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Introduction 3 Purpose and scope of report 4 Acknowledgments 5 Properties of water 5 Composition of the earth's crust 6 Water as a geochemical agent The role of water in erosion Chemistry of weathering processes Collection of quality-of-water data Collection of water samples Surface-water sampling Ground-water sampling Completeness of sample coverage Analyses of water samples Field testing of water Electric logs as indicators of ground-water quality Laboratory procedures Expression of water analyses Analyses reported in terms of hypothetical combinations Analyses expressed in terms of ions Determinations included in analyses Units used in reporting analyses Weight-per-weight units Weight-per-volume units Equivalent-weight units Composition of anhydrous residue Parts per million as calcium carbonate Comparison of units of expression Significance of properties and constituents reported in water analyses_ _ Specific electrical conductance Units for reporting conductance Physical basis of conductance Range of conductance values Accuracy and reproducibility of conductance values Hydrogen-ion concentration (pH) Hydrolysis Buffered solutions Interpretation of pH data Range of pH values Accuracy and reproducibility of pH values Color Sources and significance of color in water 49 Residue on evaporition 49 Theoretical basis of determination 50 Range of concentration 51 Accuracy and reproducibility of results 51 III Significance of properties and constituents reported in water analyses-Continued Acidity Sources of acidity of natural water Chemistry of acidity determination Range of concentration Reproducibility of acidity data Sulfate Sources of sulfate in natural water Chemistry of sulfate in natural water Range of concentration Accuracy and reproducibility of results Chloride Sources of chloride in water Chemistry of chloride in natural water Oceanic chloride Juvenile chloride Cyclic chloride Range of concentration Accuracy and reproducibility of results Fluoride Source of fluoride in water Chemistry of fluoride in natural water Range of concentration Accuracy and reproducibility of results Nitrate Source of nitrate in water Chemistry of nitrate in natural water 116 Range of concentration Accuracy and reproducibility of results Phosphate Sources of phosphate Chemistry of phosphate in natural water 119 Range of concentration 120 Accuracy and reproducibility of results 120 Boron 120 Sources of boron 120 Chemistry of boron in natural water Range of concentration 122 Accuracy and reproducibility of results 122 Trace or minor constituents-Cations 124 Heavy metals 124 Titanium 124 Chromium 124 Zinc 125 Nickel and cobalt 126 Copper 126 Tin 127 Lead 127 Cadmium 128 Mercury 128 Arsenic 129 Selenium 130 Significance of properties and constituents reported in water analyses-Continued Trace or minor constituents-Cations-Continued Alkaline-earth metals Beryllium Strontium Barium Alkali metals and ammonium Lithium Rubidium Cesium Ammonium Radioactive components Uranium Radium Radon Thorium Trace or minor constituents-Anions Bromide Iodide Sulfite and thiosulf ate Total dissolved solids-Computed Chemistry of dissolved solids determination Accuracy and reproducibility of results Dissolved gases Biochemical oxygen demand Hardness Utilization Range of concentration Accuracy and reproducibility of results Percent sodium Sodium-adsorption ratio Density Organization and study of water-analysis data Evaluation of water analyses Tabulation Study techniques Inspection and comparison Use of ratios Use of averages 156 Palmer's geochemical classification 162 Graphical representation 164 Scatter diagrams 165 Ionic-concentration diagrams 168 Percentage-composition diagrams Frequency diagrams Chemical analyses plotted against nonchemical variables 186 Hydrographs 186 Dissolved-solids rating curves 188 Water-quality profiles 192 Quality-of-water maps 192 Selection of study techniques 10. Effect of temperature on solubility of calcium carbonate (calcite) in water in the presence of CO2 VIII CONTENTS Page FIGURE 11. Solubility of magnesium carbonate in water at 25C in the presence of CO2 81 12. Relation of conductance to chloride, hardness, and sulfate concentrations, Gila River at Bylas, Ariz., Oct. 1, 1943 to Sept. 30, 1944 13. Sodium-chloride relationship, Gila River at Bylas, Ariz., Oct. 1, 1943, to Sept. 30, 1944 14. Analyses represented by vertical bar graphs of equivalents per million 15. Analyses represented by bar graphs of parts per million 16. Bar graph of equivalents per million which also shows hardness values in parts per million 17. Analyses in equivalents per million represented by vectors__ _ 18. Analyses represented by patterns based on equivalents per million 19. Analyses represented by linear plotting of cumulative percentage composition based on parts per million 20. Analyses represented by logarithmic plotting of concentrations in parts per million 21. Analyses represented by circular diagrams subdivided on the basis of percent of total equivalents per million 22. Analyses represented by bar-patterns based on percent of total equivalents per million 23. Analyses represented by patterns drawn on radial coordinates.. 24. Analyses represented by three points plotted in trilinear diagram (after A. M. Piper) 25. Number of samples having percent sodium within ranges indicated, San Simon artesian basin, Ariz 26. Cumulative frequency curve of specific conductance, Allegheny, Monongahela and Ohio River waters, Pittsburgh area, Pennsylvania, 1944-50 27. Specific conductance of daily samples and daily mean discharge, San Francisco River at Clifton, Ariz., Oct. 1, 1943 to Sept. 30, 1944 28. Bicarbonate, sulfate, hardness, and pH of samples collected in cross section of Susquehanna River at Harrisburg, Pa., July 8, 1947 29. Temperature and dissolved solids of water in Lake Mead in Virgin and Boulder Canyons, 1948 194 30. Total concentration and hardness of water from deeper wells in Prairie Creek Unit, Nebr 31. Ratio of alkalinity to sulfate in water from unconsolidated deposits in the Torrington area, Nebr 32. Map of portions of Apache and Navajo counties, Ariz., showing mineral content of ground water in the Coconino sandstone_ 198 33. Analyses of waters associated with igneous rocks 206 34. Analyses of waters associated with resistate sediments 209 35. Analyses of waters associated with hydrolyzate sediments_ _ _ 36. Weighted-average analyses for Rio Grande at San Acacia, N. Mex., for two periods in the 1945-46 water year 212 37. Analyses of waters associated with precipitate-type sediments.. 38. Analyses of waters associated with evaporate sediments 215 39. Analyses of waters associated with metamorphic rocks 217 40. Diagram for use in interpreting the analysis of irrigation water_ 251 2 CHEMICAL CHARACTERISTICS OF NATURAL WATER

