I linguaggi per la programmazione web-based: l'alternativa Dart

Il Web ha subito numerose trasformazioni rispetto al passato. Si è passati da un Web statico, in cui l'unica possibilità era quella di leggere i contenuti della pagina, ad un Web dinamico e interattivo come quello dei social network. Il Web moderno è, ancora oggi, un universo in espansione. La possibilità di arricchire le pagine con contenuti interattivi, video, foto e molto altro, rende l'esperienza web sempre più coinvolgente. Inoltre la diffusione sempre più ampia di mobile device ha reso necessaria l'introduzione di nuovi strumenti per sfruttare al meglio le funzionalità di tali dispositivi. Esistono al momento tantissimi linguaggi di scripting e di programmazione, ma anche CMS che offrono a chiunque la possibilità di scrivere e amministrare siti web. Nonostante le grandi potenzialità che offrono, spesso queste tecnologie si occupano di ambiti specifici e non permettono di creare sistemi omogenei che comprendano sia client che server. Dart si inserisce proprio in questo contesto. Tale linguaggio dà a i programmatori la possibilità di poter sviluppare sia lato client sia lato server. L'obiettivo principale di questo linguaggio è infatti la risoluzione di alcune problematiche comuni a molti programmatori web. Importante in questo senso è il fatto di rendere strutturata la costruzione di programmi web attraverso l'uso di interfacce e classi. Fornisce inoltre un supporto per l'integrazione di svariate funzionalità che allo stato attuale sono gestite da differenti tecnologie. L'obiettivo della presente tesi è quello di mettere a confronto Dart con alcune delle tecnologie più utilizzate al giorno d'oggi per la programmazione web-based. In particolare si prenderanno in considerazione JavaScript, jQuery, node.js e CoffeeScript.

Psychosoziale Belastung bei Herztransplantationskandidaten

Fragestellung: In der vorliegenden Untersuchung wurde geprüft, ob sich neu gelistete Herztransplantationskandidaten mit ischämischer Herzinsuffizienz (IKMP) und dilatativer Kardiomyopathie (DKMP) nach Kontrolle von Krankheitsschwere und Geschlecht in psychosozialen Variablen unterscheiden. Zudem wurde untersucht, ob psychosoziale Risikofaktoren für IKMP-Patienten stärker mit der 1-Jahres-Mortalität auf der Warteliste assoziiert sind als für DKMP-Patienten. Methode: Im Rahmen der multizentrischen, prospektiv angelegten Studie „Warten auf ein neues Herz“ bearbeiteten 160 DKMP- (16 % Frauen) und 122 IKMP-Patienten (13 % Frauen) kurz nach Aufnahme auf die Warteliste einen Fragebogen, der subjektiven Gesundheitszustand, Angst, Depressivität, soziale Unterstützung, Netzwerkgröße, Ärger, Ärgerausdruck und wartezeitspezifische Belastungen erfasste. Medizinische Daten zum Zeitpunkt der Listung und Veränderungen im Wartelistenstatus im 1-Jahres-Follow-Up wurden von Eurotransplant bereitgestellt. Ergebnisse: IKMP-Patienten waren im Vergleich mit DKMP-Patienten älter, häufiger ehemalige Raucher, hatten häufiger vorherige Herzoperationen erlebt, litten seltener unter einer Erregungsleitungsstörung, wurden seltener mit Aldosteronantagonisten sowie häufiger mit Katecholaminen behandelt. Nach Kontrolle dieser Variablen und unter Einbezug des Geschlechts berichteten nur Männer mit IKMP höhere Angstwerte und mehr Anger-In als Männer mit DKMP. Ein höheres Mortalitätsrisiko für IKMP-Patienten ein Jahr nach Aufnahme auf die Warteliste konnte nicht belegt werden. Auch zeigte sich keine Interaktion zwischen psychosozialer Belastung und Grunderkrankung hinsichtlich der Mortalität. Niedrige emotionale Unterstützung ging unabhängig von der Grunderkrankung mit einem dreifach erhöhten Mortalitätsrisiko einher. Fazit: Männer mit IKMP sind stärker durch negative Emotionen belastet als Männer mit DKMP. Eine weitere Risikogruppe stellen Personen mit niedriger emotionaler Unterstützung dar. Psychosoziale Risikofaktoren sollten daher bereits bei Aufnahme auf die Warteliste erfasst und Betreuungsangebote gezielt auf diese beiden Gruppen abgestimmt werden.

