We perform an analysis of Higgs portal models of dark matter (DM), where DM is light enough to contribute to invisible Higgs decays. Using effective field theory we show that DM can be a thermal relic only if there are additional light particles present with masses below a few 100 GeV. We give three concrete examples of viable Higgs portal models of light DM: (i) the SM extended by DM scalar along with an electroweak triplet and a singlet, (ii) a Two Higgs Doublet Model of type II with additional scalar DM, (iii) SM with DM and an extra scalar singlet that is lighter than DM. In all three examples the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$ \mathcal{B} $\end{document}(h → invisible) constraint is not too restrictive, because it is governed by different parameters than the relic abundance. Additional light particles can have implications for flavor violation and collider searches.

This work focuses on the design of driver for PiezoLegs actuator used for high precision positioning purposes. Motor is driven with the set of periodical voltages with known frequency, amplitude and phase shift between the phases. Motor's operation has been investigated in detail and clear relationships between the amplitude and phase shifts between the driving voltages and motor step size have been established. According to the motor analysis, set of design requirements for the design of driver is given. Particular solution meeting these requirements and satisfying the given design constraints is presented. Driver is experimentally evaluated for power consumption and analyzed in frequency domain, it's performance is compared to the performance of a commercially available driver for the same motor. Designed driver shows improved performance.

This paper deals with the comparison of different methods of speech features extraction for a neural network classifier. We have used a Kohohen self-organizing feature map (SOM) for output-stage classifier which is a specific type of artificial neural nets. The result of this research deals with the accuracy of emotion classifier and compares the two input combinations.

Abstract The importance of corporate communication to build, protect and maintain corporate reputation has been advocated in numerous publications in recent years. The main goal of this paper is to provide an understanding of the impact of corporate reputation and information sharing on value creation. Both reputation and information sharing represent signals that customers observe in the process of value creation, which is seen as the end focus for corporate marketing. The paper draws on signaling theory and corporate marketing literature from the European and American schools of thought. The empirical test of the hypothesized model focuses on the banking industry. Organizational customers from a South East European country shared their views about banks they currently cooperate with. The research instrument contained multi-item scales adapted from the existing literature. An analysis using structural equation modeling confirmed that corporate reputation positively and significantly influences customer perceived value. The effect of information sharing on customer perceived value is not direct but mediated by corporate reputation. This finding contributes to the existing discussion on the role of corporate reputation and communication as antecedents in the process of value creation.