Search

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

Chronic wounds are associated with significant clinical, economic and quality‐of‐life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5‐week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter‐rater and intra‐rater reliability between clinical and TA‐based measurements were analysed using intra‐class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5‐week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter‐rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799‐0.919, P < 0.001). Separate measurements of intra‐rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967‐0.985 and range 0.977‐0.984 respectively, P < 0.001). Inter‐rater reliability between TA used on the iOS and Android systems was also excellent (0.987‐0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity. [ABSTRACT FROM AUTHOR]

In 2019, the World Health Organization recognised diabetes as a clinically and pathophysiologically heterogeneous set of related diseases. Little is currently known about the diabetes phenotypes in the population of low- and middle-income countries (LMICs), yet identifying their different risks and aetiology has great potential to guide the development of more effective, tailored prevention and treatment. This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied. The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken. Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application. Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems' weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches. [ABSTRACT FROM AUTHOR]

Over the past century, conscientiousness has become seen as the preeminent trait for predicting performance. This consensus is due in part to these employees' ability to work with traditional 20th-century technology. Such pairings balance the systematic nature of conscientious employees with the technology's need for user input and direction to perform tasks--resulting in a complementary match. However, the 21st century has seen the incorporation of intelligent machines (e.g., artificial intelligence, robots, and algorithms) into employees' jobs. Unlike traditional technology, these new machines are equipped with the capability to make decisions autonomously. Thus, their nature overlaps with the orderliness subdimension of conscientious employees--resulting in a non-complementary mismatch. This calls into question whether the consensus about conscientious employees' effectiveness with 20th-century technology applies to 21st-century jobs. Integrating complementarity and role theory, we refine this consensus. Across three studies using distinct samples (an experience sampling study, a field experiment, and an online experiment from working adults in Malaysia, Taiwan, and the United States), each focused on a different type of intelligent machine, we show not only that using intelligent machines has benefits and consequences, but, importantly, that conscientious (i.e., orderly) employees are less likely to benefit from working with them. [ABSTRACT FROM AUTHOR]

Palm oil is a major contributor to Malaysia's GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to correctly count the oil palm trees. Currently, the audit is done manually using aerial view images. Therefore, an effective and efficient method is imperative. This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. The acquired images were first cropped and sampled into small size of sub-images, which were divided into a training set, a validation set, and a testing set. A DL algorithm based on Faster-RCNN was employed to build the model, extracts features from the images and identifies the oil palm trees, and gives information on the respective locations. The model was then trained and used to detect individual oil palm tree based on data from the testing set. The overall accuracy of oil palm tree detection was measured from three different sites with 97.06%, 96.58%, and 97.79% correct oil palm detection. The results show that the proposed method is more effective, accurate detection, and correctly counts the number of oil palm trees from the UAV images. [ABSTRACT FROM AUTHOR]

Soil Moisture and Ocean Salinity (SMOS) mission has successfully contributed to global soil moisture products since 2009. Validation and calibration activities were conducted worldwide, yet some of the validation results do not fulfill the targeted accuracy of ±0.04 $\text{m}^{3}\text{m}^{-3}$. This paper presented the site-specific calibration of the V620 retrieval algorithm with in situ data collected at selected agricultural sites in the humid tropical regions, Malaysia. This set of data has been validated where low accuracy of SMOS soil moisture products was found. To improve the SMOS soil moisture retrieval, calibration of SMOS soil moisture retrieval algorithm based on the L-band Microwave Emission and Biosphere model and SMOS Level 1C $\text{T}_{\mathrm {B}}$ products, considering the local parameters was conducted. The calibration proves that these site-specific parameters improve the product’s accuracy. Validation of SMOS Level 2 product with in situ data showed bias, root-mean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 $\text{m}^{3}\text{m}^{-3}$ , 0.068 to 0.142 $\text{m}^{3}\text{m}^{-3}$ , and 0.069 to 0.103 $\text{m}^{3}\text{m}^{-3}$ , respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020–0.056 $\text{m}^{3}\text {m}^{-3}$ and RMSE of 0.026–0.065 $\text{m}^{3}\text{m}^{-3}$. The ubRMSE ranges from 0.017 to 0.034 $\text{m}^{3}\text{m}^{-3}$. Recently released SMOS-IC V105 product was also validated, where small improvements were noticed when compared to the accuracy of SMOS Level 2. This paper shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global generalized parameters that are used in the original SMOS soil moisture retrieval algorithm. [ABSTRACT FROM AUTHOR]

