Abstract

Abstract
The emergence of accessible virtual reality headsets in the past decade multiplied educational uses of immersive virtual reality. Higher education, in particular, has seen many such reports emerge. However, there are scarce frameworks for higher education professionals to plan and deploy immersive virtual reality within their pedagogical practice. To attain a perspective on this field, we conducted a systematic literature review using SCOPUS search, focusing on Instructional Design Models for Immersive Virtual Reality in online Higher Education. This review aimed to provide a comprehensive overview of these models, their respective phases, and distinctive characteristics. The review identified two categories of Instructional Design Models for Immersive Virtual Reality in Higher Education: 1) Models specific to such contexts, with aspects such as managing immersion time or providing prior contact with the immersive environment; 2) Models developed for other contexts and adapted to immersive virtual reality, addressing aspects such as the importance of creating objectives, assessment elements, or defining resource purpose. We conclude that current instructional models used for immersive virtual reality in higher education lack the combination of the overall pedagogical concerns with the specific ones for immersive virtual reality. Thus, we recommend further research to develop instruction models that combine both aspects of learning design concerns.

Abstract

Abstract
The use of technology applied in sports comes each year becoming a great tool to help athletes train. Moreover, the post-pandemic world is undergoing dramatic changes in the way of thinking and acting, with new ways of exercising emerging, but without leaving home. Thus this paper describes the development of a platform for training, focusing on Kendo practitioners (Japanese fencing) using virtual reality tools to allow athletes and training the distance. Through the use of a HMD (Head Mounted Device), kendokas will be able to practice blows and improve their reflex by a gamified experience in a virtual environment.

Abstract
According to the European Clean Energy Package (2019) Distribution System Operators (DSOs) shall effectively use flexibility services from local and regional assets to safely host more renewable energy sources in the electricity grid. Electricity prosumers become crucial players due to their potential to provide flexibility by adapting their production and consumption behaviour. Yet, integrating new types of assets into the distribution grid to use flexibility creates complexity and hardly predictable power flows in the distribution networks. The European H2020 demonstration project EUniversal aims to overcome the existing limitations in the use of flexibility. For that purpose, smart grid tools for grid state assessment and active system management are developed. A demonstration pilot is set up to test the flexibility value chain from congestion detection to market-based flexibility procurement via a local flexibility market. The pilot is conducted in the LV grids of the German DSO MITNETZ STROM, examining the use of flexible resources in the LV grid for congestion management. The article describes the set-up of the flexibility value chain and shows how all individual parts are integrated into the complete process. © The Institution of Engineering and Technology 2023.

Abstract
Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species' niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatiotemporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by Quinta da Cholda, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm - maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.