In this reissued edition of the classic work Developing Countries in the GATT Legal System, Robert E. Hudec's clear insight on the situation of developing countries within the international trade system is once again made available. Hudec is regarded as one of the most prominent commentators on the evolution of the current international trade regime, and this long out-of-print book offers his analysis of the dynamics playing out between developed and developing nations. A significant contribution when the book was first published, this work continues to serve as a thoughtful and important guide to how current and future trade policy must seriously adapt to the demands of the developing world. This new edition includes a new introduction by J. Michael Finger that examines Hudec's work to understand how the GATT got into its current historical-institutional predicament and the lasting impact of his work on current research on international trade systems.

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This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show that it is a powerful method for colour texture classification.

The employees’ health and well-being are an actual topic in our fast-moving world. Employers lose money when their employees suffer from different health problems and cannot work. The major problem is the spinal pain caused by the poor sitting posture on the office chair. This paper deals with the proposal and realization of the system for the detection of incorrect sitting positions. The smart chair has six flexible force sensors. The Internet of Things (IoT) node based on Arduino connects these sensors into the system. The system detects wrong seating positions and notifies the users. In advance, we develop a mobile application to receive those notifications. The user gets feedback about sitting posture and additional statistical data. We defined simple rules for processing the sensor data for recognizing wrong sitting postures. The data from smart chairs are collected by a private cloud solution from QNAP and are stored in the MongoDB database. We used the Node-RED application for the whole logic implementation.

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