THE PSYCHOTHERAPIST’S SOCIAL ROLE UNDER A DIALOGICAL PERSPECTIVE: A STUDY OF THE PERSONAL CONSTRUCTION OF ‘I AS PSYCHOTHERAPIST’

To become a psychotherapist is a self-organizing challenge for anyone who assumes that role, involving a dynamic dialogical interplay between social expectations and personal features. This involves subjective and intersubjective processes in which self-image (or “internal I-position”) emerges as co-relative others’ images (or “external I-positions”). The classical distinction between the motives of agency and communion is considered here a valuable theoretical tool for this dialogical approach, because it may help to distinguish and classify diversity in terms of two kinds of orientations towards clients: one more self-centred (focused on the therapist’s abilities and power) and the other a more other-centred (focused on the contact and empathy with the client). Following these assumptions, clearly rooted in a dialogical approach of self-identity, we analyse the discourse of three psychotherapists about two different clients (one referred to as a “positive client” and another referred to as a “negative client”). The results suggest that this adaptation is a very dynamic process and that different therapists create different meanings to their occupational role. Moreover, this analysis also allows a distinction between those different selfimages in terms of their global orientation. One of the therapists seems to engage in self-organization processes focused in self-needs, other seems focused on client’s needs and the third seems to keep a balance between thosetwo orientations. The implication of these results for future research and their practical and theoretical implications are discussed

COMMENTARY ON COMMENTARIES: A SPACE FOR DIALOGUE AMONG DIFFERENT PERSPECTIVES

In this commentary we discuss our reactions towards the six contributions on our article “The psychotherapist’s social role under a dialogical perspective: A study of the personal construction of «I as psychotherapist»” (in this issue). These commentaries discuss a multiplicity of problems and potentials, providing us with a meaningful space for dialogue among our multiple and sometimes discrepant perspectives. We have organized our reaction around three issues: (1) the importance of context influence on the process of being a psychotherapist; (2) the use of the motives as a tool to organize the psychotherapists’ diversity; and (3) the methodology for studying the dialogical processes

Drawing on the Innovative Moments Model during Career Construction Counseling to explain and foster client change

Career Construction Counseling (CCC) is a narrative intervention that supports individuals in the elaboration of narrative identity and career construction. The theory, research, and practice of this approach to career counseling has benefited from the Innovative Moments Model (IMM) to explain client change. Similar to CCC, the IMM is grounded on a narrative conception of human functioning, in which psychological difficulties arise from problematic self-narratives that constrain the meaning-making. Change takes place when clients challenge problematic self-narratives and construct new meanings that lead to new ways of behaving, thinking, or feeling. These novelties are termed innovative moments. The integration of IMM into the study of CCC has provided empirical evidence about the processes of client change throughout this intervention. Findings show that the transformation of a client’s self-narrative is associated with the aims of each session revealing a movement from a focus in structuring the past to an increased engagement in projecting the future. Moreover, results suggest the possibility of using IMs as process markers to guide counselors in facilitating client change during counseling sessions. The purpose of this chapter is to explain the contribution of IMM to CCC theory, research, and practice. We begin by presenting the Innovative Moments framework. Then we review CCC process research using the Innovative Moment’s framework. Finally, research implications for theory and practice of CCC are discussed

Dirac points merging and wandering in a model Chern insulator

We present a model for a Chern insulator on the square lattice with complex first and second neighbor hoppings and a sublattice potential which displays an unexpectedly rich physics. Similarly to the celebrated Haldane model, the proposed Chern insulator has two topologically non-trivial phases with Chern numbers $\pm1$. As a distinctive feature of the present model, phase transitions are associated to Dirac points that can move, merge and split in momentum space, at odds with Haldane's Chern insulator where Dirac points are bound to the corners of the hexagonal Brillouin zone. Additionally, the obtained phase diagram reveals a peculiar phase transition line between two distinct topological phases, in contrast to the Haldane model where such transition is reduced to a point with zero sublattice potential. The model is amenable to be simulated in optical lattices, facilitating the study of phase transitions between two distinct topological phases and the experimental analysis of Dirac points merging and wandering

Conseguimos treinar a mente? : o caso de estudo de como impulsionar efetivamente o desempenho dos atletas

