Developing a framework for mobile payments integration

This paper derives a theoretical framework for consideration of both the technologically driven dimensions of mobile payment solutions, and the associated value proposition for customers. Banks promote traditional payment instruments whose value proposition is the management of risk for both consumers and merchants. These instruments are centralised, costly and lack decision support functionality. The ubiquity of the mobile phone has provided a decentralised platform for managing payment processes in a new way, but the value proposition for customers has yet to be elaborated clearly. This inertia has stalled the design of sustainable revenue models for a mobile payments ecosystem. Merchants and consumers in the meantime are being seduced by the convenience of on-line and mobile payment solutions. Adopting the purchase and payment process as the unit of analysis, the current mobile payment landscape is reviewed with respect to the creation and consumption of customer value. From this analysis, a framework is derived juxtaposing customer value, related to what is being paid for, with payment integration, related to how payments are being made. The framework provides a theoretical and practical basis for considering the contribution of mobile technologies to the payment industry. The framework is then used to describe the components of a mobile payments pilot project being run on a trial population of 250 students on a campus in Ireland. In this manner, weaknesses in the value proposition for consumers and merchants were highlighted. Limitations of the framework as a research tool are also discussed.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Exercise Prescription for Patients with Non-specific Chronic Low Back Pain: a Qualitative Study of Patients' Experiences in Decision Making

Background The culture of current clinical practice calls for collaboration between therapists and patients, sharing power and responsibility. This paper reports on the findings of a qualitative study of exercise prescription for patients with NSCLBP, taking into account issues such as decision making and how this accords with patient preferences and experiences. Objective To understand the treatment decision making experiences, information and decision support needs of patients with NSCLBP who have been offered exercise as part of their management plan. Design A qualitative study using a philosophical hermeneutic approach. Methods Semi-structured interviews with eight patients (including use of brief patient vignettes) was undertaken to explore their personal experiences of receiving exercise as part of the management of their NSCLBP, and their involvement in decisions regarding their care. Findings The findings provide a detailed insight into patients’ perceptions and experiences of receiving exercise-based management strategies. Four themes were formed from the texts: (1) patients’ expectations and patients’ needs are not synonymous, (2) information is necessary but often not sufficient, (3) not all decisions need to be shared, and (4) wanting to be treated as an individual. Conclusions Shared decision making did not appear to happen in physiotherapy clinical practice, but equally may not be what every patient wants. The overall feeling of the patients was that the therapist was dominant in structuring the interactions, leaving the patients feeling disempowered to question and contribute to the decision making.

How can science contribute for the sustainable management of natural resource? : Praia da Vitória Bay and Guadiana Estuary, a comparative study

Tese de Doutoramento, Ciências do Ambiente (Ordenamento do Território), 5 de Abril de 2013, Universidade dos Açores.

A cost-efficiency and health benefit approach to improve urban air quality

When ambient air quality standards established in the EU Directive 2008/50/EC are exceeded, Member States are obliged to develop and implement Air Quality Plans (AQP) to improve air quality and health. Notwithstanding the achievements in emission reductions and air quality improvement, additional efforts need to be undertaken to improve air quality in a sustainable way - i.e. through a cost-efficiency approach. This work was developed in the scope of the recently concluded MAPLIA project "Moving from Air Pollution to Local Integrated Assessment", and focuses on the definition and assessment of emission abatement measures and their associated costs, air quality and health impacts and benefits by means of air quality modelling tools, health impact functions and cost-efficiency analysis. The MAPLIA system was applied to the Grande Porto urban area (Portugal), addressing PM10 and NOx as the most important pollutants in the region. Four different measures to reduce PM10 and NOx emissions were defined and characterized in terms of emissions and implementation costs, and combined into 15 emission scenarios, simulated by the TAPM air quality modelling tool. Air pollutant concentration fields were then used to estimate health benefits in terms of avoided costs (external costs), using dose-response health impact functions. Results revealed that, among the 15 scenarios analysed, the scenario including all 4 measures lead to a total net benefit of 0.3M€·y(-1). The largest net benefit is obtained for the scenario considering the conversion of 50% of open fire places into heat recovery wood stoves. Although the implementation costs of this measure are high, the benefits outweigh the costs. Research outcomes confirm that the MAPLIA system is useful for policy decision support on air quality improvement strategies, and could be applied to other urban areas where AQP need to be implemented and monitored.

Diagnóstico qualidade do processo do projeto com foco na comunicação e integração entre agentes: FAPP, uma ferramenta de apoio

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Arquitetura e Urbanismo, Programa de Pós-Graduação em Arquitetura e Urbanismo, 2015.

Un modelo de predicción de tráfico en la ciudad de Ambato

Este artículo contiene el estudio inicial de un modelo de predicción de tráfico, que intenta mostrar cómo puede complementarse la toma de decisiones que afecten a la ciudad a través de una buena planificación vial. Esto permitirá dar alternativas posibles de solución mediante la predicción de flujos de tráfico y determinando las intersecciones de mayor influencia dentro de la red vial, lo que por consecuencia reduciría costes en tiempo, combustible, contaminación, etc., obteniendo así una herramienta de ayuda en la toma de decisiones respecto del tráfico. Específicamente, se utiliza modelos dinámicos lineales para predecir el tráfico en distintos puntos de una ciudad y, en consecuencia, pronosticar su eventual saturación. Se puede así predecir puntos de la ciudad en la que es necesario actuar para aliviar los problemas de tráfico antes de que éstos lleguen a manifestarse.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.

