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Recent advancements in transition metal dichalcogenides (TMDs) have unveiled exceptional optical and electronic characteristics, opened up new opportunities, and provided a unique platform for exploring light-matter interactions under the strong coupling regime. The exploitation of exciton-polaritons, with their peculiar hybrid light-matter properties, for the development of spintronic customizable devices that enhance both the information capacity and functionality at ambient temperatures is often suggested as a promising route. However, although TMD polaritons have shown promising potential, the microscopic mechanisms leading to nonlinearities in TMD polaritons are complex and their spin-anisotropy, a crucial requirement for many proposed polaritonic devices, has been missing. Here, we demonstrate the absence of spin-anisotropic interaction in a monolayer WS2 microcavity (at room temperature) and show how spin-dependent interactions can be controlled and spin anisotropy recovered by engineering double WS2 layer structures with varied interlayer spacing. We attribute this phenomenon to a distinctive feature in exciton-polariton physics: layer-dependent polariton-phonon coupling. We use theoretical calculations of the phonon electrostatic potentials finding a drastically different coupling strength for single and double monolayer samples and discuss qualitatively how this explains the observed spin-anisotropic response. This is further consistent with experiments on multi WS2 layer samples and the identification of a critical separation distance, above which an effective single monolayer spin-anisotropic response is recovered, both in experiment and theory. Our work lays the groundwork for the development of spin-optronic polaritonic devices at room temperature.

Single-photon sources (SPS) hold the potential to enhance the performance of quantum key distribution (QKD). QKD systems using SPS often require cryogenic cooling, while recent QKD attempts using SPS operating at room-temperature have failed to achieve long-distance transmission due to the SPS not operating at telecommunication wavelength. In this work, we have successfully demonstrated QKD using a room-temperature SPS at telecommunication wavelength. The SPS used in this work is based on point defects hosted by gallium nitride (GaN) thin films grown on sapphire substrates. We employed a time-bin and phase encoding scheme to perform the BB84 and reference-frame-independent QKD protocols over a 33 km fiber spool, achieving a secure key rate of $7.58\times 10^{-7}$ per pulse. Moreover, we also implemented a metropolitan QKD experiment over a 30 km deployed fiber, achieving a secure key rate of $6.06\times 10^{-8}$ per pulse. These results broaden the prospects for future use of SPS in commercial QKD applications.

Solid-state defects susceptible of spin manipulation hold great promise for scalable quantum technology. To broaden their utility, operating at room temperature and emitting in the telecom wavelength range are desired, eliminating cryogenic requirements and leveraging existing optical fiber infrastructure for transmitting the quantum information. To that end, we report that telecom single photon emitters (SPEs) in gallium nitride (GaN) exhibit optically detected magnetic resonance (ODMR) at room temperature. The analysis of ODMR as a function of magnetic field orientation enables the determination of the orientation of the spin quantization axis with respect to the GaN crystalline lattice. The optical transitions dynamics are analyzed to gain further insight into the transition rates dominating ODMR. Our findings, coupled with GaN's mature fabrication technology, could facilitate the realization of scalable quantum technology.

An adaptive control approach for a three-phase grid-interfaced solar photovoltaic system based on the new Neuro-Fuzzy Inference System with Rain Optimization Algorithm (ANROA) methodology is proposed and discussed in this manuscript. This method incorporates an Adaptive Neuro-fuzzy Inference System (ANFIS) with a Rain Optimization Algorithm (ROA). The ANFIS controller has excellent maximum tracking capability because it includes features of both neural and fuzzy techniques. The ROA technique is in charge of controlling the voltage source converter switching. Avoiding power quality problems including voltage fluctuations, harmonics, and flickers as well as unbalanced loads and reactive power usage is the major goal. Besides, the proposed method performs at zero voltage regulation and unity power factor modes. The suggested control approach has been modeled and simulated, and its performance has been assessed using existing alternative methods. A statistical analysis of proposed and existing techniques has been also presented and discussed. The results of the simulations demonstrate that, when compared to alternative approaches, the suggested strategy may properly and effectively identify the best global solutions. Furthermore, the system's robustness has been studied by using MATLAB/SIMULINK environment and experimentally by Field Programmable Gate Arrays Controller (FPGA)-based Hardware-in-Loop (HLL)., Comment: The paper was published in Energy Reports journal (ELSEVIER). Cite as: Prasad, D., Kumar, N., Sharma, R., Malik, H., M\'arquez, F. P. G., & Pinar-P\'erez, J. M. (2023). A novel ANROA based control approach for grid-tied multi-functional solar energy conversion system. Energy Reports, 9, 2044-2057