Background and purpose: This paper reviews the case study of a Mental Coaching Program, with the intent of enhancing Mental Coaching professional practices, and provides a teaching note discussing improvements suggested by the literature. Method and results: Using a mix of coaching and training techniques, the 8-month program completed by two athletes, has encountered some setbacks due to athletes’ lack of motivation and commitment. The self-assessment results were positive however not fully supported by the peer assessment. Conclusions: Apart from overall improvements in the assessment method (implementing the TOPS 2), the session (add more mental skills, tools practice in session) and program structure (follow the education, acquisition, and implementation phases for one mental skill, tool or plan at a time), the most significant breakthrough was that there is no idyllic MST program, personalization is required in order to meet and develop the individual's psychological weaknesses.Contexto e objetivo: Este artigo analisa o estudo de caso de um Programa de Treino Mental, com o intuito de aprimorar as práticas profissionais de treinadores mentais e fornece uma nota de ensino discutindo melhorias sugeridas pela literatura. Método e resultados: Utilizando uma mistura de técnicas de coaching e training, o programa de 8 meses concluído por dois atletas enfrentou alguns contratempos devido à falta de motivação e comprometimento dos atletas. Os resultados da autoavaliação foram positivos, porém não totalmente apoiados pela avaliação de pessoas com relações próximas. Conclusões: Além das melhorias gerais no método de avaliação (implementando o TOPS 2), na sessão (adicionando mais tempo de prática de habilidades e ferramentas mentais) e na estrutura do programa (seguindo as fases de educação, aquisição e implementação para uma habilidade, ferramenta ou plano mental de cada vez), a descoberta mais relevante foi que não existe um programa de Mental Coaching ideal, sendo necessária a personalização para atender e desenvolver as fraquezas psicológicas individuais

Entanglement entropy scaling in critical phases of 1D quasiperiodic systems

We study the scaling of the entanglement entropy in different classes of one-dimensional fermionic quasiperiodic systems with and without pairing, focusing on multifractal critical points/phases. We find that the entanglement entropy scales logarithmically with the subsystem size $N_{A}$ with a proportionality coefficient $\mathcal{C}$, as in homogeneous critical points, apart from possible additional small oscillations. In the absence of pairing, we find that the entanglement entropy coefficient $\mathcal{C}$ is non-universal and depends significantly and non-trivially both on the model parameters and electron filling, in multifractal critical points. In some of these points, $\mathcal{C}$ can take values close to the homogeneous (or ballistic) system, although it typically takes smaller values. We find a close relation between the behaviour of the entanglement entropy and the small-$q$ (long-wavelength) dependence of the momentum structure factor $\mathcal{S}(q)$. $\mathcal{S}(q)$ increases linearly with q as in the homogeneous case, with a slope that grows with $\mathcal{C}$. In the presence of pairing, we find that even the addition of small anomalous terms affects very significantly the scaling of the entanglement entropy compared to the unpaired case. In particular, we focused on topological phase transitions for which the gap closes with either extended or critical multifractal states. In the former case, the scaling of the entanglement entropy mirrors the behaviour observed at the critical points of the homogeneous Kitaev chain, while in the latter, it shows only slight deviations arising at small length scales. In contrast with the unpaired case, we always observe $\mathcal{C}\approx1/6$ for different critical points, the known value for the homogeneous Kitaev chain with periodic boundary conditions

Strategic Interaction in Local Fiscal Policy: Evidence from Portuguese Municipalities

This paper aims at testing the degree of interaction between Portuguese municipalities’ expenditure levels by estimating a dynamic panel model, based on jurisdictional reaction functions. The analysis is performed for all 278 Portuguese mainland municipalities from 1986 to 2006, using alternative ways to measure neighbourhood. Results indicate that local governments’ spending decisions are significantly influenced by the actions of neighbouring municipalities. For total expenditures, there is evidence that a 10% increase in nearby municipalities’ expenditures boosts expenditures in a given municipality by around 3.8%.spending interactions, local government, spatial econometrics, dynamic panel data

The Conception and Realization of a Mobile Windows Phone Location-based Augmented Reality Application

