Fast and accurate wideband sparse spatial spectrum estimation in an underwater strong interference environment.

Wideband sparse spatial spectrum estimation is an important direction-of-arrival (DOA) estimation method that can obtain a high resolution with few snapshots and a low signal-to-noise ratio. However, in an underwater strong interference environment, the accuracy of DOA estimation may be seriously affected, and even the weak targets could be completely masked. In this paper, we propose a fast matrix filter design method based on truncated nuclear norm regularization to attenuate strong interferences while passing weak targets. The matrix filter operator and the exact covariance matrix after filtering can be obtained simultaneously by solving a convex optimization problem that contains the output power term and non-Toeplitz error propagation control term. Then the modified sparse spectrum fitting algorithm based on the matrix filter is used to estimate spatial spectrum over closely spaced wideband signals. Compared with existing methods, the proposed method achieves higher DOA estimation accuracy and lower computational time for matrix filter design. Meanwhile, the estimation accuracy of the proposed method is verified with the experimental results.

Spill-over effect of Wuhan travel ban on population flow in the outbreak stage of COVID-19 in China.

This paper investigates the imppact of COVID-19 travel restrictions on population flow in the People's Republic of China. We discover an "unreasonable" surge in population flow after the Wuhan travel ban. We further find out that such a sure of population flow is attributed to the "spill-over" effect of the Wuhan travel ban. We utilize a logistic regression model to quantify that the spill-over effect linearly decays with the travel distance to the Pandemic center city. Because of the "spill-over" effect of the travel ban policy, government authorities should design redundancy polity to simultaneously implement a travel ban for the pandemic center city and its neighboring cities to restrain human movement and pandemic transmission.

Research on Predicting the Safety Factor of Plain Shotcrete Support in Laneways Based on BO-CatBoost Model.

In general, the design of a safe and rational laneway support scheme signifies a crucial prerequisite for ensuring the security and efficiency of mining exploitation in mines. Nevertheless, the conventional empirical support system for mining laneways faces challenges in assessing the rationality of support methods, which can compromise the safety and reliability of the laneways. To address this issue, the safety factor was incorporated into research on laneway support, and a safety evaluation method for laneway support in line with the safety factor was established. In light of the data from a specific iron mine laneway in central China, the CRITIC method was employed to preprocess the sample data. Going one step further, a Bayesian algorithm was utilized to optimize the hyperparameters of the CatBoost model, followed by proposing a prediction model based on the BO-CatBoost model for evaluating laneway safety factors of plain shotcrete support. Furthermore, the performance indexes, such as the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R2), the variance accounts for (VAF), and the a-20 index, were determined to examine the predictive performance of each proposed model. In contrast to the other models, the BO-CatBoost model demonstrated the optimal predictive output item for safety factors with the lowest RMSE and MAE, the largest R2 and VAF, and an appropriate a-20 index value of 0.5688, 0.4074, 0.9553, 95.25%, and 0.9167 in the test set, respectively. Therefore, the BO-CatBoost model was proven to be the most appropriate machine learning method that can more accurately predict the safety factor, which will provide a novel approach for optimizing laneway support design and laneway safety evaluation.

Association of systemic inflammation response index with all-cause mortality as well as cardiovascular mortality in patients with chronic kidney disease.

Background: Chronic Kidney Disease (CKD) stands as a formidable health challenge, recognized not only for its growing prevalence but also for its association with elevated mortality rates. Emerging evidence suggests that CKD is inherently linked to inflammatory processes, marking it as an inflammatory disorder. In this landscape, the systemic inflammatory response index (SIRI) emerges as a novel inflammation marker, yet to be applied for assessing the risk of mortality in CKD patients. Objective: This study aims to investigate the prognostic significance of the SIRI in all-cause and cardiovascular disease (CVD) mortality among patients with CKD. Method: This study conducted a retrospective observational study using the National Health and Nutrition Examination Survey (NHANES) database, encompassing data from 1999 to 2018. This analysis included 9,115 CKD patients, categorized based on SIRI quartiles. Key outcomes were all-cause and CVD mortality, analyzed using Kaplan-Meier survival curves, restricted cube splines (RCS) and cox proportional hazards models. Result: In this study of 9,115 CKD patients, the Kaplan-Meier survival analysis revealed a greater incidence of all-cause death among groups with higher SIRI (P-log rank <0.001). In the fully adjusted model (Model 3), each unit increase in SIRI led to a 20% increase in the risk of all-cause mortality. Additionally, higher SIRI quartiles (Q3 and Q4) were associated with increased risk compared to the lowest quartile (Q1) (Q3: HR: 1.16, 95% CI: 1.01-1.34; Q4: HR: 1.63, 95% CI: 1.40-1.90; P for trend <0.001). Similarly, for CVD mortality, each unit increase in SIRI in Model 3 increased the risk by 33%, with Q3 and Q4 showing higher risk than Q1 (Q3: HR: 1.39, 95% CI: 1.11-1.70; Q4: HR: 2.26, 95% CI: 1.72-2.98; P for trend <0.001). Conclusion: SIRI was positively associated with all-cause and CVD mortality in patients with CKD.

Precise detection of coal and gangue based on natural γ-ray.

To address the technical limitations of automatic coal and gangue detection technology in fully mechanized top coal caving mining operations, the low radiation level radioactivity measurement method is utilized to assess the degree of coal-gangue mixture in top coal caving process. This approach is based on the distinguishing radiation characteristics of natural γ-rays between coal and gangue. This study analyzed the distribution characteristics of natural γ-rays in coal and rock layers of thick coal seams and the applicability of this method, introduced the basic principle of coal-gangue detection technology based on natural γ-ray, developed the test system about automatic coal-gangue detection, studied the radiation characteristics of coal and gangue, proposed determination model of the coal-gangue mixed degree, combined with the time sequence characteristics of the top coal's releasing flow and the energy spectrum characteristics of different layers of rock, realized the precise coal-gangue detection technology in complex structure thick coal seam with multiple gangue. Field tests were conducted in Lilou, Xiaoyu and Tashan Coal Mine. The test results were well corroborated with the research results and achieved the expected results, which laid the foundation for the field application of intelligent coal mining.