Barriers to and facilitators of older adult’s adherence to health recommendations: Towards an EngAging two-way health communication

Non-adherence to health recommendations (e.g. medical prescriptions) presents potential costs for healthcare, which could be prevented or mitigated. This is often attributed to a person’s rational choice, to not adhere. However, this may also be determined by individual and contextual factors implied in the recommendations communication process. In accordance, this chapter focuses specifically on barriers to and facilitators of adherence to recommendations and engagement with the healthcare process, particularly concerning the communication between health professionals and patients. For this, the authors present examples of engagement increment through different degrees of participation, from a one-way/directive towards a two-way/engaging communication process. This focuses specifically on a vulnerable population group with increasing healthcare needs: older adults. Future possibilities for two-way engaging communications are discussed, aimed at promoting increased adherence to health recommendations and people’s self-regulation of their own health.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

Proposal of a trust and reputation model to provide recommendation in open quality oriented environments

El auge y penetración de las nuevas tecnologías junto con la llamada Web Social están cambiando la forma en la que accedemos a la medicina. Cada vez más pacientes y profesionales de la medicina están creando y consumiendo recursos digitales de contenido clínico a través de Internet, surgiendo el problema de cómo asegurar la fiabilidad de estos recursos. Además, un nuevo concepto está apareciendo, el de pervasive healthcare o sanidad ubicua, motivado por pacientes que demandan un acceso a los servicios sanitarios en todo momento y en todo lugar. Este nuevo escenario lleva aparejado un problema de confianza en los proveedores de servicios sanitarios. Las plataformas de eLearning se están erigiendo como paradigma de esta nueva Medicina 2.0 ya que proveen un servicio abierto a la vez que controlado/supervisado a recursos digitales, y facilitan las interacciones y consultas entre usuarios, suponiendo una buena aproximación para esta sanidad ubicua. En estos entornos los problemas de fiabilidad y confianza pueden ser solventados mediante la implementación de mecanismos de recomendación de recursos y personas de manera confiable. Tradicionalmente las plataformas de eLearning ya cuentan con mecanismos de recomendación, si bien están más enfocados a la recomendación de recursos. Para la recomendación de usuarios es necesario acudir a mecanismos más elaborados como son los sistemas de confianza y reputación (trust and reputation) En ambos casos, tanto la recomendación de recursos como el cálculo de la reputación de los usuarios se realiza teniendo en cuenta criterios principalmente subjetivos como son las opiniones de los usuarios. En esta tesis doctoral proponemos un nuevo modelo de confianza y reputación que combina evaluaciones automáticas de los recursos digitales en una plataforma de eLearning, con las opiniones vertidas por los usuarios como resultado de las interacciones con otros usuarios o después de consumir un recurso. El enfoque seguido presenta la novedad de la combinación de una parte objetiva con otra subjetiva, persiguiendo mitigar el efecto de posibles castigos subjetivos por parte de usuarios malintencionados, a la vez que enriquecer las evaluaciones objetivas con información adicional acerca de la capacidad pedagógica del recurso o de la persona. El resultado son recomendaciones siempre adaptadas a los requisitos de los usuarios, y de la máxima calidad tanto técnica como educativa. Esta nueva aproximación requiere una nueva herramienta para su validación in-silico, al no existir ninguna aplicación que permita la simulación de plataformas de eLearning con mecanismos de recomendación de recursos y personas, donde además los recursos sean evaluados objetivamente. Este trabajo de investigación propone pues una nueva herramienta, basada en el paradigma de programación orientada a agentes inteligentes para el modelado de comportamientos complejos de usuarios en plataformas de eLearning. Además, la herramienta permite también la simulación del funcionamiento de este tipo de entornos dedicados al intercambio de conocimiento. La evaluación del trabajo propuesto en este documento de tesis se ha realizado de manera iterativa a lo largo de diferentes escenarios en los que se ha situado al sistema frente a una amplia gama de comportamientos de usuarios. Se ha comparado el rendimiento del modelo de confianza y reputación propuesto frente a dos modos de recomendación tradicionales: a) utilizando sólo las opiniones subjetivas de los usuarios para el cálculo de la reputación y por extensión la recomendación; y b) teniendo en cuenta sólo la calidad objetiva del recurso sin hacer ningún cálculo de reputación. Los resultados obtenidos nos permiten afirmar que el modelo desarrollado mejora la recomendación ofrecida por las aproximaciones tradicionales, mostrando una mayor flexibilidad y capacidad de adaptación a diferentes situaciones. Además, el modelo propuesto es capaz de asegurar la recomendación de nuevos usuarios entrando al sistema frente a la nula recomendación para estos usuarios presentada por el modo de recomendación predominante en otras plataformas que basan la recomendación