Generalized nets in neurology: an example of mathematical modelling

The purpose of this paper is to explain the basic theory of generalized nets (GNs) and their applications in the context of the differential diagnosis of neurological diseases. We define formally the concepts of a GN and transitions of a GN and also outline some remarks on their theory. The work here constructs an example which aims to trace the process of diagnosing different signs and symptoms in neurology. This will enable the interested reader to see the scope of nets in general as tools for the modelling, simulation, optimization and control of real processes.     

A mathematical model for a distributed attack on targeted resources in a computer network

A mathematical model has been developed to analyze the spread of a distributed attack on critical targeted resources in a network. The model provides an epidemic framework with two sub-frameworks to consider the difference between the overall behavior of the attacking hosts and the targeted resources. The analysis focuses on obtaining threshold conditions that determine the success or failure of such attacks. Considering the criticality of the systems involved and the strength of the defence mechanism involved, a measure has been suggested that highlights the level of success that has been achieved by the attacker. To understand the overall dynamics of the system in the long run, its equilibrium points have been obtained and their stability has been analyzed, and conditions for their stability have been outlined.     

A Self-Assembled Cage with Endohedral Acid Groups both Catalyzes Substitution Reactions and Controls Their Molecularity

A self-assembled Fe₄L₆ cage complex internally decorated with acid functions is capable of accelerating the thioetherification of activated alcohols, ethers and amines by up to 1000-fold. No product inhibition is seen, and effective supramolecular catalysis can occur with as little as 5 % cage. The substrates are bound in the host with up to micromolar affinities, whereas the products show binding that is an order of magnitude weaker. Most importantly, the cage host alters the molecularity of the reaction: whereas the reaction catalyzed by simple acids is a unimolecular, S_N1-type substitution process, the rate of the host-mediated process is dependent on the concentration of nucleophile. The molecularity of the cage-catalyzed reaction is substrate-dependent, and can be up to bimolecular. In addition, the catalysis can be prevented by a large excess of nucleophile, where substrate inhibition dominates, and the use of tritylated anilines as substrates causes a negative feedback loop, whereby the liberated product destroys the catalyst and stops the reaction.     

The inventory models under conditional trade credit in a supply chain system

In several recent investigations dealing with the economic order quantity with permissible delay in payments, the following assumptions are made:     

Discrete filter operators for large‐eddy simulation using high‐order spectral difference methods

The combination of a high‐order unstructured spectral difference (SD) spatial discretization scheme with sub‐grid scale (SGS) modeling for large‐eddy simulation is investigated with particular focus on the consistent implementation of a structural mixed model based on the scale similarity hypothesis. The difficult task of deriving a consistent formulation for the discrete filter within the SD element of arbitrary order led to the development of a new class of three‐dimensional constrained discrete filters. The discrete filters satisfy a set of selected criteria and are completely local within the SD element. Their weights can be automatically computed at run time from the number of solution points within each element and the expected filter cutoff length scale. The novel discrete filters can be applied to any SGS model involving explicit filtering and to a broad class of high‐order discontinuous finite element numerical schemes. The code is applied to the computation of turbulent channel flows at three Reynolds numbers, namely Re_τ = 180, 395, and 590 (based on the friction velocity u_τ and channel half‐width δ). Results from computations with and without the SGS model are compared against results from direct numerical simulation. The numerical experiments suggest that the results are sensitive to the use of the SGS model, even when a high‐order numerical scheme is used, especially when the grid resolution is kept relatively low and mostly in terms of resolved Reynolds stresses. Results obtained using existing filters based on the projection of the solution over lower‐order polynomial bases are also shown and demonstrate that these filters are inadequate for SGS modeling purposes, mostly because of their inability to enforce the selected cutoff length scale with sufficient accuracy. The use of the similarity mixed formulation proved to be particularly accurate in reproducing SGS interactions, confirming that its well‐known potential can be realized in conjunction with state‐of‐the‐art high‐order numerical schemes.Copyright © 2012 John Wiley & Sons, Ltd.     

Uniform persistence for population interaction models with time delay

In this paper uniform persistence is establised for kolmogorov type Prey‐Predator and competion models with per capitals ent growth rates that are dependent on time‐lagged population densities.     

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Homogeneity of variance and correlation coefficients is one of assumptions in the analysis of longitudinal data.However, the assumption can be challenged. In this paper, we mainly propose and analyze nonlinear mixed effects models for longitudinal data with exponential correlation covariance structure, intend to introduce Huber's function in the log likelihood function and get robust estimation (M-estimation) by Fisher scoring method. Score test statistics for homogeneity of variance and correlation coefficient based on M-estimation are then studied. A simulation study is carried to assess the performance of test statistics and the method we proposed in the paper is illustrated by an actual data example.     

Ratio-conjugation tool and its application in linear optimization models†

The ratio-conjugation-tool (RC) is constructed systematically, and its applicability for analysis of economic models is demonstrated. One of the main results establishes a general formula for RC of aggregative function. This tool is applied to the analysis of linear economic dynamics models of Neumann–Gale type which can be solved by a dynamic programming method: for each direct problem we construct the corresponding dual RC-problem. Moreover, we consider three examples of the most important economic dynamics models and look at these now from a unified general position.     

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.