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This new volume provides the information needed to understand the
simplex method, the revised simplex method, dual simplex method,
and more for solving linear programming problems. Following a
logical order, the book first gives a mathematical model of the
linear problem programming and describes the usual assumptions
under which the problem is solved. It gives a brief description of
classic algorithms for solving linear programming problems as well
as some theoretical results. It goes on to explain the definitions
and solutions of linear programming problems, outlining the
simplest geometric methods and showing how they can be implemented.
Practical examples are included along the way. The book concludes
with a discussion of multi-criteria decision-making methods.
Advances in Optimization and Linear Programming is a highly useful
guide to linear programming for professors and students in
optimization and linear programming.
This volume offers a gradual exposition to matrix theory as a
subject of linear algebra. It presents both the theoretical results
in generalized matrix inverses and the applications. The book is as
self-contained as possible, assuming no prior knowledge of matrix
theory and linear algebra. The book first addresses the basic
definitions and concepts of an arbitrary generalized matrix inverse
with special reference to the calculation of {i,j,...,k} inverse
and the Moore-Penrose inverse. Then, the results of LDL*
decomposition of the full rank polynomial matrix are introduced,
along with numerical examples. Methods for calculating the
Moore-Penrose's inverse of rational matrix are presented, which are
based on LDL* and QDR decompositions of the matrix. A method for
calculating the A(2)T;S inverse using LDL* decomposition using
methods is derived as well as the symbolic calculation of A(2)T;S
inverses using QDR factorization. The text then offers several ways
on how the introduced theoretical concepts can be applied in
restoring blurred images and linear regression methods, along with
the well-known application in linear systems. The book also
explains how the computation of generalized inverses of matrices
with constant values is performed. It covers several methods, such
as methods based on full-rank factorization, Leverrier-Faddeev
method, method of Zhukovski, and variations of the partitioning
method.
This volume offers a gradual exposition to matrix theory as a
subject of linear algebra. It presents both the theoretical results
in generalized matrix inverses and the applications. The book is as
self-contained as possible, assuming no prior knowledge of matrix
theory and linear algebra. The book first addresses the basic
definitions and concepts of an arbitrary generalized matrix inverse
with special reference to the calculation of {i,j,...,k} inverse
and the Moore-Penrose inverse. Then, the results of LDL*
decomposition of the full rank polynomial matrix are introduced,
along with numerical examples. Methods for calculating the
Moore-Penrose's inverse of rational matrix are presented, which are
based on LDL* and QDR decompositions of the matrix. A method for
calculating the A(2)T;S inverse using LDL* decomposition using
methods is derived as well as the symbolic calculation of A(2)T;S
inverses using QDR factorization. The text then offers several ways
on how the introduced theoretical concepts can be applied in
restoring blurred images and linear regression methods, along with
the well-known application in linear systems. The book also
explains how the computation of generalized inverses of matrices
with constant values is performed. It covers several methods, such
as methods based on full-rank factorization, Leverrier-Faddeev
method, method of Zhukovski, and variations of the partitioning
method.
As one of the greatest advances of the twentieth century in
science, quantum mechanics explains the behavior of matter and
energy. It states that the elementary particles can behave as
particles in a given time and waves in the next or the previous.
The book explains the following two pillars of this theory:
Particles exchange energy at integer multiples of a minimum
possible, denominating, quantum (quantum) of energy; The position
of the particles are defined by a function that describes the
probability that the particle is in that position at that moment.
The advancement of technology, its uses and how they influence on
people have generated a great impact on today's society. This book
intends to expand our knowledge on the subject and better
understand the current state of the art in this field. It is
something we have to be aware of, since it is increasingly present
in our lives. We must understand the new technologies in order to
use them correctly and optimize them in the future. The problem
that certain jobs can be replaced by machines, generates a change
in the way of human thinking and doing, which must adopt these
technologies and trained to use them.In this book, the changes to
the world caused by the use of Artificial Intelligence and Machine
Learning are investigated. It investigates the impact of the use of
artificial intelligence in everyday life, emphasizing technologies
such as Artificial Intelligence, Machine Learning and Deep
Learning. In recent years, advances in these areas have influenced
considerably the technology as we know it, are opening doors to new
possibilities that once seemed unimaginable.
