The author obtains an estimate for the spatial gradient of solutions of the heat equation, subject to a homogeneous Neumann boundary condition, in terms of the gradient of the initial data. The proof is accomplished via the maximum principle; the main assumption is that the sufficiently smooth boundary be convex.
There are several ways that can be implemented in a vehicle tracking system such as recognizing a vehicle color, a shape or a vehicle plate itself. In this paper, we will concentrate ourselves on recognizing a vehicle on a highway through vehicle plate recognition. Generally, recognizing a vehicle plate for a toll-gate system or parking system is easier than recognizing a car plate for the highway system. There are many cameras installed on the highway to capture images and every camera has different angles of images. As a result, the images are captured under varied imaging conditions and not focusing on the vehicle itself. Therefore, we need a system that is able to recognize the object first. However, such a system consumes a large amount of time to complete the whole process. To overcome this drawback, we installed this process with grid computing as a solution. At the end of this paper, we will discuss our obtained result from an experiment.
A gut-specific chitinase gene was cloned from the mulberry longicorn beetle, Apriona germari. The A. germari chitinase (AgChi) gene spans 2894 bp and consists of five introns and six exons coding for 390 amino acid residues. AgChi possesses the chitinase family 18 active site signature and three N-glycosylation sites. Southern blot analysis of genomic DNA suggests that AgChi is a single copy gene. The AgChi cDNA was expressed as a 46-kDa polypeptide in baculovirus-infected insect Sf9 cells and the recombinant AgChi showed activity in a chitinase enzyme assay. Treatment of recombinant virus-infected Sf9 cells with tunicamycin, a specific inhibitor of N-linked glycosylation, revealed that AgChi is N-glycosylated, but the carbohydrate moieties are not essential for chitinolytic activity. Northern and Western blot analyses showed that AgChi was specifically expressed in the gut; AgChi was expressed in three gut regions, indicating that the gut is the prime site for AgChi synthesis in A. germari larvae.
As part of a search for natural enemies of the gypsy moth (Lymantria dispar), virus-infected samples were collected near Toulouse, France. Light and electron microscope studies confirmed that the French strain is a multinucleocapsid nuclear polyhedrosis virus (MNPV). In vivo bioassays using the New Jersey strain of L. dispar, and comparing L. dispar MNPV (LdMNPV) strains from France, North America and Korea, showed that the French strain was the least active, whereas the North American strain had the highest activity. The viral efficacy of all strains was enhanced 200 to 1300-fold in the presence of 1% fluorescent brightener. The enhancement was highest in the American strain and lowest in the French strain. French LdMNPV (LdMNPVF) DNA cut with four restriction enzymes (BamHI, EcoRI, HindIII, and NotI) revealed minor fragment size differences, but many similarities when compared to the North American and the Korean strain. PCR amplification of a LdMNPV early gene (G22) was detected in the North American and the Korean strain, but not in the French strain.
The split graph $K_r+\overline {K_s}$ on $r+s$ vertices is denoted by $S_{r,s}$. A non-increasing sequence $\pi =(d_1,d_2,\ldots ,d_n)$ of nonnegative integers is said to be potentially $S_{r,s}$-graphic if there exists a realization of $\pi $ containing $S_{r,s}$ as a subgraph. In this paper, we obtain a Havel-Hakimi type procedure and a simple sufficient condition for $\pi $ to be potentially $S_{r,s}$-graphic. They are extensions of two theorems due to A. R. Rao (The clique number of a graph with given degree sequence, Graph Theory, Proc. Symp., Calcutta 1976, ISI Lect. Notes Series 4 (1979), 251–267 and An Erdős-Gallai type result on the clique number of a realization of a degree sequence, unpublished).
This paper presents an observation on adaptation of Hopfield neural network dynamics configured as a relaxation-based search algorithm for static optimization. More specifically, two adaptation rules, one heuristically formulated and the second being gradient descent based, for updating constraint weighting coefficients of Hopfield neural network dynamics are discussed. Application of two adaptation rules for constraint weighting coefficients is shown to lead to an identical form for update equations. This finding suggests that the heuristically-formulated rule and the gradient descent based rule are analogues of each other. Accordingly, in the current context, common sense reasoning by a domain expert appears to possess a corresponding mathematical framework.
This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy demand of the transportation sector in Million Barrels Oil Equivalent (MBOE). This paper proposes a hierarchical artificial neural network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual data of Iran from 1968-2007 is used to train the hierarchical ANNs and to illustrate capability of the approach in this regard. Comparison of the model predictions with conventional regression model predictions shows its superiority. Furthermore, the transport energy demand of Iran for the period of 2008 to 2020 is estimated.
Artificial neural networks (ANN) are one of the highly preferred artificial intelligence techniques for brain image segmentation. The commonly used ANN is the supervised ANN, namely Back Propagation Neural Network (BPN). Even though BPNs guarantee high efficiency, they are computationally non-feasible due to the huge convergence time period. In this work, the aspect of computational complexity is tackled using the proposed high speed BPN algorithm (HSBPN). In this modified approach, the weight vectors are calculated without any training methodology. Magnetic resonance (MR) brain tumor images of three stages, namely severe, moderate and mild, are used in this work. An extensive feature set is extracted from these images and used as input for the neural network. A comparative analysis is performed between the conventional BPN and the HSBPN in terms of convergence time period and segmentation efficiency. Experimental results show the superior nature of HSBPN in terms of the performance measures.