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The objective of this study is to present and discuss how data visualization can be incorporated into teaching approaches by business faculty in introductory business statistics to strengthen business students' practical skills. Data visualization lessens difficulties in learning statistics by providing opportunities to illustrate analytical findings in graphic form, which is essential for learners with different learning styles. Familiarizing students with Excel, Python, or other software in introductory business statistics is beneficial in helping them attain statistical literacy by analyzing real-world data such as COVID-19 statistics. Using such data equips students with knowledge of statistical implementation--a core skill in the business world.

Scalable secret image sharing (SSIS) is a new secret image sharing technique. The feature of scalability refers to the fact that the revealed secret information is proportional to the number of gathered shadows. Once all of the valid shadows are collected, the complete secret can be revealed easily. The kernel of secret information, however, may be leaked out with a few shadows collected in the existing SSIS mechanisms. This is because researchers seldom concerned about secret distribution. Thus, we introduce the Salient map to develop a new method, in which the ROIs (region of interesting) of secret image can be revealed progressively. Additionally, we introduce the concepts of meaningful shadow and verification to SSIS. To the best of our knowledge, this is the first SSIS that employs a meaningful shadow. The leading adoption can greatly help reduce the attention of attackers in order to enhance the security, while the second concept can avoid malicious behaviors from outside attackers or dishonest members. [ABSTRACT FROM AUTHOR]

The article offers a study to define awareness, control of hypertension in adults in Southwest China and to investigate relevant personal characteristics, antihypertensive drug use among patients with hypertension. Topics include data from a population-based high-risk cardiovascular screening study was collected; and concluded that Hypertension awareness, treatment, and control in community-dwelling residents are poor, and the proportion of drug combination therapy is low in Southwest China.

Background To investigate the effects of body mass index (BMI) variability on cardiovascular and cerebrovascular events. Methods A prospective cohort study was conducted in participants from Kailuan Study. A total of 75,012 subjects were divided into 4 groups according to the standard deviation of BMI (BMI-SD), Q1: ≤0.51, Q2: >0.51–0.91, Q3: >0.91–1.47, and Q4: >1.47 kg/m2. The date of the last physical examination was taken as the starting point of follow-up. Cardiovascular and cerebrovascular events during follow-up were used as the endpoint. Kaplan–Meier curves and the Cox proportional hazards model were used to analyze the impact of BMI-SD on cardiovascular and cerebrovascular events. Results There were 3,569 cases of cardiovascular and cerebrovascular events during an average follow-up of 7.01 years among 75,012 subjects. The cumulative incidence of cardiovascular and cerebrovascular events in Q1, Q2, Q3, and Q4 groups was 4.13%, 4.36%, 4.73%, and 5.32%, respectively. The cumulative incidence of stroke was 3.28%, 3.47%, 3.80%, and 4.21%, and the cumulative incidence of myocardial infarction was 0.95%, 0.93%, 1.01%, and 1.22% in Q1, Q2, Q3, and Q4 groups, respectively. Log-rank test showed that there was significant difference in the cumulative incidence of endpoint events among the 4 groups (P < 0.05). Cox regression analysis indicated that the hazard ratio values (95% confidence interval) of cardiovascular and cerebrovascular events, stroke and myocardial infarction in Q4 were 1.20 (1.09–1.32), 1.15 (1.04–1.28), and 1.36 (1.10–1.67) compared with Q1 after adjusting for other confounding factors. Conclusions BMI variability is an independent risk factor of cardiovascular and cerebrovascular events. The risk of cardiovascular and cerebrovascular events increases with an increase of BMI variability. [ABSTRACT FROM AUTHOR]