The novelty of this paper is to ascertain a nonlinear relationship between housing supply and house price. This study is conducted based on panel dataset of four different types of houses in Malaysia from 2002Q3 through 2016Q4. Although housing supply has been theoretically assumed to be positively and linearly related to house price, we observed that the number of new houses build in Malaysia has declined despite the increasing house prices. Hence, we posit that housing supply and house price are nonlinearly related. The results from pooled mean group estimation show the existence of inverted U-shaped housing supply curve. The threshold level of house price index is found at 186.92 where the effect of house price on housing starts will become negative after this point. We also find that the marginal effects of house price evaluated at the minimum and maximum levels are positive and negative, respectively, and statistically significant. This paper suggests that the squared term of house price should be included in estimating housing supply in Malaysia. The evidence of inverted U-shaped housing supply curve in Malaysia shows that housing authorities have taken steps to overcome the challenges of oversupply by reducing the approvals for housing development projects. [ABSTRACT FROM AUTHOR]

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts. [ABSTRACT FROM AUTHOR]

Over two decades, analysis of seismic attributes had been an integral part of seismic reflection interpretation. Seismic attributes are an influential assistance to seismic interpretation, delivering geoscientists with alternative images of structural (faults) and stratigraphic features (channels), which can be utilised as mechanisms to identify prospects, ascertain depositional environment and structural deformation history more rapidly even provide direct hydrocarbon indicators. The additional steps are obligatory to compute and interpret the attributes of faults and channels from seismic images, which are often sensitive to noise due to the characteristically computed as discontinuities of seismic reflections. Furthermore, on a conventional seismic profile or poor quality data, faults and channels are hard to visible. The current research review these geological structures through a case study of 3D seismic data from N-field in the viewpoint of Malay Basin. This study aimed to characterise the structure and stratigraphic features by using seismic attributes on the N-field below seismic resolution. Also, two different methods are proposed to improve seismic reflections, i.e., faults and channels that are hard to see on the conventional 3D data set. The first method, to detect faults in seismic data, which this paper employs the ant tracking attribute as a unique algorithm to be an advanced forwarding that introduces a new tool in the interpretation of fault. The effective implementation of ant tracking can be achieved when the output of other faults sensitive attributes are used as input data. In this work, the seismic data used are carefully conditioned using a signal. Chaos and variance that are sensitive to faults are applied to the seismic data set, and the output from these processes are used as input data that run the ant tracking attribute, which the faults were seen difficult to display on the raw seismic data. Meanwhile, for the second method, spectral decomposition was adopted to deliberate the way its method could be utilised to augment stratigraphic features (channels) of the N-field, where the channel is ultimately considered being one of the largest formations of the petroleum entrapment. The spectral decomposition analysis method is an alternative practice concentrated on processing S-transform that can offer better results. Spectral decomposition has been completed over the Pleistocene channels, and results propose that application of its methods directs to dependable implications. Respective channel in this area stands out more obviously within the specific frequency range. The thinner layer demonstrates higher amplitude reading at a higher frequency, and the thicker channel displays higher amplitude reading at a lower frequency. Implementation of spectral decomposition assists in deciding the channels that were placed within incised valleys and helps in recognising the orientation and the relative thickness of each channel. By doing this, the ant tracking attribute and spectral decomposition approach have generated the details of subsurface geologic features through attributes by obtaining enhanced reflections and channels and sharpened faults, respectively. [ABSTRACT FROM AUTHOR]

