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Il volume contiene in forma compatta il programma svolto negli insegnamenti introduttivi di statistica e tratta alcuni argomenti indispensabili per l'attività di ricerca, come ad esempio i metodi di simulazione Monte Carlo, le procedure di minimizzazione e le tecniche di analisi dei dati di laboratorio. Gli argomenti vengono sviluppati partendo dai fondamenti, evidenziandone gli aspetti applicativi, fino alla descrizione dettagliata di molti casi di particolare rilevanza in ambito scientifico e tecnico. Numerosi esempi ed esercizi risolti valorizzano l'opera ed aiutano il lettore nella comprensione dei punti più difficili ed importanti. Come ulteriore supporto, questa seconda edizione contiene molti programmi applicativi scritti col software libero Scilab, scaricabili dal sito web creato dagli autori. Il testo è rivolto agli studenti universitari dei corsi ad indirizzo scientifico e a tutti quei ricercatori che devono risolvere problemi concreti che coinvolgono aspetti statistici e  di simulazione. Per i programmi in Scilab e per il materiale ausiliario si veda: http://www.mi.imati.cnr.it/~marco/springer/index.html .

Mathematical statistics. --- Scilab. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics .

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The overall energy sector calls for a transformation from a fossil-based system to a low-carbon one. At a technology level, significant efforts have been made to provide energy solutions that contribute to a sustainable energy system. However, the actual suitability of these solutions is often not checked. In this sense, the assessment of energy systems from a life-cycle perspective is of paramount importance when it comes to effectively planning the energy sector. While environmental issues are commonly addressed through the use of the Life Cycle Assessment (LCA) methodology, the comprehensive evaluation of the economic and social aspects of energy systems often remains ignored or underdeveloped. This book consists of a set of scientific works addressing the analysis of energy systems from a (life-cycle) technical, economic, environmental and/or social standpoint. Case studies at and beyond the technology level are included, some of them involving a combination of life cycle and non-life cycle approaches for the thorough evaluation of energy systems under the umbrella of sustainability.

Research & information: general --- zinc (Zn) --- electrowinning (EW) --- activated Carbons (ACs) --- adsorbate --- liquid phase space velocity (LHSV) --- temperature --- bioeconomy --- life cycle assessment --- multi-criteria decision analysis --- sustainability --- thermal energy --- wood --- LCC optimization --- building energy simulation --- energy system optimization --- energy renovation --- historic building district --- district heating system --- biobutanol --- clean combustion --- Scilab simulations --- SimaPro --- CO2 emission --- fuel production management --- environmental impact --- non-edible resources for biofuel production --- GIS --- concentrated solar power --- solar thermochemistry --- life-cycle costs --- cost supply --- geographical potential --- sustainable --- alternative

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Scilab is a free open-source software package for scientific computation. It includes hundreds of general purpose and specialized functions for numerical computation, organized in libraries called toolboxes, which cover such areas as simulation, optimization, systems and control, and signal processing. One important Scilab toolbox is Scicos. Scicos provides a block diagram graphical editor for the construction and simulation of dynamical systems. The objective of this book is to provide a tutorial for the use of Scilab/Scicos with a special emphasis on modeling and simulation tools. While it will provide useful information to experienced users it is designed to be accessible to beginning users from a variety of disciplines. Students and academic and industrial scientists and engineers should find it useful. The book is divided into two parts. The first part concerns Scilab and includes a tutorial covering the language features, the data structures and specialized functions for doing graphics, importing, exporting data and interfacing external routines. It also covers in detail Scilab numerical solvers for ordinary differential equations and differential-algebraic equations. Even though the emphasis is placed on modeling and simulation applications, this part provides a global view of Scilab. The second part is dedicated to modeling and simulation of dynamical systems in Scicos. This type of modeling tool is widely used in industry because it provides a means for constructing modular and reusable models. This part contains a detailed description of the editor and its usage, which is illustrated through numerous examples. All codes used in the book is made available to the reader. This new edition includes expanded chapters, new exercises and major rewrites for examples to work with the new Maple.

