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.

Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine?

Precision medicine is an emerging approach to disease treatment and prevention that considers variability in patient genes, environment, and lifestyle. However, little has been written about how such research impacts emergency care. Recent advances in analytical techniques have made it possible to characterize patients in a more comprehensive and sophisticated fashion at the molecular level, promising highly individualized diagnosis and treatment. Among these techniques are various systematic molecular phenotyping analyses (e.g., genomics, transcriptomics, proteomics, and metabolomics). Although a number of emergency physicians use such techniques in their research, widespread discussion of these approaches has been lacking in the emergency care literature and many emergency physicians may be unfamiliar with them. In this article, we briefly review the underpinnings of such studies, note how they already impact acute care, discuss areas in which they might soon be applied, and identify challenges in translation to the emergency department (ED). While such techniques hold much promise, it is unclear whether the obstacles to translating their findings to the ED will be overcome in the near future. Such obstacles include validation, cost, turnaround time, user interface, decision support, standardization, and adoption by end-users.

Simplified Predictive Instrument to Rule Out Acute Coronary Syndromes in a High-Risk Population.

BACKGROUND: It is unclear whether diagnostic protocols based on cardiac markers to identify low-risk chest pain patients suitable for early release from the emergency department can be applied to patients older than 65 years or with traditional cardiac risk factors. METHODS AND RESULTS: In a single-center retrospective study of 231 consecutive patients with high-risk factor burden in which a first cardiac troponin (cTn) level was measured in the emergency department and a second cTn sample was drawn 4 to 14 hours later, we compared the performance of a modified 2-Hour Accelerated Diagnostic Protocol to Assess Patients with Chest Pain Using Contemporary Troponins as the Only Biomarker (ADAPT) rule to a new risk classification scheme that identifies patients as low risk if they have no known coronary artery disease, a nonischemic electrocardiogram, and 2 cTn levels below the assay's limit of detection. Demographic and outcome data were abstracted through chart review. The median age of our population was 64 years, and 75% had Thrombosis In Myocardial Infarction risk score ≥2. Using our risk classification rule, 53 (23%) patients were low risk with a negative predictive value for 30-day cardiac events of 98%. Applying a modified ADAPT rule to our cohort, 18 (8%) patients were identified as low risk with a negative predictive value of 100%. In a sensitivity analysis, the negative predictive value of our risk algorithm did not change when we relied only on undetectable baseline cTn and eliminated the second cTn assessment. CONCLUSIONS: If confirmed in prospective studies, this less-restrictive risk classification strategy could be used to safely identify chest pain patients with more traditional cardiac risk factors for early emergency department release.

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.

Using AHP and the IPMA Competence Baseline in the project managers selection process

This paper proposes the joint use of the AHP (Analytic Hierarchy Process) and the ICB (IPMA Competence Baseline), as a tool for the decision-making process of selecting the most suitable managers for projects. A hierarchical structure, comprising the IPMA’s ICB 3.0 contextual, behavioural and technical competence elements, is constructed for the selection of project managers. It also describes the AHP implementation, illustrating the whole process with an example using all the 46 ICB competence elements as model criteria. This tool can be of high interest to decision-makers because it allows comparing the candidates for managing a project using a systematic and rigorous process with a rich set of proven criteria.

Accessibility Instruments In Planning Practice: Bridging The Implementation Gap

Accessibility concepts are increasingly acknowledged as fundamental to understand cities and urban regions. Accordingly, accessibility instruments have been recognised as valuable support tools for land-use and transport planning. However, despite the relatively large number of instruments available in the literature, they are not widely used in planning practice. This paper aims to explore why accessibility instruments are not widely used in planning practice. To this end, we focus our research on perceived user-friendliness and usefulness of accessibility instruments. First, we surveyed some instrument developers, providing an overview of the characteristics of accessibility instruments available and on developers’ perceptions of their user-friendliness in planning practice. Second, we brought together developers and planning practitioners in some local workshops across Europe and Australia, where participants were asked to use insights provided by accessibility instruments for the development of planning strategies. We found that most practitioners are convinced of the usefulness of accessibility instruments in planning practice, as they generate new and relevant insights for planners. Findings suggest that not only user-friendliness problems but mainly organisational barriers and lack of institutionalisation of accessibility instruments, are the main causes of the implementation gap. Thus user-friendliness improvement may provide limited contributions to the successful implementation of accessibility concepts in planning practice. In fact, there seems to be more to gain from the active and continued engagement of instrument developers with planning practitioners and the institutionalisation of accessibility planning.

Decisions, decisions everywhere (in the open data era).

Abstract: Decision support systems have been widely used for years in companies to gain insights from internal data, thus making successful decisions. Lately, thanks to the increasing availability of open data, these systems are also integrating open data to enrich decision making process with external data. On the other hand, within an open-data scenario, decision support systems can be also useful to decide which data should be opened, not only by considering technical or legal constraints, but other requirements, such as "reusing potential" of data. In this talk, we focus on both issues: (i) open data for decision making, and (ii) decision making for opening data. We will first briefly comment some research problems regarding using open data for decision making. Then, we will give an outline of a novel decision-making approach (based on how open data is being actually used in open-source projects hosted in Github) for supporting open data publication. Bio of the speaker: Jose-Norberto Mazón holds a PhD from the University of Alicante (Spain). He is head of the "Cátedra Telefónica" on Big Data and coordinator of the Computing degree at the University of Alicante. He is also member of the WaKe research group at the University of Alicante. His research work focuses on open data management, data integration and business intelligence within "big data" scenarios, and their application to the tourism domain (smart tourism destinations). He has published his research in international journals, such as Decision Support Systems, Information Sciences, Data & Knowledge Engineering or ACM Transaction on the Web. Finally, he is involved in the open data project in the University of Alicante, including its open data portal at http://datos.ua.es

Discrete-event Simulation and Optimization to Improve the Performance of a Healthcare System

Thesis (Ph.D.)--University of Washington, 2016-08

Modelo de decisão multicritério para seleções interciclo das universidades novas: estudo na Universidade Federal do Rio Grande do Norte

The discussions wherein develop proposals for university reform in Brazil include, among other things, the conception of the university titled "New University", whose structural origin comes from the bill of higher education reform and unification of the foundations of education European upper (Bologna process). At its core, the Bologna process has imposed a series of transformations, among which, the promotion of mobility, as a stimulus to interinstitutional cooperation to enable an better and bigger qualification of the students. Nevertheless, what we see is that this point is one of the main points made flawed by Brazilian institutions that have adopted this model of higher education. An example is the Bachelor of Science and Technology - BC&T, Federal University of Rio Grande do Norte - UFRN, where there are problems of the internal order, represented by the problem of the reusing of the disciplines, such also of external order, in cases of transfers interinstitutional. Because of this, and knowing that this is a typical problem in which multiple criteria are involved, the aim of this study is to propose a multicriteria model for selection of interciclo of the BC&T of the UFRN which addresses the issue of mobility. For this, this study was of exploratory and study case nature, use as tools of data collection, the bibliographic and documentary research, as well as semi-structured interviews. For the elaboration of the model, were used the five phases most commonly used in the modeling of problems in operational research in a sample of 91 students of BC&T. As a result, we obtained a model that addresses the issue of internal and external mobility of the school and that, moreover, was also more robust and fair than the current model of BC&T and also what is used in other courses of the UFRN, taking into consideration the expected results by the decision makers

Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.