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Raman Scattering on Emerging Semiconductors and Oxides
Raman Scattering on Emerging Semiconductors and Oxides presents Raman scattering studies.It describes the key fundamental elements in applying Raman spectroscopies to various semiconductors and oxides without complicated and deep Raman theories. Across nine chapters, it covers:• SiC and IV-IV semiconductors,• III-GaN and nitride semiconductors,• III-V and II-VI semiconductors,• ZnO-based and GaO-based semiconducting oxides,• Graphene, ferroelectric oxides, and other emerging materials,• Wide-bandgap semiconductors of SiC, GaN, and ZnO, and• Ultra-wide gap semiconductors of AlN, Ga2O3, and graphene. Key achievements from the author and collaborators in the above fields are referred to and cited with typical Raman spectral graphs and analyses.Written for engineers, scientists, and academics, this comprehensive book will be fundamental for newcomers in Raman spectroscopy. Zhe Chuan Feng has had an impressive career spanning many years of important work in engineering and tech, including as a professor at the Graduate Institute of Photonics & Optoelectronics and Department of Electrical Engineering, National Taiwan University, Taipei; establishing the Science Exploring Lab; joining Kennesaw State University as an adjunct professor, part-time; and at the Department of Electrical and Computer Engineering, Southern Polytechnic College of Engineering and Engineering Technology.Currently, he is focusing on materials research for LED, III-nitrides, SiC, ZnO, other semiconductors/oxides, and nanostructures and has devoted time to materials research and growth of III-V and II-VI compounds, LED, III nitrides, SiC, ZnO, GaO, and other semiconductors/oxides. Professor Feng has also edited and published multiple review books in his field, alongside authoring scientific journal papers and conference/proceeding papers.He has organized symposiums and been an invited speaker at different international conferences and universities.He has also served as a guest editor for special journal issues.
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Deep Learning for the Life Sciences : Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Deep learning has already achieved remarkable results in many fields.Now it’s making waves throughout the sciences broadly and the life sciences in particular.This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields.Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives.You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges.Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
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Applied Raman Spectroscopy : Concepts, Instrumentation, Chemometrics, and Life Science Applications
Applied Raman Spectroscopy: Concepts, Instrumentation, Chemometrics, and Life Science Applications synthesizes recent developments in the field, providing an updated overview.The book focuses on the modern concepts of Raman spectroscopy techniques, recent technological innovations, data analysis using chemometric methods, along with the latest examples of life science applications relevant in academia and industries.It will be beneficial to researchers from various branches of science and technology, and it will point them to modern techniques coupled with data analysis methods.In addition, it will help instruct new readers on Raman spectroscopy and hyphenated Raman spectroscopic techniques. The book is primarily written for analytical and physical chemistry students and researchers at a more advanced level who require a broad introductory overview of the applications of Raman spectroscopy, as well as those working in applied industry and clinical laboratories.Students, researchers, and industry workers in related fields, including X-ray and materials science, agriculture, botany, molecular biology and biotechnology, mineralogy, and environmental science will also find it very useful.
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Magneto-Optics and Spectroscopy of Antiferromagnets
Certain magnetic materials have optical properties that make them attractive for a wide variety of applications such as optical switches.This book describes the physics of one class of such magnetooptic materials, the insulating antiferromagnets.The authors summarize recent results concerning the structure, optical properties, spectroscopy, and magnetooptical properties of these materials.In particular, they consider magnetic phase transitions, symmetry effects, the linear magnetooptical effect, magnons, spectroscopic study of spin waves, photoinduced magnetic effects, and the effects of impurities.
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What are examples of learning materials?
Examples of learning materials include textbooks, workbooks, online courses, educational videos, interactive software, flashcards, and educational games. These materials are designed to help students acquire new knowledge and skills in various subjects such as math, science, language arts, and history. They can be used in traditional classroom settings, homeschooling, or for self-study purposes.
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What are the learning materials for the MedAT?
