Lignocellulosic Recycled Materials to Design Molded Products: Optimization of Physical and Mechanical Properties

The object is to contribute to the reduction of environmental pollution, by reusing a fraction of urban solid waste, forestry and agroindustrial waste: newspaper (ONP), office paper (OWP), corrugated cardboard (OCC), pine sawdust, eucalyptus sawdust and sugar cane bagasse as raw material to design biocontainers suitable for growing plants, by applying pulp molding technology. The purpose is to evaluate the effects of the combination of these lignocellulosic materials on the physical-mechanical properties and optimize responses in order to select an ideal mixture on basis the product?s necessities. An experimental design of type mixture of extreme vertices was followed, considering secondary fibers as base material, in a 0-100% proportion, and pine sawdust, eucalyptus sawdust and bagasse fibers as reinforcement, in a 0-40% proportion. An experimental matrix by each reinforcing material was proposed. Properties were evaluated: density, tensile, bursting, tearing, compression, stiffness, wet tensile, permeability and water retention, testing handsheets weighing 150 g/m2. Responses were optimized using a statistical program. It was found that OWP pulps increase strength properties; OCC pulps increases tear and wet tensile; ONP pulps increase stiffness and reinforcement materials increase permeability. Factors that allow reaching the objectives are a mixture of pulp OWP/OCC in a 50/50 proportion.Fil: Aguerre, Yanina Susel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; ArgentinaFil: Gavazzo, Graciela Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; Argentin

“Caracterización de alteraciones posturales y su asociación con factores de riesgo en adolescentes de nivel medio superior de la UAEM”

Antecedentes: La postura es la capacidad de mantener el centro de gravedad en una base de sustentación y está determinada por factores como la personalidad, actitud mental, ocupación, genética, vestimenta, edad, estado de salud y modelos socioculturales; sin embargo existen algunos de los factores que predisponen el desarrollo de alteraciones posturales como el uso prolongado de dispositivos tecnológicos, obesidad, malos hábitos y uso de calzado, mochila o estación de trabajo inadecuados. Métodos: La muestra del estudio (n=69) se obtuvo por conveniencia en estudiantes de nivel medio superior de la UAEM y el estudio fue prospectivo, no experimental, transversal, descriptivo y de asociación cuantitativa. Se diseñó, estandarizó y validó un instrumento para evaluar la presencia de alteraciones posturales y factores de riesgo para posteriormente analizar su asociación. Resultados: La muestra se conformó por 71% mujeres, con una edad media de 14,7 ± 0,4 años. La prevalencia total de alteraciones posturales fue del 21%, presentando lateralización y rotación de la cabeza (62%), anteproyección de cabeza y hombros (58%), escoliosis y descenso de hombros (48%) cifosis (38%), pie cavo (39%), genu recurvatum (23%), genu valgum (12%), pie plano (12%) y valgo del tobillo (3%). Se asoció el uso de dispositivos electrónicos con presencia de lateralización de cabeza e hiperlordosis; el uso de mochilas con escoliosis y el tipo de calzado y obesidad con alteraciones de rodilla y tobillo. Conclusiones: Evaluar frecuente y eficazmente a los adolescentes y eliminar los factores de riesgo que predisponen al desarrollo de alteraciones posturales como un estilo de vida sedentario, obesidad, posturas inadecuadas al realizar las actividades diarias y la exposición a cargas excesivas, disminuirá la tasa de defectos y mejorará la calidad de vida durante el crecimiento

The effect of downsizing on affective organisational commitment: a contextual proximity perspective

Two assumptions constrain the literature on the effects of downsizing: that all survivors are affected to a similar extent and that the effect of layoffs can be extended to all workforce reductions. Furthermore, there is inconclusive evidence on the long-term effects of downsizing. I address these issues with two empirical studies on a multinational pharmaceutical company analysing the differential effect of four downsizing methods on affective commitment depending on the contextual proximity of employees. Study 1 shows decreasing levels of affective commitment among employees exposed to layoffs and closure of units (lower commitment corresponds to greater exposure) but the opposite was observed in voluntary redundancies and divestment. Study 2 indicates that downsizing has long-term negative effects which are worse for those exposed a second time

