Mining heterogeneous information graph for health status classification

In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting people’s health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patients’health status and reveal the specific disease potentially suffered by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of people’s health with accessibility to the patterns in various observations

Mining health knowledge graph for health risk prediction

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health

Fermions Tunneling from Apparent Horizon of FRW Universe

In the paper [arXiv:0809.1554], the scalar particles' Hawking radiation from the apparent horizon of Friedmann-Robertson-Walker(FRW) universe was investigated by using the tunneling formalism. They obtained the Hawking temperature associated with the apparent horizon, which was extensively applied in investigating the relationship between the first law of thermodynamics and Friedmann equations. In this paper, we calculate Fermions' Hawking radiation from the apparent horizon of FRW universe via tunneling formalism. Applying WKB approximation to the general covariant Dirac equation in FRW spacetime background, the radiation spectrum and Hawking temperature of apparent horizon are correctly recovered, which supports the arguments presented in the paper [arXiv:0809.1554].Comment: 8 pages, no figure

Life fingerprints of nuclear reactions in the body of animals

Nuclear reactions are a very important natural phenomenon in the universe. On the earth, cosmic rays constantly cause nuclear reactions. High energy beams created by medical devices also induce nuclear reactions in the human body. The biological role of these nuclear reactions is unknown. Here we show that the in vivo biological systems are exquisite and sophisticated by nature in influence on nuclear reactions and in resistance to radical damage in the body of live animals. In this study, photonuclear reactions in the body of live or dead animals were induced with 50-MeV irradiation. Tissue nuclear reactions were detected by positron emission tomography (PET) imaging of the induced beta+ activity. We found the unique tissue "fingerprints" of beta+ (the tremendous difference in beta+ activities and tissue distribution patterns among the individuals) are imprinted in all live animals. Within any individual, the tissue "fingerprints" of 15O and 11C are also very different. When the animal dies, the tissue "fingerprints" are lost. The biochemical, rather than physical, mechanisms could play a critical role in the phenomenon of tissue "fingerprints". Radiolytic radical attack caused millions-fold increases in 15O and 11C activities via different biochemical mechanisms, i.e. radical-mediated hydroxylation and peroxidation respectively, and more importantly the bio-molecular functions (such as the chemical reactivity and the solvent accessibility to radicals). In practice biologically for example, radical attack can therefore be imaged in vivo in live animals and humans using PET for life science research, disease prevention, and personalized radiation therapy based on an individual's bio-molecular response to ionizing radiation

Study on atmospheric corrosion behaviour and mechanism of Q235 steel after passivation

Q235 steel components have poor corrosion resistance and are susceptible to erosion by corrosive media, so they are generally passivated before being put into service. This paper investigates the corrosion behaviour and corrosion mechanism of passivated Q235 steel in atmospheric environments through macro and micro morphological characterisation and electrochemical simulation analysis