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Argonne and Parallel Works Win Highest Honor from Federal Laboratory Consortium

Argonne and Parallel Works Win Highest Honor from Federal Laboratory Consortium
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Argonne and Parallel Works win highest honor from Federal Laboratory Consortium for Excellence in Technology Transfer

Argonne and Parallel Works win highest honor from Federal Laboratory Consortium for Excellence in Technology Transfer
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Human Activity Recognition Based on Wavelet-Based Features along with by Mahmudul Hasan Abid, Abdullah Al Nahid et al

Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected features also have been ranked according to their importance based on their SHAP values. In the venture, an interesting inspection has been found. That is in HAR datasets, signal values lay into lower frequency regions mostly. The highest accuracy and f1-score which have been got are 94.74%, 94.73%, and 89.98%, 89.67% for the feature extracted and feature selected dataset respectively.

Modelling of autogenerative high-pressure anaerobic digestion in a batch reactor for the production of pressurised biogas | Biotechnology for Biofuels and Bioproducts

Pressurised anaerobic digestion allows the production of biogas with a high content of methane and, at the same time, avoid the energy costs for the biogas upgrading and injection into the distribution grid. The technology carries potential, but the research faces practical constraints by a.o. the capital investment needed in high-pressure reactors and sensors and associated sampling limitations. In this work, the kinetic model of an autogenerative high-pressure anaerobic digestion of acetate, as the representative compound of the aceticlastic methanogenesis route, in batch configuration, is proposed to predict the dynamic performance of pressurised digesters and support future experimental work. The modelling of autogenerative high-pressure anaerobic digestion in batch configuration, which is not extensively studied and simulated in the present literature, was developed, calibrated, and validated by using experimental results available from the literature. Under high-pressure conditio

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