Biliary tract cancer, or cholangiocarcinoma, comprises a heterogeneous group of malignant tumors that can emerge at any part of the biliary tree. This group is the second most common type of primary liver cancer. Diag...Biliary tract cancer, or cholangiocarcinoma, comprises a heterogeneous group of malignant tumors that can emerge at any part of the biliary tree. This group is the second most common type of primary liver cancer. Diagnosis is usually based on symptoms, which may be heterogeneous, and nonspecific biomarkers in serum and biopsy specimens, as well as on imaging techniques. Endoscopy-based diagnosis is essential, since it enables biopsy specimens to be taken. In addition, it can help with locoregional staging of distal tumors. Endoscopic retrograde cholangiopancreatography is a key technique for the evaluation and treatment of malignant biliary tumors. Correct staging of cholangiocarcinoma is essential in order to be able to determine the degree of resectability and assess the results of treatment. The tumor is staged based on the TNM classification of the American Joint Committee on Cancer. The approach will depend on the classification of the tumor. Thus, some patients with early-stage disease could benefit from surgery;complete surgical resection is the cornerstone of cure. However, only a minority of patients are diagnosed in the early stages and are suitable candidates for resection. In the subset of patients diagnosed with locally advanced or metastatic disease, chemotherapy has been used to improve outcome and to delay tumor progression. The approach to biliary tract tumors should be multidisciplinary, involving experienced endoscopists, oncologists, radiologists, and surgeons.展开更多
Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer su...Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.展开更多
Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the c...Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m<sup>2</sup>] in the validation dataset.展开更多
文摘Biliary tract cancer, or cholangiocarcinoma, comprises a heterogeneous group of malignant tumors that can emerge at any part of the biliary tree. This group is the second most common type of primary liver cancer. Diagnosis is usually based on symptoms, which may be heterogeneous, and nonspecific biomarkers in serum and biopsy specimens, as well as on imaging techniques. Endoscopy-based diagnosis is essential, since it enables biopsy specimens to be taken. In addition, it can help with locoregional staging of distal tumors. Endoscopic retrograde cholangiopancreatography is a key technique for the evaluation and treatment of malignant biliary tumors. Correct staging of cholangiocarcinoma is essential in order to be able to determine the degree of resectability and assess the results of treatment. The tumor is staged based on the TNM classification of the American Joint Committee on Cancer. The approach will depend on the classification of the tumor. Thus, some patients with early-stage disease could benefit from surgery;complete surgical resection is the cornerstone of cure. However, only a minority of patients are diagnosed in the early stages and are suitable candidates for resection. In the subset of patients diagnosed with locally advanced or metastatic disease, chemotherapy has been used to improve outcome and to delay tumor progression. The approach to biliary tract tumors should be multidisciplinary, involving experienced endoscopists, oncologists, radiologists, and surgeons.
文摘Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.
文摘Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m<sup>2</sup>] in the validation dataset.
