Introdução: As Lesões por Esforços Repetitivos (LER) ou Distúrbios Osteomusculares Relacionados ao Trabalho (DORT) são afecções músculo esqueléticas. A enfermagem é uma das categorias da área da saúde mais atingidas por estas, devido aos fatores de risco da profissão. Apresentar desconforto e dor em regiões específicas do corpo, faz parte do cotidiano de muitos destes trabalhadores, que acabam apresentando alterações em sua qualidade de vida. Estes fatores levam ao aparecimento do presenteísmo observado quando o trabalhador está presente no trabalho apesar de doente ou com algum problema físico ou psicológico e ao absenteísmo que ocorre quando este trabalhador falta ao trabalho. Objetivo: Avaliar o impacto de um Curso de Ensino à Distância (EAD) de Prevenção em LER/DORT na qualidade de vida destes trabalhadores e avaliar também desconforto e dor, presenteísmo e absenteísmo. Métodos: Sessenta funcionários da enfermagem de um hospital geral universitário participaram do ensaio clínico, randomizados aleatoriamente em dois grupos.Os critérios de inclusão foram: ser contratado do hospital nas categorias profissionais enfermeiros, técnicos e auxiliares de enfermagem e atuarem em unidades de internação. Foram excluídos trabalhadores com diagnóstico de LER/DORT auto-referido.O grupo intervenção participou de um curso em EAD com duração de 8 horas, composto por estratégias e dicas laborais para a prevenção de LER/DORT, literaturas disponíveis e provas teóricas, o grupo controle foi submetido a uma palestra educativa com duração de 30 minutos, com os mesmos conteúdos do curso em EAD. Foram avaliados antes da intervenção, duas e oito semanas após. Resultados: Quando comparada a qualidade de vida entre os grupos não observamos melhores resultados na percepção do grupo intervenção. O domínio de dor apresentou pior percepção. Presenteísmo foi observado em ambos os grupos, observamos redução do presenteísmo nos grupos sendo no grupo controle uma diferença significativa.Em relação ao desconforto e dor, ambos os grupos apresentaram alta prevalência, houve mudança significativa no grupo controle, com redução na escala do basal para a 8ª semana depois na região das costas inferior. No grupo intervenção não há uma diferença estatisticamente significativa, apesar de haver uma importante redução. Já o absenteísmo apresentou diferença entre os grupos, sendo que no vii grupo intervenção teve redução do número de horas de afastamento por doença. Conclusão: Não há evidência do impacto de um curso de ensino a distância (EAD) de prevenção em LER/DORT na melhora da percepção de qualidade de vida de um grupo de enfermagem. / Introduction: Repetitive Strain Injuries (RSI) or Work-Related Musculoskeletal Disorders (WMSDs) are musculoskeletal disorders. Nursing is one of the health categories that area most affected by these, due to the profession risk factors. Many of these workers show discomfort and pain in specific regions of the body as part of the daily life that end up presenting changes in their quality of life. These factors lead to the appearance of presenteeism observed when the worker is present at work despite being ill or with some physical or psychological problem and to absenteeism that occurs when this worker is absent from work. Objective: To evaluate the effects of a distance learning (DL) course on prevention of RSI and WMSDs in the quality of life of these workers and also to assess discomfort and pain, presenteeism and absenteeism. Methods: Sixty nursing staff from a general university hospital participated in the randomized trial, randomly divided into two groups. The inclusion criteria were: to be a hospital employee in the categories professional nurses, technicians, nurse assistants and to work in hospitalization units. Workers with self-reported diagnosis of RSI/WMSDs were excluded. The intervention group participated in an 8h DL course, consisting of strategies and work tips for the prevention of RSI/ WMSDs, available literature and theoretical evidence, the control group was submitted to a 30 minute educational lecture, with the same content of the DL course. They were evaluated before the intervention, two and eight weeks after. Results: When comparing the quality of life between the groups, the we did not observe better results in the perception of the intervention group. The pain domain presented worse perception. Presenteeism was observed in both groups, we observed reduction of presenteeism in the groups being a significant difference in the control group. Regarding to discomfort and pain, both groups presented a high prevalence, there was a significant change in the control group, with a reduction in the baseline scale to the 8th week later in the lower back region. In the intervention group there is no significant difference statistically, although there is a significant reduction. On the other hand, absenteeism presented a difference between the groups and in the intervention group there was a reduction in the number of hours of sick leave. Conclusion: There is no evidence of the impact of a distance learning prevention course on RSI/WMSDs on the improvement of the perception of the quality of life of a nursing group