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings, various WS prediction methods have been used. Predictive data mining offers variety of methods for WS predictions where artificial neural network (ANN) is one of the reliable and accurate methods. It is observed from the result of this study that ANN gives better accuracy in comparison conventional model. The accuracy of WS prediction models is found to be dependent on input parameters and architecture type algorithms utilized. So the selection of most relevant input parameters is important research area in WS predicton field. The objective of the paper is twofold: first extensive review of ANN for wind power and WS prediction is carried out. Discussion and analysis of feature selection using Relief Algorithm (RA) in WS prediction are considered for different Indian sites. RA identify atmospheric pressure, solar radiation and relative humidity are relevant input variables. Based on relevant input variables Cascade ANN model is developed and prediction accuracy is evaluated. It is found that root mean square error (RMSE) for comparison between predicted and measured WS for training and testing wind speed are found to be 1.44 m/s and 1.49 m/s respectively. The developed cascade ANN model can be used to predict wind speed for sites where there are not WS measuring instruments are installed in India., Comment: Malik, H., Yadav, A. K., M\'arquez, F. P. G., & Pinar-P\'erez, J. M. (2022). Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India. Energy Strategy Reviews, 41, 100864

In this paper we address the problem of allocating the efforts of a collection of repairmen to a number of deteriorating machines in order to reduce operation costs and to mitigate the cost (and likelihood) of unexpected failures. Notwithstanding these preventive maintenance interventions are aimed at returning the machine to a so-called as-good-as-new state, unforeseeable factors may imply that maintenance interventions are not perfect and the machine is only returned to an earlier (uncertain) state of wear. The problem is modelled as a restless bandit problem and an index policy for the sequential allocation of maintenance tasks is proposed. A series of numerical experiments shows the strong performance of the proposed policy. Moreover, the methodology is of interest in the general context of dynamic resource allocation and restless bandit problems, as well as being useful in the particular imperfect maintenance model described., Comment: Published in Computers and Operations Research (ELSEVIER), July 2020. DOI: https://doi.org/10.1016/j.cor.2020.104927 Article available under the terms of the CC-BY-NC-ND licence

The polarization discontinuity across interfaces in polar nitride-based heterostructures can lead to the formation of two-dimensional electron and hole gases. In the past, the observation of these electron and hole gases has been achieved through various experimental techniques, most often by electronic measurements but occasionally by optical means. However, the occurrence of a two-dimensional hole gas has never been demonstrated optically. The objective of this article is to demonstrate, thanks to the combination of various optical spectroscopy techniques coupled to numerical simulations, the presence of a two-dimensional hole gas in a GaN/AlGaN/GaN heterostructure. This is made possible thanks to a GaN/AlGaN/GaN heterostructure displaying a micrometer-thick AlGaN layer and a GaN cap thicker than in conventional GaN-based HEMTs structures. The band structure across the whole heterostructure was established by solving self-consistently the Schr\"odinger and Poisson equations and by taking into account the experimentally determined strain state of each layer. Continuous and quasi-continuos photoluminescence reveal the presence of a broad emission band at an energy around 50 meV below the exciton emission and whose energy blueshifts with increasing excitation power density, until it is completely quenched due to the complete screening of the internal electric field. Time-resolved measurements show that the emission arising from the two-dimensional hole gas can be assigned to the recombination of holes in the potential well with electrons located in the top GaN as well as electron from the bottom AlGaN, each of them displaying different decay times due to unequal electric fields. Besides the optical demonstration of a two-dimensional hole gas in a nitride-based heterostructure, our work highlights the optical recombination processes involved in the emission from such a hole gas.