Progettazione e realizzazione di un'applicazione social georeferenziata per dispositivi android

In questo lavoro di tesi abbiamo ideato e implementato un nuovo social network combinando in una sola applicazione per dispositivi mobili alcune nuove idee con altre già esistenti, ma ancora poco sviluppate. Dopo aver analizzato le app attualmente esistenti, si sono definite le caratteristiche dell'applicazione che si vuole creare, traendo ispirazione da Snapchat, Foursquare e Whisper. L'obiettivo è un'applicazione che permette agli utenti di inviare messaggi con testo e immagini geotaggati sulla loro posizione. Essi saranno anonimi, dato che gli utenti avranno la possibilità di creare e utilizzare facilmente più identità. I messaggi potranno essere votati e commentati e avranno una durata definita dall'autore, dopo di che si autodistruggeranno. Tutti i messaggi saranno visibili su una mappa che li mostrerà nel punto in cui l'autore si trovava quando li ha inviati. Gli utenti riceveranno una notifica per i nuovi messaggi inviati nelle loro vicinanze. I primi mesi di lavoro sul progetto sono stati dedicati al perfezionamento dell'idea originale e alla realizzazione della parte server. In questa fase è stato acquisito e configurato un Virtual Private Server, è stato progettato e creato il database MySql, ed è stata implementata la logica applicativa lato server in PHP. Successivamente, ci si è concentrati sulla progettazione e realizzazione dell'applicazione stessa, scegliendo la piattaforma Android e programmando in linguaggio Java. In questa fase si è definita e implementata l'interfaccia utente e la logica applicativa lato client. Giunti ad un buon livello di sviluppo, si è cominciato a distribuire limitatamente una versione alpha dell'applicazione per ottenere feedback dagli utenti, soprattutto riguardanti l'usabilità. La seguente tesi descrive in dettaglio l'idea di partenza, l'architettura del progetto e le funzionalità realizzate, per concludersi con una previsione degli sviluppi futuri. Vi è inoltre in allegato il codice sorgente dell'applicazione Android realizzata.

Ago e filo 3.0: quali opportunità di business per l'artigianato locale con l'evoluzione dei social? C'è ancora spazio e visibilità per loro?

Con l’evoluzione dei social network e degli strumenti del 2.0 (blog, wiki, forum), internet si avvia verso nuove forme di business cui artigiani e pmi possono sfruttare per nuove opportunità, unendo social network e e-commerce e capire come si è evoluto il processo di creazione e finanziamento di un prodotto. La tesi è un viaggio per scoprire quali sono queste nuove opportunità per chi pratica artigianato a livello locale, e come è cambiata la figura stessa dell’artigiano, tra questi un nuovo movimento chiamato makers, che sfruttano i nuovi strumenti, tra cui anche mezzi rivoluzionari come la stampante 3D ora accessibile a tutti per creare oggetti e nuove idee. Sarà necessario capire quali sono le vetrine online dove e come poter vendere i nostri prodotti e profilare attraverso una segmentazione l’utente medio che già utilizza il social-commerce. In seguito si tratta il caso empirico di una start-up italiana, Blomming, una dei primi a differenziarsi dagli altri marketplace più famosi come contenitore adatto ad artigiani, ma non solo. Si seguiranno le varie fasi di crescita da un’idea, introduzione nel mercato fino alla direzione a cui si sta avviando con le ultime novità effettuando un’analisi dei rischi. Infine per capire tendenze e storie di chi sta già usando queste piattaforme è stato necessario fare un profilo dell’artigiano fai-da-te medio intervistando, e successivamente creando grafici, 100 artigiani che già vendono online.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Investigation of the Network Construction Behaviour on Social Networking Sites

By enabling connections between individuals, Social Networking Sites, such as Facebook, promise to create significant individual as well as social value. Encouraging connections between users is also crucial for service providers who increasingly rely on social advertising and viral marketing campaigns as important sources of their revenue. Consequently, understanding user’s network construction behavior becomes critical. However, previous studies offer only few scattered insights into this research question. In order to fill this gap, we employ Grounded Theory methodology to derive a comprehensive model of network construction behavior on social networking sites. In the following step we assess two Structural Equation Models to gain refined insights into the motivation to send and accept friendship requests – two network expansion strategies. Based on our findings, we offer recommendations for social network providers.