Introdução: As Lesões por Esforços Repetitivos (LER) ou Distúrbios Osteomusculares Relacionados ao Trabalho (DORT) são afecções músculo esqueléticas. A enfermagem é uma das categorias da área da saúde mais atingidas por estas, devido aos fatores de risco da profissão. Apresentar desconforto e dor em regiões específicas do corpo, faz parte do cotidiano de muitos destes trabalhadores, que acabam apresentando alterações em sua qualidade de vida. Estes fatores levam ao aparecimento do presenteísmo observado quando o trabalhador está presente no trabalho apesar de doente ou com algum problema físico ou psicológico e ao absenteísmo que ocorre quando este trabalhador falta ao trabalho. Objetivo: Avaliar o impacto de um Curso de Ensino à Distância (EAD) de Prevenção em LER/DORT na qualidade de vida destes trabalhadores e avaliar também desconforto e dor, presenteísmo e absenteísmo. Métodos: Sessenta funcionários da enfermagem de um hospital geral universitário participaram do ensaio clínico, randomizados aleatoriamente em dois grupos.Os critérios de inclusão foram: ser contratado do hospital nas categorias profissionais enfermeiros, técnicos e auxiliares de enfermagem e atuarem em unidades de internação. Foram excluídos trabalhadores com diagnóstico de LER/DORT auto-referido.O grupo intervenção participou de um curso em EAD com duração de 8 horas, composto por estratégias e dicas laborais para a prevenção de LER/DORT, literaturas disponíveis e provas teóricas, o grupo controle foi submetido a uma palestra educativa com duração de 30 minutos, com os mesmos conteúdos do curso em EAD. Foram avaliados antes da intervenção, duas e oito semanas após. Resultados: Quando comparada a qualidade de vida entre os grupos não observamos melhores resultados na percepção do grupo intervenção. O domínio de dor apresentou pior percepção. Presenteísmo foi observado em ambos os grupos, observamos redução do presenteísmo nos grupos sendo no grupo controle uma diferença significativa.Em relação ao desconforto e dor, ambos os grupos apresentaram alta prevalência, houve mudança significativa no grupo controle, com redução na escala do basal para a 8ª semana depois na região das costas inferior. No grupo intervenção não há uma diferença estatisticamente significativa, apesar de haver uma importante redução. Já o absenteísmo apresentou diferença entre os grupos, sendo que no vii grupo intervenção teve redução do número de horas de afastamento por doença. Conclusão: Não há evidência do impacto de um curso de ensino a distância (EAD) de prevenção em LER/DORT na melhora da percepção de qualidade de vida de um grupo de enfermagem. / Introduction: Repetitive Strain Injuries (RSI) or Work-Related Musculoskeletal Disorders (WMSDs) are musculoskeletal disorders. Nursing is one of the health categories that area most affected by these, due to the profession risk factors. Many of these workers show discomfort and pain in specific regions of the body as part of the daily life that end up presenting changes in their quality of life. These factors lead to the appearance of presenteeism observed when the worker is present at work despite being ill or with some physical or psychological problem and to absenteeism that occurs when this worker is absent from work. Objective: To evaluate the effects of a distance learning (DL) course on prevention of RSI and WMSDs in the quality of life of these workers and also to assess discomfort and pain, presenteeism and absenteeism. Methods: Sixty nursing staff from a general university hospital participated in the randomized trial, randomly divided into two groups. The inclusion criteria were: to be a hospital employee in the categories professional nurses, technicians, nurse assistants and to work in hospitalization units. Workers with self-reported diagnosis of RSI/WMSDs were excluded. The intervention group participated in an 8h DL course, consisting of strategies and work tips for the prevention of RSI/ WMSDs, available literature and theoretical evidence, the control group was submitted to a 30 minute educational lecture, with the same content of the DL course. They were evaluated before the intervention, two and eight weeks after. Results: When comparing the quality of life between the groups, the we did not observe