The impact of scheduling on the implementation of the new Ontario curriculum: teachers' perceptions /

This qualitative study explored secondary teachers' perceptions of scheduling in relation to pedagogy, curriculum, and observation of student learning. Its objective was to determine the best way to organize the scheduling for the delivery of Ontario's new 4-year curriculum. Six participants were chosen. Two were teaching in a semestered timetable, 1 in a traditional timetable, and 3 had experience in both schedules. Participants related a pressure cooker "lived experience" with weaker students in the semester system experiencing a particularly harsh environment. The inadequate amount of time for review in content-heavy courses, gap scheduling problems, catch-up difficulties for students missing classes, and the fast pace of semestering are identified as factors negatively impacting on these students. Government testing adds to the pressure by shifting teachers' time and attention in the classroom from deeper learning to a superficial coverage of material, from curriculum as lived to curriculum as text to be covered. Scheduling choice should be available in public education to accommodate the needs of all students. Curriculum guidelines need to be revamped to reflect the content that teachers believe is necessary for a successful course delivery. Applied level courses need to be developed for students who are not academically inferior but learn differently.

Improving Short DNA Sequence Alignment with Parallel Computing

Variations in different types of genomes have been found to be responsible for a large degree of physical diversity such as appearance and susceptibility to disease. Identification of genomic variations is difficult and can be facilitated through computational analysis of DNA sequences. Newly available technologies are able to sequence billions of DNA base pairs relatively quickly. These sequences can be used to identify variations within their specific genome but must be mapped to a reference sequence first. In order to align these sequences to a reference sequence, we require mapping algorithms that make use of approximate string matching and string indexing methods. To date, few mapping algorithms have been tailored to handle the massive amounts of output generated by newly available sequencing technologies. In otrder to handle this large amount of data, we modified the popular mapping software BWA to run in parallel using OpenMPI. Parallel BWA matches the efficiency of multithreaded BWA functions while providing efficient parallelism for BWA functions that do not currently support multithreading. Parallel BWA shows significant wall time speedup in comparison to multithreaded BWA on high-performance computing clusters, and will thus facilitate the analysis of genome sequencing data.

Bundling under the Threat of Parallel Trade

This paper examines the use of bundling by a firm that sells in two national markets and faces entry by parallel traders. The firm can bundle its main product, - a tradable good- with a non-traded service. It chooses between the strategies of pure bundling, mixed bundling and no bundling. The paper shows that in the low-price country the threat of grey trade elicits a move from mixed bundling, or no bundling, towards pure bundling. It encourages a move from pure bundling towards mixes bundling or no bundling in the high-price country. The set of parameter values for which the profit maximizing strategy is not to supply the low price country is smaller than in the absence of bundling. The welfare effects of deterrence of grey trade are not those found in conventional models of price arbitrage. Some consumers in the low-price country may gain from the threat of entry by parallel traders although they pay a higher price. This is due to the fact that the firm responds to the threat of arbitrageurs by increasing the amount of services it puts in the bundle targeted at consumers in that country. Similarly, the threat of parallel trade may affect some consumers in the hight-price country adversely.

Multi-Prover and parallel repetition in non-classical interactive games

Depuis l’introduction de la mécanique quantique, plusieurs mystères de la nature ont trouvé leurs explications. De plus en plus, les concepts de la mécanique quantique se sont entremêlés avec d’autres de la théorie de la complexité du calcul. De nouvelles idées et solutions ont été découvertes et élaborées dans le but de résoudre ces problèmes informatiques. En particulier, la mécanique quantique a secoué plusieurs preuves de sécurité de protocoles classiques. Dans ce m´emoire, nous faisons un étalage de résultats récents de l’implication de la mécanique quantique sur la complexité du calcul, et cela plus précisément dans le cas de classes avec interaction. Nous présentons ces travaux de recherches avec la nomenclature des jeux à information imparfaite avec coopération. Nous exposons les différences entre les théories classiques, quantiques et non-signalantes et les démontrons par l’exemple du jeu à cycle impair. Nous centralisons notre attention autour de deux grands thèmes : l’effet sur un jeu de l’ajout de joueurs et de la répétition parallèle. Nous observons que l’effet de ces modifications a des conséquences très différentes en fonction de la théorie physique considérée.