Contribution to proactivity in mobile context-aware recommender systems

Los sistemas de recomendación son potentes herramientas de filtrado de información que permiten a usuarios solicitar sugerencias sobre ítems que cubran sus necesidades. Tradicionalmente estas recomendaciones han estado basadas en opiniones de los mismos, así como en datos obtenidos de su consumo histórico o comportamiento en el propio sistema. Sin embargo, debido a la gran penetración y uso de los dispositivos móviles en nuestra sociedad, han surgido nuevas oportunidades en el campo de los sistemas de recomendación móviles gracias a la información contextual que se puede obtener sobre la localización o actividad de los usuarios. Debido a este estilo de vida en el que todo tiende a la movilidad y donde los usuarios están plenamente interconectados, la información contextual no sólo es física, sino que también adquiere una dimensión social. Todo esto ha dado lugar a una nueva área de investigación relacionada con los Sistemas de Recomendación Basados en Contexto (CARS) móviles donde se busca incrementar el nivel de personalización de las recomendaciones al usar dicha información. Por otro lado, este nuevo escenario en el que los usuarios llevan en todo momento un terminal móvil consigo abre la puerta a nuevas formas de recomendar. Sustituir el tradicional patrón de uso basado en petición-respuesta para evolucionar hacia un sistema proactivo es ahora posible. Estos sistemas deben identificar el momento más adecuado para generar una recomendación sin una petición explícita del usuario, siendo para ello necesario analizar su contexto. Esta tesis doctoral propone un conjunto de modelos, algoritmos y métodos orientados a incorporar proactividad en CARS móviles, a la vez que se estudia el impacto que este tipo de recomendaciones tienen en la experiencia de usuario con el fin de extraer importantes conclusiones sobre "qué", "cuándo" y "cómo" se debe notificar proactivamente. Con este propósito, se comienza planteando una arquitectura general para construir CARS móviles en escenarios sociales. Adicionalmente, se propone una nueva forma de representar el proceso de recomendación a través de una interfaz REST, lo que permite crear una arquitectura independiente de dispositivo y plataforma. Los detalles de su implementación tras su puesta en marcha en el entorno bancario español permiten asimismo validar el sistema construido. Tras esto se presenta un novedoso modelo para incorporar proactividad en CARS móviles. Éste muestra las ideas principales que permiten analizar una situación para decidir cuándo es apropiada una recomendación proactiva. Para ello se presentan algoritmos que establecen relaciones entre lo propicia que es una situación y cómo esto influye en los elementos a recomendar. Asimismo, para demostrar la viabilidad de este modelo se describe su aplicación a un escenario de recomendación para herramientas de creación de contenidos educativos. Siguiendo el modelo anterior, se presenta el diseño e implementación de nuevos interfaces móviles de usuario para recomendaciones proactivas, así como los resultados de su evaluación entre usuarios, lo que aportó importantes conclusiones para identificar cuáles son los factores más relevantes a considerar en el diseño de sistemas proactivos. A raíz de los resultados anteriores, el último punto de esta tesis presenta una metodología para calcular cuán apropiada es una situación de cara a recomendar de manera proactiva siguiendo el modelo propuesto. Como conclusión, se describe la validación llevada a cabo tras la aplicación de la arquitectura, modelo de recomendación y métodos descritos en este trabajo en una red social de aprendizaje europea. Finalmente, esta tesis discute las conclusiones obtenidas a lo largo de la extensa investigación llevada a cabo, y que ha propiciado la consecución de una buena base teórica y práctica para la creación de sistemas de recomendación móviles proactivos basados en información contextual. ABSTRACT Recommender systems are powerful information filtering tools which offer users personalized suggestions about items whose aim is to satisfy their needs. Traditionally the information used to make recommendations has been based on users’ ratings or data on the item’s consumption history and transactions carried out in the system. However, due to the remarkable growth in mobile devices in our society, new opportunities have arisen to improve these systems by implementing them in ubiquitous environments which provide rich context-awareness information on their location or current activity. Because of this current all-mobile lifestyle, users are socially connected permanently, which allows their context to be enhanced not only with physical information, but also with a social dimension. As a result of these novel contextual data sources, the advent of mobile Context-Aware Recommender Systems (CARS) as a research area has appeared to improve the level of personalization in recommendation. On the other hand, this new scenario in which users have their mobile devices with them all the time offers the possibility of looking into new ways of making recommendations. Evolving the traditional user request-response pattern to a proactive approach is now possible as a result of this rich contextual scenario. Thus, the key idea is that recommendations are made to the user when the current situation is appropriate, attending to the available contextual information without an explicit user request being necessary. This dissertation proposes a set of models, algorithms and methods to incorporate proactivity into mobile CARS, while the impact of proactivity is studied in terms of user experience to extract significant outcomes as to "what", "when" and "how" proactive recommendations have to be notified to users. To this end, the development of this dissertation starts from the proposal of a general architecture for building mobile CARS in scenarios with rich social data along with a new way of managing a recommendation process through a REST interface to make this architecture multi-device and cross-platform compatible. Details as regards its implementation and evaluation in a Spanish banking scenario are provided to validate its usefulness and user acceptance. After that, a novel model is presented for proactivity in mobile CARS which shows the key ideas related to decide when a situation warrants a proactive recommendation by establishing algorithms that represent the relationship between the appropriateness of a situation and the suitability of the candidate items to be recommended. A validation of these ideas in the area of e-learning authoring tools is also presented. Following the previous model, this dissertation presents the design and implementation of new mobile user interfaces for proactive notifications. The results of an evaluation among users testing these novel interfaces is also shown to study the impact of proactivity in the user experience of mobile CARS, while significant factors associated to proactivity are also identified. The last stage of this dissertation merges the previous outcomes to design a new methodology to calculate the appropriateness of a situation so as to incorporate proactivity into mobile CARS. Additionally, this work provides details about its validation in a European e-learning social network in which the whole architecture and proactive recommendation model together with its methods have been implemented. Finally, this dissertation opens up a discussion about the conclusions obtained throughout this research, resulting in useful information from the different design and implementation stages of proactive mobile CARS.