Ontology-Based Architecture to Improve Driving Performance Using Sensor Information for Intelligent Transportation Systems

Intelligent transportation systems are advanced applications with aim to provide innovative services relating to road transport management and enable the users to be better informed and make safer and coordinated use of transport networks. A crucial element for the success of these systems is that vehicles can exchange information not only among themselves but with other elements in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be located into vehicles or as part of an infrastructure element, such as bridges or traffic signs. The sensor can provide information related to the weather conditions and the traffic situation, which is useful to improve the driving process. In this paper a multiagent system using ontologies to improve the driving environment is proposed. The system performs different tasks in automatic way to increase the driver safety and comfort using sensor information

Preaching to the converted: the value of organisationally supported carbon reduction initiatives

Organisation led carbon reduction initiatives are bridging the gap between purely individual environmental action and higher level intervention and regulation. This research looks into one of those initiatives aimed at engaging employees in carbon reduction. We adopt a single case study approach following learning activities of volunteers within a UK organisation over four months. Observations of their activities were conducted, in addition to interviews with participants and organisers. Our core findings are, firstly, that rather than engaging a wide range of employees, only a very narrow group chose to participate. This group bears similarities with Barr and Gilg’s (2006) classification of ‘committed’ environmentalists which suggests that those willing to participate in these initiatives may be those for whom environmentalism is personally relevant. That there was little evidence of the initiative engaging a broader network of employees raises questions on the effectiveness of these activities in the wider population of employees. Secondly, we identified attitude behaviour gaps to varying degrees among participants who still made carbon intensive choices especially in relation to air travel, for example. We conclude by analysing the effectiveness of the intervention and the associated challenges. Recommendations are made covering several dimensions such as the potential role of technology in facilitating behaviour change, and organisational policy making with regards to employee engagement to carbon reduction

Aplicación de Técnicas de Machine Learning en la Predicción de Hospitalizaciones y Reingresos de pacientes con Esquizofrenia en Castilla y León

