"Effective Demand in the Recent Evolution of the US Economy"

We present strong empirical evidence favoring the role of effective demand in the US economy, in the spirit of Keynes and Kalecki. Our inference comes from a statistically well-specified VAR model constructed on a quarterly basis from 1980 to 2008. US output is our variable of interest, and it depends (in our specification) on (1) the wage share, (2) OECD GDP, (3) taxes on corporate income, (4) other budget revenues, (5) credit, and the (6) interest rate. The first variable was included in order to know whether the economy under study is wage led or profit led. The second represents demand from abroad. The third and fourth make up total government expenditure and our arguments regarding these are based on Kalecki's analysis of fiscal policy. The last two variables are analyzed in the context of Keynes's monetary economics. Our results indicate that expansionary monetary, fiscal, and income policies favor higher aggregate demand in the United States.Effective Demand; Wage Shares; Monetary Policy; Fiscal Policy; Model Evaluation

Doctrina Social de la Iglesia y desarrollo humano: ejemplo de responsabilidad social

13 p.El objetivo central de este escrito es presentar los principios más importantes de la Doctrina Social de la Iglesia (DSI) y los lineamientos esenciales para la aplicación a la vida individual y social de la persona. Luego, se realiza una discusión sobre los fundamentos del desarrollo humano y la articulación de este último concepto con la DSI.Ponencia presentada para el VII Coloquio Interno de Profesores de la Universidad Católica de Colombia.Introducción La Doctrina Social de la Iglesia y la Misión de la Universidad La articulación de la DSI con el desarrollo humano Consideraciones finales Bibliografí

Motivating Students with Learning Disabilities to Succeed in Education

Many students with learning disabilities may feel incapable of achieving in their academic goals and are in need of greater motivation. They may believe that abilities are innate and fixed and that their ability to learn and achieve is unchangeable. However, they may not be aware of growth mindset strategies that can support their academic success. To address the need for motivation, I created three lessons to teach seventh grade students in a resource classroom at Aptos Jr. High strategies for a growth mindset and to stay motivated throughout their academic challenges

Study of a hot asphalt mixture response based on energy concepts

The main objective of the research reported in this paper is to determine the response of a hot mix asphalt (HMA) in terms of both the tensile strength and energy parameters (based on the assessment of the force-displacement curve) as potential tools for improving the HMA mixture design. The HMAs analyzed were fabricated using a 60-70 penetration asphalt binder, dense-graded aggregate, mineral filler, and different types and contents of mineral filler replacements (i.e., lime, cement, and fly ash). The indirect tensile test was conducted to determine both the HMA tensile strength and force-displacement curve, which allowed for the computation of the HMA toughness as well as the energies involved in the process before and after reaching the tensile strength. Corresponding results suggest that the replacement of mineral filler by cement, lime, and fly ash modified the HMA response in terms of both the tensile strength and energy parameters. In addition, analysis of the energy parameters discussed proved to be useful for determining the optimum mineral filler content of HMA. Consequently, analysis of these energy parameters can benefit the HMA mixture design process. // El objetivo principal de la investigación fue determinar el comportamiento de una mezcla asfáltica en función de la resistencia a la tracción indirecta y parámetros de energía (calculados en función de la curva fuerza-desplazamiento) como herramientas potenciales para mejorar el diseño de mezclas asfálticas. Las mezclas asfálticas analizadas fueron fabricadas con asfalto de penetración 60/70, granulometría cerrada md10 y relleno mineral en diferentes porcentajes y materiales (cal, cemento y ceniza volante). El ensayo de tracción indirecta se utilizó para determinar la resistencia máxima a la tracción y la curva fuerza-desplazamiento, a partir de la cual se calcularon la tenacidad de la mezcla y las energías involucradas en el proceso antes y después de alcanzar la resistencia máxima. Los resultados obtenidos sugieren que reemplazar el relleno mineral por cemento, cal o ceniza volante modifica el comportamiento de la mezcla asfáltica en términos de la resistencia a la tracción y los parámetros de energía. Adicionalmente, el análisis de los parámetros de energía discutidos es útil para establecer el contenido óptimo del relleno mineral. En consecuencia, el análisis de estos parámetros de energía puede beneficiar el proceso de diseño de mezclas asfálticas.Peer ReviewedPostprint (published version

