Alain BENSOUSSAN
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Global Marketing Services Henning Schoenenberger 09.2011 Code
Code Description English A Science A11007 Science, general A12000 History of Science B Biomedicine B0000X Biomedicine general B11001 Cancer Research B12008 Human Genetics B12010 Gene Expression B12020 Gene Therapy B12030 Gene Function B12040 Cytogenetics B12050 Microarrays B13004 Human Physiology B14000 Immunology B14010 Antibodies B15007 Laboratory Medicine B16003 Medical Microbiology B16010 Vaccine B16020 Drug Resistance B1700X Molecular Medicine B18006 Neurosciences B18010 Neurochemistry B19002 Parasitology B21007 Pharmacology/Toxicology B21010 Pharmaceutical Sciences/Technology B22003 Virology B23000 Forensic Science C Chemistry C00004 Chemistry/Food Science, general C11006 Analytical Chemistry C11010 Mass Spectrometry C11020 Spectroscopy/Spectrometry C11030 Chromatography C12002 Biotechnology C12010 Applied Microbiology C12029 Biochemical Engineering C12037 Genetic Engineering C12040 Microengineering C12050 Electroporation C13009 Computer Applications in Chemistry C14005 Documentation and Information in Chemistry C15001 Food Science C16008 Inorganic Chemistry C17004 Math. Applications in Chemistry C18000 Nutrition C19007 Organic Chemistry C19010 Bioorganic Chemistry C19020 Organometallic Chemistry C19030 Carbohydrate Chemistry C21001 Physical Chemistry C21010 Electrochemistry C22008 Polymer Sciences C23004 Safety in Chemistry, Dangerous Goods C24000 Textile Engineering C25007 Theoretical and Computational Chemistry C26003 Gmelin C27000 Industrial Chemistry/Chemical Engineering C28000 Medicinal Chemistry C29000 Catalysis C31000 Nuclear -
Advances in Energy Forecasting Models Based on Engineering
20 Sep 2004 14:55 AR AR227-EG29-10.tex AR227-EG29-10.SGM LaTeX2e(2002/01/18) P1: GCE 10.1146/annurev.energy.29.062403.102042 Annu. Rev. Environ. Resour. 2004. 29:345–81 doi: 10.1146/annurev.energy.29.062403.102042 First published online as a Review in Advance on July 30, 2004 ADVANCES IN ENERGY FORECASTING MODELS ∗ BASED ON ENGINEERING ECONOMICS Ernst Worrell,1 Stephan Ramesohl,2 and Gale Boyd3 1Energy Analysis Department, Lawrence Berkeley National Laboratory, Berkeley, California 94720; email: [email protected] 2Energy Division, Wuppertal Institute for Climate, Environment, Energy, 42103 Wuppertal, Germany; email: [email protected] 3Decision and Information Sciences Division, Argonne National Laboratory, Argonne, Illinois 60439; email: [email protected] KeyWords energy modeling, industry, policy, technology ■ Abstract New energy efficiency policies have been introduced around the world. Historically, most energy models were reasonably equipped to assess the impact of classical policies, such as a subsidy or change in taxation. However, these tools are often insufficient to assess the impact of alternative policy instruments. We evalu- ate the so-called engineering economic models used to assess future industrial en- ergy use. Engineering economic models include the level of detail commonly needed to model the new types of policies considered. We explore approaches to improve the realism and policy relevance of engineering economic modeling frameworks. We also explore solutions to strengthen the policy usefulness of engineering eco- nomic analysis that can be built from a framework of multidisciplinary coopera- tion. The review discusses the main modeling approaches currently used and eval- uates the weaknesses in current models. -
On Variational Inequalities in Semi-Inner Product Spaces
TAMKANG JOURNAL OF MATHEMATICS Volume 24, Number 1, Spring 1993 ON VARIATIONAL INEQUALITIES IN SEMI-INNER PRODUCT SPACES MUHAMMAD ASLAM NOOR Abstract. In this paper, we consider and study the variational inequal• ities in the setting of semi-inner product spaces. Using the auxiliary principle, we propose and analyze an innovative and novel iterative al• gorithm for finding the approximate solution of variational inequalities. We also discuss the convergence criteria. 