Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators.

Intraoperative changes in platelet function in relation to moderate haemorrhage

Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved. Funding The study was funded by TENOVUS Scotland (G12/14). Jan Jansen is in receipt of salary support through the NHS Research Scotland (NRS) fellowship scheme. Acknowledgements The authors are extremely grateful for the expert assistance and contributions of Dr Neil Scott (Medical Statistician, University of Aberdeen), Win Culley (Research Nurse, Woodend Hospital), Dr Karen Cranfield (Consultant Anaesthetist), and Ms Sharon Wood, (Research Technician, Rowett Institute of Nutrition and Health) for fibrinogen measurement.

Difference Blindness vs. Bias Awareness: Why Law Firms with the Best of Intentions Have Failed to Create Diverse Partnerships

This Article uses the example of BigLaw firms to explore the challenges that many elite organizations face in providing equal opportunity to their workers. Despite good intentions and the investment of significant resources, large law firms have been consistently unable to deliver diverse partnership structures - especially in more senior positions of power. Building on implicit and institutional bias scholarship and on successful approaches described in the organizational behavior literature, we argue that a significant barrier to systemic diversity at the law firm partnership level has been, paradoxically, the insistence on difference blindness standards that seek to evaluate each person on their individual merit. While powerful in dismantling intentional discrimination, these standards rely on an assumption that lawyers are, and have the power to act as, atomistic individuals - a dangerous assumption that has been disproven consistently by the literature establishing the continuing and powerful influence of implicit and institutional bias. Accordingly, difference blindness, which holds all lawyers accountable to seemingly neutral standards, disproportionately disadvantages diverse populations and normalizes the dominance of certain actors - here, white men - by creating the illusion that success or failure depends upon individual rather than structural constraints. In contrast, we argue that a bias awareness approach that encourages identity awareness and a relational framework is a more promising way to promote equality, equity, and inclusion.

Development of a Household Pharmaceutical Waste Collection Program for Atlanta, Georgia: An Analysis of Risk Factors and Public Awareness with a Comprehensive Collection Plan

Decades of mixed messages from three federal agencies left many Americans unaware of the hazards associated with the indiscriminate disposal of unwanted or expired medicines. For this Capstone project, a systematic review of state and federal regulations was undertaken to determine how these laws obstruct household pharmaceutical waste collection. In addition, a survey of 654 Atlanta residents was conducted to evaluate unwanted medicine disposal habits, awareness of pharmaceutical compounds being detected in drinking water, surface, and ground waters, and willingness to participate in a household pharmaceutical waste collection program. Survey responses were tabulated to provide overall results and by age group, gender, and race. A household pharmaceutical waste collection plan was developed for the city and included as an appendix.

Mapping the awareness of inequalities in health programs in Spanish communities: moving forward together

The aim of this study is to map the awareness of gender, socioeconomic, immigrant and ethnic health inequalities in health at schools, maternal health and traffic injury health prevention programs. The study was conducted in the 19 health descentralized areas in Spain, 17 autonomous community (ACs) and the 2 autonomous cities (ACities). The data were collected from May 2008 to January 2009. The unit of analysis was the collection of policy documents setting out the programs mentioned above and the related support material in each AC. A reading guide was used to analyze the awareness of inequalities. With regard to health at schools, 2 of 10 programs show a high awareness of inequalities and include many specific proposals to be implemented at the local level. Regarding maternal health, 13 ACs have prepared support material with high awareness of inequalities to be implemented. A traffic injury program has been created in two ACs. We map the whole situation in Spain regarding the health programs that we have used as examples and their awareness of inequalities. We can conclude that there are differences between the regions studied in Spain and in general, the awareness of inequalities is low.

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

Awareness of L1 and L2 word-formation mechanisms for the development of a more autonomous L2 learner

Unlike traditional approaches, new communicative trends disregard the role of word-formation mechanisms. They tend to focus on syntax and/or vocabulary without analyzing the mechanisms involved in the creation of lexical items. In this paper, based on the analysis of the use of prefixes by L2 learners in oral and written productions, as provided by the SULEC, we emphasize the advantages that word-formation awareness and knowledge may have for the learners in terms of production, creativity, understanding, autonomy, and proficiency. Through the teaching of word-formation learners may more easily decipher, decode and/or encode messages, create words they have never seen before, etc.

A proposal for teaching with authentic texts in order to develop social awareness in a language subject in tertiary education

The University of the 21st century has to establish links with society and prepare students for the demands of the working world. Therefore, this article is a contribution to the integral preparation of university students by proposing the use of authentic texts with social content in English lessons so that students acquire emotional and social competencies while still learning content. This article will explain how the choice of texts on global issues such as racism and gender helps students to develop skills such as social awareness and critical thinking to deepen their understanding of discrimination, injustice or gender differences in both oral and written activities. A proposal will be presented which involves using the inauguration speech from Mandela's presidency and texts with photographs of women so that students analyse them whilst utilising linguistic tools that allow them to explore a text's social dimension.