Smartphone-based human activity recognition

Cotutela Universitat Politècnica de Catalunya i Università degli Studi di GenovaHuman Activity Recognition (HAR) is a multidisciplinary research field that aims to gather data regarding people's behavior and their interaction with the environment in order to deliver valuable context-aware information. It has nowadays contributed to develop human-centered areas of study such as Ambient Intelligence and Ambient Assisted Living, which concentrate on the improvement of people's Quality of Life. The first stage to accomplish HAR requires to make observations from ambient or wearable sensor technologies. However, in the second case, the search for pervasive, unobtrusive, low-powered, and low-cost devices for achieving this challenging task still has not been fully addressed. In this thesis, we explore the use of smartphones as an alternative approach for performing the identification of physical activities. These self-contained devices, which are widely available in the market, are provided with embedded sensors, powerful computing capabilities and wireless communication technologies that make them highly suitable for this application. This work presents a series of contributions regarding the development of HAR systems with smartphones. In the first place we propose a fully operational system that recognizes in real-time six physical activities while also takes into account the effects of postural transitions that may occur between them. For achieving this, we cover some research topics from signal processing and feature selection of inertial data, to Machine Learning approaches for classification. We employ two sensors (the accelerometer and the gyroscope) for collecting inertial data. Their raw signals are the input of the system and are conditioned through filtering in order to reduce noise and allow the extraction of informative activity features. We also emphasize on the study of Support Vector Machines (SVMs), which are one of the state-of-the-art Machine Learning techniques for classification, and reformulate various of the standard multiclass linear and non-linear methods to find the best trade off between recognition performance, computational costs and energy requirements, which are essential aspects in battery-operated devices such as smartphones. In particular, we propose two multiclass SVMs for activity classification:one linear algorithm which allows to control over dimensionality reduction and system accuracy; and also a non-linear hardware-friendly algorithm that only uses fixed-point arithmetic in the prediction phase and enables a model complexity reduction while maintaining the system performance. The efficiency of the proposed system is verified through extensive experimentation over a HAR dataset which we have generated and made publicly available. It is composed of inertial data collected from a group of 30 participants which performed a set of common daily activities while carrying a smartphone as a wearable device. The results achieved in this research show that it is possible to perform HAR in real-time with a precision near 97\% with smartphones. In this way, we can employ the proposed methodology in several higher-level applications that require HAR such as ambulatory monitoring of the disabled and the elderly during periods above five days without the need of a battery recharge. Moreover, the proposed algorithms can be adapted to other commercial wearable devices recently introduced in the market (e.g. smartwatches, phablets, and glasses). This will open up new opportunities for developing practical and innovative HAR applications.El Reconocimiento de Actividades Humanas (RAH) es un campo de investigación multidisciplinario que busca recopilar información sobre el comportamiento de las personas y su interacción con el entorno con el propósito de ofrecer información contextual de alta significancia sobre las acciones que ellas realizan. Recientemente, el RAH ha contribuido en el desarrollo de áreas de estudio enfocadas a la mejora de la calidad de vida del hombre tales como: la inteligència ambiental (Ambient Intelligence) y la vida cotidiana asistida por el entorno para personas dependientes (Ambient Assisted Living). El primer paso para conseguir el RAH consiste en realizar observaciones mediante el uso de sensores fijos localizados en el ambiente, o bien portátiles incorporados de forma vestible en el cuerpo humano. Sin embargo, para el segundo caso, aún se dificulta encontrar dispositivos poco invasivos, de bajo consumo energético, que permitan ser llevados a cualquier lugar, y de bajo costo. En esta tesis, nosotros exploramos el uso de teléfonos móviles inteligentes (Smartphones) como una alternativa para el RAH. Estos dispositivos, de uso cotidiano y fácilmente asequibles en el mercado, están dotados de sensores embebidos, potentes capacidades de cómputo y diversas tecnologías de comunicación inalámbrica que los hacen apropiados para esta aplicación. Nuestro trabajo presenta una serie de contribuciones en relación al desarrollo de sistemas para el RAH con Smartphones. En primera instancia proponemos un sistema que permite la detección de seis actividades físicas en tiempo real y que, además, tiene en cuenta las transiciones posturales que puedan ocurrir entre ellas. Con este fin, hemos contribuido en distintos ámbitos que van desde el procesamiento de señales y la selección de características, hasta algoritmos de Aprendizaje Automático (AA). Nosotros utilizamos dos sensores inerciales (el acelerómetro y el giroscopio) para la captura de las señales de movimiento de los usuarios. Estas han de ser procesadas a través de técnicas de filtrado para la reducción de ruido, segmentación y obtención de características relevantes en la detección de actividad. También hacemos énfasis en el estudio de Máquinas de soporte vectorial (MSV) que son uno de los algoritmos de AA más usados en la actualidad. Para ello reformulamos varios de sus métodos estándar (lineales y no lineales) con el propósito de encontrar la mejor combinación de variables que garanticen un buen desempeño del sistema en cuanto a precisión, coste computacional y requerimientos de energía, los cuales son aspectos esenciales en dispositivos portátiles con suministro de energía mediante baterías. En concreto, proponemos dos MSV multiclase para la clasificación de actividad: un algoritmo lineal que permite el balance entre la reducción de la dimensionalidad y la precisión del sistema; y asimismo presentamos un algoritmo no lineal conveniente para dispositivos con limitaciones de hardware que solo utiliza aritmética de punto fijo en la fase de predicción y que permite reducir la complejidad del modelo de aprendizaje mientras mantiene el rendimiento del sistema. La eficacia del sistema propuesto es verificada a través de una experimentación extensiva sobre la base de datos RAH que hemos generado y hecho pública en la red. Esta contiene la información inercial obtenida de un grupo de 30 participantes que realizaron una serie de actividades de la vida cotidiana en un ambiente controlado mientras tenían sujeto a su cintura un smartphone que capturaba su movimiento. Los resultados obtenidos en esta investigación demuestran que es posible realizar el RAH en tiempo real con una precisión cercana al 97%. De esta manera, podemos emplear la metodología propuesta en aplicaciones de alto nivel que requieran el RAH tales como monitorizaciones ambulatorias para personas dependientes (ej. ancianos o discapacitados) durante periodos mayores a cinco días sin la necesidad de recarga de baterías.Postprint (published version