1. Introduction Variational inequality theory has been used to study a wide class of free, moving and equilibrium problems arising in various branches of pure and applied science in a general and unified framework. This theory has been extended and generalized in several directions using new and powerful methods that have led to the solution of basic and fundamental problems thought to be inaccessible previously. Much work in this field has been done either in inner product spaces or in Hilbert spaces and it is generally thought that this is desirable, if not essential, for the results to hold. We, in this paper, consider and study the variational inequalities in the setting of semi-inner product spaces, which are more general than the inner product space. It is observed that the projection technique can not be applied to show that the variational inequality problem is Received January 6, 1991; revised March 20, 1992. (1980) AMS(MOS) Subject Classifications: 49J40, 65K05. Keywords and phrases: Variational inequalities, Auxiliary principle, Iterative algorithms, Semi-inner product spaces. 91 92 MUHAMMAD ASLAM NOOR equivalent to a fixed point problem in semi-inner product spaces. This motivates us to use the auxiliary principle technique to suggest and analyze an iterative algorithm to compute the approximate solution of variational inequalities. -
Engineering Economy for Economists
AC 2008-2866: ENGINEERING ECONOMY FOR ECONOMISTS Peter Boerger, Engineering Economic Associates, LLC Peter Boerger is an independent consultant specializing in solving problems that incorporate both technological and economic aspects. He has worked and published for over 20 years on the interface between engineering, economics and public policy. His education began with an undergraduate degree in Mechanical Engineering from the University of Wisconsin-Madison, adding a Master of Science degree in a program of Technology and Public Policy from Purdue University and a Ph.D. in Engineering Economics from the School of Industrial Engineering at Purdue University. His firm, Engineering Economic Associates, is located in Indianapolis, IN. Page 13.503.1 Page © American Society for Engineering Education, 2008 Engineering Economy for Economists 1. Abstract The purpose of engineering economics is generally accepted to be helping engineers (and others) to make decisions regarding capital investment decisions. A less recognized but potentially fruitful purpose is to help economists better understand the workings of the economy by providing an engineering (as opposed to econometric) view of the underlying workings of the economy. This paper provides a review of some literature related to this topic and some thoughts on moving forward in this area. 2. Introduction Engineering economy is inherently an interdisciplinary field, sitting, as the name implies, between engineering and some aspect of economics. One has only to look at the range of academic departments represented by contributors to The Engineering Economist to see the many fields with which engineering economy already relates. As with other interdisciplinary fields, engineering economy has the promise of huge advancements and the risk of not having a well-defined “home base”—the risk of losing resources during hard times in competition with other academic departments/specialties within the same department. -
Iterated Importance Sampling in Missing Data Problems
Iterated importance sampling in missing data problems Gilles Celeux INRIA, FUTURS, Orsay, France Jean-Michel Marin ∗ INRIA, FUTURS, Orsay, France and CEREMADE, University Paris Dauphine, Paris, France Christian P. Robert CEREMADE, University Paris Dauphine and CREST, INSEE, Paris, France Abstract Missing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling schemes. A population Monte Carlo scheme taking advantage of the latent structure of the problem is proposed. The potential of this approach and its specifics in missing data problems are illustrated in settings of increasing difficulty, in comparison with existing approaches. The improvement brought by a general Rao–Blackwellisation technique is also discussed. Key words: Adaptive algorithms, Bayesian inference, latent variable models, population Monte Carlo, Rao–Blackwellisation, stochastic volatility model ∗ Corresponding author: CEREMADE, Place du Mar´echal De Lattre de Tassigny, 75775 Paris Cedex 16, France, [email protected] Preprint submitted to Computational Statistics and Data Analysis 25 July 2005 1 Introduction 1.1 Missing data models Missing data models, that is, structures such that the distribution of the data y can be represented via a marginal density Z f(y|θ) = g(y, z|θ)dz , Z where z ∈ Z denotes the so-called ”missing data”, have often been at the forefront of computational Statistics, both as a challenge to existing techniques and as a benchmark for incoming techniques. This is for instance the case with the EM algorithm (Dempster et al., 1977), which was purposely designed for missing data problems although it has since then been applied in a much wider setting. -
