This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.

We study the interactions between NPs and liquid crystal smectic topological defects that can ultimately lead to unique NP orientations.

Smectic liquid crystals can be viewed as model systems for lamellar structures for which there has been extensive theoretical development. We demonstrate that a nonlinear energy description is required with respect to the usual Landau-de Gennes elasticity in order to explain the observed layer spacing of highly curved smectic layers. Using x-ray diffraction we have quantitatively determined the dilation of bent layers distorted by antagonistic anchoring (as high as 1.8% of dilation for the most bent smectic layers) and accurately described it by the minimal nonlinear expression for energy. We observe a 1° tilt of planar layers that are connected to the curved layers. This value is consistent with simple energetic calculations, demonstrating how the bending energy impacts the overall structure of a thin distorted smectic film. Finally, we show that combined x-ray measurements and theoretical modeling allow for the quantitative determination of the number of curved smectic layers and of the resulting thickness of the dilated region with unprecedented precision.

Controlled activation of the polarization of single photon emitters is a challenge. We use CdSe/CdS dot-in-rods (DRs) confined and oriented in smectic topological grain boundaries to activate the fluorescence polarization through temperature variation. We show that temperature acts as a knob to switch on/off the polarization of DR emitted light between smectic and isotropic phase. This occurs through the orientational motion of the DR assemblies, which is induced in isotropic phase due to the disappearing of the defects. In addition, we evidence a significant improvement of DR emitted light polarization after cooling back from the isotropic phase. It is measured by the increase in polarization degree from 0.28 to 0.36 on average for DR assemblies. This improvement is managed by the smectic phase diagram near the smectic/nematic transition. Near the smectic/nematic transition, the smectic grain boundaries entirely cover the sample surface and allow for a reorientation of all DRs, even of those initially not confined in smectic grain boundaries.