Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia.

The coronavirus disease 2019 (COVID-19) pandemic led to rapid and unexpected changes across the world, particularly in road safety. Thus, this work assesses the impact of COVID-19 accompanied by government preventive policies on road safety in Saudi Arabia by investigating the crash frequency and crash rates. A 4-year crash dataset relating to 2018-2021 was collected, covering about 71,000 km in total road length. It covers all intercity roads and some of the major intercity roads in Saudi Arabia with over 40,000 data logs of involved crashes. We considered three different time phases to observe road safety. These time phases were identified by the duration of government curfew measures against COVID-19 (before, during, and after). The crash frequency analysis showed that the curfew during COVID-19 significantly impacted the crash reduction. At a national level, the crash frequency decreased during 2020 and reached a 33.2% reduction compared to 2019 (the previous year), and it surprisingly continued decreasing in 2021 (the consequent year) to another 37.7% reduction although the government measures were lifted. Moreover, considering the traffic volume and road geometry, we analyzed crash rates for 36 selected segments, and the results showed a significant reduction in the crash rate before and after the COVID-19 pandemic. Additionally, a random effect negative binomial model was developed to quantify the impact of the COVID-19 pandemic. The results showed that the reduction in crashes was significant during and after COVID-19. Also, single roads (two-lane, two-way) were found to be more dangerous than other types of roads.

PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic

The COVID-19 pandemic has struck health service providers around the world with dire shortages, inflated prices, and volatile demand of personal protective equipment (PPE). This paper discusses supply chain resilience in the context of a Canadian provincial healthcare provider during the COVID-19 pandemic. A multi-period multi-objective mixed-integer programming model is presented for PPE supply planning under disruption risk. The deterministic formulation is extended to consider both two-stage and multi-stage uncertainty in the supply, price, and demand of PPE using stochastic programming (SP) and chance-constrained programming (CCP). The first objective is to minimize a risk measure of the stochastic total cost, either its Expected Value (EV) or its Value-at-Risk (VaR), and the second objective is to minimize the maximum shortage of any product in any time period. The ϵ-constraint method is used to generate sets of Pareto-optimal solutions and analyze the trade-off between these two competing objectives. Numerical experiments are conducted to analyze the efficacy of emergency inventory and increased inventory levels as risk mitigation strategies. We consider uncertainty scenarios based on plausible and actual pandemic trajectories seen around the world during the COVID-19 pandemic including single-wave, two-wave, and exponential growth. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

The mental health of healthcare workers in GCC countries during the COVID-19 pandemic: A systematic review and meta-analysis

Objectives: The aim of this study was to summarize the available evidence on the prevalence of stress, burnout, anxiety and depression among healthcare providers in the Gulf Cooperation Council (GCC) countries (KSA, Bahrain, Kuwait, Oman, Qatar, and the United Arab Emirates) during the COVID-19 pandemic. Methods: We searched PubMed, PsycINFO, Scopus, and Google scholar for related studies published between January 2020 and April 2021 and conducted a systematic review and meta-analysis. Results: Of the 1815 identified studies, 29 met the inclusion criteria, and 19 studies were included in the meta-analysis. The pooled estimate of prevalence for moderate to severe anxiety as reported using GAD-7 was 34.57% (95% CI = 19.73%, 51.12%), that for moderate to severe depression using PHQ-9 was 53.12% (95% CI = 32.76%, 72.96%), and that for moderate to severe stress using the 10-item Perceived Stress Scales was 81.12% (95% CI = 72.15%, 88.70%). Meta-analysis was not performed for burnout due to the small number of identified studies and the different tools used;however, the highest prevalence was reported at 76% (95% CI = 64%, 85%). Overall, a positive trend was observed over time for moderate to severe anxiety and depression, p = 0.0059 and 0.0762, respectively. Of note, the heterogeneity was significant among the studies, and many studies were of poor quality. Conclusion: The prevalence of mental health disorders during the current pandemic among healthcare workers in GCC countries is high. However, the results could be affected by the high heterogeneity and low quality studies. © 2022 [The Author/The Authors]

Juglans regia Linn.: A Natural Repository of Vital Phytochemical and Pharmacological Compounds.

Juglans regia Linn. is a valuable medicinal plant that possesses the therapeutic potential to treat a wide range of diseases in humans. It has been known to have significant nutritional and curative properties since ancient times, and almost all parts of this plant have been utilized to cure numerous fungal and bacterial disorders. The separation and identification of the active ingredients in J. regia as well as the testing of those active compounds for pharmacological properties are currently of great interest. Recently, the naphthoquinones extracted from walnut have been observed to inhibit the enzymes essential for viral protein synthesis in the SARS-CoV-2. Anticancer characteristics have been observed in the synthetic triazole analogue derivatives of juglone, and the unique modifications in the parent derivative of juglone have paved the way for further synthetic research in this area. Though there are some research articles available on the pharmacological importance of J. regia, a comprehensive review article to summarize these findings is still required. The current review, therefore, abridges the most recent scientific findings about antimicrobial, antioxidant, anti-fungal, and anticancer properties of various discovered and separated chemical compounds from different solvents and different parts of J. regia.

