Is Equity Crowdfunding the Leapfrog to Companies' Success? Financial Performance in China.

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper.

Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data.

In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city scale but also reveals the potentiality and mechanism of the comprehensive ANLI to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.

Association between socioeconomic factors at diagnosis and survival in breast cancer: A population-based study.

BACKGROUND: The associations between socioeconomic statuses and survival outcomes of breast cancer remain unclear. No model has included both histological and socioeconomic factors to predict the survival of breast cancer. This study was designed to develop nomograms to predict breast cancer-specific survival (BCSS) and overall survival (OS) with consideration of socioeconomic factors for breast cancer patients. MATERIALS AND METHODS: We included a total of 207 749 female patients, diagnosed with malignant breast cancer between 2007 and 2012 from the Surveillance, Epidemiology, and End Results database. BCSS and OS were evaluated with Gray's test and log-rank tests, respectively. Marital statuses, insurance statuses, residence, median household income, poverty rate, unemployment rate, and education level were included as socioeconomic factors in univariate and multivariate Cox regression analyses. Clinicopathological factors and socioeconomic factors were integrated to construct nomograms. Calibration plots and concordance indexes (C-indexes) were used to evaluate the accuracy and discrimination of the models. RESULTS: Four and three socioeconomic factors were involved in constructing the nomograms for 3-, 5-, and 7-year BCSS and OS, respectively. The C-indexes of the final nomograms were higher than those of the TNM staging system for predicting BCSS (0.835 vs 0.782; P < .001) and OS (0.773 vs 0.676; P < .001). The performance of the nomograms for predicting OS was significantly lower when excluding socioeconomic factors (P < .001). CONCLUSION: These findings may highlight the importance of developing health-related policies and the necessity of targeted social support-based interventions for high-risk patients.

Assessment of low-velocity impact damage in composites by the measure of second-harmonic guided waves with the phase-reversal approach.

Structural strength and integrity of composites can be considerably affected by the low-velocity impact damage due to the unique characteristics of composites, such as layering bonded by adhesive and the weakness to impact. For such damage, there is an urgent need to develop advanced nondestructive testing approaches. Despite the fact that the second harmonics could provide information sensitive to the structural health condition, the diminutive amplitude of the measured second-order harmonic guided wave still limits the applications of the second-harmonic generation-based nonlinear guided wave approach. Herein, laminated composites suffered from low-velocity impact are characterized by use of nonlinear guided waves. An enhancement in the signal-to-noise ratio for the measure of second harmonics is achieved by a phase-reversal method. Results obtained indicate a monotonic correlation between the impact-induced damage in composites and the relative acoustic nonlinear indicator of guided waves. The experimental finding in this study shows that the measure of second-order harmonic guided waves with a phase-reversal method can be a promising indicator to impact damage rendering in an improved and reliable manner.

Factor analysis of the influence of neighborhood satisfaction of the elderly: Evidence from Xuzhou, China.

According to a recent survey of the elderly in Xuzhou, China, neighborhood relationships (NP) have a significant impact on neighborhood satisfaction (0.420), followed by shopping and travel (ST) (0.262). At the same time, public facilities (PF) have a significant impact on neighborhood relationships (NP) (0.323), followed by environmental sanitation (ES) (0.295). This means that Chinese older people pay more attention to neighborhood relationships, while community public facilities and environmental sanitation have a significant impact on neighborhood relationships. Some of the environmental aspects have an impact on neighborhood satisfaction through the social environment.

Contrast-enhanced MRI in preoperative assessment of myometrial and cervical invasion, and lymph node metastasis: diagnostic value and error analysis in endometrial carcinoma.

OBJECTIVE: To determine the ability of contrast-enhanced magnetic resonance imaging to predict myometrial invasion, cervical invasion, and pelvic lymph node metastasis in endometrial carcinoma and to analyze factors that lead to errors in this identification. DESIGN: A retrospective study. SETTING: University general hospital. POPULATION: A total of 167 women diagnosed with endometrial carcinoma. METHODS: All patients received a preoperative contrast-enhanced magnetic resonance imaging scan. Histopathological findings were used as the definitive diagnosis. MAIN OUTCOME MEASURES: The results were compared with histopathological findings, factors that make accurate assessment of myometrial invasion, cervical invasion, and pelvic lymph node metastasis difficult by contrast-enhanced magnetic resonance imaging were analyzed. RESULTS: The sensitivity, specificity, diagnostic accuracy, positive predictive values, and negative predictive values of contrast-enhanced magnetic resonance imaging were 90.9, 91.8, 91.6, 73.2 and 97.6%, respectively, for identifying deep myometrial invasion; 84.2, 96.0, 94.6, 72.7 and 97.9%, respectively, for identifying cervical invasion; and 45.0, 91.2, 85.6, 40.9 and 92.4%, respectively, for identifying pelvic lymph node metastasis. The main causes of error in contrast-enhanced magnetic resonance imaging were myomas, cornual lesions, deep myometrial invasion, large tumor size, non-endometrioid tumor type, and lower tumor grade. CONCLUSION: Contrast-enhanced magnetic resonance imaging has a high accuracy and a low tendency to produce false-negative predictive values. Gynecological oncologists should combine the imaging data and clinical information to make therapeutic decisions and avoid diagnostic errors.