Gas and solid behaviours during defluidisation of Geldart-A particles

Bed collapsing experiments were carried out in a cold-air transparent column 192 mm in diameter and 2 m high. Typical Fluid Catalytic Cracking (FCC) catalyst with a mean particle size of 76 μm and a density of 1400 kg/m3 was used. Both single and double-drainage protocols were tested. The local pressure drop and bed surface collapse height were acquired throughout the bed settling.Typical results were found regarding dense phase voidage of a fluidised bed and the bed surface collapse velocity. In addition, bubble fraction was calculated based on the collapse curve.Experimental results showed that windbox effect is significantly reduced compared to previous works since the volume of air within the windbox was reduced. The comparison of single/double-drainage protocols revealed a new period in the defluidisation of Geldart-A particles concerning gas compressibility. Through the temporal analysis of local pressure drop, the progress of the solid sedimentation front from bottom to top was determined, analysed and modelled

Whalesong

HESS debates student loan bill -- Task force studies ways to slice university budget -- Jones House for daycare facility -- Fellowship: a chance to see the Orient -- Anders nabs Fulbright: selected from field of 400 -- EDITORIAL -- LETTERS -- Message in Perseverance potent today as in 1937 -- Study Center extends benefits to UAJ students -- March ski-a-thon raises 18,000 in pledges -- Basketball season bounces to a finish -- Intramural BB season ends -- Update: House Bill 161 -- Hubbard featured in statewide UA publication -- Dunne busy organizing UAJ Native Club -- Graduates urged to prepare for tremendous change -- In search of the perfect Juneau meal -- What value on human life? -- One of five V.C. finalists visits campus -- Peer advisement: a new concept in helping people -- Artists for Peace to tour USSR this fall -- UAJ's Rosenthal back on tour in Alaska -- Historical/contemporary art on display -- Classified -- UAJ students named to Chancellor's and Dean's lists -- Are the costs of UAJ athletics/activities worth it? -- Benefits of Maharishi Technology discussed at UA

MPCI : An R Package for Computing Multivariate Process Capability Indices

Manufacturing processes are often based on more than one quality characteristic. When these variables are correlated the process capability analysis should be performed using multivariate statistical methodologies. Although there is a growing interest in methods for evaluating the capability of multivariate processes, little attention has been given to developing user friendly software for supporting multivariate capability analysis. In this work we introduce the package MPCI for R, which allows to compute multivariateprocess capability indices. MPCI aims to provide a useful tool for dealing with multivariate capability assessment problems. We illustrate the use of MPCI package through both simulated and real examples

Análisis de la sentencia c-720 de 2007, bajo el parámetro legal de la retención transitoria como medida de protección

El presente artículo hace un análisis de la evolución de la norma: Detención Transitoria como Medida de Protección , y específicamente de la sentencia de la corte constitucional C-720 de 11 de septiembre de 2007. La investigación tiene como referente en el tiempo la sentencia donde la Corte Constitucional determina los límites y escenarios puntuales en la aplicación de la medida, dejando claro el objetivo principal de salvaguardar al ciudadano que sobre pase los limites emocionales establecidos. Este artículo no pretende realizar juicios o proponer cambios en la jurisprudencia, lo que busca es brindar y abonar en las líneas de investigación y así generar mayor correlación entre los actores de la norma, para que esta integración permita la adopción de las mejores recomendaciones e indicaciones a la fuerza de la ley y de la policía para garantizar su cumplimiento"This article analyzes the evolution of the standard: ""Temporary Detention as a measure for protection, and specifically the constitutional court ruling C-720 of September 11, 2007. The research is the time reference in the sentence where the Constitutional Court determines the limits and specific scenarios in the implementation of the measure, making clear the main objective to safeguard the citizen to pass limits on emocionales1 established. This article will not make judgments or to propose changes in case law, which aim is to provide and pay in the research and generate greater correlation between the actors of the standard for integration for the adoption of the best recommendations and guidelines the force of law and police to ensure compliance

