A Systematic Literature Review on Risk Analysis of Health and Safety in Oil Refinery and Onshore Pipeline Construction Projects

Oil industry construction is a very high risk from a safety and health perspective. Thousands of workers die while working in onshore oil refineries and pipeline projects worldwide, and despite many advancements in research and technology, fatal injuries are still happening. Construction products involving oil refineries and pipelines always need successful strategies in mitigating health and safety risks. After the recent Covid-19 pandemic, the industry became more conscious of increasing workers' safety on construction sites. The lack of a comprehensive literature review involving raking and prioritization of critical health and safety risk factors is the reason behind conducting a new secondary study. This study aimed to show the Systematic Literature Review (SLR) on risk analysis of health and safety issues construction workers face in onshore oil refineries and pipeline construction projects. The SLR methodology involved searching and reviewing the most relevant research papers from the perspective of safety risk factors and proven mitigation techniques. The SLR involves 30 research papers that are of high significance from 2011 to 2022. Fifteen health and safety risk factors are ranked according to arguments from previous studies, with falling from height at the top and scaffolding failure at the lowest position. The successful mitigation techniques are discussed in the existing literature, and the study provides positive theoretical and practical implications for the workers in oil refinery and pipeline construction projects. © 2022 IEEE.

Using Artificial Intelligence to Predict Customer Satisfaction with E-Payment Systems during the COVID-19 Pandemic

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID-19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID-19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network-based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.

A real-world data study of coronavirus-2019 disease severity in patients with multiple sclerosis treated with ocrelizumab

Background: Ocrelizumab (OCR) is a B-cell depleting monoclonal antibody approved for the treatment of multiple sclerosis (MS), including relapsing and primary progressive forms of MS. The Coronavirus-2019 disease (COVID-19) pandemic raises concerns about clinical course and outcomes of COVID-19 in MS patients undergoing immunosuppressive treatment. Objectives: To describe the clinical course and outcomes of COVID-19 in multiple sclerosis (MS) patients treated with ocrelizumab (OCR). Methods: A retrospective cohort study of OCR treated MS patients with COVID-19 diagnosis who received treatment for COVID-19 in the Optum® de-identified COVID-19 Electronic Health Record (EHR) dataset. Inclusion criteria: confirmed COVID-19 diagnosis (ICD10 diagnosis, or positive diagnostic lab test since Feb 20th 2020), and OCR treatment ≤6 months prior to COVID-19 diagnosis. Patients with less than 28 days of follow-up were excluded. COVID-19 severity was categorized according to a 4 level ordinal scale based on worst status experienced during COVID-19 clinical course: 1) not hospitalized, 2) hospitalized, 3) hospitalized requiring invasive mechanical ventilation, 4) death. Secondary outcomes included the proportion of hospitalized patients diagnosed with respiratory failure, bacterial pneumonia, and sepsis on admission. Results: As of 13 July 2020, there were EHRs for almost 128,000 patients with laboratory or clinically confirmed diagnosis of COVID-19. Forty-seven OCR treated patients were identified (32% male, median age 47 years, median Charlson comorbidity index 1.0, mean BMI 29.4 kg/m2, mean time since OCR initiation 1.3 years). Per COVID-19 severity scale, 75% (n=35) were not admitted to hospital, 21% (n=10) were hospitalized, 2% (n=1) required invasive ventilation and 2% (n=1) died. Compared to OCR cohort, Hospitalized patients (n=12) were older (median age 57.0 years) and consisted of proportionally more males (50%). On hospital admission, of patients not requiring ventilation, 40% (n=4) had respiratory failure, 10% (n=1) bacterial pneumonia and 0% sepsis, while both patients requiring ventilation, one of whom subsequently died, had respiratory failure and sepsis on admission. Conclusions: In this large US cohort of confirmed/clinically COVID- 19, a few treated with OCR were identified with majority experiencing mild disease not requiring hospitalization, and two patients suffering critical illness. This study provides initial real-world insights on the impact of COVID-19 in MS patients treated with OCR.

Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.