How can irrigated agriculture adapt to climate change? Insights from the Guadiana Basin in Spain

Climate change is already affecting many natural systems and human environments worldwide, like the semiarid Guadiana Basin in Spain. This paper illustrates a systematic analysis of climate change adaptation in the Guadiana irrigation farming region. The study applies a solution-oriented diagnostic framework structured along a series of sequential analytical steps. An initial stage integrates economic and hydrologic modeling to evaluate the effects of climate change on the agriculture and water sectors. Next, adaptation measures are identified and prioritized through a stakeholder-based multi-criteria analysis. Finally, a social network analysis identifies key actors and their relationships in climate change adaptation. The study shows that under a severe climate change scenario, water availability could be substantially decreased and drought occurrence will augment. In consequence, farmers will adapt their crops to a lesser amount of water and income gains will diminish, particularly for smallholder farms. Among the various adaptation measures considered, those related to private farming (new crop varieties and modern irrigation technologies) are ranked highest, whereas public-funded hard measures (reservoirs) are lowest and public soft measures (insurance) are ranked middle. In addition, stakeholders highlighted that the most relevant criteria for selecting adaptation plans are environmental protection, financial feasibility and employment creation. Nonetheless, the social network analysis evidenced the need to strengthen the links among the different stakeholder groups to facilitate the implementation of adaptation processes. In sum, the diagnostic framework applied in this research can be considered a valuable tool for guiding and supporting decision making in climate change adaptation and communicating scientific results.

Efectos territoriales de las denominaciones de origen protegidas de aceite de oliva en España

