Abstract
In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. © 2024 The Author(s). Published by Elsevier B.V.

Abstract

Abstract
Digital transformation has been taking place for several decades in different sectors of activity and is contributing significantly to mitigating the environmental impacts of those sectors. Various digital solutions are related to energy consumption and production, which is crucial to ensure continuous decarbonisation. Most of them are targeted to be used by general consumers. Therefore, it is essential to consider consumers' attitudes towards those solutions and their adoption behaviour to ensure a broad diffusion of them. This study uses the Technology Acceptance Model to understand the adoption of energy-related digital solutions in Europe. We conclude that the perceived usefulness of the solutions is more relevant in attitude formation than the perceived ease of use. Moreover, attitude highly influences adoption behaviour, as reported in the literature. Finally, these relations seem to be highly influenced by the belief that, by adopting digital solutions, consumers contribute to a better balance between energy supply and demand.

Abstract
METIS, the Mid-Infrared ELT Imager and Spectrograph is a first-generation ELT instrument scheduled to see first light in 2029. Its two main science modules are supported by an adaptive optics system featuring a pyramid sensor with 90x90 sub-apertures working in H- and K-band. The wavefront control concept for METIS' singleconjugate adaptive optics relies on a synthetic calibration that uses a model of the telescope and instrument to generate the interaction and control matrices, as well as the final projection on a modal command vector. This concept is enforced owing to the absence of a calibration source in front of the ELT's main deformable mirror. The core of the synthetic calibration functionality is the Command Matrix Optimiser module, which consists of several components providing models for various parts and aspects of the instrument, as well as the entire reconstructor. Many are present in the simulation environment used during the design phases, but need to be re-written and/or adapted for real-life use. In this paper, we present the design of the full command matrix optimisation module, the status of these efforts and the overall final concept of METIS' soft real-time system.

Abstract
This work aims to identify teaching and learning practices in practical classes of Computer Network Technology courses, which promote the use of the Physical Laboratory (PL) as an epistemic tool to improve learning in epistemic terms. Content analysis of Multimodal Narrations (MN) of three classes by two teachers were used. An MN aggregates and organizes the data collected in the PL environment. Based on the results, we infer that the student and the teacher, under certain conditions, use the physical laboratory as an epistemic tool since the physical interactions prove its use and reuse. In addition, this study allows, in the context of work in the physical laboratory of networks, to identify that the orchestrations of mediation patterns adopted by the teacher influence the students' epistemic practices and the use of the laboratory as a tool to produce new knowledge. The following contributions are presented: (1) The quality of the students' epistemic practices is increased if, in the teacher's dynamics of mediation, the control of the students' action is reduced; (2) The orchestration of the teacher's mediation patterns is essential to achieve beneficial results in student learning with the use of artifacts from the physical laboratory of Computer Networks; (3) For the physical laboratory to become an epistemic tool, it is necessary that the mediation standards allow students to develop epistemic practices to a high or very high degree and there is a certain mediation orchestration.

Abstract
Hardware specialization is seen as a promising venue for improving computing efficiency, with reconfigurable devices as excellent deployment platforms for application-specific architectures. One approach to hardware specialization is via the popular RISC-V, where Instruction Set Architecture (ISA) extensions for domains such as Edge Artifical Intelligence (AI) are already appearing. However, to use the custom instructions while maintaining a high (e.g., C/C++) abstraction level, the assembler and compiler must be modified. Alternatively, inline assembly can be manually introduced by a software developer with expert knowledge of the hardware modifications in the RISC-V core. In this paper, we consider a RISC-V core with a vectorization and streaming engine to support the Unlimited Vector Extension (UVE), and propose an approach to automatically transform annotated C loops into UVE compatible code, via automatic insertion of inline assembly. We rely on a source-to-source transformation tool, Clava, to perform sophisticated code analysis and transformations via scripts. We use pragmas to identify code sections amenable for vectorization and/or streaming, and use Clava to automatically insert inline UVE instructions, avoiding extensive modifications of existing compiler projects. We produce UVE binaries which are functionally correct, when compared to handwritten versions with inline assembly, and achieve equal and sometimes improved number of executed instructions, for a set of six benchmarks from the Polybench suite. These initial results are evidence towards that this kind of translation is feasible, and we consider that it is possible in future work to target more complex transformations or other ISA extensions, accelerating the adoption of hardware/software co-design flows for generic application cases.