An analysis of framing in British news media representations of China and the Chinese

At the beginning of the twenty-first century, with China’s remarkable success in economic developments and greater openness to the outside world, two sharply opposing views of China have appeared in the Western perception of China - a rising superpower as well as a threat to the West, economically, militarily and environmentally. The West, particularly the US and Britain fears that China is likely to take advantage of its growing economic and geopolitical influence in order to change the world’s power pattern. Within such a social context, this thesis sets out to explore if the old concepts of Orientalism on China has ever changed in modern times and how the modern images China and the Chinese are framed in the contemporary British news media. It is carried out through four cases – Chinese migration, Hong Kong handover (1997), Tibet issue and Sichuan Great Earthquake (2008). More specifically, the thesis examines: how the two dominating masterframes – ethno-nationalist and liberal individualist masterframes coexist or compete with each other in the reporting; and what the differences are between newspapers in terms of frame choice and the ratio of struggle between two frames. The study implies that the old Orientalist stereotypes, such as ‘Yellow Peril’, which were used to describe China and the Chinese have not often appeared in the recent British news media representations in the selected four cases. Instead, the liberal individualist views have been widely and deeply embedded in the British news reporting, criticising China being essentially a Communist dictatorship as opposed to Western democracy. Additionally, the relations between two masterframes appear in three forms – coexistence or intertwining, supporting each other, and struggle

Isolasi Dan Identifikasi Bakteri Aerob Yang Berpotensi Menjadi Sumber Penularan Infeksi Nosokomial Di Irina a Rsup Prof. Dr. R. D. Kandou Manado

: Nosocomial infection or Hospital Acquired Infection (HAI) is an infection caused by bacteria, parasite, or virus in the hospital, infection occur at least 72 hours since hospitalized. This infection occurs due to lack of hygiene of the environment causing microorganism infection from environment to human, infection can also occur due to transmission of microorganism from one patient to other patients. Inpatients potentially have very high risk of nosocomial infection occur due to continuous requiring treatment for more than 24 hours. Purpose: To determine the existence of aerobic bacteria that could potentially be the source of transmission of nosocomial infection in Irina A RSUP Prof. Dr. R. D. Kandou Manado. Method: This research was descriptive with cross sectional approach. Fourteen samples were taken from the surface of medical equipment, bed, floor, and wall of the treatment room and eight samples were taken from the air. Identification of bacteria was performed by culture on agar medium, staining gram, and biochemical test. Result: Bacillus subtilis found in nine samples (41%), Serratia liquefaciens found in five samples (22,7%), Lactobacillus found in two samples (9,1%), Staphylococcus found in two samples (9,1%), Coccus Gram negative found in two samples (9,1%), Enterobacter aerogenes found in one sample (4,5%), and Enterobacter agglomerans found in one sample (4,5%). Conclusion: Bacillus subtilis is the most bacteria which had been found in this research

Development of Deep Learning Based Methodology on Rotating Machine Fault Diagnosis

Rotating machines are widely used in various industrial applications. It is necessary to implement the condition based maintenance for rotating machines to prevent failures, increase reliability and decrease maintenance cost. Traditionally, the most critical issue in developing rotating machine fault detection and diagnosis methods is to extract and quantify the complicated signal processing-based fault features. With the combination of data mining techniques, faults can be diagnosed accurately using previously extracted features. However, nowadays there are challenges in using existing methods for rotating machinery fault diagnosis. In the age of Internet of Things and Industrial 4.0, massive real-time data were collected from health monitoring systems for fault diagnosis. The traditional methods to extract features from monitoring data manually with expertise in signal processing and prior knowledge in fault diagnosis is rarely accomplishable on a machinery big data platform. Therefore, a novel methodology that can automatically extract the adaptive fault features from monitoring data and, diagnose the fault pattern intelligently, is expected to realize rotating machinery fault detection and diagnosis on machinery big data platform. With its deep architecture, deep learning can automatically extract features from the data and hence eliminate the process of handcrafting features from the data. Though there is a growing interest in using deep learning for machinery fault detection and diagnosis, some challenges still exist. However, the raw monitoring data were processed with complicated signal processing algorithms such as wavelet-package transform (WPT) , or pre-processed to obtain features . The complicated signal processing is still required in many reported deep learning based fault diagnosis applications in literature. The current selection of deep learning architecture is trial and error based. The selection of deep learning architecture has not been well investigated yet. Until now, only the vibration condition monitoring data was studied with the application of deep learning based approaches. Other monitoring data such as acoustic emission (AE) data and piezoelectric (PE) data have yet to be processed with deep learning based approaches. In this research, novel deep learning based methodologies that can automatically extract the adaptive fault features from monitoring data and intelligently diagnose the faults with machinery big data is developed to address the issues stated above. Specifically, the following new effective and efficient rotating machine fault diagnosis are presented: a deep learning based approach for bearing fault diagnosis using AE signals, a deep learning based approach for simultaneous bearing fault diagnosis and fault severity detection using vibration signals, a deep learning based approach for planetary gear box (PGB) fault diagnosis, a signal processing integrated deep learning approach for bearing fault diagnosis using vibration signals. The realization of adaptive feature extraction and learning can reduce the ratio of training samples to testing samples. Furthermore, a novel signal processing integrated deep learning method is proposed to capture the hidden time and frequency features in the monitoring data. The introduction of signal processing into deep learning method provides a view of effective deep learning method on time series monitoring signals. To validate the proposed methodology on rotating machinery fault diagnosis, data collected from a bearing test rig and a planetary gear box (PGB) test rig were used. The data was collected from the runs on bearings and gears with seeded typical faults. Vibration and AE data were collected at the bearing test rig, while vibration, AE, and PE data were collected on the PGB test rig

