Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Fish classification using extraction of appropriate feature set

The field of wild fish classification faces many challenges such as the amount of training data, pose variation and uncontrolled environmental settings. This research work introduces a hybrid genetic algorithm (GA) that integrates the simulated annealing (SA) algorithm with a back-propagation algorithm (GSB classifier) to make the classification process. The algorithm is based on determining the suitable set of extracted features using color signature and color texture features as well as shape features. Four main classes of fish images have been classified, namely, food, garden, poison, and predatory. The proposed GSB classifier has been tested using 24 fish families with different species in each. Compared to the back-propagation (BP) algorithm, the proposed classifier has achieved a rate of 87.7% while the elder rate is 82.9%

Investigating Performance Outcomes under Institutional Pressures and Environmental Orientation Motivated Green Supply Chain Management Practices

Purpose: The study investigates the role of institutional pressures and environmental orientation in implementing green supply chain practices (GSCPs) in firms. The aim is to construct a comprehensive model based on institutional theory (InT) and resource-based view (RBV) that will help study the effect of GSCPs on performance-based outcomes of industrial firms. Study Design: The study adopted a cross-sectional design, and data were collected from 351 supply chain management professionals from different manufacturing companies in Saudi Arabia. Furthermore, a questionnaire was structured to collect data, and the hypothesis of the study was tested using the PLS-SEM modeling. Findings: The study findings showed a significant effect of institutional pressure on GSCPs. Also, another significant impact of environmental orientation on GSCPs was noted. Lastly, GSCPs of manufacturing companies have a significantly positive effect on economic and ecological performances. Originality: This paper is one of the first to include institutional theory, the resource-based view, institutional pressures, environmental orientation, GSCPs, and company performances outcomes. Also, the paper provides details about performance outcomes by scattering Green Supply Chain Management (GSCM) practices and gives direction to managers for the successful implementation of these practices

Investigating Performance Outcomes under Institutional Pressures and Environmental Orientation Motivated Green Supply Chain Management Practices

Purpose: The study investigates the role of institutional pressures and environmental orientation in implementing green supply chain practices (GSCPs) in firms. The aim is to construct a comprehensive model based on institutional theory (InT) and resource-based view (RBV) that will help study the effect of GSCPs on performance-based outcomes of industrial firms. Study Design: The study adopted a cross-sectional design, and data were collected from 351 supply chain management professionals from different manufacturing companies in Saudi Arabia. Furthermore, a questionnaire was structured to collect data, and the hypothesis of the study was tested using the PLS-SEM modeling. Findings: The study findings showed a significant effect of institutional pressure on GSCPs. Also, another significant impact of environmental orientation on GSCPs was noted. Lastly, GSCPs of manufacturing companies have a significantly positive effect on economic and ecological performances. Originality: This paper is one of the first to include institutional theory, the resource-based view, institutional pressures, environmental orientation, GSCPs, and company performances outcomes. Also, the paper provides details about performance outcomes by scattering Green Supply Chain Management (GSCM) practices and gives direction to managers for the successful implementation of these practices

Robust features extraction for general fish classification

Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family.  A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively

Comparison of specific segmentation methods used for copy move detection

In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature

Digitalization of learning in Saudi Arabia during the COVID-19 outbreak: A survey

Following the outbreak of the novel coronavirus (COVID-19) in China in late December 2019, more than 217 countries became almost immediately infected in the resulting pandemic. Consequently, many of them decided to close their educational institutions as a way of preventing the spread of this virus. For many of them, though, the closure made them unable to deliver learning materials to students owing to their inability to provide the right technology for the purpose. To assist with the digitalizing of learning during this time, this study reviews the most common technologies used in the delivery of learning materials, with the experience of most infected countries being considered. Major challenges in online learning are discussed in this study as well. Further, Saudi Arabia was considered as a case study for the effectiveness of distance learning during the 2020 spring semester, where 300 undergraduate students were surveyed on their opinions of distance learning. The responses to the survey indicated that distance learning was effective in providing the required knowledge to the students during the outbreak of COVID-19. The findings showed that although the lack of interaction and poor internet connections were factors affecting comfortable and successful learning of physics and mathematics, 63% of students were satisfied with learning management systems, 75% of students found it easy to understand course materials, and 67% of students found it easy to understand assignments and could deal with them comfortably. The study findings can encourage educational institutions to digitalize their learning materials in the future

The Potential of Corchorus olitorius Seeds Buccal Films for Treatment of Recurrent Minor Aphthous Ulcerations in Human Volunteers

Aphthous ulcers are very common disorders among different age groups and are very noxious and painful. The incidence of aphthous ulcer recurrence is very high and it may even last for a maximum of 6 days and usually, patients cannot stand its pain. This study aims to prepare a buccoadhesive fast dissolving film containing Corchorus olitorius seed extract to treat recurrent minor aphthous ulceration (RMAU) in addition to clinical experiments on human volunteers. An excision wound model was used to assess the in vivo wound healing potential of Corchorus olitorius L. seed extract, with a focus on wound healing molecular targets such as TGF-, TNF-, and IL-1. In addition, metabolomic profiling using HR-LCMS for the crude extract of Corchorus olitorius seeds was explored. Moreover, molecular docking experiments were performed to elucidate the binding confirmation of the isolated compounds with three molecular targets (TNF-α, IL-1β, and GSK3). Additionally, the in vitro antioxidant potential of C. olitorius seed extract using both H2O2 and superoxide radical scavenging activity was examined. Clinical experiments on human volunteers revealed the efficiency of the prepared C. olitorius seeds buccal fast dissolving film (CoBFDF) in relieving pain and wound healing of RMAU. Moreover, the wound healing results revealed that C. olitorius seed extract enhanced wound closure rates (p ≤ 0.001), elevated TGF-β levels and significantly downregulated TNF-α and IL-1β in comparison to the Mebo-treated group. The phenotypical results were supported by biochemical and histopathological findings, while metabolomic profiling using HR-LCMS for the crude extract of Corchorus olitorius seeds yielded a total of 21 compounds belonging to diverse chemical classes. Finally, this study highlights the potential of C. olitorius seed extract in wound repair uncovering the most probable mechanisms of action using in silico analysis