SUMMARY – Multifocal motor neuropathy (MMN) is a chronic demyelinating neuropathy mainly characterized by multifocal distribution; affecting only motor nerve fibers of two or more peripheral nerves, with the absence of symptoms and signs of upper motor neuron; chronic, sometimes cascading progressive course; demyelination with partial block of motor conduction; immune-mediated pathogenesis and good response to intravenous immunoglobulin treatment (IVIG). The diagnosis of MMN is based on clinical, laboratory and electrophysiological characteristics. Steroids are ineffective in MMN and may lead to worsening of the disease. Similarly, therapeutic plasma exchange is negligibly effective in this neuropathy. However, more than 80% of patients with MMN experience improvement after IVIG. We present our three cases of MMN with positive response to IVIG.

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.

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.

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.

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.