sólo en las opiniones de otros usuarios. Por último, el paradigma de agentes inteligentes ha probado su valía a la hora de modelar plataformas virtuales complejas orientadas al intercambio de conocimiento, especialmente a la hora de modelar y simular el comportamiento de los usuarios de estos entornos. La herramienta de simulación desarrollada ha permitido la evaluación del modelo de confianza y reputación propuesto en esta tesis en una amplia gama de situaciones diferentes. ABSTRACT Internet is changing everything, and this revolution is especially present in traditionally offline spaces such as medicine. In recent years health consumers and health service providers are actively creating and consuming Web contents stimulated by the emergence of the Social Web. Reliability stands out as the main concern when accessing the overwhelming amount of information available online. Along with this new way of accessing the medicine, new concepts like ubiquitous or pervasive healthcare are appearing. Trustworthiness assessment is gaining relevance: open health provisioning systems require mechanisms that help evaluating individuals’ reputation in pursuit of introducing safety to these open and dynamic environments. Technical Enhanced Learning (TEL) -commonly known as eLearning- platforms arise as a paradigm of this Medicine 2.0. They provide an open while controlled/supervised access to resources generated and shared by users, enhancing what it is being called informal learning. TEL systems also facilitate direct interactions amongst users for consultation, resulting in a good approach to ubiquitous healthcare. The aforementioned reliability and trustworthiness problems can be faced by the implementation of mechanisms for the trusted recommendation of both resources and healthcare services providers. Traditionally, eLearning platforms already integrate recommendation mechanisms, although this recommendations are basically focused on providing an ordered classifications of resources. For users’ recommendation, the implementation of trust and reputation systems appears as the best solution. Nevertheless, both approaches base the recommendation on the information from the subjective opinions of other users of the platform regarding the resources or the users. In this PhD work a novel approach is presented for the recommendation of both resources and users within open environments focused on knowledge exchange, as it is the case of TEL systems for ubiquitous healthcare. The proposed solution adds the objective evaluation of the resources to the traditional subjective personal opinions to estimate the reputation of the resources and of the users of the system. This combined measure, along with the reliability of that calculation, is used to provide trusted recommendations. The integration of opinions and evaluations, subjective and objective, allows the model to defend itself against misbehaviours. Furthermore, it also allows ‘colouring’ cold evaluation values by providing additional quality information such as the educational capacities of a digital resource in an eLearning system. As a result, the recommendations are always adapted to user requirements, and of the maximum technical and educational quality. To our knowledge, the combination of objective assessments and subjective opinions to provide recommendation has not been considered before in the literature. Therefore, for the evaluation of the trust and reputation model defined in this PhD thesis, a new simulation tool will be developed following the agent-oriented programming paradigm. The multi-agent approach allows an easy modelling of independent and proactive behaviours for the simulation of users of the system, conforming a faithful resemblance of real users of TEL platforms. For the evaluation of the proposed work, an iterative approach have been followed, testing the performance of the trust and reputation model while providing recommendation in a varied range of scenarios. A comparison with two traditional recommendation mechanisms was performed: a) using only users’ past opinions about a resource and/or other users; and b) not using any reputation assessment and providing the recommendation considering directly the objective quality of the resources. The results show that the developed model improves traditional approaches at providing recommendations in Technology Enhanced Learning (TEL) platforms, presenting a higher adaptability to different situations, whereas traditional approaches only have good results under favourable conditions. Furthermore the promotion period mechanism implemented successfully helps new users in the system to be recommended for direct interactions as well as the resources created by them. On the contrary OnlyOpinions fails completely and new users are never recommended, while traditional approaches only work partially. Finally, the agent-oriented programming (AOP) paradigm has proven its validity at modelling users’ behaviours in TEL platforms. Intelligent software agents’ characteristics matched the main requirements of the simulation tool. The proactivity, sociability and adaptability of the developed agents allowed reproducing real users’ actions and attitudes through the diverse situations defined in the evaluation framework. The result were independent users, accessing to different resources and communicating amongst them to fulfil their needs, basing these interactions on the recommendations provided by the reputation engine.

