The critical roles of hydrophobicity, surface Ru0 and active O2-/O22- sites on toluene combustion on Ru/ZSM-5 with varied Si/Al ratios.

By targeting more feasible catalysts for VOC combustion, 2%Ru/ZSM-5 catalysts were fabricated by supporting RuO2, a relatively cheaper noble metal, onto HZSM-5 supports with varied Si/Al ratios for toluene combustion. The valence state distribution of Ru and the Ru/RuO2-support interaction have been explored and elucidated. It has been revealed that the catalytic activity increases with the increase of the Si/Al ratio in the order 2%Ru/ZSM-5-18 < 2%Ru/ZSM-5-40 < 2%Ru/ZSM-5-72 < 2%Ru/ZSM-5-110 < 2%Ru/ZSM-5-255 < 2%Ru/SiO2-MFI. Interestingly, the hydrophobicity of the samples improves also with the increase in the Si/Al ratio, which impedes H2O adsorption effectively and its competition for the surface-active sites with the reactants. Both RuO2 and Ru0 are detected on all the catalysts, and the Ru0 amount/ratio increases significantly with increasing the Si/Al ratio, which promotes the adsorption/activation of both toluene and O2 molecules. Furthermore, the amount of surface-active O2- and O22- is evidently improved. Therefore, the mixed interaction of higher hydrophobicity, more surface Ru0 and active oxygen sites is the major reason for the enhancement in the activity of a Ru/ZSM-5 having a higher Si/Al ratio. It is concluded that the optimal catalyst can be designed by loading Ru/RuO2 onto an MFI framework structure support with the highest Si content.

Evaluation of a rat model of exercise-induced fatigue using treadmill running with progressively increasing load.

The mechanism behind exercise-induced fatigue is a significant topic in the field of sports physiology. Therefore, establishing and evaluating an acute exercise-induced fatigue animal model that explores the limits of the motor system may provide greater insight into these mechanisms. Heart rate is an important quantitative parameter that accurately reflects the immediate change in physical function due to exercise load. And there is likely to be an important correlation between heart rate and behavioral performance. In this study, changes in heart rate and behavioral indexes during exercise-induced fatigue were quantitatively analyzed in rats using heart rate telemetry and video methods respectively. The behavioral indexes were used as independent variables and the degree of fatigue was used as the forecast value. Ternary quadratic function curve fitting was used to deduce a formula to calculate a fatigue score: Y = 15.2548+0.4346∙xa-0.1154∙xb+0.6826∙xc+0.0044∙xa∙xb-0.0021∙xb∙xc-0.0013∙xc∙xa-0.0023∙xa2-0.0016∙xb2 (r2=0.906). It identified a linear relationship between heart rate and exercise intensity, with a plateau in heart rate occurring during difference periods. It will serve as an effective reference for the modeling of exercise-induced fatigue. In addition, it also provides a theoretical method for analyzing the correlation between peripheral and central parameters.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories. We design an autoencoder with a latent space where an orthogonal constraint is imposed to separate the space of global trajectories from individual trajectories. We also devise a cross-reconstruction loss to ensure consistency of global trajectories and enhance the orthogonality between representation spaces. To evaluate UOMETM, we conducted simulation experiments on images to verify that every component functions as intended. Furthermore, we evaluated its performance and robustness using longitudinal brain cortical thickness from two Alzheimer's disease (AD) datasets. Comparative analyses with state-of-the-art methods revealed UOMETM's superiority in identifying global and individual longitudinal patterns, achieving a lower reconstruction error, superior orthogonality, and higher accuracy in AD classification and conversion forecasting. Remarkably, we found that the space of global trajectories did not significantly contribute to AD classification compared to the space of individual trajectories, emphasizing their clear separation. Moreover, our model exhibited satisfactory generalization and robustness across different datasets. The study shows the outstanding performance and potential clinical use of UOMETM in the context of longitudinal data analysis.

When Does Diversity Help Generalization in Classification Ensembles?

Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element-"diversity." The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization of classification ensembles, we investigate three issues on diversity, that is, the measurement of diversity, the relationship between the proposed diversity and the generalization error, and the utilization of this relationship for ensemble pruning. In the diversity measurement, we measure diversity by error decomposition inspired by regression ensembles, which decompose the error of classification ensembles into accuracy and diversity. Then, we formulate the relationship between the measured diversity and ensemble performance through the theorem of margin and generalization and observe that the generalization error is reduced effectively only when the measured diversity is increased in a few specific ranges, while in other ranges, larger diversity is less beneficial to increasing the generalization of an ensemble. Besides, we propose two pruning methods based on diversity management to utilize this relationship, which could increase diversity appropriately and shrink the size of the ensemble without much-decreasing performance. The empirical results validate the reasonableness of the proposed relationship between diversity and ensemble generalization error and the effectiveness of the proposed pruning methods.

Subarchitecture Ensemble Pruning in Neural Architecture Search.

Neural architecture search (NAS) is gaining more and more attention in recent years because of its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computations that might not be affordable for researchers and practitioners. Although recent attempts have employed ensemble learning methods to mitigate the enormous computational cost, however, they neglect a key property of ensemble methods, namely diversity, which leads to collecting more similar subarchitectures with potential redundancy in the final design. To tackle this problem, we propose a pruning method for NAS ensembles called " subarchitecture ensemble pruning in neural architecture search (SAEP)." It targets to leverage diversity and to achieve subensemble architectures at a smaller size with comparable performance to ensemble architectures that are not pruned. Three possible solutions are proposed to decide which subarchitectures to prune during the searching process. Experimental results exhibit the effectiveness of the proposed method by largely reducing the number of subarchitectures without degrading the performance.

Ensemble Pruning Based on Objection Maximization With a General Distributed Framework.

Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors, while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy. Then we propose an ensemble pruning method, including a centralized version and a distributed version, in which the latter is to speed up the former. Finally, we extract a general distributed framework for ensemble pruning, which can be widely suitable for most of the existing ensemble pruning methods and achieve less time-consuming without much accuracy degradation. Experimental results validate the efficiency of our framework and methods, particularly concerning a remarkable improvement of the execution speed, accompanied by gratifying accuracy performance.