Short-Term Effects of Duodenocecostomy on Body Weight, Glucose Absorption, Serum Components, and Intestinal Histopathology in Four Normal Horses

The objective of this study was to evaluate duodenocecostomy in horses performed through a ventral midline laparotomy and report its influence oil body weight, glucose absorption, serum components, and characteristics of jejunum, cecum, and large colon histology. Four horses were submitted to the duodenocecostomy technique through a ventral midline laparotomy with animals in dorsal recumbency under inhalation anesthesia, followed by abdominal exploration. A side-to-side anastomosis was performed between the duodenojejunal flexure and the base of the cecum with two simple continuous suture lines of the serosal and muscular layers. The size of the opening created was approximately 2 cm in diameter. The mucosa layer was not Sutured. After 30 days, animals were submitted to a second laparotomy to check the patency of the duodenocaecal fistula. During both laparotomy procedures, excisional biopsies of different segments of the gastrointestinal tract were performed. Information on physical examination findings, results of hematologic and histopathologic evaluations, and oral glucose absorption test were recorded. The horses did not have significant weight loss from baseline, and absorption curve of glucose did not significantly vary from baseline. Only triglycerides had significant alterations. Histologic evaluation of jejunum, cecum, and large colon did not show alterations of intestinal structure and morphology. We concluded that the proposed technique, principally in relation to the fistula size and not suturing the mucosa layer, allowed partial or total Occlusion of the fistulae without the necessity of a second surgery and avoided the permanent bypass of ingesta and weight loss.

Short-term in situ/ex vivo study of the anticariogenic potential of a resin-modified glass-ionomer cement associated with adhesive systems

Objective: As resin-modified glass-ionomer cement (RMGIC) is an adhesive material, its association to dentin bonding agents (DBAs) was previously proposed. This study investigated the adjunctive behavior of an RMGIC with etch-and-rinse bonding systems under in situ/ex vivo cariogenic challenge. Method and Materials: Bovine enamel blocks (3 3 2 mm) were randomly assigned to group VP, Vitremer + its own primer (3M ESPE); group VSB, Vitremer + Single Bond (3M ESPE); and group VPB, Vitremer + Prime & Bond 2.1 (Dentsply). Two blocks of each group were randomly placed in an acrylic palatal appliance, so each appliance included six blocks. Volunteers (n = 10) wore these appliances according to given instructions to promote a sucrose challenge eight times/day for 15 days. After this period, the blocks were removed from the devices and cleaned, and demineralization was assessed through longitudinal microhardness analysis (Knoop indenter, 25 g/5 s). Data were submitted to three-way ANOVA and Tukey test (P < .05). Results: No treatment was able to completely avoid demineralization. All materials showed a statistically significant difference in mineral loss when the microhardness on the outer enamel was compared with deeper regions (P < .05). Conclusion: Association of the tested RMGICs with etch-and-rinse DBAs did not seem to be more beneficial against caries than the conventional treatment with RMGIC. (Quintessence Int 2010; 41: e192-e199)

Effects of short- and long-term exposure to c-AMP and c-GMP on the noradrenaline transporter

The effects of short- and long-term exposure of cells to elevated cyclic adenosine monophosphate (c-AMP), using dibutyryl-c-AMP, 8-bromo-c-AMP, cholera toxin or forskolin, or cyclic guanosine monophosphate (c-GMP), using dibutyryl-c-GMP or 8-bromo-c-GMP, on the activity and expression of the noradrenaline transporter (NAT) were examined. Short- or long-term c-GMP elevation had no effects on H-3-noradrenaline uptake by rat PC12 phaeochromocytoma cells or human SK-N-SH-SY5Y neuroblastoma cells. Short-term c-AMP elevation (for 17 min experiment duration) caused a decrease in H-3-noradrenaline uptake by PC12 cells, but had no effects on SK-N-SH-SY5Y cells or COS-7 cells transfected with human or rat NAT cDNA. c-AMP did not affect H-3-nisoxetine binding to PC12 cells. Long-term (24 h) exposure to elevated c-AMP levels caused a decrease in H-3-noradrenaline uptake and NAT mRNA in PC12 cells, but had no effects on SK-N-SH-SY5Y cells and caused a small increase in H-3-noradrenaline uptake in COS-7 cells heterologously expressing rat or human NAT. Hence, c-AMP, but not c-GMP, causes a cell type-dependent reduction in NAT activity after short-term exposure and a reduction in NAT expression after long-term exposure. (C) 2001 Elsevier Science Ltd. All rights reserved.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market

In the proposed model, the independent system operator (ISO) provides the opportunity for maintenance outage rescheduling of generating units before each short-term (ST) time interval. Long-term (LT) scheduling for 1 or 2 years in advance is essential for the ISO and the generation companies (GENCOs) to decide their LT strategies; however, it is not possible to be exactly followed and requires slight adjustments. The Cournot-Nash equilibrium is used to characterize the decision-making procedure of an individual GENCO for ST intervals considering the effective coordination with LT plans. Random inputs, such as parameters of the demand function of loads, hourly demand during the following ST time interval and the expected generation pattern of the rivals, are included as scenarios in the stochastic mixed integer program defined to model the payoff-maximizing objective of a GENCO. Scenario reduction algorithms are used to deal with the computational burden. Two reliability test systems were chosen to illustrate the effectiveness of the proposed model for the ST decision-making process for future planned outages from the point of view of a GENCO.

Short-term electricity prices forecasting in a competetive market by a hybrid Pso-Anfis approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn.

Short-term Wind Forecasting to Support Virtual Power Player Operation

This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed in the GECAD renewable energy lab was used. A new wind speed forecasting model has been implemented and it estimated accuracy was evalu-ated and compared with a previous developed forecast-ing model. Using as input attributes the information of the wind speed concerning the previous 3 hours enables to obtain results with high accuracy for the wind short-term forecasting.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

Multiagent System Architecture for Short-term Operation of Integrated Microgrids

The forthcoming smart grids are comprised of integrated microgrids operating in grid-connected and isolated mode with local generation, storage and demand response (DR) programs. The proposed model is based on three successive complementary steps for power transaction in the market environment. The first step is characterized as a microgrid’s internal market; the second concerns negotiations between distinct interconnected microgrids; and finally, the third refers to the actual electricity market. The proposed approach is modeled and tested using a MAS framework directed to the study of the smart grids environment, including the simulation of electricity markets. This is achieved through the integration of the proposed approach with the MASGriP (Multi-Agent Smart Grid Platform) system.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.