Strong coupling limits and quantum isomorphisms of the gauged Thirring model

We have studied the quantum equivalence in the respective strong coupling limits of the bidimensional gauged Thirring model with both Schwinger and Thirring models. It is achieved following a nonperturbative quantization of the gauged Thirring model into the path-integral approach. First, we have established the constraint structure via the Dirac's formalism for constrained systems and defined the correct vacuum--vacuum transition amplitude by using the Faddeev-Senjanovic method. Next, we have computed exactly the relevant Green's functions and shown the Ward-Takahashi identities. Afterwards, we have established the quantum isomorphisms between gauged Thirring model and both Schwinger and Thirring models by analyzing the respective Green's functions in the strong coupling limits, respectively. A special attention is necessary to establish the quantum isomorphism between the gauged Thirring model and the Thirring model.Comment: 14 page

Taalgebruik - Sensuur of nie?*

Dit gaan hier, redelik oppervlakkig-populêr, oor taalhoudings en enkele knelpunte rondom taalgebruiksbeoordeling. Ek wil vooraf kortliks ’n paar opmerkings oor tegniese taal en vakterminologie maak, vóórdat ek iets meer in die algemeen gaan sê oor taalsuiwerheid en Anglisismes

Prosthetics Guide for Occupational Therapy Students and New Graduates

The prosthetic technology is effectively an interface between that person and the life they wish to lead (Gallagher, 2004, p. 828). Occupational therapy can help individuals with prosthetics deal with psychosocial and physical aspects of his or her condition (Gulick, 2011). Current literature covers all aspects of prosthetics, but it is scattered in many different places. The results of a study conducted by Mitchell, Gorelick, Anderson, and Atkins (2014), approximately 3-5 hours or less are spent on prosthetic training, while 85% of respondents felt that it was considered to be “very important”. This scholarly project focuses to bridge this gap even when additional education cannot be provided in school. An extensive literature review was conducted on topics relating to prosthetic guides, Occupational therapy, prosthetics guide for occupational therapy, orthopedic prosthesis, psychosocial, and prosthesis. The search databases used include Pubmed, Google Scholar, cinahl, and psychinfo. The literature review provided the authors with the introductory tools to competently treat an individual with a prosthesis as a novice student/therapist. The finished product is the Prosthetics Guide for Occupational Students and New Graduates. This guide includes resources for occupational therapy students and new graduates in regards to, but not limited to, useful assessments, psychosocial components, physical aspects, and care of prosthetics

Oficina RDC

Apresentação sobre o tema RDC contemplando experiências, legislação, inovações nos procedimentos e a contratação integrada

Promenade

"Promenade" fue filmado íntegramente en la Casa Curutchet, única obra del arquitecto Le Corbusier en Latinoamérica, recientemente declarada Patrimonio de la Humanidad por la UNESCO. Nuestro video promueve un acercamiento entre la danza, el video y la arquitectura, con el objetivo de encontrar nuevas lecturas del espacio, tejiendo una trama que diseñará un recorrido virtual entre las dimensiones funcionales y simbólicas de la arquitectura y las dimensiones poéticas y geométricas del espacio. Articulados por la propuesta coreográfica, los cuerpos y la mirada de la cámara buscarán nuevas asociaciones y sentidos.Eje 4: Arte, cuerpo y nuevas tecnologías. Performances y videosFacultad de Humanidades y Ciencias de la Educació

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images

An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set