Engineering Economics for Public Water Utilities
Engineering Economics for Public Water Utilities Max Maurer Eawag: Swiss Federal Institute of Aquatic Science and Technology Imprint Author Prof. Dr. Max Maurer, Institute of Environmental Engineering, ETH-Zürich, Switzerland [email protected] Aim This script constitutes part of my lecture ‘Infrastructure Systems in Urban Water Management’ given at ETH Zürich, Switzerland Version 4.7, Jan 2019 License Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) Title photo Wastewater Treatment Plant (Mönchaltorf, ZH) Maurer: Engineering Economics Contents 1. Aim of this script ......................................................................................................................... - 5 - Inflation, Interest & Discount Rate ...................................................................................................... - 7 - 2. The strange concept of money .................................................................................................. - 8 - 3. Inflation........................................................................................................................................ - 8 - 4. Interest rates ............................................................................................................................. - 10 - 5. Discount rate ............................................................................................................................. - 10 - 6. Replacement value .................................................................................................................. -
Economics and Engineering” Pedro Garcia Duarte and Yann Giraud
Introduction: From “Economics as Engineering” to “Economics and Engineering” Pedro Garcia Duarte and Yann Giraud The Transformation of Economics into an Engineering Science: From Analogies to Interactions In recent years, economists, who in the past had mostly insisted on their discipline’s strength as a rigorous social science, have turned to its larger role in transforming society. As early as 2002, Alvin Roth, the Stan- ford-educated economist who received the Nobel memorial prize in 2012, has claimed that members of his community should think of themselves as engineers rather than scientists. By this he meant that they should not be interested solely in the making of theoretical models but also in con- fronting these models with the complexities of real-life situations, which is what engineering is allegedly about. He pointed to the rise of the new sub eld of market design, which he had helped develop, as characteristic of an engineer’s stance and provided several examples of engineering practices applied to economics: the design of labor clearinghouses—such as the entry-level labor market for American physicians—and that of the Federal Communications Commission spectrum auctions (Roth 2002). We want to thank the Center for the History of Political Economy and Duke University Press for their support, as well as the many referees who helped us during the editorial process. Yann Giraud wishes to point out that his research has been supported by the project Labex MME-DII (ANR11–LBX-0023–01). Pedro Duarte acknowledges the nancial support of the Institute for Advanced Studies, at the Université de Cergy-Pontoise, for visiting professorships (2016, 2018) that were critical for the shaping of this joint project. -
Testing Adverse Selection and Moral Hazard on French Car Insurance Data
TESTING ADVERSE SELECTION AND MORAL HAZARD ON FRENCH CAR INSURANCE DATA Guillaume CARLIER1 Université Paris Dauphine Michel GRUN-REHOMME2 Université Paris Panthéon Olga VASYECHKO3 Université Nationale d'Economie de Kyiv This paper is a modest contribution to the stream of research devoted to find empirical evidence of asymmetric information. Building upon Chiappori and Salanié's (2000) work, we propose two specific tests, one for adverse selection and one for moral hazard. We implement these tests on French car insurance data, circumventing the lack of dynamic data in our data base by a proxy of claim history. The first test suggests presence of adverse selection whereas the second one seems to contradict presence of pure moral hazard. Keywords: empirical evidence of moral hazard, adverse selection, car insurance. JEL Classification: C35, D82. 1 Université Paris Dauphine, CEREMADE, Pl. de Lattre de Tassigny, 75775 Paris Cedex 16, FRANCE, [email protected] 2 Université Paris Panthéon, ERMES, 12 Pl. du Panthéon, 75005 Paris, FRANCE, [email protected] 3 Université Nationale d'Economie de Kyiv, UKRAINE, [email protected] BULLETIN FRANÇAIS D’ACTUARIAT, Vol. 13, n° 25, janvier – juin 2013, pp. 117- 130 118 G. CARLIER – M. GRUN-REHOMME – O. VASYECHKO 1. INTRODUCTION There has been an intensive line of research in the last decades devoted to find empirical evidence of asymmetric information on insurance data. Under adverse selection, the insured has some private information about his type of risk, which the insurer cannot observe before the subscription of an insurance contract. The adverse selection assumption stipulates that the high-risks tend to choose more coverage than the low-risks (Rothschild and Stiglitz (1976), Wilson (1977), Spence (1978)). -
Dirk Hundertmark Department of Mathematics, MC-382 University of Illinois at Urbana-Champaign Altgeld Hall 1409 W
Dirk Hundertmark Department of Mathematics, MC-382 University of Illinois at Urbana-Champaign Altgeld Hall 1409 W. Green Street Urbana, IL 61801 +1 (217) 333-3350 (217) 333-9516 (fax) [email protected] 1401 W. Charles, Champaign, IL 61821 +1 (217) 419-1088 (home) http://www.math.uiuc.edu/∼dirk Personal Data German and American citizen, married, one daughter. Research interests Partial differential equations, analysis, variational methods, functional analysis, spectral theory, motivated by problems from Physics, especially quantum mechanics, and Engineering. Spectral theory of random operators and and its connection to statistical mechanics and some probabilistic problems from solid state physics. More recently, mathematical problems in non-linear fiber-optics, especially properties of dispersion managed solitons. Education 5/2003 Habilitation in Mathematics, Ludwig{Maximilians-Universit¨atM¨unchen. 7/1992 { 11/1996 Ph.D. (Dr. rer. nat., summa cum laude) in Mathematics, Ruhr- Universit¨atBochum, Germany. Thesis: \ On the theory of the magnetic Schr¨odingersemigroup." Advisor: Werner Kirsch 11/1985 { 2/1992 Study of Physics, Friedrich-Alexander-Universit¨atErlangen, Germany. Graduated with Diplom. Advisor: Hajo Leschke. Employment Since 9/2007 Member of the Institute of Condensed Matter Theory at UIUC. Since 8/2006 Associate Professor (tenured), Department of Mathematics, University of Illinois at Urbana-Champaign (on leave 8/2006 { 8/2007). 8/2006 {8/2007 Senior Lecturer, School of Mathematics, University of Birmingham, England. 1/2003 { 7/2006 Assistant Professor, Department of Mathematics, University of Illinois at Urbana-Champaign. 9 { 12/2002 Research fellow at the Institut Mittag-Leffler during the special program \Partial Differential Equations and Spectral Theory." 9/1999{8/2002 Olga Taussky-John Todd Instructor of Mathematics, Caltech. -
Curriculum Vitae
Umberto Mosco WPI Harold J. Gay Professor of Mathematics May 18, 2021 Department of Mathematical Sciences Phone: (508) 831-5074, Worcester Polytechnic Institute Fax: (508) 831-5824, Worcester, MA 01609 Email: [email protected] Curriculum Vitae Current position: Harold J. Gay Professor of Mathematics, Worcester Polytechnic Institute, Worcester MA, U.S.A. Languages: English, French, German, Italian (mother language) Specialization: Applied Mathematics Research Interests:: Fractal and Partial Differential Equations, Homog- enization, Finite Elements Methods, Stochastic Optimal Control, Variational Inequalities, Potential Theory, Convex Analysis, Functional Convergence. Twelve Most Relevant Research Articles 1. Time, Space, Similarity. Chapter of the book "New Trends in Differential Equations, Control Theory and Optimization, pp. 261-276, WSPC-World Scientific Publishing Company, Hackenseck, NJ, 2016. 2. Layered fractal fibers and potentials (with M.A.Vivaldi). J. Math. Pures Appl. 103 (2015) pp. 1198-1227. (Received 10.21.2013, Available online 11.4.2014). 3. Vanishing viscosity for fractal sets (with M.A.Vivaldi). Discrete and Con- tinuous Dynamical Systems - Special Volume dedicated to Louis Niren- berg, 28, N. 3, (2010) pp. 1207-1235. 4. Fractal reinforcement of elastic membranes (with M.A.Vivaldi). Arch. Rational Mech. Anal. 194, (2009) pp. 49-74. 5. Gauged Sobolev Inequalities. Applicable Analysis, 86, no. 3 (2007), 367- 402. 6. Invariant field metrics and dynamic scaling on fractals. Phys. Rev. Let- ters, 79, no. 21, Nov. (1997), pp. 4067-4070. 7. Variational fractals. Ann. Scuola Norm. Sup. Pisa Cl. Sci. (4) 25 (1997) No. 3-4, pp. 683-712. 8. A Saint-Venant type principle for Dirichlet forms on discontinuous media (with M. -
Engineering Economics and the Law
Session 1339 Engineering Economics and the Law J. Timothy Cromley BANK ONE CORPORATION Abstract Patent and Trade Secret Law is an area of the Law that is very important to engineering and the economy, but it is not among the topics prominently covered (if at all) in a typical course in Engineering Economy (or “Engineering Economics”). To help remedy this apparent deficiency in course coverage, the author presents: a rationale for inclusion of this topic in such a course, an overview of patent and trade secret law, classroom suggestions, and steps for valuing a patent. 1. Why Patent & Trade Secret Law is an Apt Topic for Courses in Engineering Economics It is widely recognized that relationships exist between law and economics . The University of Chicago, for example, has had a Journal of Law and Economics since 1958. 1 The Encyclopedia of Law and Economics , which is published in the Netherlands, has two Nobel Laureates in Economics on its Editorial Board. 2 Because important relationships exist between law and economics , it is appropriate to inquire: What area(s) of the law (if any) are most relevant to a course in engineering economics? Environmental law might be a candidate, as it is relevant to environmental engineers, but it is too specialized to be of general interest in a course on engineering economics. Contract law is a possibility, but its appeal is too broad to properly fit into a focused course like engineering economics. Many other areas of the law are not good fits for similar reasons. Patent law and product liability law are good candidates, but as between these two areas of law, patent law is more highly relevant to both engineering and economics because of its unique links to both technology and monopoly . -
Basics of Engineering Economics Performance:Slide501.Doc Engineering Economics
Construction Management - II / Basics of Engineering Economics Performance:Slide501.doc Engineering Economics Principles § During our examinations we assume a consolidated economy. ( Free of extremities, such as war, hyperinflation, corruption, etc., and fundamentally operated by pure market mechanisms and by stabil legal and regulations’ systems ) § Our examinations are aiming at economic comparisions of functionally equivalent technical and/or financial options. Figures resulted by any analysis of any option can not be evaluated as themselves but as values to be measured against others. § Conclusions of our examinations are at most supporting decision makers at their work when elaborating their market policy and/or strategy, but are not substituting any decisions to be made by them. BUTE DCTM / Engineering Programs in English / 2000- Dr. Zoltán András Vattai Construction Management - II / Basics of Engineering Economics Performance:Slide502.doc Using External Resources Foreign Capital Due to the fact that a typical investment in civil engineering and/or in construction industry moves huge amount of technical and financial resources, it is frequently unavoidable to invoke external („foreign”) resources and/or capital temporarily. Liquidity: Promp available own economic resources. („self-financing capability”) Loan: External economic resource temporarily let for use and to be paid back later increased by some extra fee („foreign capital”, „loan capital”). Interest: „Rent” („price”) of using foreign capital. Its extent is highly defined by the actual „demand versus supply” conditions. BUTE DCTM / Engineering Programs in English / 2000- Dr. Zoltán András Vattai Construction Management - II / Basics of Engineering Economics Performance:Slide503.doc Using External Resources Foreign Capital Term / Pay-Back Period / Lending Period: A time period in which a foreign capital is let for use.