BIGDATA e IoT: claves del modelo de negocio para la empresa industrial del siglo XXI

En este trabajo se discute cómo los datos sobre los productos producidos en la empresa industrial van a contribuir a transformar el modelo de negocio de ésta mucho más allá del sistema productivo en sí mismo. Para poder adaptarse a esos cambios de paradigma y aumentar la cantidad de valor aportado al consumidor de sus productos será menester que estas compañías den decididos pasos en el ámbito de la Inteligencia de Negocio, lo que significará tener que lidiar con nuevos conceptos provenientes del ámbito de las tecnologías de la información y las comunicaciones, como BIGDATA e IoT (Internet de las cosas)

Relevant framework for social applications of IoT by means of Machine Learning techniques

With the rapid development of Internet of Things (IoT) technology, billions of smart devices are being connected into a whole network and streaming out a huge amount of data every moment. Unimaginable potential value can be mined from these data with the help of "Cloud Computing" and "Machine Learning" techniques. The target of our research is to address the benefits of IoT in social applications, especially in healthcare area, by developing a multilayer framework. Low cost data collection, efficient data transfer, flexible data management and accurate data analysis mechanisms will be included in the framework. A Smart Decision Support System is supposed to be developed on the basis of this framework

Incorporation of human knowledge to the stock markets for improving forecasts

This thesis attempts to explore the possibility to model human behavior and how it guides financial markets. According to Behavioral Finance theory the stock market ecosystem is influenced by the decision making of the individuals trading in it. The traders are heterogeneous in nature, with each group having their own belief and expectation. This thesis tries to answer the question Can human behavior and its responses to macroeconomic events be modelled and used as an indicator to predict price directions? To answer the former question, the research has delved deep into exploring human behavior guiding financial markets. Different exogenous variables representing stock broker behavior has been explored. These variables are derived from market data of local markets like the Madrid Stock Exchange, and also from micro blogging sites and website visit statistics. A local market microstructure is guided mostly by its local players and macroeconomic events. Where as more global stock markets are more guided by global macroeconomic events. This research constructs exogenous variables which effect the small stock exchanges and bigger stock exchanges alike. In this research different data set are constructed from web search volumes, sentiment scores of Twitter posts to page visit statistics of Wikipedia articles. The exogenous time series constructed is then used as a predictor variable for different supervised and unsupervised machine learning algorithms for future price predictions. In this research different categories of machine learning algorithm were used from simple tree based ensemble learning models to SVM (support vector machine) and kernel based models to more complex Deep Learning algorithms. The implication of the research is that it will help financial managers and traders use these correlations with social sentiment indexes to predict financial markets with certain accuracies. It will also provide them with early warnings of market downturns risk and indication of crisis

New Exploration of Comparative Advantage Theoretical Model for International Trade in the Context of Global Carbon Emissions Reduction

With the increasing world's greenhouse gas (GHG) emissions and pressing global warming, the Earth's ecological environment for human beings survival is ever more threatened by the carbon dioxide (CO2), which is caused by the production and livelihood of the human beings themselves. Therefore, it is essential for the whole world to take strong measures for carbon emissions reduction. Under such circumstances, the traditional comparative advantage theory of international trade is bound to be challenged. Based on the classic comparative advantages of international trade and H-O theoretical model, this paper constructs a new Ricardian model and H-O theoretical model in combination of the carbon factor, using methodologies of theoretical deduction and comparative analyses. The results indicate: considering the carbon factor, the original comparative advantage of international trade will disappear, and the original direction of trade flow changes. What is more, the country that has a comparative advantage in the production of certain products turns into the country that has the disadvantage; in the case of remaining the same nature of factors, when taking the carbon factor into account, the original comparative advantage of international trade will be reversed. Based on the results of these analyses, this paper proposes relevant suggestions

Local scale air pollution forecasting by artificial intelligence techniques and assess the pollution-related social effects

Since the air pollution can cause serious health problem, the concerns about forecasting air pollution timely and accurately arise by researchers in order to alert the public avoiding high level pollution and help the government make decisions. In our research, we take Hong Kong (finished research) and the cities in Mexico (finishing) and Mainland China (starting), especially the high pollutant areas such as Mexico City and Beijing, as study cases. Meanwhile, various types of artificial intelligence techniques are employed to achieve the goals. Furthermore, we also extent our research from forecast pollution to assess the air pollution – related social effects, for example health impact and its economic loss, which are also the concerned by public and the governments

The HOSHIN KANRI TREE. Cross-Plant Lean Shopfloor Management

Shopfloor Management (SM) empowerment methodologies have traditionally focused on two aspects: goal achievement following rigid structures, such as SQDCME, or evolutional aspects of empowerment factors away from strategic goal achievement. Furthermore, SM Methodologies have been organized almost solely around the hierarchical structure of the organization, failing systematically to cope with the challenges that Industry 4.0 is facing. The latter include the growing complexity of value-stream networks, sustainable empowerment of the workforce (Learning Factory), an autonomous and intelligent process management (Smart Factory), the need to cope with the increasing complexity of value-stream networks (VSN) and the leadership paradigm shift to strategic alignment. This paper presents a novel Lean SM Method (LSM) called ?HOSHIN KANRI Tree? (HKT), which is based on standardization of the communication patterns among process owners (POs) by PDCA. The standardization of communication patterns by HKT technology should bring enormous benefits in value stream (VS) performance, speed of standardization and learning rates to the Industry 4.0 generation of organizations. These potential advantages of HKT are being tested at present in worldwide research

Predictive capabilities of twitter, applied for general elections in Spain based on comments and geolocated users

Sentiment analysis is based on subjectivity research, in other words, determining the feelings of messages from users regarding some topics, products, movies, music, etc. That should be judged in terms to classify the polarity of a document, sentence, or aspect levels

The use of computers for graduate education in Project Management. Improving the integration to the industry.

This paper presents an initiative for monitoring the competence acquisition by a team of students with different backgrounds facing the experience of being working by projects and in a project. These students are graduated bachelor engineering are inexperienced in the project management field and they play this course on a time-shared manner along with other activities. The goal of this experience is to increase the competence levels acquired by using an structured web based portfolio tool helping to reinforce how relevant different project management approaches can result for final products and how important it becomes to maintain the integration along the project. Monitoring is carried out by means of have a look on how the work is being done and measuring different technical parameters per participant. The use of this information could make possible to bring additional information to the students involved in terms of their individual competencies and the identification of new opportunities of personal improvement. These capabilities are strongly requested by companies in their daily work as well as they can be very convenient too for students when they try to organize their PhD work

An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly

The goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior

Strategic Lean Organizational Design: Towards Lean World-Small World Configurations through Discrete Dynamic Organizational Motifs

Organizations face strong international competition in the global market arena in achieving strategic goals such as high quality of product or service at lower cost while increasing their ability to respond quickly to requirements of the market. These challenges concern strategically designing organizations that can meet global challenges and specialize locally to meet performance constraints. After introducing the concept of organizational functional and structural motifs as small organizational building block, our findings suggest the hypothesis that a strategic organizational design (SOD) approach to meet these challenges involves maximizing the number and diversity of functional motifs, while minimizing the repertoire of structural motifs. By detecting characteristic structural motifs, we provide organizational leaders with specific Lean SOD solutions with which to meet local and global challenges simultaneously. As a matter of application, we show the implementation of such an SOD approach in nine US hospitals that form one large health care holding