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Soil and Climate

Soil and Climate

Climate is a soil-forming factor and soil can mitigate climate change through a reduction in the emissions of greenhouse gases and sequestration of atmospheric CO2. Thus there is a growing interest in soil management practices capable of mitigating climate change and enhancing environmental quality. Soil and Climate addresses global issues through soil management and outlines strategies for advancing Sustainable Development Goals (SDGs). This volume in the Advances in Soil Science series is specifically devoted to describe state-of-the-knowledge regarding the climate–soil nexus in relation to:Soil Processes: weathering decomposition of organic matter erosion leaching salinization biochemical transformations gaseous flux and elemental cycling Soil Properties: physical chemical biological and ecological Atmospheric Chemistry: gaseous concentrations of (CO2 CH4 N2O) water vapors soot dust and particulate matter Mitigation and Adaptation: source and sink of GHGs (CO2 CH4 N2O) land use and soil management soil C sink capacity permafrost Soil Management: sequestration of organic and inorganic C nutrient requirements water demands coupled cycling of H2O N P S andPolicy and Outreach: carbon farming payments for ecosystem services COP21 SDGs land degradation neutrality Special topics on soil as a source or sink of CO2 silicate weathering and carbon sequestration nutrients required for carbon sequestration physical protection and the mean resident time and predicting soil carbon stocks are discussed in detail throughout the book.

GBP 44.99
1

Spectroscopic Techniques and Hindered Molecular Motion

Spectroscopic Techniques and Hindered Molecular Motion

Spectroscopic Techniques and Hindered Molecular Motion presents a united theoretical approach to studying classical local thermal motion of small molecules and molecular fragments in crystals by spectroscopic techniques. Mono- and polycrystalline case studies demonstrate performance validity. The book focuses on small molecules and molecular fragments such as N2 HCl CO2 CH4 H2O NH4 BeF4 NH3 CH2 CH3 C6H6 SF6 and other symmetrical atomic formations which exhibit local hindered motion in molecular condensed media: molecular and ionic crystals molecular liquids liquid crystals polymeric solids and biological objects. It reviews the state of studying the hindered molecular motion (HMM) phenomenon and the experimental works on the basis of the latest theoretical research. Case StudiesPhysical models of hindered molecular motionGeneral solution of the stochastic problem for the hindered molecular motion in crystalsFormulae of the angular autocorrelation function symmetrized on the crystallographic point symmetry groups Formulae of the spectral line shapes concerning the dielectric infrared Raman nuclear magnetic relaxation and neutron scattering spectroscopy in the presence of the hindered molecular motionExperimental probation of the theoretical outcomesProton relaxation in three-atomic molecular fragments undergoing axial symmetry hindered motion Structural distortion in the ordered phase of crystalline ammonium chlorideOrganic compounds polymers pharmaceutical products and biological systems consist of the molecular fragments which possess rotational or conformational degrees of freedom or an atomic exchange within the fragme

GBP 59.99
1

Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R which includes using various R packages such as glmnet h2o ranger xgboost keras and others to effectively model and gain insight from their data. The book favors a hands-on approach providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book the reader will be exposed to the entire machine learning process including feature engineering resampling hyperparameter tuning model evaluation and interpretation. The reader will be exposed to powerful algorithms such as regularized regression random forests gradient boosting machines deep learning generalized low rank models and more! By favoring a hands-on approach and using real word data the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages understand when and how to tune the various hyperparameters and be able to interpret model results. By the end of this book the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
1