Deep Neural Networks and Applications makes the readers aware about
the various artificial neural networks (ANN) and the topologies
related to main neural networks (MNN). The book throws light on the
prospect of artificial intelligence and the applications it has in
risk management. It further elaborates on the artificial neural
networks in detail and discusses the practical applications of the
deep neutral networks. Also discussed in the book is the
optimization of deep learning for the best performance of
e-learning data, the methodology and the research framework,
development of the algorithms that quicken the data processing over
complex network architectures and the optimization of database
query structures using deep learning.
Stochastic Processes and their Applications illustrates the
theoretical knowledge of random variables along with some practical
skills to analyze various stochastic dynamical systems in
economics, engineering and other fields. It includes the most
appropriate process for modelling in particular situations arising
in economics, engineering and other fields. Provide the readers
with the insights into the development of different processes and
theories like Poisson processes and the application of stochastic
processes in biology.
Parallel Programming talks about a type of computation "parallel
programming" and the parallel algorithm designed by technique
"PCAM". It includes the description of parallel computer systems
and parallelization of web compatibility tests in software
development. It provides the reader with the understanding of
parallel programing so as to analyze the differences in modular
programming, recursive programming and dynamic programming and
precise knowledge related to Turing's Hypothesis. This book also
discusses about Theoretical Framework of parallel programming,
Modular Programming, Recursive Programming and Dynamic Programming
and Turing's Hypothesis
Mathematical Analysis and Analytical Modeling provides the readers
with the preliminary knowledge on mathematics and explains them the
meaning of normal families. It talks about the iteration functions,
fixed points and other kinds of sets in the mathematics. Also
discusses in the book is the Baker theorem for the complexity
invariant components in the set of Fatou, mathematical analysis of
significant transformations, theoretical framework for it,
application of analytical functions in educational research, and
the methodological framework. The book also gives some concluding
remarks on the subject matter.
Applied Neural Networks and Soft Computing examines the relation
between neural networks and soft computing. Neural network is a
system of hardware and software designed after the operations of
neurons. Applied neural networks has a plethora of applications and
the text tries to touch every aspect to give readers a wider
perspective. Further, artificial neural networks (vaguely inspired
by biological neural network) are also discussed to keep readers up
to date regarding the latest innovations taking place.
Hybrid Algorithms, Techniques and Implementations of Fuzzy Logic
studies various aspects of Fuzzy logic including an extensive
overview of hybrid algorithms, techniques and implementations. It
includes definitions of evolutionary computation and fuzzy systems,
ideologies and strategies. Provides the reader with insights into
the development of its knowledge, so as to understand the
applications and implementations of fuzzy logic using hybrid
algorithms.
Computational and Numerical Simulations examines various aspects of
simulations including an extensive overview of Computational and
Numerical Simulations. It includes introduction to system dynamics
simulations, implementation of system dynamics for urban planning
in a municipality, dynamic integrated framework for improving
software processes, vehicle aerodynamic analysis using CFD
simulation, parallelization in hydraulic simulations. Provides the
reader with insights into the development of its history, so as to
understand the lapbot positioning in a three-dimensional virtual
environment using simulated interface and derive conclusions.
A biometric recognition system that uses as a biometric feature a
static digital image of the human face is developed. Detecting and
recognizing human faces in photographs and video sequences is an
increasing problem in the field of computer vision, and there are
many practical applications at present, such as surveillance,
videoconferencing, access control, etc. The objective is to return
as a result the five people in the database which most resemble the
person of the test image. The problem of face recognition can be
divided into two phases: Detection of the face within the image and
recognition. The detection phase is mainly based on the detection
of skin in the image. Subsequently, a selection of candidate skin
regions to be expensive and validated through ""maps"" of eyes and
mouth. In addition, an alternative system of face detection, if the
previous method has not detected any. This method takes the largest
region of skin found in the image and generates an ellipse with its
characteristics to return as face the part of the image that
coincides with the ellipse. In the recognition phase the areas of
the detected images are taken as persons. PCA is used to extract
the characteristics that represent the images. These
characteristics are then used to train and simulate neural
networks. With the outputs of the neural networks, the images of
the database that most closely resemble the face of the test image.
