80 research outputs found
Nonlinear energy harvesting based on a rotating system
Acknowledgement The authors acknowledge the financial supports of the National Natural Science Foundation of China (No.11702111, 11732014,11872201, 12172166) and the State Scholarship Fund of CSCPeer reviewedPostprin
Mechanical Deformation Induced Continuously Variable Emission for Radiative Cooling
Passive radiative cooling drawing the heat energy of objects to the cold
outer space through the atmospheric transparent window (8 um - 13 um) is
significant for reducing the energy consumption of buildings. Daytime and
nighttime radiative cooling have been extensively investigated in the past.
However, radiative cooling which can continuously regulate its cooling
temperature, like a valve, according to human need is rarely reported. In this
study, we present a concept of reconfigurable photonic structure for the
adaptive radiative cooling by continuously varying the emission spectra in the
atmospheric window region. This is realized by the deformation of the
one-dimensional PDMS grating and the nanoparticles embedded PDMS thin film when
subjected to mechanical strain. The proposed structure reaches different
stagnation temperatures under certain strains. A dynamic exchange between two
different strains results in the fluctuation of the photonic structure's
temperature around a set temperature
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
Script event prediction aims to predict the subsequent event given the
context. This requires the capability to infer the correlations between events.
Recent works have attempted to improve event correlation reasoning by using
pretrained language models and incorporating external knowledge~(e.g.,
discourse relations). Though promising results have been achieved, some
challenges still remain. First, the pretrained language models adopted by
current works ignore event-level knowledge, resulting in an inability to
capture the correlations between events well. Second, modeling correlations
between events with discourse relations is limited because it can only capture
explicit correlations between events with discourse markers, and cannot capture
many implicit correlations. To this end, we propose a novel generative approach
for this task, in which a pretrained language model is fine-tuned with an
event-centric pretraining objective and predicts the next event within a
generative paradigm. Specifically, we first introduce a novel event-level blank
infilling strategy as the learning objective to inject event-level knowledge
into the pretrained language model, and then design a likelihood-based
contrastive loss for fine-tuning the generative model. Instead of using an
additional prediction layer, we perform prediction by using sequence
likelihoods generated by the generative model. Our approach models correlations
between events in a soft way without any external knowledge. The
likelihood-based prediction eliminates the need to use additional networks to
make predictions and is somewhat interpretable since it scores each word in the
event. Experimental results on the multi-choice narrative cloze~(MCNC) task
demonstrate that our approach achieves better results than other
state-of-the-art baselines. Our code will be available at
https://github.com/zhufq00/mcnc
Identification and characterization of bone/cartilage-associated signatures in common fibrotic skin diseases
Background: Fibrotic skin diseases are characterized by excessive accumulation of the extracellular matrix (ECM) and activation of fibroblasts, leading to a global healthcare burden. However, effective treatments of fibrotic skin diseases remain limited, and their pathological mechanisms require further investigation. This study aims to investigate the common biomarkers and therapeutic targets in two major fibrotic skin diseases, namely, keloid and systemic sclerosis (SSc), by bioinformatics analysis.Methods: The keloid (GSE92566) and SSc (GSE95065) datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then constructed a protein–protein interaction (PPI) network for the identification of hub genes. We explored the possibility of further functional enrichment analysis of hub genes on the Metascape, GeneMANIA, and TissueNexus platforms. Transcription factor (TF)–hub gene and miRNA–hub gene networks were established using NetworkAnalyst. We fixed GSE90051 and GSE76855 as the external validation datasets. Student’s t-test and receiver operating characteristic (ROC) curve were used for candidate hub gene validation. Hub gene expression was assessed in vitro by quantitative real-time PCR.Results: A total of 157 overlapping DEGs (ODEGs) were retrieved from the GSE92566 and GSE95065 datasets, and five hub genes (COL11A1, COL5A2, ASPN, COL10A1, and COMP) were identified and validated. Functional studies revealed that hub genes were predominantly enriched in bone/cartilage-related and collagen-related processes. FOXC1 and miR-335-5p were predicted to be master regulators at both transcriptional and post‐transcriptional levels.Conclusion: COL11A1, COL5A2, ASPN, COL10A1, and COMP may help understand the pathological mechanism of the major fibrotic skin diseases; moreover, FOXC1 and miR-355-5p could build a regulatory network in keloid and SSc
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