fracture initiation and substantially followed crack propagation until the final failure. Compared with existing damage accumulation models, the proposed fatigue model provides a more accurate estimation of damage accumulation. For each case, the cut-off positions were examined. The SAC-Q amplification factor increased linearly to 83% of its overall life, which was much higher than that of SAC305 and SAC-R. This study identified three distinct failure modes: ductile, brittle, and near intermetallic compound (IMC) failure. It was also observed that SAC-Q with an organic solderability preservatives (OSP) surface finish was more susceptible to brittle failure owing to the excessive brittleness of the alloy material.

Symmetry of gamma distribution data about the mean after processing with EWMA function.

Statistical Process Control (SPC) plays a vital role in maintaining quality and reducing variability in manufacturing processes. Among SPC techniques, the Exponentially Weighted Moving Average (EWMA) stands out for its ability to detect small process shifts quickly, making it a valuable tool in ensuring product consistency and preventing quality issues. EWMA constructs control charts to monitor process mean shifts, tracks product/service quality by identifying variations, and monitors manufacturing process parameters for early detection of deviations and necessary adjustments. EWMA control chart has been proposed as an alternative to the Shewhart control chart. Sequential measurements are processed using the EWMA function before being placed on the control chart. One of the crucial concerns about the EWMA control chart is the asymmetry of the data around the mean. Although processing with the EWMA function reduces data skewness, the problem of asymmetric data may not be solved. The control chart is designed to leave in front of the upper control limit (UCL) α/2 of the data and behind the lower control limit (LCL) another α/2 of the data, and this does not occur in the case of symmetric data. α/2 represents the significance level for each tail in a two-tailed hypothesis test, indicating the probability of incorrectly rejecting the null hypothesis for each side of the distribution. Since many of the distributions in real life can be approximated by the Gamma distribution, the Gamma distribution was adopted in this study. The Monte Carlo simulation methodology was implemented to generate Gamma distributed data, process it with EWMA function and assess the skewness and kurtosis. The purpose of this paper is to evaluate the effect of EWMA parameters on the performance of the EWMA control chart. Moreover, it focuses on skewness and kurtosis reduction after data processing using the EWMA function. The findings help researchers and practitioners to select the best parameters. Further, the research investigates the effect of EWMA parameter on the shape of distribution.

Strategical selection of maintenance type under different conditions.

Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration.

Effect of Thermal Aging on the Mechanical Properties of SAC305.

Many electronic products are subjected to heat for long periods, depending on their operations. Thus, it is expected that the physical and mechanical properties of electronic elements, including the soldering joints, will be affected. In this study, the impact of thermal aging time and temperature on the microstructure and mechanical properties of 96.5Sn-3.0Ag-0.5Cu (SAC305) was investigated. The samples used were SAC305 solder balls attached to copper pads. The research began by examining the microstructure of the aged samples at 150 °C for 100 and 1000 h. Then, this was compared to the microstructure of the same samples without thermal aging. Then, five groups of 10 samples were prepared from a shear stress-shear stain experiment. The first group was as produced, the second group was aged for 2 h, the third group was aged for 10 h, the fourth group was aged for 100 h, and the fifth group was aged for 1000 h. All groups were aged at a temperature of 150 °C. An Instron testing machine was used to plot a shear stress-shear stain curve until the ball was completely sheared off the pad. The mechanical properties, including the ultimate shear strength, the ultimate energy used to shear the ball, and the total energy used to shear the ball at all thermal aging times were then estimated. The results of this study indicated the formation of a layer of Cu6Sn5 over the copper pad, which thickened with thermal aging time. Furthermore, the ultimate and total shear strengths decreased with thermal aging time. The same procedure was repeated to assess the ultimate shear strength at 100 °C. The decrease in ultimate shear strength was more severe with increasing thermal aging temperature.

Markovian analysis of unreliable multi-machine flexible manufacturing cell.

