The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training datasets and unlimited testing datasets. It is vitally significant to enhance the GP of the pretrained ASISR models by making full use of the testing samples. The ASISR models usually employ an open-loop architecture from low-resolution (LR) images to super-resolution (SR) images. The degradation model from SR samples to LR samples is known bicubic down-sampling for the classical ASISR, is supposed down-sampling with additive random noise for the blind ASISR, and is learnable for the real-world ASISR. Combining the ASISR and degradation models, it is potentially possible to adopt a closed-loop architecture based on the automatic control theory for strengthening the GP of the ASISR methods. Therefore, this paper proposes a closed-loop architecture, circular ASISR (CASISR), to lift the capability of image reconstruction. A mathematical nonlinear loop equation is established to describe the CASISR, the reasonability of the CASISR is proven by conditional probability theory, and the stability of the CASISR is proven by Taylor series approximation. The first-order and second-order absolute difference images are defined to compare the image reconstruction performance of the ASISR and the CASISR methods. Comprehensive simulation experiments show that the proposed CASISR approach outperforms the eight state-of-the-art ASISR approaches in the quality of image reconstruction. Especially, the proposed CASISR is extraordinarily suitable for fractional SR scale factors and is extremely effective for text and stripe images with drastically changed edges.
In healthcare, clinical trial datasets are often high-dimensional and have small sample sizes, increasing the risk of overfitting. Furthermore, research in this context usually focuses not only on the accuracy of the resulting models but also on their explainability. Feature selection can mitigate overfitting and provide valuable insights into relevant features. Using SHAP (SHapley Additive exPlanation) values has proved to be a useful tool to identify significant features. In this paper, we propose implementing Kernel SHAP in the shap-select module to extend its functionality to non-tree-based models. This is done by introducing a masker that generates background values that SHAP uses to simulate the removal of a feature. The performance is then evaluated on a small high-dimensional dataset, and the resulting feature selection is evaluated. The results show that, despite higher instability in the performance metrics, the method’s capacity to identify significant features remains promising. These findings can serve as a starting point for further research into the relationship between feature characteristics and feature selection methods, as well as for improving the capacity to identify relevant features using SHAP values for feature selection.
This report addresses a challenge proposed by Comissão de Viticultura da Região dos Vinhos Verdes (CVRVV) at the 181st European Study Group in Industry. Here we investigate the refinement of the sensory evaluation methodology of wine employed by the CVRVV laboratory, with a focus on improving statistical rigor and tasters' assessments. We perform a detailed statistical analysis of the sensory evaluation data, including measures of central tendency, dispersion, and distribution, to identify sources of variability. We also develop a script to assess tasters consistency by analyzing deviations from the panel median and repeatability across tests. These approaches improve the aggregation of sensory data, detect errors, and establish sound statistical criteria for sample validation and test series acceptance, ensuring more precise and reliable sensory analysis.
In an effort to reduce waste and manufacturing costs in automotive assembly tooling, this study evaluates the potential of replacing the traditional machining of technical plastic connector (CH) supports with Fused Deposition Modelling (FDM) using Polylactic Acid (PLA). Comprehensive experimental evaluations were conducted using different printing parameters to cover mechanical properties (tensile, flexural and compressive strength), environmental durability (thermal ageing, moderate heat exposure and chemical resistance) and functional performance (torque, drop and cyclic load). The results confirm that FDM-manufactured PLA components meet the operational requirements for low-volume automotive tooling. Specifically, a cubic infill density of 20% was found to provide the optimal balance of structural integrity, resource efficiency, and mechanical durability, offering a sustainable and cost-effective alternative to conventionally machined plastics.
Exceptional points (EPs) correspond to specific values of the system parameters that yield defective eigenvalues. The concept is demonstrated experimentally in the case of a simple mechanical system consisting of two coupled linearized pendulums. The latter is designed and instrumented in order to allow the encircling of an EP: the modulation of both the mass and stiffness matrices is achieved by controlling the length of one pendulum, whereas the damping is controlled by using eddy current brake mechanisms. The time evolution of the state vector, identified with the help of the extended Kalman filter, is described using the instantaneous modal basis. This allows one to quantify and observe mode coupling mechanisms, non-adiabatic effects and chiral behaviour. The paper ends with a numerical study, via the analysis of the monodromy matrix and the multiple-scale approach, which illustrates both the effect on chirality of the period of encircling and the presence of an EP in the loop.
