Bringing emotion to work: Emotional intelligence, resistance, and the reinvention of character

This article centrally examines the sociological significance of emotional intelligence (EI) as a nascent managerial discourse. Through developing a three-way reading of the writers Richard Sennett, Daniel Goleman, and George Ritzer, it is contended that EI can be understood to signal ‘new rules’ for work involving demands for workers to develop moral character better attuned to the dynamics of the flexible workplace - character that is more ‘intelligent’, adaptive, and reflexive. Furthermore, it is argued that while EI appears in some important respects to open the scope for worker discretion, it might also signal diminished scope for worker resistance. However, ultimately, the case of EI is used to problematise recent discussions of worker resistance - to suggest the possibility of ‘resistant’ worker agency exercised through collusion with, as well as transgression of, corporate norms and practices

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM

SARS-CoV-2 mRNA vaccination of aplastic anemia patients is safe and effective

Immunobiology of allogeneic stem cell transplantation and immunotherapy of hematological disease

Towards automated binding affinity prediction using an iterative linear interaction energy approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only. © 2014 by the authors; licensee MDPI, Basel, Switzerland

Uso de funções ortogonais para descrever a produção de leite no dia de controle por meio de modelos de regressão aleatória Genetic modelling of daily milk yield using orthogonal polynomials in random regression

Registros de produção de leite de 68.523 controles leiteiros de 8.536 vacas da raça Holandesa, filhas de 537 reprodutores, distribuídas em 266 rebanhos, com parições nos anos de 1996 a 2001, foram utilizados na comparação de modelos de regressão aleatória, para estimação de componentes de variância. Os modelos de regressão aleatória diferiram entre si pelo grau do polinômio de Legendre utilizado para descrever a trajetória da curva de lactação dos animais. Os modelos incluíram os efeitos rebanho-mês-ano do controle, composição genética dos animais, freqüência de ordenhas diárias, regressão polinomial em cada classe de idade-estação de parto para descrever a parte fixa da lactação e regressão polinomial aleatória relacionadas aos efeitos genético direto e de ambiente permanente. As estimativas de herdabilidade obtidas oscilaram de 0,122 a 0,291. Verificou-se que o modelo de regressão aleatória que utilizou a maior ordem para os polinômios de Legendre descreveu melhor a variação genética da produção de leite, de acordo com o critério de Akaike.<br>Data comprising 68,523 test day milk yield of 8,536 cows of the Holstein breed, daughters of 537 sires, distributed in 266 herds, calving from 1996 to 2001, were used to compare random regression models, for estimating variance. Test day records (TD) were analyzed by different random regression models regarding the function used to describe the trajectory of the lactation curve of the animals. Legendre orthogonal polynomials function of second, third and fourth order were used. The random regression models included the effects of herd-month-year of the control, genetic group of the animals; the frequency of the daily milk; regression coefficients for each class of age-season (in order to describe the fixed part of the lactation curve) and random regression coefficients related to the direct genetic and the permanent environmental effects. The heritability estimates obtained using the random regression models ranged from 0.122 to 0.291. The random regression model which used the fourth order Legendre polynomials was the model which better described the genetic variation of the milk yield, according to AIC test