This contribution is a completely updated and expanded version of the four prior analogous reviews that were published in this journal in 1997, 2003, 2007, and 2012. In the case of all approved therapeutic agents, the time frame has been extended to cover the 34 years from January 1, 1981, to December 31, 2014, for all diseases worldwide, and from 1950 (earliest so far identified) to December 2014 for all approved antitumor drugs worldwide. As mentioned in the 2012 review, we have continued to utilize our secondary subdivision of a "natural product mimic", or "NM", to join the original primary divisions and the designation "natural product botanical", or "NB", to cover those botanical "defined mixtures" now recognized as drug entities by the U.S. FDA (and similar organizations). From the data presented in this review, the utilization of natural products and/or their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from around the 1940s to the end of 2014, of the 175 small molecules approved, 131, or 75%, are other than "S" (synthetic), with 85, or 49%, actually being either natural products or directly derived therefrom. In other areas, the influence of natural product structures is quite marked, with, as expected from prior information, the anti-infective area being dependent on natural products and their structures. We wish to draw the attention of readers to the rapidly evolving recognition that a significant number of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated", and therefore it is considered that this area of natural product research should be expanded significantly.

Natural selection is an immense and important subject, yet there have been few attempts to summarize its effects on natural populations, and fewer still which discuss the problems of working with natural selection in the wild. These are the purposes of John Endler's book. In it, he discusses the methods and problems involved in the demonstration and measurement of natural selection, presents the critical evidence for its existence, and places it in an evolutionary perspective. Professor Endler finds that there are a remarkable number of direct demonstrations of selection in a wide variety of animals and plants. The distribution of observed magnitudes of selection in natural populations is surprisingly broad, and it overlaps extensively the range of values found in artificial selection. He argues that the common assumption that selection is usually weak in natural populations is no longer tenable, but that natural selection is only one component of the process of evolution; natural selection can explain the change of frequencies of variants, but not their origins.

This review is an updated and expanded version of two prior reviews that were published in this journal in 1997 and 2003. In the case of all approved agents the time frame has been extended to include the 251/2 years from 01/1981 to 06/2006 for all diseases worldwide and from 1950 (earliest so far identified) to 06/2006 for all approved antitumor drugs worldwide. We have continued to utilize our secondary subdivision of a "natural product mimic" or "NM" to join the original primary divisions. From the data presented, the utility of natural products as sources of novel structures, but not necessarily the final drug entity, is still alive and well. Thus, in the area of cancer, over the time frame from around the 1940s to date, of the 155 small molecules, 73% are other than "S" (synthetic), with 47% actually being either natural products or directly derived therefrom. In other areas, the influence of natural product structures is quite marked, with, as expected from prior information, the antiinfective area being dependent on natural products and their structures. Although combinatorial chemistry techniques have succeeded as methods of optimizing structures and have, in fact, been used in the optimization of many recently approved agents, we are able to identify only one de novo combinatorial compound approved as a drug in this 25 plus year time frame. We wish to draw the attention of readers to the rapidly evolving recognition that a significant number of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated", and therefore we consider that this area of natural product research should be expanded significantly.

Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks. 1

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

A method of ‘‘natural population analysis’’ has been developed to calculate atomic charges and orbital populations of molecular wave functions in general atomic orbital basis sets. The natural analysis is an alternative to conventional Mulliken population analysis, and seems to exhibit improved numerical stability and to better describe the electron distribution in compounds of high ionic character, such as those containing metal atoms. We calculated ab initio SCF-MO wave functions for compounds of type CH3X and LiX (X=F, OH, NH2, CH3, BH2, BeH, Li, H) in a variety of basis sets to illustrate the generality of the method, and to compare the natural populations with results of Mulliken analysis, density integration, and empirical measures of ionic character. Natural populations are found to give a satisfactory description of these molecules, providing a unified treatment of covalent and extreme ionic limits at modest computational cost.

Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments - including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances - are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.

We need scarcely add that the contemplation in natural science of a wider domain than the actual leads to a far better understanding of the actual.' (p. 267,The, Nature of the Physical World.)x PREFACE evolutionary theory was thus chiefly retrogressive, the mighty body of Mendelian researches throughout the world has evidently out- grown the fallacies with which it was at first fostered.As a pioneer of genetics he has done more than enough to expiate the rash polemics of his early writings.To treat Natural Selection as an agency based independently on its own foundations is not to mimimize its importance in the theory of evolution.On the contrary, as soon as we require to form opinions by other means than by comparison and analogy, such an indepen- dent deductive basis becomes a necessity.This necessity is particu- larly to be noted for mankind ; since we have some knowledge of the structure of society, of human motives, and of the vital statistics of this species, the use of the deductive method can supply a more intimate knowledge of the evolutionary processes than is elsewhere possible.In addition it will be of importance for our subject to call ) attention to several consequences of the principle of Natural Selection!which, since they do not consist in the adaptive modification of specific I forms, have necessarily escaped attention.The genetic phenomena of I dominance and linkage seem to offer examples of this class, the future ' investigation of which may add greatly to the scope of our subject.No efforts of mine could avail to make the book easy reading.I have endeavoured to assist the reader by giving short summaries at the ends of all chapters, except Chapter IV, which is summarized conjointly with Chapter V.Those who prefer to do so may regardChapter IV as a mathematical appendix to the corresponding part of the summary.The deductions respecting Man are strictly in- separable from the more general chapters, but have been placed together in a group commencing with Chapter VIII.I believe no one will be surprised that a large number of the points considered demand a far fuller, more rigorous, and more comprehensive treat- ment.It seems impossible that full justice should be done to the subject in this way, until there is built up a tradition of mathematical work devoted to biological problems, comparable to the researches upon which a mathematical physicist can draw in the resolution of special difficulties.

Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements. Bradford Books imprint

Historically, management theory has ignored the constraints imposed by the biophysical (natural) environment. Building upon resource-based theory, this article attempts to fill this void by proposing a natural-resource-based view of the firm—a theory of competitive advantage based upon the firm's relationship to the natural environment. It is composed of three interconnected strategies: pollution prevention, product stewardship, and sustainable development. Propositions are advanced for each of these strategies regarding key resource requirements and their contributions to sustained competitive advantage.

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation, and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions, and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

The book examines theories (models) of how systems (those of humans, nature, and combined humannatural systems) function, and attempts to understand those theories and how they can help researchers develop effective institutions and policies for environmental management. The fundamental question this book asks is whether or not it is possible to get beyond seeing environment as a sub-component of social systems, and society as a sub-component of ecological systems, that is, to understand human-environment interactions as their own unique system. After examining the similarities and differences among human and natural systems, as well as the means by which they can be accounted for in theories and models, the book examines five efforts to describe human-natural systems. The point of these efforts is to provide the means of learning about those systems so that they can be managed adaptively. The final section of the book uses case studies to examine the application of integrated theories/models to the real world.