Resistivity and seismic refraction are conventional methods in preinvestigations of subsurface structures, which are commonly employed and interpreted separately to reduce ambiguities from each method. Although integration of the two methods' models into a single model was recently introduced, the integration still requires enhancement to generate an accurate subsurface profile. Therefore, an enhanced algorithm called closure coupling technique was developed to integrate 2-dimensional (2-D) models of resistivity and seismic refraction to become a single integrated model where one model influences the other model. The resultant integrated model is superior in mapping the subsurface compared with singular resistivity and seismic models. These methods were then applied on a pre-investigative field dataset in finding ancient river for archaeological point of interest. Due to complex geology, only slight changes were observed in the inverted model of the integrated data inversion for this archetype. Still, the combined model enhanced subsurface interpretation by highlighting the distribution of buried alluvial soil. [ABSTRACT FROM AUTHOR]

Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential of the emotional artificial neural network-genetic algorithm (EANN-GA), and three ensemble techniques, i.e. emotional artificial neural network (EANN), feedforward neural network (FFNN), and neural network ensemble (NNE), to predict DO concentration in the Kinta River basin of Malaysia. The performance of EANN-GA, EANN, FFNN, and NNE models in predicting DO was evaluated using statistical metrics and visual interpretation. Appraisal of the results revealed a promising performance of the NNE-M3 model (Nash-Sutcliffe efficiency (NSE) = 0.8743/0.8630, correlation coefficient (CC) = 0.9351/0.9113, mean square error (MSE) = 0.5757/0.6833 mg/L, root mean square error (RMSE) = 0.7588/0.8266 mg/L, and mean absolute percentage error (MAPE) = 20.6581/14.1675) during the calibration/validation period compared to EANN-GA, EANN, and FFNN models in DO prediction in the study basin. [ABSTRACT FROM AUTHOR]

Total suspended sediment (TSS) is a water quality parameter that is used to understand sediment transport, aquatic ecosystem health, and engineering problems. The majority of TSS in water bodies is due to natural and human factors such as brought by river runoff, coastal erosion, dredging activities, and waves. It is an important parameter that should be monitored periodically, particularly over the dynamic coastal region. This study aims to monitor spatiotemporal TSS concentration over Teluk Lipat, Malaysia. To date, there are two commonly used methods to monitor TSS concentration over wide water regions. Firstly, field sampling is known very expensive and time-consuming method. Secondly, the remote sensing technology that can monitor spatiotemporal TSS concentration freely. Although remote sensing technology could overcome these problems, universal empirical or semiempirical algorithms are still not available. Most of the developed algorithms are on a regional basis. To measure TSS concentration over the different regions, a new regional algorithm needs to develop. To do so, two field trip was conducted in the study area concurrent with the passing of Landsat 8. A total of 30 field samples were collected from 30 sampling points during the first field trip and 30 samples from 30 samplings from the second field trip. The samples were then analyzed using an established method to develop the TSS algorithm. The data obtained from the first field trip were then used to develop a regional TSS algorithm using the regression analysis technique. The developed algorithm was then validated by using data obtained from the second field trip. The results demonstrated that TSS in the study area is highly correlated with three Landsat 8 bands, namely green, near-infrared (NIR), and short-wavelength (SWIR) bands, with R2 = 0.79. The TSS map is constructed using the algorithm. Analyses of the image suggest that the highest TSSs are mainly observed along the coastal line and over the river mouth. It suggested that the main contributing factors over the study area are river runoff and wave splash. [ABSTRACT FROM AUTHOR]

The availability of the large volume of remote sensing data has allowed for the developing of several automated algorithms for detecting linear geological features and more reliable analysis. However, most of the algorithms focus on edge detection and tone change on a satellite image, which represents all geological and non-geological features. In this study, a topographic fabric algorithm, which calculates the slope and aspect at each point in a DEM, is applied for automatically geological linear features mapping in Bau Goldfield, Malaysia using the new version of the Shuttle Radar Topographic Mission (SRTM) DEM. A series of topographic fabric input parameters was tested using different combinations of input values in order to decide the optimal parameters that provided the suitable detection parameters, best fit and the highest accuracy. Comparison with the geological map demonstrated that the tested parameters made the algorithm able to automatically detect geological structures. [ABSTRACT FROM AUTHOR]

