Evolution of small-scale magnetic elements in the vicinity of granular-size swirl convective motions

Advances in solar instrumentation have led to a widespread usage of time series to study the dynamics of solar features, specially at small spatial scales and at very fast cadences. Physical processes at such scales are determinant as building blocks for many others occurring from the lower to the upper layers of the solar atmosphere and beyond, ultimately for understanding the bigger picture of solar activity. Ground-based (SST) and space-borne (Hinode) high-resolution solar data are analyzed in a quiet Sun region displaying negative polarity small-scale magnetic concentrations and a cluster of bright points observed in G-band and Ca II H images. The studied region is characterized by the presence of two small-scale convective vortex-type plasma motions, one of which appears to be affecting the dynamics of both, magnetic features and bright points in its vicinity and therefore the main target of our investigations. We followed the evolution of bright points, intensity variations at different atmospheric heights and magnetic evolution for a set of interesting selected regions. A description of the evolution of the photospheric plasma motions in the region nearby the convective vortex is shown, as well as some plausible cases for convective collapse detected in Stokes profiles.Comment: 9 figure

Complex noise in diffusion-limited reactions of replicating and competing species

We derive exact Langevin-type equations governing quasispecies dynamics. The inherent multiplicative noise has both real and imaginary parts. The numerical simulation of the underlying complex stochastic partial differential equations is carried out employing the Cholesky decomposition for the noise covariance matrix. This noise produces unavoidable spatio-temporal density fluctuations about the mean field value. In two dimensions, the fluctuations are suppressed only when the diffusion time scale is much smaller than the amplification time scale for the master species.Comment: 10 pages, 2 composite figure

Using reservoir computing to construct scarred wavefunctions

Scar theory is one of the fundamental pillars in the field of quantum chaos, and scarred functions a superb tool to carry out studies in it. Several methods, usually semiclassical, have been described to cope with these two phenomena. In this paper, we present an alternative method, based on the novel machine learning algorithm known as Reservoir Computing, to calculate such scarred wavefunctions together with the associated eigenstates of the system. The resulting methodology achieves outstanding accuracy while reducing execution times by a factor of ten. As an illustration of the effectiveness of this method, we apply it to the widespread chaotic two-dimensional coupled quartic oscillator.Comment: arXiv admin note: text overlap with arXiv:2310.0745

Pre-tertiary engagement with online learning : Exploring uses of online learning environments and digital technology for progression into and through Higher Education

This report outlines work undertaken by the Institute of Education to explore how pre-tertiary experiences of online learning influences students? successful transitions into and through Higher Education. The work was commissioned by Pamoja Education, and the studies that were undertaken focused on the experiences of students and staff taking part in Pamoja Education courses offered as part of the International Baccalaureate Diploma Programme. The work involved reviewing previous literature about the role of technology in preparing students for University study; undertaking a survey of International Baccalaureate students (including Pamoja Education alumni) to explore their experiences; interviewing Pamoja Education alumni as a way of explaining and elaborating these patterns of experience; and asking teachers to reflect on how they worked with learners to support them online. Each of these areas of work is reported in a separate section of this report

Anticipating food price crises by reservoir computing

Anticipating price crises in the market of agri-commodities is critical to guarantee both the sustainability of the food system and to ensure food security. However, this is not an easy task, since the problem implies analyzing small and very volatile time series, which are highly influenced by external factors. In this paper, we show that suitable reservoir computing algorithms can be developed that outperform traditional approaches, by reducing the Mean Absolute Error and, more importantly, increasing the Market Direction Accuracy. For this purpose, the applicability of five variants of such method to forecast this market is explored, and their performance evaluated by comparing the results with those obtained with the standard LSTM and SARIMA benchmarks. We conclude that decomposing the time series and modeling each component with a separate RC is essential to successfully anticipate price trends, and that this method works even in the complex changing temporal scenario of the Covid-19 pandemic, when part of the data were collectedThe project that gave rise to these results received the support of a fellowship from ‘‘la Caixa’’ Foundation (ID 100010434). The fellowship code is LCF/BQ/DR20/11790028. This work has also been partially supported by the Spanish Ministry of Science, Innovation and Universities, Gobierno de España, under Contract No. PID2021-122711NB-C21; and by DG of Research and Technological Innovation of the Community of Madrid (Spain) under Contract No. IND2022/TIC-2371