better results in the perception of the intervention group. The pain domain presented worse perception. Presenteeism was observed in both groups, we observed reduction of presenteeism in the groups being a significant difference in the control group. Regarding to discomfort and pain, both groups presented a high prevalence, there was a significant change in the control group, with a reduction in the baseline scale to the 8th week later in the lower back region. In the intervention group there is no significant difference statistically, although there is a significant reduction. On the other hand, absenteeism presented a difference between the groups and in the intervention group there was a reduction in the number of hours of sick leave. Conclusion: There is no evidence of the impact of a distance learning prevention course on RSI/WMSDs on the improvement of the perception of the quality of life of a nursing group

Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-12-13T15:34:18Z No. of bitstreams: 1 Dissertação - Mitsuyoshi N. Carvalho.pdf: 2218487 bytes, checksum: dee728ee846d66a58515c2e64c63ff4f (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-12-13T15:35:18Z (GMT) No. of bitstreams: 1 Dissertação - Mitsuyoshi N. Carvalho.pdf: 2218487 bytes, checksum: dee728ee846d66a58515c2e64c63ff4f (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-12-13T15:35:51Z (GMT) No. of bitstreams: 1 Dissertação - Mitsuyoshi N. Carvalho.pdf: 2218487 bytes, checksum: dee728ee846d66a58515c2e64c63ff4f (MD5) / Made available in DSpace on 2016-12-13T15:35:51Z (GMT). No. of bitstreams: 1 Dissertação - Mitsuyoshi N. Carvalho.pdf: 2218487 bytes, checksum: dee728ee846d66a58515c2e64c63ff4f (MD5) Previous issue date: 2013-02-28 / FINEP - Financiadora de Estudos e Projetos / The Common Channel Signaling System No. 7 (SS7) is one of the most important signaling systems used in today’s telecommunication networks and continues to be used on new architectures of fixed and mobile telephony. This dissertation presents an implementation for the control functions of the Signaling System Number 7 level 2 (referred in this work as MTP2-H) using the VHDL as the hardware description language. The specification of the control functions for the SS7 level 2 is made by using SDL diagrams described in Recommendation Q.703 of ITU-T. To accomplish the implementation, a methodology was developed for conversion of systems described in SDL to VHDL, consisting of a set of rules and a standard VHDL model that were applied on those SDL diagrams. The implementation was performed in order to provide the modularity and portability characteristics to the generated code. This way, the developed module can be replicated as many times as necessary in a programmable logic component (respecting the limitations of the component) and can also be synthesized in components from different manufacturers. The validation of implementation was done by means of functional tests using the Modelsim simulation tool. The developed code was compiled in development tools from different manufacturers to validate the portability feature and to estimate the amount of resources required in programmable logic components. / O Sistema de Sinalização por Canal Comum número 7 (SS7) é um dos mais importantes sistemas de sinalização utilizado em redes atuais de telecomunicações e continua a ser usado em novas arquiteturas tanto de telefonia fixa como móvel. O presente trabalho de dissertação apresenta a implementação das funções de controle do nível 2 do Sistema de Sinalização número 7 (denominado nesse trabalho como MTP2-H) utilizando a linguagem de descrição de hardware VHDL. A especificação das funções de controle do nível 2 do SS7 é feita através de diagramas em linguagem SDL na recomendação Q.703 do ITU-T. Para realizar a implementação, foi desenvolvida uma metodologia para conversão de sistemas descritos em SDL para VHDL, composta por um conjunto de regras e um modelo padrão em VHDL que foram aplicados nos referidos diagramas SDL. A implementação foi realizada de forma a proporcionar características de modularidade e portabilidade ao código gerado. Com isso, o módulo desenvolvido poderá ser replicado o número de vezes em que for necessário em um componente de lógica programável (respeitando as limitações do componente) e também poderá ser sintetizado em componentes de diferentes fabricantes. A validação da implementação foi feita por meio de testes funcionais utilizando a ferramenta de simulação Modelsim. O código desenvolvido foi compilado em ferramentas de desenvolvimento de diferentes fabricantes para validar a característica de portabilidade e para estimar a quantidade de recursos necessários nos componentes de lógica programáveis.