Improving matching process in social network

Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

Bioequivalence of two tablet formulations of capecitabine and exploration of age, gender, body surface area, and creatinine clearance as factors influencing systemic exposure in cancer patients

Purpose: The objective of the study was to assess the bioequivalence of two tablet formulations of capecitabine and to explore the effect of age, gender, body surface area and creatinine clearance on the systemic exposure to capecitabine and its metabolites. Methods: The study was designed as an open, randomized two-way crossover trial. A single oral dose of 2000 mg capecitabine was administered on two separate days to 25 patients with solid tumors. On one day, the patients received four 500-mg tablets of formulation B (test formulation) and on the other day, four 500-mg tablets of formulation A (reference formulation). The washout period between the two administrations was between 2 and 8 days. After each administration, serial blood and urine samples were collected for up to 12 and 24 h, respectively. Unchanged capecitabine and its metabolites were determined in plasma using LC/MS-MS and in urine by NMRS. Results: Based on the primary pharmacokinetic parameter, AUC(0-∞) of 5'-DFUR, equivalence was concluded for the two formulations, since the 90% confidence interval of the estimate of formulation B relative to formulation A of 97% to 107% was within the acceptance region 80% to 125%. There was no clinically significant difference between the t(max) for the two formulations (median 2.1 versus 2.0 h). The estimate for C(max) was 111% for formulation B compared to formulation A and the 90% confidence interval of 95% to 136% was within the reference region 70% to 143%. Overall, these results suggest no relevant difference between the two formulations regarding the extent to which 5'-DFUR reached the systemic circulation and the rate at which 5'-DFUR appeared in the systemic circulation. The overall urinary excretions were 86.0% and 86.5% of the dose, respectively, and the proportion recovered as each metabolite was similar for the two formulations. The majority of the dose was excreted as FBAL (61.5% and 60.3%), all other chemical species making a minor contribution. Univariate and multivariate regression analysis to explore the influence of age, gender, body surface area and creatinine clearance on the log-transformed pharmacokinetic parameters AUC(0-∞) and C(max) of capecitabine and its metabolites revealed no clinically significant effects. The only statistically significant results were obtained for AUC(0-∞) and C(max) of intact drug and for C(max) of FBAL, which were higher in females than in males. Conclusion: The bioavailability of 5'-DFUR in the systemic circulation was practically identical after administration of the two tablet formulations. Therefore, the two formulations can be regarded as bioequivalent. The variables investigated (age, gender, body surface area, and creatinine clearance) had no clinically significant effect on the pharmacokinetics of capecitabine or its metabolites.

Wireless network-coded three-way relaying using Latin Cubes

The design of modulation schemes for the physical layer network-coded three-way wireless relaying scenario is considered. The protocol employs two phases: Multiple Access (MA) phase and Broadcast (BC) phase with each phase utilizing one channel use. For the two-way relaying scenario, it was observed by Koike-Akino et al. [4], that adaptively changing the network coding map used at the relay according to the channel conditions greatly reduces the impact of multiple access interference which occurs at the relay during the MA phase and all these network coding maps should satisfy a requirement called exclusive law. This paper does the equivalent for the three-way relaying scenario. We show that when the three users transmit points from the same 4-PSK constellation, every such network coding map that satisfies the exclusive law can be represented by a Latin Cube of Second Order. The network code map used by the relay for the BC phase is explicitly obtained and is aimed at reducing the effect of interference at the MA stage.

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.

Grand Trunk Railway way bills, 1900

The Grand Trunk Railway initially ran from Montreal to Toronto, then with expansion of Canada operated to British Columbia, linking major cities together. In 1900, two way bill forms were completed; one for the Niagara Falls Wine Co. and the other for T.G. Bright & Co. Both companies were headquartered in Niagara Falls, Ont. The consignors were John Mayberry & Co. and John Eleareys?.

Les pratiques d'attraction et de rétention de la génération Y dans les PME

Les dirigeants-propriétaires des petites et moyennes entreprises (PME) sont de plus en plus intéressés par la gestion des ressources humaines (GRH); certains y voient un avantage concurrentiel face à la pénurie de main-d’œuvre actuelle. Considérant que la compatibilité entre les caractéristiques des travailleurs et celles de l’organisation peut générer des résultats positifs (Kristof-Brown et Guay, 2011), notre étude s’intéresse aux pratiques de GRH associées aux valeurs au travail de la génération Y ainsi que leur effet sur la capacité des PME à attirer et retenir cette cohorte. Cette étude qualitative s’est réalisée grâce à des données primaires colligées à la suite d’entrevues avec des dirigeants de quatre PME du secteur de la construction et seize employés appartenant à la génération Y œuvrant au sein de ces entreprises. Par nos résultats, nous avons relevé que la qualité des relations, autant avec les collègues que les superviseurs, demeure généralement la principale source d’attraction et de rétention des Y dans les PME. Nos résultats soutiennent aussi que leur attraction et rétention peut être très fortement favorisée grâce à des pratiques de communication bidirectionnelle et illimitée, une communication stratégique et une liberté dans la gestion du temps et des méthodes de travail. La conciliation travail et vie personnelle, les défis variés, les possibilités d’avancement, la gestion des ressources humaines socialement responsable, la reconnaissance des compétences ainsi que la gestion participative sont aussi des pratiques pouvant être fortement liées à l’attraction et la rétention de cette génération. Nos résultats montrent aussi que l’attraction et la rétention des Y dans les PME sont modérément favorisées par le travail d’équipe, les conditions de travail équitables et objectives et la rémunération globale concurrentielle. À l’inverse, la présence de technologies de l’information et des communications et la formation continue sont des sources plus faibles d’attraction et de rétention en comparaison aux autres pratiques abordées dans cette étude. En somme, cette étude contribue à la littérature sur la GRH dans les PME, puisque les spécificités relatives à ces entreprises ont été peu considérées jusqu’à aujourd’hui. Elle permet aussi la recommandation de pratiques utiles aux dirigeants-propriétaires et professionnels en ressources humaines œuvrant avec le défi d’attraction et de rétention de la génération Y au sein de leur entreprise.