Integrated studies of coupled human and natural systems reveal new and complex patterns and processes not evident when studied by social or natural scientists separately. Synthesis of six case studies from around the world shows that couplings between human and natural systems vary across space, time, and organizational units. They also exhibit nonlinear dynamics with thresholds, reciprocal feedback loops, time lags, resilience, heterogeneity, and surprises. Furthermore, past couplings have legacy effects on present conditions and future possibilities.

Hydrate research has expanded substantially over the past decade, resulting in more than 4,000 hydrate-related publications. Collating this vast amount of information into one source, Clathrate Hydrates of Natural Gases, Third Edition presents a thoroughly updated, authoritative, and comprehensive description of all major aspects of natural gas cla

The Origin of Species, by Charles Darwin, is part of the Barnes & Noble Classicsseries, which offers quality editions at affordable prices to the student and the general reader, including new scholarship, thoughtful design, and pages of carefully crafted extras. Here are some of the remarkable features of Barnes & Noble New introductions commissioned from today's top writers and scholars Biographies of the authors Chronologies of contemporary historical, biographical, and cultural events Footnotes and endnotes Selective discussions of imitations, parodies, poems, books, plays, paintings, operas, statuary, and films inspired by the work Comments by other famous authors Study questions to challenge the reader's viewpoints and expectations Bibliographies for further reading Indices & Glossaries, when appropriate All editions are beautifully designed and are printed to superior specifications; some include illustrations of historical interest. Barnes & Noble Classics pulls together a constellation of influencesbiographical, historical, and literaryto enrich each reader's understanding of these enduring works.On December 27, 1831, the young naturalist Charles Darwin left Plymouth Harbor aboard the HMS Beagle. For the next five years, he conducted research on plants and animals from around the globe, amassing a body of evidence that would culminate one of the greatest discoveries the history of mankindthe theory of evolution.Darwin presented his stunning insights a landmark book that forever altered the way human beings view themselves and the world they live in. In The Origin of Species, he convincingly demonstrates the fact of evolution: that existing animals and plants cannot have appeared separately but must have slowly transformed from ancestral creatures. Most important, the book fully explains the mechanism that effects such a transformation: natural selection, the idea that made evolution scientifically intelligible for the first time.One of the few revolutionary works of science that is engrossingly readable, The Origin of Species not only launched the science of modern biology but also has influenced virtually all subsequent literary, philosophical, and religious thinking.George Levine, Kenneth Burke Professor of English Literature at Rutgers University, has written extensively about Darwin and the relation of science and literature, particularly in Darwin and the Novelists. He is the author of many related books, including The Realistic Imagination, Dying to Know, and his birdwatching memoirs, Lifebirds.

Numerical assessments of global air quality and potential changes in atmospheric chemical constituents require estimates of the surface fluxes of a variety of trace gas species. We have developed a global model to estimate emissions of volatile organic compounds from natural sources (NVOC). Methane is not considered here and has been reviewed in detail elsewhere. The model has a highly resolved spatial grid (0.5°×0.5° latitude/longitude) and generates hourly average emission estimates. Chemical species are grouped into four categories: isoprene, monoterpenes, other reactive VOC (ORVOC), and other VOC (OVOC). NVOC emissions from oceans are estimated as a function of geophysical variables from a general circulation model and ocean color satellite data. Emissions from plant foliage are estimated from ecosystem specific biomass and emission factors and algorithms describing light and temperature dependence of NVOC emissions. Foliar density estimates are based on climatic variables and satellite data. Temporal variations in the model are driven by monthly estimates of biomass and temperature and hourly light estimates. The annual global VOC flux is estimated to be 1150 Tg C, composed of 44% isoprene, 11% monoterpenes, 22.5% other reactive VOC, and 22.5% other VOC. Large uncertainties exist for each of these estimates and particularly for compounds other than isoprene and monoterpenes. Tropical woodlands (rain forest, seasonal, drought‐deciduous, and savanna) contribute about half of all global natural VOC emissions. Croplands, shrublands and other woodlands contribute 10–20% apiece. Isoprene emissions calculated for temperate regions are as much as a factor of 5 higher than previous estimates.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTIntermolecular interactions from a natural bond orbital, donor-acceptor viewpointAlan E. Reed, Larry A. Curtiss, and Frank WeinholdCite this: Chem. Rev. 1988, 88, 6, 899–926Publication Date (Print):September 1, 1988Publication History Published online1 May 2002Published inissue 1 September 1988https://pubs.acs.org/doi/10.1021/cr00088a005https://doi.org/10.1021/cr00088a005research-articleACS PublicationsRequest reuse permissionsArticle Views19552Altmetric-Citations15820LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts