Historias debidas: transformaciones y experiencias migrantes en el espacio socio productivo del periurbano platense

A partir de trabajos realizados desde fines de los „80 en el espacio social del periurbano platense con trabajadores hortícolas migrantes, pretendemos -a través del registro e indagación de diversas experiencias, percepciones y representaciones- acercarnos al mundo significativo de integrantes de esas mismas poblaciones en la actualidad. Habida cuenta de transformaciones sucedidas durante casi 30 años en las condiciones objetivas de vida y producción, la inserción en el sector, la zona y el país en tanto migrantes y la necesidad-posibilidad de desarrollar instancias organizativas, haremos hincapié en algunos ejes que, entendemos, ligan esas condiciones con la elaboración subjetiva de la producción de la vida en variados contextos. En este marco –y entre la diversidad de aspectos posibles– atenderemos a auto-percepciones en términos de continuidades y cambios en las condiciones de trabajo, vida y producción, en particular a formas de vinculación e inserción locales y a la aparición de nuevas/otras formas de asociación y organización socio-política. Esbozaremos articulaciones posibles entre esos diferentes aspectos. Esta aproximación nos permitirá avanzar en el conocimiento de cómo se vivieron e interpretaron esos pasajes temporales y la variedad de las experiencias acumuladas, cómo se manifiestan las transformaciones acontecidas en el ámbito del periurbano en estas décadas en relación con la condición de migrantes, la organización de la vida y el trabajo, los grupos y procesos de pertenencia e identificación, la organización y participación en sociedad. Este texto es un producto parcial de nuestra participación en un proyecto PIO de investigación interdisciplinario y en proyectos de investigación y extensión en la zona. Para su desarrollo realizamos una revisión de la bibliografía producida en la región sobre la temática a partir de resultados de investigaciones de la década de 1990. Empleamos metodología cualitativa, en particular entrevistas semi-estructuradas.GT21 – Ruralidades en transformación en el marco del capitalismo global: Trabajo, políticas públicas, medio ambiente, mercados y alimentación.Universidad Nacional de La Plat

Intermediate-thrust arcs and their optimality in a central, time-invariant force field

This paper presents the general equations of the intermediate-thrust arcs in a general, time-invariant, central force field. Two families of planar arcs, namely, the family of Lawden's spirals in the equatorial plane of an oblate planet and the family of intermediate-thrust arcs in a gravitational field of the form μ/ r n , have been considered in detail. The Kelley-Contensou condition has been used to test their optimality condition. It is shown that, in the first case, there exist portions of the arcs at a finite distance satisfying the condition, while, in the second case, the entire family satisfies the condition for n ≥ 3. Hence, in a perturbed Newtonian gravitational force field, the intermediate-thrust arcs, under certain favorable conditions, can be part of an optimal trajectory.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45193/1/10957_2004_Article_BF00935198.pd

Data Analytics in Maintenance Planning – DAIMP

Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area.The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics.The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activitiesThe goals were further divided into specific goals and six work packages were designed to execute the project.WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start.WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances.WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective.WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities.WP5 focused on the evaluation and demonstration for different project resultsWP6 focused on coordination project managementIn WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors.The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users

Data Analytics in Maintenance Planning – DAIMP

Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area.The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics.The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activitiesThe goals were further divided into specific goals and six work packages were designed to execute the project.WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start.WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances.WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective.WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities.WP5 focused on the evaluation and demonstration for different project resultsWP6 focused on coordination project managementIn WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors.The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users

Secular Trends in the Prevalence of Overweight and Obesity in Sicilian Schoolchildren Aged 11–13 Years During the Last Decade

