Application of cellular neural network (CNN) method to the nuclear reactor dynamics equations

This paper describes the application of a multilayer cellular neural network (CNN) to model and solve the nuclear reactor dynamic equations. An equivalent electrical circuit is analyzed and the governing equations of a bare, homogeneous reactor core are modeled via CNN. The validity of the CNN result is compared with numerical solution of the system of nonlinear governing partial differential equations (PDE) using MATLAB. Steady state as well as transient simulations, show very good comparison between the two methods. We used our CNN model to simulate space-time response of different reactivity excursions in a typical nuclear reactor.     

P distribution in different sediment fraction of a constructed wetland

The aim of this work was to study the accumulation and fractionation of P in the inlet and outlet sediment of a constructed wetland for the wastewater treatment of a metallurgic plant in Argentina. It was important to predict whether P could be released into the water again by changing environmental conditions or retained over time. P-fractionation was performed using a sequential extraction method. Sediment cores were sliced at depths of: 0-3; 3-7 and 7-10 cm. Sediment showed high pH values and anoxic conditions. In the inlet area, P was principally bound to the carbonate fraction, whereas in the outlet area, it was mainly bound in the residual fraction. This behavior was justified by the effluent composition, which is rich in Ca2+ and Fe3+ and presents high values of pH and conductivity. These conditions favor CaCO3 and Fe(OOH)n precipitation and the subsequent sorption of P to their surface. The sediment active layer involved in the exchange reactions was the superficial one (0-3 cm). The wetland is highly efficient in P retention. P was retained by sediment in fractions that will not release it to the water while chemical and environmental conditions of the system are maintained.     

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.     

Investigation on minimum quantity lubricant-MQL grinding of 100Cr6 hardened steel using different abrasive and coolant–lubricant types

Large quantities of coolant–lubricants are still widely used in the metal working industry, generating high consumption and discard costs and impacting the environment. Alternatives to current practices are getting more serious consideration in response to environmental and operational cost pressures. In the grinding process, promising alternatives to conventional dry and fluid coolant applications are minimum quantity lubrication (MQL) or near dry grinding process. Despite several researches, there have been a few investigations about the influence of different types of coolant–lubricants and grinding wheels on the process results. The current study aims to show the effects of the above parameters on grinding performance such as grinding forces and surface quality. The tests have been performed in presence of fluid, air jet and eleven types of coolant–lubricants, as well as, in dry condition. The grinding wheels employed in this study were vitrified bond corundum, resin bond corundum and vitrified bond SG wheels. The results indicate that SG wheels and MQL oils have potential for the development of the MQL process in comparison to vitrified and resin bond corundums and water miscible oils. Also, the lowest thermal damages, material side flow on the ground surface and wheel loading were generated by using the SG grinding wheel in MQL grinding process.     

Pharmacokinetic analysis and antiepileptic activity of two new isomers of N-valproyl glycinamide

Valproyl glycinamide (TV 1901-VPGD) is a new antiepileptic drug, which is currently undergoing clinical trials. The present study explored the pharmacokinetics and pharmacodynamics (anticonvulsant activity and neurotoxicity) of two new isomers of valproyl glycinamide: valnoctyl glycinamide (VCGD) and diisopropylacetyl (DIGD). Both VCGD and DIGD showed anticonvulsant activity and a safety margin in mice similar to those of VPGD. Following i.v. administration (556 mg) to six dogs, VCGD had a clearance (Cl) value of 3.8 +/- 1.1 Lh-1 (mean +/- SD), a volume of distribution (Vss) of 15 +/- 2 L, and a half-life (t1/2) of 1.9 +/- 0.3 h. DIGD had Cl, Vss, and t1/2 values of 10 +/- 0.8 Lh-1, 19 +/- 3 L, and 1.6 +/- 0.2 h, respectively. Neither VCGD nor DIGD operated as chemical drug delivery systems (CDDSs) of glycine, valnoctic acid, or diisopropyl acetic acid and both showed antiepileptic profiles different from that of valproic acid (VPA). Both glycinamides were biotransformed to their glycine analogues with similar fractions metabolized (fm): 59 +/- 5% (VCGD) and 62 +/- 15% (DIGD). The two glycine metabolites, valnoctyl glycine (VCGA) and diisopropylacetyl glycine (DIGA), were also administered to the same dogs in order to calculate the above fm values. Both VCGA and DIGA had higher Cl and lower Vss values than VCGD and DIGD and therefore their mean t1/2 values were 0.43 +/- 0.02 and 0.30 +/- 0.07 h, respectively. VCGA and DIGA were excreted mainly intact in the urine, with fractions excreted unchanged (fe) of 60 +/- 9 and 55 +/- 7%, respectively. The improved pharmacokinetic profile of VCGD and DIGD relative to their glycine analogues may explain the similarity of their anticonvulsant activity to that of valproyl glycinamide. The current study demonstrates the benefit of the structure-pharmacokinetic-pharmacodynamic relationship (SPPR) approach in developing and selecting a potent antiepileptic compound in intact animals based not only on its intrinsic pharmacodynamic activity but also on its improved pharmacokinetic profile.     

Pavlovian conditioning of sexual arousal: First- and second-order effects.

