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AbstractWe present a comprehensive study using finite element numerical analysis of the acoustic localized phonons supported by a gold nanoridges dimer-based multilayer design. The latter consists in a SiO2-substrate over which a gold film covered with a thin polymer is deposited. We investigate first the mechanical eigen-modes analysis of a single monomer ridge, where we find flexural and compressional type modes in the sub-GHz frequency range. This is realized by either setting the ridge in a periodic structure, which enables to get the dispersion curve of the modes, or by considering an isolated system bounded by perfect matched layers, where we use the equivalent of the local density of states to track the modes. A good agreement is obtained between the two methods. Similarly, we find in case of the coupled dimer ridges hybridized modes, namely the flexural and compressional modes of a monomer split-up into in- and -out-of-phase type modes. We demonstrate efficient coupling between the monomer/dimer localized phonons with surface acoustic waves (SAWs) as the simulated transmission spectra show dips at the frequencies of the monomer/dimer eigenmodes. For symmetry reasons, some of the dimer modes are expected to be optomechanically active. The proposed SAW-based device is meant to help design acousto-optic modulators or ultrasensitive sensors.

Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health. Nonetheless, in the last decades a reduction in grain quality has been observed. Therefore, it is central to develop efficient quality-related phenotyping tools. In this sense, one of the most relevant wheat features related to grain quality traits is grain nitrogen content, which is directly linked to grain protein content and monitorable with remote sensing approaches. Moreover, the relation between nitrogen fertilization and grain nitrogen content (protein) plays a central role in the sustainability of agriculture. Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status, this paper studies the efficacy of various sensors, multispectral and visible red–greenblue (RGB), at different scales, ground and unmanned aerial vehicle (UAV), and phenological stages (anthesis and grain filling) to estimate grain nitrogen content. Linear models were calculated using vegetation indices at each sensing level, sensor type and phenological stage. Furthermore, this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data. We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation (R2 = 0.42, RMSE = 0.18%) in comparison with those models built with RGB imagery at ground and aerial level, as well as with those built with widely used ground-level multispectral sensors. We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively (R2 = 0.40, RMSE = 0.29%) at actual wheat fields.

It is essential to study the porosity, thermal conductivity, and P-wave velocity of calcarenites, as well as the anisotropy coefficients of the thermal conductivity and P-wave velocity, for civil engineering, and conservation and restoration of historical monuments. This study focuses on measuring the thermal conductivity using the thermal conductivity scanning (TCS) technique and measuring the P-wave velocity using portable equipment. This was applied for some dry and saturated calcarenite samples in the horizontal and vertical directions (parallel and perpendicular to the bedding plane, respectively). The calcarenites were selected from some historical monuments in Morocco. These physical properties were measured in the laboratory to find a reliable relationship between all of these properties. As a result of the statistical analysis of the obtained data, excellent linear relationships were observed between the porosity and both the thermal conductivity and porosity. These relationships are characterized by relatively high coefficients of determination for the horizontal and vertical samples. Based on the thermal conductivity and P-wave velocity values in these two directions, the anisotropy coefficients of these two properties were calculated. The internal structure and the pore fabric of the calcarenite samples were delineated using scanning electron microscopy (SEM), while their chemical and mineral compositions were studied using the energy dispersive X-ray analysis (EDXA) and X-ray diffraction (XRD) techniques.

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications.

By using DFT simulations employing the GGA/PBE and LDA/CA-PZ approximations, the effects of the Hubbard U correction on the crystal structure, electronic properties, and chemical bands of the cubic phase (Pm3¯m) of STO were investigated. Our findings showed that the cubic phase (Pm3¯m) STO’s band gaps and lattice parameters/volume are in reasonably good accordance with the experimental data, supporting the accuracy of our model. By applying the DFT + U method, we were able to obtain band gaps that were in reasonably good agreement with the most widely used experimental band gaps of the cubic (Pm3¯m) phase of STO, which are 3.20 eV, 3.24 eV, and 3.25 eV. This proves that the Hubbard U correction can overcome the underestimation of the band gaps induced by both GGA/PBE and LDA/CA-PZ approximations. On the other hand, the Sr-O and Ti-O bindings appear predominantly ionic and covalent, respectively, based on the effective valence charges, electron density distribution, and partial density of states analyses. In an attempt to enhance the performance of STO for new applications, these results might also be utilized as theoretical guidance, benefitting from our precise predicted values of the gap energies of the cubic phase (Pm3¯m).