The learning materials for the MedAT (Medical Admission Test) typically include study guides, practice tests, and online resources. These materials cover the specific subjects tested in the exam, such as biology, chemistry, physics, mathematics, and text comprehension. Many test preparation companies offer comprehensive study materials that are designed to help students familiarize themselves with the format and content of the MedAT. Additionally, there are also online courses and tutoring services available to provide additional support and guidance for students preparing for the exam.
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Can recipients of ALG2 borrow learning materials at school?
Yes, recipients of ALG2 can typically borrow learning materials at school. Schools often have programs in place to support students in need, including providing access to textbooks, workbooks, and other necessary materials. Students receiving ALG2 can usually take advantage of these resources to ensure they have what they need to succeed academically. It's always a good idea for students to check with their school's administration or counseling office to learn more about the specific borrowing policies and procedures in place.
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Which app can be used to scan learning materials?
An app that can be used to scan learning materials is Adobe Scan. This app allows users to quickly and easily scan documents, books, and other learning materials using their smartphone camera. The scanned materials can then be saved as PDFs or images, making it convenient for students and educators to digitize and organize their study materials. Additionally, Adobe Scan offers features such as text recognition and the ability to convert scanned documents into editable text, making it a useful tool for academic purposes.
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Nanotechnology in Electronics : Materials, Properties, Devices
Nanotechnology in Electronics Enables readers to understand and apply state-of-the-art concepts surrounding modern nanotechnology in electronics Nanotechnology in Electronics summarizes numerous research accomplishments in the field, covering novel materials for electronic applications (such as graphene, nanowires, and carbon nanotubes) and modern nanoelectronic devices (such as biosensors, optoelectronic devices, flexible electronics, nanoscale batteries, and nanogenerators) that are used in many different fields (such as sensor technology, energy generation, data storage and biomedicine). Edited by four highly qualified researchers and professionals in the field, other specific sample topics covered in Nanotechnology in Electronics include: Graphene-based nanoelectronics biosensors, including the history, properties, and fundamentals of graphene, plus fundamentals of graphene derivatives and the synthesis of graphene Zinc oxide piezoelectronic nanogenerators for low frequency applications, with an introduction to zinc oxide and zinc oxide piezoelectric nanogenerators Investigation of the hot junctionless mosfets, including an overview of the junctionless paradigm and a simulation framework of the hot carrier degradation Conductive nanomaterials for printed/flexible electronics application and metal oxide semiconductors for non-invasive diagnosis of breast cancer The fundamental aspects and applications of multiferroic-based spintronic devices and quartz tuning fork based nanosensors. Containing in-depth information on the topic and written intentionally to help with the practical application of concepts described within, Nanotechnology in Electronics is a must-have reference for materials scientists, electronics engineers, and engineering scientists who wish to understand and harness the state of the art in the field.
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A Milliliter-Scale Setup for the Efficient Characterization of Multicomponent Vapor-Liquid Equilibria Using Raman Spectroscopy
Vapor-liquid equilibrium (VLE) data are of major importance for the chemical industry.Despite significant progress in predictive methods, experimental VLE data are still indispensable.In this work, we address the need for experimental VLE data.Commonly, the characterization of VLE requires significant experimental effort.To limit the experimental effort, VLE measurements are frequently conducted by synthetic methods which employ samples of known composition and avoid complex analytics and sampling issues.In contrast, analytical methods provide independent information on phase compositions, commonly based on sampling and large amounts of substance. In the first part of this work, we employ a synthetic method, the well-established Cailletet setup, to characterize the high pressure VLE of two promising binary biofuel blends.The Cailletet method serves as a state of the art reference method that enables collecting data of remarkable accuracy.However, extensive infrastructure is needed. In the second part, to avoid extensive infrastructure and overcome limitations of previous methods, we develop a novel analytical milliliter-scale setup for the noninvasive and efficient characterization of VLE: RAMSPEQU (Raman Spectroscopic Phase Equilibrium Characterization).The novel setup saves substance and rapidly characterizes VLE.Sampling and its associated errors are avoided by analyzing phase compositions using Raman spectroscopy.Thereby, volumes of less than 3 ml are sufficient for reliable phase equilibrium measurements.To enable rapid data generation and save substance, we design an integrated workow combining Raman signal calibration and VLE measurement.As a result, RAMSPEQU gives access to up to 15 pT xy-data sets per workday.RAMSPEQU is successfully validated against pure component and binary VLE data from literature. However, mixtures with only two components rarely depict real industrial applications.As the number of experiments increases strongly with a rising number of components, the efficient RAMSPEQU setup seems particularly suited for multicomponent systems.In the third part of this work, we employ the RAMSPEQU setup for the characterization of a quaternary system and its binary subsystems. 22 ml and 105 ml of the binary and quaternary mixtures are sufficient for an extensive VLE characterization. The RAMSPEQU setup and its integrated workow enable the characterization of multicomponent VLE while saving significant amounts of substance and laboratory time.