Four experiments examined the conditioning of sexual arousal in 81 male Long-Evans hooded rats. In each case, the unconditioned response (UCR) was unconsummated arousal after exposure to a female. There was evidence of a substantial conditioned effect, as shown by decreases in the time to complete copulation during postconditioning conditioned stimulus/stimuli (CS) tests. It was also possible to establish a 2nd-order conditioned response (CR), which retained its strength even after extinction of the 1st-order response. Results confirm the power of Pavlovian contingencies in sexual responding. The locus and mode of action of this CR and its function are discussed. (20 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An experimental investigation of cylindrical wire electrical discharge turning process

The cylindrical wire electrical discharge turning (CWEDT) process was developed to generate precise cylindrical forms on hard, difficult to machine materials. A precise, flexible, and corrosion-resistant submerged rotary spindle was designed and added to a conventional five-axis CNC wire electrical discharge machine (EDM) to enable the generation of free-form cylindrical geometries. The hardness and strength of the work material are no longer the dominating factors that affect the tool wear and hinder the machining process. In this study, the effect of machining parameters on surface roughness (R _a) and roundness in cylindrical CWEDT of a AISI D3 tool steel is investigated. The selection of this material was made taking into account its wide range of applications in tools, dies, and molds and in industries such as punching, tapping, reaming, and so on in cylindrical forms. Surface roughness and roundness are chosen as two of the machining performances to verify the process. In addition, power, pulse off-time, voltage, and spindle rotational speed are adopted for evaluation by full factorial design of experiments. In this case, a 2²?×?3² mixed full factorial design has been selected considering the number of factors used in the present study. The main effects of factors and interactions were considered in this paper, and regression equations were derived using response surface methodology. Finally, the surfaces of the CWEDT parts were examined using scanning electron microscopy (SEM) to identify the macro-ridges and craters on the surface. Cross sections of the EDM parts were examined using the SEM and microhardness tests to quantify the sub-surface recast layers and heat-affected zones under specific process parameters.     

Fractography, Mechanical Properties, and Microstructure of Commercial Silicon Nitride–Titanium Nitride Composites

Commercial-grade Si₃N₄–TiN composites with 0, 10, 20, and 30 wt% TiN content have been characterized. Submicrometer grain-size Si₃N₄ was reinforced with fine TiN grains. Density, Young's modulus, coefficient of thermal expansion, and fracture toughness increased linearly with TiN content. Increased strength was observed in the Si₃N₄+20 wt% TiN, and Si₃N₄+30 wt% TiN composites. Fractography was used to characterize the different types of fracture origins. Improvements in toughness and strength are due to residual stresses in the Si₃N₄ matrix and the TiN particles. A threefold improvement in dry wear resistance of the Si₃N₄+30 wt% TiN composite over the Si₃N₄ matrix was observed.     

Simultaneous generation and correlation of complete sets of generalized pairwise complementary sequences

New coding schemes are increasingly being developed to adapt multisensor and communication systems to the requirements of the current technological environment. In some cases, the practical implementation of these schemes involves a great amount of signal processing operations. In order to achieve and make suitable their use in different platforms, efficient algorithms are required to process coding. In the last years, a new kind of complementary sequences, known as Generalized Pairwise Complementary sequences, has been proposed. These sequences provide high noise immunity when they are applied to multiuser systems, as well as a low cross‐correlation, which is convenient in those applications. The aim of this paper is to propose optimized generation and correlation architectures for Generalized Pairwise Complementary sequences that would allow to process a complete set using a reduced amount of operations. An analytical demonstration is provided, with a final comparison to evaluate the reduction with regard to other algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

Clinical, bacteriological, and histopathological study of toxic puerperal metritis in Iraqi buffalo

Data were collected from 42 buffalo with toxic puerperal metritis in 2 large herds, with a history of dystocia, prolapse, and retained placenta. All buffalo were subjected to detailed clinical examination including external inspection, vaginoscopy, and transrectal palpation of the cervix, uterus, and ovaries. Swabs for bacteriology and biopsies for histopathology were collected from the uterine lumen from each cow. Character, odor, and estimation of polymorphonuclear cells of the vaginal mucus were scored. Blood samples were collected from cows for creatine kinase and aspartate amino-transferase measurement. The most predisposing factor causing toxic puerperal metritis was retained placenta (52.4%), and the most prevalent bacteria in uterine lumen were Escherichia coli, Arcanobacterium pyogenes, Staphylococcus aureus, and Fusobacterium necrophorum (18.5, 16.7, 13.0, and 9.3%, respectively). High levels of polymorphonuclear cells were observed in buffalo infected with A. pyogenes and gram-negative anaerobic bacteria (62.1 and 76.4%). A high prevalence of gram-negative anaerobes was isolated from uteri harboring A. pyogenes (13.0%). Buffalo with toxic puerperal metritis had significantly higher creatine kinase and aspartate aminotransferase activities than controls (499.2 ± 23.9 and 208.3 ± 11.3 vs. 242.7 ± 12.9 and 166.8 ± 11.5 U/L, respectively). In a conclusion, gram-negative anaerobes and other facultative pathogens including A. pyogenes were important pathogens that cause severe uterine inflammation.