Investigation of magnesium hydroxide functionalized by polydopamine/transition metal ions on flame retardancy of epoxy resin.

Aiming to impart epoxy resin (EP) with flame retardancy, magnesium hydroxide (MDH) was sequentially functionalized with four transition metals and polydopamine (PDA) to prepare MDH@M-PDA (M includes Fe3+, Co2+, Cu2+, Ni2+). Compared with MDH, MDH@M-PDA presented better dispersion in EP matrix. The results illustrated that a 30 mass% of MDH@Fe-PDA imparted the EP matrix with best fire retardancy and thermal stability. Specifically, the resultant EP/MDH/MDH@Fe-PDA composites remarkably reduced flammability, which is reflected by high LOI value of 29.3% and UL-94 V-0 ratings. The peak heat release rate (PHRR) and total smoke production (TSP) were reduced by 52% and 21%, respectively. Moreover, the impact and tensile strength of EP/MDH/MDH@M-PDA composites are improved compared with EP/MDH due to the better chemical compatibility of PDA in the EP matrix. Notably, this work provided a feasible design for organo-modified MDH and enriched its practical applications of MDH as functional fillers to polymers.post-print2133 K

Bismuth-doped zinc aluminosilicate glasses and glass-ceramics with ultra-broadband infrared luminescence

Abstract Broadband infrared luminescence covering the optical telecommunication wavelength region of O, E and S bands was observed from bismuth-doped zinc aluminosilicate glasses and glass-ceramics. The spectroscopic properties of the glasses and glass-ceramics depend on the thermal-treatment history. With the appearance of gahnite (ZnAl 2 O 4 ) crystalline phase, the fluorescent peak moves to longer wavelength, but the fluorescent intensity decreases. The $1300 nm fluorescence with a FWHM larger than 250 nm and a lifetime longer than 600 ls possesses these optical materials with potential applications in laser devices and broadband amplifiers. The broad infrared luminescence from the bismuth-doped zinc aluminosilicate glasses and glass-ceramics might be from BiO or bismuth clusters rather than from Bi 5+ and Bi 3+

Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan

Retinex mine image enhancement algorithm based on TopHat weighted guided filtering

The uneven distribution of light sources and weak light in coal mines lead to low brightness and unclear image. The traditional Retinex algorithm has the problems of detail loss, edge blur and halo when processing low illumination images of coal mines. In order to solve the above problems, a new algorithm named THWGIF-Retinex based on TopHat weighted guided filtering is proposed to enhance the mine image. Firstly, the image is transformed from RGB space to HSV space. Then the image is separated into three channel components of hue, saturation and brightness. Secondly, the TopHat transform is used to improve the weight factor of the weighted guided filtering. The illumination component of the image is extracted from the brightness component. The edge enhancement of the brightness component is realized. Thirdly, the illumination component and the saturation component are corrected by adopting a self-adaptive gamma correction function. The reflection component is obtained from the illumination component by the Retinex algorithm. The details and color effect of the image light source are further improved. Finally, the hue component, the corrected saturation component and the reflection component are combined and converted to RGB space to obtain an enhanced mine image. The THWGIF-Retinex algorithm, multi-scale Retinex (MSR) algorithm and weighted guided filtering Retinex (WGIF-Retinex) algorithm are compared and verified from subjective evaluation and objective evaluation. The subjective evaluation results show that the original image of low illumination without strong light is enhanced by the THWGIF-Retinex algorithm. The color reproduction degree of the image is higher, the image edge is clearer, and the visual effect is obviously enhanced. The THWGIF-Retinex algorithm has a good effect on halo reduction for the mine low-illumination original image with strong light. The THWGIF-Retinex algorithm is better than the WGIF-Retinex algorithm in restoring the details and clarity of dark areas. The objective evaluation results show that the information entropy, the average gradient, the standard deviation and the no-reference structural sharpness (NRSS) of the image enhanced by the THWGIF-Retinex algorithm are increased by 12.50%, 109.07%, 52.44% and 45.46% respectively for the low illumination images without strong light. Compared with the MSR algorithm, the information entropy, average gradient, standard deviation and NRSS of the image enhanced by the THWGIF-Retinex algorithm are increased by 1.24%, 81.44%, 18.23% and 36.67% respectively for the mine low illumination image with strong light. Compared with the WGIF-Retinex algorithm, the THWGIF-Retinex algorithm has lower information entropy. However, the average gradient and NRSS are improved by 72.34% and 23.87% respectively

The complete chloroplast genome of Salix cupularis Rehder, a sand binder in alpine hillslope, China

The complete chloroplast genome of Salix cupularis Rehder was assembled and subjected to phylogenetic analysis. The chloroplast genome of S. cupularis was 155,518 bp in length, containing a large single-copy region (84,373 bp), a small single-copy region (16,226 bp), and two inverted repeat regions (27,458 bp). The overall GC content of S. cupularis chloroplast genome was 36.70%. The chloroplast genome of S. cupularis contained 127 unique genes, including 82 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic analysis showed that S. cupularis was most related to Salix magnifica

The complete chloroplast genome sequence of Sibiraea angustata, a traditional Chinese medicine in Sichuan Province, China

The complete chloroplast genome of Sibiraea angustata was assembled and subjected to phylogenetic analysis. The chloroplast genome of S. angustata was 155,869 bp in length, containing a large single-copy region (84,343 bp), a small single-copy region (18,820 bp), and two inverted repeat regions (26,353 bp). The overall GC content of S. angustata chloroplast genome was 36.80%. The chloroplast genome of S. angustata contained 127 unique genes, including 83 protein-coding genes, 36 tRNA genes and eight rRNA genes. Phylogenetic analysis revealed that S. angustata was related to Malus ioensis, Malus florentina and Malus trilobata

Non-orthogonal beam coordinate system wave propagation and reverse time migration

Grid size has a significant influence on the computation efficiency and accuracy of finite-difference seismic modeling and can change the workload of reverse time migration (RTM) remarkably. This paper proposes a non-orthogonal analytical coordinate system, beam coordinate system (BCS), for the solution of seismic wave propagation and RTM. Starting with an optical Gaussian beam width equation, we expand the representation on vertically variable velocity media, which is the most common scenario in seismic exploration. The BCS based on this representation can be used to implement an irregular-grid increment finite-difference that improves the efficiency of RTM. Based on the Laplacian expression in Riemannian space, we derive the wave equation in the BCS. The new coordinate system can generate an irregular grid with increment increasing vertically along depth. Through paraxial ray tracing, it can be extended to non-analytical beam coordinate system (NBCS). Experiments for the RTM on the Marmousi model with the BCS demonstrate that the proposed method improves the efficiency about 52% while maintaining good image quality

Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04

Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04