Genotypic and allelic distributions of rs2486668 in NSOFC cases and controls.

<p>Genotypic and allelic distributions of rs2486668 in NSOFC cases and controls.</p

Model 3.

Case (a), (b), and (c) represent the three aforementioned cases. The horizontal axis represents the constructed variable, while the vertical axis represents the temporal change of the opinion score difference. The scatter points illustrate the distribution of these two variables, with darker colors indicating overlapping points. The deep blue fitted lines are drawn using the OLS method.</p

Genotypic and allelic distributions of rs545809 in NSOFC cases and controls.

<p>Genotypic and allelic distributions of rs545809 in NSOFC cases and controls.</p

Statistical test results of Model 2.

We propose two analytical relationships between affinity and opinion change. The first one focuses on value homophily, while the second one incorporates affinity in opinion dynamics. Three analytical test models are derived based on these relationships: the value homophily model, the temporal evolution of opinion summation, and the evolution of opinion difference between two individuals. We test these models using data from a previous experiment, and the results demonstrate their validity.</div

Frequency distribution of differences between the summations of opinion scores at two different time points.

(a) Between sum(2) and sum(1), (b) Between sum(3) and sum(2).</p

Lack of Association between Missense Variants in <i>GRHL3</i> (rs2486668 and rs545809) and Susceptibility to Non-Syndromic Orofacial Clefts in a Han Chinese Population

<div><p>Background</p><p>Grainyhead-like-3 (<i>GRHL3</i>) was recently identified as the second gene that, when mutated, can leads to Van der Woude syndrome, which is characterized by orofacial clefts (OFC) and lower lip pits. In addition, a missense variant (rs41268753) in <i>GRHL3</i> confers risk for non-syndromic cleft palate cases of European ancestry. Together with interferon regulatory factor 6 (<i>IRF6</i>), <i>GRHL3</i> may be associated with the risk of NSOFC which awaits for being verified across different ethnic populations.</p><p>Objective</p><p>The aim of this study was to investigate the possible relationship between common functional variants in <i>GRHL3</i> and susceptibility to NSOFC, especially cleft palate cases, in a Han Chinese population, one of the ethnic groups with the highest birth prevalence of orofacial clefting.</p><p>Methods</p><p>Because the allele frequency for rs41268753 minor alleles was zero in our Chinese population, we selected functional single nucleotide polymorphisms (SNPs) spanning <i>GRHL3</i> with minor allele frequencies (MAFs) > 5% in the Han Chinese population. Two SNPs which meet the above criteria were then genotyped in a case-control cohort comprising 1145 individuals using the TaqMan 5′-exonuclease allelic discrimination assay.</p><p>Results</p><p>SNPs rs2486668 and rs545809 were used in this study. Overall genotype and allele distributions of both SNPs in general and stratified genotyping analyses revealed no statistically significant differences between cases and controls. Further logistic regression analyses using different genetic models failed to reveal any evidence that these markers influence risk to NSOFC.</p><p>Conclusions</p><p>The variant rs41268753 in <i>GRHL3</i> increases the risk for cleft palate in European population, but our findings failed to detect the link between two <i>GRHL3</i> SNPs (rs2486668 and rs545809) and risk to NSOFC in the Han Chinese cohort. Although the present study did not provide any evidence that common functional variants in <i>GRHL3</i> may contribute to NSOFC etiology in this Chinese population, further studies with a larger sample size, additional SNPs, and a more diverse ethnic cohort are still warranted.</p></div

Model 2.

Case (a), (b), and (c) represent the three aforementioned cases. The horizontal axis represents the opinion summation at the current time point, while the vertical axis represents the opinion summation at the next time point. The scatter points depict the distribution of these two sums, with darker colors indicating overlapping points. The deep blue fitted lines are generated using the OLS method.</p