The development of homogeneity psycho cognition learning strategy in physical education learning

Many future studies have been developed by scientists today in the form of methods, models, strategies, and techniques in improving student learning outcomes that are oriented to psychology and the development of students' intelligence. One of the latest innovations in learning offered in this study is the homogeneity psycho cognition (HPC) strategy. The research objective was to develop the latest learning strategies in physical education, sports, and health learning. This development research uses a 4D model consisting of four stages: define, design, develop, and disseminate to produce products in the form of an HPC learning strategy. This study involved 115 elementary school students in several sample schools in Ambon City as participants. This study found that the HPC learning strategy had been developed following the relevant development directions and procedures. The validation of the HPC strategy by experts indicates that the HPC strategy is feasible to implement with due regard to minor revisions. The results of small and medium-scale trials show that the HPC strategy can improve student learning outcomes

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Technologies and Innovations in Regional Development: The European Union and its Strategies

The subsequent volume revolves around the Social-Fields-Approach (SOFIA) as an approach to conceptualization and operationalisation for the purpose of empirical research. It contributes a new perspective and approach in research on innovation. We believe that SOFIA can have implications for both academic research and practical applications in reshaping the existing instruments and governance arrangements in innovation policy. Whilst applying SOFIA, we urge researchers to leverage the plurality of different qualitative, quantitative and mixed-method approaches in innovation studies, including less conventional methods, such as QCA (Ragin, 2008). Diligent application of SOFIA can also subsequently lead to the development of high-level theoretical contributions

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtualmachines; this provides a high-performance simulation environment that is accessible to multidomain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a largescale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.European Union’s Horizon 2020 Framework Programme 785907 945539European Union’s Horizon 2020 800858MEXT (hp200139, hp210169) MEXT KAKENHI grant no. 17H06310

AAPOR Report on Big Data

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.There is a great potential in Big Data but there are some fundamental challenges that have to be resolved before its full potential can be realized. In this report we give examples of different types of Big Data and their potential for survey research. We also describe the Big Data process and discuss its main challenges