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Advances in Mechanics of Materials for Environmental and Civil Engineering
Deals with both mathematical modeling and experimental studies related to systems relevant for various civil engineering fields. The book addresses several key topics, including artificial intelligence applied to the control and monitoring of construction site personnel, finite element models for endplate beam-to-column connections under various load conditions, random functionally graded micropolar beams, and many others. The book explores the design and study of microstructures aimed at increasing the toughness and durability of novel materials in building and construction, based also on the re-utilization of residues and wastes of metallurgical industry produces.
Advances in Mathematical and Statistical Modeling
Enrique Castillo is a leading figure in several mathematical, statistical, and engineering fields, having contributed seminal work in such areas as statistical modeling, extreme value analysis, multivariate distribution theory, Bayesian networks, neural networks, functional equations, artificial intelligence, linear algebra, optimization methods, numerical methods, reliability engineering, as well as sensitivity analysis and its applications. Organized to honor Castillo's significant contributions, this volume is an outgrowth of the International Conference on Mathematical and Statistical Modeling and covers recent advances in the field. Also presented are applications to safety, reliability and life-testing, financial modeling, quality control, general inference, as well as neural networks and computational techniques.
Advances and applications of DSmT for information fusion: Collected works ; Vol.3
One of the most comprehensive and flexible fusion theory based on belief functions. It can work in all fusion spaces: power set, hyper-power set, and super-power set, and has various fusion and conditioning rules that can be applied depending on each application. Some new generalized rules are introduced in this volume with codes for implementing some of them. For the qualitative fusion, the DSm Field and Linear Algebra of Refined Labels (FLARL) is proposed which can convert any numerical fusion rule to a qualitative fusion rule. When one needs to work on a refined frame of discernment, the refinement is done using Smarandache s algebraic codification. New interpretations and implementations of the fusion rules based on sampling techniques and referee functions are proposed, including the probabilistic proportional conflict redistribution rule.
Advances and applications of DSmT for information fusion ; Vol. 4
One of the most comprehensive and flexible fusion theory based on belief functions. It can work in all fusion spaces: power set, hyper-power set, and super-power set, and has various fusion and conditioning rules that can be applied depending on each application. Some new generalized rules are introduced in this volume with codes for implementing some of them. For the qualitative fusion, the DSm Field and Linear Algebra of Refined Labels (FLARL) is proposed which can convert any numerical fusion rule to a qualitative fusion rule. When one needs to work on a refined frame of discernment, the refinement is done using Smarandache s algebraic codification. New interpretations and implementations of the fusion rules based on sampling techniques and referee functions are proposed, including the probabilistic proportional conflict redistribution rule.
Adaptive Machine Learning Algorithms with Python : Solve Data Analytics and Machine Learing Problems on Edge Devices
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. You will: Apply adaptive algorithms to practical applications and examples / Understand the relevant data representation features and computational models for time-varying multi-dimensional data / Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data / Speed up your algorithms and put them to use on real-world stationary and non-stationary data / Master the applications of adaptive algorithms on critical edge device computation applications
A Theory of Distributed Objects : Asynchrony - Mobility - Groups - Components
Distributed and communicating objects are becoming ubiquitous. In global, Grid and Peer-to-Peer computing environments, extensive use is made of objects interacting through method calls. So far, no general formalism has been proposed for the foundation of such systems. Caromel and Henrio are the first to define a calculus for distributed objects interacting using asynchronous method calls with generalized futures, i.e., wait-by-necessity -- a must in large-scale systems, providing both high structuring and low coupling, and thus scalability. The authors provide very generic results on expressiveness and determinism, and the potential of their approach is further demonstrated by its capacity to cope with advanced issues such as mobility, groups, and components.
A Geometric Approach to Differential Forms
The modern subject of differential forms subsumes classical vector calculus. This text presents differential forms from a geometric perspective accessible at the undergraduate level. The book begins with basic concepts such as partial differentiation and multiple integration and gently develops the entire machinery of differential forms. Each new concept is presented with a natural picture that students can easily grasp. Algebraic properties then follow. This facilitates the development of differential forms without assuming a background in linear algebra. Throughout the text, emphasis is placed on applications in 3 dimensions, but all definitions are given so as to be easily generalized to higher dimensions. A centerpiece of the text is the generalized Stokes' theorem. Although this theorem implies all of the classical integral theorems of vector calculus, it is far easier for students to both comprehend and remember.
A Generalization of Bohr-Mollerup's Theorem for Higher Order Convex Functions
This book develops a far-reaching generalization of Bohr-Mollerup's theorem to higher order convex functions, along lines initiated by Wolfgang Krull, Roger Webster, and some others but going considerably further than past work. In particular, this generalization shows using elementary techniques that a very rich spectrum of functions satisfy analogues of several classical properties of the gamma function, including Bohr-Mollerup's theorem itself, Euler's reflection formula, Gauss' multiplication theorem, Stirling's formula, and Weierstrass' canonical factorization.
