Integration of curriculum is meant to make the teaching/learning activities meaningful; however, the interpretation of 'integration' varies in different institutions and among individuals. Many medical schools find it hard to change their existing curriculum or develop a new integrated curriculum mainly because of lack of will, infrastructure and understanding the process of change.
This paper describes the method used to develop the One Stop Crisis Centre (OSCC) Portal, an open source web-based electronic patient record system (EPR) for the One Stop Crisis Center, Hospital Universiti Sains Malaysia (HUSM) in Kelantan, Malaysia. Features and functionalities of the system are presented to demonstrate the workflow. Use of the OSCC Portal improved data integration and data communication and contributed to improvements in care management. With implementation of the OSCC portal, improved coordination between disciplines and standardisation of data in HUSM were noticed. It is expected that this will in turn result in improved data confidentiality and data integrity. The collected data will also be useful for quality assessment and research. Other low-resource centers with limited computer hardware and access to open-source software could benefit from this endeavour.
The investments and costs of infrastructure, communication, medical-related equipments, and software within the global healthcare ecosystem portray a rather significant increase. The emergence of this proliferation is then expected to grow. As a result, information and cross-system communication became challenging due to the detached independent systems and subsystems which are not connected. The overall model fit expending over a sample size of 320 were tested with structural equation modelling (SEM) using AMOS 20.0 as the modelling tool. SPSS 20.0 is used to analyse the descriptive statistics and dimension reliability. Results of the study show that system utilisation and system impact dimension influences the overall level of services of the healthcare providers. In addition to that, the findings also suggest that systems integration and security plays a pivotal role for IT resources in healthcare organisations. Through this study, a basis for investigation on the need to improvise the Malaysian healthcare ecosystem and the introduction of a cloud computing platform to host the national healthcare information exchange has been successfully established.
Signal transducers and activators of transcription (STAT) proteins are key signalling molecules in metazoans, implicated in various cellular processes. Increased research in the field has resulted in the accumulation of STAT sequence and structure data, which are scattered across various public databases, missing extensive functional annotations, and prone to effort redundancy because of the dearth of community sharing. Therefore, there is a need to integrate the existing sequence, structure and functional data into a central repository, one that is enriched with annotations and provides a platform for community contributions. Herein, we present STATdb (publicly available at http://statdb.bic.nus.edu.sg/), the first integrated resource for STAT sequences comprising 1540 records representing the known STATome, enriched with existing structural and functional information from various databases and literature and including manual annotations. STATdb provides advanced features for data visualization, analysis and prediction, and community contributions. A key feature is a meta-predictor to characterise STAT sequences based on a novel classification that integrates STAT domain architecture, lineage and function. A curation policy workflow has been devised for regulated and structured community contributions, with an update policy for the seamless integration of new data and annotations.
Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.
In recent times, the size of biological databases has increased significantly, with the continuous growth in the number of users and rate of queries; such that some databases have reached the terabyte size. There is therefore, the increasing need to access databases at the fastest rates possible. In this paper, the decision tree indexing model (PDTIM) was parallelised, using a hybrid of distributed and shared memory on resident database; with horizontal and vertical growth through Message Passing Interface (MPI) and POSIX Thread (PThread), to accelerate the index building time. The PDTIM was implemented using 1, 2, 4 and 5 processors on 1, 2, 3 and 4 threads respectively. The results show that the hybrid technique improved the speedup, compared to a sequential version. It could be concluded from results that the proposed PDTIM is appropriate for large data sets, in terms of index building time.
Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.
Radiotracer experiments are carried out in order to determine the mean residence time (MRT) as well as percentage of dead zone, V dead (%), in an integrated mixer consisting of Rushton and pitched blade turbine (PBT). Conventionally, optimization was performed by varying one parameter and others were held constant (OFAT) which lead to enormous number of experiments. Thus, in this study, a 4-factor 3-level Taguchi L9 orthogonal array was introduced to obtain an accurate optimization of mixing efficiency with minimal number of experiments. This paper describes the optimal conditions of four process parameters, namely, impeller speed, impeller clearance, type of impeller, and sampling time, in obtaining MRT and V dead (%) using radiotracer experiments. The optimum conditions for the experiments were 100 rpm impeller speed, 50 mm impeller clearance, Type A mixer, and 900 s sampling time to reach optimization.
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
The Integrated Telehealth Project of Malaysia is considered a principal enabler for the nation's Vision 2020 as well as the National Health Vision. Being in such an unenviable position, of being not only the pioneer for such an integrated project, but also with no benchmark to compare with, the project implementers have faced manifold challenges along the way. This chapter deals with some of the challenges and lessons learnt that have accumulated as the project progressed.
Malaysia's experience in implementing the Integrated Telehealth Project has placed her way ahead in the arena of world Telehealth. Thus, she has become the focus point, reference point and benchmark for similar endeavors around the world. In fact, it would not be presumptuous to state that the Integrated Telehealth project is a trail-blazing pioneer with e-leadership experience and skills developed over the last few years. It is hoped that the Integrated Telehealth concept will find acceptance and credence globally.
Thermophilic treatment of palm oil mill effluent (POME) was studied in a novel integrated anaerobic-aerobic bioreactor (IAAB). The IAAB was subjected to a program of steady-state operation over a range of organic loading rate (OLR)s, up to 30 g COD/L day in order to evaluate its treatment capacity. The thermophilic IAAB achieved high chemical oxygen demand (COD), biochemical oxygen demand (BOD) and total suspended solids (TSS) removal efficiencies of more than 99% for OLR up to 18.5 g COD/L day. High methane yield of 0.32 LCH(4) (STP)/g COD(removed) with compliance of the final treated effluent to the discharge limit were achieved. This is higher than that of the mesophilic system due to the higher maximum specific growth rate (μ(max)) of the thermophilic microorganisms. Besides, coupling the model of Grau second order model (anaerobic system) with the model of Monod (aerobic system) will completely define the IAAB system.
Bacterial proteases are an important group of enzymes that have very diverse biochemical and cellular functions. Proteases from prokaryotic sources also have a wide range of uses, either in medicine as pathogenic factors or in industry and therapeutics. ProLysED (Prokaryotic Lysis Enzymes Database), our meta-server integrated database of bacterial proteases, is a useful, albeit very niche, resource. The features include protease classification browsing and searching, organism-specific protease browsing, molecular information and visualisation of protease structures from the Protein Data Bank (PDB) as well as predicted protease structures.