The effects of moisture conditioning on the mechanical properties of the fused deposition modeling (FDM) printed carbon fiber reinforced composites

The aims of this thesis is to evaluate the effects of moisture conditioning on the mechanical and tribological properties of the fused deposition modeling (FDM) printed carbon fiber reinforced composites. The work provided a theoretic foundation for the design and development of high wear resistant fiber-reinforced polymer composites using 3D printing technology for various tribology applications

Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each essential parameters in wavelet-based methods - on the estimated values of network diagnostics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of network diagnostics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of network diagnostics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.Comment: working pape

Direct single-molecule dynamic detection of chemical reactions.

Single-molecule detection can reveal time trajectories and reaction pathways of individual intermediates/transition states in chemical reactions and biological processes, which is of fundamental importance to elucidate their intrinsic mechanisms. We present a reliable, label-free single-molecule approach that allows us to directly explore the dynamic process of basic chemical reactions at the single-event level by using stable graphene-molecule single-molecule junctions. These junctions are constructed by covalently connecting a single molecule with a 9-fluorenone center to nanogapped graphene electrodes. For the first time, real-time single-molecule electrical measurements unambiguously show reproducible large-amplitude two-level fluctuations that are highly dependent on solvent environments in a nucleophilic addition reaction of hydroxylamine to a carbonyl group. Both theoretical simulations and ensemble experiments prove that this observation originates from the reversible transition between the reactant and a new intermediate state within a time scale of a few microseconds. These investigations open up a new route that is able to be immediately applied to probe fast single-molecule physics or biophysics with high time resolution, making an important contribution to broad fields beyond reaction chemistry

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://github.com/linuxsino/rPPG-MAE

Multi-representations Space Separation based Graph-level Anomaly-aware Detection

Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem. The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies. Furthermore, abnormal graphs that have subtle differences from normal graphs are easily escaped detection by the existing methods. Thus, we propose a multi-representations space separation based graph-level anomaly-aware detection framework in this paper. To consider the different importance of node-level and graph-level anomalies, we design an anomaly-aware module to learn the specific weight between them in the abnormal graph evaluation process. In addition, we learn strictly separate normal and abnormal graph representation spaces by four types of weighted graph representations against each other including anchor normal graphs, anchor abnormal graphs, training normal graphs, and training abnormal graphs. Based on the distance error between the graph representations of the test graph and both normal and abnormal graph representation spaces, we can accurately determine whether the test graph is anomalous. Our approach has been extensively evaluated against baseline methods using ten public graph datasets, and the results demonstrate its effectiveness.Comment: 11 pages, 12 figure

21 cm foreground removal using AI and frequency-difference technique

The deep learning technique has been employed in removing foreground contaminants from 21 cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input, which can substantially reduce the dynamic range of foreground amplitudes by approximately two orders of magnitude. This reduction proves to be highly advantageous for the U-Net foreground removal. We observe that the HI signal can be reliably recovered, as indicated by the cross-correlation power spectra showing unity agreement at the scale of $k < 0.3 h^{-1}$Mpc in the absence of instrumental effects. Moreover, accounting for the systematic beam effects, our reconstruction displays consistent auto-correlation and cross-correlation power spectrum ratios at the $1\sigma$ level across scales $k \lesssim 0.1 h^{-1}$Mpc, with only a 10% reduction observed in the cross-correlation power spectrum at $k\simeq0.2 h^{-1}$Mpc. The effects of redshift-space distortion are also reconstructed successfully, as evidenced by the quadrupole power spectra matching. In comparison, our method outperforms the traditional Principal Component Analysis method, which derived cross-correlation ratios are underestimated by around 75%. We simulated various white noise levels in the map and found that the mean cross-correlation ratio $\bar{R}_\mathrm{cross} \gtrsim 0.75$ when the level of the thermal noise is smaller than or equal to that of the HI signal. We conclude that the proposed frequency-difference technique can significantly enhance network performance by reducing the amplitude range of foregrounds and aiding in the prevention of HI loss.Comment: 18 pages, 16 figure

Environmental Sustainable Development: Study on the Value Realization Mechanism and Diversified Realization Path of Ecological Products under the Background of "Double Carbon"

Under the background of carbon neutrality and common prosperity, the importance of carbon sinks is constantly highlighted. Realizing the value of carbon sink ecological products is not only conducive to the realization of the goal of carbon neutrality, but also an effective way to promote the endogenous development of rural areas and promote common prosperity. Broadening the value transformation channel of carbon sink ecological products and realizing the sustainable transformation from "green water and green hills" to "Jinshan and Yinshan" provide a new way to achieve the goal of carbon neutrality and common prosperity. Based on the theoretical analysis of the traditional connotation, formation mechanism and value of carbon sink ecological products, this paper summarizes the main ways and existing problems of realizing carbon sink ecological value in China, systematically analyzes the two-way promotion relationship between the double carbon target and the realization of carbon sink ecological product value, and emphasizes the important role of carbon sink ecological value realization and participation in carbon market transactions in carbon emission reduction. It also summarizes the experience of international typical cases. Finally, suggestions and reflections were put forward for redistributing the supply of ecological products based on carbon sinks, improving the basic system for calculating the value of ecological products, strengthening the government's guiding role, improving the ecological rights trading market, and innovating financial models, providing reference for optimizing the innovative mechanism and path for realizing the value of ecological products in China under the "dual carbon" goal