Civil Society Organizations and Community Politics in Postcolonial Hong Kong

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The politics of social policy development in Hong Kong: mobilization by civil society in a semi-democracy

This paper studies the politics of social policy development in postcolonial Hong Kong, focusing on how societal mobilization has affected social policymaking in a liberal autocratic and semi-democratic setting. The significance of social mobilization in affecting social policy development has been receiving more scholarly attention. For instance, Kwon (2002, 2005) has adopted the concept of advocacy coalitions to study the politics of health care reform in Korea (Kwon, 2002) and comparative social policy reform in Korea and Taiwan (2005), paying particular attention to the collaboration between actors across the state and nonstate institutions. Hsiao’s (2001) study of the social welfare movement in Taiwan has also shown how the structure of civil society and the linkages of important societal actors may have a direct impact on social policy development....postprintThe 9th International Conference of the International Society for Third Sector Research, Istanbul, Turkey, 7-10 July 2010

A Study of High School Graduates

The specific problem of this report is to portray the results or findings of a survey conducted as a Follow-up study of the graduates of the Stafford High School

Predicting systolic blood pressure using machine learning

In this paper, a new study based on machine learning technique, specifically artificial neural network, is investigated to predict the systolic blood pressure by correlated variables (BMI, age, exercise, alcohol, smoke level etc.). The raw data are split into two parts, 80% for training the machine and the remaining 20% for testing the performance. Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the prediction system. Based on a database with 498 people, the probabilities of the absolute difference between the measured and predicted value of systolic blood pressure under 10mm Hg are 51.9% for men and 52.5% for women using the back-propagation neural network With the same input variables and network status, the corresponding results based on the radial basis function network are 51.8% and 49.9% for men and women respectively. This novel method of predicting systolic blood pressure contributes to giving early warnings to young and middle-aged people who may not take regular blood pressure measurements. Also, as it is known an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff. Our experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. © 2014 IEEE.published_or_final_versio

Bio-medical application on predicting systolic blood pressure using neural networks

This paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff.published_or_final_versio

A Prediction Model of Blood Pressure for Telemedicine

This paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressure through the correlation of variables (age, BMI, exercise level, alcohol consumption level, smoking status, stress level, and salt intake level). The study was carried out using a database containing a variety of variables/factors. Each database of raw data was split into two parts: one part for training the neural network and the remaining part for testing the performance of the network. Two neural network algorithms, back-propagation and radial basis function, were used to construct and validate the prediction system. According to the experiment, the accuracy of our predictions of systolic blood pressure values exceeded 90%. Our experimental results show that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This new method of predicting systolic blood pressure helps to give an early warning to adults, who may not get regular blood pressure measurements that their blood pressure might be at an unhealthy level. Also, because an isolated measurement of blood pressure is not always very accurate due to daily fluctuations, our predictor can provide the predicted value as another figure for medical staff to refer to.postprin

Alternative Approach to Improving Survival of Patients With Out-of-Hospital Primary Cardiac Arrest

Out-of-hospital cardiac arrest (OHCA) is a common cause of death. In spite of recurring updates of guidelines, the survival of patients with OHCA was essentially unchanged from the mid 1970s to the mid 2000s, averaging 7.6% for all OHCA and 17.7% for OHCA due to ventricular fibrillation. In the past, changes in one's approach to resuscitation had to await the semi-decennial publications of guidelines. Following approved guidelines (at times based on consensus), survival rates of patients with OHCA were extremely variable, with only a few areas having good results. An alternative approach to improving survival is to use continuous quality improvement (CQI), a process often used to address public health problems. Continuous quality improvement advocates that one obtain baseline data and, if not optimal, make changes and continuously re-evaluate the results. Using CQI, we instituted cardiocerebral resuscitation as an alternative approach and found significant improvement in survival of patients with OHCA. The changes we made to the therapy of patients with primary OHCA, called cardiocerebral resuscitation, were based primarily on extensive experimental laboratory data. Using cardiocerebral resuscitation as a model for CQI, neurologically intact survival of patients with OHCA in ventricular fibrillation improved in 2 rural counties in Wisconsin, from 15% to 39%, and in 60 emergency medical systems in Arizona, to 38%. By advocating chest compression only CPR for bystanders of patients with primary OHCA and encouraging the use of cardiocerebral resuscitation by emergency medical systems, survival of patients with primary cardiac arrest in Arizona increased over a 5-year period from 17.7% to 33.7%. We recommend that all emergency medical systems determine their baseline survival rates of patients with OHCA and a shockable rhythm, and consider implementing the CQI approach if the community does not have a neurologically intact survival rate of at least 30%

New two-dimensional global longitudinal strain and strain rate imaging for assessment of systemic right ventricular function

Objectives: To determine the usefulness of new two-dimensional strain indices, based on speckle tracking imaging, for assessment of systemic right ventricular (RV) function after an atrial switch operation for transposition of the great arteries. Design: Cross-sectional study. Setting: Tertiary paediatric cardiac centre. Methods: 26 patients, mean (SD) age 21.0 (3.6) years at 19.9 (3.2) years after an atrial switch operation, and 27 age-matched controls were studied. Two-dimensional imaging at the four-chamber view was obtained with tracing of the entire RV endocardial border. The RV global longitudinal strain (GLS) and GLS rate were derived using automated software (EchoPAC, GE Medical) and correlated with tissue Doppler-derived RV isovolumic acceleration (IVA), and, in the patient cohort, with cardiac magnetic resonance-derived RV ejection fraction. Results: Intra- and interobserver variability for measurement of GLS, as determined from the mean (SD) of differences in two consecutive results from 20 studies, were 0.06 (1.39)% and 0.24 (1.77)%, respectively. Compared with controls, patients had lower RV GLS (17.1 (1.9)% vs 26.3 (2.9)%, p<0.001), a reduced GLS rate (0.78 (0.11)/s vs 1.33 (0.23)/s, p<0.001), lower RV IVA (1.10 (0.36) m/s 2 vs 1.56 (0.53) m/s 2, p<0.001) and increased RV myocardial performance index (0.52 (0.09) vs 0.38 (0.09), p<0.001). Both RV GLS and GLS rate correlated positively with RV IVA (r = 0.43, p = 0.001 and r = 0.46, p<0.001, respectively), and negatively with RV myocardial performance index (r = -0.65, p<0.001 and r = -0.57, p<0.001, respectively). In patients, the GLS rate correlated positively with RV ejection fraction (r = 0.62, p = 0.001). Conclusions: Two-dimensional RV GLS and GLS rate are new, potentially useful indices for assessment of systemic RV function.published_or_final_versio