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Fe3Co67Cr3Si15B12 ribbons with a high degree of flexibility and excellent corrosion stability were produced by rapid quenching technique. Their structural, magnetic, and thermomagnetic (Anomalous Nernst Effect) properties were studied both in an as-quenched (NR) state and after stress annealing during 1 h at the temperature of 350 °C and a specific load of 230 MPa (AR). X-ray diffraction was used to verify the structural characteristics of our ribbons. Static magnetic properties were explored by inductive technique and vibrating sample magnetometry. The thermomagnetic curves investigated through the Anomalous Nernst Effect are consistent with the obtained magnetization results, presenting a linear response in the thermomagnetic signal, an interesting feature for sensor applications. Additionally, Anomalous Nernst Effect coefficient SANE values of 2.66μV/K and 1.93μV/K were estimated for the as-quenched and annealed ribbons, respectively. The interplay of the low magnetostrictive properties, soft magnetic behavior, linearity of the thermomagnetic response, and flexibility of these ribbons place them as promising systems to probe curved surfaces and propose multifunctional devices, including magnetic field-specialized sensors.

Titanium-copper alloy films with stoichiometry given by Ti1−xCux were produced by magnetron co-sputtering technique and analyzed in order to explore the suitability of the films to be applied as resistive temperature sensors with antimicrobial properties. For that, the copper (Cu) amount in the films was varied by applying different DC currents to the source during the deposition in order to change the Cu concentration. As a result, the samples showed excellent thermoresistivity linearity and stability for temperatures in the range between room temperature to 110 °C. The sample concentration of Ti0.70Cu0.30 has better characteristics to act as RTD, especially the αTCR of 1990 ×10−6°C−1. The antimicrobial properties of the Ti1−xCux films were analyzed by exposing the films to the bacterias S. aureus and E. coli, and comparing them with bare Ti and Cu films that underwent the same protocol. The Ti1−xCux thin films showed bactericidal effects, by log10 reduction for both bacteria, irrespective of the Cu concentrations. As a test of concept, the selected sample was subjected to 160 h reacting to variations in ambient temperature, presenting results similar to a commercial temperature sensor. Therefore, these Ti1−xCux thin films become excellent antimicrobial candidates to act as temperature sensors in advanced coating systems.

We investigated the magnetization dynamics through the magnetoimpedance effect in an integrated YIG/Pt-stripline system in the frequency range of 0.5 up to 2.0 GHz. Specifically, we explore the dependence of the dynamic magnetic behavior on the field orientation by analyzing beyond the traditional longitudinal magnetoimpedance effect of the transverse and perpendicular setups. We disclose here the strong dependence of the effective damping parameter on the field orientation, as well as verification of the very-low damping parameter values for the longitudinal and transverse configurations. We find considerable sensitivity results, bringing to light the facilities to integrate ferrimagnetic insulators in current and future technological applications.

Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.

We introduce a novel technique for designing color filter metasurfaces using a data-driven approach based on deep learning. Our innovative approach employs inverse design principles to identify highly efficient designs that outperform all the configurations in the dataset, which consists of 585 distinct geometries solely. By combining Multi-Valued Artificial Neural Networks and back-propagation optimization, we overcome the limitations of previous approaches, such as poor performance due to extrapolation and undesired local minima. Consequently, we successfully create reliable and highly efficient configurations for metasurface color filters capable of producing exceptionally vivid colors that go beyond the sRGB gamut. Furthermore, our deep learning technique can be extended to design various pixellated metasurface configurations with different functionalities.

The main objective of this research is to propose an active and reactive power injection control in order to mitigate voltage sags. The proposed control strategy works in conjunction with a modified version of an automatic voltage regulator (AVR), where it will act on the active and reactive powers injected by the inverter to reduce the effects of voltage sags. In this way, the control will avoid possible shutdowns and damage to the equipment connected to the grid. The voltage improvement can be perceived for consumers connected to the power system. Modifications in AVR model and parameters are performed to speed up its performance, thus identifying the short-duration voltage variations (SDVV) and, consequently, the control acts to alter the powers, decreasing the active power injection and increasing the reactive power based on inverter capacity during the momentary voltage dip (MVD). Finally, when the fault is cleared, all values return to the pre-fault condition, so that the inverter only operates with active power. A 75 kW three-phase grid-connected photovoltaic system (GCPVS) equipped with the proposed control was inserted in a distribution grid of the city of Palmas, state of Tocantins, Brazil, and all of the computer simulations were performed on the Matlab/Simulink®.

We introduce a novel technique for designing color filter metasurfaces using a data-driven approach based on deep learning. Our innovative approach employs inverse design principles to identify highly efficient designs that outperform all the configurations in the dataset, which consists of 585 distinct geometries solely. By combining Multi-Valued Artificial Neural Networks and back-propagation optimization, we overcome the limitations of previous approaches, such as poor performance due to extrapolation and undesired local minima. Consequently, we successfully create reliable and highly efficient configurations for metasurface color filters capable of producing exceptionally vivid colors that go beyond the sRGB gamut. Furthermore, our deep learning technique can be extended to design various pixellated metasurface configurations with different functionalities.Optical color filters are structures or materials designed to discriminate and manipulate distinct light wavelengths through the selective transmission or reflection of particular colors while simultaneously absorbing or attenuating undesired colors 1,2 . Conventional color filters rely on the manipulation of chemical composition to achieve the desired optical properties, which can lead to issues such as absorption losses, thermal effects, and alterations in chemical characteristics 3 . An alternative approach involves the utilisation of structural color filters, offering distinct advantages and applications in diverse fields such as photorealistic color printing, color holography, anti-counterfeiting devices, and much more 4-6 .Metasurfaces have emerged as a promising platform for structural color filters 7,8 , owing to its peculiar capability of controlling all the light properties at the nanoscale, enabling a plethora of applications 9-12 . Dielectric metasurfaces play a crucial role in color filter applications, especially within the visible spectrum range where the plasmonic conterpart based on metals is less performing owing to intrinsic optical losses. The limited losses of dielectrics (e.g. Si 3 N 4 , GaN, TiO 2 , ZrO 2 , HfO 2 ) make them highly desirable for designing efficient devices with sharp resonance responses 13-16 . Resonant dielectric metasurfaces achieve precise control over the phase of reflected and transmitted light by leveraging various resonant phenomena (e.g. Mie resonances) 17,18 . Through meticulous engineering of the resonators, selective interaction with different wavelengths is enabled, leading to efficient and vivid color filters. Such kind of metasurfaces offer exceptional phase control, high-quality factors, and sharp resonances, resulting in enhanced color purity and spectral selectivity 19-21 . Yet, the design of an ideal color filter demands capability to selectively filter all colors across the optical spectrum. In other words, at each desired wavelength, it is crucial to eliminate any background resonances in order to achieve a pure color response characterized by sharp reflection or transmission amplitudes. Given the fabrication constraints, finding the appropriate resonator shape to achieve a desired response, is a challenging task that has garnered significant attention in the research community. Numerous studies explored this area, employing sophisticated optimization algorithms including advanced Deep Learning (DL) approaches to tackle the inherent complexity of the problem 20-29 . However, relying on classical optimization approaches requires several costly simulations when optimizing various color targets simultaneously 30-32 . A viable solution for the design of vivid metasurface color

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.