Desarrollo de modelos basados en patrones para la predicción de series temporales en entornos Big Data

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111Esta Tesis Doctoral se presenta mediante la modalidad de compendio de publicaciones y en ella se aportan distintas contribuciones científicas en Congresos Internacionales y revistas con alto índice de impacto en el Journal of Citation Reports (JCR). Durante los cinco años de investigación a tiempo parcial, se ha realizado una investigación encaminada al estudio, análisis y predicción de grandes conjuntos de series temporales, principalmente de tipo energético. Para ello, se han seguido las últimas tendencias tecnológicas en el ámbito de la computación distribuida, desarrollando la experimentación íntegramente en Scala, el lenguaje nativo del framework Apache Spark, realizando las pruebas experimentales en entornos reales como Amazon Web Services u Open Telekom Cloud. La primera fase de la Tesis Doctoral se centra en el desarrollo y aplicación de una metodología que permite analizar de manera eficiente conjuntos de datos que contienen series temporales de consumo eléctrico, generados por la red de contadores eléctricos inteligentes instalados en la Universidad Pablo de Olavide. La metodología propuesta se enfoca principalmente en la correcta aplicación en entornos distribuidos del algoritmo de clustering K-means a grandes conjuntos de datos, permitiendo segmentar conjuntos de $n$ observaciones en $k$ grupos distintos con características similares. Esta tarea se realiza utilizando una versión paralelizada del algoritmo llamado K-means++, incluido en la Machine Learning Library de Apache Spark. Para la elección del número óptimo de clusters, se adopta una estrategia en la que se evalúan distintos índices de validación de clusters tales como el Within Set Sum of Squared Error, Davies-Bouldin, Dunn y Silhouette, todos ellos desarrollados para su aplicación en entornos distribuidos. Los resultados de esta experimentación se expusieron en 13th International Conference on Distributed Computing and Artificial Intelligence. Posteriormente, se amplió la experimentación y la metodología, resultando en un artículo publicado en la revista Energies, indexada en JCR con categoría Q3. La segunda parte del trabajo realizado consiste en el diseño de una metodología y desarrollo de un algoritmo capaz de pronosticar eficazmente series temporales en entornos Big Data. Para ello, se analizó el conocido algoritmo Pattern Sequence-based Forecasting (PSF), con dos objetivos principales: por un lado, su adaptación para aplicarlo en entornos escalables y distribuidos y, por otro lado, la mejora de las predicciones que realiza, enfocándolo a la explotación de grandes conjuntos de datos de una manera eficiente. En este sentido, se ha desarrollado en lenguaje Scala un algoritmo llamado bigPSF, que se integra en el marco de una completa metodología diseñada para a pronosticar el consumo energético de una Smart City. Finalmente, se desarrolló una variante del algoritmo bigPSF llamada MV-bigPSF, capaz de predecir series temporales multivariables. Esta experimentación se ha plasmado en dos artículos científicos publicados en las revistas Information Sciences (para el artículo relativo al algoritmo bigPSF) y Applied Energy (relativo al estudio de la versión multivariable del mismo), ambas con un índice de impacto JCR con categoría Q1.Universidad Pablo de Olavide de Sevilla. Escuela de Doctorad

Seafood processing, preservation, and analytical techniques in the age of industry 4.0

Fish and other seafood products are essential dietary components that are highly appreciated and consumed worldwide. However, the high perishability of these products has driven the development of a wide range of processing, preservation, and analytical techniques. This development has been accelerated in recent years with the advent of the fourth industrial revolution (Industry 4.0) technologies, digitally transforming almost every industry, including the food and seafood industry. The purpose of this review paper is to provide an updated overview of recent thermal and nonthermal processing and preservation technologies, as well as advanced analytical techniques used in the seafood industry. A special focus will be given to the role of different Industry 4.0 technologies to achieve smart seafood manufacturing, with high automation and digitalization. The literature discussed in this work showed that emerging technologies (e.g., ohmic heating, pulsed electric field, high pressure processing, nanotechnology, advanced mass spectrometry and spectroscopic techniques, and hyperspectral imaging sensors) are key elements in industrial revolutions not only in the seafood industry but also in all food industry sectors. More research is still needed to explore how to harness the Industry 4.0 innovations in order to achieve a green transition toward more profitable and sustainable food production systems.José S. Câmara and Rosa Perestrelo acknowledge FCT-Fundação para a Ciência e a Tecnologia through the CQM Base Fund—UIDB/00674/2020, and Programmatic Fund—UIDP/00674/2020, Madeira 14–20 Program, project PROEQUIPRAM—Reforço do Investimento em Equipamentos e Infraestruturas Científicas na RAM (M1420-01-0145-FEDER-000008), and ARDITI—Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação, through M1420-01-0145- FEDER-000005—Centro de Química da Madeira—CQM+ (Madeira 14–20 Program) for their support. The research leading to these results was supported by MICINN supporting the Ramón y Cajal grant for M.A. Prieto (RYC-2017-22891); by Xunta de Galicia for supporting the program EXCELENCIAED431F 2020/12; and the pre-doctoral grant of P. Garcia-Oliveira (ED481A-2019/295); and by the program BENEFICIOS DO CONSUMO DAS ESPECIES TINTORERA-(CO-0019-2021).info:eu-repo/semantics/publishedVersio

