Character in Context: Informing Character Education Programming

The Positive Youth Development (PYD) perspective is a strength-based conceptualization of youth. It highlights the importance of mutually beneficial relationships between youth and their environment to develop the “Five Cs”, key assets that include character. Character has long been a subject of programming due to its focus on helping children lead moral, empathic, and prosocial lives. There are, however, many limitations in character research, including poorly operationalized definitions of character; a failure to examine the developmental and broader social context in which character exists; and a lack of evaluation of more practical character programming. The goal of this dissertation was to address these gaps in knowledge and inform the character education programming literature. The first study examined the relationships among age, gender, the school social context, and character. Moral character was negatively associated with grade, and being a girl was positively associated with moral character. The relationships between positive peer interactions at school and character (fairness, integrity) were stronger among students who reported low initial moral character when positive peer interactions was high. In the second study, the Build Character: Build Success Program, a character education program, was evaluated over six months to examine its effects on character behaviours, victimization, and school climate. No program effects were found for students in grades 1 to 3, but a slight decrease in victimization in one experimental school was found for students in grades 4 to 8. This lack of general program effects may be due to the short-term nature of the intervention, which may not have been long enough to result in measurable behaviour change. Implementation data indicated that teachers did not teach all program elements, which also may have influenced the results of the program evaluation. The present dissertation contributes to knowledge about character and its programming by: introducing new measures to operationalize character, discovering developmental patterns in character in school-aged children, highlighting gender differences in character, examining character within its broad social context, and evaluating short-term character education programming.

Code pattern analysis of object-oriented programming languages

Code patterns, including programming patterns and design patterns, are good references for programming language feature improvement and software re-engineering. However, to our knowledge, no existing research has attempted to detect code patterns based on code clone detection technology. In this study, we build upon the previous work and propose to detect and analyze code patterns from a collection of open source projects using NiPAT technology. Because design patterns are most closely associated with object-oriented languages, we choose Java and Python projects to conduct our study. The tool we use for detecting patterns is NiPAT, a pattern detecting tool originally developed for the TXL programming language based on the NiCad clone detector. We extend NiPAT for the Java and Python programming languages. Then, we try to identify all the patterns from the pattern report and classify them into several different categories. In the end of the study, we analyze all the patterns and compare the differences between Java and Python patterns.

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.

Factors influencing success in introductory programming courses.

This study looked at the reasons why Vanier College students in computer programming are encountering difficulties in their learning process, Factors such as prior academic background, prior computer experience, mother tongue, and learning styles were examined to see how they play a role in students' success in programming courses. The initial research hypotheses were the following : Computer science students using understanding and integrating succeed better than students using following coding, or problem solving. Students using problem solving succeed better than those who use participating and enculturation. Students who use coding perform better than those who prefer participating ans enculturation. In addition, this study hoped to examine whether there is a gender difference in how students learn programming.||Résumé :||La présente étude a examiné les raisons pour lesquelles les étudiants en informatique du Collège Vanier rencontrent des difficultés dans leurs études en programmation. Les facteurs tel que le niveau des études précédentes, l'expérience en informatique, la langue maternelle e les méthodes d'apprentissage ont été considérés pour voir quel rôle ces facteurs jouent pour promouvoir la réussite dans les cours de programmation.Les hypothèses initiales de recherche ont été formulées comme suit : 1. Les étudiants en informatique utilisant la compréhension et l'intégration réussissent mieux que ceux utilisant «suivre», le codage ou la résolution des problèmes. 2, Les étudiants utilisant la résolution des problèmes réussissent mieux que ceux qui utilisent la participation dans la culture informatique. 3, Les étudiants utilisant le codage réussissent mieux que ceux qui utilisent la participation dans la culture informatique.

Constraint Programming-based Job Dispatching for Modern HPC Applications

A High-Performance Computing job dispatcher is a critical software that assigns the finite computing resources to submitted jobs. This resource assignment over time is known as the on-line job dispatching problem in HPC systems. The fact the problem is on-line means that solutions must be computed in real-time, and their required time cannot exceed some threshold to do not affect the normal system functioning. In addition, a job dispatcher must deal with a lot of uncertainty: submission times, the number of requested resources, and duration of jobs. Heuristic-based techniques have been broadly used in HPC systems, at the cost of achieving (sub-)optimal solutions in a short time. However, the scheduling and resource allocation components are separated, thus generates a decoupled decision that may cause a performance loss. Optimization-based techniques are less used for this problem, although they can significantly improve the performance of HPC systems at the expense of higher computation time. Nowadays, HPC systems are being used for modern applications, such as big data analytics and predictive model building, that employ, in general, many short jobs. However, this information is unknown at dispatching time, and job dispatchers need to process large numbers of them quickly while ensuring high Quality-of-Service (QoS) levels. Constraint Programming (CP) has been shown to be an effective approach to tackle job dispatching problems. However, state-of-the-art CP-based job dispatchers are unable to satisfy the challenges of on-line dispatching, such as generate dispatching decisions in a brief period and integrate current and past information of the housing system. Given the previous reasons, we propose CP-based dispatchers that are more suitable for HPC systems running modern applications, generating on-line dispatching decisions in a proper time and are able to make effective use of job duration predictions to improve QoS levels, especially for workloads dominated by short jobs.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

Avoiding Pitfalls Of Intraoperative Peripheral Endovascular Surgery With The Aid Of Osirix: Expanding The Use Of Virtual Fluoroscopy.

