Population pharmacokinetics of nitroglycerin and of its two metabolites after a single 24-hour application of a nitroglycerin transdermal matrix delivery system

OBJECTIVES: To determine whether oxytocin exists in the cerebrospinal fluid (CSF) of dogs and whether the amount of oxytocin in the CSF of dogs with neck or back pain caused by spinal cord compression is significantly different than that in the CSF of clinically normal dogs. STUDY DESIGN: Prospective controlled study. ANIMAL POPULATION: A total of 15 purpose-bred beagles and 17 client-owned dogs. METHODS: CSF was collected by needle puncture of the cerebellar medullary cistern after induction of general anesthesia. Oxytocin levels within the samples were determined through radioimmunoassay. RESULTS: Dogs with spinal cord compression had significantly more oxytocin in their CSF than the clinically normal dogs (13.76 +/- 2.0 pg/mL and 3.61 +/- 0.63 pg/mL, respectively; P < .0001). Dogs with chronic signs (>7 days) had significantly more oxytocin in their CSF than dogs with acute signs (<7 days) (21.60 +/- 0.86 pg/mL and 6.80 +/- 0.81 pg/mL, respectively; P < .0001). Both acutely and chronically affected dogs had significantly more oxytocin in their CSF than the controls (P < .005 and P < .0001 respectively). CONCLUSIONS: Dogs with neck and back pain caused by spinal cord compression have significantly more oxytocin in their CSF than clinically normal dogs. Dogs with chronic clinical signs have significantly more oxytocin in their CSF than dogs with acute clinical signs. CLINICAL RELEVANCE: In humans, intrathecal injection of oxytocin is effective in treating low back pain for up to 5 hours. Intrathecal oxytocin may be a logical choice for perioperative analgesia in dogs undergoing myelography because the intrathecal space is accessed for injection of contrast agent.     

Stability preservation analysis in direct discretization of fractional order transfer functions

In this paper, a class of the direct methods for discretization of fractional order transfer functions is studied in the sense of stability preservation. The stability boundary curve is exactly determined for these discretization methods. Having this boundary helps us to recognize whether the original system and its discretized model are the same in the sense of stability. Finally, some illustrative examples are presented to evaluate achievements of the paper.     

Calculation of multicomponent vapor–liquid critical point using the Dividing Rectangles global optimization algorithm

In this paper, the Dividing Rectangles (D.R.) global optimization technique is used to predict the critical temperature and pressure for multicomponent systems. The D.R. optimization algorithm is a fast and reliable optimization method without any need for adjustable parameters, initial guesses or objective function derivation. For calculating the critical point the tangent plane distance (TPD) in terms of the Helmholtz energy is selected. The Peng Robinson (PR) equation of state is applied for determining thermodynamic relations. The procedure of the critical point calculation by means of global optimization is described stepwise in order to clarify the concept and method of programing. The D.R.-based computer program is tested for some multicomponent systems and the results are compared with the Simulated Annealing (SA) global optimization method and with experimental data. The results indicated that the number of the objective function calls (the main parameter that increases the speed and efficiency of an optimization algorithm) is reduced to one twentieth with almost the same accuracy.     

Radiation pattern design of photonic crystal LED optimized by using multi-objective grey wolf optimizer

Photonic Network Communications - This paper proposes an effective method for shaping the radiation pattern intensity of photonic crystal (PhC) light-emitting diode (LED). In this method, the...     

Applying MLP-ANN as a novel and accurate method to estimate gas density

In the gas industries, to increase the degree of accuracy of calculation and estimation in different processes, the importance of accurate prediction of gas properties is highlighted. The gas density, as one of the key properties in gas engineering, has a major effect in calculations. So, in the present paper, multi-layer perceptron artificial neural network (MLP-ANN) was used to predict the gas density based on molecular weight, critical pressure and critical temperature of gas, pressure, and temperature. To this end, a total number of 1240 reliable data of gas density were gathered from literature for the training and testing phases. The MLP-ANN outputs were compared with the actual data in different manners, such as statistical and graphical analyses. The coefficient of determination (R²), average absolute relative deviation (AARD), and root mean squared error (RMSE) for overall process were calculated as 1, 0.0088444, and 0.0259, respectively. The determined parameters and graphical analysis showed that the MLP-ANN has great potential and high degree of accuracy in gas density estimation.     

Development of a novel ANFIS-GA method for investigation of effect of inhibitors on asphaltene precipitation

Asphaltene precipitation and deposition in different parts of petroleum industry increase considerably cost and problems in oil production. Due to these facts controlling asphaltene precipitation becomes one of valuable topics for research in petroleum engineering. Utilization of Asphaltene inhibitors is known as one of the dominant methods for controlling asphaltene precipitation so in this paper Adaptive neuro-fuzzy inference system (ANFIS) is joint with Genetic Algorithm (GA) to study effectiveness of asphaltene inhibitors on precipitation in terms of oil and inhibitors properties. In order to prepare and evaluate the ANFIS-GA algorithm, some reliable experimental data were gathered. The obtained results from the comparison shows the coefficient of determination (R²) for training and testing phases are 0.98804 and 0.9916 respectively. The determined indexes and graphical comparisons expresses that ANFIS-GA has enough accuracy and potential to estimate effectiveness of inhibitors on asphaltene precipitation reductions.     

UNICORE 6 — Recent and Future Advancements

UNICORE is a European Grid Technology with more than 10 years of history. Originating from the Supercomputing domain, the latest version UNICORE 6 has turned into a general-purpose Grid technology that follows established standards and offers a rich set of features to its users. The paper starts with an architectural insight into UNICORE 6, highlighting the workflow features, standards and the different clients. Next, the current state of advancement is presented by describing recent developments. The paper closes with an outlook on future planned developments.