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Background: This manuscript aimed to map the spatial distributions of health clinics for public and private sectors in Malaysia. It would assist the stakeholders and responsible authorities in the planning for health service delivery. Methods: Data related to health clinic were gathered from stakeholders. The location of health facilities was geo-coded using a Global Positioning System (GPS) handheld. The average nearest neighbour was used to identify whether health clinics were spatially clustered or dispersed. Hot spot analysis was used to assess high density of health clinics to population ratio and average distance of health clinics distribution. A Geographically Weighted Regression (GWR) was used to analyse the requirement of health clinic in a sub-district based on population density and number of health clinics with significant level (p < 0.001). Results: The results of the average nearest neighbour analysis revealed that the distribution of public health clinics was dispersed (p < 0.001) with z-scores 3.95 while the distribution of private clinics was clustered (p < 0.001) with z-score ?29.26. Several locations especially urban area was also identified as high density in the sub-district. Conclusions: There is a significant difference in the spatial pattern of public health clinics and private clinics in Malaysia. The information can assist stakeholder and responsible authorities in planning health service delivery.
Missing values are
prevalent in real-world datasets and they may reduce predictive performance of
a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable
methods for detecting and predicting incipient faults in power transformers is
one of the casualties. Thus, this paper proposes filling-in the missing values
found in a DGA dataset using the k-nearest neighbor imputation method with two
different distance metrics: Euclidean and Cityblock. Thereafter, using these
imputed datasets as inputs, this study applies Support Vector Machine (SVM) to
built models which are used to classify transformer faults. Experimental
results are provided to show the effectiveness of the proposed approach.
Solid polymer electrolytes (SPEs) of polyacrylamide-co-acrylic acid (PAA) as the polymer host and zinc acetate (ZnA) as an ionic dopant were prepared using a single solvent by the solution casting technique. The amorphous and crystalline structures of film were investigated by X-ray diffraction (XRD). The surface morphology of samples was examined by scanning electron microscopy (SEM). The composition and complex formation of films were characterized by Fourier transform infrared (FTIR) spectroscopy. The conductivity of the PAA-ZnA films was determined by electrochemical impedance spectroscopy. According to the XRD and FTIR analyses, all electrolyte films were in amorphous state and the existence of interaction between Zn2+ cations and the PAA structure confirms that the film was successfully prepared. The SEM observations reveal that the electrolyte films appeared to be rough and flat with irregularly shaped surfaces. The highest ionic conductivity (σ) of 1.82 × 10-5 Scm-1 was achieved at room temperature (303 K) for the sample containing 10 wt % ZnA.