A sensor technology survey for a stress aware trading process

The role of the global economy is fundamentally important to our daily lives. The stock markets reflect the state of the economy on a daily basis. Traders are the workers within the stock markets who deal with numbers, statistics, company analysis, news and many other factors which influence the economy in real time. However, whilst making significant decisions within their workplace, traders must also deal with their own emotions. In fact, traders have one of the most stressful professional occupations. This survey merges current knowledge about stress effects and sensor technology by reviewing, comparing, and highlighting relevant existing research and commercial products that are available on the market. This assessment is made in order to establish how sensor technology can support traders to avoid poor decision making during the trading process. The purpose of this article is: 1) to review the studies about the impact of stress on the decision making process and on biological stress parameters that are applied in sensor design; 2) to compare different ways to measure stress by using sensors currently available in the market according to basic biometric principles under trading context; and 3) to suggest new directions in the use of sensor technology in stock markets

Preparation of aromatic geraniol analogues via a Cu(I)-mediated Grignard coupling

Difunctional allylic terpenes are important synthetic building blocks. Functionalization of protected geranyl derivatives by SeO2/t-BuO2H adsorbed on SiO2 provides a convenient route to such compounds. The chosen protecting groups clearly influence the oxidation process. Also, an efficient synthesis of 2-geranylphenol derivatives via a Cu(I)-mediated Grignard coupling of 2-lithiophenols and geranyl substrates was developed.Terpenos alílicos difuncionais constituem-se em importantes blocos de construção sintéticos. A funcionalização de derivados geranílicos protegidos por SeO2/t-BuO2H adsorvido em SiO2, propicia uma rota conveniente para tais compostos. Os grupos protetores escolhidos efetivamente influenciam o processo de oxidação. Também, desenvolveu-se uma eficiente síntese de derivados 2-geranilfenóis através de um acoplamento de Grignard mediado por Cu(I) entre derivados de 2-lítiofenóis e substratos geranílicos.975981Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Crossover between the Dense Electron-Hole Phase and the BCS Excitonic Phase in Quantum Dots

Second order perturbation theory and a Lipkin-Nogami scheme combined with an exact Monte Carlo projection after variation are applied to compute the ground-state energy of $6\le N\le 210$ electron-hole pairs confined in a parabolic two-dimensional quantum dot. The energy shows nice scaling properties as N or the confinement strength is varied. A crossover from the high-density electron-hole phase to the BCS excitonic phase is found at a density which is roughly four times the close-packing density of excitons.Comment: Improved variational and projection calculations. 17 pages, 3 ps figures. Accepted for publication in Int. J. Mod. Phys.

Context-aware system for cardiac condition monitoring and management: a survey

Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also propose "mCardiac", an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation

Context-aware support for cardiac health monitoring using federated machine learning

Context-awareness provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters such as heart rate, electrocardiogram (ECG) signals and activity data. It involves the use of digital technologies to monitor the health condition of a patient in an intelligent environment. Feedback gathered from relevant professionals at earlier stages of the project indicates that physical activity recognition is an essential part of cardiac condition monitoring. However, the traditional machine learning method f developing a model for activity recognition suffers two significant challenges; model overfitting and privacy infringements. This research proposes an intelligent and privacy-oriented context-aware decision support system for cardiac health monitoring using the physiological and the activity data of the patient. The system makes use of a federated machine learning approach to develop a model for physical activity recognition. Experimental analysis shows that the federated approach has advantages over the centralized approach in terms of model generalization whilst maintaining the privacy of the user

Context-aware approach for cardiac rehabilitation monitoring

As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. Existing technologies for cardiac patient monitoring are generally based on the physiological information, mostly the heart rate or Electrocardiogram(ECG) Signals. Other important factors such as physical activities and time of the day are usually ignored. We propose a context-aware solution for cardiac rehabilitation monitoring using multiple vital signs from the physiological and activity data of the patient. This research considers the activity of the patient alongside the time of the activity to facilitate physicians decision-making process. We provide a personalised physical activity recognition processing by generating a personalised model for each user. A prototype is presented to illustrate our proposed approach

Beyond the Landau Criterion for Superfluidity

According to the Landau criterion for superfluidity, a Bose-Einstein condensate flowing with a group velocity smaller than the sound velocity is energetically stable to the presence of perturbing potentials. We found that this is strictly correct only for vanishingly small perturbations. The superfluid critical velocity strongly depends on the strength and shape of the defect. We quantitatively study, both numerically and with an approximate analytical model, the dynamical response of a one-dimensional condensate flowing against an istantaneously raised spatially periodic defect. We found that the critical velocity $v_c$ decreases by incresing the strength of the defect $V_0$, up to to a critical value of the defect intensity where the critical velocity vanishes

The influence of factors characterizing the performance of ports, measured by operational, financial and efficiency indicators

This paper aims to study the performance of ports by characterization factors and understand its importance. The methods used are data envelopment analysis (DEA), factor analysis and linear regression. The sample consists of 43 European ports. The results indicate the existence of a relationship between performance and several variables that characterize the port and confirm the impact of location, governance, size, infrastructure, port specialisation, logistics integration and maritime services in the operational and financial performance and efficiency of ports .Portos, desempenho, factores de caracterização

Discovering frequent user-environment interactions in intelligent environments

Intelligent Environments are expected to act proactively, anticipating the user's needs and preferences. To do that, the environment must somehow obtain knowledge of those need and preferences, but unlike current computing systems, in Intelligent Environments the user ideally should be released from the burden of providing information or programming any device as much as possible. Therefore, automated learning of a user's most common behaviors becomes an important step towards allowing an environment to provide highly personalized services. In this paper we present a system that takes information collected by sensors as a starting point, and then discovers frequent relationships between actions carried out by the user. The algorithm developed to discover such patterns is supported by a language to represent those patterns and a system of interaction which provides the user the option to fine tune their preferences in a natural way, just by speaking to the system