The present study evaluates trends in the prevalence of overweight and obesity in relation to gender and area of residence between two cohorts of students aged 11–13 years in Sicily. The analysis was performed on 1,839 schoolchildren, with 924 and 915 children being studied in 1999–2001 and 2009–2010, respectively. The children who were enrolled during 2009–2010 had significantly higher body mass indexes (BMI), BMI z-scores, and waist circumferences than the children who were studied during 1999–2001 (p<0.0001 for all); these differences was also observed when the cohort was subdivided according to gender or residence area The prevalence of obesity increased significantly from 7.9% in 1999–2001 to 13.7% in 2009–2010 (p<0.0001), whereas thinness decreased significantly from 10.1% to 2.3% (p<0.0001) in the same periods. The increase of trends in the prevalence of obesity was significantly higher in males (9.7% vs. 17.6%, p = 0.0006) than in females (6.3% vs. 9.8%, p = 0.04) and was slightly higher in urban areas (8.8% vs. 14.3%, p = 0.002) than in rural areas (7.8% vs. 13.0%, p = 0.012). The male gender was associated with a higher risk of being overweight or obese (odds ratio: 1.63; 95% confidence intervals: 1.24–2.15; p = 0.0005) in 2009–2010 than in 1999–2001, after adjusting for age and the residence area. In conclusion, this study showed an increasing trend in the prevalence of overweight and obesity in Sicilian schoolchildren during the last decade and that this trend was related to gender, age and the area of residence. More specifically, our data indicated that the prevalence of obesity increased by 5.8%, the prevalence of thinness decreased by 7.8% and the prevalence of normal-weight children did not change over the course of a decade. These results suggest a shift in the body weights of Sicilian children toward the upper percentiles

Identification and Analysis of Linked SystemsDynamic Accuracy Proposal of a Measurement Approach and Instrumentation

In this paper a measurement approach and instrumentation for capturing the dynamic stiffness of a linked system is proposed. The approach is based on the loaded double ball bar (LDBB) in combination with an integral dynamic shaker. The innovation is the introduction of a dynamic load besides the static one given by the LDBB and consequently able to extract the frequency response functions, taking advantage of the possibility of orienting the LDBB in different directions of the machine tool. The dynamic behaviour is studied through experimental modal analysis. The test conditions are therefore more similar to a cutting situation in which both a static and a dynamic component of force characterise the system. However, it must be underlined that the introduced dynamic force does not replicate the one arising in a cutting operation, but it is chosen instead for its spectrum characteristics, i.e. the energy introduced must equally cover a given range of frequencies. The test method is able to reveal machine tool characteristics not obtainable with existing methods, for instance the variation of dynamic stiffness in the working space. The paper will include some theoretical aspects on the approach as well as some experimental investigation

Dynamic parameter identification in nonlinear machining systems

The demand for enhanced performance of production systems in terms of quality, cost and reliability is ever increasing while, at the same time, there is a demand for shorter design cycles, longer operating life, minimisation of inspection and maintenance needs. Experimental testing and system identification in operational conditions still represent an important technique for monitoring, control and optimization. The term identification refers in the present paper to the extraction of information from experimental data and is used to estimate operational dynamic parameters for machining systems. Such an approach opens up the possibility of monitoring the dynamics of machining systems during operational conditions, and can also be used for control and/or predictive purposes The machining system is considered nonlinear and excited by random loads. Parametric and nonparametric techniques are developed for the identification of the nonlinear machining system and their application is demonstrated both by numerical simulations and in actual machining operations. Discrimination between forced and self-excited vibrations is also presented. The ability of the developed methods to estimate operational dynamic parameters ODPs is presented in practical machining operations

Hybrid machining: abrasive waterjet technologies used in combination with conventional metal cutting

Abrasive Waterjet technology is one of the fastest growing metal cutting technologies. Even so, very little published material is available on hybrid processing where abrasive waterjet cutting is one of two or more metal cutting methods. There is also limited published material on thin-walled components cut with abrasive waterjet technology. This paper makes a comparison of conventional metal cutting methods to the more unconventional abrasive waterjet technique. It will serve as a stepping stone in building knowledge aiding in hybrid machining development. It will show the possibilities and limitations during milling of thin-walled Aluminum components and then compare this to the capabilities of abrasive waterjet cutting the same components. Differences in material removal and revert control as well as in vibrations and force requirements will be reviewed. In addition, the environmental issues will be discussed and it will be determined which of the methods is more sustainable. The paper also includes a large section on process methodology

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