Introdução: As Lesões por Esforços Repetitivos (LER) ou Distúrbios Osteomusculares Relacionados ao Trabalho (DORT) são afecções músculo esqueléticas. A enfermagem é uma das categorias da área da saúde mais atingidas por estas, devido aos fatores de risco da profissão. Apresentar desconforto e dor em regiões específicas do corpo, faz parte do cotidiano de muitos destes trabalhadores, que acabam apresentando alterações em sua qualidade de vida. Estes fatores levam ao aparecimento do presenteísmo observado quando o trabalhador está presente no trabalho apesar de doente ou com algum problema físico ou psicológico e ao absenteísmo que ocorre quando este trabalhador falta ao trabalho. Objetivo: Avaliar o impacto de um Curso de Ensino à Distância (EAD) de Prevenção em LER/DORT na qualidade de vida destes trabalhadores e avaliar também desconforto e dor, presenteísmo e absenteísmo. Métodos: Sessenta funcionários da enfermagem de um hospital geral universitário participaram do ensaio clínico, randomizados aleatoriamente em dois grupos.Os critérios de inclusão foram: ser contratado do hospital nas categorias profissionais enfermeiros, técnicos e auxiliares de enfermagem e atuarem em unidades de internação. Foram excluídos trabalhadores com diagnóstico de LER/DORT auto-referido.O grupo intervenção participou de um curso em EAD com duração de 8 horas, composto por estratégias e dicas laborais para a prevenção de LER/DORT, literaturas disponíveis e provas teóricas, o grupo controle foi submetido a uma palestra educativa com duração de 30 minutos, com os mesmos conteúdos do curso em EAD. Foram avaliados antes da intervenção, duas e oito semanas após. Resultados: Quando comparada a qualidade de vida entre os grupos não observamos melhores resultados na percepção do grupo intervenção. O domínio de dor apresentou pior percepção. Presenteísmo foi observado em ambos os grupos, observamos redução do presenteísmo nos grupos sendo no grupo controle uma diferença significativa.Em relação ao desconforto e dor, ambos os grupos apresentaram alta prevalência, houve mudança significativa no grupo controle, com redução na escala do basal para a 8ª semana depois na região das costas inferior. No grupo intervenção não há uma diferença estatisticamente significativa, apesar de haver uma importante redução. Já o absenteísmo apresentou diferença entre os grupos, sendo que no vii grupo intervenção teve redução do número de horas de afastamento por doença. Conclusão: Não há evidência do impacto de um curso de ensino a distância (EAD) de prevenção em LER/DORT na melhora da percepção de qualidade de vida de um grupo de enfermagem. / Introduction: Repetitive Strain Injuries (RSI) or Work-Related Musculoskeletal Disorders (WMSDs) are musculoskeletal disorders. Nursing is one of the health categories that area most affected by these, due to the profession risk factors. Many of these workers show discomfort and pain in specific regions of the body as part of the daily life that end up presenting changes in their quality of life. These factors lead to the appearance of presenteeism observed when the worker is present at work despite being ill or with some physical or psychological problem and to absenteeism that occurs when this worker is absent from work. Objective: To evaluate the effects of a distance learning (DL) course on prevention of RSI and WMSDs in the quality of life of these workers and also to assess discomfort and pain, presenteeism and absenteeism. Methods: Sixty nursing staff from a general university hospital participated in the randomized trial, randomly divided into two groups. The inclusion criteria were: to be a hospital employee in the categories professional nurses, technicians, nurse assistants and to work in hospitalization units. Workers with self-reported diagnosis of RSI/WMSDs were excluded. The intervention group participated in an 8h DL course, consisting of strategies and work tips for the prevention of RSI/ WMSDs, available literature and theoretical evidence, the control group was submitted to a 30 minute educational lecture, with the same content of the DL course. They were evaluated before the intervention, two and eight weeks after. Results: When comparing the quality of life between the groups, the we did not observe better results in the perception of the intervention group. The pain domain presented