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Hybrid halide perovskites (HHPs) have attracted significant attention due to their remarkable optoelectronic properties that combine the advantages of low cost-effective fabrication methods of organic-inorganic materials. Notably, low-dimensional hybrid halide perovskites including two-dimensional (2D) layers and one-dimensional (1D) chains, are recognized for their superior stability and moisture resistance, making them highly appealing for practical applications. Particularly, DMAPbI3 has attracted attention due to other interesting behaviors and properties, such as thermally induced order-disorder processes, dielectric transition, and cooperative electric ordering of DMA dipole moments. In this paper, we investigated the interplay between low-temperature SPT undergone by the low-dimensional (1D) hybrid halide perovskite-like material DMAPbI3 and its optoelectronic properties. Our approach combines synchrotron X-ray powder diffraction, Raman spectroscopy, thermo-microscopy, differential scanning calorimetry (DSC), and photoluminescence (PL) techniques. Temperature-dependent Synchrotron powder diffraction and Raman Spectroscopy reveal that the modes associated with I-Pb-I and DMA+ ion play a crucial role in the order-disorder SPT in DMAPbI3. The reversible SPT modifies its optoelectronic properties, notably affecting its thermochromic behavior and PL emission. The origin of the PL phenomenon is associated to self-trapped excitons (STEs), which are allowed due to a strong electron-phonon coupling quantified by the Huang-Rhys factor (S = 97+-1). Notably, we identify the longitudinal optical (LO) phonon mode at 84 cm-1 which plays a significant role in electron-phonon interaction. Our results show these STEs not only intensify the PL spectra at lower temperatures but also induce a shift in the color emission, transforming it from a light orange-red to an intense bright strong red., Comment: 38 pages, 8 figures

We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr\"odinger equation in configuration space. Traditional Configuration Interaction (CI) methods, while powerful, are computationally expensive due to the large number of determinants required. Our approach leverages RBMs to efficiently identify and sample the most significant determinants, accelerating convergence and reducing computational cost. This method achieves up to 99.99\% of the correlation energy even by four orders of magnitude less determinants compared to full CI calculations and up to two orders of magnitude less than previous state of the art works. Additionally, our study demonstrate that the RBM can learn the underlying quantum properties, providing more detail insights than other methods . This innovative data-driven approach offers a promising tool for quantum chemistry, enhancing both efficiency and understanding of complex systems., Comment: Preprint to be submitted to Computer Physics Communications

Bismuth selenide, a benchmark topological insulator, grows in a trigonal structure at ambient conditions and exhibits a number of enticing properties related to the formation of Dirac surface states. Besides this polytype, a metastable orthorhombic modification with Pnma space group has been produced by electrodeposition and high-pressure high-temperature synthesis displaying upon Sb doping significant thermoelectric properties in the midtemperature range. However, very little experimental information is available on the fundamental properties of this polytype, such as, e.g., the electronic band gap and the lattice dynamics. We report here the temperature dependence of the Raman spectra of orthorhombic Bi2Se3 between 10 K and 300 K, which displays an anharmonic behavior of the optical phonons that can be modelled with a two-phonon decay channel. In order to analyze the data we performed ab initio calculations of the electronic bandstructure, the phonon frequencies at the center of the Brillouin zone, and the phonon dispersion relations along the main symmetry directions, examining the effect of spin-orbit coupling in both phonon and electronic energies. Lastly, we report here cathodoluminescence experiments at 83 K that set a lower limit to the electronic bandgap at 0.835 eV, pointing to an indirect nature, in agreement with our calculations. These results shed light to essential properties of orthorhombic Bi2Se3 for further understanding of the potential of this semiconductor for thermoelectrics and new applications., Comment: 10 figures

