Employer Perspectives on Secondary School Cooperative Education in a Southern Ontario City in Canada

Despite 2 Ontario Ministry of Education policy documents which mandate that regular program effectiveness surveys be completed in secondary school co-op programs, research was either not occurring or data were not being made available. A lack of co-op research also existed at the postsecondary level. The primary reason for this study was to determine the perspectives of current secondary school co-op employers in the Hamilton-Wentworth District School Board (HWDSB) and to identify any program strengths as well as any areas in which improvement can be made. A secondary aim of the study was to discover the reasons that some employers decline to participate in the co-op program, and why other employers decide to discontinue their co-op partnerships. An online survey was utilized with 2 Likert scales and open-ended questions to solicit responses from the 100 participants. The findings from this study strongly supported previous secondary and postsecondary co-op research. Overall, the HWDSB co-op program was found to be very strong, and employer satisfaction very high. There were, however, areas in which improvement could be made. Although most employers felt supported by institutions and felt that expectations were clearly communicated and were reasonable, there was evidence that many employers perceived a lack of institutional support which included factors such as communication, student placement and fit, and institutional responsiveness. In addition, some employers felt that students were underprepared for the workplace and lacked basic employability skills such as dependability and responsibility.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Percepción entre el docente y el alumno de los niveles de aplicación del modelo por competencias en la asignatura de inglés en la preparatoria 9 de la U.A.N.L. en un período regular.

Tesis (Maestría en Psicología con orientación en Psicología Laboral y Organizacional) UANL, 2013.

Percepción entre el docente y el alumno de los niveles de aplicación del modelo por competencias en la asignatura de inglés en la preparatoria 9 de la U.A.N.L., en un período regular.

Tesis (Maestría en Psicología con orientación en Psicología Laboral y Organizacional) UANL, 2013.

Virtudes y milagros en vida, y muerte del venerable padre Fr. Sebastián de Aparicio, religioso lego de la regular observancia de nuestro seráfico Padre San Francisco, e hijo de la Provincia del Santo Evangelio de México, en la Nueva España, que floreció en el Convento de la Puebla de los Angeles.

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Vida prodigiosa del v. siervo de Dios Fray Sebastián de Aparicio, religioso lego de la regular obfervancia de N.S.P.S. Francifeo, é hijo de la Provincia del Santo Evangelio de México

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Historia Bethlehemítica : vida exemplar y admirable del venerable siervo de Dios, y padre Pedro de San Joseph Betancur, fundador de el regular Instituto de Bethlehen en las Indias Occidentales; frutos singulares de su fecundo espíritu y sucesos varios de de esta religión

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Sermón fúnebre en las solemnes exequias que celebraron en la Santa Iglesia Metropolitana de Valencia su excmo. e illmo. Arzobispo y cabildo con asistencia de la M. I. ciudad y de todo el clero secular y regular en sufragio por el alma de su difunto prelado el excmo. illmo. Señor Don Francisco Fabian y Fuero, el dia 13 de octubre de 1801,

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Primera carta pastoral del Ilustrísimo Señor Dr. Fr. Buenaventuradel Sagrado Corazón de María, Portillo y Tejeda tercer Obispo de Zacatecas, dirigida al V. clero secular y regular a todos los fieles de la diócesis

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Circular dirigida por el Ilmo. Sr. Arzobispo de México al Ilmo. y Ve. Cabildo Metropolitano, al de la insigne Colegiata de Guadalupe, al clero secular y regular y a todos sus diocesanos

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Carta pastoral del Illmo. y Rmo. Sr. D. F. Buenaventura del Sagrado Corazón de María, Portillo y Tejeda, tercer Obispo de Zacatecas dirigido al M.I. y venerable cabildo al clero secular y regular y a todos sus amados diocesanos, convocándolos para la próxima peregrinación que Dios mediante tendrá lugar el día tres del próximo octubre...

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Carta pastoral que el Ilmo. Sr. Dr. D. Francisco de P. Verea Obispo de Linares, dirige al venerable clero secular y regular y a todos los fieles de su Diócesis, con motivo de la enciclica que su santidad el señor Pio IX dirigió al orbe católico, el 19 de Enero del corriente año

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