The main purposes of this paper are to derive Bayesian acceptance sampling plans regarding the number of defects per unit of product, and to illustrate how to apply the methodology to the paper pulp industry. The sampling plans are obtained following an economic criterion: minimize the expected total cost of quality. It has been assumed that the number of defects per unit of product follows a Poisson distribution with process average 5 , whose prior information is described either for a gamma or for a non- informative distribution. The expected total cost of quality is composed of three independent components: inspection, acceptance and rejection. Both quadratic and step-loss functions have been used to quantify the cost incurred for the acceptance of a lot containing units with defects. Combining the prior information on 5 with the loss functions, four different sampling plans are obtained. When the quadratic-loss function is used, an analytical relation between the optimum settings of the sample size and the acceptance number is derived. The robustness analysis indicates that the sampling plans obtained are robust with respect to the prior distribution of the process average as well as to the misspecification of its mean and variance.
The utilisation of biofuels in gas turbines is a promising alternative to fossil fuels for power generation. It would lead to a significant reduction of CO2 emissions using an existing combustion technology, although considerable changes appear to be required and further technological development is necessary. The goal of this work is to conduct energy and exergy analyses of the behaviour of gas turbines fired with biogas, ethanol and synthesis gas (bio-syngas), compared with natural gas. The global energy transformation process (i.e., from biomass to electricity) also has been studied. Furthermore, the potential reduction of CO2 emissions attained by the use of biofuels has been determined, after considering the restrictions regarding biomass availability. Two different simulation tools have been used to accomplish this work. The results suggest a high interest in, and the technical viability of, the use of Biomass Integrated Gasification Combined Cycle (BioIGCC) systems for large scale power generation
Dynamic Trees are a tree-based machine learning technique specially designed for online environments where data are to be analyzed sequentially as they arrive. Our purpose is to test this methodology for the very first time for Electricity Price Forecasting (EPF) by using data from the Iberian market. For benchmarking the results, we will compare them against another tree-based technique, Random Forest, a widely used method that has proven its good results in many fields. The benchmark includes several versions of the Dynamic Trees approach for a very short term EPF (one-hour ahead) and also a short term (one-day ahead) approach but only with the best versions. The numerical results show that Dynamic Trees are an adequate method, both for very short and short term EPF-even improving upon the performance of the Random Forest method. The comparison with other studies for the Iberian market suggests that Dynamic Trees is a proper and promising method for EPF.
The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.
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