We have shown how the analysis of the angiotomography reconstruction through OsiriX program has assisted in endovascular perioperative programming. We presented its application in situations when an unexpected existence of metallic overlapping artifact (orthopedic osteosynthesis) compromised the adequate visualization of the arterial lesion during the procedure. Through manipulation upon OsiriX software, with assistance of preview under virtual fluoroscopy, it was possible to obtain the angles that would avoid this juxtaposition. These angles were reproduced in the C-arm, allowing visualization of the occluded segment, reducing the need for repeated image acquisitions and contrast overload, allowing the continuation of the procedure.

Projeto de um cortador de base para colhedora de cana-de-açúcar utilizando otimização matemática

A base-cutter represented for a mechanism of four bars, was developed using the Autocad program. The normal force of reaction of the profile in the contact point was determined through the dynamic analysis. The equations of dynamic balance were based on the laws of Newton-Euler. The linkage was subject to an optimization technique that considered the peak value of soil reaction force as the objective function to be minimized while the link lengths and the spring constant varied through a specified range. The Algorithm of Sequential Quadratic Programming-SQP was implemented of the program computational Matlab. Results were very encouraging; the maximum value of the normal reaction force was reduced from 4,250.33 to 237.13 N, making the floating process much less disturbing to the soil and the sugarcane rate. Later, others variables had been incorporated the mechanism optimized and new otimization process was implemented .

Modelo de programação quadrática para análise da movimentação logística e comercialização da soja brasileira

The purpose of this work was to analyze the logistical distribution of Brazilian soybean by applying a quadratic programming to a spatial equilibrium model. The soybean transportation system is an important part of the soybean complex in Brazil, since the major part of the costs of this commodity derives from the transportation costs. Therefore, the optimization of this part of the process is essential to a better competitiveness of the Brazilian soybean in the international market. The Brazilian soybean complex have been increasing its agricultural share in the total of the exportation value in the last ten years, but due to other countries' investments the Brazilian exportations cannot be only focused on increasing its production but it still have to be more efficient. This way, a model was reached which can project new frames by switching the transportation costs and conduce the policy makers to new investments in the sector.

Programação esportiva na TV Brasil : a influência da ideologia e indústria cultural

Universidade Estadual de Campinas . Faculdade de Educação Física

Socorros de urgencia e a preparação do profissional de Educação Fisica

Universidade Estadual de Campinas. Faculdade de Educação Física

Pode a atividade física materna modular a programação fetal induzida pela nutrição?

Existe considerável evidência para a indução de diferentes fenótipos em reposta às variações no ambiente fetal e neonatal. O aporte inadequado de nutrientes no período crítico do desenvolvimento está associado ao risco alto de doenças metabólicas na vida adulta, este fenômeno biológico é chamado de programação. A atividade física durante a gestação resulta em adaptações fisiológicas da mãe e no aumento da disponibilidade de nutrientes e oxigênio no espaço feto-placentário. Este trabalho tem como objetivo discutir os mecanismos da indução de programação fetal pela nutrição e o provável efeito modulador da atividade física durante a gestação. Foram utilizadas as bases de dados do Medline Pubmed, Lilacs e Bireme, com publicações entre 1990 até 2008. Os termos de indexação utilizados foram: nutrition, fetal programming, gestation, physical activity, physical exercise, metabolism. Em conclusão, o aporte inadequado de nutrientes programa o aparecimento de doenças metabólicas na vida adulta, enquanto que a atividade física durante a gestação aumenta a disponibilidade de nutrientes e oxigênio, repercutindo positivamente no crescimento fetal e no peso ao nascer.

Using sentinels to detect intersections of convex and nonconvex polygons

We describe finite sets of points, called sentinels, which allow us to decide if isometric copies of polygons, convex or not, intersect. As an example of the applicability of the concept of sentinel, we explain how they can be used to formulate an algorithm based on the optimization of differentiable models to pack polygons in convex sets. Mathematical subject classification: 90C53, 65K05.

Relationship between birthweight and arterial elasticity in childhood

There is a considerable debate about the potential influence of fetal programming on cardiovascular diseases in adulthood. In the present prospective epidemiological cohort study, the relationship between birthweight and arterial elasticity in 472 children between 5 and 8 years of age was assessed. LAEI (large artery elasticity index), SAEI (small artery elasticity index) and BP (blood pressure) were assessed using the HDI/PulseWave CR-2000 CardioVascular Profiling System. Blood concentrations of glucose, total cholesterol and its fractions [LDL (low-density lipoprotein)-cholesterol and HDL (high-density lipoprotein)-cholesterol] and triacylglycerols (triglycerides) were determined by automated enzymatic methods. Insulin was assessed by a chemiluminescent method, insulin resistance by HOMA (homoeostasis model assessment) and CRP (C-reactive protein) by immunonephelometry. Two linear regression models were applied to investigate the relationship between the outcomes, LAEI and SAEI, and the following variables: birthweight, gestational age, glucose, LDL-cholesterol, HDL-cholesterol, triacylglycerols, insulin, CRP, HOMA, age, gender, waist circumference, per capita income, SBP (systolic BP) and DBP (diastolic BP). LAEI was positively associated with birthweight (P=0.036), waist circumference (P<0.001) and age (P<0.001), and negatively associated with CRP (P=0.024) and SBP (P<0.001). SAEI was positively associated with birthweight (P=0.04), waist circumference (P=0.001) and age (P<0.001), and negatively associated with DBP (P<0.001). Arterial elasticity was decreased in apparently healthy children who had lower birthweights, indicating an earlier atherogenetic susceptibility to cardiovascular diseases in adolescence and adult life. Possible explanations for the results include changes in angiogenesis during critical phases of intrauterine life caused by periods of fetal growth inhibition and local haemodynamic anomalies