worse perception. Presenteeism was observed in both groups, we observed reduction of presenteeism in the groups being a significant difference in the control group. Regarding to discomfort and pain, both groups presented a high prevalence, there was a significant change in the control group, with a reduction in the baseline scale to the 8th week later in the lower back region. In the intervention group there is no significant difference statistically, although there is a significant reduction. On the other hand, absenteeism presented a difference between the groups and in the intervention group there was a reduction in the number of hours of sick leave. Conclusion: There is no evidence of the impact of a distance learning prevention course on RSI/WMSDs on the improvement of the perception of the quality of life of a nursing group

Memoria (licenciado en ciencias jurídicas y sociales)

Action planning has been used in the field of robotics for solving long-running tasks. In the robot architectures field, it is also known as the deliberative layer. However, there is still a gap between the symbolic representation on the one hand and the low-level control and sensor representation on the other. In addition, the definition of a planning problem for a complex, real-world robot is not trivial. The planning process could become intractable as its search spaces become large. As the defined planning problem determines the complexity and the computationability for solving the problem, it should contain only relevant states. In this work, a novel approach which amalgamates Description Logic (DL) reasoning with Hierarchical Task Network (HTN) planning is introduced. The planning domain description as well as fundamental HTN planning concepts are represented in DL and can therefore be subject to DL reasoning; from these representations, concise planning problems are generated for HTN planning. The method is presented through an example in the robot navigation domain. In addition, a case study of the RoboCup@Home domain is given. As proof of concept, a well-known planning problem that often serves as a benchmark, namely that of the blocks-world, is modeled and solved using this approach. An analysis of the performance of the approach has been conducted and the results show that this approach yields significantly smaller planning problem descriptions than those generated by current representations in HTN planning.

White matter hyperintensities (WMH) are commonly found as bright regions in brain MRI images in older individuals. They are associated with various neurological and vascular diseases, such as stroke, dementia, and cardiovascular disorders. WMH is also one of the seven radiological parameters included in the idiopathic normal pressure hydrocephalus (iNPH) Radscale, used to grade the radiological signs of normal pressure hydrocephalus. The radiological patterns are highly heterogenous, making quantification and classification of WMHs complex. In this project, an automated method for quantitative and qualitative assessment of white matter hyperintensities was developed based on the deep learning architecture nnU-Net.  Different configurations of the nnU-Net network were trained on a publicly available dataset from the 2017 Medical Image Computing and Computer Assisted Invention Society (MICCAI) WMH segmentation challenge constituting different grades of WMHs, and eventually ensembled to produce the final output of the segmentation model. Based on the segmentation result, a volumetric assessment was performed using Fazekas scale, which can then be used as one of the essential radiological parameters in the iNPH Radscale. Furthermore, the pipeline subdivides and classifies the hyperintense regions based on spatial information and T1-signal intensity, which is believed to have a significant impact on the pathology of the WMHs.  The final pipeline accurately segments WMHs from T1-weighted and FLAIR MRI images with a Dice’s similarity coefficient of 0.81, quantitatively classifies each case according to Fazekas scale and further subdivides each hyperintense voxel based on its location in the brain and intensity in the T1-weighted image. Hopefully, this can serve as a meaningful tool in the diagnosis of iNPH as well as future research aiming to fully understand the clinical implications of different types of WMHs.

