ABSTRACT
More than one year has passed since the outbreak of a new phenomenon in the world, a phenomenon that has affected and transformed all aspects of human life, it is nothing but pandemic of COVID-19. The field of electrical energy is no exception to this rule and has faced many changes and challenges over the 2020. In this paper, by applying artificial intelligence and the integrated clustering model, by k-means technique, combined with the meta-heuristic artificial bee colony (ABC) algorithm a new methodology is presented in order to optimal positioning of the repair crew based on annual data of power grid under situation of COVID-19 to improve the reliability and resiliency of the network due to the importance of electricity for medical purposes, home quarantine, telecommuting, and electronic services. Current research benefits from real interruption data related to year 2020 in Isfahan Province (Iran), reflexing both the huge changes in patterns of power consumption and dispatching as well as novel geographical distribution of blackouts due to COVID pandemic. The temporal distribution of interruptions is very close to the uniform distribution and the geographical distribution of interruptions relative to the density of subscribers had a normal distribution. Accordingly, proposed model is implemented for clustering the spatial data of blackouts recorded during 2020. The number of clusters is equal to the number of repair teams which in this study is considered equal to three. In the next step, the average spatial coordinates of the points of each cluster are calculated, which after reviewing the geographical conditions in the geo-spatial information system (GIS), indicates the optimal point for the deployment of electrical repair crew related to that cluster. The research findings show that after using the optimal points for a month, system average interruption duration index (SAIDI) decreased by an average of 23% compared to the same period of the 2020.
ABSTRACT
Background: The European Medicine Agency has authorized COVID-19 vaccination in young adults from 12 years onwards. COVID-19 vaccination is associated with a negative effect on the quality of anticoagulation stability in adult vitamin K antagonists (VKA) users, due to an increased risk of supra-and subtherapeutic INRs after the first vaccination. It is unknown whether this effect is also observed in adolescents and young adults (AYA) using VKAs. Aim(s): To investigate whether the COVID-19 vaccine also affects anticoagulation stability in AYAs using VKA. Method(s): A case-crossover study was performed in a cohort of AYAs (12-30 years) using VKA. INR results before vaccination, the reference period, were compared with the first INR after the first and, if applicable, second vaccination. Vaccination is deemed safe when the INR is <3.5. Anticoagulation clinics were encouraged to measure the INR within 2 weeks after vaccination. Result(s): Ninety-six AYAs were included, with a median age [IQR] of 25 [7] years, of whom 53.1% were female and 67.7% used acenocoumarol. The majority of AYAs (69.8%) received the BNT162b2 vaccine. [Table 1]. The percentage of INR results within range was significantly lower after the first vaccination (60/97 (62.5%) vs. 40/97 (41.7%), p = 0.004) due to an increase in supratherapeutic INRs (12/97 (12.5%) vs. 30/97 (31.3%), p = 0.005) [Figure 1]. The percentages of subtherapeutic INRs (24/97 (25.0%) vs. 26/97 (27.1%), p = 0.864) and INRs >=5 (1/97 (1.0%) vs. 2/97 (2.1%), p = 1.000) before and after first vaccination were similar. No differences were observed after the second vaccination compared to before or after the first vaccination. Complications after vaccination occurred less often than before vaccination (3.0 vs. 20.0, p = 0.012) and were non-severe. Conclusion(s): COVID-19 vaccination is also associated with a negative effect on anticoagulation stability in AYA VKA users, but not with an increase in complications. Still, it is advisable to monitor the INR shortly after vaccination.
