Cross-Sector Partnerships and the Co-creation of Dynamic Capabilities for Stakeholder Orientation

This is the final version of the article. Available from Springer Verlag via the DOI in this record.This paper explores the relationship between business experience in cross-sector partnerships (CSPs) and the co-creation of what we refer to as ‘dynamic capabilities for stakeholder orientation,’ consisting of the four dimensions of (1) sensing, (2) interacting with, (3) learning from and (4) changing based on stakeholders. We argue that the co-creation of dynamic capabilities for stakeholder orientation is crucial for CSPs to create societal impact, as stakeholder-oriented organizations are more suited to deal with “wicked problems,” i.e., problems that are large, messy, and complex (Rittel and Webber, Policy Sciences 4:155–169, 1973; Waddock, Paper presented at the 3rd international symposium on cross sector social interactions, 2012). By means of a grounded theory approach of inductive research, we collected and interpreted data on four global agri-food companies which have heterogeneous experience in participating in CSPs. The results of this paper highlight that only companies’ capability of interacting with stakeholders continually increases, while their capabilities of sensing, learning from, and changing based on stakeholders first increase and then decrease as companies gain more experience in CSP participation. To a large extent, this can be attributed to the development of corporate strategies on sustainability after a few years of CSP participation, which entails a shift from a reactive to a proactive attitude towards sustainability issues and which may decrease the need or motivation for stakeholder orientation. These findings open up important issues for discussion and for future research on the impact of CSPs in a context of wicked problems

Heat transfer for various angular positions about a pipe at right angles to forced air flow

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1931.Includes bibliographical references (leaf 51).by Paul V. Bitzer, Maurice W. Mayer.M.S

Constructing a protocol for the evaluation of residents' competency with office hysteroscopy

There is an increasing need for clinician self-evaluation. The need becomes bigger when it comes to assess residents in operative procedures; office hysteroscopy in its current form is one of the best examples to teach and to assess them. We propose a simple protocol for the evaluation of residents in office hysteroscopy that can be used as a platform for future improvement. This will improve their learning experience and ensure that they do not miss any steps of the procedure. As each task is outlined on the evaluation checklist, it is easier to objectively demonstrate the strengths and deficiencies of each one with respect to the given procedure. This can be the basis for application of extra attention and highlights the areas in which each individual needs to improve. The advantage of recording parameters, such as duration of the procedure and pain scores, is that they can serve as tools that demonstrate acquisition of experience and of confidence. © 2013 Springer-Verlag Berlin Heidelberg

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Influence of solvent granularity on the effective interaction between charged colloidal suspensions

We study the effect of solvent granularity on the effective force between two charged colloidal particles by computer simulations of the primitive model of strongly asymmetric electrolytes with an explicitly added hard sphere solvent. Apart from molecular oscillating forces for nearly touching colloids which arise from solvent and counterion layering, the counterions are attracted towards the colloidal surfaces by solvent depletion providing a simple statistical description of hydration. This, in turn, has an important influence on the effective forces for larger distances which are considerably reduced as compared to the prediction based on the primitive model. When these forces are repulsive, the long-distance behaviour can be described by an effective Yukawa pair potential with a solvent-renormalized charge. As a function of colloidal volume fraction and added salt concentration, this solvent-renormalized charge behaves qualitatively similar to that obtained via the Poisson-Boltzmann cell model but there are quantitative differences. For divalent counterions and nano-sized colloids, on the other hand, the hydration may lead to overscreened colloids with mutual attraction while the primitive model yields repulsive forces. All these new effects can be accounted for through a solvent-averaged primitive model (SPM) which is obtained from the full model by integrating out the solvent degrees of freedom. The SPM was used to access larger colloidal particles without simulating the solvent explicitly.Comment: 14 pages, 16 craphic

Neuraminidase-deficient Sendai virus HN mutants provide protection from homologous superinfection

Binding of hemagglutinin-neuraminidase proteins (HN) to sialylated receptors initiates the infection process of several paramyxoviruses, whereas later in the viral life cycle, the neuramindase (NA) activity of newly synthesized HN destroys all receptors. Prior to NA action, expressed HN has to bind the receptor. To evaluate this HN–receptor complex with respect to receptor inactivation, three temperature-sensitive Sendai virus HN mutants carrying amino acid exchanges at positions 262, 264 and/or 461 were created that uncoupled NA activity from receptor binding at 39°C. Interestingly, at elevated temperature, when there is no detectable neuramindase activity, all infected cells are protected against homologous superinfection. Mutated HN protein on the cell surface is mainly bound to sialylated cell-surface components but can be released by treatment with NA. Thus, continuous binding to HN already inactivates the receptors quantitatively. Furthermore, mutant HN bound to receptors is prevented from being incorporated into virus particles in the absence of NA. It is shown here for the first time that during paramyxoviral infection, quantitative receptor inactivation already occurs due to binding of receptors to expressed HN protein without involvement of NA and is independent of NA activity of viral progeny. NA subsequently functions in the release of HN from the complex, coupled with desialysation of receptors. These findings could have implications for further antiviral drug development

Pleosporales

One hundred and five generic types of Pleosporales are described and illustrated. A brief introduction and detailed history with short notes on morphology, molecular phylogeny as well as a general conclusion of each genus are provided. For those genera where the type or a representative specimen is unavailable, a brief note is given. Altogether 174 genera of Pleosporales are treated. Phaeotrichaceae as well as Kriegeriella, Zeuctomorpha and Muroia are excluded from Pleosporales. Based on the multigene phylogenetic analysis, the suborder Massarineae is emended to accommodate five families, viz. Lentitheciaceae, Massarinaceae, Montagnulaceae, Morosphaeriaceae and Trematosphaeriaceae