It is considered very evolved having Augmented Reality (AR) to be used in an application. This raises the need to have good AR frameworks and AR engines to facilitate the development. Most of the available engines and frameworks are either hard to understand, due to poor documentation or do not provide a sufficient insight, or are proprietary, which force the developer to pay for it. This thesis introduces a location-based AR engine from scratch, which is in its dynamic structure easy to understand and to integrate it in any custom application. The usage of user controls and the possibility to extend the available classes provide a good basis to individualize the engine. This engine is based on the original AREA for iOS[1] and uses advanced calculations to enhance performance. This engine is made for Windows Phone 8.1 using C# with XAML(Extensible Application Markup Language) to create the UI

Desenvolvimento de um modelo de comportamento elastoplástico através de inteligência artificial

In the past few years, there has been tremendous advances in the accuracy and predictive capabilities of tools for the simulation of materials. Predictive modeling has now become a powerful tool that can also deliver real value through application and innovation to the global industry. Simulation of forming operations, particularly using the nite element method, is clearly dependent on the accuracy of the constitutive models. In the last years, several methodologies were developed to improve the accuracy of constitutive models through parameter identi cation and calibration methodologies. However, independently of the e cacy of the calibration methods, the accuracy of a constitutive model is always constrained to its prede ned mathematical formulation. Additionally, using known elastoplastic formulations, it is impossible to reproduce the material phenomena if these phenomena are not formulated mathematically. In the past several years, arti cial intelligence (AI) techniques have become more robust and complex. This eld has set the ambitious goal of making machines either seemingly or genuinely intelligent. The sub- eld of arti cial intelligence known as machine learning attempts to make computers learn from observations. Machine-learning algorithms are general tools that can be tted to a vast number of problems, including predicting the stress-strain relationship of the material. This work proposes to model the behavior of a metal material using machinelearning (ML) techniques and use this ML in forming simulations. Initially, the ML model is designed and trained using a known plane stress elastoviscoplasticity model to evaluate its competence to replace classical models. Di erent ML topologies and optimization techniques are used to train the model. Then, the AI model is introduced into a nite element analysis (FEA) code, as a user subroutine, and its attainment in forming simulations is evaluated. The replacement of classical formulations by AI techniques for the material behavior de nition is analysed and discussed.Nos últimos anos, tem havido enormes avanços na precisão e capacidades preditivas de ferramentas para a simulação de materiais. A modelação preditiva tornou-se numa ferramenta poderosa que também pode agregar um grande valor por meio de aplicações e inovações para a indústria global. A simulação das operações de conformação, particularmente usando o método dos elementos finitos, é claramente dependente da precisão dos modelos constitutivos. Nos últimos anos, várias metodologias foram desenvolvidas para melhorar a precisão de modelos constitutivos através de metodologias de identificação e calibração de parâmetros. No entanto, independentemente da eficácia dos métodos de calibração, a precisão de um modelo constitutivo é sempre restrita a sua formulação matemática predefinida. Adicionalmente, usando formulações elastoplasticas conhecidas, e impossível reproduzir os fenomenos do comportamento de materiais se estes comportamentos não forem eficazmente formulados matematicamente. Recentemente, as tecnicas de inteligencia artificial (IA) tornaram-se mais robustas e complexas. Este campo estabeleceu o objetivo ambicioso de tornar as maquinas aparentemente ou genuinamente inteligentes. O sub-campo da inteligencia artificial conhecido como aprendizagem computacional tenta fazer com que os computadores aprendam com as observações. Os algoritmos de aprendizagem computacional são ferramentas gerais que podem ser adaptadas a um grande numero de problemas, incluindo a previsão da relação tensao-deformação do material. Este trabalho propõe modelar o comportamento de um material metalico utilizando tecnicas de aprendizagem computacional (ML) e utilizar este ML na modelação de simulações. Inicialmente, o modelo ML e projetado e treinado usando um modelo de elastoviscoplasticidade em estado plano de tensão de forma a avaliar a sua eficacia na substituição de modelos classicos. Diferentes topologias ML e tecnicas de otimização são usadas para treinar o modelo. Em seguida, o modelo IA e introduzido num codigo de analise de elementos finitos (FEA), como user subroutine, e a sua concretização em simulações de conformação e avaliada. A substituição de formulações classicas por tecnicas de IA para a definiçao do comportamento do material e analisada e discutida.Mestrado em Engenharia Mecânic