MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different sizes of infection sites, some researchers have improved the segmentation accuracy by adding model complexity. However, this approach has severe limitations. Increasing the computational complexity and the number of parameters is unfavorable for model transfer from laboratory to clinic. Meanwhile, the current COVID-19 infections segmentation DCNN-based methods only apply to a single modality. To solve the above issues, this paper proposes a symmetric Encoder-Decoder segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that uses a shift-window mechanism similar to the Transformer to acquire self-attention and achieve local-to-global semantic dependency. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to expand the receptive field and extract multi-scale features. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other U-shape models. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results

Effect of storage time on the silage quality and microbial community of mixed maize and faba bean in the Qinghai-Tibet Plateau

Tibetan Plateau is facing serious shortage of forage in winter and spring season due to its special geographical location. Utilization of forages is useful to alleviate the forage shortage in winter and spring season. Consequently, the current study was aimed to evaluate the influence of storage time on the silage quality and microbial community of the maize (Zea mays L.) and faba bean (Vicia faba L.) mixed silage at Qinghai-Tibet Plateau. Maize and faba bean were ensiled with a fresh weight ratio of 7:3, followed by 30, 60, 90, and 120 days of ensiling. The results showed the pH value of mixed silage was below 4.2 at all fermentation days. The LA (lactic acid) content slightly fluctuated with the extension of fermentation time, with 33.76 g/kg DM at 90 days of ensiling. The AA (acetic acid) and NH3-N/TN (ammonium nitrogen/total nitrogen) contents increased with the extension of fermentation time and no significantly different between 90 and 120 days. The CP (crude protein) and WSC (water soluble carbohydrate) contents of mixed silage decreased significantly (P < 0.05) with ensiling time, but the WSC content remained stable at 90 days. The Proteobacteria was the predominant phyla in fresh maize and faba bean, and Pseudomonas and Sphingomonas were the predominant genera. After ensiling, Lactobacillus was the prevalent genus at all ensiling days. The relative abundance of Lactococcus increased rapidly at 90 days of ensiling until 120 days of fermentation. Overall, the storage time significant influenced the silage fermentation quality, nutrient content, and microbial environment, and it remained stable for 90 days of ensiling at Qinghai-Tibet Plateau. Therefore, the recommended storage time of forage is 90 days in Qinghai-Tibet Plateau and other cool areas

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks

Impacts of land use at multiple buffer scales on seasonal water quality in a reticular river network area.

The assessment and prediction of regional water quality are fundamental inputs to environmental planning and watershed ecological management. This paper explored spatiotemporal changes in the correlation of water quality parameters (WQPs) and land-use types (LUTs) in a reticular river network area. Water samples of 44 sampling sites were collected every quarter from 2016 to 2018 and evaluated for dissolved oxygen (DO), total phosphorus (TP), ammonia nitrogen (NH3-N), and permanganate index (CODMn). A redundancy analysis (RDA) and stepwise multiple linear regression (SMLR) were applied to analyze the land-use type impacts on seasonal WQPs at five buffer scales (100, 200, 500, 800, and 1000 m). The Kruskal-Wallis test results revealed significant seasonal differences in NH3-N, TP, CODMn, and DO. The area percentages of farmland, water area and built-up land in the study area were 38.96%, 22.75% and16.20%, respectively, for a combined total area percentage of nearly 80%. Our study showed that orchard land had an especially favorable influence on WQPs. Land-use type impacts on WQPs were more significant during the dry season than the wet season. The total variation explained by LUTs regarding WQPs at the 1 km buffer scale was slightly stronger than at smaller buffer scales. Built-up land had a negative effect on WQPs, but orchard and forest-grassland had a positive effect on WQPs. The effects of water area and farmland on WQPs were complex on different buffer scales. These findings are helpful for improving regional water resource management and environmental planning

Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 µM to 3.9 µM based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk

Microstructure and Mechanical Properties of Diffusion-Bonded CoCrNi-Based Medium-Entropy Alloy to DD5 Single-Crystal Superalloy Joint

This study focuses on the diffusion bonding of a CoCrNi-based medium-entropy alloy (MEA) to a DD5 single-crystal superalloy. The microstructure and mechanical properties of the joint diffusion-bonded at variable bonding temperatures were investigated. The formation of diffusion zone, mainly composed of the Ni3(Al, Ti)-type γ′ precipitates and Ni-rich MEA matrix, effectively guaranteed the reliable joining of MEA and DD5 substrates. As the bonding temperature increased, so did the width of the diffusion zone, and the interfacial microvoids significantly closed, representing the enhancement of interface bonding. Both tensile strength and elongation of the joint diffusion-bonded at 1110 °C were superior to those of the joints diffusion-bonded at low temperatures (1020, 1050, and 1080 °C), and the maximum tensile strength and elongation of 1045 MPa and 22.7% were obtained. However, elevated temperature produced an adverse effect that appeared as grain coarsening of the MEA substrate. The ductile fracture of the joint occurred in the MEA substrate (1110 °C), whereas the tensile strength was lower than that of the MEA before diffusion bonding (approximately 1.3 GPa)

Performance optimization and carbon reduction effect of solid waste-based cementitious materials from iron and steel metallurgical slags and ammonia-soda residue

The utilization of solid waste as a resource is a beneficial approach to achieve pollution reduction and carbon reduction simultaneously. In this paper, we developed a quaternary solid waste-based cementitious materials (SWCMs) that can be used as a substitute for cement by utilizing four types of solid waste, namely ground granulated blast furnace slag (GGBS), steel slag (SS), ammonia-soda residue (ASR) and desulfurization gypsum (DG). The performance optimization and carbon emissions of SWCMs are investigated by response surface methodology and emission factor calculations. The results showed that a second-order polynomial model can accurately predict the compressive strength of mortar specimens of SWCMs, with prediction accuracies of 96.78 % and 87.17 % for compressive strengths at 3 days and 28 days, respectively. In terms of raw materials, DG content positively correlates with the compressive strength of the mortar containing SWCMs, moreover, ratios of GGBS to ASR of less than two or more than eight are beneficial. In addition, the production process of each ton of SWCMs emits 71.51 kg CO2, which is only 10 % of the production process of ordinary Portland cement. Overall, this work elucidates the influence of raw materials on the mechanical properties of quaternary SWCMs and quantifies their carbon reduction effects as a substitute for traditional cement, advancing the investigation and application of SWCMs in the realm of low-carbon materials