Corporate Branding: a bibliographic review

The success of a company is not only based on the quality of its products but also on the way its brand is remembered in the market and this process is known as corporate branding. This type of strategy has been recognized as an important area in management sciences and needs a detailed review. The purpose of this article is to show the evolution of corporate branding through a review of the literature. For this purpose, a network analysis supported by bibliometric techniques is carried out. For this purpose, aWeb of Science consultation was carried out, after which the most important documents were classified through the Tree of Science web platform and, at the end, an analysis of co-citations is carried out. This last analysis showed that corporate branding presents three perspectives: the effect of the brand generated by corporate alliances, the influence of social responsibility and the management of brand identity.El éxito de una empresa no solo se basa en la calidad de sus productos si no también en la forma como es recordada su marca en el mercado y aeste proceso se le conoce como branding corporativo. Este tipo de estrategia ha sido reconocida como un área importante en las ciencias administrativas y necesita de una revisión detallada. Este artículo tiene como propósito mostrar la evolución del branding corporativo a través una revisión de la literatura, para ello se realiza un análisis de red apoyado en técnicas bibliométricas, además, se presentan las perspectivas de este campo de estudio en la actualidad. Para esto, se realizó una consulta en Web of Science, después se clasificaron los documentos más importantes a través de la plataforma web Tree of Science y, al final, se efectúa un análisis de co-citaciones. Este último análisis mostró que el branding corporativo presenta tres perspectivas: efecto de la marca generado por las alianzas corporativas, la influencia de la responsabilidad social y la gestión de la identidad de la marca

Branding Corporativo: una revisión bibliográfica

The success of a company is not only based on the quality of its products but also on the way its brand is remembered in the market and this process is known as corporate branding. This type of strategy has been recognized as an important area in management sciences and needs a detailed review. The purpose of this article is to show the evolution of corporate branding through a review of the literature. For this purpose, a network analysis supported by bibliometric techniques is carried out. For this purpose, a Web of Science consultation was carried out, after which the most important documents were classified through the Tree of Science web platform and, at the end, an analysis of co-citations is carried out. This last analysis showed that corporate branding presents three perspectives: the effect of the brand generated by corporate alliances, the influence of social responsibility and the management of brand identity.El éxito de una empresa no solo se basa en la calidad de sus productos si no también en la forma como es recordada su marca en el mercado y a este proceso se le conoce como branding corporativo. Este tipo de estrategia ha sido reconocida como un área importante en las ciencias administrativas y necesita de una revisión detallada. Este artículo tiene como propósito mostrar la evolución del branding corporativo a través una revisión de la literatura, para ello se realiza un análisis de red apoyado en técnicas bibliométricas, además, se presentan las perspectivas de este campo de estudio en la actualidad. Para esto, se realizó una consulta en Web of Science, después se clasificaron los documentos más importantes a través de la plataforma web Tree of Science y, al final, se efectúa un análisis de co-citaciones. Este último análisis mostró que el branding corporativo presenta tres perspectivas: efecto de la marca generado por las alianzas corporativas, la influencia de la responsabilidad social y la gestión de la identidad de la marca

