Effects of COVID-19 prevention procedures on other common infections: a systematic review

Introduction: Since the outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began, necessary measures to prevent virus transmission and reduce mortality have been implemented, including mandatory public use of masks, regular hand-sanitizing and hand-washing, social distancing, avoidance of crowds, remote work, and cancellation of public events. During and after the introduction of COVID-19 lockout, we performed a systematic review of available published literature to investigate the incidence of seasonal influenza and other respiratory viral infections. Methods: PubMed, Embase, Web of Science, Scopus, Science Direct, Google Scholar, Research Gate, and the World Health Organization databases and websites were systematically searched for original studies concerning the impact of COVID-19 prevention means and measures on other common respiratory infectious diseases during the pandemic published by March 2021. Results: The findings showed that the adherence to health protocols to prevent COVID-19 could help to reduce the incidence of other infectious diseases such as influenza, pneumonia, and Mycobacterium tuberculosis. Conclusion: The implemented prevention measures and protocols might have reduced the incidence of influenza and some other common respiratory infections. However, controversies exist on this matter and future large population-based studies might provide further information to address these controversies. © 2021, The Author(s)

Psychometric properties revised reinforcement sensitivity theory (r-RST) scale in chronic pain patients

Sohrab Amiri,1 Sepideh Behnezhad,2 Esfandiar Azad-Marzabadi3 1Faculty of Literature and Humanities, Urmia University, Urmia, Iran; 2Faculty of Psychology and Educational Sciences, Kharazmi University, Tehran, Iran; 3Baqiyatallah University of Medical Sciences, Behavioral Sciences Research Center, Tehran, Iran Objective: The aim of present study was to evaluate the psychometric properties of the Reinforcement Sensitivity Questionnaire (RSQ) in patients with chronic pain.Methods: For this purpose, 312 (first study) and 70 (second study) patients with chronic pain were selected, and the Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ) and Pain Beliefs and Perceptions Inventory (PBPI) were distributed among them for their response. The reliability of the questionnaire was evaluated by Cronbach’s alpha, retest, and split-half coefficient; then, the criterion validity with other questionnaires was evaluated to determine the psychometric properties of the RSQ. The factor structure was assessed via confirmatory factor analysis.Results: The results of the factor analysis indicated that the RSQ has five factors, and checking the validity by using Cronbach’s alpha, retest, and split-half coefficient reflected the stability of the scale; the criterion validity of the RSQ with other questionnaires showed desirable discriminant and convergent validity.Conclusion: Overall, the findings indicated that the RSQ has good psychometric properties in chronic pain samples, and the tool can be used in studies of chronic pain. It seems that the RSQ is a good predictor for pain in patients with chronic pain. Keywords: chronic pain, factor analysis, reinforcement sensitivit

Connected Components on a PRAM in Log Diameter Time

We present an O(log d + log logm/n n)-time randomized PRAM algorithm for computing the connected components of an n-vertex, m-edge undirected graph with maximum component diameter d. The algorithm runs on an ARBITRARY CRCW (concurrent-read, concurrent-write with arbitrary write resolution) PRAM using O(m) processors. The time bound holds with good probability. Our algorithm is based on the breakthrough results of Andoni et al. [FOCS'18] and Behnezhad et al. [FOCS'19]. Their algorithms run on the more powerful MPC model and rely on sorting and computing prefix sums in O(1) time, tasks that take Ω(log n / log log n) time on a CRCW PRAM with poly(n) processors. Our simpler algorithm uses limited-collision hashing and does not sort or do prefix sums. It matches the time and space bounds of the algorithm of Behnezhad et al., who improved the time bound of Andoni et al. It is widely believed that the larger private memory per processor and unbounded local computation of the MPC model admit algorithms faster than that on a PRAM. Our result suggests that such additional power might not be necessary, at least for fundamental graph problems like connected components and spanning forest