Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.     

Tree-shaped flow structures designed by minimizing path lengths

This paper outlines a direct route to the construction of effective tree-shaped flow structures. Dendritic flow structures dominate the design of natural and engineered flow systems, especially in thermal and fluid systems. The starting point is the optimization of the shape of each elemental area or volume, such that the length of the flow path housed by the element is minimized. Proceeding toward larger and more complex structures - from elements, to first constructs, second constructs, etc. - the paper develops tree-shaped flow structures between one point and a straight line, one point and a plane, a circle and its center, and a point and many points distributed uniformly over an area. In the latter, the construction method is applied to a fluid flow configuration with laminar fully developed flow. The constructions reveal several features that are supported by empirical observations of natural tree-shaped flows: asymmetry, flow rate imbalance, pairing or bifurcation, angles between branches, and Y-shaped constructs that lie in a plane. It is shown that these basic features are necessary because of “packing”, i.e., assembling optimized elements into a fixed space, and filling the space completely. For the flow between an area and one point, the best elemental shape is the regular hexagon. It is shown that the emergence of string-shaped links that connect two or more elements are necessary features, which are also required by packing. Strings cover some of the inner zones of the tree network, particularly the inner zones of large and complex trees. Dichotomous Y-shaped constructs dominate the tree structure, especially the peripheral zones of the tree canopy. The practical importance of the simplified design method is discussed.     

从江苏苏南数字微波线路看广播电视专用网发展前景

新建微波干线电路采用高质量的数字微波设备是广电部科技工作会议的精神之一。本文介绍了江苏苏南数字微波线路，并从方案确定、技术实现、设备选型等方面进行分析比较，从而得到一个合理的整体方案。本文还着重阐述了数字传输的关键设备－编码器的现状与发展趋势，提出广播电视专用网多功能应用的设想。     

我国有线电视事业的现状和发展

本文详细系统地介绍了我国有线是视发展历史和现状，及其经营和管理方法。重点介绍了北美的ＣＡＴＶ技术发展动向。最后，指出我国ＣＡＴＶ事业发展中的应用重视的问题。     

GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.     

光学全交叉网络与SW榕树（F＝S＝2）网络拓扑等价的多样性

本文对有关互连网络拓扑等价的分析方法作了简单评述。并采用互连网络拓扑等价的图分析法分析了自由空间光学互连全交叉网络与ＳＷ榕树网络的拓扑等价及其多样性，拓展了全交叉网络在数字光计算、光子交换及多处理机系统的潜在应用。     

Energy minimization and design for testability

The problem of fault detection in general combinational circuits is NP-complete. The only previous result on identifying easily testable circuits is due to Fujiwara who gave a polynomial time algorithm for detecting any single stuck fault inK-bounded circuits. Such circuits may only contain logic blocks with no more thanK input lines and the blocks are so connected that there is no reconvergent fanout among them. We introduce a new class of combinational circuits called the (k, K)-circuits and present a polynomial time algorithm to detect any single or multiple stuck fault in such circuits. We represent the circuit as an undirected graphG with a vertex for each gate and an edge between a pair of vertices whenever the corresponding gates have a connection. For a (k, K)-circuit,G is a subgraph of ak-tree, which, by definition, cannot have a clique of size greater thank+1. Basically, this is a restriction on gate interconnections rather than on the function of gates comprising the circuit. The (k, K)-circuits are a generalization of Fujiwara'sK-bounded circuits. Using the bidirectional neural network model of the circuit and the energy function minimization formulation of the fault detection problem, we present a test generation algorithm for single and multiple faults in (k, K)-circuits. This polynomial time aggorithm minimizes the energy function by recursively eliminating the variables.     

The Effect of Lactates on the Quality of Microwave-Cooked Chicken Patties during Storage

ABSTRACT:  The aim of this study was to determine the effect of adding 3.3% sodium lactate (SL), 0.25% calcium lactate (CL), and 1.65% SL along with 0.125% CL (SL+CL) on the physicochemical properties, cooking characteristics, and microbiological quality of microwave-cooked chicken patties compared to control (no added lactates). The addition of lactates did not affect the proximate composition of cooked chicken patties. The pH of CL- or SL+CL-containing patties was significantly ( P < 0.05) reduced compared to control or SL patties. The SL-containing patties had lower ( P < 0.05) a _w values compared to control or CL patties. The CL- or SL+CL-containing patties had lower denatured myoglobin and higher ( P < 0.05) cooking yield, surface redness (CIE a *), and chroma values. However, addition of CL alone resulted in higher total expressible fluid, indicating lower water-holding capacity. The CL-containing patties exhibited lower Warner–Bratzler shear force values and higher ( P < 0.05) penetrometer reading compared to the SL-containing patties, indicating soft texture. Thiobarbituric acid (TBA) values were significantly ( P < 0.05) reduced in all lactate-containing patties compared to control up to the 7th day of refrigerated storage under aerobic conditions. No difference ( P > 0.05) was observed in aerobic plate counts between control and lactate-added patties during 28-d storage.