No description available.

According to the World Health Organization, almost all human population (99%) lives in 117 countries with over 6000 cities, where air pollutant concentration exceeds recommended thresholds. The most common, so-called criteria, air pollutants that affect human lives, are particulate matter (PM) and gas-phase (SO2, CO, NO2, O3 and others). Therefore, many countries or regions worldwide have imposed regulations or interventions to reduce these effects. Whenever an intervention occurs, air quality changes due to changes in ambient factors, such as weather characteristics and human activities. One approach for assessing the effects of interventions or events on air quality is through the use of the Weather Normalized Model (WNM). However, current deterministic models struggle to accurately capture the complex, non-linear relationship between pollutant concentrations and their emission sources. Hence, the primary objective of this thesis is to examine the power of machine learning (ML) and deep learning (DL) techniques to develop and improve WNMs. Subsequently, these enhanced WNMs are employed to assess the impact of events on air quality. Furthermore, these ML/DL-based WNMs can serve as valuable tools for conducting exploratory data analysis (EDA) to uncover the correlations between independent variables (meteorological and temporal features) and air pollutant concentrations within the models.  It has been discovered that DL techniques demonstrated their efficiency and high performance in different fields, such as natural language processing, image processing, biology, and environment. Therefore, several appropriate DL architectures (Long Short-Term Memory - LSTM, Recurrent Neural Network - RNN, Bidirectional Recurrent Neural Network - BIRNN, Convolutional Neural Network - CNN, and Gated Recurrent Unit - GRU) were tested to develop the WNMs presented in Paper I. When comparing these DL architectures and Gradient Boosting Machine (GBM), LSTM-based methods (LSTM, BiRNN) have obtained superior results in developing WNMs. The study also showed that our WNMs (DL-based) could capture the correlations between input variables (meteorological and temporal variables) and five criteria contaminants (SO2, CO, NO2, O3 and PM2.5). This is because the SHapley Additive exPlanations (SHAP) library allowed us to discover the significant factors in DL-based WNMs. Additionally, these WNMs were used to assess the air quality changes during COVID-19 lockdown periods in Ecuador. The existing normalized models operate based on the original units of pollutants and are designed for assessing pollutant concentrations under “average” or consistent weather conditions. Predicting pollution peaks presents an even greater challenge because they often lack discernible patterns. To address this, we enhanced the Weather Normalized Models (WNMs) to boost their performance specifically during daily concentration peak conditions. In the second paper, we accomplished this by developing supervised learning techniques, including Ensemble Deep Learning methods, to distinguish between daily peak and non-peak pollutant concentrations. This approach offers flexibility in categorizing pollutant concentrations as either daily concentration peaks or non-daily concentration peaks. However, it is worth noting that this method may introduce potential bias when selecting non-peak values. In the third paper, WNMs are directly applied to daily concentration peaks to predict and analyse the correlations between meteorological, temporal features and daily concentration peaks of air pollutants.

<p dir="ltr">A camera's image signal processor (ISP) is responsible for taking the mosaiced and noisy image signal from the image sensor and processing it such a way that an end-result image is produced that is informative and accurately captures the scene. Real-time video capture in photon-limited environments remains a challenge for many ISP's today. In these conditions, the image signal is dominated by the photon shot noise. Deep learning methods show promise in extracting the underlying image signal from the noise, but modern AI-based ISPs are too computationally complex to be realized as a fast and efficient hardware ISP. An ISP algorithm, BLADE2 has been designed, which leverages AI in a computationally conservative manner to demosaic and denoise low-light images. The original implementation of this algorihtm is in Python/PyTorch. This Thesis explores taking BLADE2 and implementing it on a general purpose GPU via a suite of Nvidia optimization toolkits, as well as a low-level implementation in C/C++, bringing the algorithm closer to FPGA realization. The GPU implementation demonstrated significant throughput gains and the C/C++ implementation demonstrated the feasibility of further hardware development.</p>