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Operations research (OR) is a core discipline in military and
defense management. Coming to the forefront initially during World
War II, OR provided critical contributions to logistics, supply
chains, and strategic simulation, while enabling superior
decision-making for Allied forces. OR has grown to include
analytics and many applications, including artificial intelligence,
cybersecurity, and big data, and is the cornerstone of management
science in manufacturing, marketing, telecommunications, and many
other fields. The Handbook of Military and Defense Operations
Research presents the voices leading OR and analytics to new
heights in security through research, practical applications, case
studies, and lessons learned in the field. Features Applies the
experiences of educators and practitioners working in the field
Employs the latest technology developments in case studies and
applications Identifies best practices unique to the military,
security, and national defense problem space Highlights
similarities and dichotomies between analyses and trends that are
unique to military, security, and defense problems
Computational Methods for Numerical Analysis with R is an overview
of traditional numerical analysis topics presented using R. This
guide shows how common functions from linear algebra,
interpolation, numerical integration, optimization, and
differential equations can be implemented in pure R code. Every
algorithm described is given with a complete function
implementation in R, along with examples to demonstrate the
function and its use. Computational Methods for Numerical Analysis
with R is intended for those who already know R, but are interested
in learning more about how the underlying algorithms work. As such,
it is suitable for statisticians, economists, and engineers, and
others with a computational and numerical background.
Online education has become a major component of higher education
worldwide. In mathematics and statistics courses, there exists a
number of challenges that are unique to the teaching and learning
of mathematics and statistics in an online environment. These
challenges are deeply connected to already existing difficulties
related to math anxiety, conceptual understanding of mathematical
ideas, communicating mathematically, and the appropriate use of
technology. Teaching and Learning Mathematics Online bridges these
issues by presenting meaningful and practical solutions for
teaching mathematics and statistics online. It focuses on the
problems observed by mathematics instructors currently working in
the field who strive to hone their craft and share best practices
with our professional community. The book provides a set of
standard practices, improving the quality of online teaching and
the learning of mathematics. Instructors will benefit from learning
new techniques and approaches to delivering content. Features Based
on the experiences of working educators in the field Assimilates
the latest technology developments for interactive distance
education Focuses on mathematical education for developing early
mathematics courses
Online education has become a major component of higher education
worldwide. In mathematics and statistics courses, there exists a
number of challenges that are unique to the teaching and learning
of mathematics and statistics in an online environment. These
challenges are deeply connected to already existing difficulties
related to math anxiety, conceptual understanding of mathematical
ideas, communicating mathematically, and the appropriate use of
technology. Teaching and Learning Mathematics Online bridges these
issues by presenting meaningful and practical solutions for
teaching mathematics and statistics online. It focuses on the
problems observed by mathematics instructors currently working in
the field who strive to hone their craft and share best practices
with our professional community. The book provides a set of
standard practices, improving the quality of online teaching and
the learning of mathematics. Instructors will benefit from learning
new techniques and approaches to delivering content. Features Based
on the experiences of working educators in the field Assimilates
the latest technology developments for interactive distance
education Focuses on mathematical education for developing early
mathematics courses
This Brief presents a benefit-cost analysis of the National Flood
Insurance Program (NFIP) as well as an evaluation of its cumulative
socioeconomic effects. Created by Congress in 1968, the NFIP
provides flood insurance protection to property owners, in return
for local government commitment to sound floodplain management.
Since 1994, the NFIP has included a Flood Mitigation Assistance
(FMA) program to provide local communities with support for flood
mitigation. This book offers quantitative evidence of the net
social benefit of the NFIP for the years 1996-2010, including an
independent assessment of the consumer benefit. Second, it provides
distributionally weighted analysis to show the socioeconomic
effects of payments and claims. Finally, this Brief includes an
analysis of the change in government revenue attributable to the
NFIP and FMA programs. The models used in each component of the
analysis are usable by others for extending and revising the
analysis. Providing a comprehensive analysis of this increasingly
important federal policy, this Brief will be of use to students of
environmental economics and public policy as well as those
interested in risk management in the era of climate change.
Applies the experiences of educators and practitioners working in
the field Employs the latest technology developments in case
studies and applications Identifies best practices unique to the
military, security, and national defense problem space Highlights
similarities and dichotomies between analyses and trends that are
unique to military, security, and defense problems
Computational Methods for Numerical Analysis with R is an overview
of traditional numerical analysis topics presented using R. This
guide shows how common functions from linear algebra,
interpolation, numerical integration, optimization, and
differential equations can be implemented in pure R code. Every
algorithm described is given with a complete function
implementation in R, along with examples to demonstrate the
function and its use. Computational Methods for Numerical Analysis
with R is intended for those who already know R, but are interested
in learning more about how the underlying algorithms work. As such,
it is suitable for statisticians, economists, and engineers, and
others with a computational and numerical background.
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