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.

Psychometric properties of the Portuguese version of the State-Trait Anxiety Inventory applied to college students: factor analysis and relation to the Beck Depression Inventory

The psychometric properties of the Portuguese version of the trait form of the State-Trait Anxiety Inventory (STAI-T) and its relation to the Beck Depression Inventory (BDI) were evaluated in a large Brazilian college student sample containing 845 women and 235 men. STAI-T scores tended to be higher for women, singles, those who work, and subjects under 30 years. Factor analysis of the STAI-T for total sample and by gender yielded two factors: the first representing a mood dimension and the second being related to worrying or cognitive aspects of anxiety. In order to study the relation between anxiety and depression measures, factor analysis of the combination of the 21 BDI items and the 20 STAI-T items was also carried out. The analysis resulted in two factors that were analyzed according to the tripartite model of anxiety and depression. Most of the BDI items (measuring positive affectivity and nonspecific symptoms of depression) were loaded on the first factor and four STAI-T items that measure positive affectivity. The remaining STAI-T items, all of them measuring negative affect, remained in the second factor. Thus, factor 1 represents a depression dimension and factor 2 measures a mood-worrying dimension. The findings of this study suggest that, although widely used as an anxiety scale, the STAI-T in fact measures mainly a general negative affect.

Forecasting financial weather - can we foresee market sentiment? Spectrum of stock price behavior - NYSE case study

The desire to create a statistical or mathematical model, which would allow predicting the future changes in stock prices, was born many years ago. Economists and mathematicians are trying to solve this task by applying statistical analysis and physical laws, but there are still no satisfactory results. The main reason for this is that a stock exchange is a non-stationary, unstable and complex system, which is influenced by many factors. In this thesis the New York Stock Exchange was considered as the system to be explored. A topological analysis, basic statistical tools and singular value decomposition were conducted for understanding the behavior of the market. Two methods for normalization of initial daily closure prices by Dow Jones and S&P500 were introduced and applied for further analysis. As a result, some unexpected features were identified, such as a shape of distribution of correlation matrix, a bulk of which is shifted to the right hand side with respect to zero. Also non-ergodicity of NYSE was confirmed graphically. It was shown, that singular vectors differ from each other by a constant factor. There are for certain results no clear conclusions from this work, but it creates a good basis for the further analysis of market topology.

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.

Permanent magnet synchronous generator design solution for large directdrive wind turbines: Thermal behavior of the LC DD-PMSG

Wind is one of the most compelling forms of indirect solar energy. Available now, the conversion of wind power into electricity is and will continue to be an important element of energy self-sufficiency planning. This paper is one in a series intended to report on the development of a new type of generator for wind energy; a compact, high-power, direct-drive permanent magnet synchronous generator (DD-PMSG) that uses direct liquid cooling (LC) of the stator windings to manage Joule heating losses. The main param-eters of the subject LC DD-PMSG are 8 MW, 3.3 kV, and 11 Hz. The stator winding is cooled directly by deionized water, which flows through the continuous hollow conductor of each stator tooth-coil winding. The design of the machine is to a large degree subordinate to the use of these solid-copper tooth-coils. Both steady-state and timedependent temperature distributions for LC DD-PMSG were examined with calculations based on a lumpedparameter thermal model, which makes it possible to account for uneven heat loss distribution in the stator conductors and the conductor cooling system. Transient calculations reveal the copper winding temperature distribution for an example duty cycle during variable-speed wind turbine operation. The cooling performance of the liquid cooled tooth-coil design was predicted via finite element analysis. An instrumented cooling loop featuring a pair of LC tooth-coils embedded in a lamination stack was built and laboratory tested to verify the analytical model. Predicted and measured results were in agreement, confirming the predicted satisfactory operation of the LC DD-PMSG cooling technology approach as a whole.

The Competitive Analysis of Selected Biorefineries in Finland

Biorefineries is a perspective field of study that covers many opportunities of a successful business unit with respect to sustainability. The thesis focuses on the following key objective: identification of a competitive biorefineries production process in small and medium segments of the chemical and forest industries in Finland. The scope of the research relates to the selected biorefineries operations in Finland and the use of hemicellulose, as a raw material. The identification of the types of biorefineries and the important technical and process characteristics opens the advantage in the company’s competitive analysis. The study concentrates on the practical approach to the scientific methods of the market and companies research with the help of Quality Function Deployment and House of Quality tool. The thesis’s findings provide mindset version of the expert’s House of Quality application, identification of crucial biorefineries technical and design characteristics’ correlation and their effect on the competitive behavior of a company. The theoretical background helps to build the picture of the problematic issues within the field and provides scientific possible solutions. The analysis of the biorefineries’ market and companies operations bring the practical-oriented aptitude of the research. The results of the research can be used for the following investigations in a field and may be applied as a company’s management analytic and strategic application.

