Background: Hypertension is a chronic medical condition, which needs an adequate management. The patients' adherence with a proper medication of hypertension is the key factor to control hypertension and to reduce the associated complications. However, poor adherence to the antihypertensive drugs is a worldwide problem, which is responsible for adverse health outcomes and thus, increases different health complexities along with health management costs. Here we aim to identify the associated factors of medication adherence among the hypertensive patients in Nepal.

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.

Alkaloids Alkaloids U 0600Synthesis of New Tetracyclic Aristotelia Alkaloids. -The synthesis of two novel analogues (X) and (XII) of the rare aristotelia indole alkaloids starting from (R)-carvone (I) is described. -(HASHEM*, M. A.; FERDOUSH, Z.; Indian J.