The evaluation of the implemented system shows the great influence
of the type of images used for recognition, with much better
results when the images meet certain characteristics. The framework
of this book is the digital restoration of images, that is, the
process which recovers an original image that has been degraded by
imperfections of the acquisition system: blur and noise. Restoring
this degradation is a problem poorly conditioned because the direct
investment by least squares amplifies the noise at high
frequencies. Therefore, regularization is used mathematics as a
means to include a priori information of the image that it achieves
to stabilize the solution. During the first part of the report a
review of certain state-of-the-art algorithms, which will later be
used as methods of comparison in the experiments. To solve the
problem of regularization, the restoration of images has
prerequisites. In the first place, it is necessary to behavior of
the image outside its borders, due to non-local ownership of the
convolution modeling degradation. The absence of border in the
restoration gives rise to the artifact known as boundary ringing.
In the second one, the restoration algorithms depend on a
significant number of parameters divided into three groups:
parameters with respect to the degradation, noise and the original
image. All of them need to estimate a priori sufficiently precise,
because small errors with respect to their real values produce
significant deviations in restoration results. The problem border
and sensitivity to estimates are the objectives to be resolved in
this book using iterative algorithms. The first of the algorithms
addresses the border problem starting from an image truncated in
the field of vision as real observation. To solve this no
linearity, a neural network is used that minimizes a defined cost
function mainly due to regularization by total variation, but not
including any type of information on frontiers or require prior
training. As a result, a restored image without ringing effects is
field of view and in addition the truncated borders are
reconstructed until the original size. The algorithm is based on
the technique of retro-propagation of energy, with that the network
becomes an iterative cycle of two processes: forward and backward,
which simulate a restoration and degradation for each iteration.
Following the same iterative concept of restoration-degradation, a
second algorithm in the frequency domain to reduce dependence with
respect to parameter estimates. For this, a new filter of
desensitized restoration as a result of applying an iterative
algorithm on an original filter. Studying the sensitivity
properties of this filter and establishing a criterion for the
number of iterations, we arrive at an expression for the
desensitization algorithm particularized to the Wiener and Tikhonov
filters. The results of the experiments demonstrate the good
behavior of the filter with respect to noise-dependent error,
making the estimate that is made more robustness is the one
corresponding to the parameters of the noise, although
desensitization is also extended to other estimates.
Recently, a new field of computer science was derived, including
methods and techniques of problem solving that cannot be easily
described by traditional algorithms. This field, called ""cognitive
computing"" or ""real-world computing"", has a varied set of
working methodologies, such as: fuzzy logic, approximate reasoning,
genetic algorithms, chaos theory, and the Artificial Neural
Networks (ANN). The objective of the present work is to introduce
the problematic of the latter: definitions, principles and
typology, as well as concrete applications in the field of
information retrieval. During the past decade in the field of
information retrieval has been experimented with artificial
Intelligence (AI) techniques based on rules and knowledge. These
techniques seem to have many limitations and difficulties of
application, so that already in the present decade work has begun
with the more recent. AI techniques, based on inductive learning:
symbolic learning, genetic algorithms and neural networks (Chen,
1995). The earliest work in neural computing dates back to the
early 1940s, which neuro-physicist Warren McCulloch and
mathematician Walter Pitts proposed, based on their system studies.
A formal neuron model implemented by electrical circuits
(McMulloch, 1943), whose enthusiasm aroused the neuronal model
drove research in this line during the 1950s and 1960s. In 1957
Frank Rosenblatt developed the Perceptron, a network model that
possesses the generalization capability, so it has been used to
this day in various applications, generally in recognition of
patterns. In 1959 Bernard Widrow and Marcial Hoff of Stanford
University developed the model ADALINE (ADAptative LINear
Elements), first ANN applied to a real problem (noise filters in
lines phone calls). In 1969 Marvin Minsky and Seymour Papert, of
MIT, published a work in which they attack the neural model and
consider that any research along these lines was sterile (Minsky,
1969). Due to this criticism the works on ANN stop to a new impetus
during the 80's. Despite this pause, several researchers continued
to work in that direction during the 1970s. Such is the case of the
American James Anderson which develops the BSB
(Brain-State-in-a-Box) model, or Finnish Teuvo Kohonen who does the
same with one based on self-organizing maps. As of 1982 the
interest for the neuronal computation began to take force again.