In this work, we report diffuse reflectivity measurements in InNbO4, ScNbO4, YNbO4, and eight different rare-earth niobates. From a comparison with the established values of the band gap of InNbO4 and ScNbO4, we have found that the broadly used Tauc plot analysis leads to erroneous estimates of the band-gap energy of niobates. In contrast, accurate results are obtained considering excitonic contributions using the Elliot-Toyozawa model. We have found that YNbO4 and the rare-earth niobates are wide band-gap materials. The band-gap energy is 3.25 eV for CeNbO4, 4.35 eV for LaNbO4, 4.5 eV for YNbO4, and 4.73 - 4.93 eV for SmNbO4, EuNbO4, GdNbO4, DyNbO4, HoNbO4, and YbNbO4. An explanation for the obtained results will be presented. The fact that the band-gap energy is nearly not affected by the rare-earth substitution from SmNbO4 to YbNbO4 and the circumstance that these are the compounds with the largest band gap are a consequence of the fact that the band structure near the Fermi level originates mainly from Nb 4d and O 2p orbitals. We hypothesize that YNbO4, CeVO4, and LaNbO4 have smaller band gaps because of the contribution from rare-earth atom 4d or 5f states to the states near the Fermi level., Comment: 18 pages, 1 Table, 4 Figures

We report a characterization of the high-pressure behavior of zinc-iodate, Zn(IO3)2. By the combination of x-ray diffraction, Raman spectroscopy, and first-principles calculations we have found evidence of two subtle isosymmetric structural phase transitions. We present arguments relating these transitions to a non-linear behavior of phonons and changes induced by pressure on the coordination sphere of the iodine atoms. This fact is explained as a consequence of the formation of metavalent bonding at high-pressure which is favored by the lone-electron pairs of iodine. In addition, the pressure dependence of unit-cell parameters, volume, and bond is reported. An equation of state to describe the pressure dependence of the volume is presented, indicating that Zn(IO3)2 is the most compressible iodate among those studied up to now. Finally, phonon frequencies are reported together with their symmetry assignment and pressure dependence., Comment: 33 pages, 3 tables, 15 figures

Halide perovskite has attracted intense research interest owing to its multifaceted and versatile applications in optoelectronics. This intrigue is further fueled by their propensity to undergo intricate structural modifications under extreme conditions, thereby instigating property changes. Within this context, our study delves deep into the intricate interplay of structural and vibrational attributes within the inorganic-metal halide perovskite-like CsCuCl3. Our approach employs Raman spectroscopy and Synchrotron Powder X-Ray Diffraction (SPXRD) techniques harnessed under the dual conditions of low temperatures and high pressures. We have observed a distinct spin-phonon coupling mechanism by employing Raman spectroscopy at low temperatures; this coupling has been manifested as a renormalization phonon phenomenon that occurs notably at T* = 15 K. The correlation between spin and phonon dynamics becomes pronounced through a notable hardening of phonon temperature dependence, a behavior intricately linked to the material antiferromagnetic transition at TN = 10.7 K. The SPXRD under high pressure showed a first-order structural phase transition (SPT) at the critical pressure Pc = 3.69 GPa, leading to the transformation from the hexagonal P6522 to a base-centered monoclinic cell. Notably, the coexistence of both phases is discernible within the pressure range from 2.79 to 3.57 GPa, indicating that the SPT involves the reorganization of the internal [Cu2Cl9]5- dimer unit, with the Cl-Cu-Cl bending contributing more than stretching modes. Furthermore, we demonstrate that the SPT is reversible, but residual strain pressure influences the modification of the critical pressure Pc value upon pressure decrease., Comment: 27 pages, 8 figures

In this article, we propose a new metaheuristic inspired by the morphogenetic cellular movements of endothelial cells (ECs) that occur during the tumor angiogenesis process. This algorithm starts with a random initial population. In each iteration, the best candidate selected as the tumor, while the other individuals in the population are treated as ECs migrating toward the tumor's direction following a coordinated dynamics through a spatial relationship between tip and follower ECs. This algorithm has an advantage compared to other similar optimization metaheuristics: the model parameters are already configured according to the tumor angiogenesis phenomenon modeling, preventing researchers from initializing them with arbitrary values. Subsequently, the algorithm is compared against well-known benchmark functions, and the results are validated through a comparative study with Particle Swarm Optimization (PSO). The results demonstrate that the algorithm is capable of providing highly competitive outcomes. Furthermore, the proposed algorithm is applied to real-world problems (cantilever beam design, pressure vessel design, tension/compression spring and sustainable explotation renewable resource). The results showed that the proposed algorithm worked effectively in solving constrained optimization problems. The results obtained were compared with several known algorithms., Comment: 16 pages; 7 figures; added real-world problems in section 4 (pressure vessel design, tension/compression spring and sustainable explotation renewable resource); added references for section 4; typos corrected

Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels. Thus, a significant amount of research has been carried out to investigate and apply computer vision techniques to address this problem. One of the most promising approaches for wildfire fighting is the use of drones equipped with visible and infrared cameras for the detection, monitoring, and fire spread assessment in a remote manner but in close proximity to the affected areas. However, implementing effective computer vision algorithms on board is often prohibitive since deploying full-precision deep learning models running on GPU is not a viable option, due to their high power consumption and the limited payload a drone can handle. Thus, in this work, we posit that smart cameras, based on low-power consumption field-programmable gate arrays (FPGAs), in tandem with binarized neural networks (BNNs), represent a cost-effective alternative for implementing onboard computing on the edge. Herein we present the implementation of a segmentation model applied to the Corsican Fire Database. We optimized an existing U-Net model for such a task and ported the model to an edge device (a Xilinx Ultra96-v2 FPGA). By pruning and quantizing the original model, we reduce the number of parameters by 90%. Furthermore, additional optimizations enabled us to increase the throughput of the original model from 8 frames per second (FPS) to 33.63 FPS without loss in the segmentation performance: our model obtained 0.912 in Matthews correlation coefficient (MCC),0.915 in F1 score and 0.870 in Hafiane quality index (HAF), and comparable qualitative segmentation results when contrasted to the original full-precision model. The final model was integrated into a low-cost FPGA, which was used to implement a neural network accelerator., Comment: This paper has been accepted at the 22nd Mexican International Conference on Artificial Intelligence (MICAI 2023)

GaGeTe is a layered topological semimetal that has been recently found to exist in at least two different polytypes, $\alpha$-GaGeTe ($R\bar{3}m$) and $\beta$-GaGeTe ($P6_3 mc$). Here we report a joint experimental and theoretical study of the structural, vibrational, and electronic properties of these two polytypes at high pressure. Both polytypes show anisotropic compressibility and two phase transitions, above 7 and 15 GPa, respectively, as confirmed by XRD and Raman spectroscopy measurements. Although the nature of the high-pressure phases is not confirmed, comparison with other chalcogenides and total-energy calculations allow us to propose possible high-pressure phases for both polytypes with an increase in coordination for Ga and Ge atoms from 4 to 6. In particular, the simplification of the X-ray patterns for both polytypes above 15 GPa suggests a transition to a structure of relatively higher symmetry than the original one. This result is consistent with the rocksalt-like high-pressure phases observed in parent III-VI semiconductors, such as GaTe, GaSe, and InSe. Pressure-induced amorphization is observed upon pressure release. The electronic band structures of $\alpha$-GaGeTe and $\beta$-GaGeTe and their pressure dependence also show similarities to III-VI semiconductors, thus suggesting that the germanene-like sublayer induces a semimetallic character in both GaGeTe polytypes. Above 3 GPa, both polytypes lose their topological features, due to the opening of the direct band gap, while the reduction of the interlayer space increases the thermal conductivity at high pressure., Comment: Main manuscript: 47 pages, 12 figures, 4 tables; ESI: 20 pages, 12 figures, 9 tables

Aspects of cell metabolism are modeled by ordinary differential equations describing the change of intracellular chemical concentrations. There is a correspondence between this dynamical system and a complex network. As in the classic Erd\H{o}s--R\'enyi model, the reaction network can evolve by the iterative addition of edges to the underlying graph. In the biochemical context, each added reaction implies a metabolic mutation. In this work it is shown that modifications to the graph topology by gradually adding mutations lead here too to the formation of a giant connected component, i.e., to a percolation--like phase transition. It triggers an abrupt change in the functionality of the corresponding network. This percolation is mapped into a bifurcation in the intracellular dynamics. It acts as a shortcut in biological evolution, so that the most probable metabolic state for the cell is suddenly switched from cellular stagnation to exponential growth., Comment: 10 pages, 5 figures

Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or overdiagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features of the ROI from magnetic resonance images, as this process can greatly facilitate many diagnostic and therapeutic applications. However, the lack of clear prostate boundaries, the heterogeneity inherent to the prostate tissue, and the variety of prostate shapes makes this process very difficult to automate.In this work, six deep learning models were trained and analyzed with a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and Universitat Politecnica de Catalunya. We carried out a comparison of multiple deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy loss function. The analysis was performed using three metrics commonly used for image segmentation: Dice score, Jaccard index, and mean squared error. The model that give us the best result segmenting all the zones was R2U-Net, which achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error, respectively.