An exploratory descriptive study of factors influencing students to seek or not to seek personal counselling on a college of applied arts and technology campus located in Southern Ontario /

This descriptive-exploratory study examined factors which were perceived by students at a College of Applied Arts and Technology (CAAT) campus as influencing them in choosing to come or not to come for personal counselling and why they would or would not retum. A total of 250 students selected through a sample of convenience were surveyed. A questionnaire survey was conducted with quantitative data collected using a 4-point, forced-choice Likert scale and yes/no questions and qualitative data collected using open-ended questions and invited comments. The responses were analyzed using means and modes for the Likert responses and percentages for the yes/no and check-off questions. The narrative responses were subjected to content analysis to identify themes. The mean score findings on factors influencing students to come for personal counselling were at or close to the mid- point of 2.5. Personal distress was the only variable found to have a negative response, meaning students would not come to counselling if they were in personal distress. On factors that would keep them from choosing to come to counselling, students seemed to trust counsellors and feel accepted by them and rejected the notion that peer pressure or the first session being unhelpful would keep them away from counselling. The counsellor's relationship with the student is the major determinant for repeat sessions. When asked what factors would influence students to not retum for personal counselling, students rejected the variables of peer pressure, the extra time needed for counselling, and not getting what they wanted in a session, but, in one instance, indicated that variables regarding the counselling relationship would keep them from returning.

Exploring the potential of early identification and intervention within a college of applied arts

This study probed for an answer to the question, "How do you identify as early as possible those students who are at risk of failing or dropping out of college so that intervention can take place?" by field testing two diagnostic instruments with a group of first semester Seneca College Computer ,Studies students. In some respects, the research approach was such as might be taken in a pilot study_ Because of the complexity of the issue, this study did not attempt to go beyond discovery, understanding and description. Although some inferences may be drawn from the results of the study, no attempt was made to establish any causal relationship between or among the factors or variables represented here. Both quantitative and qualitative data were gathered during four resea~ch phases: background, early identification, intervention, and evaluation. To gain a better understanding of the problem of student attrition within the School of Computer Studies at Seneca College, several methods were used, including retrospective analysis of enrollment statistics, faculty and student interviews and questionnaires, and tracking of the sample population. The significance of the problem was confirmed by the results of this study. The findings further confirmed the importance of the role of faculty in student retention and support the need to improve the quality of teacher/student interaction. As well, the need for skills assessmen~-followed by supportive counselling, and placement was supported by the findings from this study. strategies for reducing student attrition were identified by faculty and students. As part of this study, a project referred to as "A Student Alert Project" (ASAP) was undertaken at the School of Computer Studies at Seneca college. Two commercial diagnostic instruments, the Noel/Levitz College Student Inventory (CSI) and the Learning and Study Strategies Inventory (LASSI), provided quantitative data which were subsequently analyzed in Phase 4 in order to assess their usefulness as early identification tools. The findings show some support for using these instruments in a two-stage approach to early identification and intervention: the CSI as an early identification instrument and the LASSI as a counselling tool for those students who have been identified as being at risk. The findings from the preliminary attempts at intervention confirmed the need for a structured student advisement program where faculty are selected, trained, and recognized for their advisor role. Based on the finding that very few students acted on the diagnostic results and recommendations, the need for institutional intervention by way of intrusive measures was confirmed.

