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Purpose: The dramatic increase in stress ulcer prophylaxis (SUP) prescribing patterns over the past several years has raised concerns regarding to their appropriate utilization. This prospective study attempted to evaluate the trend of adherence to stress ulcer prophylaxis from admission until discharge in non- Intensive care unit (ICU) setting. Additionally, we attempted to find those variables associated with appropriate SUP administration. Methods: Data collection was performed prospectively to evaluate 195 randomly selected adult patients who received SUP or had indication for that in non-ICU wards of one of the largest referral center in Iran, during 6 months. Adherence was studied according to widely accepted American Society of Health system Pharmacists (ASHP) guideline. Univariate and multivariate logistic regression was also performed to detect associations related to misuse of SUP. Results: We recognized total inappropriate use of SUP upon admission, during hospital stay and at discharge were somewhat identical at different time points (61%, 80% and 77.4% respectively). On the other hand, since small number of patients experienced SUP underutilization, unfortunately this was not possible to elucidate factors that may have effect on this flawed behavior. However, increasing age was identified to be significant variable in SUP overutilization. Conclusion: This prospective study highlighted inappropriate overutilization of SUP within non-critically ill patients and found factors which predicted this behavior. Adherence during hospital stay was also calculated for the first time in this study, which was related to SUP adherence upon hospital admission.

In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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