Interactions between grain size and composition of sediments: two examples

Two contrasting case studies of sediment and detrital mineral composition are investigated in order to outline interactions between chemical composition and grain size. Modern glacial sediments exhibit a strong dependence of the two parameters due to the preferential enrichment of mafic minerals, especially biotite, in the fine-grained fractions. On the other hand, the composition of detrital heavy minerals (here: rutile) appears to be not systematically related to grain-size, but is strongly controlled by location, i.e. the petrology of the source rocks of detrital grains. This supports the use of rutile as a well-suited tracer mineral for provenance studies. The results further suggest that (i) interpretations derived from whole-rock sediment geochemistry should be flanked by grain-size observations, and (ii) a more sound statistical evaluation of these interactions require the development of new tailor-made statistical tools to deal with such so-called two-way compositions

Brazil’s electorate : still polarised but in a new way

Back in November, we wrote about an analysis of tweets in Brazil that illustrated the extreme polarisation of the country’s voters on the eve of the presidential election on October 26. A striking image (seen in miniature on the left) generated by Marco Aurélio Ruediger and colleagues at the Fundação Getúlio Vargas, an educational institution in Rio de Janeiro, showed voters on each side of the two-way race talking exclusively among themselves and almost never to each other.

Comparison between two methods for cardiac output measurement in propofol-anesthetized dogs: thermodilution and Doppler

ObjectiveTo compare cardiac output (CO) measured by Doppler echocardiography and thermodilution techniques in spontaneously breathing dogs during continuous infusion of propofol. To do so, CO was obtained using the thermodilution method (CO(TD)) and Doppler evaluation of pulmonary flow (CO(DP)) and aortic flow (CO(DA)).Study designProspective cohort study.AnimalsEight adult dogs weighing 8.3 +/- 2.0 kg.MethodsPropofol was used for induction (7.5 +/- 1.9 mg kg-1 IV) followed by a continuous rate infusion at 0.7 mg kg-1 minute-1. The animals were positioned in left lateral recumbency on an echocardiography table that allowed for positioning of the transducer at the 3rd and 5th intercostal spaces of the left hemithorax for Doppler evaluation of pulmonary and aortic valves, respectively. CO(DP) and CO(DA) were calculated from pulmonary and aortic velocity spectra, respectively. A pulmonary artery catheter was inserted via the jugular vein and positioned inside the lumen of the pulmonary artery in order to evaluate CO(TD). The first measurement of CO(TD), CO(DP) and CO(DA) was performed 30 minutes after beginning continuous infusion (T0) and then at 15-minute intervals (T15, T30, T45 and T60). Numeric data were submitted to two-way anova for repeated measurements, Pearson's correlation coefficient and Bland & Altman analysis. Data are presented as mean +/- SD.ResultsAt T0, CO(TD) was lower than CO(DA). CO(DA) was higher than CO(TD) and CO(DP) at T30, T45 and T60. The difference between the CO(TD) and CO(DP), when all data were included, was -0.04 +/- 0.22 L minute-1 and Pearson's correlation coefficient (r) was 0.86. The difference between the CO(TD) and CO(DA) was -0.87 +/- 0.54 L minute-1 and r = 0.69. For CO(TD) and CO(DP), the difference was -0.82 +/- 0.59 L minute-1 and r = 0.61.ConclusionDoppler evaluation of pulmonary flow was a clinically acceptable method for assessing the CO in propofol-anesthetized dogs.

Pulp response after application of two resin modified glass ionomer cements (RMGICs) in deep cavities of prepared human teeth

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)