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.     

Deterministic Resource Discovery in Distributed Networks

The resource discovery problem was introduced by Harchol-Balter, Leighton, and Lewin. They developed a number of algorithms for the problem in the weakly connected directed graph model. This model is a directed logical graph that represents the vertices’ knowledge about the topology of the underlying communication network. The current paper proposes a deterministic algorithm for the problem in the same model, with improved time, message, and communication complexities. Each previous algorithm had a complexity that was higher at least in one of the measures. Specifically, previous deterministic solutions required either time linear in the diameter of the initial network, or communication complexity $O(n^3)$ (with message complexity $O(n^2)$), or message complexity $O(|E_0| \log n)$ (where $E_0$ is the arc set of the initial graph $G_0$). Compared with the main randomized algorithm of Harchol-Balter, Leighton, and Lewin, the time complexity is reduced from $O(\log^2n)$ to\pagebreak[4] $O(\log n )$, the message complexity from $O(n \log^2 n)$ to $O(n \log n )$, and the communication complexity from $O(n^2 \log^3 n)$ to $O(|E_0|\log ^2 n )$. \par Our work significantly extends the connectivity algorithm of Shiloach and Vishkin which was originally given for a parallel model of computation. Our result also confirms a conjecture of Harchol-Balter, Leighton, and Lewin, and addresses an open question due to Lipton.     

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.     

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.     

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.     

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.     

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.