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Biofuels Production and Processing Technology
| Biofuels Production and Processing Technology |
| Autore | Tropea Alessia |
| Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
| Descrizione fisica | 1 electronic resource (250 p.) |
| Soggetto topico |
Technology: general issues
Biotechnology |
| Soggetto non controllato |
biofuels
corn extraction enzyme-assisted protein soybean molecular sieve water removal rotary shaking electromagnetic stirring biofuel gasohol trend analysis promotion policy regulatory measure bottleneck synthesis gas fermentation volumetric mass transfer coefficient Tween 80® surfactant gasification multi-objective optimization bioethanol syngas fermentation modeling sustainability soapberry pericarp carbonization biochar pore property surface chemistry biomethane food waste co-production biorefinery bioelectrochemical system (BES) carbon dioxide sequestration extracellular electron transfer (EET) electroactive microorganisms microbial biocatalyst electro-fermentation circular economy downstream processing (DSP) gene manipulation biogas compost leachate pressurized anaerobic digestion ethanol simultaneous saccharification and fermentation Saccharomyces cerevisiae single cell protein pineapple waste cell wall sugar fermentation spent sugar beet pulp model economics pretreatment saccharification B. ceiba biomass second-generation biofuel bioenergy biodiesel non-fossil fuel empty fruit bunches response surface methodology central composite design biofuel production technologies downstream processing energy bioethanol production agroforest and industrial waste feedstock valorization microorganisms for biofuel |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595067503321 |
Tropea Alessia
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| Basel, : MDPI Books, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Information Bottleneck : Theory and Applications in Deep Learning
| Information Bottleneck : Theory and Applications in Deep Learning |
| Autore | Geiger Bernhard |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (274 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
information theory
variational inference machine learning learnability information bottleneck representation learning conspicuous subset stochastic neural networks mutual information neural networks information bottleneck compression classification optimization classifier decision tree ensemble deep neural networks regularization methods information bottleneck principle deep networks semi-supervised classification latent space representation hand crafted priors learnable priors regularization deep learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Information Bottleneck |
| Record Nr. | UNINA-9910557582803321 |
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Geiger Bernhard
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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