Abstract
Handling major challenges like traffic volume estimation, mobility pattern detection and feature extraction in mobility network usually form a weak balance among them. Most of the works are focused towards one of these areas which fail in improving altogether. In this paper, we present a model with modified conventional methods meeting all three above challenges to an extent. Extracting new temporal & directional feature, we introduce Reciprocity metric. It proves to be more informative and efficient in capturing mobility pattern of the network than existing metrics. We introduce the idea of network skeleton which is a reduced form of mobility network but captures approx 90% of its inherent characteristics. Network Skeleton can extract higher level of information from the network while enhancing network's short-term predictability. Our work has the following steps: 1) extracting and building "link reciprocity", a more informative feature; 2) pattern detection in random mobility introduced by "convergence of mobility network"; and 3) estimation of network skeleton formed using a link based approach for short-term forecasting. Our network convergence method outperforms conventional approaches and detects active regions at a very fast rate compared to other approaches. Long ShortTerm Memory (LSTM), a kind of Recursive Neural Networks (RNN) capable of learning long-term dependencies is used to estimate network traffic. Indicating link based network-skeleton helps to reduce short-term forecasting error up to 6% and 3/4 times in different time-slots. Our network skeleton approach can be used to meet the general problems of the traffic-rules formulation by characterizing important routes (links), detecting regions of high importance in less time and predicting short-term traffic volume in a more accurate way. Moreover, network skeleton with reduced network-size can be easily operable with existing methodologies, which is another essential contribution of our work.

Abstract
In this review the objective is to search for technologies that supervise the exercise or physical activity of people suffering from Peripheral Arterial Disease (PAD) at home or in the community. Patients with PAD have walking limitations and their quality of life progressively deteriorates. The regular practice of exercise can help mitigate these effects and even improve their health status. The methodology used was to search for scientific articles published since 2008, with the final result of 18 articles. The results show the most frequent technologies used are based on the accelerometer device, with the tests being performed on a treadmill at a hospital. The hospital tests are expensive, so a useful and viable alternative is the usage of mobile devices to help the health professionals record the exercise performed by their patients suffering with PAD. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract

Abstract
In today's society, there is a compelling need for innovative approaches for the solution of many pressing problems, such as understanding the fluctuations in the performance of an individual when involved in complex and high-stake tasks. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. Human stress can be viewed as an agent, circumstance, situation, or variable that disturbs the normal functioning of an individual, that when not managed can bring mental problems, such as chronic stress or depression. In this paper, we propose a different approach for this problem. The EUStress application is a non-intrusive and non-invasive performance monitoring environment based on behavioural biometrics and real time analysis, used to quantify the level of stress of individuals during online exams.

Abstract

Abstract
O nosso estudo propôs a implementação de Gamificação numa Plataforma Social Académica de uma Universidade Virtual, para verificar de que forma esta influenciava a Interação e a Aprendizagem Social. Para isso usámos uma Metodologia de Design Based Research numa configuração de Métodos Mistos. Começámos por recolher opiniões dos utilizadores dessa Plataforma. Esses resultados informaram na construção de um protótipo gamificado. Seguidamente efetuaram-se testes de usabilidade, recolhendo dados da performance e das opiniões dos utilizadores e foi construída uma nova Plataforma. Nesta fase foi efetuada uma Observação sistemática e recolhidas Analytics do uso. Foram discutidos os resultados e de que forma estes podem ser usados para posteriores intervenções.;Our work proposed the Gamification of an Academic Social Platform from a Virtual University, to inspect the impact on the Interaction and Social Learning of members. We employed Design Based Research with Mixed-Methods. First we gathered information about the users of the original platform, them we designed a prototype. After, we made usability tests and implemented a second platform with Gamification Elements. On this second platform we made a Systematic Observation and gathered the Analytics. We discuss the findings and report ways where they can be used for future implementations.