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

Decision support and data mining for direct consumer-to-consumer trading

This paper describes a decision support system that integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. The seller would provide the condition and number of years of usage of the used item, and the intelligent system would provide real-time search on related items in the marketplace and suggest a price for trading. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. Related items to a recently purchased item would also be suggested with an aim of providing friendly reminders and recommendations so that the user of the website would obtain a pleasant trading experience. © 2014 Infonomics Society.published_or_final_versio

Design intelligence of web application for internet direct consumer-to-consumer trading

An online web application called Student-Trade has been developed. It is a state-of-the-art platform for direct consumer-to-consumer trading in the Internet. The platform is targeted for direct consumer-to-consumer trading among university students. The items for trading include books, household items, electronics, housing rental, sports equipment and tutoring services. This paper is on the design intelligence of the Student-Trade web application. One objective is to help the user to decide on the selling price of his item when the item is being posted in the web application. The system integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. The aim is to provide a pleasant trading experience for the user. © 2015 IEEE.published_or_final_versio

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

An optimization model for a battery swapping station in Hong Kong

In this paper, a battery swapping station (BSS) model is proposed as an economic and convenient way to provide energy for the batteries of the electric vehicles (EVs). This method would overcome some drawbacks to the use of electric vehicles like long charging time and insufficient running distance. On the economic concern of a battery swapping station, the station would optimize the availability of the batteries in stock, and at the same time determine the best strategy for recharging the batteries on hand. By optimizing the charging method of the batteries, an optimization model of BSS with the maximum number of batteries in stock has been developed for the bus terminal at the Hong Kong International Airport. The secondary objective would be to minimize a cost on the batteries due to the use of different charging schemes. The genetic algorithm (GA) has been used to implement the optimization model, and simulation results are shown.published_or_final_versio

Parental emotional management benefits family relationships: A randomized controlled trial in Hong Kong, China

There is a shortage of culturally appropriate, brief, preventive interventions designed to be sustainable and acceptable for community participants in nonwestern cultures. Parents’ ability to regulate their emotions is an important factor for psychological well-being of the family. In Chinese societies, emotional regulation may be more important in light of the cultural desirability of maintaining harmonious family relationships. The objectives of our randomized controlled trial were to test the effectiveness of our Effective Parenting Programme (EPP) to increase the use of emotional management strategies (primary outcome) and enhance the parent-child relationship (secondary outcome). We utilized design characteristics that promoted recruitment, retention, and intervention sustainability. We randomized a community sample of 412 Hong Kong middle- and low-income mothers of children aged 6–8 years to the EPP or attention control group. At 3, 6 and 12- month follow up, the Effective Parent Program group reported greater increases in the use of emotion management strategies during parent-child interactions, with small to medium effect size, and lower negative affect and greater positive affect, subjective happiness, satisfaction with the parent–child relationship, and family harmony, compared to the control group, with small to medium effect size. Our results provided evidence of effectiveness for a sustainable, preventive, culturally appropriate, cognitive behaviorally-based emotion management program, in a non-clinical setting for Chinese mothers.postprin

Bringing scientific rigor to community-developed programs in Hong Kong

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A community based intervention program to enhance neighborhood cohesion: The Learning Families Project in Hong Kong

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Phase Diagram of the Two-Channel Kondo Lattice

The phase diagram of the two-channel Kondo lattice model is examined with a Quantum Monte Carlo simulation in the limit of infinite dimensions. Commensurate (and incommensurate) antiferromagnetic and superconducting states are found. The antiferromagnetic transition is very weak and continuous; whereas the superconducting transition is discontinuous to an odd-frequency channel-singlet and spin-singlet pairing state.Comment: 5 pages, LaTeX and 4 PS figures (see also cond-mat/9609146 and cond-mat/9605109

Two-Channel Kondo Lattice: An Incoherent Metal

The two-channel Kondo lattice model is examined with a Quantum Monte Carlo simulation in the limit of infinite dimensions. We find non-fermi-liquid behavior at low temperatures including a finite low-temperature single-particle scattering rate, the lack of a fermi edge and Drude weight. However, the low-energy density of electronic states is finite. Thus, we identify this system as an incoherent metal. We discuss the relevance of our results for concentrated heavy fermion metals with non-Fermi-Liquid behavior.Comment: LaTex, 5 pages, 3 Postscript files. Revision - in reference 5 and 6(a