MULTI-SOURCE LEARNING FROM SOCIAL NETWORK DATA

Ph.DDOCTOR OF PHILOSOPHY (NGS

Job search and over-education: Evidence from China’s labour market for postgraduates

This article investigates the relationship between the number of informational channels and overeducation in the outcome of job search, using the survey data of postgraduates in China. The empirical results show: (1) the more the informational channels of job search are used, the lower the probability and the less the intensity of over-education will be; (2) graduates from prestigious “985” universities have lower probability and less intensity of over-education than those of their counterparts from “none-985” universities. Based on the findings above, we argue that helping graduates to get more job information and improving the quality of universities will lighten the problem of the over-education under the situation of great higher education expansion

Job search and over-education: Evidence from China’s labour market for postgraduates

This article investigates the relationship between the number of informational channels and overeducation in the outcome of job search, using the survey data of postgraduates in China. The empirical results show: (1) the more the informational channels of job search are used, the lower the probability and the less the intensity of over-education will be; (2) graduates from prestigious “985” universities have lower probability and less intensity of over-education than those of their counterparts from “none-985” universities. Based on the findings above, we argue that helping graduates to get more job information and improving the quality of universities will lighten the problem of the over-education under the situation of great higher education expansion

Development and validation of a predictive model for stroke associated pneumonia in patients after thrombectomy for acute ischemic stroke

ObjectiveThis study aims to identify the risk factors associated with stroke-associated pneumonia (SAP) in patients who have undergone thrombectomy for acute ischemic stroke and to develop a nomogram chart model for predicting the occurrence of pneumonia.MethodsConsecutive patients who underwent thrombectomy for acute ischemic stroke were enrolled from three hospitals at Taizhou Enze Medical Center. They were randomly divided into a training group and a validation group in a 7:3 ratio. The training group data was used to screen for effective predictive factors using LASSO regression. Multiple logistic regression was then conducted to determine the predictive factors and construct a nomogram chart. The model was evaluated using the validation group, analyzing its discrimination, calibration, and clinical decision curve. Finally, the newly constructed model was compared with the AIS-APS, A2DS2, ISAN, and PANTHERIS scores for acute ischemic stroke-associated pneumonia.ResultsOut of 913 patients who underwent thrombectomy, 762 were included for analysis, consisting of 473 males and 289 females. The incidence rate of SAP was 45.8%. The new predictive model was constructed based on three main influencing factors: NIHSS ≥16, postoperative LMR, and difficulty swallowing. The model demonstrated good discrimination and calibration. When applying the nomogram chart to threshold probabilities between 7 and 90%, net returns were increased. Furthermore, the AUC was higher compared to other scoring systems.ConclusionThe constructed nomogram chart in this study outperformed the AIS-APS, A2DS2 score, ISAN score, and PANTHERIS score in predicting the risk of stroke-associated pneumonia in patients with acute ischemic stroke after thrombectomy. It can be utilized for clinical risk prediction of stroke-associated pneumonia in patients after thrombectomy for acute ischemic stroke

Discovery of Invocation Model and Dynamic Test Configuration Model based on TTCN-3 Test Systems

Abstract. Aimed at the comprehensibility, reusability and maintainability, the thesis presents the reverse model recovery for the legacy code developed by TTCN-3. It can also help tester and maintainers to verify the test implement, etc. The thesis introduces the discovery of invocation model and dynamic test configuration model based on the reverse model discovery system framework

Recognizing Complex Activities by a Probabilistic Interval-Based Model

A key challenge in complex activity recognition is the fact that a complex activity can often be performed in several different ways, with each consisting of its own configuration of atomic actions and their temporal dependencies. This leads us to define an atomic activity-based probabilistic framework that employs Allen's interval relations to represent local temporal dependencies. The framework introduces a latent variable from the Chinese Restaurant Process to explicitly characterize these unique internal configurations of a particular complex activity as a variable number of tables.It can be analytically shown that the resulting interval network satisfies the transitivity property, and as a result, all local temporal dependencies can be retained and are globally consistent.Empirical evaluations on benchmark datasets suggest our approach significantly outperforms the state-of-the-art methods

Effect of Selected Mercapto Flavor Compounds on Acrylamide Elimination in a Model System