ABSTRACT
More than one year has passed since the outbreak of a new phenomenon in the world, a phenomenon that has affected and transformed all aspects of human life, it is nothing but pandemic of COVID-19. The field of electrical energy is no exception to this rule and has faced many changes and challenges over the 2020. In this paper, by applying artificial intelligence and the integrated clustering model, by k-means technique, combined with the meta-heuristic artificial bee colony (ABC) algorithm a new methodology is presented in order to optimal positioning of the repair crew based on annual data of power grid under situation of COVID-19 to improve the reliability and resiliency of the network due to the importance of electricity for medical purposes, home quarantine, telecommuting, and electronic services. Current research benefits from real interruption data related to year 2020 in Isfahan Province (Iran), reflexing both the huge changes in patterns of power consumption and dispatching as well as novel geographical distribution of blackouts due to COVID pandemic. The temporal distribution of interruptions is very close to the uniform distribution and the geographical distribution of interruptions relative to the density of subscribers had a normal distribution. Accordingly, proposed model is implemented for clustering the spatial data of blackouts recorded during 2020. The number of clusters is equal to the number of repair teams which in this study is considered equal to three. In the next step, the average spatial coordinates of the points of each cluster are calculated, which after reviewing the geographical conditions in the geo-spatial information system (GIS), indicates the optimal point for the deployment of electrical repair crew related to that cluster. The research findings show that after using the optimal points for a month, system average interruption duration index (SAIDI) decreased by an average of 23% compared to the same period of the 2020.
ABSTRACT
Objective: Amid the barriers created by Covid-19 and the enforced strict governments' containments measures, adolescents were negatively impacted more than any other age group. Adolescents are known to display a pivotal role in spreading the virus because of their low conformity. To find the contributing and mediating factors associated with teens' extensive noncompliance, this study focuses on the mechanisms of adopting preventive behaviors. Method: This cross-sectional study was administered online using convenience sampling over four weeks during severe lockdown 4th peak outbreak period. Data were gathered online from 422 adolescents with an average age of 15.48 years (SD=2.74) and their parents. Analysis of data was done by PROCESS macro for SPSS version 25. Results: Personal fable and sensation seeking were predictive of rule-disrespecting in adolescents of both genders. Mediation modeling demonstrated that parents' risk perception partially mediated the predictive relationship between personal fable and sensation seeking with pandemic preventive behaviors in girls ( personal fable: [B = -0.034, 95% CI = (-0.076, -0.005)];sensation seeking [B = -0.047, 95% CI = (-0.102, -0.008)]). Conclusion: These findings highlight the importance of some peculiar characteristics of this developmental stage in explaining adherence to pandemic-related preventative behaviors. It seems that teen boys are more vulnerable in pandemic time frames regarding their inattention to parents' recommendations.
ABSTRACT
Aim: The Purpose of this study was to examine the relationship between bright and dark aspects of personality dimensions and self-interest and other interest preventive related behaviors during Covid-19 pandemic. Method: This cross-sectional study was advertised on social media. 541 participants completed four self-reported questionnaires through the online link. Analysis was performed to assess the kind of association by SPSS. Results: Findings indicate different significant associations between the personality dimensions and preventive behaviors. Regression analysis indicated that conscientiousness (B=.27, P=.00) and agreeableness (B=.11, P=.03) positively and extraversion (B= -.14 P =.02) negatively predict self-protective behaviors. Likewise, conscientiousness (B=.40, P=. 00) and agreeableness (B=.14, P=.00) and antisocial tendencies (B= -.23, P=.00) could have predictive role in other-oriented behaviors. Conclusion: Personality traits have role in explaining compliance and noncompliance with Covid-19 guideline protocols and protecting self and others.
ABSTRACT
Objective: Despite the development of various vaccines to combat Covid-19, adopting preventive behaviors are still the first line of struggle. There exist vast differences in the way people comply with the rules. Some are committed to restrictions laid down by the government, while others disrespect the rules. Therefore, understanding the underlying structures of behavioral management was the purpose of the present study. We aim to examine the contribution of moral standards, sense of social alienation, and institutional trust in predicting compliance with Covid-19 rules. Method: The study design is cross-sectional and data collection was done for two months through online social media. The sample of the study was obtained from 465 of the general population with an average age of 30.01 years (SD=7.04). Analysis of data was done by SPSS version 25. Results: correlation and regression analysis were done to obtain the predictive power of study variables. Findings show that social alienation, institutional trust, and moral standards explain 33% of the variance of compliance [R2 = 0.33, F (461) = 7.68, p = 0.001]. Conclusion: The results of the present study not only contribute to theory development but also provide implications to the governance of Covid-19. It seems that strengthening trust and moral standards along with expanding the sense of cohesion will help to improve compliance with health regulations imposed during Covid-19 and end this tedious period as soon as possible.