Hazardous Gases: Risk Assessment on Environment and Human Health examines all relevant routes of exposure, inhalation, skin absorption and ingestion, and control measures of specifics hazardous gases resulting from workplace exposure from industrial processes, traffic fumes, and the degradation of waste materials and how they impacts the health and environment of workers. The book examines the risk assessment and effect of poisonous gases on the environment human health. It also covers necessary emergency guidelines, safety measures, physiological impact, hazard control measures, handling and storage of hazardous gases. Each chapter is formatted to include an introduction, historical background, physicochemical properties, physiological role discussing mechanisms of toxicity, its effect on human health as well as environment, followed by case studies and recent research on toxic gases. Hazardous Gases: Risk Assessment on Environment and Human Health is a helpful resource for academics and researchers in toxicology, occupational health and safety, and environmental sciences as well as those in the field who work to assess and mitigate the impact of toxic gases on the work environment and the health of the workforce. - Emphasizes the environmental monitoring in the workplace of hazardous materials - Includes all relevant storage and handling information required for detailing all personnel on the hazards and risks from the substances with which they work - Offers practical examples and case studies related to toxic gases and their impact on health

Nonostante lo sviluppo di una definizione matematica e rigorosa del continuo attraverso i lavori di Cantor e lo sviluppo teoria degli insiemi a fine ‘800, la continuità del tempo rimane un problema per la filosofia contemporanea. Questo vale soprattutto per quelle teorie che accentuano la natura dinamica del tempo e del cambiamento, come la teoria A del tempo e in particolare il presentismo. Come è possibile pensare il tempo come continuo e perciò come esteso, se esso è, in quanto dinamico, in eterno divenire? Come possiamo concepire la continuità del tempo in contrapposizione alla continuità dello spazio? Attraverso un analisi di diverse concezioni del continuo nella storia della filosofia così, il presente volume intende esplorare diverse risposte a tali domande.

The conference was aimed at promoting contacts between scientists involved in solar-terrestrial physics, space physics, astroparticle physics and cosmology both from the theoretical and the experimental approach. The conference was devoted to physics and physics requirements, survey of theoretical models and performances of detectors employed (or to be employed) in experiments for fundamental physics, astroparticle physics, astrophysics research and space environment - including Earth magnetosphere and heliosphere and solar-terrestrial physics. Furthermore, cosmic rays have been used to extent the scientific research experience to teachers and students with air shower arrays and other techniques. Presentations included the following subjects: advances in physics from present and next generation ground and space experiments, dark matter, double-beta decay, high-energy astrophysics, space environment, trapped particles, propagation of cosmic rays in the Earth atmosphere, Heliosphere, Galaxy and broader impact activities in cosmic rays science. The open and flexible format of the Conference was conducive to fruitful exchanges of points of view among participants and permitted the evaluation of the progresses made and indicated future research directions. The participants were experienced researchers but also graduate students (MSc and PhD) and recent postdoctoral fellows.

