The Ca II infrared triplet's performance as an activity indicator compared to Ca II H and K

Aims. A large number of Calcium Infrared Triplet (IRT) spectra are expected from the GAIA- and CARMENES missions. Conversion of these spectra into known activity indicators will allow analysis of their temporal evolution to a better degree. We set out to find such a conversion formula and to determine its robustness. Methods. We have compared 2274 Ca II IRT spectra of active main-sequence F to K stars taken by the TIGRE telescope with those of inactive stars of the same spectral type. After normalizing and applying rotational broadening, we subtracted the comparison spectra to find the chromospheric excess flux caused by activity. We obtained the total excess flux, and compared it to established activity indices derived from the Ca II H & K lines, the spectra of which were obtained simultaneously to the infrared spectra. Results. The excess flux in the Ca II IRT is found to correlate well with $R_\mathrm{HK}'$ and $R_\mathrm{HK}^{+}$, as well as $S_\mathrm{MWO}$, if the $B-V$-dependency is taken into account. We find an empirical conversion formula to calculate the corresponding value of one activity indicator from the measurement of another, by comparing groups of datapoints of stars with similar B-V.Comment: 16 pages, 15 figures. Accepted for publication in Astronomy & Astrophysic

Controlling anomalous stresses in soft field-responsive systems

We report a new phenomenon occurring in field-responsive suspensions: shear-induced anomalous stresses. Competition between a rotating field and a shear flow originates a multiplicity of anomalous stress behaviors in suspensions of bounded dimers constituted by induced dipoles. The great variety of stress regimes includes non-monotonous behaviors, multi-resonances, negative viscosity effect and blockades. The reversibility of the transitions between the different regimes and the self-similarity of the stresses make this phenomenon controllable and therefore applicable to modify macroscopic properties of soft condensed matter phasesComment: 5 pages, 6 figures, submitted to PR

The Dark Matter Problem in Light of Quantum Gravity

We show how, by considering the cumulative effect of tiny quantum gravitational fluctuations over very large distances, it may be possible to: ($a$) reconcile nucleosynthesis bounds on the density parameter of the Universe with the predictions of inflationary cosmology, and ($b$) reproduce the inferred variation of the density parameter with distance. Our calculation can be interpreted as a computation of the contribution of quantum gravitational degrees of freedom to the (local) energy density of the Universe.Comment: 13 pages, LaTeX, (3 figues, not included

Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis

Machine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information. © 2022, The Author(s)

Modeling the effect of the electrode potential in SERS by electronic structure calculations.

Surface Enhanced Raman Spectroscopy (SERS), due to the ability of greatly intensify the weak Raman signal of molecules adsorbed to metal surfaces, has proven to be a very useful tool to investigate changes in the electronic structure of metal-molecule surface complex. A deep knowledge of the electronic structure of these metal-molecule hybrid systems is key in electrochemistry, catalysis, plasmonics, molecular electronics, and in the development of selective and ultra-sensitive analytical sensors. The origin of this huge enhancement in SERS is due to two contributions: the electromagnetic (EM), related to surface plasmons, and the chemical mechanism, due to resonant charge transfer (CT) process between the adsorbate and the metal (CTSERS). Unfortunately, the SERS implies very complex phenomena where the molecule and the metal nanoparticle are involved. This fact makes challenging to build realistic theoretical models that take into account both the metal and the molecule at quantum level. We propose a methodology, based on DFT and ab initio electronic calculations, to simulate the effect of the electrode potential on the absorption, on the charge transfer states energies, and on the electronic excitations in metal-molecule hybrid systems from a microscopic point of view. This methodology consists on the prediction of Raman intensities from ab initio calculations of the geometries or the energy gradients at the excited states Franck-Condon point, bringing the possibility to predict the intensities in CTSERS as well as in resonance Raman without the need to know the excited state geometries, not always feasible to compute. The microscopic model adopted to mimic the effect of the interphase electric potential consist in a molecule adsorbed to a linear silver cluster [Agn-Adsorbate]q, were n is the number of silver atoms, and the total charge of the system (q) is zero for n=2 and q=±1 for n=1, 3 and 7.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech