We provide an ultraviolet (UV) complete model for the R(D(∗)) anomalies, in which the additional contribution to semi-tauonic b → c transitions arises from decay to a right-handed sterile neutrino via exchange of a TeV-scale SU(2)L singlet W′. The model is based on an extension of the Standard Model (SM) hypercharge group, U(1)Y , to the SU(2)V × U(1)′ gauge group, containing several pairs of heavy vector-like fermions. We present a comprehensive phenomenological survey of the model, ranging from the low-energy flavor physics, direct searches at the LHC, to neutrino physics and cosmology. We show that, while the W′ and Z′-induced constraints are important, it is possible to find parameter space naturally consistent with all the available data. The sterile neutrino sector also offers rich phenomenology, including possibilities for measurable dark radiation, gamma ray signals, and displaced decays at colliders.

INTRODUCTION In spring 2017, the first case of bovine anthrax in 20 years in Switzerland occurred in the canton of Jura. Carcasses of anthrax-deceased animals should not be opened due to the formation of highly resistant spores bearing the risk of environmental contamination and aerosolization. Nevertheless, in the course of this local outbreak, one sick cow from the affected farm, whose blood repeatedly tested negative for Bacillus anthracis, was necropsied after euthanasia under special biosafety precautions at the Institute of Animal Pathology, Vetsuisse-Faculty Bern. Necropsy revealed ventral edema, fetal death, necro-hemorrhagic placentitis and necrotizing iliac lymphadenitis. Bacillus anthracis was isolated only from placenta and altered lymph node. The biosafety measures taken during and after necropsy prevented a contamination of the necropsy environment, which was proven with bacteriological swabs. This case shows that anthrax may elicit unspecific symptoms mimicking other diseases, and veterinarians must be aware of these non-septicemic cases.

Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography “pocket” phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the “pocket” phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET “pocket” phantom has the potential to reduce and/or check for standardized uptake value measurement errors.

The research in this paper is oriented to the blended learning and teaching model with a study group on the faculty of the science and education, to determine the effectiveness of such an approach. The study included graduate students of Faculty of Science and Education, University of Mostar who attended a course E-learning systems (N=39). The teaching process was organized and implemented according to the sub-model of the rotation model, called the flipped classroom. The teaching process included a period of the traditional teaching (approx. 30% of the total time) and a period of the online delivery of content (approx. 70% of the total time). The research has provided a stimulating experience for both teachers and students.

Article history: Received: 04 July, 2018 Accepted: 20 August, 2018 Online: 05 September, 2018

Self-supervised methods are interesting for remote sensing because there are not many human labeled datasets available, but there is practically unlimited amount of data that can be used for self-supervised learning. In this paper we analyze the use of split-brain autoencoders in the context of remote sensing image classification. Weinvestigate the importance of training set size, choice of color space and size of the model to the classification accuracy. We show that even with small amount of unlabeled training images, if we finetune the weights learned by the autoencoder, we can achieve almost state of the art results of 89.27% on AID dataset.

Purpose- Paper aims to give empirical evidence on the impact of control of corruption on happiness as a proxy of social progress by collecting panel data for 59 countries over the period 2007-2016. Methodology- Initial as well as extended model that controls for government consumption are estimated using linear static and dynamic panel data estimators. In order to test for the sensitivity of the results and to estimate short- and long-run coefficients, panel ARDL framework is employed. Findings- The results of linear static and dynamic panel data estimators indicate the significant positive impact of control of corruption on happiness in both, initial and extended models. ARDL model reports a significant relationship between control of corruption and happiness in initial and extended model only in the long-run. Conclusion- The convergence process from corrupt to hones politics is complex and is likely to be achieved only in the long-run period.

One of the main challenges of future industrial systems is to flexibly (re)deploy control applications on hardware controllers - a feature that is missing in most state of the art automation systems. Due to the heterogeneous structure of control systems, e.g. different standards and hardware, a silver bullet architecture for flexible deployment seems to be infeasible. Instead we propose an abstract architecture covering different concrete reconfiguration system architectures along with a taxonomy for design decisions for the realization of such a system. The capability of the abstract architecture is demonstrated by mapping three fundamentally different concrete reconfiguration system architectures and is verified on an industrial use case of a logistic system of an aluminum cold rolling plant.

Children with disability deserve equal access to quality education which enable them develop into useful member of the society and contribute to the economic growth of their immediate community irrespective of their areas of special needs. The Individual Education Plan (IEP) is a written document specifically developed for students with disabilities in inclusive education. The main goal of this article is to present a checklist of the essential elements required for an IEP and it is intended that these will form the basis for good inclusive practicein the future. The IEP is a working document and should be useful, available and comprehensible to all those dealing directly with the student. It needs to be considered in the context of home, school and classroom organisation.Effective individual education plans have key characteristics: Individualised and child-centred, Inclusive, Holistic, Collaborative and Accessible.

This paper describes the transient characteristics and control of the output DC voltage of a stand-alone switched reluctance generator (SRG). A mathematical model of switched reluctance machine (SRM) is developed and implemented in Matlab/Simulink software. The mathematical model is verified experimentally. The robust controller based on the discrete-time sliding mode control (DT-SMC) technique is proposed for the SRG voltage control. The robustness is achieved using the disturbance estimator. The proposed control technique was implemented through simulations on a three phase 12/8-pole SRG with a variable speed and load. The proposed DT-SMC based controller is compared with a standard PI controller. Obtained results show the effectiveness and quality of DT-SMC based voltage control technique for the SRG.

Datalog is a deductive query language for relational databases. We introduce LogiQL, a language based on Datalog and show how it can be used to specify mixedinteger linear optimization models and solve them. Unlike pure algebraic modeling languages, LogiQL allows the user to both specify models, and manipulate and transform the inputs and outputs of the models. This is an advantage over conventional optimization modeling languages that rely on reading data via plug-in tools or importing data from external sources via files. In this chapter, we give a brief overview of LogiQL and describe two mixed integer programming case studies: a production-transportation model and a formulation of the traveling salesman problem.