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The aim of Sudoku puzzle is to fill in the blank cells in a square matrix consisting of nine 3x3 blocks with the numbers 1-9 in a way that no number appears more than once in any row, column, or block. We combined image processing, a convolutional neural network (CNN), and a Sudoku game algorithm to automatically place the numbers 1-9 in the blank cells of a Sudoku square matrix. An image of the Sudoku square matrix is first captured using a camera, and then the vertical lines, horizontal lines, and outer frame of the Sudoku square matrix are detected using Hough transform (HT). Based on the OpenCV module, we proposed an image processing algorithm that captures the numbers in the image and calculates the location coordinates of the numbers in the image. We trained the CNN using the MNIST handwritten digit dataset to recognize the numbers in the Sudoku square matrix. Finally, we used the Python programming language to design a Sudoku puzzle backtrace algorithm that automatically deduces and fills in the blank cells in the square matrix. This study provides further understanding of the critical operating principles of CNNs and lays down a foundation for future research. &nbsp

Ni-rich layered oxide cathode materials LiNixCoyMnzO2 (x>0.6) have been widely used in high specific energy Li-ion batteries. Compared with polycrystalline LiNixCoyMnzO2, single crystal Ni-rich cathode materials have attracted much attention for their unique advantages. However, they also suffer from some drawbacks, such rapid capacity decay and intragranular crack during cycling. In order to enhance the structure and electrochemical performance of single crystal LiNi0.8Co0.1Mn0.1O2 (SC811), different amount of aluminium (1 mol%, 2 mol% and 3 mol%) is utilized to substitute the materials. Physical characterizations of SC811 show the successful synthesis and Al doping of the single-crystal materials as well as lower Li/Ni intermixing after Al doping. Electrochemical tests illustrate that the single-crystal materials doped with 2mol% Al (2Al-SC811) shows superior electrochemical performance, and maintains 83.5 % of the initial discharge capacity after 100 cycles at 1 C in comparison with 68.9 % of the pristine SC811. It is hoped this work should shed light on the development of advanced cathode materials for Li-ion batteries.

The mergers of supermassive black hole binaries (SMBHBs) can serve as standard sirens: the gravitational wave (GW) analog of standard candles. The upcoming space-borne GW detectors will be able to discover such systems and estimate their luminosity distances precisely. Unfortunately, weak gravitational lensing can induce significant errors in the measured distance of these standard sirens at high redshift, severely limiting their usefulness as precise distance probes. The uncertainty due to weak lensing can be reduced if the lensing magnification of the siren can be estimated independently, a procedure called 'delensing'. With the help of up-to-date numerical simulations, here we investigate how much the weak-lensing errors can be reduced using convergence maps reconstructed from shear measurements. We also evaluate the impact of delensing on cosmological parameter estimation with bright standard sirens. We find that the weak-lensing errors for sirens at $z_s = 2.9$ can be reduced by about a factor of two on average, but to achieve this would require expensive ultra-deep field observations for every siren. Such an approach is likely to be practical in only limited cases, and the reduction in the weak-lensing error is therefore likely to be insufficient to significantly improve the cosmological parameter estimation. We conclude that performing delensing corrections is unlikely to be worthwhile, in contrast to the more positive expectations presented in previous studies. For delensing to become more practicable and useful in the future will require significant improvements in the resolution/depth of weak-lensing surveys and/or the methods to reconstruct convergence maps from these surveys., 20 pages, 22 figures, to be published in MNRAS

Si-si Xie,1 Xiao-ting Luo,2 Ming-hua Dong,3 Qi Wang,3 Jian Li,4 Qing-feng Wu3 1School of Graduate, Gannan Medical University, Ganzhou, Jiangxi, People’s Republic of China; 2School of General Medicine, Gannan Medical University, Ganzhou, Jiangxi, People’s Republic of China; 3School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, People’s Republic of China; 4School of Basic Medical, Gannan Medical University, Ganzhou, Jiangxi, People’s Republic of ChinaCorrespondence: Qing-feng Wu, Gannan Medical University, Rongjiang New District College Park, Ganzhou, Jiangxi, 341000, People’s Republic of China, Tel/Fax +86 18970786090, Email wuqf0920@163.comPurpose: Hemoglobin glycation index (HGI) is used to describe the difference between estimated and measured glycated hemoglobin A1c (HbA1c). The present study aimed to investigate the association between metabolic syndrome (MetS) and HGI in middle-aged and elderly Chinese.Patients and Methods: In this cross-sectional study, a multi-stage random sampling method was used to select objects from the permanent residents aged 35 years and above living in Ganzhou, Jiangxi, China. The demographic information, history of illness, physical examination, and blood biochemistry data were obtained. HGI was calculated from fasting plasma glucose (FPG) and HbA1c (HGI = measured HbA1c value - predicted HbA1c value). All participants were divided into low HGI and high HGI groups using the median HGI as a cut-off value. Univariate analysis was used to detect the influencing factors of HGI, and Logistic regression analysis was adopted to analyze the relationship between significant variables found in univariate analysis, MetS, or MetS’s components and HGI.Results: A total of 1826 participants were enrolled in the study, and the prevalence of MetS was 27.4%. There were 908 in the low HGI group and 918 in the high HGI group, and the prevalence of MetS was 23.7% and 31.0%, respectively. Logistic regression analysis showed that the prevalence of MetS in the high HGI group was higher than that in the low HGI group (OR=1.384, 95% CI:1.110~1.725), further analysis showed that HGI was related with abdominal obesity (OR=1.287, 95% CI:1.061~1.561), hypertension (OR=1.349, 95% CI:1.115~1.632), and hypercholesterolemia (OR=1.376, 95% CI:1.124~1.684) (all P < 0.05). After adjusting for age, sex, and serum uric acid (UA), the relationship still existed.Conclusion: This study found that HGI is directly associated with MetS.Keywords: hemoglobin glycation index, metabolic syndrome, cross-sectional study

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China’s Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K-means clustering was used to purify the factor data factors before data-driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K-means method is of great significance for the drawing of landslide sensitivity map.

Insect monitoring in the field is an extremely important part of the agricultural production system. Recent advances in computer technology have provided the technical foundation for automatic field insect monitoring. In insect automatic monitoring, insect recognition and classification based on images is one of the most active research areas. Rapid advancements in computer vision technology based on deep learning have provided new ideas for implementing automatic field insect monitoring. Firstly, the field insect images are preprocessed and input to the lightweight algorithm for feature extraction, and the prediction networks of different sizes are output by multiscale feature fusion; then, the joint cross-merge ratio is introduced for automatic identification and classification of field insects. Compared with other algorithms, the simulation results show that the proposed algorithm has higher accuracy, less time consumption, and stronger robustness. It effectively solves the insect accumulation and background interference problems and can identify field insects online in real time.

Anxious depression is one of the subtypes of major depressive disorder (MDD), usually defined as "patients with MDD and high levels of anxiety symptoms". Compared to non-anxious MDD (naMDD), patients with anxious MDD (aMDD) have more severe depressive symptoms and suicidal ideation, worse treatment outcomes and remission rates, and poorer prognosis. Current research suggests that the Papez circuit is an important brain structure closely related to emotion, memory, and cognition. This study applied DTI to explore the altered white matter integrity in Papez circuit of patients with aMDD.DTI data were acquired from 30 medication-naive outpatients with naMDD and 55 with aMDD and 88 demographically similar healthy control (HC) subjects. Voxel-based analysis (VBM) and region of interest (ROI) analysis were conducted to explore the significant difference of fractional anisotropy (FA) values among 3 groups. Pearson's correlations were performed to analyze the correlation between FA values and the score of HAMA-14 and HAMD-17.We found that aMDD patients had significantly higher FA values in left fornix (belong to Papez circuit) and left posterior thalamic radiation and right anterior corona radiata (belong to limbic-thalamo-cortical circuitry) compared with HC. And there was variability in the white matter integrity in right posterior thalamic radiation (belong to limbic-thalamo-cortical circuitry) and left fornix (belong to Papez circuit) between aMDD and naMDD patients.The cross-sectional study and the population vary between aMDD group and naMDD group are limitations.Abnormal white matter integrity in Papez circuit and Limbic-Thalamo-Cortical circuitry may play an important role in the neuropathology of aMDD and might help to identify aMDD.