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

Implementation of synthetic fast-ion loss detector and imaging heavy ion beam probe diagnostics in the 3D hybrid kinetic-MHD code MEGA

A synthetic fast-ion loss (FIL) detector and an imaging Heavy Ion Beam Probe (i-HIBP) have been implemented in the 3D hybrid kinetic-magnetohydrodynamic code MEGA. First synthetic measurements from these two diagnostics have been obtained for neutral beam injection-driven Alfvén Eigenmode (AE) simulated with MEGA. The synthetic FILs show a strong correlation with the AE amplitude. This correlation is observed in the phase-space, represented in coordinates (P, E), being toroidal canonical momentum and energy, respectively. FILs and the energy exchange diagrams of the confined population are connected with lines of constant E, a linear combination of E and P. First i-HIBP synthetic signals also have been computed for the simulated AE, showing displacements in the strike line of the order of ∼1 mm, above the expected resolution in the i-HIBP scintillator of ∼100 μm.This work received funding from the European Starting Grant (ERC) from project 3D-FIREFLUC and from the Spanish Ministry of Science under Grant No. FPU19/02267. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission

Implementation of synthetic fast-ion loss detector and imaging heavy ion beam probe diagnostics in the 3D hybrid kinetic-MHD code MEGA

A synthetic fast-ion loss (FIL) detector and an imaging Heavy Ion Beam Probe (i-HIBP) have been implemented in the 3D hybrid kinetic-magnetohydrodynamic code MEGA. First synthetic measurements from these two diagnostics have been obtained for neutral beam injection-driven Alfvén Eigenmode (AE) simulated with MEGA. The synthetic FILs show a strong correlation with the AE amplitude. This correlation is observed in the phase-space, represented in coordinates (Pϕ, E), being toroidal canonical momentum and energy, respectively. FILs and the energy exchange diagrams of the confined population are connected with lines of constant E′, a linear combination of E and Pϕ. First i-HIBP synthetic signals also have been computed for the simulated AE, showing displacements in the strike line of the order of ∼1 mm, above the expected resolution in the i-HIBP scintillator of ∼100 μm

Mechanical Biosensors in Biological and Food Area: a Review

A review of state the art about the structure, classification and operation of biosensors applied in food and biological areas is presented. This review is focused to mechanical biosensors that use mill, micro and nanocantilevers. Basic concepts of atomic force microscopy and optical systems, used as testing platform of biosensors are described. The most funcionalized strategies and geometrical configurations are also explained. Mathematical methods for evaluating the performance in static and dynamic mode of the mechanical biosensors are reviewed and examples of application in biological and food areas are provided. An overall description of the operational effect of operation conditions and design variables on the sensitivity devices is also proposed. A brief description of the design processes and manufacturing of cantilevers based silicon technology as well as information about BioMEMS and BioNEMS are provided. Finally, overall tends in research, development and commercialization of biosensors are described briefly as well as probable areas of development in food biosensors. Thereby, this review provides an overall view of biosensors, as an exploratory guide to identify the most important aspects of this technology

Success evaluation factors in construction project management : some evidence from medium and large portuguese companies

The construction industry plays a very important role in the Portuguese economy. In 2009, it was among the top five economic sectors, representing 13% of total employment. Nevertheless, project failures are still frequent mainly due to inadequate management practices and to the intrinsic characteristics of projects of the construction industry. Even though Portuguese construction has improved in recent years, cost and schedule overruns, low productivity and final product quality problems are still common. In this context, project management is a crucial tool for improving construction operations and for the overall success of projects. The aim of this article is to contribute to the discussion on success evaluation factors in a field where little has been written – the construction industry. Through a survey of 40 medium and large Portuguese companies several factors were identified which are currently considered in the evaluation of project success, as found in the literature review. The results show that the traditional factors, often referred to as the “Atkinson elements triangle” (cost, time and quality), are still the most relevant for evaluating the success of a project, but others, such as customer involvement and acceptance, have gained importance in recent years

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084