La implementación de indicaciones geográficas, generan distintos efectos socio-económicos, tecnológicos y ambientales, no sólo a nivel de los productores directamente involucrados, sino también en el marco del sistema agroalimentario local (SIAL). Entre estos últimos, se destaca la organización colectiva de los productores y el desarrollo de una gobernanza, a partir del conjunto de actividades desarrolladas por el Consejo Regulador (CR), en torno al control de calidad y la promoción del producto. El concepto de gobernanza territorial implica llevar a cabo procesos de organización colectiva en red en los que tienen lugar procesos de coordinación multi-nivel entre los agentes, las empresas y las instituciones locales, en un contexto de asimetría en la información y de la existencia de numerosos decisores. El objetivo de esta Tesis Doctoral es realizar un aporte metodológico para el estudio de la gobernanza territorial en sistemas agroalimentarios localizados, a partir de la construcción de indicadores (de calidad, innovación, efectos económicos y prácticas ambientales) y el análisis de las redes de colaboración en materia de difusión del conocimiento e innovaciones técnicas, organizativas y comerciales en la Comarca de Sierra Mágina. Para responder el objetivo se aplica análisis de redes sociales y se construyen indicadores sintéticos para valorizar los efectos. Se realizan encuestas a la totalidad de almazaras presentes en la Comarca de Sierra Mágina, tanto a las que poseen el signo de calidad como a las que no cuentan con dicha marca territorial y entrevistas a testigos privilegiados. En las mismas se consulta sobre las actividades y proyectos que realizan en forma conjunta con otras almazaras e instituciones, así como los actores o referentes a quiénes consultan en temas relacionados con la calidad en la producción olivícola y la obtención de aceite, así como aquellos actores a quienes consultan en aspectos referidos a la Comercialización y gestión. A partir de la información recabada, se obtienen cuatro indicadores sintéticos de efectos de la implantación de una DOP en las almazaras adheridas, referidos a la adopción de innovaciones productivas y organizativas, a la calidad en producto y procesos, a las rentas de diferenciación y a las prácticas ambientales. Asimismo se generan las matrices bi-narias y valuadas para las redes de colaboración en la difusión del conocimiento y se calculan los indicadores particulares. Esto nos permite estudiar las características de las redes que se generan (tamaño, conectividad), el papel del CR en la gobernanza territorial y de otras instituciones del medio, así como identificar actores centrales y grupos en el proceso de difusión de conocimientos. Los resultados muestran que los SIAL pueden beneficiarse de la organización interprofesional y de cooperación interinstitucional a escala local que se producen tras la implantación de una DOP. Se desarrolla un proceso de articulación y un fuerte intercambio de conocimientos entre actores ligados al propio proceso de control de la calidad diferencial. Pero, junto con ello, se desarrollan actividades de formación y de promoción del patrimonio comarcal y se consolidan redes locales de innovación y colaboración mutua en distintas actividades, tanto entre actores de la propia cadena de producción, como entre éstos y las instituciones del medio. Estas actividades, junto con la presencia de un entramado institucional denso, permiten el desarrollo de una gobernanza territorial alrededor del sistema agroalimentario del aceite de oliva. ABSTRACT The implementation of geographical indications has different socio-economic, technological and environmental effects not only for the producers involved but also for the local agro food system (LAFS). Among the latter, we can highlight the collective organization of producers and the development of territorial governance through a series of tasks developed by the Regulatory Board (RB) as regards quality control and product marketing. The concept of territorial governance involves processes of collective organization in networks, where processes of multi-level coordination among agents, enterprises and local institutions take place within a context of asymmetry of information and numerous decision makers. The objective of this Doctoral Thesis is to contribute with methodological tools for the study of territorial governance in local agro food systems by constructing indicators (of quality, innovation, economic effects and environmental practices) and by analyzing networks of collaboration in terms of dissemination of technical, organizational and commercial innovation and knowledge in Comarca de Sierra Mágina. To achieve that objective, social network analysis is applied and synthetic indicators are elaborated to valorize the effects. Interviews to key actors and surveys to every oil mill in Comarca de Sierra Mágina are carried out, not only in those oil mills that host a label of quality but also in the ones that do not hold it. These interviews inquire about tasks and projects performed together with other oil mills and institutions and about actors or referents consulted in topics related to quality in olive production and oil extraction as well as in management and marketing. From all the information collected, four synthetic indicators of the effects of establishing Protected Designations of Origen (PDOs) in the associate oil mills are obtained, in terms of adoption of productive and organizational innovations, process and product quality, income differentiation and environmental practices. In the same way, binary and valorized matrixes are generated for those networks collaborating in the dissemination of knowledge and specific indicators are calculated. This allows to study networks (size, connectivity), the role of RB in territorial governance and other institutions involved and to identify main actors and groups in the process of knowledge dissemination. The results show that the LAFS can benefit from inter-professional organizations and inter- institutional cooperation at local levels by implementing PDOs. A process of articulation and strong exchange of knowledge is developed among those actors involved in controlling differential quality. But, at the same time, activities for the formation and promotion of the “comarca” patrimony are developed and local networks of innovation and mutual collaboration are built for different activities, not only among actors in the production process itself, but between these actors and local institutions as well. These tasks, together with the existence of a dense spatial entrepreneurial network, allow the development of territorial governance on the olive oil agro food system.

