Effects of Hf, B, Cr and Zr alloying on mechanical properties and oxidation resistance of Nb-Si based ultrahigh temperature alloy

Multi-component Nb-Si based ultrahigh temperature alloys were prepared by vacuum non-consumable arc melting. The effects of Hf, B, Zr and Cr alloying on the phase selection, phase stability, both non-equilibrium and equilibrium microstructure, room-temperature fracture toughness, hardness and oxidation resistance at 1250 oC of the alloys have been investigated and estimated systematically. The results show that the addition of B or Cr promotes the formation of hypereutectic structures. The alloying with both Hf and B suppresses the formation of β(Nb,X)5Si3 and promotes the formation of α(Nb,X)5Si3 and γ(Nb,X)5Si3, while the alloying with Cr has no effect on the crystal structures of 5-3 silicides. The room-temperature fracture toughness of the alloys is always degraded by the addition of Cr but almost not influenced by the combined additions of Hf and B. The hardness of 5-3 silicides exhibits a tendency of γ \u3e α \u3e β. The macrohardness of the alloys increases with Cr addition, and it obviously reduces in the presence of Hf after 1450 oC/50 h heat-treatment. The best oxidation-resistant performance has been obtained for the alloy with both B and Cr additions. However, in the presence of B and/or Cr, the oxidation resistance of the alloys has been degraded by further addition of Hf. Both sizes and amounts of primary γ-(Nb, X)5Si3 increase with Zr contents in the alloy. Both adhesion and compactness of the scales are improved effectively by increase in Zr content. The mass gain and thickness of the scale decrease with increase in Zr contents, indicating that Zr addition can improve the oxidation resistance of the alloys significantly. Please click Additional Files below to see the full abstract

Towards Intelligent Decision Making in Emotion-aware Applications

In this paper, we propose an intelligent emotion-aware system (IES), which aims to provide a systematic approach that can make use of the online technology to improve the intelligence of different emotion-aware mobile applications. IES is constructed to provide multi-dimensional online social community data collection and processing approaches for decision making, so as to recommend intelligent services for emotion-aware mobile applications. Furthermore, we present a flow of intelligent decision making process designed on IES, and highlight the implementation and orchestration of several key technologies and schemes applied in this system for different emotion-aware mobile applications in run-time. We demonstrate the feasibility of the proposed IES by presenting a novel emotion-aware mobile application - iSmile, and evaluate the system performance based on this application

Associations between air pollutant and pneumonia and asthma requiring hospitalization among children aged under 5 years in Ningbo, 2015–2017

IntroductionExposure to ambient air pollutants is associated with an increased incidence of respiratory diseases such as pneumonia and asthma, especially in younger children. We investigated the relationship between rates of hospitalization of children aged under 5 years for pneumonia and asthma and the concentration of air pollutants in Ningbo between January 1, 2015 and August 29, 2017.MethodsData were obtained from the Ningbo Air Quality Data Real-time Publishing System and the big data platform of the Ningbo Health Information Center. A generalized additive model was established via logarithmic link function and utilized to evaluate the effect of pollutant concentration on lag dimension and perform sensitivity analysis.ResultsA total of 10,301 cases of pneumonia and 115 cases of asthma were identified over the course of this study. Results revealed that PM2.5, PM10, SO2 and NO2 were significantly associated with hospitalization for pneumonia and asthma in children under 5 years of age. For every 10-unit increase in lag03 air pollutant concentration, hospitalization for pneumonia and asthma due to PM2.5, PM10, SO2 and NO2 increased by 2.22% (95%CI: 0.64%, 3.82%), 1.94% (95%CI: 0.85%, 3.04%), 11.21% (95%CI: 4.70%, 18.10%) and 5.42% (95%CI: 3.07%, 7.82%), respectively.DiscussionAdverse effects of air pollutants were found to be more severe in children aged 1 to 5 years and adverse effects due to PM2.5, PM10 and SO2 were found to be more severe in girls. Our findings underscore the need for implementation of effective public health measures to urgently improve air quality and reduce pediatric hospitalizations due to respiratory illness

Egg consumption associated with all-cause mortality in rural China: A 14-year follow-up study

Background: Dietary recommendations regarding egg intake remain controversial topic for public health. We hypothesized that there was a positive association between egg consumption and all-cause mortality. Methods: To test this hypothesis, we enrolled 9885 adults from a community-based cohort in Anhui Province, China during 2003-05. Egg consumption was assessed by food questionnaire. Stratified analyses were performed for age, sex, body mass index (BMI), blood pressure, smoking, drinking and laboratory tests. Results: After an average follow-up of 14.1 years, 9444 participants were included for analysis. A total of 814 deaths were recorded. Participants\u27 BMI and lipid profile had no significantly difference between three egg consumption groups. BMI was 21.6±2.7 of the whole population, especially BMI\u3e24 was only 17.3%. A bivariate association of egg consumption \u3e6/week with increased all-cause mortality was observed compared with ≤6/week (RR: 1.35, 95% CI: 1.05, 1.73, P = 0.018). A significant interaction was observed for BMI ≥ 21.2 kg/m2 vs. BMI\u3c21.2 kg/m2 (P for interaction: 0.001). No other significant interactions were found. Conclusions: In this study, consuming \u3e6 eggs/week increased risk of all-cause mortality, even among lean participants, especially who with BMI ≥ 21.2 kg/m2. Eggs are an easily accessible and constitute an affordable food source in underdeveloped regions. Consuming \u3c6 eggs/week may be the most suitable intake mode

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis

Alterations of the gut microbiota associated with the occurrence and progression of viral hepatitis

BackgroundGut microbiota is the largest population of microorganisms and is closely related to health. Many studies have explored changes in gut microbiota in viral hepatitis. However, the correlation between gut microbiota and the occurrence and progression of viral hepatitis has not been fully clarified.MethodsPubMed and BioProject databases were searched for studies about viral hepatitis disease and 16S rRNA gene sequencing of gut microbiota up to January 2023. With bioinformatics analyses, we explored changes in microbial diversity of viral hepatitis, screened out crucial bacteria and microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting risks for the occurrence and progression of viral hepatitis based on ROC analysis.ResultsOf the 1389 records identified, 13 studies met the inclusion criteria, with 950 individuals including 656 patient samples (HBV, n = 546; HCV, n = 86; HEV, n = 24) and 294 healthy controls. Gut microbial diversity is significantly decreased as the infection and progression of viral hepatitis. Alpha diversity and microbiota including Butyricimonas, Escherichia-Shigella, Lactobacillus, and Veillonella were identified as the potential microbial markers for predicting the risk of development of viral hepatitis (AUC>0.7). Microbial functions including tryptophan metabolism, fatty acid biosynthesis, lipopolysaccharide biosynthesis, and lipid metabolism related to the microbial community increased significantly as the development of viral hepatitis.ConclusionsThis study demonstrated comprehensively the gut microbiota characteristics in viral hepatitis, screened out crucial microbial functions related to viral hepatitis, and identified the potential microbial markers for predicting the risk of viral hepatitis