The progress made in hardware and software, methodological advances
around learning algorithms for ANN, and the new techniques of
artificial intelligence, favored this rebirth. The same year, the
first conference between neuronal computing researchers from the US
and Japan. In 1985 the American Institute of physics establishes
annual meeting Neural Networks for Computing. In 1987 the IEEE held
the first conference on ANN. That same year the International
Society of Neural Networks was created (INNS). An automatic
learning system that identifies the expressions of denial and
speculation in biomedical texts is presented, specifically in the
collection of BioScope documents. The objective of the work is to
compare the efficiency of this approach centered in automatic
learning with which it is based on regular expressions. Between the
systems that follow this latter approach, we have used NegEx
because of its availability and popularity. The evaluation has been
carried out on the three subcollections that form BioScope:
clinical documents, scientific articles and abstracts of scientific
articles. The results show the superiority of the approach based on
automatic learning regarding the use of regular expressions. In the
identification of negation expressions, the system improves the F1
measure of NegEx between 20 and 30%, depending on the collection of
documents. In the identification of speculation, the proposed
system exceeds the measure F1 of the best baseline algorithm
between 10 and 20%.
The theory of decision deals with analyzing how a person chooses an
action that, among a set of possible actions, leads to the best
result given the preferences. If a person should invest or not in
capital goods, what career a person is going to choose, what car is
foing to buy, are very common problems that affect us in our daily
life and to the which - in formal terms - is faced with the theory
of decision. On the other hand, in recent years its influence in
disciplines such as psychology and economics has been so great,
along with applied mathematics, sociology, political science and
philosophy - which have that it is very difficult to address some
of these specialties without having a knowledge of theory of the
decision. Decision theory has become an indispensable working tool
in disciplines as varied as economics, psychology, science
politics, sociology or philosophy. Nevertheless, he remains a great
stranger for many social scientists despite its great influence. In
this work the basic elements of decision theory are presented first
then focus on decision theory in situations of uncertainty. Thus,
after explaining some classic decision criteria under uncertainty,
we discuss the normative model of expected subjective utility
(SEU). The limitations of this theory lead us to the more recent
descriptive models that are Support of Herbert Simon's limited
rationality theory as the model the adaptive decision maker or the
theory of ecological rationality. Life abounds in situations where
it is necessary to make decisions. In some cases, the consequences
of decisions depend only on one side, which makes the decision. For
example, a programmer makes a decision in any programming language
will encrypt algorithm that solves a given problem. However, the
consequences of decisions often depend not only on one side but
also on the interaction with the decisions taken by the other side,
so that the outcome of the decision on the one hand depends on the
decisions of others or other parties. It is often the case that
such a situation characterized by conflicting-antagonistic
interests of the participants in decision-making, ie. we say that
the parties to make decisions in the conflict. In the game of
chess, the result of the game depends not only moves one player
more than another move and their interests are conflicting, because
each side wants to win another. This situation of uncertainty in
decision-making in mathematical games is the field of operations
research that deals with the analysis of these problems and finding
optimal solutions, and is called game theory. Game theory means the
mathematical theory of decision-making process by the opponent (the
participants, players) that are in conflict (conflict) or are
involved in competitive conditions. The term game means a model of
real conflict situations. The game can be added to the relevant
rules, which define the rules of conduct of participants in the
game, and the goal of game theory is that the exact mathematical
algorithm analyzes the conflict situation and determine the
reasonable behavior of the players and the course of the conflict,
ie. to determine the optimal strategy for each of the participants
in the game. Game theory has the task of finding solutions in
situations of competition, which is partially or completely
conflicting interests are at least two opponents (ie. According to
this theory among the participants in the game). The solution of
the conflict is determined by the actions of all parties involved
in the conflict. Game theory deals with situations that have the
following characteristics: a) there must be at least two players;
b) The game starts by having one or more players choose between
defined alternatives; c) after the selection is associated with the
first move, the result is determined by the situation that
determines who makes the next selection and what are his options
open; d) the rules of the game are certain rules for determining
which specifies the mode of behavior of players; e) any move in the
game ends the situation that determines the payout of each
bonificiranog player (extra nine player is the one who makes