GaGeTe is a layered material composed of germanene and GaTe sublayers that has been recently predicted to be a basic Z2 topological semimetal. To date, only one polytype of GaGeTe is known with trigonal centrosymmetric structure (alpha phase, space group R-3m, No. 166). Here we show that as-grown samples of GaGeTe show traces of at least another polytype with hexagonal non-centrosymmetric structure (beta phase, space group P63mc, No. 186). Moreover, we suggest that another bulk hexagonal polytype (gamma phase, space group P-3m1, No. 164) could also be found near room conditions. Both alpha and beta polytypes have been identified and characterized by means of X-ray diffraction and Raman scattering measurements with the support of ab initio calculations. We provide the vibrational properties of both polytypes and show that the Raman spectrum reported for GaGeTe almost forty years ago and attributed to the alpha phase, was, in fact, that of the secondary beta phase. Additionally, we show that a Fermi resonance occurs in alpha-GaGeTe under non-resonant excitation conditions, but not under resonant excitation conditions. Theoretical calculations show that bulk beta-GaGeTe is a non-centrosymmetric weak topological semimetal with even smaller lattice thermal conductivity than centrosymmetric bulk alpha-GaGeTe. In perspective, our work paves the way for the control and engineering of GaGeTe polytypes to design and implement complex van der Waals heterostructures formed by a combination of centrosymmetric and non-centrosymmetric layers of up to three different polytypes in a single material, suitable for a number of fundamental studies and technological applications.

Among the different kinds of fire accidents that can occur during industrial activities that involve hazardous materials, jet fires are one of the lesser-known types. This is because they are often involved in a process that generates a sequence of other accidents of greater magnitude, known as domino effect. Flame impingement usually causes domino effects, and jet fires present specific features that can significantly increase the probability of this happening. These features become relevant from a risk analysis perspective, making their proper characterization a crucial task. Deep Learning approaches have become extensively used for tasks such as jet fire characterization; however, these methods are heavily dependent on the amount of data and the quality of the labels. Data acquisition of jet fires involve expensive experiments, especially so if infrared imagery is used. Therefore, this paper proposes the use of Generative Adversarial Networks to produce plausible infrared images from visible ones, making experiments less expensive and allowing for other potential applications. The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras. The obtained results are compared with some previous experiments, and it is shown that similar results were obtained., Comment: Pre-print submitted to Engineering Science and Technology, an International Journal

We report a comparative theoretical study of the \textit{Pnma} and \textit{R-3m} phases of Sb$_2$S$_3$, Bi$_2$S$_3$, and Sb$_2$Se$_3$ close to ambient pressure. Our enthalpy calculations at 0 K show that at ambient pressure the \textit{R-3m} (tetradymite-like) phase of Sb$_2$Se$_3$ is energetically more stable than the \textit{Pnma} phase, contrary to what is observed for Sb$_2$S$_3$ and Bi$_2$S$_3$, and irrespective of the exchange-correlation functional employed in the calculations. The result for Sb$_2$Se$_3$ is in contradiction to experiments where all three compounds are usually grown in the \textit{Pnma} phase. This result is further confirmed by free-energy calculations taking into account the temperature dependence of the unit-cell volumes and phonon frequencies. Lattice dynamics and elastic tensor calculations further show that both \textit{Pnma} and \textit{R-3m} phases of Sb$_2$Se$_3$ are dynamically and mechanically stable at zero applied pressure. Since these results suggest that the formation of the \textit{R-3m} phase for Sb$_2$Se$_3$ should be feasible at close to ambient conditions, we provide a theoretical crystal structure and simulated Raman and infrared spectra to help in its identification. We also discuss the results of the two published works that have claimed to have synthesized tetradymite-like Sb$_2$Se$_3$. Finally, the stability of the \textit{R-3m} phase across the three group-15 \textit{A$_2$X$_3$} sesquichalcogenides is analysed based on their van der Waals gap and X-X in-plane geometry., Comment: 17 pages, 9 figures