It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and a...It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.展开更多
A varied and balanced diet has always been essential in the optimal management of diabetes. The objective of this work was to evaluate dietary surveys among 50 diabetic patients hospitalized in the metabolic and endoc...A varied and balanced diet has always been essential in the optimal management of diabetes. The objective of this work was to evaluate dietary surveys among 50 diabetic patients hospitalized in the metabolic and endocrine diseases department of the Brazzaville Hospital and University Center. This survey was carried out using two methods: dietary history and 24-hour recall. The results relating to the dietary history revealed in the patients a dietary imbalance characterized by snacking at meals, non-compliance with a balanced diet and a high frequency of consumption of foods rich in simple sugar. and saturated fats. Regarding the 24-hour recall, the survey showed that the average blood sugar levels of hospitalized patients increased depending on the number of meals. This meant that these hyperglycemias (2 to 5 g/L) observed in these patients exceeded three meals per day and required, among other things, an increase in insulin intake or doses. The age groups of diabetic patients were also divided. These age groups had partly defined the types of diabetes encountered. Regarding body mass index, women had a body mass index greater than 30 kg/m2 compared to men. This increase in body mass index was explained by being overweight or even obese due to excess body fat.展开更多
Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take ca...Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.展开更多
文摘It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.
文摘A varied and balanced diet has always been essential in the optimal management of diabetes. The objective of this work was to evaluate dietary surveys among 50 diabetic patients hospitalized in the metabolic and endocrine diseases department of the Brazzaville Hospital and University Center. This survey was carried out using two methods: dietary history and 24-hour recall. The results relating to the dietary history revealed in the patients a dietary imbalance characterized by snacking at meals, non-compliance with a balanced diet and a high frequency of consumption of foods rich in simple sugar. and saturated fats. Regarding the 24-hour recall, the survey showed that the average blood sugar levels of hospitalized patients increased depending on the number of meals. This meant that these hyperglycemias (2 to 5 g/L) observed in these patients exceeded three meals per day and required, among other things, an increase in insulin intake or doses. The age groups of diabetic patients were also divided. These age groups had partly defined the types of diabetes encountered. Regarding body mass index, women had a body mass index greater than 30 kg/m2 compared to men. This increase in body mass index was explained by being overweight or even obese due to excess body fat.
文摘Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.