Statistical analysis for the pitch of mask-wearing Arabic speech

According to Fourier analysis, any periodic function can be analyzed as an infinite series of trigonometric functions (sets of sines and cosines). The kernel of decay cosine yields an extension for the previous frequency-based, sieve-type detection algorithm by giving smooth peaks for decaying amplitudes with the harmonics of the signal correlation. The sequential outline of the RAPT algorithm is: 1) Providing speech samples with their sampling rate and with a reduced sampling rate. 2) Periodically, computing normalized cross-correlation function (NCCF) of the reduced sampling rate speech signal with lags in the F0 range. 3) Indicating the locations of maximum at the 1st pass of NCCF. 4) For the vicinity of the peaks in that 1st pass, calculate the NCCF for the original sampling rate. 5) Again, finding the maximum in that NCCF. Obtaining the location and amplitude of the modified peak. 6) For each peak obtained from the NCCF (high resolution), estimate the F0 of the processed frame. 7) The hypothesis of the frame for unvoiced/voiced is advanced for each frame. 8) Finding the group of the NCCF peaks via optimization process for the unvoiced/voiced hypotheses for all the frames which have the best match with the above characteristics. 9) Using the well-known speech pitch tracking algorithm (PTA), RAPT has the following differences: - PTA computes the NCCF in the linear prediction coding (LPC).

CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging.

Since the end of 2019, novel coronavirus disease (COVID-19) has brought about a plethora of unforeseen changes to the world as we know it. Despite our ceaseless fight against it, COVID-19 has claimed millions of lives, and the death toll exacerbated due to its extremely contagious and fast-spreading nature. To control the spread of this highly contagious disease, a rapid and accurate diagnosis can play a very crucial part. Motivated by this context, a parallelly concatenated convolutional block-based capsule network is proposed in this article as an efficient tool to diagnose the COVID-19 patients from multimodal medical images. Concatenation of deep convolutional blocks of different filter sizes allows us to integrate discriminative spatial features by simultaneously changing the receptive field and enhances the scalability of the model. Moreover, concatenation of capsule layers strengthens the model to learn more complex representation by presenting the information in a fine to coarser manner. The proposed model is evaluated on three benchmark datasets, in which two of them are chest radiograph datasets and the rest is an ultrasound imaging dataset. The architecture that we have proposed through extensive analysis and reasoning achieved outstanding performance in COVID-19 detection task, which signifies the potentiality of the proposed model.

Therapeutic Management of COVID-19 Patients: Clinical Manifestation and Limitations.

Coronavirus disease-2019 (COVID-19) is a respiratory tract infection accompanied by severe or fatal pneumonia-like symptoms and sometimes death. It has posed to be an ongoing global health emergency caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to a sudden outbreak and a large number of infections and deaths, it became a major concern all over the world. The options available as effective therapeutics should be urgently exercised to handle this pandemic. So far, no specific and accurate anti- SARS-CoV-2 treatment is recommended because of the absence of sufficient clinical evidence. In such cases, the clinical use of available drugs is always considered to be on top priority. A broad-spectrum antiviral agent, remdesivir, is found effective in many cases and recommended by many clinicians in many countries. This drug acts as a potential inhibitor of viral RNA-dependent RNA polymerase protein and thus likely to be efficacious in SARS-CoV-2 infection. Tocilizumab is currently recommended by many hospitals as an alternative treatment for critically ill COVID-19 patients. Tocilizumab has been administered to control cytokine storms that occur due to the release of proinflammatory cytokine, including interleukin 6. Chloroquine and hydroxychloroquine are also used in hospitals to handle severe COVID-19 patients. Currently, plasma therapy has been exercised as a therapeutic alternative, especially to handle severe COVID-19 patients. In addition, herbal medicines are expected to play a significant role in the control and prevention of COVID-19. All these therapeutic options have their advantages and limitations. This review highlights the therapeutic potential of these available drugs, along with their mechanism of action and shortcomings. We have provided detailed information on available therapeutic options, which have proved to be effective in improving clinical symptoms of severe COVID-19 patients.

Covid-19 Diagnosis Based on CT Images Using Pre-Trained Models

the NOVEL (COVID-19) coronavirus has recently grown into a pandemic in the world due to the severe acute respiratory syndrome (SARSCoV-2). According to studies in this area, about 34, 440, 235 people are infected with COVID-19, 1, 023, 430 is the number of deaths, and around 25, 633, 956 patients are being subjected to treatment worldwide. In this paper researchers used five pre-trained models. They are: ResNet-50, ResNet-101, AlexNet, VGG11, and SqueezeNetV-1.0. DTL (deep transfer learning) is used to diagnose the NOVEL (COVID-19) by training the COVID-19 coronavirus dataset with 32-batch size and 25 epochs. In training, ResNet-50 gives the best value in loss rate (0.22) with an accuracy of 93.2%, whereas, VGG11 showed the worst value (0.38). Also, in validation, the results showed that ResNet-50 (0.28) is the best, and VGG11 achieved (0.39) as the worst value. © 2021 IEEE.

Coronavirus disease 2019 (COVID-19) pandemic: Review of guidelines for resuming non-urgent imaging and procedures in radiology during Phase II.

Since the spread of the coronavirus disease 2019 (COVID-19) was designated as a pandemic by the World Health Organization, health care systems have been forced to adapt rapidly to defer less urgent care during the crisis. The United States (U.S.) has adopted a four-phase approach to decreasing and then resuming non-essential work. Through strong restrictive measures, Phase I slowed the spread of disease, allowing states to safely diagnose, isolate, and treat patients with COVID-19. In support of social distancing measures, non-urgent studies were postponed, and this created a backlog. Now, as states transition to Phase II, restrictions on non-essential activities will ease, and radiology departments must re-establish care while continuing to mitigate the risk of COVID-19 transmission all while accommodating this backlog. In this article, we propose a roadmap that incorporates the current practice guidelines and subject matter consensus statements for the phased reopening of non-urgent and elective radiology services. This roadmap will focus on operationalizing these recommendations for patient care and workforce management. Tiered systems are proposed for the prioritization of elective procedures, with physician-to-physician communication encouraged. Infection control methods, provision of personal protective equipment (PPE), and physical distancing measures are highlighted. Finally, changes in hours of operation, hiring strategies, and remote reading services are discussed for their potential to ease the transition to normal operations.