Background Coronavirus disease (COVID-19) is an infectious disease caused by a new variable of the Coronaviridae family. COVID-19 spreads primarily by contacting the virus either from a COVID-19-infected individual through coughing or sneezing or from COVID-19-contaminated surfaces. On March 12, 2020, the World Health Organization (WHO) announced COVID-19 as a pandemic. The government of Saudi Arabia was among the first countries in the world to take quick and serious precautions. The Ministry of Health (MOH) has made the public aware of the virus transmission patterns and the importance of quarantine and curfew. Despite strict measures taken, the awareness of people towards infectious viruses remains the most important factor in limiting the widespread of diseases. Method A cross-sectional survey of 1767 participants, was conducted to explore the awareness, attitude and practice of COVID-19 in relation to socioeconomic data among residents in the city of Riyadh. Results Of all the participants, 58% showed a moderate level of awareness, 95% presented a high attitude and 81% presented an adequate practice regarding COVID-19. Significant positive correlation between awareness-attitude (r = 0.132, p-value < 0.001) and attitude-practice (r = 0.149, p-value < 0.001) were found. The gender of the participants was the only common characteristic significantly associated with both awareness and practice. This study revealed that males showed a slight increase (60%) in the level of awareness compared to female participants (57%), however, when it comes to the practice towards COVID-19, females showed slightly better practice (82%) than males (80%). The World health organization (WHO) and the Ministry of Health (MOH) were the main sources of information. Conclusion Despite the moderate public awareness, their attitude and practice were better. Therefore, public awareness must be improved to be prepared for epidemic and pandemic situations. A comprehensive public health education program is important to increase awareness and to reach sufficient knowledge.

Objective. To investigate the efficacy of using “Instagram application” with a “home-exercise program” as a motivational stimulus in improving physical activity (PA) adherence levels among female college students. Methods. Fifty-eight female undergraduate students with the mean age 20.3 ± 0.96 years participated. Participants were divided into two groups: intervention and the control group; both the groups received an exercise program and the intervention group was additionally motivated by “Instagram.” Adherence to PA was measured by using an adherence sheet. The Exercise Motivation Inventory (EMI-2) was used to assess the motivational factors. Results. The most frequent motivational factors were extrinsic as assessed using the EMI-2. “Positive health” was the most frequent factor mentioned of the two types with 47% of the sample. The intervention group adhered with 17% more to the activity program compared to the control group. Moreover, 72% of the participants in the intervention and control groups found the activity program flexible enough to be performed at home; they agreed about its effectiveness on adherence (53%). Conclusions. The use of Instagram with the home exercise program as a motivational modality could be attractive and effective to reinforce adherence and maintain an appropriate PA level.

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract-This paper proposes and compares feature construction and calibration methods for clustering daily electricity load curves. Such load curves describe electricity demand over a period of time. A rich body of the literature has studied clustering of load curves, usually using temporal features. This limits the potential to discover new knowledge which may not be best represented as models consisting of all time points on load curves. This paper presents three new methods to construct features: conditional filters on time-resolution based features, calibration and normalization, and using profile errors. These new features extend the potential of clustering load curves. Moreover, smart metering is now generating high-resolution time series, and so the dimensionality reduction offered by these features is welcome.The clustering results using the proposed new features are compared with clusterings obtained from temporal features as well as clusterings with Fourier features, using household electricity consumption time series as test data. The experimental results suggest that the proposed feature construction methods offer new means for gaining insight in energy consumption patterns.

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A highly accelerating number of people around the world have been infected with novel Coronavirus disease 2019 (COVID-19). Mass screening programs were suggested by the World Health Organization (WHO) as an effective precautionary measure to contain the spread of the virus. On 16 April 2020, a COVID-19 mass screening program was initiated in Saudi Arabia in multiple phases. This study aims to analyze the number of detected COVID-19 cases, their demographic data, and regions most affected in the initial two phases of these mass screening programs. A retrospective cross-sectional study was conducted among the high-risk population as part of the COVID-19 mass screening program across all regions in Saudi Arabia during April and May 2020. A Chi-square-test was used to determine the associations between positive cases and various demographic variables. Out of 71,854 screened individuals, 13.50% (n = 9701) were COVID-19 positive, of which 83.27% (n = 59,835) were males. Among positive cases, in the 30–39 years age group, 6.36% were in the active phase, and 2.19% were in the community phase. Based on our experience, launching mass screening programs is crucial for early case detection, isolation, and pattern recognition for immediate public interventions.

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