Preventing School-bullying through Automated Video Analysis

Currently, humanity strives to prevent discrimination, whether through offensive words or violent attitudes. Most teenagers who suffer bullying in school have difficulties in the learning process and consequently low grades. Most of the recent studies carried out by professionals in the health department show that the marks left by events of this type can bring illnesses such as depression, low self-esteem, and self-destructive behaviors. To address this problem non-profit institutions appear to prevent this kind of action through sensibility campaigns. However, these institutions have limitations that make it impossible to diagnose most of these occurrences, creating a lack of assistance for the victim. These reasons motivate us to search for new solutions with the help of automated systems that will make it possible to detect, at the exact moment, the persons involved in bullying actions in school property. With the help of a Portuguese non-profit bullying organization, a study was made to collect information about the most known behaviors of persons involved in bullying actions and their effects on society to have good guidelines to identify this events. Next, we carried out an investigation about technologies used in computer vision and artificial intelligence that allow the analysis of videos captured by surveillance cameras and can predict which type of action is inhered in each one. We present a variety of architectures since the first model capable to classify human behavior on videos, until the current times, where state-of-the art architectures, composed by two 3D convolutions streams, able to extract spatial and temporal features were developed. To search previous studies in the deep learning area related to bullying recognition in school videos, three scientific papers were found that already had investigated this kind of problem. Our analysis derived by the studies shows us the need to create a novel dataset able to represent all types of existing bullying actions and a new model architecture capable of identifying these events with high accuracy. Following the previous studies made in Chapter 2 and 3, a few guidelines were created to mimic bullying behavior on school grounds with a group of teenagers. Three hundred fifty clips were shot in bathrooms, classrooms, hallways, and canteens with five kids aged 7 to 18 years old. Another 200 films were acquired from the Internet and categorized alongside the recorded videos, producing a balanced dataset of 550 trimmed videos. The data cleaning process removed audio and black sidebars. The Kinetics 400 was downloaded and applied for fine-tuning deep learning pipelines. In terms of models, the SlowFast, I3D, C2D, and FGN architectures were used to construct the application. The FGN was the only model that produced plausible results when trained from scratch, finishing the training process with an accuracy on the test dataset of around 70%. However, when the ideal threshold is employed, this value drops to around 51%. Following the successful training from scratch with the FGN, a training strategy known as K-Fold Cross Validation was implemented, which divided the dataset into ten pieces to test the entire dataset. The final result is the average of the ten models, which attained an accuracy of 65.67%. When trained from scratch, the other three models could not converge to a minimum and only got satisfying performance when fine-tuned using the Kinetics 400 weights. These three models do not perform well when trained from scratch since they contain numerous parameters that must be changed, signaling that more extensive datasets are required. The SlowFast model obtained approximately 83% when selecting the class with highest probability. However, this score was maintained when adopting the optimum threshold. The I3D model scored 81% on the test dataset, when considered the class with highest probability. However, determining the appropriate threshold achieved the best accuracy of approximately 87%. Finally, the C2D model obtained approximately 77% accuracy on the test dataset. This model maintained this performance when computed and utilizing the optimum threshold. These thresholds were determined using the ROC Curve, which looked for the best threshold with the highest number of true positives and the lowest amount of false positives. Ultimately, this study offered a unique bullying dataset with activities that highlight the bullying theme and have more attributes than well-known conflict datasets. After cleaning and labeling the dataset, 550 bullying and non-bullying trimming films were produced. Due to the sensitivity of the topic and the requirement for authorization from the student’s responsible entity, the filming procedure of the movies, getting the school locations and students, was challenging. It was suggested for future work to use network compression techniques through knowledge distillation, teaching a student model with a smaller size with knowledge derived from a huge model, to reduce the number of parameters and thus the number of computing resources while maintaining accuracy. This approach has advantages since it allows the model to be performed in inference mode on IoT devices rather than transferring data over the Internet to large data centers. This method provides an additional security layer to an application because of the sensitive bullying topic and school video information. Another enhancement proposal is to record new bullying and non-bullying films to offer more features and variation to the dataset.Atualmente, a humanidade luta contra a discriminação, seja ela praticada através de palavras ofensivas ou atitudes violentas. Muitos dos adolescentes que sofrem de bullying na escola têm dificuldades no processo de aprendizagem e consequentemente resultados negativos. Os mais recentes estudos feitos por profissionais da área de saúde mostram que o bullying pode deixar marcas na vida dos adolescentes através do surgimento de doenças tais como depressão, baixa autoestima, comportamentos auto-destrutivos, entre outras. Obviamente, estes problemas reduzem drasticamente a qualidade de vida da pessoa, uma vez que podem despoletar traumas socais, físicos e psicológicos na vítima. Foram criadas organizações sem fins lucrativos com o intuito de prevenir a ocorrência de ações de bullying nas escolas através de campanhas de sensibilização. Mas para além dessas campanhas, as instituições têm dificuldade em identificar esses acontecimentos, o que impede que se possa dar um correto e rápido suporte à vitima. Estes fatores levam-nos a procurar novas soluções com ajuda de sistemas automáticos, capazes de detetar, no exato momento, a ocorrência