Synthesis and evaluation of Mn-Sn modified Ru-Ir electrode for electrocatalytic treatment of high chloride acrylonitrile wastewater.

Acrylonitrile wastewater was an organic wastewater with strong toxicity and poor biodegradability. Therefore, electro-catalytic technology became a promising acrylonitrile wastewater treatment technology because of no secondary pollution, wide application range and low water quality requirements. The optimal Mn-Sn modified Ru-Ir electrode material was synthesized by thermal method and applied in electro-catalytic treatment of acrylonitrile wastewater. The electrode materials were characterized by SEM, TEM, XRD, XPS and electrochemical characterization. SEM, TEM, XRD and XPS indicated that Mn and Sn were capable of incorporating and replacing the part of Ru or Ir and could alter the microstructure of Ru-Ir and the types of Mn and Sn oxides, raising the oxygen evolution potential (OEP) and voltampere charge. When the molar ratio of Mn-Sn was 1:1, OEP, voltampere charge and exchange current density could reach 1.303 V, 1.51 C/cm2 and 6.29×10-4 A/cm2, respectively. The co-doping of Mn-Sn had significant influence on the electrocatalytic performance of Ru-Ir electrode materials. The optimum synthesis conditions of Mn-Sn modified Ru-Ir electrode were as follows: the molar ratio of Mn-Sn was 1:1, calcination time was 4.0 hours, calcination temperature was 450℃, and solvent was water. Under certain conditions, the removal rate of acrylonitrile with Mn-Sn modified Ru-Ir electrode was 100%. Mn-Sn modified Ru-Ir electrode had high oxygen evolution potential and good removal effect of acrylonitrile, which was higher than that of ruthenium iridium electrode and RuO2 electrode.

An attention base U-net for parotid tumor autosegmentation.

A parotid neoplasm is an uncommon condition that only accounts for less than 3% of all head and neck cancers, and they make up less than 0.3% of all new cancers diagnosed annually. Due to their nonspecific imaging features and heterogeneous nature, accurate preoperative diagnosis remains a challenge. Automatic parotid tumor segmentation may help physicians evaluate these tumors. Two hundred eighty-five patients diagnosed with benign or malignant parotid tumors were enrolled in this study. Parotid and tumor tissues were segmented by 3 radiologists on T1-weighted (T1w), T2-weighted (T2w) and T1-weighted contrast-enhanced (T1wC) MR images. These images were randomly divided into two datasets, including a training dataset (90%) and an validation dataset (10%). A 10-fold cross-validation was performed to assess the performance. An attention base U-net for parotid tumor autosegmentation was created on the MRI T1w, T2 and T1wC images. The results were evaluated in a separate dataset, and the mean Dice similarity coefficient (DICE) for both parotids was 0.88. The mean DICE for left and right tumors was 0.85 and 0.86, respectively. These results indicate that the performance of this model corresponds with the radiologist's manual segmentation. In conclusion, an attention base U-net for parotid tumor autosegmentation may assist physicians to evaluate parotid gland tumors.

Application of Pd-Sn modified Ru-Ir electrode for treating high chlorine ammonia-nitrogen wastewater.

Electro-catalytic technology is a promising approach for wastewater treatment, owing to its easy operation, minimal generation of secondary pollution, small foot-print and rapid start-up. In this work, the chlorine evolution potential of the Pd-Sn modified ruthenium(Ru)-iridium(Ir) electrode was investigated for the electro-catalytic treatment of high chlorine ammonia-nitrogen wastewater. The effect of reaction conditions on the removal of ammonia-nitrogen, kinetics and apparent activation energy of ammonia-nitrogen removal were studied. The possible denitrification process of high chlorine ammonia-nitrogen wastewater was discussed. The results indicated that the chlorine evolution potential of the Pd-Sn modified Ru-Ir electrode was 1.0956 V(vs. SCE). The electro-catalytic treatment of high chlorine ammonia-nitrogen conformed to zero-order kinetic law, and the apparent activation energy of removal process was 14.089 kJ/mol. With a current was 0.5 A, the removal efficiency of ammonia-nitrogen could achieve 100% at a reaction time of 40 min. Indirect oxidation played an essential role in the electro-catalytic ammonia-nitrogen removal using the Pd-Sn modified Ru-Ir electrode. This paper demonstrated that the electro-catalytic technology was a promising approach for efficiently treating the high chlorine ammonia-nitrogen wastewater.

Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing.

With the gradual improvement of people's living standards, the production and drinking of all kinds of food is increasing. People's disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network.

Objective.Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects.Approach.This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people.Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models.Significance.Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.

Nonhongiella spirulinensis gen. nov., sp. nov., a bacterium isolated from a cultivation pond of Spirulina platensis in Sanya, China.

A Gram-negative, aerobic, motile rod strain, designated Ma-20(T), was isolated from a pool of marine Spirulina platensis cultivation, Sanya, China, and was subjected to a polyphasic taxonomy study. Strain Ma-20(T) can grow in the presence of 0.5-11 % (w/v) NaCl, 10-43 °C and pH 6-10, and grew optimally at 30 °C, pH 7.5-9.0 in natural seawater medium. The polar lipids were composed of phosphatidylethanolamine, three unidentified phospholipids and three unidentified polar lipids. The respiratory quinone was ubiquinone 8 (Q-8) and the major fatty acids were C18:1ω6c/C18:1ω7c (summed feature 8, 32.84 %), C16:1ω6c/C16:1ω7c (summed feature 3, 30.76 %), C16:0 (13.54 %), C12:03-OH (4.63 %), and C12:0 (4.09 %). The DNA G+C content of strain Ma-20(T) was 58 mol %. Phylogenetic analyses based on 16S rRNA gene sequences showed that strain Ma-20(T) belonging to Gammaproteobacteria, it shared 88.46-91.55 and 89.21-91.26 % 16S rRNA gene sequence similarity to the type strains in genus Hahella and Marinobacter, respectively. In addition to the large 16S rRNA gene sequence difference, Ma-20(T) can also be distinguished from the reference type strains Hahella ganghwensis FR1050(T) and Marinobacter hydrocarbonoclasticus sp. 17(T) by several phenotypic characteristics and chemotaxonomic properties. On the basis of phenotypic, chemotaxonomic and phylogenetic properties, strain Ma-20(T) is suggested to represent a novel species of a new genus in Gammaproteobacteria, for which the name Nonhongiella spirulinensis gen. nov., sp. nov. is proposed. The type strain is Ma-20(T) (=KCTC 32221(T)=LMG 27470(T)).