Temporal decision making using unsupervised learning

With the explosion of ubiquitous continuous sensing, on-line streaming clustering continues to attract attention. The requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. In this dissertation, we develop a streaming clustering algorithm, MU Streaming Clustering (MUSC), that is based on coupling a Gaussian mixture model (GMM) with possibilistic clustering to build an adaptive system for analyzing streaming multi-dimensional activity feature vectors. For this reason, the possibilistic C-Means (PCM) and Automatic Merging Possibilistic Clustering Method (AMPCM) are combined together to cluster the initial data points, detect anomalies and initialize the GMM. MUSC achieves our goals when tested on synthetic and real-life datasets. We also compare MUSC's performance with Sequential k-means (sk-means), Basic Sequential Clustering Algorithm (BSAS), and Modified BSAS (MBSAS) here MUSC shows superiority in the performance and accuracy. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Incremental internal Validity Indices (iCVIs) are used to monitor the performance of an on-line clustering algorithm. We study the internal incremental Davies-Bouldin (DB), Xie-Beni (XB), and Dunn internal cluster validity indices in the context of streaming data analysis. We extend the original incremental DB (iDB) to a more general version parameterized by the exponent of membership weights. Then we illustrate how the iDB can be used to analyze and understand the performance of MUSC algorithm. We give examples that illustrate the appearance of a new cluster, the effect of different cluster sizes, handling of outlier data samples, and the effect of the input order on the resultant cluster history. In addition, we investigate the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. We analyze the effect of large numbers of samples on the values of the iCVI (iDB). We also develop online versions of two modified generalized Dunn's indices that can be used for dynamic evaluation of evolving (cluster) structure in streaming data. We argue that this method is a good way to monitor the ongoing performance of online clustering algorithms and we illustrate several types of inferences that can be drawn from such indices. We compare the two new indices to the incremental Xie-Beni and Davies-Bouldin indices, which to our knowledge offer the only comparable approach, with numerical examples on a variety of synthetic and real data sets. We also study the performance of MUSC and iCVIs with big streaming data applications. We show the advantage of iCVIs in monitoring large streaming datasets and in providing useful information about the data stream in terms of emergence of a new structure, amount of outlier data, size of the clusters, and order of data samples in each cluster. We also propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space. Another example of streaming is the data acivity data coming from TigerPlace and other elderly residents' apartments in and around Columbia. MO. TigerPlace is an eldercare facility that promotes aging-in-place in Columbia Missouri. Eldercare monitoring using non-wearable sensors is a candidate solution for improving care and reducing costs. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. We propose an unsupervised method for detecting abnormal behavior patterns based on a new context preserving representation of daily activities. A preliminary analysis of the method was conducted on data collected in TigerPlace. Sensor firings of each day are converted into sequences of daily activities. Then, building a histogram from the daily sequences of a resident, we generate a single data vector representing that day. Using the proposed method, a day with hundreds of sequences is converted into a single data point representing that day and preserving the context of the daily routine at the same time. We obtained an average Area Under the Curve (AUC) of 0.9 in detecting days where elder adults need to be assessed. Our approach outperforms other approaches on the same datset. Using the context preserving representation, we develoed a multi-dimensional alert system to improve the existing single-dimensional alert system in TigerPlace. Also, this represenation is used to develop a framework that utilizes sensor sequence similarity and medical concepts extracted from the EHR to automatically inform the nursing staff when health problems are detected. Our context preserving representation of daily activities is used to measure the similarity between the sensor sequences of different days. The medical concepts are extracted from the nursing notes using MetamapLite, an NLP tool included in the Unified Medical Language System (UMLS). The proposed idea is validated on two pilot datasets from twelve Tiger Place residents, with a total of 5810 sensor days out of which 1966 had nursing notes

Predictive Modelling Approach to Data-driven Computational Psychiatry

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016)

En la presente edición, más de 150 trabajos de alto nivel científico van a ser presentados en 18 sesiones paralelas y 3 sesiones de póster, que se centrarán en áreas relevantes de la Ingeniería Biomédica. Entre las sesiones paralelas se pueden destacar la sesión plenaria Premio José María Ferrero Corral y la sesión de Competición de alumnos de Grado en Ingeniería Biomédica, con la participación de 16 alumnos de los Grados en Ingeniería Biomédica a nivel nacional. El programa científico se complementa con dos ponencias invitadas de científicos reconocidos internacionalmente, dos mesas redondas con una importante participación de sociedades científicas médicas y de profesionales de la industria de tecnología médica, y dos actos sociales que permitirán a los participantes acercarse a la historia y cultura valenciana. Por primera vez, en colaboración con FENIN, seJane Campos, R. (2017). XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/79277EDITORIA

Formaciones imaginarias del diseñador gráfico en el discurso del campo académico.

En este trabajo se describe un proyecto de tesis doctoral en el que se analiza el discurso sobre el diseñador gráfico. Se parte del supuesto de que existe una tricotomía de su perfil: 1) el campo profesional, 2) el campo educativo y, 3) el campo académico. Proponemos que dicha tricotomía permite la identificación de imaginarios sobre el tema, y no solo eso, sino que también aporta elementos que conforman la identidad (Bauman, 2002) de un diseñador gráfico. La pregunta de investigación es ¿Cuál es la identidad discursiva del diseñador gráfico en el campo académico? La investigación descrita es de tipo cualitativo y deductivo; para la construcción la identidad discursiva (Van Dijk, T; 2008) del diseñador gráfico, se toman en cuenta diversas publicaciones: principalmente investigaciones y breves artículos difundidos en comunidades/foros de reflexión y debate en torno a la temática, además de memorias de congresos y libros. En apoyo al desarrollo del proyecto se ha diseñado un Laboratorio de Intervención en el Diseño, cuyos objetivos son impulsar el desarrollo social y cultural de los diseñadores gráficos por medio de la investigación, educación continua, producción y vinculación. En un primer acercamiento a las formaciones imaginarias (Pêcheux, 1978) sobre la identidad del diseñador gráfico se centran en el grado de erudición para la ejecución de su trabajo, en la cultura que demuestran y en la autonomía con la que producen