The Split and Rephrase (SPRP) task, which consists in splitting complex sentences into a sequence of shorter grammatical sentences, while preserving the original meaning, can facilitate the processing of complex texts for humans and machines alike. It is also a valuable testbed to evaluate natural language processing models, as it requires modelling complex grammatical aspects. In this work, we evaluate large language models on the task, showing that they can provide large improvements over the state of the art on the main metrics, although still lagging in terms of splitting compliance. Results from two human evaluations further support the conclusions drawn from automated metric results. We provide a comprehensive study that includes prompting variants, domain shift, fine-tuned pretrained language models of varying parameter size and training data volumes, contrasted with both zero-shot and few-shot approaches on instruction-tuned language models. Although the latter were markedly outperformed by fine-tuned models, they may constitute a reasonable off-the-shelf alternative. Our results provide a fine-grained analysis of the potential and limitations of large language models for SPRP, with significant improvements achievable using relatively small amounts of training data and model parameters overall, and remaining limitations for all models on the task.

We propose a novel formulation of elastic materials based on high-order interpolants, which fits accurately complex elastic behaviors, but remains conservative. The proposed high-order interpolants can be regarded as a high-dimensional extension of radial basis functions, and they allow the interpolation of derivatives of elastic energy, in particular stress and stiffness. Given the proposed parameterization of elasticity models, we devise an algorithm to find optimal model parameters based on training data. We have tested our methodology for the homogenization of 2D microstructures, and we show that it succeeds to match complex behaviors with high accuracy., Comment: Published at Computer Graphics Forum (Proc. of ACM/SIGGRAPH SCA), 2022. Project website http://mslab.es/projects/HiOInterp/

The stimulated emission of exciton-polaritons was proposed as a means of lowering the lasing threshold because it does not require the dissociation of excitons to obtain an electron-hole plasma, as in a classical semiconductor laser based on population inversion. In this work we propose a method to prove unambiguously the polaritonic nature of lasing by combining experimental measurements with a model accounting for the permittivity change as a function of the carrier density. To do so we use angle resolved photoluminescence to observe the lasing at cryogenic temperature from a polariton mode in a zinc oxide waveguide structure, and to monitor the continuous shift of the polaritonic dispersion towards a photonic dispersion as the optical intensity of the pump is increased (up to 20 times the one at threshold). This shift is reproduced thanks to a model taking into account the reduction of the oscillator strength and the renormalization of the bandgap due to the screening of the electrostatic interaction between electrons and holes. Furthermore, the measurement of the carriers lifetime at optical intensities in the order of those at which the polariton lasing occurs enables us to estimate the carrier density, confirming it is lower than the corresponding Mott density for zinc oxide reported in the literature.

Full wavefront control by photonic components requires that the spatial phase modulation on an incoming optical beam ranges from 0 to 2{\pi}. Because of their radiative coupling to the environment, all optical components are intrinsically non-Hermitian systems, often described by reflection and transmission matrices with complex eigenfrequencies. Here, we show that Parity-Time symmetry breaking -- either explicit or spontaneous -- moves the position of Zero singularities of the reflection or transmission matrices from the real axis to the upper part of the complex frequency plane. A universal 0 to 2{\pi}-phase gradient of an output channel as a function of the real frequency excitation is thus realized whenever the discontinuity branch bridging a Zero and a Pole, i.e a pair of singularities, is crossing the real axis. This basic understanding is applied to engineer electromagnetic fields at interfaces, including, but not limited to, metasurfaces. Non-Hermitian topological features associated with exceptional degeneracies or branch cut crossing are shown to play a surprisingly pivotal role in the design of resonant photonic systems.

In this paper, we present a new approach based on dynamic factor models (DFMs) to perform nowcasts for the percentage annual variation of the Mexican Global Economic Activity Indicator (IGAE in Spanish). The procedure consists of the following steps: i) build a timely and correlated database by using economic and financial time series and real-time variables such as social mobility and significant topics extracted by Google Trends; ii) estimate the common factors using the two-step methodology of Doz et al. (2011); iii) use the common factors in univariate time-series models for test data; and iv) according to the best results obtained in the previous step, combine the statistically equal better nowcasts (Diebold-Mariano test) to generate the current nowcasts. We obtain timely and accurate nowcasts for the IGAE, including those for the current phase of drastic drops in the economy related to COVID-19 sanitary measures. Additionally, the approach allows us to disentangle the key variables in the DFM by estimating the confidence interval for both the factor loadings and the factor estimates. This approach can be used in official statistics to obtain preliminary estimates for IGAE up to 50 days before the official results., Comment: 29 pages, 8 figures, 1 table and 1 annex