Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. SSD for R serves as a guide for those desiring to conduct single-subject data analysis and introduces readers to the various functions available in SSD for R, a new, free, and innovative software package written in R--the open-source statistical programming language that was written by the book's authors. This second edition of SSD for R is the most comprehensive guide to the numerous graphing and charting functions for conducting robust visual analysis including line graphs and more complex standard deviation lines. This book also contains numerous tests of statistical significance, such as t-tests, chi-squares, and the conservative dual criteria. Auerbach and Zeitlin guide readers through the analytical process based on the characteristics of their data. In addition to presentations and assignments, this new edition contains more examples and illustrations to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation. SSD for R is the only book of its kind to describe single-subject data analysis while providing free statistical software to do so. For more instructional videos, blogs, and a growing community of researchers interested in single-subject designs, visit the authors' website: http://ssdanalysis.com.

"In this chapter you will learn how to measure target behaviors and use Excel or other software to record and edit client data. You will then be able to import these data into R and use the SSD for R functions to analyze them. The first part of this chapter will focus on the types of data you will want to record and some common issues related to collecting these. While an overview of this material is covered in this chapter, additional resources that include these topics in-depth are listed in Appendix D. The second part of this chapter will show you how to use Excel or another spreadsheet program to quickly and effectively record these data"--

Solid State Drives (SSDs) are gaining momentum in enterprise and client applications, replacing Hard Disk Drives (HDDs) by offering higher performance and lower power. In the enterprise, developers of data center server and storage systems have seen CPU performance growing exponentially for the past two decades, while HDD performance has improved linearly for the same period. Additionally, multi-core CPU designs and virtualization have increased randomness of storage I/Os. These trends have shifted performance bottlenecks to enterprise storage systems. Business critical applications such as online transaction processing, financial data processing and database mining are increasingly limited by storage performance. In client applications, small mobile platforms are leaving little room for batteries while demanding long life out of them. Therefore, reducing both idle and active power consumption has become critical. Additionally, client storage systems are in need of significant performance improvement as well as supporting small robust form factors. Ultimately, client systems are optimizing for best performance/power ratio as well as performance/cost ratio. SSDs promise to address both enterprise and client storage requirements by drastically improving performance while at the same time reducing power. Inside Solid State Drives walks the reader through all the main topics related to SSDs: from NAND Flash to memory controller (hardware and software), from I/O interfaces (PCIe/SAS/SATA) to reliability, from error correction codes (BCH and LDPC) to encryption, from Flash signal processing to hybrid storage. We hope you enjoy this tour inside Solid State Drives.

There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming. Get advice for setting up an R programming environment Explore general programming concepts and R coding techniques Understand the ingredients of an efficient R workflow Learn how to efficiently read and write data in R Dive into data carpentry—the vital skill for cleaning raw data Optimize your code with profiling, standard tricks, and other methods Determine your hardware capabilities for handling R computation Maximize the benefits of collaborative R programming Accelerate your transition from R hacker to R programmer

The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

This book constitutes the proceedings of the 21st European Conference on Advances in Databases and Information Systems, ADBIS 2017, held in Nicosia, Cyprus, in September 2017. The 26 regular papers presented together with one keynote paper and one keynote abstract were carefully selected and reviewed from numerous submissions. The papers are organized in topical sections such as conceptual modeling and human factors; subsequence matching and streaming data; OLAP; graph databases; spatial data management; parallel and distributed data processing; query optimization, recovery, and databases on modern hardware; semantic data processing; and additional database and information systems topics.

'Radiation Oncology: MCQs for Exams' (ROME) will cover the essential aspects of radiation physics, radiobiology, and clinical radiation oncology designed to meet the needs of a large scale of examinees. Topics of this new book will be in the order of our previous "Basic Radiation Oncology" (Springer, 2010) with additional two new chapters (Pediatric tumors and Rare tumors-Benign Diseases) making a total of 15 chapters and instead of old style question and answer format,current MCQ examination pattern helpful for both oral exams and written exams is used in this comprehensive bedside recall book complementing the "Basic Radiation Oncology"1st Edition.

This book constitutes the refereed proceedings of six workshops of the 14th International Conference on Web-Age Information Management, WAIM 2013, held in Beidaihe, China, June 2013. The 37 revised full papers are organized in topical sections on the six following workshops: The International Workshop on Big Data Management on Emerging Hardware (HardBD 2013), the Second International Workshop on Massive Data Storage and Processing (MDSP 2013), the First International Workshop on Emergency Management in Big Data Age (BigEM 2013), the International Workshop on Trajectory Mining in Social Networks (TMSN 2013), the First International Workshop on Location-based Query Processing in Mobile Environments (LQPM 2013), and the First International Workshop on Big Data Management and Service (BDMS 2013).

Examining the legal structure of the mental health system, this book explains the legal principles. It places them in the context of their practical application, the realities of patient life, and the complexities of organising care. This edition gives an analysis of the Mental Capacity Act, 2005 and the Draft Mental Health Bill.