Schizophrenia is a severe mental disorder characterized by symptoms such as hallucinations, delusions, thought and behavior disorders. People with schizophrenia are associated with an increased risk of substance abuse, suicide, and mortality compared to the general population. They present hospitalization rates of 20-40% in a year, which results in high costs in the health system and affects the life quality of patients and family members. In Spain, hospital stay accounts for 37.6% of total healthcare costs. The use of Machine Learning (ML) techniques makes it possible to analyze data patterns using statistical methods and to create models that learn and generalize the behavior of the data. In Castilla y León (CyL), reducing the number of hospitalizations and readmissions is of great importance for psychiatric services. Therefore, in this Doctoral Thesis it is hypothesized that the application of ML algorithms helps to identify risk factors for hospitalization and predict readmission of patients with schizophrenia. Consequently, the main objective of this research is to develop and evaluate new predictive models using ML algorithms, in order to help in the prediction of hospitalizations and readmissions of patients with schizophrenia in CyL. To achieve this objective, 11,126 administrative records were used, corresponding to 5,412 hospitalized patients with schizophrenia from 11 public hospitals in CyL, in two different time periods. The records are global data, not based on the clinical psychopathology of the patient; they include demographic information, characteristics of hospitalization episodes, diagnoses and procedures concerning the hospitalized patient. These records were automatically analyzed using ML classification techniques, and predictive models were created to predict the readmission risk of these patients. In this sense, a methodological approach was proposed where a preprocessing and feature selection phase is applied where the predictive variables of the research were determined. The cross-validation method was used in the validation of the models and the ROC curves for their interpretation. Finally, a web application was developed to transfer the main contribution of this Doctoral Thesis to clinical practice. The different models created based on their performance metrics were compared, and the Random Forest (RF) algorithm was found to be the best predictor of the readmission risk of patients with schizophrenia in CyL. This RF model achieved an accuracy of 0.817 and an area under the ROC curve (AUC) of 0.879. These values suggest that the model has a reasonable discrimination capacity to predict the readmission of these patients. Variables such as age, length of stay, V-code diagnoses, substance abuse, and mental disorders were identified as the most predictive variables of the model. These variables indicate possible risk factors associated with the readmission of patients with schizophrenia. Therefore, the results obtained in this Doctoral Thesis suggest that ML algorithms such as RF have the ability to learn complex features from the data and predict the risk of readmission of hospitalized patients with schizophrenia in CyL. It is considered that the developed models can help decision-making, improving the quality of patient care and developing preventive treatments in function of reducing the number of hospitalizations. In addition, the implementation of the web application developed in this research, in public hospitals in CyL, can be very useful to health personnel in terms of reducing the costs associated with these hospitalizations.La esquizofrenia es un trastorno mental grave que se caracteriza por síntomas como las alucinaciones, delirios, trastornos del pensamiento y la conducta. Las personas con esquizofrenia se asocian con un mayor riesgo de abuso de sustancias, suicidio y mortalidad en comparación con la población general. Presentan tasas de hospitalización de un 20-40% en un año, lo que deriva en altos costes en el sistema sanitario y afecta la calidad de vida de los pacientes y los familiares. En España, la estancia hospitalaria corresponde al 37.6% de los costes sanitarios totales. El uso de técnicas de Machine Learning (ML), permite analizar patrones de los datos mediante métodos estadísticos, y crear modelos que aprenden y generalizan el comportamiento de los datos. En Castilla y León (CyL), reducir el número de hospitalizaciones y de reingresos es de suma importancia para los servicios de psiquiatría. Por tanto, en esta Tesis Doctoral se plantea la hipótesis que la aplicación de algoritmos de ML ayuda a identificar los factores de riesgo de hospitalización y predecir el reingreso de pacientes con esquizofrenia. En consecuencia, el objetivo principal de esta investigación es desarrollar y evaluar nuevos modelos predictivos utilizando algoritmos de ML, con el fin de ayudar en la predicción de hospitalizaciones y reingresos de pacientes con esquizofrenia en CyL. Para alcanzar este objetivo, se utilizaron 11 126 registros administrativos que corresponden a 5 412 pacientes hospitalizados con esquizofrenia, de 11 hospitales públicos de CyL, en dos períodos de tiempo diferentes. Los registros son datos globales, no están basados en la psicopatología clínica del paciente; incluyen información demográfica, características de episodios de hospitalización, diagnósticos y procedimientos referentes al paciente hospitalizado. Estos registros se analizaron automáticamente utilizando técnicas de clasificación de ML, y se crearon modelos predictivos para predecir el riesgo de reingreso de estos pacientes. En este sentido, se propuso un enfoque metodológico donde se aplica una fase de preprocesamiento y de selección de características donde se determinaron las variables predictivas de la investigación. El método de validación cruzada se utilizó en la validación de los modelos y las curvas ROC para su interpretación. Por último, se ha desarrollado una aplicación web que permite trasladar la principal contribución de esta Tesis Doctoral a la práctica clínica. Se compararon los diferentes modelos creados a partir de sus métricas de rendimiento, y se obtuvo que el algoritmo Random Forest (RF) es el que mejor predice el riesgo de reingreso de los pacientes con esquizofrenia en CyL. Este modelo RF alcanzó una exactitud (accuracy) de 0.817 y un área bajo la curva ROC (AUC) del 0.879. Estos valores sugieren que el modelo tiene una capacidad de discriminación razonable para predecir el reingreso de estos pacientes. Variables como la edad, la duración de la estancia, diagnósticos con códigos V, de abuso de sustancias y trastornos mentales, se identificaron como las variables más predictivas del modelo. Estas variables indican posibles factores de riesgo asociados al reingreso de pacientes con esquizofrenia. Por tanto, los resultados obtenidos en esta Tesis Doctoral sugieren que algoritmos de ML como el RF, tienen la capacidad de aprender características complejas de los datos y predecir el riesgo de reingreso de pacientes hospitalizados con esquizofrenia, en CyL. Se considera que los modelos desarrollados pueden ayudar a la toma de decisiones, mejorando la calidad de la atención al paciente y desarrollando tratamientos preventivos en función de reducir el número de hospitalizaciones. Además, la implementación de la aplicación web desarrollada en esta investigación, en los hospitales públicos de CyL, puede ser de gran utilidad al personal sanitario en función de reducir los costos asociados a estas hospitalizaciones.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione