Object-oriented Programming Applied to the Development of Structural Analysis and Optimization Software

This paper presents the development of a two-dimensional interactive software environment for structural analysis and optimization based on object-oriented programming using the C++ language. The main feature of the software is the effective integration of several computational tools into graphical user interfaces implemented in the Windows-98 and Windows-NT operating systems. The interfaces simplify data specification in the simulation and optimization of two-dimensional linear elastic problems. NURBS have been used in the software modules to represent geometric and graphical data. Extensions to the analysis of three-dimensional problems have been implemented and are also discussed in this paper.

Applying Qt to programming courses

Työn tavoitteena on sovittaa Qt opetussuunnitelmaan. Työ sisältää Qt:n lyhyen historian sekä katsauksen sen nykytilaan. Nykytilakatsaus sisältää kolme näkökulmaa: miten ja missä Qt:ta voidaan käyttää, sekä sen käyttötarkoitukset teollisuudessa ja opetuksessa. Työn tuloksena syntyy luentodemonstraatiota varten pieni ohjelma, joka on luotu C++:n ja Qt Designerin avulla ja käyttää olennaisia käyttöliittymäkirjaston olioita. Toisena tuotteena työssä syntyy luonnos Lappeenrannan Teknillisen Yliopiston ohjelmointikursseista, joissa Qt:ta voitaisiin käyttää avustamaan opiskelijoita näkemään, miten graafinen ohjelma luodaan sekä valmentaa heitä ymmärtämään viitekehyksien ja graafisten kirjastojen tuomat edut.

Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

Nonconventional amide bond formation catalysis: programming enzyme specificity with substrate mimetics

This article reports on the design and characteristics of substrate mimetics in protease-catalyzed reactions. Firstly, the basis of protease-catalyzed peptide synthesis and the general advantages of substrate mimetics over common acyl donor components are described. The binding behavior of these artificial substrates and the mechanism of catalysis are further discussed on the basis of hydrolysis, acyl transfer, protein-ligand docking, and molecular dynamics studies on the trypsin model. The general validity of the substrate mimetic concept is illustrated by the expansion of this strategy to trypsin-like, glutamic acid-specific, and hydrophobic amino acid-specific proteases. Finally, opportunities for the combination of the substrate mimetic strategy with the chemical solid-phase peptide synthesis and the use of substrate mimetics for non-peptide organic amide synthesis are presented.

Developing an introductory object-oriented programming course

The state of the object-oriented programming course in Lappeenranta University of Technology had reached the point, where it required changes to provide better learning opportunities and thus the learning outcomes. Based on the student feedback the course was partially dated and ineffective. The components of the course were analysed and the ineffective elements were removed and new methods were introduced to improve the course. The major changes included the change from traditional teaching methods to reverse classroom method and the use of Java as the programming language. The changes were measured by the student feedback, lecturer’s observations and comparison to previous years. The feedback suggested that the changes were successful; the course received higher overall grade than before.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

Open source solutions for multiplatform graphics programming

New emerging technologies in the recent decade have brought new options to cross platform computer graphics development. This master thesis took a look for cross platform 3D graphics development possibilities. All platform dependent and non real time solutions were excluded. WebGL and two different OpenGL based solutions were assessed via demo application by using most recent development tools. In the results pros and cons of the each solutions were noted.

Can future systemic financial risks be quantified?: ergodic vs nonergodic stochastic processes

Different axioms underlie efficient market theory and Keynes's liquidity preference theory. Efficient market theory assumes the ergodic axiom. Consequently, today's decision makers can calculate with actuarial precision the future value of all possible outcomes resulting from today's decisions. Since in an efficient market world decision makers "know" their intertemporal budget constraints, decision makers never default on a loan, i.e., systemic defaults, insolvencies, and bankruptcies are impossible. Keynes liquidity preference theory rejects the ergodic axiom. The future is ontologically uncertain. Accordingly systemic defaults and insolvencies can occur but can never be predicted in advance.