Climate change in mountain regions: how local communities adapt to extreme events

This paper examines how local communities adapt to climate change and how governance structures can foster or undermine adaptive capacity. Climate change policies, in general, and disaster risk management in mountain regions, in particular, are characterised by their multi-level and multi-sectoral nature during formulation and implementation. The involvement of numerous state and non-state actors at local to national levels produces a variety of networks of interaction and communication. The paper argues that the structure of these relational patterns is critical for understanding adaptive capacity. It thus proposes an expanded concept of adaptive capacity that incorporates (horizontal and vertical) actor integration and communication flow between these actors. The paper further advocates the use of formal social network analysis to assess these relational patterns. Preliminary results from research on adaptation to climate change in a Swiss mountain region vulnerable to floods and other natural hazards illustrate the conceptual and empirical significance of the main arguments.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Performance Comparison of MATLAB and Neuro Solution Software on Estimation of Fuel Economy by Using Artificial Neural Network

In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.

Balancing push and pull information management within the supply chain

The aim of this paper is to explore the management of information in an aerospace manufacturer's supply chain by analysing supply chain disruption risks. The social network perspective will be used to examine the flows of information in the supply chain. The examination of information flows will also be explored in terms of push and pull information management. The supply chain risk management (SCRM) strategy is to assess the management of information that allows companies to gather information which will allow them to mitigate that risk before any disruption to the supply chain occurs. There is a shortage of models in analysing the supply chain risk associated with information flows, possibly due to the omission of appropriate modelling techniques in this area (Tang and Nurmaya, 2011). This paper uses an exploratory case study consisting of a multi method qualitative approach using fifteen interviews and four focus groups.

Évaluation de la modélisation de la taille de grain de neige du modèle multi-couches thermodynamique SNOWPACK: implication dans l'évaluation des risques d'avalanches

Résumé: L’Institut pour l'étude de la neige et des avalanches en Suisse (SLF) a développé SNOWPACK, un modèle thermodynamique multi-couches de neige permettant de simuler les propriétés géophysiques du manteau neigeux (densité, température, taille de grain, teneur en eau, etc.) à partir desquelles un indice de stabilité est calculé. Il a été démontré qu’un ajustement de la microstructure serait nécessaire pour une implantation au Canada. L'objectif principal de la présente étude est de permettre au modèle SNOWPACK de modéliser de manière plus réaliste la taille de grain de neige et ainsi obtenir une prédiction plus précise de la stabilité du manteau neigeux à l’aide de l’indice basé sur la taille de grain, le Structural Stability Index (SSI). Pour ce faire, l’erreur modélisée (biais) par le modèle a été analysée à l’aide de données précises sur le terrain de la taille de grain à l’aide de l’instrument IRIS (InfraRed Integrated Sphere). Les données ont été recueillies durant l’hiver 2014 à deux sites différents au Canada : parc National des Glaciers, en Colombie-Britannique ainsi qu’au parc National de Jasper. Le site de Fidelity était généralement soumis à un métamorphisme à l'équilibre tandis que celui de Jasper à un métamorphisme cinétique plus prononcé. Sur chacun des sites, la stratigraphie des profils de densités ainsi des profils de taille de grain (IRIS) ont été complétés. Les profils de Fidelity ont été complétés avec des mesures de micropénétromètre (SMP). L’analyse des profils de densité a démontré une bonne concordance avec les densités modélisées (R[indice supérieur 2]=0.76) et donc la résistance simulée pour le SSI a été jugée adéquate. Les couches d’instabilités prédites par SNOWPACK ont été identifiées à l’aide de la variation de la résistance dans les mesures de SMP. L’analyse de la taille de grain optique a révélé une surestimation systématique du modèle ce qui est en accord avec la littérature. L’erreur de taille de grain optique dans un environnement à l’équilibre était assez constante tandis que l’erreur en milieux cinétique était plus variable. Finalement, une approche orientée sur le type de climat représenterait le meilleur moyen pour effectuer une correction de la taille de grain pour une évaluation de la stabilité au Canada.