Network design for cylinder gas distribution

Purpose: Network design of the supply chain is an important and strategic aspect of logistics management. In this paper, we address the network design problem specific to packaged gases (cylinder) supply chain. We propose an integrated framework that allows for the determination of the optimal facility locations, the filling plant production capacities, the inventory at plants and hubs, and the number of packages to be routed in primary and secondary transportation. Design/methodology/approach: We formulate the problem as a mixed integer program and then develop a decomposition approach to solve it. We illustrate the proposed framework with numerical examples from real-life packaged gases supply chain. The results show that the decomposition approach is effective in solving a broad range of problem sizes. Findings: The main finding of this paper is that decomposing the network design problem into two sub-problems is very effective to tackle the real-life large scale network design problems occurring in cylinder gas distribution by optimizing strategic and tactical decisions and approximating the operational decisions. We also benchmark the results from the decomposition approach by solving the complete packaged gases network design model for smaller test cases. Originality/value: The main contribution of our work is that it integrates supply chain network design decisions without fixing the fillings plant locations with inventory and resource allocation decisions required at the plants. We also consider the transportation costs for the entire supply chain including the transhipment costs among different facilities by deciding the replenishment frequency.Peer Reviewe

Investigating the Psychological Impact of COVID-19 Among Healthcare Workers: A Meta-Analysis

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Previous meta-analyses were conducted during the initial phases of the COVID-19 pandemic, which utilized a smaller pool of data. The current meta-analysis aims to provide additional (and updated) evidence related to the psychological impact among healthcare workers. The search strategy was developed by a medical librarian and bibliographical databases, including Medline, Embase, CINAHL, PsycINFO, and Scopus were searched for studies examining the impact of the COVID-19 pandemic on the psychological health of healthcare workers. Articles were screened by three reviewers. Heterogeneity among studies was assessed by I2 statistic. The random-effects model was utilized to obtain the pooled prevalence. A subgroup analysis by region, gender, quality of study, assessment methods, healthcare profession, and exposure was performed. Publication bias was assessed by Funnel plot and Egger linear regression test. Sixty-five studies met the inclusion criteria and the total sample constituted 79,437 participants. The pooled prevalence of anxiety, depression, stress, post-traumatic stress syndrome, insomnia, psychological distress, and burnout was 34.4%, 31.8%, 40.3%, 11.4%, 27.8%, 46.1%, and 37.4% respectively. The subgroup analysis indicated higher anxiety and depression prevalence among females, nurses, and frontline responders than males, doctors, and second-line healthcare workers. This study highlights the need for designing a targeted intervention to improve resilience and foster post-traumatic growth among frontline responders

Assessing the Testability of the Multi-Theory Model (MTM) in Predicting Vaping Quitting Behavior among Young Adults in the United States: A Cross-Sectional Survey.

Purpose: Given the increased exposure to e-cigarettes and nicotine among young adults, difficulty in quitting vaping is likely, which supports the need for effective behavioral interventions. Therefore, this cross-sectional study aims to assess the testability of the contemporary multi-theory model of health behavior change in predicting the vaping quitting behavior among young adults in the United States. Methods: A nationally representative sample of 619 young adults engaged in vaping behavior and aged 18–24 years was recruited to complete a 49-item web-based survey. A structural equation model was used to test relationships between MTM constructs. Hierarchical multiple regression was utilized to predict the variance in the initiation and sustenance of vaping quitting behavior by predictor variables, such as demographic characteristics, history of behaviors, and MTM constructs. Results: Of 619 respondents, over 75% were White and nearly 70% had educational attainment equal to high school or some college. In total, 62% of respondents were using nicotine, followed by 33.3% were using cannabis. About 80% of the respondents reported being engaged in drinking alcohol, and nearly 45% were engaged in cigarette smoking. The predictive effect of all MTM constructs on vaping quitting initiation (adjusted R2 = 0.417, F (23, 595) = 20.215, p \u3c 0.001) and sustenance (adjusted R2 = 0.366, F (23, 595) = 16.533, p \u3c 0.001) was statistically significant. Conclusions: The findings of this study point to the usability and applicability of MTM in operationalizing and developing vaping quitting behavior interventions targeting young adults

Role of diffusion weighted MR imaging in differentiating benign from malignant prostate lesions

Background: The purpose of the study was to determine the diagnostic accuracy of diffusion weighted MR imaging and to propose a cut off ADC value in differentiating benign from malignant prostatic lesions considering histopathology as gold standard.Methods: It is a descriptive type of observational study done on 40 patients with clinical suspicion of prostate carcinoma and elevated PSA level more than 4ng/ml. The patients underwent Multiparametric prostate MRI and ADC values were calculated using ADC maps.Results: Of the 40 cases included in the study histopathology revealed a diagnosis of abscess (1), chronic prostatitis (2), BPH with chronic prostatitis (4), BPH (12), and malignancy (21). The mean and standard deviation (SD) of ADC values for the abscess (0.59), CP (0.83+0.16), BPH with CP (0.94+0.22), BPH (1.14+0.14) and malignancy (0.72+0.15) (x10-3mm2/s) were found in our study. The mean ADC value of malignant lesion was lower (0.727+0.149) as compare to benign lesion (1.034+0.216) and this difference was found to be statistically significant with p<0.001. By using ROC curve, ADC cut off value was calculated as 0.92 x 10-3mm2/s and sensitivity, specificity at this cut off value of ADC were 95.24% and 73.68% respectively. The PPV, NPV, diagnostic accuracy of at this cut off value of ADC were 80%, 93.33%, 85% respectively.Conclusions: Our study shows that DWI with ADC calculation helps in differentiation of Benign from Malignant prostatic lesions with high accuracy and this quantitative analysis should be incorporated in routine MRI evaluation of prostatic lesion

Assessing the Testability of the Multi-Theory Model (MTM) in Predicting Vaping Quitting Behavior among Young Adults in the United States: A Cross-Sectional Survey

Purpose: Given the increased exposure to e-cigarettes and nicotine among young adults, difficulty in quitting vaping is likely, which supports the need for effective behavioral interventions. Therefore, this cross-sectional study aims to assess the testability of the contemporary multi-theory model of health behavior change in predicting the vaping quitting behavior among young adults in the United States. Methods: A nationally representative sample of 619 young adults engaged in vaping behavior and aged 18–24 years was recruited to complete a 49-item web-based survey. A structural equation model was used to test relationships between MTM constructs. Hierarchical multiple regression was utilized to predict the variance in the initiation and sustenance of vaping quitting behavior by predictor variables, such as demographic characteristics, history of behaviors, and MTM constructs. Results: Of 619 respondents, over 75% were White and nearly 70% had educational attainment equal to high school or some college. In total, 62% of respondents were using nicotine, followed by 33.3% were using cannabis. About 80% of the respondents reported being engaged in drinking alcohol, and nearly 45% were engaged in cigarette smoking. The predictive effect of all MTM constructs on vaping quitting initiation (adjusted R2 = 0.417, F (23, 595) = 20.215, p \u3c 0.001) and sustenance (adjusted R2 = 0.366, F (23, 595) = 16.533, p \u3c 0.001) was statistically significant. Conclusions: The findings of this study point to the usability and applicability of MTM in operationalizing and developing vaping quitting behavior interventions targeting young adults