Suppose that $A$ is an $n\times n$ nonnegative matrix whose eigenvalues are $\lambda = \rho (A), \lambda _2,\ldots , \lambda _n$. Fiedler and others have shown that $\det (\lambda I - A) \le \lambda ^n - \rho ^n$, for all $\lambda > \rho $, with equality for any such $\lambda $ if and only if $A$ is the simple cycle matrix. Let $a_i$ be the signed sum of the determinants of the principal submatrices of $A$ of order $i\times i$, $i = 1,\ldots ,n - 1$. We use similar techniques to Fiedler to show that Fiedler’s inequality can be strengthened to: $\det (\lambda I - A) + \sum _{i = 1}^{n - 1} \rho ^{n - 2i}|a_i|(\lambda - \rho )^i \le \lambda ^n -\rho ^n$, for all $\lambda \ge \rho $. We use this inequality to derive the inequality that: $\prod _{2}^{n}(\rho - \lambda _i) \le \rho ^{n - 2}\sum _{i = 2}^{n}(\rho - \lambda _i)$. In the spirit of a celebrated conjecture due to Boyle-Handelman, this inequality inspires us to conjecture the following inequality on the nonzero eigenvalues of $A$: If $\lambda _1 = \rho (A),\lambda _2,\ldots , \lambda _k$ are (all) the nonzero eigenvalues of $A$, then $\prod _{2}^{k}(\rho - \lambda _i) \le \rho ^{k-2}\sum _{i = 2}^{k}(\rho -\lambda )$. We prove this conjecture for the case when the spectrum of $A$ is real.
In this paper we proposed a fuzzy neural network model which can
einbody a fuzzy Takagi-Sugeno model and carry out fuzzy inference and support structure of fuzzy rules. The algorithm of model properties improvement consists of several new procedures námely input space partition, fuzzy terms number and rule number extending, low-effective fuzzy terms and rules extraction and consequent structure Identification. A fuzzy neural network is constructed based on fuzzy model. By learning of the neural network we can tuně of embedded initial fuzzy model. To show the applicability of new method and to niake a possibility to reál systerns rnodelling, we designed the fuzzy-neural network prograrnrne tool FUZNET. Next, we perforrned numerical experiment to do fuzzy rnodelling for an artifical tirne series and reál non-linear complex systém.
Methotrexate (MTX) was investigated for possible effect on the metabolism of ethoxyresorufin, pentoxyresorufin and ethjxycoumarin, the model substrates of cytochrome P450. The investigation was carried out in liver microsomes of rats pretreated with classical inducers of cytochrome P450 as well as in microsomes of two human livers. Firthermore, we measured the conversion of MTX (100 ^M) to its main metabolite, 7-hydroxymethotrexate (7-OHMTX), in microsomes and cytosolic fractions of rat and human livers. The inhibition of 7-OHMTX formation by menadion (inhibitor of aldehyde oxidase) and allopurinol (inhibitor of xanthine oxidase) was studied in the cytosol of rat and human livers. In both species, MTX in the concentration range 0.5-500 /¿M exerted no inhibitory effect on enzymatic activities associated with cytochrome P450. Moreover, we did not observe any measurable formation of 7-OHMTX in liver microsomes. MTX was metabolized at a similar rate in the cytosol of rat and human liver. Allopurinol (100 /iM) reduced the rate of MTX hydroxylation by 31.5 % in the cytosol of human livers but had no effect in the rat. Menadion (100 y/M) decreased the rate of 7-OHMTX formation in the cytosol of human and rat liver by 69 % and 94 %, respectively. Our results confirmed that MTX is oxidized by a soluble enzymatic system in both the rat and human liver. In human tissues, both aldehyde oxidase and xanthine oxidase may play an important role in the metabolism of MTX. Depression of cytochrome P450 and related enzymatic activities observed in vivo cannot be explained by a direct inhibitory action of MTX on cytochrome P450
The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.
Cortisol is secreted by the central hypothalamo-pituitary-adrenal axis and affects many target organs and tissues, particularly in response to stressor demands and infection. Recent data reporting cortisol synthesis in hair follicles have shown the existence of a parallel “peripheral” HPA-axis. However, although there is evidence from in vitro studies and single-observation comparisons between groups that cortisol from hair follicles reflects endocrine changes associated with stressor demands, there are no reports to date of repeated measurements of in vivo cortisol responsivity in hair to transitory stressors. This issue was investigated with three males who underwent 1 min cold pressor test (CP). Cortisol response in hair to stressor demand appears to be (a) swift but transitory, (b) localized to the site of the demand and (c) independent of central HPA-axis activity., C. F. Sharpley, K. G. Kauter, J. R. McFarlane., and Obsahuje seznam literatury
This paper considers the data-based identification of industrial robots using an instrumental variable method that uses off-line estimation of the joint velocities and acceleration signals based only on the measurement of the joint positions. The usual approach to this problem relies on a `tailor-made' prefiltering procedure for estimating the derivatives that depends on good prior knowledge of the system's bandwidth. The paper describes an alternative Integrated Random Walk SMoothing (IRWSM) method that is more robust to deficiencies in such a priori knowledge and exploits an optimal recursive algorithm based on a simple integrated random walk model and a Kalman filter with associated fixed interval smoothing. The resultant IDIM-IV instrumental variable method, using this approach to signal generation, is evaluated by its application to an industrial robot arm and comparison with previously proposed methods.