de um ato de bullying numa escola e consequentemente as pessoas envolvidas no mesmo. Com a ajuda de uma associação sem fins lucrativos portuguesa, foi realizado um estudo que procura identificar os comportamentos mais comuns nas pessoas que se encontram envolvidas nestes atos, e os efeitos que podem trazer para a sociedade, com o objetivo de tornar claro os padrões intrínsecos aos atos de bullying, possibilitando desta forma reconhecer com maior facilidade estas ações. De seguida, foi realizado um estudo aprofundado acerca das tecnologias e ferramentas utilizadas na área de visão computacional e inteligência artificial, que possibilitam a análise de vídeos capturados em câmaras de vigilância, e consequentemente identificam os tipos de ações humanas existentes. Este estudo começa com as abordagens clássicas de aprendizagem profunda, redes neuronais convolucionais 2D e termina com a utilização de redes avançadas onde são implementadas duas redes neuronais convolucionais 3D, cada uma com funções diferentes, uma responsável pela extração de características estáticas e a outra responsável pela análise do movimento. Antes de se prosseguir para o desenvolvimento, foi realizado um estudo científico em vários trabalhos já efetuados, que abordaram o tema de bullying, no contexo das tecnologias de aprendizagem profunda. Foram encontrados três artigos que estudaram a possibilidade de utilizar diversas arquiteturas de redes convolucionais e diferentes conjuntos de dados para abordar o problema. Com a leitura e análise desses documentos, concluí-se que existe a necessidade de criar um conjunto de dados que caracterizem o problema através de um grande leque de videos com ações de bullying, e a necessidade de desenvolver um modelo que consiga identificar com uma grande taxa de acerto estas ações em vídeos capturados em cenários realistas. Depois do estudo realizado nos dois capítulos anteriores, foram criados vários guiões para planear cenários encenados de ações de bullying e não-bullying com estudantes em propriedade escolar. As gravções originaram 350 videos, tendo como cenário casas de banho, salas de aula, cantinas e parques exteriores. Outros 200 vídeos foram transferidos da Internet através do site World Star HipHop. Posteriormente, os 550 videos sofreram um processo de limpeza onde foi removido som e as barras pretas presentes nas laterais. O processo de anotação criou vídeos com sequências de tempo entre os 5s e os 12s. O dataset Kinetics 400 também foi transferido e utilizado para os métodos de destilação de conhecimento e ajuste dos pesos com o dataset YNF. Em relação aos modelos utilizados na fase de desenvolvimento, foram implementadas as arquiteturas SlowFast, I3D, C2D, e FGN. FGN foi o único modelo capaz de convergir para um mínimo quando treinado com pesos incializados aleatoriamente. No final do processo de treino e validação o modelo atingiu uma taxa de acerto no conjunto de teste perto dos 70%, sofrendo uma redução significativa para os 51% quando utilizado o valor de separação ótimo entre as duas classes. Esta redução ocorreu devido à taxa de acerto inicial ter sido calculada com base no valor de separação de 0.5, enquanto que o valor que garante o maior número de verdadeiros positivos e o menor número de falsos positivos é de aproximadamente 0.87. Uma vez que o conjunto de dados recolhido é de apenas 550 videos, o que implica um reduzido número de instâncias de teste, foi implementada a técnica de treino K-Fold Cross Validation, no modelo FGN. Este processo atingiu uma taxa de acerto de 65.67%. Os restantes 3 modelos foram incializados com os pesos do conjunto de dados Kinetics 400 e sofreram um ajuste dos pesos atráves do processo de treino com o conjunto de dados YNF. O facto de estes modelos terem um grande número de parâmetros para atualizar ao longo do treino, implica o uso de grandes conjuntos de dados para convergir para um mínimo quando treinados com pesos inicializados aleatoriamente. O facto de o conjunto de dados recolhido ter apenas 550 vídeos impediu que estes atingissem um bom desempenho quando treinados sem qualquer conhecimento prévio. A arquitetura de rede SlowFast atingiu uma taxa de acerto de aproximadamente 83%, quando utilizado o valor de separação entre as duas classes de 0.5. A taxa de acerto no conjunto de teste foi igual quando utilizado o valor ótimo de separação através da métrica ROC Curve. O segundo modelo, I3D atingiu uma taxa de acerto de 81% no conjunto de teste e quando contabilizado o valor de separação ótimo, aumentou o desempenho para aproximadamente 87%. O último modelo treinado, C2D atingiu uma taxa de acerto no conjunto de teste de aproximadamente 77%, acabando por manter a mesma taxa de acerto quando contabilizado o valor ótimo de separação entre classes. Os valores ótimos de separação foram calculados atráves da métrica ROC Curve, que procurou o melhor valor de forma a reduzir o número de instâncias falsas positivas e aumentar o número de instâncias verdadeiras positivas. Em conclusão, este trabalho apresentou um conjunto de dados que expressa várias ações de bullying e não-bullying entre estudantes em propriedade escolar. Este foi criado devido à inexistência de dados que retratem o problema de bullying na sua totalidade, para além de violência física, focando-se em situações de gozo, roubo e intimidação. Com o conjunto de dados anotado e limpo, foram utilizados no processo de treino e validação de 5 modelos de aprendizagem profunda para análise de vídeo com o intuito de criar uma aplicação capaz de diferenciar ações de bullying e não-bullying. O modelo que foi capaz de realizar essa distinção com a melhor taxa de acerto foi a arquitetura I3D, inicializado com os pesos do conjunto de dados Kinetics 400, atingindo 87 % no conjunto de teste, com o valor ótimo de separação entre classes. Para trabalho futuro é mencionada a técnica de destilação de conhecimento utilizada para reduzir o tamanho das redes profundas, diminuindo consequentemente os recursos computacionais necessários para executar os modelos. Uma das vantagens do uso desta técnica é a possibilidade de fazer o desenvolvimento de aplicações de inteligência artificial em dispositivos IoT com poucos recursos de energia e processamento, mantendo a mesma taxa de acerto adquirida com modelos de maiores dimensões. Devido à sensibilidade da comunidade relativamente ao tema de bullying e partilha de dados visuais relativos a crianças menores de idade em escolas, a possibilidade de realizar inferência sem enviar dados pela Internet para grandes data-centers, adiciona uma camada de segurança às aplicações. Outra das sugestões para melhorar o desempenho da aplicação apresentada nesta dissertação é a gravação de novos vídeos, aumentando substancialmente a variedade de ações

