During the real-aperture-scanning imaging process, terahertz (THz) images are often plagued with the problem of low spatial resolution. Therefore, an accommodative super-resolution framework for THz images is proposed. Specifically, the 3D degradation model for the imaging system is firstly proposed by incorporating the focused THz beam distribution, which determines the relationship between the imaging range and the corresponding image restoration level. Secondly, an adjustable CNN is introduced to cope with this range dependent super-resolution problem. By simply tuning an interpolation parameter, the network can be adjusted to produce arbitrary restoration levels between the trained fixed levels without extra training. Finally, by selecting the appropriate interpolation coefficient according to the measured imaging range, each THz image can be coped with its matched network and reach the outstanding super-resolution effect. Both the simulated and real tested data, acquired by a 160 ∼ 220 GHz imager, have been used to demonstrate the superiority of our method.

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial–temporal model and graph spatial–temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks).

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Myocardial energy expenditure (MEE) and 2-oxoglutarate are elevated in chronic heart failure (CHF) patients compared with healthy controls. To explore whether 2-oxoglutarate could reflect the levels of MEE and predict the prognosis of CHF, 219 CHF patients and 66 healthy controls were enrolled. 2-Oxoglutarate was assayed with Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC/MS/MS). CHF patients were divided into 4 groups according to interquartile range of MEE and followed for death or recurrent hospital admission due to CHF for the mean follow-up time 6.64±0.16months. 2-Oxoglutarate was increased in CHF patients compared with controls (P<0.01) and correlated with estimated glomerular filtration rate (r=0.142, P=0.036), age (r=-0.269, P<0.01) and MEE levels (r=0.307, P<0.01) in a multiple linear correlation analysis in CHF patients. Furthermore, 2-oxoglutarate (OR=3.470, 95% CI=1.557 to 7.730, P=0.002), N-terminal pro-B-type natriuretic peptide (OR=4.013, 95% CI=1.553 to 10.365, P=0.004), age (OR=1.611, 95% CI=1.136 to 2.283, P=0.007) and left ventricular ejection fraction (OR=7.272, 95% CI=3.110 to 17.000, P<0.001) were independently associated with MEE on multiple logistic regression analysis. Kaplan-Meier event curves showed that high 2-oxoglutarate levels were associated with adverse outcomes (Log Rank, Chi(2)=4.026, P=0.045). This study showed that serum 2-oxoglutarate is associated with MEE levels, which can be used as potential biomarkers for MEE, and it can reflect the clinical severity and short-term outcome of CHF.