Nanotube Superfiber Materials refers to different forms of macroscale materials with unique properties constructed from carbon nanotubes. These materials include nanotube arrays, ribbons, scrolls, yarn, braid, and sheets. Nanotube materials are in the early stage of development and this is the first dedicated book on the subject. Transitioning from molecules to materials is a breakthrough that will positively impact almost all industries and areas of society. Key properties of superfiber materials are high flexibility and fatigue resistance, high energy absorption, high strength, good electrical conductivity, high maximum current density, reduced skin and proximity effects, high thermal conductivity, lightweight, good field emission, piezoresistive, magnetoresistive, thermoelectric, and other properties. These properties will open up the door to dozens of applications including replacing copper wire for power conduction, EMI shielding, coax cable, carbon biofiber, bullet-proof vests, impact resistant glass, wearable antennas, biomedical microdevices, biosensors, self-sensing composites, supercapacitors, superinductors, hybrid superconductor, reinforced elastomers, nerve scaffolding, energy storage, and many others. The scope of the book covers three main areas: Part I: Processing; Part II: Properties; and Part III: Applications. Processing involves nanotube synthesis and macro scale material formation methods. Properties covers the mechanical, electrical, chemical and other properties of nanotubes and macroscale materials. Different approaches to growing high quality long nanotubes and spinning the nanotubes into yarn are explained in detail. The best ideas are collected from all around the world including commercial approaches. Applications of nanotube superfiber cover a huge field and provides a broad survey of uses. The book gives a broad overview starting from bioelectronics to carbon industrial machines. - First book to explore the production and applications of macro-scale materials made from nano-scale particles - Sets out the processes for producing macro-scale materials from carbon nanotubes, and describes the unique properties of these materials - Potential applications for CNT fiber/yarn include replacing copper wire for power conduction, EMI shielding, coax cable, carbon biofiber, bullet-proof vests, impact resistant glass, wearable antennas, biomedical microdevices, biosensors, self-sensing composites, supercapacitors, superinductors, hybrid superconductor, reinforced elastomers, nerve scaffolding, energy storage, and many others

Mirroring the growth and direction of science for a century, the Handbook, now in its 93rd edition, continues to be the most accessed and respected scientific reference in the world. An authoritative resource consisting tables of data, its usefulness spans every discipline. This edition includes 17 new tables in the Analytical Chemistry section, a major update of the CODATA Recommended Values of the Fundamental Physical Constants and updates to many other tables. The book puts physical formulas and mathematical tables used in labs every day within easy reach. The 93rd edition is the first edition to be available as an eBook.

Proudly serving the scientific community for over a century, this 96th edition of the CRC Handbook of Chemistry and Physics is an update of a classic reference, mirroring the growth and direction of science. This venerable work continues to be the most accessed and respected scientific reference in the world. An authoritative resource consisting of tables of data and current international recommendations on nomenclature, symbols, and units, its usefulness spans not only the physical sciences but also related areas of biology, geology, and environmental science. The 96th edition of the Handbook includes 18 new or updated tables along with other updates and expansions. A new series highlighting the achievements of some of the major historical figures in chemistry and physics was initiated with the 94th edition. This series is continued with this edition, which is focused on Lord Kelvin, Michael Faraday, John Dalton, and Robert Boyle. This series, which provides biographical information, a list of major achievements, and notable quotations attributed to each of the renowned chemists and physicists, will be continued in succeeding editions. Each edition will feature two chemists and two physicists. The 96th edition now includes a complimentary eBook with purchase of the print version. This reference puts physical property data and mathematical formulas used in labs and classrooms every day within easy reach. New Tables: Section 1: Basic Constants, Units, and Conversion Factors Descriptive Terms for Solubility Section 8: Analytical Chemistry Stationary Phases for Porous Layer Open Tubular Columns Coolants for Cryotrapping Instability of HPLC Solvents Chlorine-Bromine Combination Isotope Intensities Section 16: Health and Safety Information Materials Compatible with and Resistant to 72 Percent Perchloric Acid Relative Dose Ranges from Ionizing Radiation Updated and Expanded Tables Section 6: Fluid Properties Sublimation Pressure of Solids Vapor Pressure of Fluids at Temperatures Below 300 K Section 7: Biochemistry Structure and Functions of Some Common Drugs Section 9: Molecular Structure and Spectroscopy Bond Dissociation Energies Section 11: Nuclear and Particle Physics Summary Tables of Particle Properties Table of the Isotopes Section 14: Geophysics, Astronomy, and Acoustics Major World Earthquakes Atmospheric Concentration of Carbon Dioxide, 1958-2014 Global Temperature Trend, 1880-2014 Section 15: Practical Laboratory Data Dependence of Boiling Point on Pressure Section 16: Health and Safety Information Threshold Limits for Airborne Contaminants

Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 2 is about machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle accelerators or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.