The effect of different concentrations of oxygen on postoperative nausea and vomiting after spinal anesthesia

چکیده: زمینه و هدف: تهوع و استفراغ از عوارض شایع پس از عمل جراحی به روش بیهوشی عمومی و نخاعی می باشد. جهت پیشگیری یا درمان تهوع و استفراغ پس از عمل از داروهای مختلف استفاده شده که علیرغم استفاده از آنها این عارضه کماکان پس از عمل جراحی بطور شایع دیده می شود. از طرفی در برخی از تحقیقات استفاده از اکسیژن با غلظت بالا توانسته است سبب کاهش تهوع و استفراغ پس از عمل جراحی، متعاقب بیهوشی عمومی شود. با توجه به آنکه تاکنون هیچ تحقیقی در مورد تأثیر اکسیژن بر روی تهوع و استفراغ پس از عمل جراحی تحت بیهوشی نخاعی انجام نشده است در این تحقیق اثرات اکسیژن با غلظت های مختلف مورد بررسی قرار گرفته است. روش بررسی: در این مطالعه که از نوع کار آزمایی بالینی دوسوکور است تعداد 132 بیمار با مشخصات ASAI,II (American Society of Anesthesiologyists) در محدوده سنی 70-15 سال، داوطلب عمل جراحی جا اندازی و ثابت کردن باز شکستگی ساق پا تحت بِیهوشی نخاعی، بطور تصادفی به سه گروه مساوی تقسیم شدند. پس از انجام بِیهوشی نخاعی با محلول بوپی واکائین 5/. در حین عمل از اکسیژن با غلظت های 30 (گروه اول)، 50 (گروه دوم) و70 (گروه سوم) استفاده شد. تعداد دفعات استفراغ با مشاهده و شدت تهوع به کمک پرسشنامه (Visual Analogue Scale=VAS) ثبت گردید. اطلاعات بدست آمده توسط آزمون های کای دو و ANOVA و با استفاده از نرم افزار SPSS مورد تجزیه و تحلیل آماری قرار گرفته و 05/0p< معنی دار قلمداد گردید. یافته ها: نتایج این بررسی نشان داد که بین میانگین تعداد دفعات استفراغ، شدت تهوع و میزان مصرف متوکلوپرامید حین عمل و در ساعات 2، 12 و 24 پس از عمل، در گروههای 1، 2 و 3 اختلاف آماری معنی دار وجود نداشت. نتیجه گیری: استفاده از اکسیژن با غلظت های بالاتر، در مقایسه با دوزهای کمتر باعث کاهش تهوع، استفراغ و یا مصرف متوکلوپرامید و حین عمل و پس از عمل جراحی نمی شود

Age, growth and length at first maturity of Otolithes ruber in the northwestern part of the Persian Gulf, based on age estimation using otolith

Estimates of age, growth parameters, length-weight relationship and length and age at first maturity of the otolithes ruber are required for fishery management. We used counting annuli on the section of sagittal otoliths to age O.ruber from the Northwest Persian Gulf in south of Iran. Estimated ages ranged from 0 to 6 years, and maximum frequency of fishes was observed in age-group 1. The values of growth parameters L∞, k and to were calculated by von Bertalanffy model and the results were 67.57 (cm), 0.27 (year-1) and -0.43 respectively. Parameters b and an in length-weight relationship were calculated 3.19 and 0.005 respectively. Length and age at first maturity were estimated 28 cm and 1.55 year

Active Power Sharing and Frequency Restoration in an Autonomous Networked Microgrid

© 1969-2012 IEEE. Microgrid (MG) concept is considered as the best solution for future power systems, which are expected to receive a considerable amount of power through renewable energy resources and distributed generation units. Droop control systems are widely adopted in conventional power systems and recently in MGs for power sharing among generation units. However, droop control causes frequency fluctuations, which leads to poor power quality. This paper deals with frequency fluctuation and stability concerns of f-P droop control loop in MGs. Inspired from conventional synchronous generators, virtual damping is proposed to diminish frequency fluctuation in MGs. Then, it is demonstrated that the conventional frequency restoration method inserts an offset to the phase angle, which is in conflict with accurate power sharing. To this end, a novel control method, based on phase angle feedback, is proposed for frequency restoration in conjunction with a novel method for adaptively tuning the feedback gains to preserve precise active power sharing. Nonlinear stability analysis is conducted by drawing the phase variations of the nonlinear second-order equation of the δ-P droop loop and it is proved that the proposed method improves the stability margin of f-P control loop. Simulation results demonstrate the effectiveness of the proposed method

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p

Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization

A two-stage adaptive robust optimization is developed for pre-disturbance scheduling in microgrids (MGs) for handling uncertainties associated with electricity market prices, renewable generation, demand forecasts, and islanding events. The objective is to produce a reliable and optimal solution for MG operation that minimizes operational costs and the risk/failure in islanding events. In the literature, the uncertainty sets associated with islanding events cover a full scheduling period which results in a sub-optimal solution. In this paper, uncertainty sets corresponding to islanding events are modeled based on reliability/resiliency-oriented indexes of the MG/grid to achieve a more accurate/reliable solution. Besides, the Benders decomposition algorithm which is used to handle uncertainties in solving the optimization problem is time-consuming. Therefore, the column-and-constraint generation (C&CG) decomposition strategy is adopted to make the problem computationally tractable. Further, the uncertainty budget parameters are clarified to balance the conservatism and optimality (cost minimization) of the robust solution in uncertainty sets. The effectiveness of the proposed framework is evaluated and discussed by using a set of numerical studies with different scenarios in an MG. The simulations show that the proposed framework reduces operational costs by using the precise analysis of uncertainty budgets and a change in scheduling periods

Aseptic meningoencephalitis mimicking transient ischaemic attacks

Purpose: To highlight meningoencephalitis as a transient ischaemic attack (TIA) mimic and suggest clinical clues for differential diagnosis. Methods: This was an observational study of consecutively admitted patients over a 9.75-year period presenting as TIAs at a stroke unit. Results: A total of 790 patients with TIAs and seven with TIA-like symptoms but a final diagnosis of viral meningoencephalitis were recognised. The most frequent presentations of meningoencephalitis patients were acute sensory hemisyndrome (6) and cognitive deficits (5). Signs of meningeal irritation were minor or absent on presentation. Predominantly lymphocytic pleocytosis, hyperproteinorachia and a normal cerebrospinal fluid (CSF)/serum glucose index (in 5 out of 6 documented patients) were present. Meningeal thickening on a brain magnetic resonance imaging (MRI) scan was the only abnormal imaging finding. Six patients received initial vascular treatment; one thrombolysed. Finally, six patients were treated with antivirals and/or antibiotics. Although neither bacterial nor viral agents were identified on extensive testing, viral meningoencephalitis was the best explanation for all clinical and laboratory findings. Conclusions: Aseptic meningoencephalitis should be part of the differential diagnosis in patients presenting as TIA. The threshold for a lumbar puncture in such patients should be set individually and take into account the presence of mild meningeal symptoms, age and other risk factors for vascular disease, the results of brain imaging and the basic diagnostic work-up for a stroke sourc

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

© 2019 Elsevier B.V. The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data mining of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settlements. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems operators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various approaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use