choices and receive payments). There are many examples in different
areas of life that can be observed and studied as a conflict
situation. A good number of economic problems in the field of
market - competitive relations contain conflicts of different
interests, so they can be analyzed and solved using game theory. In
this book the solutions of multiobjective problems are considered
through genetic algorithms, which consist of random searches in the
search space marked by the restrictions, obtaining solutions
increasingly efficient. In order to achieve this, two new
methodologies are proposed, the first one (MOEGA), which elitism as
an interesting concept not to lose the good results that are have
achieved and obtain a Pareto border close to the real and the
second (MOEGA-P) which considers the preferences of the decision
maker in an interactive way, such that the decision maker can
direct the search of the algorithm towards the area of its
interest. MOEGA get better solutions in the problem of the backpack
comparing it with algorithms like the SPEA2 and NSGAII and MOEGA-P
allows the decision- maker to obtain only a portion of the pareto
according to your preferences acquiring knowledge of the problem,
such that for it will be much easier for him to decide between this
small group of alternatives. In addition to in the case of the
backpack, MOEGA-P offers the decision maker alternative solutions
that does not consider an algorithm without preferences and much
closer to the Pareto frontier real, because restricting the search
area with preferences, takes advantage of the cost to find more
efficient solutions instead of looking at areas that are no longer
are of interest to the decision maker. In this book, the problem of
multiobjective optimization is described, mentioning some classic
techniques of optimization as well as some meta techniques -
heuristics that are used to solve such problems. Since the interest
of this research are the genetic algorithms, will be deepened in
this type of methodology. The following chapter will describe the
main methodologies of multiobjective genetic algorithms and will
mention how the preferences of the decision maker. Taking into
account the weaknesses and strengths of these methodologies, the
next chapter describes an interactive methodological proposal where
the decision-maker intervenes in the search process for better
results and according to your preferences; But not before proposing
another new methodology of algorithms genetic algorithm that
obtains better results comparing it with the already existing ones.
Then, the results obtained with the two methodological proposals
are described along with the conclusions and some ideas of possible
future work.
At present, most of the software projects that are developed, are
run using methodologies and procedures, which causes the team in
charge of the project end up launching products and services with
failures. This is why companies need to consider the quality of
software developed from a perspective that covers not only the
final quality of the product, but a more general perspective which
covers the entire development process. It is necessary to make use
of methodologies, procedures and standards to be able to develop
and launch a software product of quality, with sufficient certainty
to obtain a lower final cost and risk. The typical software life
cycles have processes that isolate the tasks to be carried out by
the different teams of the project. These are the development,
testing, product (business) administration and operations, so that
there is an important barrier between these. In addition, during
the software life cycle, it passes through different environments
(the development, testing, production, etc.), so that it is a
source of errors that are common to produce software in an
environment making the software dependent on it and at the time of
in the environment in which the users are going to interact with
the application or service, this it does not work correctly. One of
the intermediate steps between these two environments is the tests,
where the correct functioning of the software will be verified,
that is why it is it is advisable to carry them out in an
environment as close as possible to that of production to mitigate
the possible errors produced by the difference of scenarios. In
addition to this scenario difference problem, this process of
bringing the software to the production environment, commonly
referred to as production start-up, is completely manual, of
errors, unreliable and difficult to repeat. All these disadvantages
cause that this process that goes from the software development
until its production can lead to take in most cases weeks, delay
that for some companies can translate into a high cost of
opportunities, or what is the same a high economic cost. To
overcome all these problems, new methodologies and software
development: DevOps defined as a new working philosophy in which
barriers are broken previously discussed by the different software
project teams, through the creation of an ecosystem that favors the
communication, collaboration and integration of all these
equipment. Continuous delivery or continuous supply [1] as a form
of software development that makes use of good practices for the
software to be built so that it can be put into production in an
agile and less risky way, carrying out frequent launches that allow
the cycle between the development of the software and the supply of
this to the user is as short as possible, allowing even daily
deployments to be carried out faster feedback from users.
