Abstract

Abstract
Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

Abstract
The geophysical surveys methods have not been extensively used in gold exploration in Northern Portugal. This is based on the fact that most mineralized zones contain low and erratic sulphide contents. In this work we3re tested three different types of geophysical surveys combined with geochemical surveys, in order to define anomalies that are closely correlated with the locations of known mineralized zones. The aim of the work is to study some gold deposits in Northern Portugal (Lagoa Negra and Castromil) with the purpose of obtaining valuable information of the mineralogical and petrogenetically features of these gold deposits, and how it would facilitate the understanding of the regional distribution and formation of gold deposits. Ground Penetrating Radar (GPR), Very Low frequency (VLF) electromagnetic methods combined with 2D Electrical Resistivity techniques can contribute towards defining structures, and are particularly important where extensive cover exists and may also play an important role in the mapping of lithology and the lithological contacts. The magnetic surveys revealed and confirmed two possible areas with sulphide mineralization, one with pyrrhotite and other with Fe-sulphide oxidation proven by during drilling campaign in this area. This work demonstrates how the combination of geophysics and geochemical methods, together with geological sampling, can be used to help delineate possible structures that host gold mineralization.

Abstract

Abstract
The growth of wireless sensor networks (WSN) has resulted in part from requirements for connecting sensors and advances in radio technologies. WSN nodes may be required to save energy and therefore wake up and sleep in a synchronized way. In this paper, we propose an application-driven WSN node synchronization mechanism which, by making use of cross-layer information such as application ID and duty cycle, and by using the exponentially weighted moving average (EWMA) technique, enables nodes to wake up and sleep without losing synchronization. The results obtained confirm that this mechanism maintains the nodes in a mesh network synchronized according to the applications they run, while maintaining a high packet reception ratio.

Abstract
This paper presents an integer linear programming (ILP) model to minimise the total energy-cost of a flake ice production unit in food retail stores. This work is based on a real problem in a Portuguese food retail company, where flake ice in necessary throughout the day in order to maintain fresh fish on the shelves at ideal temperature conditions and humidity levels. The proposed approach aims to provide an energy-efficient scheduling of the production to periods with a lower energy cost, as well as to reduce water consumption, by producing the exact quantities required at the right time, minimising waste. The model was tested on a set of real-world instances from the retail company, and on a set of randomly generated instances. The procedure used to create these instances is presented in the paper. For the sets of tested instances, the results show that the model is strong when compared to the lower bounds provided by the linear programming relaxation of the model. The results from the set of real instances show that it is possible to achieve an energy-efficient scheduling of the production which translates in an average annual cost savings of 34.3% for the stores.