MACVIA-ARIA Sentinel NetworK for allergic rhinitis (MASK-rhinitis): the new generation guideline implementation

Several unmet needs have been identified in allergic rhinitis: identification of the time of onset of the pollen season, optimal control of rhinitis and comorbidities, patient stratification, multidisciplinary team for integrated care pathways, innovation in clinical trials and, above all, patient empowerment. MASK-rhinitis (MACVIA-ARIA Sentinel NetworK for allergic rhinitis) is a simple system centred around the patient which was devised to fill many of these gaps using Information and Communications Technology (ICT) tools and a clinical decision support system (CDSS) based on the most widely used guideline in allergic rhinitis and its asthma comorbidity (ARIA 2015 revision). It is one of the implementation systems of Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). Three tools are used for the electronic monitoring of allergic diseases: a cell phone-based daily visual analogue scale (VAS) assessment of disease control, CARAT (Control of Allergic Rhinitis and Asthma Test) and e-Allergy screening (premedical system of early diagnosis of allergy and asthma based on online tools). These tools are combined with a clinical decision support system (CDSS) and are available in many languages. An e-CRF and an e-learning tool complete MASK. MASK is flexible and other tools can be added. It appears to be an advanced, global and integrated ICT answer for many unmet needs in allergic diseases which will improve policies and standards.

Proposta de modelagem de qualidade ambiental em áreas metropolitanas, por meio de dados de sensoriamento remoto

Tese (doutorado)—Universidade de Brasília, Instituto de Geociências, Pós-Graduação em Geociências Aplicadas, 2016.

Investigating key decision problems to optimize the operation and maintenance strategy of offshore wind farms

This paper investigates three decision problems with potential to optimize operation and maintenance and logistics strategies for offshore wind farms: the timing of pre-determined jack-up vessel campaigns; selection of crew transfer vessel fleet; and timing of annual services. These problems are compared both in terms of potential cost reduction and the stochastic variability and associated uncertainty of the outcome. Pre-determined jack-up vessel campaigns appear to have a high cost reduction potential but also a higher stochastic variability than the other decision problems. The paper also demonstrates the benefits and difficulties of considering problems together rather than solving them in isolation.

Evaluación del desempeño pronóstico de dos puntajes de predicción de mortalidad a siete días en pacientes adultos oncológicos críticamente enfermos admitidos a una unidad de cuidados intensivos en Bogotá

Introducción Los sistemas de puntuación para predicción se han desarrollado para medir la severidad de la enfermedad y el pronóstico de los pacientes en la unidad de cuidados intensivos. Estas medidas son útiles para la toma de decisiones clínicas, la estandarización de la investigación, y la comparación de la calidad de la atención al paciente crítico. Materiales y métodos Estudio de tipo observacional analítico de cohorte en el que reviso las historias clínicas de 283 pacientes oncológicos admitidos a la unidad de cuidados intensivos (UCI) durante enero de 2014 a enero de 2016 y a quienes se les estimo la probabilidad de mortalidad con los puntajes pronósticos APACHE IV y MPM II, se realizó regresión logística con las variables predictoras con las que se derivaron cada uno de los modelos es sus estudios originales y se determinó la calibración, la discriminación y se calcularon los criterios de información Akaike AIC y Bayesiano BIC. Resultados En la evaluación de desempeño de los puntajes pronósticos APACHE IV mostro mayor capacidad de predicción (AUC = 0,95) en comparación con MPM II (AUC = 0,78), los dos modelos mostraron calibración adecuada con estadístico de Hosmer y Lemeshow para APACHE IV (p = 0,39) y para MPM II (p = 0,99). El ∆ BIC es de 2,9 que muestra evidencia positiva en contra de APACHE IV. Se reporta el estadístico AIC siendo menor para APACHE IV lo que indica que es el modelo con mejor ajuste a los datos. Conclusiones APACHE IV tiene un buen desempeño en la predicción de mortalidad de pacientes críticamente enfermos, incluyendo pacientes oncológicos. Por lo tanto se trata de una herramienta útil para el clínico en su labor diaria, al permitirle distinguir los pacientes con alta probabilidad de mortalidad.

Bidding Decision of Wind-Thermal GenCo in Day-Ahead Market

This paper deals with the self-scheduling problem of a price-taker having wind and thermal power production and assisted by a cyber-physical system for supporting management decisions in a day-ahead electric energy market. The self-scheduling is regarded as a stochastic mixed-integer linear programming problem. Uncertainties on electricity price and wind power are considered through a set of scenarios. Thermal units are modelled by start-up and variable costs, furthermore constraints are considered, such as: ramp up/down and minimum up/down time limits. The stochastic mixed-integer linear programming problem allows a decision support for strategies advantaging from an effective wind and thermal mixed bidding. A case study is presented using data from the Iberian electricity market.

Analysis of Dyscalculia Evidences through Artificial Intelligence Systems

Dyscalculia is usually perceived of as a specific learning difficulty for mathematics or, more appropriately, arithmetic. Because definitions and diagnoses of dyscalculia are in their infancy and sometimes are contradictory. However, mathematical learning difficulties are certainly not in their infancy and are very prevalent and often devastating in their impact. Co-occurrence of learning disorders appears to be the rule rather than the exception. Co-occurrence is generally assumed to be a consequence of risk factors that are shared between disorders, for example, working memory. However, it should not be assumed that all dyslexics have problems with mathematics, although the percentage may be very high, or that all dyscalculics have problems with reading and writing. Because mathematics is very developmental, any insecurity or uncertainty in early topics will impact on later topics, hence to need to take intervention back to basics. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work will focus on the development of a Decision Support System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, grounded on a Case-based approach to computing, that allows for the handling of incomplete, unknown, or even self-contradictory information.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.