Glycemic control among patients with prediabetes and type 2 diabetes mellitus (T2D) in Malaysia is suboptimal, especially after the continuous worsening over the past decade. Improved glycemic control may be achieved through a comprehensive management strategy that includes medical nutrition therapy (MNT). Evidence-based recommendations for diabetes-specific therapeutic diets are available internationally. However, Asian patients with T2D, including Malaysians, have unique disease characteristics and risk factors, as well as cultural and lifestyle dissimilarities, which may render international guidelines and recommendations less applicable and/or difficult to implement. With these thoughts in mind, a transcultural Diabetes Nutrition Algorithm (tDNA) was developed by an international task force of diabetes and nutrition experts through the restructuring of international guidelines for the nutritional management of prediabetes and T2D to account for cultural differences in lifestyle, diet, and genetic factors. The initial evidence-based global tDNA template was designed for simplicity, flexibility, and cultural modification. This paper reports the Malaysian adaptation of the tDNA, which takes into account the epidemiologic, physiologic, cultural, and lifestyle factors unique to Malaysia, as well as the local guidelines recommendations. [ABSTRACT FROM AUTHOR]

Melaleuca cajuputi and Acacia auriculiforrnis trees are major sources of nectar and pollen for Apis dorsata and the colonies are a major source of honey to the rural poor, honey hunters. Honey is a supplementary income to many of these people (including school children) in the Marang district, Terengganu. In this study, Marang area with 270 square kilometers was chosen as pilot study area in Terengganu state for mapping M. cajuputi and A. auriculiformis as two dominant species in low land secondary forest in Terengganu state. To inventory and produce land use map of Melaleuca forest in Marang area, in this study SPOT-5 satellite image in multispectral mode with 10 meter resolution which is acquired in 2007 as optical satellite was utilized. Most images from optical satellites have some null data from ground because of clouds and shadow of clouds. To solve this problem, Hue, Saturation and value (HSV) and Principal Component Analysis (PCA) were used as fusion techniques to replace null data with microwave data which taken from Radarsat-1 image in C-band with 25 meter resolution image. Accordingly, fusion technique which was used in this research not only was a technique to improve information but also caused the accuracy increasing than land use map by just only SPOT-5 image. Also between two different fusion techniques, PCA shows the better result than HSV as two different fusion techniques. [ABSTRACT FROM AUTHOR]

Sequential strategies of digitized tablet drawings by 6-7-yr.-old children (N=203) of average and below-average handwriting ability were analyzed. A Beery Visual Motor Integration (BVMI) and a Bender-Gestalt (BG) pattern, each composed of two tangential shapes, were predefined into area sectors for automatic analysis and adaptive mapping of the drawings. Girls more often began on the left side and used more strokes than boys. The below-average handwriting group showed more directional diversity and idiosyncratic strategies. [ABSTRACT FROM AUTHOR]

This study focuses on the situation of health information exchange (HIE) in the context of a nationwide network. It aims to create a security framework that can be implemented to ensure the safe transmission of health information across the boundaries of care providers in Malaysia and other countries. First, a critique of the major elements of nationwide health information networks is presented from the perspective of security, along with such topics as the importance of HIE, issues, and main approaches. Second, a systematic evaluation is conducted on the security solutions that can be utilized in the proposed nationwide network. Finally, a secure framework for health information transmission is proposed within a central cloud-based model, which is compatible with the Malaysian telehealth strategy. The outcome of this analysis indicates that a complete security framework for a global structure of HIE is yet to be defined and implemented. Our proposed framework represents such an endeavor and suggests specific techniques to achieve this goal. [ABSTRACT FROM AUTHOR]

Borneo Island is experiencing rapid tree-cover loss. This loss has been quantified for the Indonesian part of the island at Landsat spatial resolution, but no recent study exists that extends across the border into Malaysia. This research focused on quantifying patterns of tree-cover loss in the Indonesia–Malaysia border zone on Borneo. The methods used for quantifying 2000–2010 tree-cover loss within 20 km on either side of the border are an internally consistent mapping algorithm used on Landsat imagery and a local indicator of spatial autocorrelation to quantify the concentration of loss. Within the 20 km zone on either side of the border, tree-cover loss rates in lowlands were high in the two countries (19.8% and 14.4%, in Indonesia and Malaysia, respectively), but rates in the Malaysian uplands were an order of magnitude higher than in the Indonesian uplands (2.95% and 0.25%, respectively). Clusters of tree-cover loss in the Malaysian uplands were considerably larger than in the Indonesian uplands. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the factors that would help in diagnosing acute myocardial infarction (AMI) using data from an electronic medical record (EMR) and generating structure decisions in the form of linguistic fuzzy rules. Topics covered include the main advantage of the generated linguistic fuzzy rules, the challenge posed by chest pain diagnosis and the use of artificial neural networks in identifying AMI in patients admitted due to chest pain.

