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.     

Randomized range-maxima in nearly-constant parallel time

Given an array ofn input numbers, therange-maxima problem is that of preprocessing the data so that queries of the type what is the maximum value in subarray [i..j] can be answered quickly using one processor. We present a randomized preprocessing algorithm that runs inO(log^* n) time with high probability, using an optimal number of processors on a CRCW PRAM; each query can be processed in constant time by one processor. We also present a randomized algorithm for a parallel comparison model. Using an optimal number of processors, the preprocessing algorithm runs inO( (n)) time with high probability; each query can be processed inO ( (n)) time by one processor. (As is standard, (n) is the inverse of Ackermann function.) A constant time query can be achieved by some slowdown in the performance of the preprocessing stage.     

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.     

Inconsistent heuristics in theory and practice

In the field of heuristic search it is usually assumed that admissible heuristics are consistent, implying that consistency is a desirable attribute. The term “inconsistent heuristic” has, at times, been portrayed negatively, as something to be avoided. Part of this is historical: early research discovered that inconsistency can lead to poor performance for A^? (nodes might be re-expanded many times). However, the issue has never been fully investigated, and was not re-considered after the invention of IDA^?.This paper shows that many of the preconceived notions about inconsistent heuristics are outdated. The worst-case exponential time of inconsistent heuristics is shown to only occur on contrived graphs with edge weights that are exponential in the size of the graph. Furthermore, the paper shows that rather than being something to be avoided, inconsistent heuristics often add a diversity of heuristic values into a search which can lead to a reduction in the number of node expansions. Inconsistent heuristics are easy to create, contrary to the common perception in the AI literature. To demonstrate this, a number of methods for achieving effective inconsistent heuristics are presented.Pathmax is a way of propagating inconsistent heuristic values in the search from parent to children. This technique is generalized into bidirectional pathmax (BPMX) which propagates values from a parent to a child node, and vice versa. BPMX can be integrated into IDA^? and A^?. When inconsistent heuristics are used with BPMX, experimental results show a large reduction in the search effort required by IDA^?. Positive results are also presented for A^? searches.     

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.     

Evolution of social learning when high expected payoffs are associated with high risk of failure

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals.     

Cycle time distribution in circulating systems

The cycle time distribution (CTD) within closed, continuously circulating systems is defined and related to the residence time distributions of flow regions which make up such systems. Examples of the application of the CTD are noted and experimental methods for determining CTDs for various systems are summarized.     

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.     

Ring-type plasmon resonance in metallic nanoshells

A simple, approximate theoretical model of surface plasmon resonance in two-dimensional metal nanoshells is developed. The model is based on the concept of short-range surface plasmons propagating around closed circular metal nanotubes. In this model, the plasmon resonance in a metal nanotube is treated as a propagating, self-interfering plasmonic wave, in a ring-type resonance, at plasmonic wavelengths matching an integer fraction of the nanotube's effective circumference. The model is validated by detailed computer simulations based on the finite-difference time-domain method and is shown to be in full agreement with the widely used plasmon hybridization model, which is based on the quasi-static approximation.