In HAART and other follow-up clinical trials, both longitudinal and survival data are generated. Joint models are used to describe the joint behaviour of such data. This study has discussed Bayesian joint modeling approaches using a five years HAART data obtained from Jimma University Specialized Hospital HIV Outpatient Clinic, Ethiopia. The objective is to compare separate and joint models of longitudinal CD4 cells counts and default time processes of HIV/AIDS patients. A linear mixed effects model, assuming homogenous and heterogenous CD4 variances, is used for modeling the CD4 counts and a Weibull survival model is used for describing the default times. Then, both processes are linked using unobserved random effects through the use of a shared parameter model. The analysis of both the separate and the joint models reveal that the assumption of heterogenous (subject-specific) CD4 variances brings a substantial improvement in the model t. The parameter estimates of both the separate and joint models are consistent. However, the joint model is parsimonious and fits the data better. The final joint model relates the hazard of defaulting to two characteristics of the repeated CD4 counts; patient-specic slopes and CD4 variability.

Background: Dyslipidemia is a highly prevalent and modifiable risk factor for atherosclerotic cardiovascular diseases. Though the problem is significant in Ethiopia, available data in this regard is very poor among alcoholic beverage industrial workers. This study aimed to assess factors associated with dyslipidemia and its prevalence among Awash wine factory employees in Addis Ababa, Ethiopia.Methods: A cross-sectional study was conducted among 335 randomly selected employees of Awash wine factory, Addis Ababa, Ethiopia from January to February 2021. Data was collected by a face-to-face interview technique using the WHO STEPwise approach. Data were entered and analyzed using Epi Info 7 and SPSS version 26, respectively. Both bivariable and multivariable logistic regression analyses were performed to identify factors associated with dyslipidemia. All statistical tests were declared significant atp-value < 0.05.Results: The overall prevalence of dyslipidemia was 67.8% (95% CI 62.5–72.7%). Elevated total cholesterol, elevated triglycerides, reduced high-density lipoprotein, and elevated low-density lipoprotein was found in 25.4%, 33.4%, 50.7%, and 21.5% of participants, respectively. Dyslipidemia was significantly associated with age group 30–39 years (AOR = 2.51; 95% CI 1.16–5.44,p = 0.019), ≥ 40 years (AOR = 6.45; 95% CI 2.01–20.71,p = 0.002), current alcohol consumption (AOR = 3.37; 95% CI 1.70–6.66,p < 0.001), eating vegetables < 2 days per week (AOR = 2.89; 95% CI 1.54–5.43,p = 0.001), sitting duration of > 4 h per day (AOR = 1.96; 95% CI 1.03–3.74,p = 0.041), and raised waist circumference (AOR = 4.56; 95% CI 2.07–10.08,p < 0.001).Conclusions: High prevalence of dyslipidemia was found among Awash wine factory employees in Addis Ababa. Periodic screening of high-risk groups along with effective health promotion and education which encourages a healthy lifestyle is essential.

A novel approach to power scheduling is introduced, focusing on minimizing both economic and environmental impacts. This method utilizes deep contextual reinforcement learning (RL) within an agent-based simulation environment. Each generating unit is treated as an independent, heterogeneous agent, and the scheduling dynamics are formulated as Markov decision processes (MDPs). The MDPs are then used to train a deep RL model to determine optimal power schedules. The performance of this approach is evaluated across various power systems, including both small-scale and large-scale systems with up to 100 units. The results demonstrate that the proposed method exhibits superior performance and scalability in handling power systems with a larger number of units.