The Nest of Engineers

Lester W. Schmerr Jr. ... 432 pages - Language:‎ English - Publisher:‎ CRC Press; (March, 2024).

This text makes use of symbolic algebra and vector-matrix algebra to demonstrate a new approach to learning statics. Symbolic solutions are obtained, together with the types of solutions covered in other texts, so that students can see the advantages of this new approach.

This innovative text is an extension of second-generation vector Statics courses to a new, third-generation matrix-vector Statics course, a course that addresses deformable as well as rigid bodies and employs MATLAB®.

MATLAB® is used as a “calculator” whose built-in functions are used to solve statics problems. This text uses vectors and matrices to solve both statically determinate rigid body problems and statically indeterminate problems for deformable bodies.

The inclusion of statically indeterminate problems is unique to this text. It is made possible by using symbolic algebra and a new, simplified vector-matrix formulation that combines the equations of equilibrium, the homogeneous solutions to those equations, and a description of the flexibilities found in the deformable elements of a structure to solve directly for the unknown forces/moments.

Kamal I. M. Al-Malah ... 592 pages - Language: ‎English - Publisher: ‎Wiley; (October, 2023) - ISBN-10: ‎1394209088 - ISBN-13:‎ 978-1394209088.

Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues.

Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) + Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response) + Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps + Retraining and creation for image labeling, object identification, regression classification, and text recognition.

Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

Pijush Samui, Sunita Kumari, Vladimir Makarov, Pradeep Kurup ... 846 pages - Publisher: ‎Academic Press; (December, 2020) - Language: ‎English.

Modeling in Geotechnical Engineering is a one stop reference for a range of computational models, the theory explaining how they work, and case studies describing how to apply them. Drawing on the expertise of contributors from a range of disciplines including geomechanics, optimization, and computational engineering, this book provides an interdisciplinary guide to this subject which is suitable for readers from a range of backgrounds.

Before tackling the computational approaches, a theoretical understanding of the physical systems is provided that helps readers to fully grasp the significance of the numerical methods. The various models are presented in detail, and advice is provided on how to select the correct model for your application.

Antonio Marco ... 160 pages - Language: ‎English - Publisher: Chapman and Hall/CRC; (December, 2023).

Statistics is central in the biosciences, social sciences and other disciplines, yet many students often struggle to learn how to perform statistical tests, and to understand how and why statistical tests work. Although there are many approaches to teaching statistics, a common framework exists between them: starting with probability and distributions, then sampling from distribution and descriptive statistics and later introducing both simple and complex statistical tests, typically ending with regression analysis (linear models).

This book proposes to reverse the way statistics is taught, by starting with the introduction of linear models. Today, many statisticians know that the one unifying principle of statistical tests is that most of them are instances of linear models. This teaching method has two advantages: all statistical tests in a course can be presented under the same unifying framework, simplifying things; second, linear models can be expressed as lines over squared paper, replacing any equation with a drawing.

This book explains how and why statistics works without using a single equation, just lines and squares over grid paper. The reader will have the opportunity to work through the examples and compute sums of squares by just drawing and counting, and finally evaluating whether observed differences are statistically significant by using the tables provided. Intended for students, scientists and those with little prior knowledge of statistics, this book is for all with simple and clear examples, computations and drawings helping the reader to not only do statistical tests but also understand statistics.

Jianye Ching ... 188 pages - Language: ‎English - Publisher: CRC Press; (August, 2024).

Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization.

Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples.

Brij B. Gupta, Mamta ... 369 pages - Language: ‎English - Publisher: ‎World Scientific Publishing Company; (December, 2023).

With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.

Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.

Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.

Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance.

Jeffrey Paul Wheeler ... 474 pages - Language: English - Publisher: ‎Chapman and Hall/CRC; (December, 2023).

The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.

This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.

The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, optimization has seemingly limitless other applications in business and industry. In short, knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines.

Many of the problems rely on using a computer. Microsoft’s Excel is most often used, as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica, and the computer science students to write their own programs in Java or Python.

Tanvir Mustafy, T. Ur Rahman ... 281 pages - Language: ‎English - Publisher: ‎Springer; (January, 2024).

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses,and apply them to their research or professional fields.

Dothang Truong ... 577 pages - Language: ‎English - Publisher: Chapman and Hall/CRC; (February, 2024) - ISBN-10:‎ 0367755386 - ISBN-13:‎ 978-0367755386.

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.

Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.

Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

Felix Zumstein ... 335 pages - Language: ‎English - Publisher: ‎O'Reilly Media; (April, 2021) - ISBN-10: 1492081000 - ISBN-13: 978-1492081005.

While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently.

Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started

Python is one of the most widely used programming languages in the world. It is used everywhere from primary school education to workaday web development, to the most advanced scientific research institutes of the world. However, like all programming languages, Python has a collection of "Pythonic" ways of accomplishing tasks that are easy to overlook, especially when habits are borrowed wholesale from work in other programming languages. Better Python Code is a guide to Pythonic programming. The book presents common mistakes that Python developers make--even Python developers who have used the language for years--often because Python sometimes presents "attractive nuisances." Throughout, the book is a guide to better programming in the core Python language.

Each section shows a concrete but concise example of some misunderstanding or bad habit in action. Each section contains a description of what is wrong with the sample code and a suggestion for one or more better ways to code equivalent functionality without the initial pitfall. Every pitfall addressed in this book reflects foibles, errors, and misunderstandings that the author as seen in concrete, widely used code bases written by experienced developers, over his 25 years of writing Python. The sections of this book each present some mistake, pitfall, or foible that developers can easily fall into, and are accompanied by descriptions of ways to avoid making them. At times those solutions simply involve a minor change in “spelling,” but in most cases they require a nuance of thought and design in your code. Many of the discussions do something else as well…

William S. Janna ... 754 pages - Language: ‎English - Publisher: CRC Press; 6th edition (March, 2020).

Introduction to Fluid Mechanics, Sixth Edition, is intended to be used in a first course in Fluid Mechanics, taken by a range of engineering majors. The text begins with dimensions, units, and fluid properties, and continues with derivations of key equations used in the control-volume approach. Step-by-step examples focus on everyday situations, and applications. These include flow with friction through pipes and tubes, flow past various two and three dimensional objects, open channel flow, compressible flow, turbomachinery and experimental methods. Design projects give readers a sense of what they will encounter in industry. A solutions manual and figure slides are available for instructors.

Mark G. Richardson ... 702 pages - Language: English - Publisher: ‎CRC Press; 2nd edition (November, 2023).

This new edition sets out the fundamental aspects of concrete durability with an emphasis on sustainability and carbon neutrality through performance-based methodologies. Global approaches to managing durability are explained from both a prescriptive and performance viewpoint. Achieving a balance between the interactive factors influencing durability and sustainability is supported by an explanation of the physical and chemical phenomena at play, determination of key performance parameters by mathematical modelling and physical testing, and current guidance for good practice. New chapters and sections examine the holistic approach to durability and significant aspects of traditional and new cementitious systems. The full range of threats to durability are covered in this single volume, including reinforcement corrosion, carbonation, chloride ingress, freeze-thaw effects, sulfate attack, acid and seawater attack, alkali-aggregate reaction, cracking, abrasion, erosion, cavitation, and weathering.The book presents a framework for specification through internationally adopted codes and standards and summarises the background to probabilistic approaches to durability design, providing a state-of-the-art review of mathematical modelling of deterioration mechanisms along with current directions in test methods for performance-based specifications.

Fundamentals of Durable Reinforced Concrete is an essential reference on concrete durability for specifiers and researchers and is also accessible to undergraduate students.

David Kaplan ... 250 pages - Language: English - Publisher: ‎The Guilford Press; 2nd edition (November, 2023) - ISBN-10:‎ 1462553540 - ISBN-13: ‎978-1462553549.

The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.com/kaplan-materials) provides data sets and code for the book's examples.

New to This Edition: • Utilizes the R interface to Stan—faster and more stable than previously available Bayesian software—for most of the applications discussed. • Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. • Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.

Jürgen Zierep, Karl Bühler ... 237 pages - Language: ‎English - Publisher: ‎Springer; (February, 2022).

This mature textbook brings the fundamentals of fluid mechanics in a concise and mathematically understandable presentation. In the current edition, a section on dissipation and viscous potential flows has been added. Exercises with solutions help to apply the material correctly and promote understanding.

This book is a translation of the original German 11th edition Grundzüge der Strömungslehre by Jürgen Zierep & Karl Bühler, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2018. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

K. Shanker, M. Pradeep Kumar ... 275 pages - Language: ‎English - Publisher: Shashawat; (June, 2022).

Engineering Mechanics, one of the oldest branches of physical science, is a subject of enormous importance. Although it is taught in the first year of engineering, its foundation is rooted in the two other fundamental subjects i.e., applied mathematics and physics. Basically, Engineering Mechanics is a subject that deals with the action of forces. It is broadly classified under Statics and Dynamics. Statics deals with the action of forces on the rigid bodies at rest whereas dynamics deals with motion characteristics of the bodies when subjected to force. The primary purpose of writing this book is to build basic concepts of engineering mechanics along with strong analytical and problem-solving abilities that would enhance the thinking capability of students. Problems are solved systematically with clear procedure that makes the students feel better in understanding the solution.