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This paper summarizes the transmission chain of “digital finance-financing constraint-firm innovation” at the theoretical and practical levels, incorporates digital finance into the empirical analysis framework of firm innovation, selects the data of Chinese GEM(Growth Enterprise Market)-listed companies from 2011 to 2020, and matches the data of the digital inclusive finance index. The paper empirically examines the incentive effect and impact mechanism of digital finance on SME innovation through the two-way fixed-effects model and mediated-effects model by matching the data of China GEM-listed companies from 2011 to 2020 with the digital financial inclusion index data. The findings show that the digital development and promotion of digital finance play a significantly positive impact in helping SMEs innovate and stimulate innovation. The effect is realized by alleviating corporate financing constraints. Further, digital finance has different incentive effects on enterprises with varying rights of property nature, as well as on other regions.

Introduction Material and methods Results Conclusions

The activity of Fischer–Tropsch synthesis (FTS) can be promoted by the reducible oxides, while their role remains elusive. Here, the authors reveal that, by varying the reduction condition to regulate the TiO x overlayer on Ru nanocatalysts, the TiOx overlayer participate in the C–O bond dissociation.

The activity of Fischer–Tropsch synthesis (FTS) on metal-based nanocatalysts can be greatly promoted by the support of reducible oxides, while the role of support remains elusive. Herein, by varying the reduction condition to regulate the TiO x overlayer on Ru nanocatalysts, the reactivity of Ru/TiO 2 nanocatalysts can be differentially modulated. The activity in FTS shows a volcano-like trend with increasing reduction temperature from 200 to 600 °C. Such a variation of activity is characterized to be related to the activation of CO on the TiO x overlayer at Ru/TiO 2 interfaces. Further theoretical calculations suggest that the formation of reduced TiO x occurs facilely on the Ru surface, and it involves in the catalytic mechanism of FTS to facilitate the CO bond cleavage kinetically. This study provides a deep insight on the mechanism of TiO x overlayer in FTS, and offers an effective approach to tuning catalytic reactivity of metal nanocatalysts on reducible oxides. The activity of Fischer–Tropsch synthesis (FTS) can be promoted by the reducible oxides, while their role remains elusive. Here, the authors reveal that, by varying the reduction condition to regulate the TiO x overlayer on Ru nanocatalysts, the TiOx overlayer participate in the C–O bond dissociation.

Resting-state electroencephalogram (EEG) microstate analysis resolves EEG signals into topographical maps representing discrete, sequential network activations. These maps can be used to identify patterns in EEGs that may be indicative of underlying neurological conditions. One such pattern is observed in EEGs of patients with Alzheimer’s disease (AD), where a global microstate disorganization is evident. We initially investigated the classification efficacy of microstate parameters as markers for AD classification. Subsequently, we compared the classification efficacy of EEG conventional features to ascertain the superiority of microstate features. We extracted raw EEG data from a public, independent database, OpenNeuro EEG. The raw EEG was subjected to preprocessing and band-pass filtering to obtain five distinct frequency bands. The SVM classifier was used to input the microstate feature set to determine the one with the best classification effect as the main band. In order to verify the advantage of the microstate features, the AD group and the healthy control group were filtered for the main frequency bands respectively. Then the microstate feature set and the regular feature set were extracted. The two feature sets were input into four different conventional machine learning classifiers, namely SVM, KNN, RF, and LR, in order to avoid the classifiers as the dependent variable. And the comparison of the classification results of simply two feature sets as the dependent variable can be obtained. The results show that in the Alpha (8–13 Hz) sub-band, the microstate feature set as model input to SVM is optimal for the recognition of AD, with a classification accuracy of 99.22%. The Alpha band, as the main frequency band, the microstate feature set as model input to the four classifiers obtains an average classification accuracy of 98.61%, and the average classification accuracy obtained by the conventional EEG feature set as model is 91.19%. Based on four different classifiers, microstate parameters can be served as markers to effectively classify the EEG of AD patients. The microstate feature set outperforms the conventional EEG feature set after excluding the effect of classifiers.

BackgroundThe high levels of C reactive protein (CRP) to albumin ratio (CAR) is thought to increase the risk of poor outcomes for cancer and cardiovascular disease (CVD). However, the association between CAR and CVD in the Chinese community population has not been investigated.ObjectiveThe aim of this study was to investigate the association between CAR and CVD in the Chinese community population.MethodsA total of 62 067 participants without a history of CVD or cancer were included in this study. Kaplan-Meier survival curves were used to calculate the cumulative incidence of endpoint events in CAR quartile groups, and the results were tested by log-rank test. Fine-Gray model was used to analyse the competing risk of death. C-index, Net Reclassification Index (NRI) and Integrated Discrimination Improvement Index (IDI) of different indicators were calculated to distinguish the predictive performance of different indicators.ResultsDuring an average follow-up period of 10.3±2.1 years, 4025 participants developed CVD. In multivariable Cox regression analysis, compared with Q1 group, model 3 showed that the hazard ratio (HR) (95% confidence interval (95%CI)) of CVD in Q4 group was 1.26 (1.15 to 1.38) (p<0.01), and the HR (95% CI) per 1 SD increase was 1.06 (1.03 to 1.08) (p<0.01). The C-index, continuous NRI and IDI for predicting 10-year CVD were 73.48%, 0.1366 (0.1049 to 0.1684) (p<0.01) and 0.0002 (0.0001 to 0.0004) (p<0.01), respectively, which were higher than those of hs-CRP (C-index:0.7344, NRI:0.0711, IDI: 0.0001) and albumin (C-index:0.7339, NRI: −0.0090, IDI: 0.0000).ConclusionHigh levels of CAR can increase the risk of CVD and the predictive performance of CAR for CVD is better than that of hs-CRP or albumin alone.

