Abstract
This special issue of the Management Letters/Cuadernos de Gestion is dedicated, on one hand, to presenting those articles that are included as part of the special issue on innovation and, on the other hand, to disclose the top priorities on innovation research taking into account challenging topics we are witnessing in the business world that fuel research creativity. The first part of this editorial presents the four articles that make up this special issue on innovation. The second part of this editorial addresses the main topics of the shifting landscape innovation faces: business model innovation, artificial intelligence, Industry 4.0, Internet of things, innovation ecosystems and gamification.

Abstract
This study aims to explore and synthesize the academic entrepreneurship indicators that can be used by university institutions to understand their degree of development and maturities in supporting entrepreneurship processes. To this end, a systematic review is used to characterize the studies published in the last decade, the researchers who have led this research field, the adopted methodologies, and the structure and dimensions of indicators. The results allowed us to identify a set of 29 indicators distributed by 8 dimensions such as patents, university spinoffs, infrastructures, industry engagement, process creativity, process efficiency, process outcomes, and training. This study is innovative in focusing its analysis on academic entrepreneurship indicators. The knowledge of these indicators is relevant from a conceptual and practical perspective and will help universities to improve their technology transfer processes which will serve as a fundamental element in the implementation of the university's third mission.

Abstract
This paper presents a deep generative model for capturing the conditional probability distribution of future wind power given its history by modeling and pattern recognition in a dynamic graph. The dynamic nodes show the wind sites while the dynamic edges reflect the correlation between the nodes. We propose a scalable optimization model, which is theoretically proved to catch distributions at nodes of the graph, contrary with all learning formulations in the sector of discriminatory pattern recognition. The density of probabilities for each node can be used as samples in our framework. This probabilistic deep convolutional Auto-encoder (PDCA), is based on the deep learning of localized first-order approximation of spectral graph convolutions, a novel evolutionary algorithm and the Bayesian variational inference concepts. The presented generative model is used for the spatiotemporal probabilistic wind power problem in a wide 25 wind sites located in California, the USA for up to 24 hr ahead prediction. The experimental findings reveal that our proposed model outperforms other competitive temporal and spatio-temporal algorithms in terms of reliability, sharpness, and continuous ranked probability score.

Abstract
This study aimed to explore the diversity of open-innovation practices that are adopted in Portuguese SMEs considering the outside-in, inside-out, and coupled paradigms. A quantitative study was carried out considering a sample of 187 Portuguese SMEs. The findings revealed that these organizations favored the adoption of the outside-in paradigm. The inside-out model was the least relevant, especially for smaller companies (i.e., small and micro-companies). The most adopted outside-in practices were the integration of external knowledge from suppliers and clients; in the inside-out model, licensing processes were more important; while in the coupled model, joint ventures and network consortiums stood out. The increase in the innovation capacity of these organizations was highlighted as the most relevant benefit, while the lack of resources and difficulties in integrating knowledge emerged as challenges. This study is especially relevant for the establishment of public-support policies that promote the involvement of Portuguese SMEs in open-innovation processes. © 2021 by the author. Licensee MDPI, Basel, Switzerland.

Abstract
Analysis of locomotion is often used as a measure for impairment and recovery following experimental peripheral nerve injury. Compared to rodents, sheep offer several advantages for studying peripheral nerve regeneration. In the present study, we compared for the first time, two-dimensional (2D) and three-dimensional (3D) hindlimb kinematics during obstacle avoidance in the ovine model. This study obtained kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. The strategy used by the sheep to bring the hindlimb over a moderately high obstacle, set to 10% of its hindlimb length, was pronounced knee, ankle and metatarsophalangeal flexion when approaching and clearing the obstacle. Despite the overall time course kinematic patterns about the hip, knee, ankle, and metatarsophalangeal were identical, we found significant differences between values of the 2D and 3D joint angular motion. Our results showed that the most apparent changes that occurred during the gait cycle were for the ankle (2D-measured STANCEmax: 157 +/- 2.4 degrees vs. 3D-measured STANCEmax: 151 +/- 1.2 degrees; P<.05) and metatarsophalangeal joints (2D-measured STANCEmin: 151 +/- 2.2 degrees vs. 3D-measured STANCEmin: 162 +/- 2.2 degrees; P<.01 and 2D-measured TO: 163 +/- 4.9 degrees vs. 3D-measured TO: 177 +/- 1.4 degrees; P<.05), whereas the hip and knee joints were much less affected. Data and techniques described here are useful for an objective assessment of altered gait after CP injury and repairin an ovine model.

Abstract
Road safety is crucial in the design of autonomous vehicles, so safety and performance tests must be carried out regularly. Visualization applications of data acquired in these tests are critical to helping the continuous progress of autonomous driving. Currently, there is an application that helps in visualizing these data through a web platform, called Kratos Visualization. This application was developed using the WebGL library, to help companies that carry out experiments with autonomous vehicles to visualize and validate the data that they collect. These data represent different sequences of autonomous driving experiences and can contain a lot of information about the vehicle and objects in the environment around it. The work that led to this paper aims to explore the visualization of these data with the help of virtual reality. To strengthen the visualization of autonomous driving data, an application was created named Kratos VR. Designed with the A-Frame framework to work in any browser, this application contains the same functionalities of Kratos Visualization and new virtual reality features. Performance tests were carried out to evaluate the application. These tests allowed us to conclude that virtual reality can be successfully used to effectively visualize Advanced Driving Assist Systems (ADAS) and Autonomous Driving (AD) data and that the developed application provides a solid basis for future virtual reality applications in this field.