Abstract
This paper aims to undertake a closer examination of the design ecosystem model, considering it has recently emerged to justify and support the implementation of design policies within the systems failure theory. It does so by identifying and analyzing diverse perspectives and some of the gaps in the literature, and to propose adaptations in the model by looking at design capabilities as its substance, and as well to identify avenues for further research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Context. Cutting-edge, ground-based astronomical instruments are fed by adaptive optics (AO) systems that are aimed at providing high performance down to the visible wavelength domain on 10 m class telescopes and in the near infrared for the first generation instruments of Extremely Large Telescopes (ELTs). Both applications lead to a large ratio between the telescope diameter, D, and the coherence length or Fried parameter, r(0), that is D/r(0). As the parameter that defines the required number of degrees of freedom of the AO system, D/r(0) drives the requirement to reconstruct the incoming wavefront with ever-higher spatial resolution. In this context, super-resolution (SR) appears as a potential game changer. Indeed, SR promises to dramatically expand the range of spatial frequencies that can be reconstructed from a set of lower resolution measurements of the wavefront. Aims. As a technique that seeks to upscale the resolution of a set of measured signals, SR retrieves higher-frequency signal content by combining multiple lower resolution sampled data sets. It is well known both in the temporal and spatial domains and widely used in imaging to reduce aliasing and enhance the resolution of coarsely sampled images. This study applies the SR technique to the bidimensional wavefront reconstruction. In particular, we show how SR is intrinsically suited for tomographic multi-wavefront sensor (WFS) AO systems, revealing many of its advantages with minimal design effort. Methods. We provide a direct space and Fourier optics description of the wavefront sensing operation and we demonstrate how SR can be exploited through signal reconstruction, especially within the framework of periodic non-uniform sampling. We investigate both meta-uniform and non-uniform sampling schemes and we show that under some conditions, both sampling schemes enable a perfect reconstruction of band-limited signals. We also provide a SR bi-dimensional model for a Shack-Hartmann (SH) WFS, along with an analysis of the characteristics of the sensitivity function. We validated the SR concept with numerical simulations of representative multi-WFS SH AO systems. Finally, we explored the extension of the method to pyramid WFSs. Results. Our results show that combining several WFS samples in a SR framework grants access to a greater number of modes than the native one offered by a single WFS (despite the fixed sub-aperture size across samples). We show that the wavefront reconstruction achieved with four WFSs can be equivalent to a single WFS providing a sampling resolution that is twice greater (linear across the telescope aperture). We also show that the associated noise propagation is not degraded under SR. Finally, we show that the concept can be extended to the signal produced by single pyramid WFS, with its four re-imaged pupils serving as multiple non-redundant samples. Conclusions. We find that SR applied to wavefront sensing and reconstruction (WFR) offers a new parameter space to explore, as it decouples the size of the sub-aperture from the desired wavefront sampling resolution. By shifting away from outdated assumptions, new and more flexible, better-performing AO designs have now become possible.

Abstract
Family businesses (FBs) are central to economies: In Portugal the impact of these structures reaches 2/3 of the GDP, 1/2 of the labour force, and 4/5 of the firms in operation, most of them being SMEs. These organisations play a central role in terms of job creation, local development, knowledge transfer, and territorial cohesion. Innovative activities are key factors for competitive economies; yet innovation increases risk exposure and FBs are conservative and risk adverse, resisting change, relying on internal factors rather than opening to the external environment, consequently postponing innovation and thus pledging their future. Their embedded culture reduces innovative propensity; still, the existence loyalty trust and informal networks enhance individual or collective innovation processes. Using a dataset of 110 FBs innovation and internationalization along with other structural characteristics are connected to their economic performance, shedding light on the determinants FB economic efficiency. Given their importance, made-to-measure policy schemes should be designed. © 2022 by IGI Global. All rights reserved.

Abstract
Currently, business organizations are struggling with the increasing demand for learning needs to address their knowledge gaps. They must have a structure that can reach all employees in terms of training and extract all the important data which is collected by Learning Management Systems during the instruction or learning process. This data will be of extreme importance for better business decisions. In this paper, it is presented a Systematic Literature Review with their respective phases duly explained and framed in the topic. It allowed us to understand the benefits, challenges, enablers, and inhibitors of the deployment and usage of a specified Teaching-Learning Analytics Framework. Finally, it is concluded, that the development of a reference model, could fulfill this gap in knowledge and help business organizations to allocate resources better and improve the decision-making process as well as an instructional and learning process. To achieve the final goal of this research, future work about the development of a Survey Research methodology will be started to fulfill this gap of knowledge.

Abstract
Robust quantification of animal behavior is fundamental in experimental neuroscience research. Systems providing automated behavioral assessment are an important alternative to manual measurements avoiding problems such as human bias, low reproducibility and high cost. Integrating these tools with closed-loop control systems creates conditions to correlate environment and behavioral expressions effectively, and ultimately explain the neural foundations of behavior. We present an integrated solution for automated behavioral analysis of rodents using deep learning networks on video streams acquired from a depth-sensing camera. The use of depth sensors has notable advantages: tracking/classification performance is improved and independent of animals' coat color, and videos can be recorded in dark conditions without affecting animals' natural behavior. Convolutional and recurrent layers were combined in deep network architectures, and both spatial and temporal representations were successfully learned for a 4-classes behavior classification task (standstill, walking, rearing and grooming). Integration with Arduino microcontrollers creates an easy-to-use control platform providing low-latency feedback signals based on the deep learning automatic classification of animal behavior. The complete system, combining depth-sensor camera, computer, and Arduino microcontroller, allows simple mapping of input-output control signals using the animal's current behavior and position. For example, a feeder can be controlled not by pressing a lever but by the animal behavior itself. An integrated graphical user interface completes a user-friendly and cost-effective solution for animal tracking and behavior classification. This open-software/open-hardware platform can boost the development of customized protocols for automated behavioral research, and support ever more sophisticated, reliable and reproducible behavioral neuroscience experiments.

Abstract
The interest in expanding the production of hops outside the traditional cultivation regions, mainly motivated by the growth of the craft brewery business, justifies the intensification of studies into its adaptation to local growing conditions. In this study, four field trials were undertaken on a twenty-year-old hop garden, over periods of up to three years to assess the effect of important agro-environmental variation factors on hop phenol and phenolic composition and to establish its relationship with the elemental composition of hop cones. All the field trials were arranged as factorial designs exploring the combined effect of: (1) plots of different vigour plants x year; (2) plots of different plant vigor x algae- and nutrient-rich foliar sprays x year; (3) plot x liming x year; and (4) cultivars (Nugget, Cascade, Columbus) x year. Total phenols in hops, were significantly influenced by most of the experimental factors. Foliar spraying and liming were the factors that least influenced the measured variables. The year had the greatest effect on the accumulation of total phenols in hop cones in the different trials and may have contributed to interactions that often occurred between the factors under study. The year average for total phenol concentrations in hop cones ranged from 11.9 mg g(-1) to 21.2 mg g(-1). Significant differences in quantity and composition of phenolic compounds in hop cones were also found between cultivars. The phenolic compounds identified were mainly flavonols (quercetin and kaempferol glycosides) and phenolic carboxylic acids (p-coumaric and caffeic acids).