A novel and anti-agglomerating Ni@yolk–ZrO₂ structure with sub-10 nm Ni core for high performance steam reforming of methane

Steam reforming of methane is a versatile technology for hydrogen production in oil refinery and fuel cell applications. Using natural gas is a promising method to produce rich-hydrogen gas. Ni@yolk–ZrO₂ catalyst is used to study steam reforming of methane under various GHSVs, steam-to-carbon (S/C) ratio, and its recyclability. The catalyst was characterized using a combination of XRD, TEM, AAS, TPR, TPH, TGA, BET, XPS, and Raman techniques. The catalyst is evaluated on time stream and identify its anti-agglomeration property and coking mechanism. From the characterization of TEM and XPS establish the information of Ni particles mobility in the catalyst, which active metal particle size was controlled under the yolk–shell structure framework. Furthermore, the results from TGA, TPH, and Raman analysis of the used Ni@yolk–ZrO₂ catalyst showed the characteristic of inhibiting formation of highly ordered carbon structure

Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss

PurposeTo establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer.MethodsThe study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset.ResultsA total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691).ConclusionThe XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors

The Survey of H5N1 Flu Virus in Wild Birds in 14 Provinces of China from 2004 to 2007

The highly pathogenic H5N1 avian influenza emerged in the year 1996 in Asia, and has spread to Europe and Africa recently. At present, effective monitoring and data analysis of H5N1 are not sufficient in Chinese mainland.)) were obviously higher than those in other 13 provinces. The results of sequence analysis indicated that the 17 strains isolated from wild birds were distributed in five clades (2.3.1, 2.2, 2.5, 6, and 7), which suggested that genetic diversity existed among H5N1 viruses isolated from wild birds. The five isolates from Qinghai came from one clade (2.2) and had a short evolutionary distance with the isolates obtained from Qinghai in the year 2005.We have measured the prevalence of H5N1 virus in 56 species of wild birds in 14 provinces of China. Continuous monitoring in the field should be carried out to know whether H5N1 virus can be maintained by wild birds

Building up AuPd@m-SiO2 nanocatalyst with alloyed noble metal core and mesoporous silica shell structure: Designed composite for enhanced p-chloronitrobenzene hydrogenation selectivity

cable-driven joint module; variable stiffness; service robot; design optimazatio

Weighting Features Before Applying Machine Learning Methods to Pulsar Search

Part 3: Big Data Analysis and Machine LearningInternational audienceIn recent years, different Artificial Intelligence methods have been applied to pulsar search, such as Artificial Neural Network method, PEACE Sorting Algorithm, Real-time Classification method. In this paper, Weighting Feature method before applying machine learning (ML) was proposed. We give weight to each feature according to its ability to distinguish pulsar and non-pulsar candidates. The ability is determined by the separation degree of the distribution of pulsars and non-pulsars on particular feature. And then use the ML methods to classify different types of candidates. The results show that this method is significant. The accuracy of identifying pulsars and modeling time were both improved after weighting