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Sequential optimization is one of the promising approaches in identifying the optimal candidate(s) (molecules, reactants, drugs, etc.) with desired properties (reaction yield, selectivity, efficacy, etc.) from a large set of potential candidates, while minimizing the number of experiments required. However, the high dimensionality of the feature space (e.g., molecular descriptors) makes it often difficult to utilize the relevant features during the process of updating the set of candidates to be examined. In this article, we developed a new sequential optimization algorithm for molecular problems based on reinforcement learning, multi-armed linear bandit framework, and online, dynamic feature selections in which relevant molecular descriptors are updated along with the experiments. We also designed a stopping condition aimed to guarantee the reliability of the chosen candidate from the dataset pool. The developed algorithm was examined by comparing with Bayesian optimization (BO), using two synthetic datasets and two real datasets in which one dataset includes hydration free energy of molecules and another one includes a free energy difference between enantiomer products in chemical reaction. We found that the dynamic feature selection in representing the desired properties along the experiments provides a better performance (e.g., time required to find the best candidate and stop the experiment) as the overall trend and that our multi-armed linear bandit approach with a dynamic feature selection scheme outperforms the standard BO with fixed feature variables. The comparison of our algorithm to BO with dynamic feature selection is also addressed. [ABSTRACT FROM AUTHOR]

Background. Nutritional status is the best indicator of global well-being. Childhood malnutrition is a major public health problem among Bangladeshi children. Objectives. The main objectives of this study are to: (i) determine the prevalence of malnutrition among under-five (U5) children; (ii) assess the association between malnutrition and selected factors; (iii) determine the high-risk factors for malnutrition and its impact on U5 mortality. Material and methods. The data was taken from the Bangladesh Demographic and Health Survey (BDHS), 2014. Chi-square analysis was performed to explore the association between malnutrition and selected potential factors. Moreover, logistic regression (LR) was also performed to determine the factors associated with malnutrition and its impact on U5 mortality based on the p-value and odds ratio (OR) along with a 95% confidence interval (CI). Results. The prevalence of stunting, wasting and underweight were 37.1%, 14.7% and 33%, respectively. Most of the selected factors were found to be highly significant (p < 0.05), which were performed via chi-square analysis. In the LR model, it was found that place of residence, region, mother’s working status, mother’s education, mother’s age at first birth, wealth index and toilet facilities were the factors mostly responsible for child malnutrition. Our study also showed that wasting and underweight have a significant impact on U5 mortality. Conclusions. It is recommended that educating mothers, delaying a mother’s age for first birth, improving sanitation facilities, reducing poverty and creating health-related services are essential to improve a child’s health and nutritional status. As a result, U5 mortality will be reduced in Bangladesh. [ABSTRACT FROM AUTHOR]