Throughout the text will be used indistinctly, as being synonymous,
the terms continuous delivery and continuous supply. One way to
facilitate the implementation of the continuous delivery model is
to integrate of an automated, repeatable and reliable process that
encompasses the set of common tasks involved in the deployment and
development of software. Tasks ranging from the developer uploads
to the software repository of the project until it is put into
production, producing a rapid feedback, so that the developments
that are not valid for production be discarded as quickly as
possible. In addition, this automation allows to solve in part the
barrier previously commented between the different project teams,
reducing the delay between phases that depend on different groups,
making the process tasks carried out by these groups can be
performed with the press of a button. Continuous software
engineering is an approach in which equipment maintains the
production of software in short cycles of time, ensuring that the
product can be released reliably in any moment. Today, this
approach is increasingly being used in organizations, especially
those that develop web or mobile. However, by releasing versions of
the product with greater frequency, more defects emerge in the
same. This is mainly due to that the time to perform the test
cycles are very short. One of the challenges that currently exists
is the acceleration of testing on the interface such as web
compatibility. A technique used in a large software development
company is presented to automate web compatibility testing,
automation image comparison when doing cross-tests between
different browsers. The results indicate that the proposed
technique adapts to the requirements of the processes of continuous
development, increasing the performance and speed of this type of
tests.
Numerical methods constitute an analytical tool scientific and
technological valuable today. The development of computers has
allowed its development to solve complex problems, from the
simulation of a phenomenon or device, to the study of complex
systems such as simulation of evolution of a galaxy or stress
analysis and stability of an aircraft. This proposal is applicable
to any introductory course on the subject. The use of software and
the use of visualizations support the learning process combined
with the basic learning programming through mathematical problems
are directly applicable to science and engineering, as well as the
proposal for consideration and programming methods more specific,
and the use of problem-based learning. This will allow the reader
to understand the relationship of discipline with applications in
engineering. We have considered a number of applications including
integrated through the horizontality of the methods for solving
complex application problems. Modern software is included directly
in each method, where each should be programmed as a mark of
professional practice due. Currently, even if one could ignore the
learning of numerical methods by using specialized software
programming it allows the reader to understand the complexity and
the need to solve certain problems by simulating and programming,
even assisted by this. The dimensioning of the firmness is the
definition of its geometric and constitutive aspects. Consideration
of it as a structure and application of the principles of
mechanics, the concept of mechanics Firmes arises. This discipline
mainly adopts two types of methods for determining the
characteristics of the firmness: empirical methods, based on
knowledge through experience, and analytical methods based on
mathematical modeling of the structure.Analytical Methods were
developed in the second half of the twentieth century, in parallel
with the evolution of computing. Models of the firmness based on
the theory of Burmister (for flexible pavements) and Westergaard
(for firmness concrete) were being computerized in increasingly
complex programs that behavior patterns were added to estimate the
service life of the structure. Thus, analytical methods resulted in
software, due to the advantages of calculation that entails. In
1906, the Russian botanist M. Tswett conducted an experiment that
led to the discovery of what is now known as chromatography. Placed
an extract pigment plant on top of a glass column filled with
calcium carbonate (CaCO3). By adding ether, noted that mixing
original separated into different colored bands that descended
through the column at different speeds. A characteristic feature of
chromatography is the presence of two phases; arranged so that
while one remains stationary within the system (stationary phase),
the other moves along it (mobile phase). The key to the separation
in chromatography is that the speed with which it moves each
substance depends on its relative affinity for both phases
(equilibrium distribution). In the experiment Tswett, separation of
pigments plant was achieved thanks to each one of them had an
affinity for different phases. In general, the components more akin
to the stationary phase are moving slowly (more retained) while
more akin to the mobile phase (less retained) move faster.