Our Lady of Ocotlán and our Lady of Guadalupe : investigation into the origins of parallel virgins

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Application du concept des transactions pour la modélisation et la simulation multicoeur des systèmes sur puce

Avec la complexité croissante des systèmes sur puce, de nouveaux défis ne cessent d’émerger dans la conception de ces systèmes en matière de vérification formelle et de synthèse de haut niveau. Plusieurs travaux autour de SystemC, considéré comme la norme pour la conception au niveau système, sont en cours afin de relever ces nouveaux défis. Cependant, à cause du modèle de concurrence complexe de SystemC, relever ces défis reste toujours une tâche difficile. Ainsi, nous pensons qu’il est primordial de partir sur de meilleures bases en utilisant un modèle de concurrence plus efficace. Par conséquent, dans cette thèse, nous étudions une méthodologie de conception qui offre une meilleure abstraction pour modéliser des composants parallèles en se basant sur le concept de transaction. Nous montrons comment, grâce au raisonnement simple que procure le concept de transaction, il devient plus facile d’appliquer la vérification formelle, le raffinement incrémental et la synthèse de haut niveau. Dans le but d’évaluer l’efficacité de cette méthodologie, nous avons fixé l’objectif d’optimiser la vitesse de simulation d’un modèle transactionnel en profitant d’une machine multicoeur. Nous présentons ainsi l’environnement de modélisation et de simulation parallèle que nous avons développé. Nous étudions différentes stratégies d’ordonnancement en matière de parallélisme et de surcoût de synchronisation. Une expérimentation faite sur un modèle du transmetteur Wi-Fi 802.11a a permis d’atteindre une accélération d’environ 1.8 en utilisant deux threads. Avec 8 threads, bien que la charge de travail des différentes transactions n’était pas importante, nous avons pu atteindre une accélération d’environ 4.6, ce qui est un résultat très prometteur.

Parallel metaheuristics for stochastic capacitated multicommodity network design

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d'horaires

Thèse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de Troyes

Uncovering parallel ribosome biogenesis pathways during pre-60S subunit maturation

Paralogs are present during ribosome biogenesis as well as in mature ribosomes in form of ribosomal proteins, and are commonly believed to play redundant functions within the cell. Two previously identified paralogs are the protein pair Ssf1 and Ssf2 (94% homologous). Ssf2 is believed to replace Ssf1 in case of its absence from cells, and depletion of both proteins leads to severely impaired cell growth. Results reveal that, under normal conditions, the Ssf paralogs associate with similar sets of proteins but with varying stabilities. Moreover, disruption of their pre-rRNP particles using high stringency buffers revealed that at least three proteins, possibly Dbp9, Drs1 and Nog1, are strongly associated with each Ssf protein under these conditions, and most likely represent a distinct subcomplex. In this study, depletion phenotypes obtained upon altering Nop7, Ssf1 and/or Ssf2 protein levels revealed that the Ssf paralogs cannot fully compensate for the depletion of one another because they are both, independently, required along parallel pathways that are dependent on the levels of availability of specific ribosome biogenesis proteins. Finally, this work provides evidence that, in yeast, Nop7 is genetically linked with both Ssf proteins.

Supporting Parallel Legal Systems as a Means of Improving Access to Justice in Guatemala

Parallel legal systems can and do exist within a single sovereign nation, and rural Guatemala offers one example. Such parallel systems are generally viewed as failures of legal penetration which compromise the rule of law. The question addressed in this paper is whether the de facto existence of parallel systems in Guatemala benefits the indigenous population, or whether the ultimate goal of attaining access to justice requires a complete overhaul of the official legal system. Ultimately, the author concludes that while the official justice system needs a lot of work in order to expand access to justice, especially for the rural poor, the existence of a parallel legal system can be a vehicle for, rather than a hindrance to, expanding such access.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Hopf bifurcation in parallel polarized Nd:YAG laser

Dynamics of Nd:YAG laser with intracavity KTP crystal operating in two parallel polarized modes is investigated analytically and numerically. System equilibrium points were found out and the stability of each of them was checked using Routh–Hurwitz criteria and also by calculating the eigen values of the Jacobian. It is found that the system possesses three equilibrium points for (Ij, Gj), where j = 1, 2. One of these equilibrium points undergoes Hopf bifurcation in output dynamics as the control parameter is increased. The other two remain unstable throughout the entire region of the parameter space. Our numerical analysis of the Hopf bifurcation phenomena is found to be in good agreement with the analytical results. Nature of energy transfer between the two modes is also studied numerically.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.