A road surface reconstruction dataset for autonomous driving.

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

Influence Analysis of Digital Financial Risk in China's Economically Developed Regions Under COVID-19: Based on the Skew-Normal Panel Data Model.

The rapid spread of COVID-19 worldwide makes an uncertain impact on the development of digital finance in China. In this background, the measurement of digital financial risk and analysis of influence factor become the focus of the financial field. Therefore, this article builds the indicator system of digital financial risk and uses the Lagrange multiplier method to obtain the optimal comprehensive weight of AHP and entropy weight. Then, this article measures the digital financial risk indexes of China's major regions with high-level economic development from 2013 to 2020. Furthermore, the maximum likelihood estimates of the unknown parameters of skew-normal panel data model are obtained based on the EM algorithm, and the comparative study of the normal and skew-normal panel data models is conducted under AIC and BIC. Finally, based on the result of the model, the influence factors of digital financial risk of China's economically developed regions under COVID-19 are analyzed to provide data support for the prevention and governance of digital financial risk.

Effects of thickness reduction in cold rolling process on the formability of sheet metals using ANFIS.

Cold rolling has detrimental effect on the formability of sheet metals. It is, however, inevitable in producing sheet high quality surfaces. The effects of cold rolling on the forming limits of stretch sheets are not investigated comprehensively in the literature. In this study, a through experimental study is conducted to observe the effect of different cold rolling thickness reduction on the formability of sheet metals. Since the experimental procedure of such tests are costly, an artificial intelligence is also adopted to predict effects of cold thickness reduction on the formability of the sheet metals. In this regard, St14 sheets are examined using tensile, metallography, cold rolling and Nakazima's hemi-sphere punch experiments. The obtained data are further utilized to train and test an adaptive neural network fuzzy inference system (ANFIS) model. The results indicate that cold rolling reduces the formability of the sheet metals under stretch loading condition. Moreover, the tensile behavior of the sheet alters considerably due to cold thickness reduction of the same sheet metal. The trained ANFIS model also successfully trained and tested in prediction of forming limits diagrams. This model could be used to determine forming limit strains in other thickness reduction conditions. It is discussed that determination of forming limit diagrams is not an intrinsic property of a chemical composition of the sheet metals and many other factors must be taken into account.

Predictive effect of J waves on cardiac compression and clinical prognosis of esophageal tumors: a retrospective study.

Background: The J wave syndromes (JWS) could be observed in patients with mediastinal tumors, though few studies have verified the statistical correlation between J waves and cardiac compression by tumors. This study aimed to investigate the relationship between J waves and cardiac compression by esophageal tumor and to compare the prediction of J waves on clinical prognosis with that of cardiac compression by esophageal tumor. Methods: We enrolled 273 patients (228 males, 45 females; mean 63.8±7.5 years) with esophageal tumors admitted to Shanghai Chest Hospital between August 2016 and November 2020. The J wave was defined as a J-point elevation of ≥0.1 mV in a 12-lead electrocardiogram (ECG) and classified into multiple types. Chest computed tomography (CT) was reviewed to clarify the anatomical relationship between the heart and the esophageal tumor. The prognosis of severe cardiac events and survival status were followed up through medical history, examination records and telephone records. Results: J waves were present in 141 patients among all 273 cases. The sensitivity and specificity of cardiac compression by the tumor for J waves were 78.1% and 67.3%, respectively. The odds ratio (OR) of cardiac compression by the tumor to J waves was 7.33 [95% confidence interval (CI): 4.21-12.74; P<0.001]. The Kappa coefficient between J waves and cardiac compression was 0.44±0.05. The significance association between J waves and cardiac compression was independent from other clinical variables (P<0.001). Decreased J wave amplitude was correlated with the disappearance of cardiac compression during follow-up (P=0.03). Patients with J waves had a higher risk of severe cardiac events than those without J waves (OR =2.84, 95% CI: 1.22-6.63; P=0.01). During the follow-up period, we found that the presence of J waves [hazard ratio (HR) =2.28; 95% CI: 1.35-3.84; P=0.002] and cardiac compression by the tumor (HR =2.51; 95% CI: 1.51-4.17; P<0.001) were both negatively correlated with the survival time of patients. Conclusions: The presence of J waves could be used as an effective mean to predict the mechanical impact of esophageal tumor on the heart, and played an important role in predicting the survival of patients.

An Empirical Study on the Equity Performance of China's Health Insurance Companies During the COVID-19 Pandemic-Based on Cases of Dominant Listed Companies.

The health insurance industry in China is undergoing great shocks and profound impacts induced by the worldwide COVID-19 pandemic. Taking for instance the three dominant listed companies, namely, China Life Insurance, Ping An Insurance, and Pacific Insurance, this paper investigates the equity performances of China's health insurance companies during the pandemic. We firstly construct a stock price forecasting methodology using the autoregressive integrated moving average, back propagation neural network, and long short-term memory (LSTM) neural network models. We then empirically study the stock price performances of the three listed companies and find out that the LSTM model does better than the other two based on the criteria of mean absolute error and mean square error. Finally, the above-mentioned models are used to predict the stock price performances of the three companies.