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Learning, Unlearning and Re-Learning Curves
Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others.It explores the differences and similarities between the various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning Curve Cost Drivers leads to the consideration of a little used but very powerful technique of Learning Curve modelling called Segmentation, which looks at an organisation’s complex learning curve as the product of multiple shallower learning curves.Perhaps the biggest benefit is that it simplifies the calculations in Microsoft Excel where there is a change in the rate of learning observed or expected.The same technique can be used to model and calibrate discontinuities in the learning process that result in setbacks and uplifts in time or cost.This technique is compared with other, better known techniques such as Anderlohr’s.Equivalent Unit Learning is another, relative new technique that can be used alongside traditional completed unit learning to give an early warning of changes in the rates of learning.Finally, a Learning Curve can be exploited to estimate the penalty of collaborative working across multiple partners.Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists, as well as students of cost engineering.
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Learning and Behavior : Active Learning Edition
Examine learning through an evolutionary lens as Chance/Furlong's LEARNING AND BEHAVIOR, 8E demonstrates how learning has evolved as a biological mechanism to aid in survival.This clear, engaging presentation embraces a scientific approach to studying behavior with updated content, numerous learning features and current examples to capture your interest and strengthen your understanding.Timely research demonstrates the relevance of what you are studying as you examine concepts such as "nature via nurture" and the co-dependency of ontogeny and phylogeny.New learning objectives direct your attention to important concepts for study and review while helpful Concept Checks ensure you understand concepts before progressing.In addition, all content corresponds with the latest 7th Edition of the APA Publication Manual.Trust this book's unique approach as you master concepts behind learning, behavior and change and apply this to everyday life.
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Where can I find teaching materials for learning Spanish?
You can find teaching materials for learning Spanish in a variety of places. Many bookstores and online retailers offer textbooks, workbooks, and other resources specifically designed for learning Spanish. Additionally, there are numerous websites and apps that provide interactive lessons, exercises, and games to help you learn the language. You can also consider enrolling in a Spanish language course at a local community college or language school, where you will have access to a wide range of teaching materials and resources.
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What learning support materials are available for adults with autism?
There are various learning support materials available for adults with autism, including visual schedules, social stories, and task analysis guides. These materials can help adults with autism understand and navigate daily routines, social interactions, and work tasks. Additionally, there are online resources, support groups, and workshops specifically designed for adults with autism to help them develop skills and strategies for independent living, employment, and social communication. It's important for individuals with autism and their caregivers to explore and utilize these resources to find the most effective support for their unique needs.
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Why was the free provision of learning materials abolished in Germany?
The free provision of learning materials was abolished in Germany in order to shift the financial burden from the government to the students and their families. This change was part of a broader effort to reduce public spending on education and to encourage students to take more responsibility for their own learning. Additionally, it was seen as a way to increase the quality and diversity of learning materials available to students, as private companies could now compete in the market for educational resources.
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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