Multipiezo Effect in Altermagnetic V2SeTeO Monolayer.

Strain engineering has been used as an efficient method to modulate various properties of quantum materials and electronic devices. One may establish piezo effects based on a disciplined response to the strain in multifunctional nanosystems. Inspired by a recent theoretical proposal on the interesting piezomagnetism and C-paired valley polarization in the V2Se2O monolayer, we predict a stable altermagnetic Janus monolayer V2SeTeO using density functional theory calculations. It exhibits a novel "multipiezo" effect combining piezoelectricity, piezovalley, and piezomagnetism. Most interestingly, the valley polarization and the net magnetization under strain in V2SeTeO exceed these in V2Se2O, along with the additional large piezoelectric coefficient. The "multipiezo" effect makes Janus monolayer V2SeTeO as a tantalizing material for potential applications in nanoelectronics, optoelectronics, spintronics, and valleytronics.

Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space.

An evolutionary approach to designing accurate classifiers with a compact fuzzy-rule base using a scatter partition of feature space is proposed, in which all the elements of the fuzzy classifier design problem have been moved in parameters of a complex optimization problem. An intelligent genetic algorithm (IGA) is used to effectively solve the design problem of fuzzy classifiers with many tuning parameters. The merits of the proposed method are threefold: 1) the proposed method has high search ability to efficiently find fuzzy rule-based systems with high fitness values, 2) obtained fuzzy rules have high interpretability, and 3) obtained compact classifiers have high classification accuracy on unseen test patterns. The sensitivity of control parameters of the proposed method is empirically analyzed to show the robustness of the IGA-based method. The performance comparison and statistical analysis of experimental results using ten-fold cross validation show that the IGA-based method without heuristics is efficient in designing accurate and compact fuzzy classifiers using 11 well-known data sets with numerical attribute values.