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

[Abstract] Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Preparation, Proximate Composition and Culinary Properties of Yellow Alkaline Noodles from Wheat and Raw/Pregelatinized Gadung (Dioscorea Hispida Dennst) Composite Flours

The steady increase of wheat flour price and noodle consumptions has driven researchers to find substitutes for wheat flour in the noodle making process. In this work, yellow alkaline noodles were prepared from composite flours comprising wheat and raw/pregelatinized gadung (Dioscorea hispida Dennst) flours. The purpose of this work was to investigate the effect of composite flour compositions on the cooking properties (cooking yield, cooking loss and swelling index) of yellow alkaline noodle. In addition, the sensory test and nutrition content of the yellow alkaline noodle were also evaluated for further recommendation. The experimental results showed that a good quality yellow alkaline noodle can be prepared from composite flour containing 20% w/w raw gadung flour. The cooking yield, cooking loss and swelling index of this noodle were 10.32 g, 1.20 and 2.30, respectively. Another good quality yellow alkaline noodle can be made from composite flour containing 40% w/w pregelatinized gadung flour. This noodle had cooking yield 8.93 g, cooking loss 1.20, and swelling index of 1.88. The sensory evaluation suggested that although the color, aroma and firmness of the noodles were significantly different (p ≤ 0.05) from wheat flour noodle, but their flavor remained closely similar. The nutrition content of the noodles also satisfied the Indonesian National Standard for noodle. Therefore, it can be concluded that wheat and raw/pregelatinized gadung composite flours can be used to manufacture yellow alkaline noodle with good quality and suitable for functional food

New Developments in Renewable Energy

Renewable energy is defined as the energy which naturally occurs, covers a number of sources and technologies at different stages, and is theoretically inexhaustible. Renewable energy sources such as those who are generated from sun or wind are the most readily-available and possible solutions to address the challenge of growing energy demands in the world. Newer and environmentally friendly technologies are able to provide different social and environmental benefits such as employment and decent environment. Renewable energy technologies are crucial contributors to world energy security, reduce reliance on fossil fuels, and provide opportunities for mitigating greenhouse gases. International public opinion indicates that there is strong support for a variety of methods for solving energy supply problems, one of which is utilizing renewable energy sources. In recent years, countries realized that that the renewable energy and its sector are key components for greener economies

Development of A Sustainable Landscape Architecture Best Practices Manual

The goal of this project was to develop a Best Practices Manual (BPM) for Stantec Inc regarding sustainable landscape architecture practices. The manual will be used by Stantec employees to help assess the feasibility of landscape architecture practices for specific projects. Potential Benefits, Potential Risks and Considerations, Estimated Costs, Recommended Site Characteristics, and Potential LEED Credits were researched and presented for each practice within the BPM. This information was then posted on an internal electronic best practices manual so that all employees within Stantec could access the information

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Waste PET-MOF-Cleanwater

In counties like South Africa, firstly, the waste PET stream has posed a serious problem to the environment, and the current recycling of waste PET remains as low as 30%. The waste PET recycling industries such as PETCO & Extrupet (South Africa) are struggling to implement innovative processes to make cooperate more profitable. Secondly, metal-organic frameworks (MOFs) as a new class of porous materials, the MOFs-based water treatment holds the promises to provide cost-effective solutions dealing with the polluted water. However, the high costs of MOFs production have raised a challenge for its effective implementations. Given that, cross-cutting advances in materials and engineering will help to solve those societal challenges. To maintain the world-class research and development associated with human capacity in South Africa, this multidisciplinary and transdisciplinary work has been strengthened along with the basic-applied research continuum under the frame of South Africa (NRF)/Poland (NCBR) Joint Science and Technology Research Collaboration