Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

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Background: Cognitive Muscular TherapyTM (CMT) is an integrated behavioural intervention developed for knee osteoarthritis. CMT teaches patients to reconceptualise the condition, integrates muscle biofeedback and aims to reduce muscle overactivity, both in response to pain and during daily activities. This nested qualitative study explored patient and physiotherapist perspectives and experiences of CMT.Methods: Five physiotherapists were trained to follow a well-defined protocol and then delivered CMT to at least two patients with knee osteoarthritis. Each patient received seven individual clinical sessions and was provided with access to online learning materials incorporating animated videos. Semi-structured interviews took place after delivery/completion of the intervention and data were analysed at the patient and physiotherapist level.Results: Five physiotherapists and five patients were interviewed. All described a process of changing beliefs throughout their engagement with CMT. A framework with three phases was developed to organise the data according to how osteoarthritis was conceptualised and how this changed throughout their interactions with CMT. Firstly, was an identification of pain beliefs to be challenged and recognition of how current beliefs can misalign with daily experiences. Secondly was a process of challenging and changing beliefs, validated through new experiences. Finally, there was an embedding of changed beliefs into self-management to continue with activities. Conclusion:This study identified a range of psychological changes which occur during exposure to CMT. These changes enabled patients to reconceptualise their condition, develop a new understanding of their body, understand psychological processes, and make sense of their knee pain.

A system of interacting agents is, by definition, very demanding in terms of computational resources. Although multi-agent systems have been used to solve complex problems in many areas, it is usually very difficult to perform large-scale simulations in their targeted serial computing platforms. Reconfigurable hardware, in particular Field Programmable Gate Arrays (FPGA) devices, have been successfully used in High Performance Computing applications due to their inherent flexibility, data parallelism and algorithm acceleration capabilities. Indeed, reconfigurable hardware seems to be the next logical step in the agency paradigm, but only a few attempts have been successful in implementing multi-agent systems in these platforms. This paper discusses the problem of inter-agent communications in Field Programmable Gate Arrays. It proposes a Network-on-Chip in a hierarchical star topology to enable agents' transactions through message broadcasting using the Open Core Protocol, as an interface between hardware modules. A customizable router microarchitecture is described and a multi-agent system is created to simulate and analyse message exchanges in a generic heavy traffic load agent-based application. Experiments have shown a throughput of 1.6Gbps per port at 100 MHz without packet loss and seamless scalability characteristics.

In order to properly monitor the health status of the hydrological resources of a region, in terms of water contamination, a scalable and low-cost system is necessary to map the water quality at different locations and allow the prioritization of more sophisticated and expensive monitoring campaigns on those areas where a suspicious behavior seems to be occurring. This paper presents the design and implementation process of such an IoT-based solution for low-cost and scalable water quality monitoring applications. To achieve that end, we propose the utilization of a low-cost inter-digital capacitance (IDC) sensor to characterize the conductivity of the water, a very telling parameter about the level of pollution in the water. Additionally, an embedded method to measure such sensor was designed and implemented, which considers the requirements of a portable platform: low computational capabilities, small memory and low power consumption. Our results show that an IDC sensor is capable of detecting the changes of the capacitance of the sample, and therefore mapping the changes in the conductivity of the water. Additionally, integrating an embedded measuring method is a valid option for in-situ characterization of water samples and the complete solution enables a new paradigm for water quality monitoring in large scale scenarios.