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The intersection of technology and mental health has spurred innovative approaches to assessing emotional well-being, particularly through computational techniques applied to audio data analysis. This study explores the application of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models on wavelet extracted features and Mel-frequency Cepstral Coefficients (MFCCs) for emotion detection from spoken speech. Data augmentation techniques, feature extraction, normalization, and model training were conducted to evaluate the models' performance in classifying emotional states. Results indicate that the CNN model achieved a higher accuracy of 61% compared to the LSTM model's accuracy of 56%. Both models demonstrated better performance in predicting specific emotions such as surprise and anger, leveraging distinct audio features like pitch and speed variations. Recommendations include further exploration of advanced data augmentation techniques, combined feature extraction methods, and the integration of linguistic analysis with speech characteristics for improved accuracy in mental health diagnostics. Collaboration for standardized dataset collection and sharing is recommended to foster advancements in affective computing and mental health care interventions.

Baryon Acoustic Oscillation (BAO) provides a powerful tool to measure cosmic expansion and consequently the nature of the Dark Energy (DE). Recent precise BAO measurements by Dark Energy Spectroscopic Instrument data release 1 (DESI DR1), when combined with Cosmic Microwave Background (CMB) data from Planck and Supernovae of Type Ia (SN Ia), favor evolving dark energy over cosmological constant. This result is strongly related to the assumed priors on the Chevallier-Polarski-Linder (CPL) parameterization of DE. We test another parametrization which introduces two free parameters $n$ and $\alpha$, only $n$ is independent. Thus, it reduces the parameter space compared to the CPL model, which derives a more robust preference for evolving DE, if any. The model potentially produces three cosmological scenarios according to the values of its parameters. For $n=3$, the $\Lambda$CDM model is recovered, quintessence for $n<3$, and phantom for $n>3$. In the present study, we test the model on the background level, and, to our knowledge for the first time, on the linear perturbation level. Bayesian evidence analysis shows a weak preference for dynamical DE in the phantom regime over the cosmological constant DE using Planck, DESI, and PantheonPlus\&SH0ES data. The model predicts current phantom DE $w_{de,0} = -1.073 \pm 0.032$ and $H_0=70.9\pm 1.4$ km/s/Mpc when Planck+DESI data is used, which decreases the tension with $H_0$ local measurements to $1.2\sigma$ level., Comment: 24 pages, 9 figures, 4 tables

In this paper, we report the long-range and wireless interrogation of multiple chipless sensors from the isoline processing of three-dimensional polarimetric radar images. A Frequency-Modulated Continuous-Wave Radar operating at 24 GHz is used for the indoor interrogation of four sensors in the basement of a Laboratory. In such cluttered environment, the proposed radar image processing based on isolines computation allows the wireless measurement range of sensors up to 5.8m.

The integration of van der Waals (vdW) materials into photonic devices has laid out a foundation for many new quantum and optoelectronic applications. Despite tremendous progress in the nanofabrication of photonic building blocks from vdW crystals, there are still limitations, specifically with large-area devices and masking. Here, we focus on hexagonal boron nitride (hBN) as a vdW material and present a double etch method that overcomes problems associated with methods that employ metallic films and resist-based films for masking. Efficacy of the developed protocol is demonstrated by designing and fabricating a set of functional photonic components including waveguides, ring resonators and photonic crystal cavities. The functionality of the fabricated structures is demonstrated through optical characterization over several key spectral ranges. These include the near-infrared and blue ranges, where the hBN boron vacancy (VB-) spin defects and the coherent B center quantum emitters emit, respectively. The double etch method enables fabrication of high-quality factor optical cavities and constitutes a promising pathway toward on-chip integration of vdW materials., Comment: 10 pages, 4 figures

This paper investigates the possibility of Dark Matter (DM) fermions production alongside a gauge boson (A$^{\prime}$) using a model based on Einstein-Cartan gravity in an electron-positron linear collider, such as the ILC, that operates at a center-of-mass energy $\sqrt{s} = 500$ GeV with a detector's integrated luminosity of 1000 fb$^{-1}$. We used the WHIZARD package as the event generator to simulate the $ e^{+} e^{-}$ interactions that lead to the production of di-muon pairs and missing transverse energy. We specifically are performing this study on a low mass dark gauge boson, M$_{A^\prime}$= 10 GeV, that can subsequently decay into a muon pair (A$^\prime\rightarrow \mu^{+}\mu^{-})$ while aiming to set upper limits on the free parameters' masses of the model, such as the torsion field (ST), if evidence for physics beyond the standard model is not found.

In this study, we aim to explore the potential for generating events related to dark matter (DM) in conjunction with a neutral heavy gauge boson (Z$^{\prime}$) decaying leptonically during proton-proton collisions at the Large Hadron Collider (LHC). These collisions occur at a center-of-mass energy of $\sqrt{s}$ = 14 TeV, with a high integrated luminosity equivalent to 1000 fb$^{-1}$. Our analysis involves interpreting the outcomes through Monte Carlo simulation of the effective field theory (EFT) framework. If no new physics is detected, we establish constraints on various parameters within the EFT context, such as the scenario cutoff scale ($\Lambda$) and the Z$^{\prime}$ mass.