Design and implementation of a windows-based parallel computing environment for large scale optimization

A parallel computing environment to support optimization of large-scale engineering systems is designed and implemented on Windows-based personal computer networks, using the master-worker model and the Parallel Virtual Machine (PVM). It is involved in decomposition of a large engineering system into a number of smaller subsystems optimized in parallel on worker nodes and coordination of subsystem optimization results on the master node. The environment consists of six functional modules, i.e. the master control, the optimization model generator, the optimizer, the data manager, the monitor, and the post processor. Object-oriented design of these modules is presented. The environment supports steps from the generation of optimization models to the solution and the visualization on networks of computers. User-friendly graphical interfaces make it easy to define the problem, and monitor and steer the optimization process. It has been verified by an example of a large space truss optimization. (C) 2004 Elsevier Ltd. All rights reserved.

Cognitive fit between conceptual schemas and internal problem representations: The case of geospatio-temporal conceptual schema comprehension

Geospatio-temporal conceptual models provide a mechanism to explicitly represent geospatial and temporal aspects of applications. Such models, which focus on both what and when/where, need to be more expressive than conventional conceptual models (e.g., the ER model), which primarily focus on what is important for a given application. In this study, we view conceptual schema comprehension of geospatio-temporal data semantics in terms of matching the external problem representation (that is, the conceptual schema) to the problem-solving task (that is, syntactic and semantic comprehension tasks), an argument based on the theory of cognitive fit. Our theory suggests that an external problem representation that matches the problem solver's internal task representation will enhance performance, for example, in comprehending such schemas. To assess performance on geospatio-temporal schema comprehension tasks, we conducted a laboratory experiment using two semantically identical conceptual schemas, one of which mapped closely to the internal task representation while the other did not. As expected, we found that the geospatio-temporal conceptual schema that corresponded to the internal representation of the task enhanced the accuracy of schema comprehension; comprehension time was equivalent for both. Cognitive fit between the internal representation of the task and conceptual schemas with geospatio-temporal annotations was, therefore, manifested in accuracy of schema comprehension and not in time for problem solution. Our findings suggest that the annotated schemas facilitate understanding of data semantics represented on the schema.

Long-term outcome of a school-based, universal approach to prevention of depression in adolescents

In this study. the authors examined the 2-, 3-, and 4-year outcomes of a school-based, universal approach to the prevention of adolescent depression. Despite initial short-term positive effects, these benefits were not maintained over time. Adolescents who completed the teacher-administered cognitive-behavioral intervention did not differ significantly from adolescents in the monitoring-control condition in terms of changes in depressive symptoms, problem solving, attributional style, or other indicators of psychopathology from preintervention to 4-year follow-up. Results were equivalent irrespective of initial level of depressive symptoms.

Approaches to training practitioners in the art and science of plant disease diagnosis

Allowing plant pathology students to tackle fictitious or real crop problems during the course of their formal training not only teaches them the diagnostic process, but also provides for a better understanding of disease etiology. Such a problem-solving approach can also engage, motivate, and enthuse students about plant pathologgy in general. This paper presents examples of three problem-based approaches to diagnostic training utilizing freely available software. The first provides an adventure-game simulation where Students are asked to provide a diagnosis and recommendation after exploring a hypothetical scenario or case. Guidance is given oil how to create these scenarios. The second approach involves students creating their own scenarios. The third uses a diagnostic template combined with reporting software to both guide and capture students' results and reflections during a real diagnostic assignment.

Systems Analysis: the Structure-and-Purpose Approach Based on Logic-linguistic Formalisation

Systems analysis (SA) is widely used in complex and vague problem solving. Initial stages of SA are analysis of problems and purposes to obtain problems/purposes of smaller complexity and vagueness that are combined into hierarchical structures of problems(SP)/purposes(PS). Managers have to be sure the PS and the purpose realizing system (PRS) that can achieve the PS-purposes are adequate to the problem to be solved. However, usually SP/PS are not substantiated well enough, because their development is based on a collective expertise in which logic of natural language and expert estimation methods are used. That is why scientific foundations of SA are not supposed to have been completely formed. The structure-and-purpose approach to SA based on a logic-and-linguistic simulation of problems/purposes analysis is a step towards formalization of the initial stages of SA to improve adequacy of their results, and also towards increasing quality of SA as a whole. Managers of industrial organizing systems using the approach eliminate logical errors in SP/PS at early stages of planning and so they will be able to find better decisions of complex and vague problems.

How to Elaborate a Demonstrator in Inspiring Science Education Platform

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Practice based learning approaches in collaborative design and engineering education:a case study investigation into the benefits of a crossdisciplinary practice based learning strategy

This study explores the ongoing pedagogical development of a number of undergraduate design and engineering programmes in the United Kingdom. Observations and data have been collected over several cohorts to bring a valuable perspective to the approaches piloted across two similar university departments while trialling a number of innovative learning strategies. In addition to the concurrent institutional studies the work explores curriculum design that applies the principles of Co-Design, multidisciplinary and trans disciplinary learning, with both engineering and product design students working alongside each other through a practical problem solving learning approach known as the CDIO learning initiative (Conceive, Design Implement and Operate) [1]. The study builds on previous work presented at the 2010 EPDE conference: The Effect of Personality on the Design Team: Lessons from Industry for Design Education [2]. The subsequent work presented in this paper applies the findings to mixed design and engineering team based learning, building on the insight gained through a number of industrial process case studies carried out in current design practice. Developments in delivery also aligning the CDIO principles of learning through doing into a practice based, collaborative learning experience and include elements of the TRIZ creative problem solving technique [3]. The paper will outline case studies involving a number of mixed engineering and design student projects that highlight the CDIO principles, combined with an external industrial design brief. It will compare and contrast the learning experience with that of a KTP derived student project, to examine an industry based model for student projects. In addition key areas of best practice will be presented, and student work from each mode will be discussed at the conference.

Menedzsmentképességek és döntéshozatali közelítésmódok longitudinális elemzése a versenyképesség-kutatások alapján (Longitudinal analysis of management capabilities and decision making approaches based on the competitiveness researches)

Számos korábbi kutatás – köztük a szerzők korábbi vizsgálatai is – azt mutatja, hogy a menedzsmentképességek és a vállalatok versenyképessége között pozitív kapcsolat áll fenn, a jobban teljesítő és a proaktívabb vállalatok rendre felkészültebb, jobb vezetői képességekkel bíró, kockázatvállalóbb vezetőkkel rendelkeznek. Az is megfigyelhető, hogy az ebből a nézőpontból sikeresebben működő vállalatok döntéseiben az átlagosnál is erősebben érvényesül a racionális közelítésmód, melynek alkalmazásával a menedzserek az optimális cselekvési alternatíva kiválasztására törekszenek. A cikkben a szerzők az elmúlt 15 év versenyképességi kutatásainak tapasztalatait összegzik, kiemelt hangsúlyt helyezve a legfrissebb felmérés eredményeire. ________________ The article summarizes the main findings of the Competitiveness Research Program with respect to the skills and capabilities of the Hungarian managers and the decision making approaches they use during their work. The results of the four surveys conducted in 1996, 1999, 2004 and 2009 are fairly stable over time: practice minded behavior, professional expertise, and problem solving skills are on the top of the list of the most developed skills of the Hungarian executives. The rational approach is the most popular among the most widespread decision making models in the authors’ sample which is rather alarming since the present turbulent economic environment may demand more adaptive and intuitive approaches.

Making Life Choices Program: A psychoeducational intervention for promoting critical cognitive and affective competencies in adolescents at-risk for problem behaviors

The long term goal of the work described is to contribute to the emerging literature of prevention science in general, and to school-based psychoeducational interventions in particular. The psychoeducational intervention reported in this study used a main effects prevention intervention model. The current study focused on promoting optimal cognitive and affective functioning. The goal of this intervention was to increase potential protective factors such as critical cognitive and communicative competencies (e.g., critical problem solving and decision making) and affective competencies (e.g., personal control and responsibility) in middle adolescents who have been identified by the school system as being at-risk for problem behaviors. The current psychoeducational intervention draws on an ongoing program of theory and research (Berman, Berman, Cass Lorente, Ferrer Wreder, Arrufat, & Kurtines 1996; Ferrer Wreder, 1996; Kurtines, Berman, Ittel, & Williamson, 1995) and extends it to include Freire's (1970) concept of transformative pedagogy in developing school-based psychoeducational programs that target troubled adolescents. The results of the quantitative and qualitative analyses indicated trends that were generally encouraging with respect to the effects of the intervention on increasing critical cognitive and affective competencies. ^

Case Based Heuristic Selection for Timetabling Problems

This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems.

Case Based Heuristic Selection for Timetabling Problems

This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems.

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

Współczesna debata telewizyjna a konflikt destruktywny

W artykule dowodzi się tezy, że współczesne debaty publiczne, emitowane w telewizji (takie jak emitowany od 2004 roku w TVP2 program Jana Pospieszalskiego „Warto rozmawiać”), są zdarzeniami komunikacyjnymi zasadzonymi na konflikcie destruktywnym, a więc takim, który wg L. Kriesberga, nastawiony jest nie na problem wywołujący frustrację, ale jest manifestacją agresji, a celem sporu staje się nie pokonanie różnic dzielących strony, ale pokonanie oponenta. Wynika to z sytuacji komunikacyjnej debaty telewizyjnej i nastawienia w pierwszej kolejności na zdobycie uwagi, a potem zaspokajanie potrzeb odbiorcy sekundarnego – widza. Podporządkowane są temu takie elementy, jak wybór tematu debaty, jej scenariusz, dobór uczestników, a w dalszej kolejności ich zachowania werbalne i niewerbalne. W rezultacie, w debacie telewizyjnej złamane zostają typowe dla debaty konstruktywnej reguły i strategie, i zastąpione zostają zachowaniami typowymi dla konfliktu destruktywnego, takimi jak: brak otwartości na argumenty drugiej strony, nastawienie na pokonanie antagonisty, niechęć do szukania innych rozwiązań, jak tylko moja wygrana - twoja przegrana, działania nastawione na prowokację i podważanie wiarygodności partnera, przenoszenie ciężaru debaty ze sprawy na osobę, posługiwanie się argumentacją niemerytoryczną, niezgoda na konstruowanie wspólnego stanowiska. Taki wzorzec debaty telewizyjnej, która stała się pośpieszną, bezproduktywną prezentacją rozbieżnych stanowisk, nie prowadzącą do żadnych konkluzji, nastawioną najwyżej na obrażenie rozmówcy i dostarczenie widzowi krótkotrwałych emocji, buduje powszechne przekonania o niemożności dialogu i porozumienia w dyskursie publicznym i dominującym w nim antagonizmie.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.