Consequently, the chromatographic medium (column, plate or paper)
works as a driver of the speed of each substance constituting the
mixture, thus achieving separation and using a detector, chemical
characterization. Although the basic principles are the same, it is
customary to classify chromatographic methods depending on the
physical state of the mobile phase: Liquid chromatography. The
mobile phase is a solvent or solvent mixture and stationary phase
solid that interacts with the substances to be separated (liquid
chromatograph-solid) or liquid immiscible with the mobile phase,
deposited on the surface of a solid ( liquid-liquid
chromatography). This form of chromatography can be performed with
different experimental arrangements: column, in a thin layer on the
paper. Gas chromatography. In this case the mobile phase is an
inert gas (helium or nitrogen) and stationary phase is a solid
(gas-solid chromatography) or a liquid "sustained" by an inert
solid (GLC). This type of chromatography column is provided as it
is the only way that the mobile phase flowing gas remains confined
within the system. Application areas are diverse and encompass
virtually every activity involved in chemistry, for example, is
used in: The analysis of drugs and drugs in biological fluids such
as saliva, blood, urine Follow the transformation of the substances
responsible for the neurological transmission Determining the
presence of contaminants in the environment Deciphering the
composition of fossil fuels Perform quality control of products
manufactured chemicals and pharmaceuticals; in short, the list of
examples is endless. In this book we have taken several analytical
methods that have been accessed through a bibliographic research
and information technology, in order to analyze the characteristics
of each of them and update their status of validity. The aim of
this study is to make a comparison between different methods and
determine the degree of acceptance and use thereof. This is
intended to bring together the available knowledge of analytical
methods to recognize its advantages and limitations, which can
serve as a basis for the development of new methods or improving
existing ones.
We define different varieties of business conducted over the
Internet or supported by this technology, which are called
e-Business. These businesses can range from sell or Internet
auctions to manage internal business processes -distribution,
production, supply, finance, etc. using such technology.
Differential equations are a powerful and versatile tool for
solving problems from the most diverse horizons: from mechanics to
biology, economics electricity, etc. The first step in solving
these problems is modeling, ie the "translation" in mathematical
relationships of the most important of the situation in question
intrinsic aspects. The mathematical treatment of a problem from
technical or other branch of science can be a difficult job, it is
the combination of two different domains: the phenomena being
studied (financial, ecological, economic, etc) and mathematics.
Solving a problem basically comprises the following steps: 1)
Determination of intrinsic qualitative aspects of the problem:
choosing a reference system of independent variables and unknown
variables. 2) Writing equations. These essentially they translate
laws or physical, biological, economic behavior, etc. 3) Processing
and study of the equations obtained. Analytical or approximate
solutions of the unknowns are being looked for. 4) Interpretation
of the mathematical results by comparison with experience. We
understand mathematical model to approximate representation of a
phenomenon by mathematical relationships (usually equations,
differential equations in our case). This approach is appropriate
if the conclusions that can be drawn have sufficient similarity to
the observed phenomena. Its formulation corresponds to steps 1) and
2) mentioned above. It is virtually impossible for a model to
represent all facets of the phenomenon. Therefore we insist on the
need to take into account the most relevant aspects of the
situation in question. Sometimes the same model can be reformulated
facilitating its resolution. As well as the equations are also
involved data models, inaccuracies in measuring these entail errors
in the results. In most cases there is no analytical solution of
the mathematical model by which we must pass numerical modeling.
This is another source of inaccuracies and in the context of
numerical analysis can establish error bounds between the solution
of the numerical model and the exact solution of the mathematical
model. The mathematical model is disturbed numeric character as
mathematical operations as the derivation or integration are not
accurate. The numerical model for its size or complexity may
require computer processing involves the disturbance arithmetic,
since the representation of real numbers and elementary arithmetic
is about. In this book, we will try to figure out how the essential
book keeping condition is utilized to keep all bookkeeping records
in adjust. We broke down business exchanges to perceive how they
influenced each piece of the bookkeeping condition. Money, credit,
income and cost, withdrawals, and speculation exchanges were
inspected for a specific business. It should be kept in mind that a
solid comprehension of the fundamental bookkeeping condition is the
way to understanding the impacts of any business exchange. In
conclusion, the resolution of a problem for numerical computation,
the result cascading different types of errors, rarely produces an
exact solution.