Nonsuicidal Self-Injury and Suicidal Behavior: A Latent Class Analysis among Young Adults

Although there is a general consensus among researchers that engagement in nonsuicidal self-injury (NSSI) is associated with increased risk for suicidal behavior, little attention has been given to whether suicidal risk varies among individuals engaging in NSSI. To identify individuals with a history of NSSI who are most at risk for suicidal behavior, we examined individual variability in both NSSI and suicidal behavior among a sample of young adults with a history of NSSI (N = 439, Mage = 19.1). Participants completed self-report measures assessing NSSI, suicidal behavior, and psychosocial adjustment (e.g., depressive symptoms, daily hassles). We conducted a latent class analysis using several characteristics of NSSI and suicidal behaviors as class indicators. Three subgroups of individuals were identified: 1) an infrequent NSSI/not high risk for suicidal behavior group, 2) a frequent NSSI/not high risk for suicidal behavior group, and 3) a frequent NSSI/high risk for suicidal behavior group. Follow-up analyses indicated that individuals in the ‘frequent NSSI/high risk for suicidal behavior’ group met the clinical-cut off score for high suicidal risk and reported significantly greater levels of suicidal ideation, attempts, and risk for future suicidal behavior as compared to the other two classes. Thus, this study is the first to identity variability in suicidal risk among individuals engaging in frequent and multiple methods of NSSI. Class 3 was also differentiated by higher levels of psychosocial impairment relative to the other two classes, as well as a comparison group of non-injuring young adults. Results underscore the importance of assessing individual differences in NSSI characteristics, as well as psychosocial impairment, when assessing risk for suicidal behavior.

Exploring the Therapeutic Alliance in Cognitive-Behavior Therapy with Children with Autism Spectrum Disorder: An Interpretative Phenomenological Approach

Abstract The therapeutic alliance (TA) is the most studied process of adult psychotherapeutic change (Zack et al., 2007) and has been found to have a moderate but robust relationship with therapeutic outcome regardless of treatment modality (Horvath, 2001). The TA is loosely described as the extent to which the therapist and the participant connect emotionally and work together towards goals. Conceptualizations of the TA with children have relied on adult models, even though it is widely acknowledged that the pediatric population will rarely willingly commit to therapy, nor readily admit to any challenges that they may be experiencing (Keeley, Geffken, McNamara & Storch, 2011). For children with Autism Spectrum Disorder (ASD) the therapeutic alliance may require an even greater retheorizing considering the communicative and social difficulties of this particular population. Despite this need, research on children with ASD and the therapeutic TA is almost non-existent. In this qualitative study, transcripts from semi-structured interviews with mothers of children with ASD were analyzed using Interpretative Phenomenological Analysis (IPA). IPA closely examines how individual people make sense of their life experiences using a theme-by-theme approach. The three interviewees were mothers whose children were participants in a nine-week Cognitive Behaviour Therapy (CBT) group for obsessive-compulsive behaviours (OCB). A total of four superordinate themes were identified: (i) Centralization and disremembering the TA, (ii) Qualities of the therapist, (iii) TA and the importance of time, and (iv) Signs of a healthy TA. The mothers’ perspectives on the TA suggest that, for them and their children, a strong TA was a required component of the therapy. Implications for clinicians and researchers are discussed.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Understanding Economic Cycle Effects on Top Executive Compensation: An Analysis Applied to American NYSE Listed Companies.

Rapport de recherche présenté à la Faculté des arts et des sciences en vue de l'obtention du grade de Maîtrise en sciences économiques.

Dielectric Behavior of Natural Rubber Composites in Microwave Fields

In this article, we report the preparation of conducting natural rubber (NR) with polyaniline (Pani). NR was made into a conductive material by the compounding of NR with Pani in powder form. NR latex was made into a conductive material by the in situ polymerization of aniline in the presence of NR latex. Different compositions of Pani- NR semi-interpenetrating networks were prepared, and the dielectric properties of all of the samples were determined in microwave frequencies. The cavity perturbation techpique was used for this study. A HP8510 vector network analyzer with a rectangular cavity resonator was used for this study. S bands 2-4 GHz in frequency were used. Thermal studies were also carried out with thermogravimetric analysis and differential scanning calorimetry.

Dielectric Behavior of Natural Rubber Composites in Microwave Fields

In this article, we report the preparation of conducting natural rubber (NR) with polyaniline (Pani). NR was made into a conductive material by the compounding of NR with Pani in powder form. NR latex was made into a conductive material by the in situ polymerization of aniline in the presence of NR latex. Different compositions of Pani- NR semi-interpenetrating networks were prepared, and the dielectric properties of all of the samples were determined in microwave frequencies. The cavity perturbation techpique was used for this study. A HP8510 vector network analyzer with a rectangular cavity resonator was used for this study. S bands 2-4 GHz in frequency were used. Thermal studies were also carried out with thermogravimetric analysis and differential scanning calorimetry.

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.