Effect of aromatherapy with Damask Rose (Rosa damascena Mill.) on anxiety in the elderly: Open-labeled quasi-experimental placebo-controlled trial

Background: Anxiety in orthopedic surgeries, especially knee replacement, is one of the most common complaints of the elderly. Aromatherapy with Damask Rose (Rosa damascena Mill.) can be one of the non-pharmacological methods in complementary medicine to control anxiety. Objectives: The present study aimed to determine the effect of aromatherapy with R. damascena on elderly anxiety after knee replacement surgery. Methods: In this quasi-experimental study, 80 elderly patients (60 to 90 years old) undergoing knee replacement surgery according to inclusion criteria were selected by convenience sampling method randomly from Moheb Mehr and Shafa Yahyaian hospitals of Tehran, Iran, and were divided into two groups of case and control. The case group was exposed to aromatherapy intervention at four intervals of 30 minutes. The instrument for measuring anxiety was the Visual Analogue scale for anxiety (VAS-A). Results: The results showed that the study elderly were homogeneous in terms of demographic variables in both case and control groups, except for two variables of education level and consumption of analgesics, which were also determined by two-way ANOVA. These parameters (education level, P = 0.54, and consumption of analgesics, P = 0.661) were not confounding variables. Significant differences were observed in the anxiety of the case group before and after the intervention (P < 0.001), while this difference was not significant in the control group (P = 0.304). Moreover, the difference in anxiety scores was significantly decreased after the intervention compared to before intervention in both case and control groups (P < 0.001). Probably Damask Rose aroma molecules produce and secrete neurotransmitters such as endorphins and encephalin, thereby reducing pain and anxiety. Conclusions: According to the findings of the study, the aromatherapy with R. damascena seems to be effective in reducing postoperative anxiety in these elderly patients. Copyright © 2020, Author(s)

Multiobjective generation and transmission expansion planning of renewable dominated power systems using stochastic normalized normal constraint

This paper proposes a comprehensive framework for generation and transmission planning of renewable dominated power systems, which is formulated as a stochastic multiobjective problem. In this regard, a Normalized Normal Constraint (NNC) solution approach is proposed to solve the introduced stochastic multiobjective generation and transmission planning (GTP) problem. The NNC is utilized in this paper as a relation between different objective functions with different dimensions to find the optimal weighting factors of these objectives. The NNC is applied for solving the GTP problem with objective functions including the investment and operation costs along with the transmission losses, while considering the cost of unserved energy, as well as the uncertainty of load and Renewable Energy Resources (RERs). A fuzzy-based decision making framework is utilized to select the best solution among the optimal non-dominated solution points. A scenario-based approach is used to model the uncertainties. The Garver 6-bus and IEEE 118-bus test systems are utilized to perform the numerical analysis. The simulation results validate the performance and importance of the proposed model, as well as the effectiveness of the NNC to find the evenly distributed Pareto solutions of the multiobjective problems.©2020 Elsevier Ltd. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Decision tree analysis to identify harmful contingencies and estimate blackout indices for predicting system vulnerability

Cascading failure is the main mechanism for progressing large blackouts in power systems. Following an initial event, it is challenging to predict whether there is a potential for starting cascading failure. In fact, the potential of an event for starting a cascading failure depends on many factors such as network structure, system operating point and nature of the event. In this paper, based on the application of decision tree, a new approach is proposed for identifying harmful line outages with the potential of starting and propagating cascading failures. For this purpose, associated with each trajectory of the cascading failure, a blackout index is defined that determines the potential of the initial event for triggering cascading failures towards power system blackout. In order to estimate the blackout indices associated with a line outage, a three stages harmful estimator decision tree (HEDT) is proposed. The proposed HEDT works based on the online operating data provided by a wide area monitoring system (WAMS). The New England 39-bus test system is utilized to show the worthiness of the proposed algorithm.©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties

Microgrids are often designed as energy systems that supply electrical and thermal loads with local resources such as combined heat and power units, renewable energy sources, diesel generators, and others. However, increasing interaction between natural gas and electrical systems, in addition to increased penetration of natural gas fired units gives rise to both opportunities and challenges in microgrid operation scheduling. In this paper, the energy hub concept is used to construct a scenario-based model for the optimal scheduling of electrical and thermal resources in a microgrid with integrated electrical and natural gas infrastructures. The objective function of the proposed model minimizes the expected operation costs while considering all network constraints and uncertainties. The natural gas and electricity flow equations are linearized and formulated as a mixed-integer linear programming problem. Scenarios associated with stochastic variables such as renewable generation and electrical and thermal loads are generated using the corresponding probability distribution functions and reduced using a scenario reduction technique. The proposed model is applied to an energy hub-based microgrid and the simulation results demonstrate the effectiveness of the approach. Furthermore, the benefits of implementing electrical and thermal demand response schemes are quantified. Also, more in-depth analyses for this network-constrained model are performed, including natural gas flow rate variations, natural gas pressures, power flow, and nodal voltages.© 2019 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed