Advancing Agro-Based Research

Taking the next sums up Universiti Putra Malaysia (UPM) approach to research. The university now aims to create an environment that inspires innovative research following its selection as a research university by the Higher Education Ministry in November 2006

the first year of experience

Strategic @ERSTalk-@WHO alliance to address tobacco use by training health professionals on brief advice resulted in establishing smoking cessation in real care settings with quit rates higher than the literature and high propensity for wider dissemination http://ow.ly/lWDF30krq5V.publishersversionpublishe

Optimal Multi-Stage Arrhythmia Classification Approach

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources

Quantitative Determination of Fibrinogen of Patients with Coronary Heart Diseases through Piezoelectric Agglutination Sensor

Fibrinogen can transform fibrin through an agglutination reaction, finally forming fibrin polymer with grid structure. The density and viscosity of the reaction system changes drastically during the course of agglutination. In this research, we apply an independently-developed piezoelectric agglutination sensor to detect the fibrinogen agglutination reaction in patients with coronary heart diseases. The terminal judgment method of determining plasma agglutination reaction through piezoelectric agglutination sensor was established. In addition, the standard curve between plasma agglutination time and fibrinogen concentration was established to determinate fibrinogen content quantitatively. The results indicate the close correlation between the STAGO paramagnetic particle method and the method of piezoelectric agglutination sensor for the detection of Fibrinogen. The correlation coefficient was 0.91 (γ = 0.91). The determination can be completed within 10 minutes. The fibrinogen concentration in the coronary heart disease group was significantly higher than that of the healthy control group (P < 0.05). The results reveal that high fibrinogen concentration is closely correlated to the incurrence, development and prognosis of coronary heart diseases. Compared with other traditional methods, the method of piezoelectric agglutination sensor has some merits such as operation convenience, small size, low cost, quick detecting, good precision and the common reacting agents with paramagnetic particle method

Small grants research competition to support ratification, implementation and/or enforcement of the Framework Convention on Tobacco Control (FCTC)

The project assesses primary care providers’ readiness for implementation of the provisions of the Framework Convention on Tobacco Control (FCTC) and makes recommendations to decision-makers on implementation and enforcement of the FCTC in China. The report provides survey results, activities and outcomes of the project including: the Shanghai Health Bureau will revise their existing policy to promote awareness of evidence-based cessation intervention among primary healthcare providers; advocacy campaigns are underway; and 32 community hospital directors attended a follow up workshop

Application of SOI pressure sensor to working resistance monitoring of hydraulic support

In order to improve reliability of monitoring of hydraulic support working resistance, a new silicon SOI pressure sensor was developed. The sensor fuses strain meter with double-deck resistance grid and stainless steel elastomer, the control circuit uses sampling mode with constant value and time to ensure effectiveness and coherence of the data. The field application results show that the new silicon SOI pressure sensor can get accurate monitoring data and can be used to analysis of mine roof supports crushing accident

Design of double layer resistance gird SOI strainometer and its application in coal mine

A double layer resistance gird SOI strainometer with micro fuse structure was designed. The strainometer is produced by techniques of thermally grown oxide, photoetching and mechanical erosion. It solves problems of normal SOI strainometer which has small bulk resistor and is vulnerable to pollution, and improves precision, insulativity and working stability of the strainometer. Field test results show that mine borehole stressmeter adopting the proposed SOI strainometer has high measuring accuracy and stable and reliable performance,and is suitable for stress monitoring of coal and rock mass

Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network

A rural built-up area is one of the most important features of rural regions. Rapid and accurate extraction of rural built-up areas has great significance to rural planning and urbanization. In this paper, the spectral residual method is embedded into a deep neural network to accurately describe the rural built-up areas from large-scale satellite images. Our proposed method is composed of two processes: coarse localization and fine extraction. Firstly, an improved Faster R-CNN (Regions with Convolutional Neural Network) detector is trained to obtain the coarse localization of the candidate built-up areas, and then the spectral residual method is used to describe the accurate boundary of each built-up area based on the bounding boxes. In the experimental part, we firstly explored the relationship between the sizes of built-up areas and the kernels in the spectral residual method. Then, the comparing experiments demonstrate that our proposed method has better performance in the extraction of rural built-up areas