Circular braiding has been successfully adapted for producing near-net shape structures for advanced fiber-reinforced composites. Net-shape manufacturing is significantly important for fabricating complex three-dimensional (3D) preforms. Geometrical modeling of braid patterns on 3D preforms plays a key role in determining their mechanical behavior. In this research work, the geometrical models of strand trajectory on the surface of cylindrical and conical mandrels with diamond, regular and triaxial braid patterns have been developed. These geometrical models of strand profiles were then simulated using Virtual Reality Modeling Language. Subsequently, the strands on complex-shaped mandrels, including ‘bottle’ and ‘funnel’, were simulated and accordingly, the braid angles have been predicted and compared with the experimental results. A virtual experiment was also conducted to compare the trajectory of the strands having constant and varying braid angles on the surface of conical mandrels.
We propose a novel design for a hermetically sealable device consisting of charged linear and nonlinear membranes driven in the Gigahertz range in vacuum setting, as a source of antibunched single phonons.Constraints for effecting phonon antibunching are found using the stationary Liouville-von Neumann master equation. Using analytical calculations, material and geometry optimization we show that sizes of the proposed system can be upscaled to near-micrometer range, in a trade-off with the system operating temperature. The results are significant to realize quantum Phononics which has much promise as a modality for sensing and computing applications.
The advances in the field of Automated Machine Learning (AutoML) have greatly reduced human effort in selecting and optimizing machine learning algorithms. These advances, however, have not yet widely made it to Recommender-Systems libraries. We introduce Auto-CaseRec, a Python framework based on the CaseRec recommender-system library. Auto-CaseRec provides automated algorithm selection and parameter tuning for recommendation algorithms. An initial evaluation of Auto-CaseRec against the baselines shows an average 13.88% improvement in RMSE for theMovielens100K dataset and an average 17.95% improvement in RMSE for the Last.fm dataset.
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