Abstract
There is no generally accepted definition for conditional Tsallis entropy. The standard definition of (unconditional) Tsallis entropy depends on a parameter alpha that converges to the Shannon entropy as alpha approaches 1. In this paper, we describe three proposed definitions of conditional Tsallis entropy suggested in the literature-their properties are studied and their values, as a function of alpha, are compared. We also consider another natural proposal for conditional Tsallis entropy and compare it with the existing ones. Lastly, we present an online tool to compute the four conditional Tsallis entropies, given the probability distributions and the value of the parameter alpha.

Abstract
Purpose The purpose of this paper is to explore the applicability of the failure mode and effects analysis (FMEA) as an effective tool for decreasing failure risk in the early phase of the new product development (NPD), which adds to existing literature on the application of FMEA in NPD. Design/methodology/approach Through the application of action research (AR) methodology, it was possible to develop a case study examining the use of FMEA to decrease NPD risk in an early phase of NPD execution. Findings The importance and immediate gains of identifying NPD failures support FMEA's usefulness for NPD risk decrease. Moreover, its user-friendliness, timeliness and cost advantages facilitate the introduction of FMEA in the early phase of NPD execution. Originality/value FMEA is a well-known method used in manufacturing companies to identify and correct failures in products, processes and systems. This article explores the lack of practice-oriented evidence on the use of FMEA in the early phase of NPD execution and provides support to its applicability and effectiveness.

Abstract
Context. Protoplanetary disks drive some of the formation process (e.g., accretion, gas dissipation, formation of structures) of stars and planets. Understanding such physical processes is one of the most significant astrophysical questions. HD 163296 is an interesting young stellar object for which infrared and sub-millimeter observations have shown a prominent circumstellar disk with gaps plausibly created by forming planets. Aims. This study aims to characterize the morphology of the inner disk in HD 163296 with multi-epoch, near-infrared interferometric observations performed with GRAVITY at the Very Large Telescope Interferometer. Our goal is to depict the K-band (lambda(0) similar to 2.2 mu m) structure of the inner rim with milliarcsecond (sub-au) angular resolution. Our data is complemented with archival Precision Integrated-Optics Near-infrared Imaging ExpeRiment (H-band; lambda(0) similar to 1.65 mu m) data of the source. Methods. We performed a gradient descent parametric model fitting to recover the sub-au morphology of our source. Results. Our analysis shows the existence of an asymmetry in the disk surrounding the central star of HD 163296. We confirm variability of the disk structure in the inner similar to 2 mas (0.2 au). While variability of the inner disk structure in this source has been suggested by previous interferometric studies, this is the first time that it is confirmed in the H- and K-bands by using a complete analysis of the closure phases and squared visibilities over several epochs. Because of the separation from the star, position changes, and the persistence of this asymmetric structure on timescales of several years, we argue that it is probably a dusty feature (e.g., a vortex or dust clouds) made by a mixing of silicate and carbon dust and/or refractory grains, inhomogeneously distributed above the mid-plane of the disk.

Abstract
Programming courses are needed for an increasing number of students in the Higher Education Institutions of today. Of all the programming languages covered in typical courses, the C and Assembly languages are among the most critical. As they are very low level languages, their knowledge helps the students to understand the inner workings of a computer. At the same time, their differences from other programming languages, demands from the learner a serious adjustment of the mental model. As the programming tools and environments are also different, there is the need of supporting the students in their learning, using a minimum of infrastructure, due to financial restrictions, and to support the maximum number of students, with the existing resources. The use of a Virtual Machine based on a Live Linux distribution, together with an enhanced set of software tests can provide students with an easy to install development platform, providing a good amount feedback, with very limited network usage. The methods described in this paper have been applied with good results, and can be used to support live or online classes.

Abstract
Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.

Abstract
Smart specialisation is a major driver of contemporary regional development policy in the European Union. Politicians, policymakers, and academics enthusiastically wave smart specialisation as the remedy that will fix the problems affecting previous policy rounds. The expectations towards the ‘remedial' effect of smart specialisation bear on the assumption that the policy approach will place the emphasis on what is unique in a given region by means of a so-called entrepreneurial process of discovery, basically a wide participatory process, underpinning a learning mechanism aimed at revealing the R&D and innovation domains in which that region can hope to excel. Universities are generally seen as central organisations in smart specialisation strategies, as made explicit in policy documents and academic papers. The chapter aims at knowing more about the power of smart specialisation policies to induce change in university-industry interactions and the promotion of sustainable growth.