Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

More than just engaging? TikTok as an effective learning tool.

With the growing popularity of social media, educators have been adopting social media platforms, such as TikTok, for learning purposes. Whilst the effectiveness of TikTok to increase learner engagement has been demonstrated, there was little evidence indicating the effectiveness of TikTok on actual learner performance through formal graded assessments. This study is one of the first attempts to investigate the effects of TikTok as a learning tool on learner performance. The findings from a controlled experiment indicate that TikTok had a beneficial impact on the learner\u27s performance as well as self-perceived engagement in an introductory statistics course

Finite Element Analysis of a Novel Tensegrity-Based Vibratory Platform

The study aims to conduct the finite element analysis (FEA) of a novel tensegrity-based vibratory platform by using IronCAD software. and analyze its deformation under external forces to verify if the platform can generate the required advancing motion. Firstly, the structure and operating principles of the proposed platform are introduced. Subsequently, individual parts are created using IronCAD software and assembled to form a solid model of the entire platform. Finally, employing Multiphysics for IronCAD, FEA is conducted to analyze the platform’s displacement under different external forces, as well as to examine its natural frequencies and mode shapes. The simulation results indicate that the proposed platform effectively moves a part in a specified direction. Additionally, the maximum stress remains below the yield strength. Moreover, the mode shapes corresponding to the initial 3 natural frequencies contribute to the advancing motion

siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods

Small interfering RNA (siRNA) has been used widely to induce gene silencing in cells. To predict the efficacy of an siRNA with respect to inhibition of its target mRNA, we developed a two layer system, siPRED, which is based on various characteristic methods in the first layer and fusion mechanisms in the second layer. Characteristic methods were constructed by support vector regression from three categories of characteristics, namely sequence, features, and rules. Fusion mechanisms considered combinations of characteristic methods in different categories and were implemented by support vector regression and neural networks to yield integrated methods. In siPRED, the prediction of siRNA efficacy through integrated methods was better than through any method that utilized only a single method. Moreover, the weighting of each characteristic method in the context of integrated methods was established by genetic algorithms so that the effect of each characteristic method could be revealed. Using a validation dataset, siPRED performed better than other predictive systems that used the scoring method, neural networks, or linear regression. Finally, siPRED can be improved to achieve a correlation coefficient of 0.777 when the threshold of the whole stacking energy is ≥−34.6 kcal/mol. siPRED is freely available on the web at http://predictor.nchu.edu.tw/siPRED

Inclusions properties at 1673 K and room temperature with Ce addition in SS400 steel

Inclusion species formed in SS400 steel with Ce-addition was predicted by thermodynamic calculation. The analysis of the inclusion morphology and size distribution was carried out by applying Scanning Electron Microscopy (SEM) and Transmission Electron Microscope (TEM). Nano-Fe3O4 particles were also found in cerium-deoxidized and -desulfurized steel and their shapes were nearly spherical. The complex Ce2O3 inclusions covering a layer of 218 nm composed by several MnS particles with similar diffraction pattern. Most importantly, the complex Ce2O3 characterized by using TEM diffraction is amorphous in the steel, indicating that Ce2O3 formed in the liquid iron and then MnS segregated cling to it

ContraQA: Question Answering under Contradicting Contexts

With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over contradicting information to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the behavior of the QA model under contradicting contexts that are mixed with both real and fake information. We create the first large-scale dataset for this problem, namely Contra-QA, which contains over 10K human-written and model-generated contradicting pairs of contexts. Experiments show that QA models are vulnerable under contradicting contexts brought by misinformation. To defend against such a threat, we build a misinformation-aware QA system as a counter-measure that integrates question answering and misinformation detection in a joint fashion.Comment: Technical repor