The effect of four mercapto flavor compounds (1,2-ethanedithiol, 1-butanethiol, 2-methyl-3-furanthiol, and 2-furanmethanethiol) on acrylamide elimination were investigated in model systems. The obtained results showed that mercaptans assayed were effective in elimination arylamide in a model system. Their reactivities for decreasing acrylamide content depended on mercaptan’s molecular structure and acrylamide disappearance decreased in the following order: 1,2-ethanedithiol > 2-methyl-3-furanthiol > 1-butanethiol > 2-furanmethanethiol. Mercaptans were added to acrylamide to produce the corresponding 3-(alkylthio) propionamides. This reaction was irreversible and only trace amounts of acrylamide were formed by thermal heating of 3-(alkylthio) propanamide. Although a large amount disappeared, only part of the acrylamide conversed into 3-(alkylthio) propionamides. All of these results constitute a fundamental proof of the complexity of the reactions involved in the removal of free acrylamide in foods. This implies mercapto flavor/aroma may directly or indirectly reduce the level of acrylamide in food processing. This study could be regarded as a pioneer contribution on acrylamide elimination in a model system by the addition of mercapto flavor compounds

Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction

Social networks contain a wealth of useful information. In this paper, we study a challenging task for integrating users' information from multiple heterogeneous social networks to gain a comprehensive understanding of users' interests and behaviors. Although much effort has been dedicated to study this problem, most existing approaches adopt linear or shallow models to fuse information from multiple sources. Such approaches cannot properly capture the complex nature of and relationships among different social networks. Adopting deep learning approaches to learning a joint representation can better capture the complexity, but this neglects measuring the level of confidence in each source and the consistency among different sources. In this paper, we present a framework for multiple social network learning, whose core is a novel model that fuses social networks using deep learning with source confidence and consistency regularization. To evaluate the model, we apply it to predict individuals' tendency to volunteerism. With extensive experimental evaluations, we demonstrate the effectiveness of our model, which outperforms several state-of-the-art approaches in terms of precision, recall and F1-score

Colorimetric Detection of Class A Soybean Saponins by G-Quadruplex-Based Hybridization Chain Reaction

Soybean saponin is one of the important secondary metabolites in seeds, which has various beneficial physiological functions to human health. GmSg-1 gene is the key enzyme gene for synthesizing class A saponins. It is of great significance to realize the visual and rapid detection of class A saponins at the genetic level. The hybridization chain reaction (HCR) was employed to the visual detection of GmSg-1 gene, which was implemented by changing the length of the target fragment to 92 bp and using the hairpin probes we designed to detect the GmSg-1a and GmSg-1b genes. The best condition of HCR reaction is hemin (1.2 μM), Triton X-100 (0.002%), ABTS (3.8 μM), and H2O2 (1.5 mM). It was found that HCR has high specificity for GmSg-1 gene and could be applied to the visual detection of different soybean cultivars containing Aa type, Ab type, and Aa/Ab type saponins, which could provide technical reference and theoretical basis for molecular breeding of soybean and development of functional soybean products

UMI-77 Modulates the Complement Cascade Pathway and Inhibits Inflammatory Factor Storm in Sepsis Based on TMT Proteomics and Inflammation Array Glass Chip

Sepsis is a systemic inflammatory response syndrome caused by infection, which has no specific drug at present. UMI-77 can significantly improve the survival rate of septic mice; the detailed role of UMI-77 and its underlying mechanisms in sepsis are not clear. Inflammation array glass chip and proteomic analyses were performed to elucidate the latent mechanism of UMI-77 in the treatment of sepsis. The results showed that 7.0 mg/kg UMI-77 improved the 5 day survival rate in septic mice compared to the LPS group (60.964 vs 9.779%) and ameliorated the pathological conditions. Inflammation array glass chip analysis showed that sepsis treatment with UMI-77 may eventually through the suppression of the characteristic inflammatory storm-related cytokines such as KC, RANTES, LIX, IL-6, eotaxin, TARC, IL-1β, and so on. Proteomics analysis showed that 213 differential expression proteins and complement and coagulation cascades were significantly associated with the process for the UMI-77 treatment of sepsis. The top 10 proteins including Apoa2, Tgfb1, Serpinc1, Vtn, Apoa4, Cat, Hp, Serpinf2, Fgb, and Serpine1 were identified and verified, which play important roles in the mechanism of UMI-77 in the treatment of sepsis. Our findings indicate that UMI-77 exerts an antisepsis effect by modulating the complement cascade pathway and inhibiting inflammatory storm factors