The economic and human losses caused by drought are increasing, driven by climate change, human activities, and increased exposure of livelihood activities in water‐dependent sectors. Mitigation of these impacts for socio‐ecological securit is necessary to gain a better understanding of how human activities contribute to the propagation of drought as water management further develops. The previous studies investigated the impact of human activities on a macro level, but they overlooked the specific effects caused by human water management measures. In addition, most studies focus on the propagation time (PT, the number of months from meteorological drought propagation to hydrological drought), while other drought propagation characteristics, such as duration, magnitude, and recovery time, are not yet sufficiently understood. To tackle these issues, the PCR‐GLOBWB v2.0 hydrological model simulated hydrological processes in China under natural and human‐influenced scenarios. The study assessed how human activities impact hydrological drought and its propagation. Result shows that human activities have exacerbated hydrological drought in northern China, while it is mitigated in the south. The propagation rate (PR, proportion of meteorological drought propagation to hydrological drought) ranges from 45% to 75%, and the PT is 6–23 months. The PR does not differ substantially between the north and south, while the PT is longer in the north. The PR decreases by 1%–60% due to human activities, and the PT decreases (1–13 months) in the north and increases (1–10 months) in the south. Human activities display significant variations in how they influence the propagation process of drought across different basins. The primary factors driving the spatial pattern of drought disparities are regional variations in irrigation methods and the storage capacity of reservoirs. Plain Language Summary Under the combined impact of climate change and human activities, economic and human losses caused by drought in China have been increasing year by year. To mitigate the impact of disasters, we conducted research using PCR‐GLOBWB v2.0 model to investigate how human activities have altered hydrological drought in China. And the role of human activities in the propagation process of drought was explored. The results indicate that human activities have intensified hydrological drought in northern China, while providing some alleviation in the southern regions. Human activities disrupt the natural processes of drought propagation, resulting in a decrease in propagation rates. Furthermore, human activities have shortened the propagation lag time of drought in the north, while increasing it in the south. Additionally, smaller basins are more sensitive to human activities compared to larger basins. Our study reveals the impact of human activities on hydrological drought and drought propagation, providing valuable insights for the development of more effective drought adaptation strategies. Key Points We used the PCR‐GLOBWB v2.0 model to study the impact of human activities on the process of drought propagation Human activities play a varying role in the propagation process of drought in different river basins Human activities has led to a decrease in drought propagation rates and shortened/prolonging the drought lag time in northern/southern China

Long-term antibody responses and neutralizing activities in response to SARS-CoV-2 infection are not yet clear. Here we quantify immunoglobulin M (IgM) and G (IgG) antibodies recognizing the SARS-CoV-2 receptor-binding domain (RBD) of the spike (S) or the nucleocapsid (N) protein, and neutralizing antibodies during a period of 6 months from COVID-19 disease onset in 349 symptomatic COVID-19 patients who were among the first be infected world-wide. The positivity rate and magnitude of IgM-S and IgG-N responses increase rapidly. High levels of IgM-S/N and IgG-S/N at 2-3 weeks after disease onset are associated with virus control and IgG-S titers correlate closely with the capacity to neutralize SARS-CoV-2. Although specific IgM-S/N become undetectable 12 weeks after disease onset in most patients, IgG-S/N titers have an intermediate contraction phase, but stabilize at relatively high levels over the 6 month observation period. At late time points, the positivity rates for binding and neutralizing SARS-CoV-2-specific antibodies are still >70%. These data indicate sustained humoral immunity in recovered patients who had symptomatic COVID-19, suggesting prolonged immunity. A better understanding of longitudinal changes in antibody responses in COVID-19 patients is needed. Here the authors analyze anti-spike and anti-nucleocapsid antibody responses to Sars-CoV-2 over a course of 6 months in a large cohort of patients with COVID-19, showing that IgM is mostly not detectable after 3 months, whereas IgG responses contract, yet remain at high levels at 6 months.

Previous studies have suggested that virtual reality (VR) can elicit emotions in different visual modes using 2D or 3D headsets. However, the effects on emotional arousal by using these two visual modes have not been comprehensively investigated, and the underlying neural mechanisms are not yet clear. This paper presents a cognitive psychological experiment that was conducted to analyze how these two visual modes impact emotional arousal. Forty volunteers were recruited and were randomly assigned to two groups. They were asked to watch a series of positive, neutral and negative short VR videos in 2D and 3D. Multichannel electroencephalograms (EEG) and skin conductance responses (SCR) were recorded simultaneously during their participation. The results indicated that emotional stimulation was more intense in the 3D environment due to the improved perception of the environment; greater emotional arousal was generated; and higher beta (21–30 Hz) EEG power was identified in 3D than in 2D. We also found that both hemispheres were involved in stereo vision processing and that brain lateralization existed in the processing.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.