Coking Resistance of Alumina Forming Cast Austenitic Stainless Steels

Coking is the process of carbon deposition from a gas phase that is encountered in many reforming, cracking and other high temperature processes. Coking in certain petrochemical processes can lead to carbon build up causing reduced process efficiency, corrosive attack and degradation of the alloy. Steam cracking of hydrocarbons is one of the most important process for manufacturing many base chemicals such as ethene, propene and other. A major influence on the energy efficiency and economics is the formation of coke on the inner wall of the reactors. With the accumulation of coke on the walls, eventually metallurgic constraints of the reactor material will force to stop the process and de-coke the reactors resulting in loss of efficiency with negative effect on the economics of the process. Materials used in these processes are fabricated from HP alloys that rely on the formation of a chromium oxide (chromia) layer as a protective layer between the bulk material and chemical byproducts. However, strong oxidation, carburization, sulfidation or nitriding can occur if the environment does not promote chromium oxide formation or if the protectivity of the scale is destroyed by other mechanisms. More recent alloys that form an alumina-based oxide layer have been recently developed for structural use in aggressive oxidizing environments. These alloys, commonly known as AFA alloys, form a protective layer of aluminum oxide (alumina) showing a promising combination of oxidation resistance, creep resistance, tensile properties, and potential for good welding behavior. An experimental high temperature coking atmosphere was constructed and used to evaluate the effects of temperature, time and metal surface roughness on the carbon deposition of two alumina forming alloys (2.6% and 3.7% Al content each). Coking conditions were simulated with multiple atmospheres including CO-H2 mixtures at moderate temperatures and ethane at higher temperatures. Carbon deposition was tracked using specific mass change of the samples as a function of exposure times and conditions. Results obtained with the alumina forming alloys were compared to a baseline HP alloy. The materials were analyzed using XRD, SEM, and optical microscopy to characterize the oxide layer formation, carbon deposition layers and carbon attack, and changes to base metal microstructure. Raman spectroscopy was used to characterize the carbon deposits. The overall resistance of the alumina-forming alloys relative to the traditional chromia forming alloys is described. Overall, AFA alloys showed better coking resistance to more aggressive environments that involve high temperature and longer times of exposure than traditional chromia-forming alloy. Therefore, this particular coking resistance make AFA alloys suitable for a wide range of energy production, chemical and process industry applications, resulting in significant cost and energy savings as well as reductions in environmental emissions

The Role of Multiple Articulatory Channels of Sign-Supported Speech Revealed by Visual Processing

Purpose The use of sign-supported speech (SSS) in the education of deaf students has been recently discussed in relation to its usefulness with deaf children using cochlear implants. To clarify the benefits of SSS for comprehension, 2 eye-tracking experiments aimed to detect the extent to which signs are actively processed in this mode of communication. Method Participants were 36 deaf adolescents, including cochlear implant users and native deaf signers. Experiment 1 attempted to shift observers' foveal attention to the linguistic source in SSS from which most information is extracted, lip movements or signs, by magnifying the face area, thus modifying lip movements perceptual accessibility (magnified condition), and by constraining the visual field to either the face or the sign through a moving window paradigm (gaze contingent condition). Experiment 2 aimed to explore the reliance on signs in SSS by occasionally producing a mismatch between sign and speech. Participants were required to concentrate upon the orally transmitted message. Results In Experiment 1, analyses revealed a greater number of fixations toward the signs and a reduction in accuracy in the gaze contingent condition across all participants. Fixations toward signs were also increased in the magnified condition. In Experiment 2, results indicated less accuracy in the mismatching condition across all participants. Participants looked more at the sign when it was inconsistent with speech. Conclusions All participants, even those with residual hearing, rely on signs when attending SSS, either peripherally or through overt attention, depending on the perceptual conditions.Unión Europea, Grant Agreement 31674