A more efficient technique to power home monitoring systems using controlled battery charging

Home energy monitoring has recently become a very important issue and a means to reduce energy consumption in the residential sector. Sensors and control systems are deployed at various locations in a house and an intelligent system is used to efficiently manage the consumed energy. Low power communication systems are used to provide low power consumption from a smart meter. Several of these systems are battery operated. Other systems use AC/DC adapters to supply power to sensors and communication systems. However, even using low-power technology, such as ZigBee, the power consumption of a router can be high because it must always be powered on. In this work, to evaluate power consumption, a system for monitoring energy usage and indoor air quality was developed. A technique is proposed to efficiently supply power to the components of the system. All sensor nodes are battery operated, and relays are used to control the battery charging process. In addition, an energy harvesting system based on solar energy was developed to power the proposed system.info:eu-repo/semantics/publishedVersio

Optimal cerebral perfusion pressure in patients with intracerebral hemorrhage: an observational case series

Introduction: Current guidelines for spontaneous intracerebral hemorrhage (ICH) recommend maintaining cerebral perfusion pressure (CPP) between 50 and 70 mmHg, depending on the state of autoregulation. We continuously assessed dynamic cerebral autoregulation and the possibility of determination of an optimal CPP (CPPopt) in ICH patients. Associations between autoregulation, CPPopt and functional outcome were explored. Methods: Intracranial pressure (ICP), mean arterial pressure (MAP) and CPP were continuously recorded in 55 patients, with 38 patients included in the analysis. The pressure reactivity index (PRx) was calculated as moving correlation between MAP and ICP. CPPopt was defined as the CPP associated with the lowest PRx values. CPPopt was calculated using hourly updated of 4 hour windows. The modified Rankin Scale (mRS) was assessed at 3 months and associations between PRx, CPPopt and outcomes were explored using Pearson correlation and Fisher’s exact test. Multivariate stepwise logistic regression models were calculated including standard outcome predictors along with percentage of time with PRx >0.2 and percentage of time within the CPPopt range. Results: An overall PRx indicating impairment of pressure reactivity was found in 47% of patients (n = 18). The mean PRx and the time spent with a PRx > 0.2 significantly correlated with mRS at 3 months (r = 0.50, P = 0.002; r = 0.46, P = 0.004). CPPopt was calculable during 57% of the monitoring time. The median CPP was 78 mmHg, the median CPPopt 83 mmHg. Mortality was lowest in the group of patients with a CPP close to their CPPopt. However, for none of the CPPopt variables a significant association to outcome was found. The percentage of time with impaired autoregulation and hemorrhage volume were independent predictors for acceptable outcome (mRS 1 to 4) at three months. Conclusions: Failure of pressure reactivity seems common following severe ICH and is associated with unfavorable outcome. Real-time assessment of CPPopt is feasible in ICH and might provide a tool for an autoregulation-oriented CPP management. A larger trial is needed to explore if a CPPopt management results in better functional outcomes