In this paper, a Markovian model is constructed to test a flexible manufacturing cell's (FMC) performance. The considered FMC includes a conveyer belt, robot, and n machines. The conveyer belt delivers the working part to the robot, and the robot picks it up and loads it onto the machines. The movement of a working part from one step to the next depends on the availability of the tool in the next step (i.e., conveyer belt, robot, and machine). Any machine is assumed to potentially fail during the processing time as a result of high loading stresses. First, a Markovian model is constructed for single-machine and double-machine FMCs. Then, a generalized FMC with an n-machine is constructed. The introduced model is illustrated with two numerical examples for both the single- and triple-machine. The Markov chain model can be used to estimate the FMC performance measures (i.e., overall utilization of machines and production rate). It is used to analyze the response of these measures under varying parameters (i.e., conveyor belt delivery rate, robot loading rate, processing rate of a machine, failure rate of a machine, and down machines' repairing rate). Moreover, an economic model based on the Markov chain model is introduced to analyze the FMC's net profit under these varying parameters.

Ultra-fine transformation of data for normality.

Normally distributed data is crucial for the application of large-scale statistical analysis. To statisticians, the most important assumptions of statistical users are the adequacy of the data and the normal distribution of the data. However, users are constantly forced to deal with unusual data. This includes changing the method used to be less sensitive to non-normal data or transforming that data to normal data. In addition, common mathematical transformation methods (for example, Box-Cox) do not work on complex distributions, and each method works on limited data shapes. In this paper, a novel approach is presented to transform any data into normally distributed data. We refer to our approach as the Ultra-fine transformation method. The article's novelty is that the proposed approach is powerful enough to accurately transform any data with any distribution to the standard normal distribution. Besides this approach's usefulness, it is simple in both theory and in application, and users can easily retrieve the original data from its transformed state. Therefore, we recommend using this method for the data used in the statistical method, even if the data are normal.

Investigating Frontline Nurse Stress: Perceptions of Job Demands, Organizational Support, and Social Support During the Current COVID-19 Pandemic.

Background: While frontline nurses employ coping alternatives to help deal with occupational stress resulting from unprecedented challenges during the COVID-19 pandemic, their access to necessary resources is unclear. Objective: This study aims to explore nurses' mental health in Alabama hospitals during the COVID-19 outbreak and investigate the impact of organizational and community support on nurse stressor levels, physio-psychosocial responses, and coping strategies employed. Methods: A cross-sectional survey was developed to bridge our understanding of stress, support, and coping mechanisms and distributed to nurses working with COVID-19-infected patients in hospital settings in Alabama. A total of 232 frontline nurses responded to 79 items in four domains (stressors, physio-psychosocial symptoms, coping, and support) between May 6, 2020, and June 30, 2020. A two-way ANOVA, regression analysis, and mediation of effects were used to analyze the data. Results: This study found that both social support and use of coping strategies contributed to the reduction of physio-psychosocial symptoms. Differences were found in how older frontline nurses perceived the efficacy of social support and certain coping strategies. This study provides further evidence of the importance of organizational support in addressing the harmful physio-psychosocial symptoms experienced by nurses.

Identifying Patterns of Turnover Intention Among Alabama Frontline Nurses in Hospital Settings During the COVID-19 Pandemic.

INTRODUCTION: Current research about frontline nurse stress and turnover intention lacks context related to rural communities' plight in providing organizational resources during the current COVID-19 pandemic. These implications have been particularly underexamined in the United States, whose regional differences may influence how frontline nurses perceive the access and utility of organizational resources. This study investigates if anxiety and stress while working during the current COVID-19 pandemic contribute to frontline nurses' desire to leave their current position in Alabama hospital settings. MATERIAL AND METHODS: A cross-sectional survey was developed and distributed as a Qualtrics survey to frontline nurses using social media and professional contacts. A total of 111 frontline nurse respondents within May 19-June 12, 2020 were included in this study. RESULTS: A significant correlation was found between gender (p= 0.002), marital status (p= 0.000) and seniority (p= 0.049) on turnover intention. A nurse's perceived anxiety and stress related to their patients' acuity (r= 0.257, p= 0.004), their personal health as a risk factor (r= 0.507, p= 0.000), their patient assignments (r= 0.239, p= 0.01), their personal protective equipment (r= 0.412, p= 0.000), and their psychological support (r= 0.316, p= 0.001) correspond to higher turnover intention among nurses working with patients infected with COVID-19. CONCLUSION: Perceived resource loss in task autonomy, PPE, and psychosocial support increased turnover intention among frontline nurses in Alabama. Research is needed to understand how intrinsic motivations and social support influence individual nurse staff's perceptions of resource loss and job demands. Further, more research is necessary to examine the implications of rurality and place in discussing turnover intention and organizational resources across multiple health systems.