Learning from the pandemic: Capitalising on opportunities and overcoming challenges for mathematics teaching and learning practices with and through technology

This new working group (WG) was created to discuss the theoretical and methodological challenges faced by the mathematics education field when the prevailing boundaries of the classroom shifted; alongside the changed nature of the classroom interactions between the humans (teachers and students) and the chosen technologies. Starting with the assumption that technology resources are being used, the WG explored the nature of these tools and their affordances for the mathematical teaching and learning. The work was framed by the following three pedagogic activities, which are proving to be particularly challenging: introducing and developing understanding of new mathematical topics; managing interaction and communication in mathematics; and assessing mathematics, both formatively and summatively. Three case studies of teachers’ practices were presented to initiate discussions with respect to these challenges and to highlight some existing theoretical and methodological frames

Learning from the pandemic: Capitalising on opportunities and overcoming challenges for mathematics teaching and learning practices with and through technology

This working group (WG), which met for the second time in June 2021, was created to discuss the theoretical and methodological challenges faced by the mathematics education field when the prevailing boundaries of the classroom shifted as a result of the COVID-19 pandemic. Following a brief introduction to the aims for the WG, we offer three further case studies of teachers’ practices and an emerging synthesis of the cases according to three pedagogic activities that are proving to be particularly challenging

Parametric search and optimisation of fast displacement hull forms using rans simulations of full-scale flow

Abstract. A methodology to derive parametric hull design candidates with a specified displacement and initial stability is introduced. A gradient-free search and optimisation algorithm coupled to a RANS CFD solver is then used to identify efficient pure-displacement hull shapes with minimal hydrodynamic resistance operating in the transition speed region without relying on dynamic lift

Relative neutronic performance of proposed high-density dispersion fuels in water-moderated and D{sub 2}O-reflected research reactors

This paper provides an overview of the neutronic performance of an idealized research reactor using several high density LEU fuels that are being developed by the RERTR program. High-density LEU dispersion fuels are needed for new and existing high-performance research reactors and to extend the lifetime of fuel elements in other research reactors. This paper discusses the anticipated neutronic behavior of proposed advanced fuels containing dispersions of U{sub 3}Si{sub 2}, UN, U{sub 2}Mo and several uranium alloys with Mo, or Zr and Nb. These advanced fuels are ranked based on the results of equilibrium depletion calculations for a simplified reactor model having a small H{sub 2}O-cooled core and a D{sub 2}O reflector. Plans have been developed to fabricate and irradiate several uranium alloy dispersion fuels in order to test their stability and compatibility with the matrix material and to establish practical loading limits

A classification of tasks for the systematic study of immune response using functional genomics data

A full understanding of the immune system and its responses to infection by different pathogens is important for the development of anti-parasitic vaccines. A growing number of large-scale experimental techniques, such as microarrays, are being used to gain a better understanding of the immune system. To analyse the data generated by these experiments, methods such as clustering are widely used. However, individual applications of these methods tend to analyse the experimental data without taking publicly available biological and immunological knowledge into account systematically and in an unbiased manner. To make best use of the experimental investment, to benefit from existing evidence, and to support the findings in the experimental data, available biological information should be included in the analysis in a systematic manner. In this review we present a classification of tasks that shows how experimental data produced by studies of the immune system can be placed in a broader biological context. Taking into account available evidence, the classification can be used to identify different ways of analysing the experimental data systematically. We have used the classification to identify alternative ways of analysing microarray data, and illustrate its application using studies of immune responses in mice to infection with the intestinal nematode parasites Trichuris muris and Heligmosomoides polygyrus

Methods and codes for neutronic calculations of the MARIA research reactor.

The core of the MARIA high flux multipurpose research reactor is highly heterogeneous. It consists of beryllium blocks arranged in 6 x 8 matrix, tubular fuel assemblies, control rods and irradiation channels. The reflector is also heterogeneous and consists of graphite blocks clad with aluminum. Its structure is perturbed by the experimental beam tubes. This paper presents methods and codes used to calculate the MARIA reactor neutronics characteristics and experience gained thus far at IAE and ANL. At ANL the methods of MARIA calculations were developed in connection with the RERTR program. At IAE the package of programs was developed to help its operator in optimization of fuel utilization

Multimodel Evaluation of Nitrous Oxide Emissions From an Intensively Managed Grassland

Process‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N$_{2}$O) fluxes remain challenging. Models are limited by our understanding of soil‐plant‐microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N$_{2}$O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N$_{2}$O fluxes on annual timescales, while APSIM was most accurate for daily N$_{2}$O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural GHG inventory (IPCC‐Swiss), but individual models were not systematically more accurate than IPCC‐Swiss. The model ensemble overestimated the N$_{2}$O mitigation effect of the clover‐based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC‐Swiss (IPCC‐Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N$_{2}$O emissions