Summary What is known and Objectives: Testing for cytochrome P450-2C9 ( CYP2C9) and vitamin K epoxide reductase complex subunit 1 ( VKORC1) variant alleles is recommended by the FDA for dosing of warfarin. However, dose prediction models derived from data obtained in one population may not be applicable to another. We therefore studied the impact of genetic polymorphisms of CYP2C9 and VKORC1 on warfarin dose requirement in Malaysia. Methods: Patients who were attending clinics at our hospital and prescribed warfarin with stabilized INR levels of 2-4 were selected. DNA was extracted from blood samples and subsequently genotyped for CYP2C9*1, *2, *3, VKORC1 (G-1639A) and VKORC1 C1173T. Linear regression modelling using age, CYP2C9 and VKORC1 genotypes, sex, weight and height was undertaken to define a warfarin dosing algorithm. An initial model was developed using data from one cohort of patients and validated using data from a second cohort. Results and Discussion: A model which included age and variants of CYP2C9 and VKORC1 account for about 37% of the variability in warfarin dose required to achieve INR of 2-4. Among the parameters evaluated, only VKORC1 (G-1639A) and (C1173T) alleles, and age correlated with warfarin dose at 6 month. The mean dose predicted using the algorithm derived from cohort 1 was lower than the actual dose for cohort 2 (3·30 mg, SD 0·84 vs. 3·45 mg, SD 1·42). There was no relationship between INR values and the dose taken by the patients. Race, sex, weight and height did not correlate with dose. What is new and Conclusion: This study identifies factors which affect warfarin dosing in the Malaysia population. However, our best model does not account sufficiently for the variability in dose requirements for it to be used in dose prediction for the individual patient. Other important influential factors affecting warfarin dose requirement remain to be identified. [ABSTRACT FROM AUTHOR]

Scattered data interpolation is important in sciences, engineering, and medical-based problems. Quartic Bézier triangular patches with 15 control points (ordinates) can also be used for scattered data interpolation. However, this method has a weakness; that is, in order to achieve C 1 continuity, the three inner points can only be determined using an optimization method. Thus, we cannot obtain the exact Bézier ordinates, and the quartic scheme is global and not local. Therefore, the quartic Bézier triangular has received less attention. In this work, we use Zhu and Han's quartic spline with ten control points (ordinates). Since there are only ten control points (as for cubic Bézier triangular cases), all control points can be determined exactly, and the optimization problem can be avoided. This will improve the presentation of the surface, and the process to construct the scattered surface is local. We also apply the proposed scheme for the purpose of positivity-preserving scattered data interpolation. The sufficient conditions for the positivity of the quartic triangular patches are derived on seven ordinates. We obtain nonlinear equations that can be solved using the regula-falsi method. To produce the interpolated surface for scattered data, we employ four stages of an algorithm: (a) triangulate the scattered data using Delaunay triangulation; (b) assign the first derivative at the respective data; (c) form a triangular surface via convex combination from three local schemes with C 1 continuity along all adjacent triangles; and (d) construct the scattered data surface using the proposed quartic spline. Numerical results, including some comparisons with some existing mesh-free schemes, are presented in detail. Overall, the proposed quartic triangular spline scheme gives good results in terms of a higher coefficient of determination (R2) and smaller maximum error (Max Error), requires about 12.5% of the CPU time of the quartic Bézier triangular, and is on par with Shepard triangular-based schemes. Therefore, the proposed scheme is significant for use in visualizing large and irregular scattered data sets. Finally, we tested the proposed positivity-preserving interpolation scheme to visualize coronavirus disease 2019 (COVID-19) cases in Malaysia. [ABSTRACT FROM AUTHOR]