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This paper aims to illustrate that the dominating rational-analytic perspective on the Fuzzy Front End (FFE) of innovation could benefit by a complementary socio-interactive perspective that addresses the social processes during the FFE. We have developed a still fledgling socio-interactive framework blending existing knowledge of the FFE with theories from design methodology and the psycho-social sciences. For the framework constructs like the interplay of design problem and design solution, mental models and social processes are used and applied to the results of an empirical study for illustration. The paper ends by discussing the results and presenting a future research agenda including implications for management.

The aim of this work is to implement an automatic method to predict and classify complete responders (CRs) patients, affected by rectal cancer and treated with neoadjuvant radiochemotherapy (RCT), by exploiting the tumor regression grade (MR-TRG) estimated by magnetic resonance imaging. For the purpose of the study, a total of 65 patients were enrolled and the magnetic resonance (MR) examinations to calculate TRG were performed using a 3.0 T scanner. By processing and testing patients’ data, the algorithm allows to determine the optimum threshold dividing CRs patients from patients that are considered non responders. The prediction accuracy of the classifier was investigated by using cross-validation statistical analysis in order to automatically determine the best testing rule. After collecting the outcomes of the performed cross-validation, the obtained results show the percentages of correct instances and misclassified patients. The automatic classification of CRs appears to be feasible and can be considered as a helpful method to predict CRs assisting clinicians to predict disease prognoses and patient survival prospects in order to provide treatments’ customization.

We propose two new variational models aimed to outperform the popular total variation (TV) model for image restoration with L$_2$ and L$_1$ fidelity terms. In particular, we introduce a space-variant generalization of the TV regularizer, referred to as TV$_p^{SV}$, where the so-called shape parameter $p\,$ is automatically and locally estimated by applying a statistical inference technique based on the generalized Gaussian distribution. The restored image is efficiently computed by using an alternating direction method of multipliers procedure. We validated our models on images corrupted by Gaussian blur and two important types of noise, namely the additive white Gaussian noise and the impulsive salt and pepper noise. Numerical examples show that the proposed approach is particularly effective and well suited for images characterized by a wide range of gradient distributions.