Biofuels : from microbes to molecules / / Edited by Xuefeng Lu

• Record Nr.: UNINA9910464683803321
• Pubbl/distr/stampa: Norfolk, England : , : Caister Academic Press, , [2014]; ©2014
• ISBN: 1-908230-63-0
• Descrizione fisica: 1 online resource (259 p.)
• Disciplina: 333.9539
• Soggetti: Biomass conversion; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Contents; Current Books of Interest; Contributors; Preface; 1: Metabolic Engineering: Key for Improving Biological Hydrogen Production; 1.1 Introduction; 1.2 Metabolic engineering of bacterial systems for hydrogen production by dark fermentation; 1.3 Metabolic engineering of green algae, cyanobacteria, and bacteria for improving hydrogen production; 1.4 Future directions; 2: Biogas-producing Microbes and Biomolecules; 2.1 Introduction; 2.2 Biogas microbiology; 2.3 Biomethane; 2.4 Molecular methods for the study and control of biogas production; 2.5 Biogas from unconventional substrates; 2.6 Future trends: algae2.7 Conclusions; 3: Engineering Recombinant Organisms for Next-generation Ethanol Production; 3.1 Introduction; 3.2 Overview of all microbial technologies for first- (1G) and second-generation (2G) ethanol production; 3.3 Xylose fermentation by Saccharomyces cerevisiae; 3.4 Hardening of S. cerevisiae against inhibitors formed during lignocellulose pretreatment; 3.5 CBP application to soluble and insoluble (raw, uncooked) starch fermentation; 3.6 Conversion of cellulose to ethanol by S. cerevisiae in a CBP configuration; 3.7 Mining microbial diversity for novel enzymes for CBP application to starch and lignocellulose, including genomic and metagenomic and/or transcriptomic libraries as sources of novel enzymes/activities3.8 Process configurations for integration of 1G and 2G processes; 3.9 Discussion and conclusions; 4: Production of Biobutanol, from ABE to ; Syngas Fermentation; 4.1 Butanol - commodity chemical and advanced biofuel; 4.2 Classic acetone-butanol-ethanol (ABE) fermentation with solventogenic clostridia; 4.3 Engineering of non-natural butanol producers and synthetic pathways; 4.4 Future trends - butanol production from greenhouse gases CO2 and/or CO5: Higher Chain Alcohols from Non-fermentative Pathways; 5.1 Introduction; 5.2 Steps to production; 5.3 Fermentative alcohol production; 5.4 2-Keto acid-based alcohols; 5.5 Conclusion; 6: Isoprene-derived Biofuels from Engineered Microbes; 6.1 Classes of isoprenoid compounds; 6.2 Metabolic pathway and host engineering to optimize isoprenoid-precursors biosynthetic pathways; 6.3 Conversions of isoprenoid precursors to fuel compounds; 6.4 Future trends in isoprene-derived biofuels; 7: Engineering Microbial Fatty Acid Biosynthetic Pathways to Make Advanced Biofuels7.1 Introduction; 7.2 Current status of biodiesel production; 7.3 Motivation for engineering fatty acid metabolism; 7.4 Brief review of fatty acid metabolism; 7.5 Regulation of fatty acid synthesis and degradation; 7.6 Genetic engineering of bacteria to improve free fatty acid production; 7.7 Genetic engineering to improve fatty alcohol production; 7.8 Genetic engineering to improve fatty acid methyl/ethyl ester production; 7.9 Genetic engineering to improve fatty alkane/alkene production; 7.10 Future perspectives
• Sommario/riassunto: The increasing worldwide demand for energy, combined with diminishing fossil fuel reserves and concerns about climate change, have stimulated intense research into the development of renewable energy sources, in particular, microbial biofuels. For a biofuel to be commercially viable, the production processes, yield, and titer have to be optimized, which can be achieved through the use of microbial cell factories. Using multidisciplinary research approaches, and through the application of diverse biotechnologies (such as enzyme engineering, metabolic engineering, systems biology, and synthetic

Understanding the Impact of Machine Learning on Labor and Education : A Time-Dependent Turing Test / / by Joseph Ganem

• Record Nr.: UNINA9910728952503321
• Autore: Ganem Joseph
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• ISBN: 3-031-31004-7
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (88 pages)
• Collana: SpringerBriefs in Philosophy, , 2211-4556
• Disciplina: 006.3101
• Soggetti: Technology - Philosophy; Artificial intelligence; Machine learning; Philosophy of Technology; Artificial Intelligence; Machine Learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Introduction: The difference between knowing and learning -- Labor Markets: Comparative learning advantages -- Learning to Work: The two dimensions of job performance -- The Judgment Game: The Turing Test as a general research framework -- The Learning Game: A time-dependent Turing Test -- Implications: Recommendations for future education and labor policies.
• Sommario/riassunto: This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, “learning algorithms”—that enable machines to modify their actions based on real-world experiences—are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology. To explore the difference between knowing and learning, Turing’s “Imitation Game,”—that he proposed as a test for machine thinking—is expanded to include time dependence. The ; arguments presented in the book introduce three novel concepts: (1) Comparative learning advantage: This is a concept analogous to comparative labor advantagebut arises from the disparate times required to learn new knowledge bases/skillsets. It is argued that in the future, comparative learning advantages between humans and machines will determine their division of labor. (2) Two dimensions of job performance—expertise and interpersonal: Job tasks can be sorted into two broad categories. Tasks that require expertise have stable endpoints, which makes these tasks inherently repetitive and subject to automation. Tasks that are interpersonal are highly context-dependent and lack stable endpoints, which makes these tasks inherently non-routine. Humans compared to machines have a comparative learning advantage along the interpersonal dimension, which is increasing in value economically. (3) The Learning Game is a time-dependent version of Turing’s “Imitation Game.” It is more than a thought experiment. The “Learning Game” provides a mathematical framework with quantitative criteria for training and assessing comparative learningadvantages. The book is highly interdisciplinary—presenting philosophical arguments in economics, artificial intelligence, and education. It also provides data, mathematical analysis, and testable criteria that researchers in these fields will find of practical use. The book calls for a rethinking of how labor markets operate and how the education system should prepare students for future jobs. It concludes with a list of counterintuitive recommendations for future education and labor policies that all stakeholders—employers, employees, educators, students, and political leaders—should heed.