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Green ecological grain storage technologies (GEGSTs) are the means of controlling stored grain quality, and quality changes of stored grain are the basis of GEGSTs control. This paper introduces that GEGSTs are widely used in China, including monitoring and early warning of stored grain pest and mould, pest control by using food-grade materials, controlled atmosphere for pest control, ventilation for lowering and equalizing temperature, low and quasi-low temperature grain storage, treatment of hot spots, etc. And it introduces that grain processing enterprises’ and market’s request for grain quality, is called “quality control”. It also clarifies that stored grain quality control is the purpose, and emphasizes that GEGSTs control is the process, so GEGSTs control should serve for quality control. Therefore, we propose that the technology application and the quality control of grain storage are equally important, and without the quality control, the technology application could be invalid, especially for sensitive areas in grain bulks. In the process of grain storage, special attention should be paid to quality changes in the sensitive areas, like real-time monitoring. Identify and utilize scientific and reasonable technology accordingly, including related technologies and equipment, to improve "overall" quality control level of stored grain bulks, and to gradually standardize them. By means of GEGSTs, positive ecological storage conditions are effectively utilized, which helps us achieve the purposes of safety, no pollution, high quality and nutrition during grain storage.

This study constructs a monthly index of skilled analysts (ISA) regarding the coverage by such analysts and explores their predictive ability on the future excess returns of Chinese A-share market. The empirical results reveal that ISA significantly and positively predicts excess stock market returns, with R2s of 4.39% and 4.13% in- and out-of-sample respectively, which surpasses the predictive ability of most commonly used economic variables. High ISA precedes a higher investor sentiment index (ISI) and lower idiosyncratic volatility (IVOL) at firm level. The conclusion remains valid after controlling for a series of other predictor variables and performing robustness tests. [ABSTRACT FROM AUTHOR]

The purpose of epilepsy detection is to determine whether epilepsy has occurred by analysing the patient's electroencephalogram (EEG) signals. Compared to traditional methods, epilepsy detection methods based on deep learning have achieved significant improvements in detection accuracy. However, when the number of training samples is limited, the model's detection performance often significantly declines. To address this issue, here a sample enhancement method based on electroencephalogram signal channel swapping is proposed. This method generates new electroencephalogram samples by exchanging electroencephalogram sequences from different channels, thereby expanding the training set and improving epilepsy detection accuracy in few‐shot scenarios. Experiments using the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB‐MIT) dataset show that for training sets with 100, 500, and 1000 samples, detection accuracy improves from 0.6797 to 0.7789, 0.6952 to 0.8210, and 0.7273 to 0.8517, respectively. Compared to the sliding window method, the proposed method demonstrates higher accuracy in extreme low sample sizes. Combining both methods can further enhances detection performance, showing an improvement of approximately 8% across various configurations. [ABSTRACT FROM AUTHOR]

AIM: To evaluate the effectiveness of heparin-binding protein (HBP) in combination with organ function indicators for early diagnosis and prognosis prediction in patients with severe trauma complicated with sepsis. METHODS: A retrospective analysis was conducted on 184 patients with multiple injuries who were admitted to the Emergency Medicine Department of the Second Affiliated Hospital of Zhejiang University Medical College between January 2019 and September 2020 and underwent HBP testing. Patients were classified according to the SEPSIS 3. 0 diagnostic criteria into a sepsis group (n=89) and a non-sepsis group (n=95). Clinical outcomes were tracked, dividing patients into a deceased group (n=43) and a survival group (n=141). HBP levels were continuously measured, and the peak values of the two groups were compared to assess the efficacy of diagnosing sepsis. Further analysis on the correlation of HBP peak value median with clinical prognosis was conducted. The effectiveness of HBP alone and in combination with total bilirubin (TBil) and white blood cell (WBC) count in prognosis assessment was evaluated. RESULTS: (1) No significant difference was found in the peak level of HBP between the sepsis group (n=89) and the non-sepsis group (n=95) (71. 7± 68. 6 vs 52. 5±56. 1, P=0. 051). (2) Among the 184 patients, the peak level of HBP was positively correlated with WBC count (r=0. 244, P<0. 01) and TBil levels (r=0. 241, P<0. 01). (3) The area under curve (AUC) for independent diagnosis of sepsis using TBil levels, WBC count, and PCT levels were 0. 618, 0.631, and 0. 718, respectively, and the combined AUC was 0. 684, with a diagnostic sensitivity of 60. 7% and specificity of 71. 6% (P<0. 05). (4) Prognostic analysis of mortality showed that patients in the high HBP level group had a significantly higher mortality rate than those in the low-level group (30. 4% vs 16. 3%, P<0. 05). The WBC count was also significantly higher in the deceased group than in the survival group (17. 5±6. 9 vs 12. 8±4. 7, P<0. 01), especially in those with sepsis (P<0. 01). The AUCs for predicting sepsis mortality prognosis using HBP peak level, TBil levels, WBC count, SOFA score, and APACHE-II score were 0. 618, 0. 603, 0. 719, 0. 823, and 0. 811, respectively. The combined AUC of HBP with TBil and WBC for assessing sepsis prognosis was 0. 750, with a sensitivity of 74. 4% and specificity of 74. 5%, showing statistically significant differences (P<0. 05). (5) The combined assessment of these three indicators showed no statistically significant difference from artificial scoring systems in predicting sepsis prognosis (P>0. 05). CONCLUSION: The combination of HBP, TBil, and WBC is highly effective in predicting the risk of sepsis in patients with multiple injuries and has significant clinical value in predicting the mortality risk of trauma patients with sepsis. [ABSTRACT FROM AUTHOR]

This paper employs a shrinkage method named Adaptive Lasso (ALasso) to predict the realized volatility (RV) of the China's stock market with numerous predictors. We observed from the out‐of‐sample predictions that the ALasso model exhibits better predictive power than its competitors, implying that the ALasso method can select stronger predictors in the forecasting process than competing models. In addition, the predictability of ALasso method is better in low volatility periods than in high volatility periods. Finally, several robustness check methods, including different forecasting windows, different low and high volatility division, and different volatility measures, supported our results. [ABSTRACT FROM AUTHOR]

This paper constructs three monthly sentiment indices based on social media, traditional newspapers, and Internet news. The predictive power of the social media sentiment index and Internet news sentiment index in the full sample is excellent, far better than that of the macroeconomic predictors, whereas the index established based on the traditional newspaper is unsatisfactory. The forecasting results of the indices across different business cycles imply that the social media sentiment index has the best predictive power during expansion periods, and the Internet news sentiment index significantly predicts stock returns during recessions. The two combination sentiment indices obtained by principal component analysis and equal combination provide more accurate forecasts of stock returns and can generate great economic value for investors. These results are broadly consistent across robustness tests. [ABSTRACT FROM AUTHOR]