This book lists general information about a Monte Carlo simulation
methodology in radio. In addition to providing general information,
the text also constitutes a specification of the first generation
of software SEAMCAT (spectrum engineering advanced Monte Carlo
analysis tool) that uses the Monte Carlo method applied to radio
cases. The problem of unwanted emissions, an important factor
affecting the efficiency of radio spectrum use is the subject of an
in-depth treatment in various forums both internal and external to
the European Conference of Postal and Telecommunications
Administrations (CEPT). While the need to re-evaluate the limits of
unwanted emissions within Section 3 of the Radio Regulations (RR)
is supported, it is generally considered to be preferable to use
for this purpose a generic method. One of the many reasons why
generic methods are preferred is their ability, a priori, to deal
with new systems and communication technologies as they arise.
Another reason is that only a generic method can aspire to become
the basis of an analytical tool widely recognized. The tool for
radio Monte Carlo simulation described in this report was developed
based on the above considerations, in the process of the European
Radiocommunications Committee (ERC). SEAMCAT is the application of
a Monte Carlo simulation model for radio, developed by a group of
CEPT administrations, members of the European Telecommunications
Standards Institute (ETSI) and international scientific bodies.
SEAMCAT is a computer program whose object code is public, being
distributed by the European Radiocommunications Office (ERO) of the
CEPT. Computing and information technology area courses have as
objectives the training of human resources for the technological
development of computing (hardware and software) in order to meet
society's needs for the application of computer technology in the
interests of society and the formation teachers for middle and
vocational education. Between the needs of society can be met with
the aid of computers can be mentioned: storage of large volumes of
information of all kinds and shapes and their recovery in an
acceptable time; computing complex mathematical calculations in
extremely short time; secure communication, fast and reliable;
automation, control and monitoring of complex systems; fast
computing repetitive calculations involving large amounts of
information; processing images from different sources; games and
tools to support teaching, etc. Application examples are found in
the daily routine of companies (computing involving economic,
financial and administrative information generated by business
activities, industrial and services); in processing images
generated by satellites for weather forecasts; in related to health
activities (hospitals, doctors' offices and public health
agencies); in air traffic control systems; communication via the
Internet; in the banking system, etc. The computation is for man an
indispensable and fundamental tool in modern life. In the context
of higher education in the field of Information Technology and its
generation processes and automation of knowledge, we must consider
the importance of curricula that can effectively prepare critical
people, active and increasingly aware of their social roles and its
contribution to scientific and technological advancement of the
country. The social, humanitarian and ethical content of such
training must orient curricula to ensure the expansion of human
capabilities in close relation to the technical and scientific
learning in the field of Computer and Information Technology. It is
therefore a higher education in which individuals are also being
trained to deal with human and ethical dimensions of knowledge and
social relations. Applications of computing and computer area
courses can be composed of four major areas of training: Basic
training, which comprises the basic principles of the area of
computing, computer science, mathematics necessary to define them
formally, physics and electricity needed to enable the
understanding and design of viable computers technical and
pedagogical training that introduces the basic knowledge the
construction of knowledge, necessary for the development of the
practice of computer education. Technological formation applying
the basic technological development in computing Additional
training allowing an interaction of the graduates of the courses
with other professions
Applications of Graph Theory gives an introduction on the subject
of graph theory and the applications related to it. It explains the
various computational complexities and the methodologies to solve
the problems using NP/P graphs. Also discussed in the book are the
theoretical applications of the graphs, the role of graphs in
education, the application of graph theory in the recognition of
language and the various special classes into which graphs and its
applications are classified. The book also gives some conclusive
remarks on the subject.
Correlation and Regression Analysis: Applications for Industrial
Organizations book discusses the important theoretical concepts
such as the Amortization System Constant, French System of Price
Amortization, comparative analysis of these methods and American
System of Amortization which provide a basic understanding of the
correlation and regression analysis. The application of these
concepts to develop economic and mathematical models in e-business
have been explained in detail. The theories and concepts related to
mathematical design in e-business, design of organizational
structure, microeconomic theory of firm, and fundamental concepts
related to banks, financial transactions, and the importance of
good relations during inflation have been elucidated. The
presentation and analysis of data along with detailed information
about macroeconomic variables, different result filters, and the
relationship of the macroeconomic variables with the result
variables has been described. This book provides a comprehensive
understanding about the application of correlation and regression
analysis in the industrial organizations.
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