Modelling the Metaverse: A Theoretical Model of Effective Team Collaboration in 3D Virtual Environments

In this paper, a theoretical model of effective team collaboration in 3D virtual environments is presented. The aim of this model is to enhance our understanding of the capabilities exerting influence on effective 3D virtual team collaboration. The model identifies a number of specific capabilities of 3D virtual worlds that can contribute to this team effectiveness. Compared to "traditional" computer-mediated collaboration technologies, 3D virtual environments support team collaboration primarily through (a) the shared virtual environment, and (b) avatar-based interaction. Through the shared virtual environment, users experience higher levels of presence (a feeling of actually "being there"), realism and interactivity. These capabilities increase the users' level of information processing.  Avatar-based interaction induces greater feelings of social presence (being with others) and control over  self-presentation (how one wants to be perceived by others), thus increasing the level of communication support in the 3D environment. Through greater levels of information and communication support, a higher level of shared understanding is reached, which in turn positively influences team performance. Our paper concludes by presenting several propositions which allow further empirical testing, implications for research and practice, and suggestions for future research. The insights obtained from this paper can help developers of these virtual worlds to design standards for the capabilities that influence effective team collaboration in 3D virtual environments.

Risk of cancer in children and young adults conceived by assisted reproductive technology

STUDY QUESTION: Do children conceived by ART have an increased risk of cancer? SUMMARY ANSWER: Overall, ART-conceived children do not appear to have an increased risk of cancer. WHAT IS KNOWN ALREADY: Despite the increasing use of ART, i.e. IVF or ICSI worldwide, information about possible long-term health risks for children conceived by these techniques is scarce. STUDY DESIGN, SIZE, DURATION: A nationwide historical cohort study with prospective follow-up (median 21 years), including all live-born offspring from women treated with subfertility treatments between 1980 and 2001. PARTICIPANTS/MATERIALS, SETTING, METHODS: All offspring of a nationwide cohort of subfertile women (OMEGA study) treated in one of the 12 Dutch IVF clinics or two fertility clinics. Of 47 690 live-born children, 24 269 were ART-conceived, 13 761 naturally conceived and 9660 were conceived naturally or through fertility drugs, but not by ART. Information on the conception method of each child and potential confounders were collected through the mothers’ questionnaires and medical records. Cancer incidence was ascertained through linkage with The Netherlands Cancer Registry from 1 January 1989 until 1 November 2016. Cancer risk in ART-conceived children was compared with risks in naturally conceived children from subfertile women (hazard ratios [HRs]) and with the general population (standardized incidence ratios [SIRs]). MAIN RESULTS AND THE ROLE OF CHANCE: The median follow-up was 21 years (interquartile range (IQR): 17–25) and was shorter in ART-conceived children (20 years, IQR: 17–23) compared with naturally conceived children (24 years, IQR: 20–30). In total, 231 cancers were observed. Overall cancer risk was not increased in ART-conceived children, neither compared with naturally conceived children from subfertile women (HR: 1.00, 95% CI 0.72–1.38) nor compared with the general population (SIR = 1.11, 95% CI: 0.90–1.36). From 18 years of age onwards, the HR of cancer in ART-conceived versus naturally conceived individuals was 1.25 (95% CI: 0.73–2.13). Slightly but non-significantly increased risks were observed in children conceived by ICSI or cryopreservation (HR = 1.52, 95% CI: 0.81–2.85; 1.80, 95% CI: 0.65–4.95, respectively). Risks of lymphoblastic leukemia (HR = 2.44, 95% CI: 0.81–7.37) and melanoma (HR = 1.86, 95% CI: 0

Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

International audienceMeasurements of electrons from νe interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectrum is derived and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of missing energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50 MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours

DUNE Phase II: scientific opportunities, detector concepts, technological solutions

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos

The influence of shop characteristics on workload control

Several order release methods have been developed for workload control in job shop production. The release methods of the traditional workload control concerts differ in how they deal with the flow of work to each station. Previous research has pointed at strengths and weaknesses of each method. Till now the choice of the appropriate method for a particular situation has hardly received attention. This research shows that shop characteristics are an important factor to this choice, A simulation study indicates that the relative performance of the release methods changes completely with for instance the presence or absence of a dominant flow direction in the shop. Adjustments to the traditional release methods are suggested which prove to make these methods more robust. (C) 2000 Elsevier Science B.V. All rights reserved

‘Kies voor de uitgeteelde Fries’ : Bart Ducro voorspelt: binnen twee jaar DNA-tests dwerggroei en waterhoofd

Interview met Bart Ducro over de toekomst van het Friese paard. Ducro bracht voor Wageningen UR alle Friese paarden uit heden en verleden in kaar