Science. --- Science --- Mathematics --- Physical Sciences & Mathematics --- Sciences - General --- Mathematics - General --- Data processing --- Computer programs --- Computer programs. --- Data processing. --- Scilab --- Scilab. --- Electronic data processing --- Mathematics. --- Operations research. --- Decision making. --- Computer simulation. --- Dynamics. --- Ergodic theory. --- Computer mathematics. --- Control engineering. --- Robotics. --- Mechatronics. --- Computational Mathematics and Numerical Analysis. --- Control, Robotics, Mechatronics. --- Dynamical Systems and Ergodic Theory. --- Operation Research/Decision Theory. --- Simulation and Modeling. --- Computer science --- Differentiable dynamical systems. --- Operations Research/Decision Theory. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Differential dynamical systems --- Dynamical systems, Differentiable --- Dynamics, Differentiable --- Differential equations --- Global analysis (Mathematics) --- Topological dynamics --- Computer mathematics --- Discrete mathematics --- Ergodic transformations --- Continuous groups --- Mathematical physics --- Measure theory --- Transformations (Mathematics) --- Dynamical systems --- Kinetics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Automation --- Machine theory --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Programmable controllers --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Decision making

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Many industries, such as transportation and manufacturing, use control systems to insure that parameters such as temperature or altitude behave in a desirable way over time. For example, pilots need assurance that the plane they are flying will maintain a particular heading. An integral part of control systems is a mechanism for failure detection to insure safety and reliability. This book offers an alternative failure detection approach that addresses two of the fundamental problems in the safe and efficient operation of modern control systems: failure detection--deciding when a failure has occurred--and model identification--deciding which kind of failure has occurred. Much of the work in both categories has been based on statistical methods and under the assumption that a given system was monitored passively. Campbell and Nikoukhah's book proposes an "active" multimodel approach. It calls for applying an auxiliary signal that will affect the output so that it can be used to easily determine if there has been a failure and what type of failure it is. This auxiliary signal must be kept small, and often brief in duration, in order not to interfere with system performance and to ensure timely detection of the failure. The approach is robust and uses tools from robust control theory. Unlike some approaches, it is applicable to complex systems. The authors present the theory in a rigorous and intuitive manner and provide practical algorithms for implementation of the procedures.

System failures (Engineering) --- Fault location (Engineering) --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Location of system faults --- System fault location (Engineering) --- Dynamic testing --- Failure of engineering systems --- Reliability (Engineering) --- Systems engineering --- A priori estimate. --- AIXI. --- Abuse of notation. --- Accuracy and precision. --- Additive white Gaussian noise. --- Algorithm. --- Approximation. --- Asymptotic analysis. --- Bisection method. --- Boundary value problem. --- Calculation. --- Catastrophic failure. --- Combination. --- Computation. --- Condition number. --- Continuous function. --- Control theory. --- Control variable. --- Decision theory. --- Derivative. --- Detection. --- Deterministic system. --- Diagram (category theory). --- Differential equation. --- Discrete time and continuous time. --- Discretization. --- Dynamic programming. --- Engineering design process. --- Engineering. --- Equation. --- Error message. --- Estimation theory. --- Estimation. --- Finite difference. --- Gain scheduling. --- Inequality (mathematics). --- Initial condition. --- Integrator. --- Invertible matrix. --- Laplace transform. --- Least squares. --- Likelihood function. --- Likelihood-ratio test. --- Limit point. --- Linear programming. --- Linearization. --- Mathematical optimization. --- Mathematical problem. --- Maxima and minima. --- Measurement. --- Method of lines. --- Monotonic function. --- Noise power. --- Nonlinear control. --- Nonlinear programming. --- Norm (mathematics). --- Numerical analysis. --- Numerical control. --- Numerical integration. --- Observational error. --- Open problem. --- Optimal control. --- Optimization problem. --- Parameter. --- Partial differential equation. --- Piecewise. --- Pointwise. --- Prediction. --- Probability. --- Random variable. --- Realizability. --- Remedial action. --- Requirement. --- Rewriting. --- Riccati equation. --- Runge–Kutta methods. --- Sampled data systems. --- Sampling (signal processing). --- Scientific notation. --- Scilab. --- Shift operator. --- Signal (electrical engineering). --- Sine wave. --- Solver. --- Special case. --- Stochastic Modeling. --- Stochastic calculus. --- Stochastic interpretation. --- Stochastic process. --- Stochastic. --- Theorem. --- Time complexity. --- Time-invariant system. --- Trade-off. --- Transfer function. --- Transient response. --- Uncertainty. --- Utilization. --- Variable (mathematics). --- Variance.