Hypoglycemic effect of extracts of Petai Papan (Parkia speciosa, Hassk)

The oml administration of the chloroform extract of Parkia speciosa to alloxan-induced diabetic rats produced a significant (p<0.01) decrease in blood glucose levels. The hypoglycemic response was approximately proportional to the square root of the dose given. The hypoglycemic activity of the extract reached a maximum 2-5 hours rifter oral administration of the extract and lasted for at least 24 hours

Review on Geographically Weighted Regression (GWR) approach in spatial analysis

In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been successfully used in spatial analysis. GWR can theoretically integrate geographical location, altitude, and other factors for spatial analysis estimations, and reflects the non-stationary spatial relationship between these variables. The main goal of this study is to review the potential of the GWR in modelling the spatial relationship between variables either dependent or independent and its used as the spatial prediction models. Based on the application of GWR such as house property indicates that GWR is the best model in estimating the parameters. Hence, from the GWR model, the significance of the variation can also be tested

Generalized linear models (GLMS) approach in modelling rainfall data over Johor and Kelantan area

Observations of rainfall data are always changing over time . With the concern over climate change , this study is done to demonstrate how Generalized Linear Models (GLMs) could be utilized to model daily rainfall amount over Johor and Kelantan areas. Hence, in modeling rainfall amount, Fourier series are used as the smoothing techn ique. This re earch concentrated on the daily rainfall series with the dura tion period of 1985 to 201 1 from three rainfall stations in Johor and another three in Kelantan area. The results indicated that the rainfall stations demonstrate different behaviours of rainfall patterns. One harmonic is sufficient to model the mean rainfall per rainy day at the stations that are located at the Johor area while four harmonics are best described the rainfall pattern at Kelantan area . Based on the resulting curve s with fitted smoothing parameters, a good summary of statistics of the six stations were obtained. The result s from the model will then be used to compare the rainfall patterns among the stations

Smoothing wind and rainfall data through functional data analysis technique

The pattern of wind and rainfall throughout Peninsular Malaysia are varied from one region to another, because of strong influences from the monsoons. In order to capture the wind and rainfall variations, a functional data analysis is introduced. The purpose of this study is to convert the wind and rainfall data into a smooth curve by using functional data analysis method. Fourier basis is used in this study since the wind and rainfall data indicated periodic pattern. In order to avoid such overfitting data, roughness penalty is added to the least square when constructing functional data object from the observed data. Result indicated that if we use a small number of bases functions, the difference is very small between with and without roughness penalty, showing that it is safer to smooth only when required. However, when a large basis function is employed, the roughness penalty should be added in order to obtain optimal fit data. Based on the contour plot of correlation and cross-correlation functions of wind and rainfall data, the relationship between both climate functions could be determined

Temperature effect on HFMD transmission in Selangor, Malaysia

Hand, foot, and mouth disease (HFMD) has become a major concern for health authorities all over the world including Malaysia. In Malaysia, it has been reported that more than fifteen thousand people were affected by this disease in the year 2016 and it is suspected that climate variables play an important role in the incidence of HFMD. Previous studies showed that HFMD disease is associated with climatic factors such as temperature, humidity, and rainfall. Hence, this paper attempts to examine the pattern of HFMD and scrutinize the effect of temperature on HFMD in Selangor from the year 2010 to 2016. Correlation analysis is conducted to measure the relationship between HFMD incidence and temperature with a lag time effect. The generalized linear model (GLM) is then carried out to determine the influence of climate variables on HFMD disease in Selangor. Our findings discovered that the weekly mean temperature is significantly associated with HFMD incidence in Selangor. A comparison between models shows that HFMD with 2 weeks lag time mean temperature is the best-fitted model of HFMD in Selangor. This result helps to lay sound evidence for the implementation of strategies to reduce the effect of climate change especially temperature towards HFMD

Assessing the effect of climate factors on dengue incidence via a generalized linear model

Changes in climate factors such as temperature, rainfall, humidity, and wind speed are natural processes that could significantly impact the incidence of infectious diseases. Dengue is a widespread disease that has often been documented when it comes to the impact of climate change. It has become a significant concern, especially for the Malaysian health authorities, due to its rapid spread and serious effects, leading to loss of life. Several statistical models were performed to identify climatic factors associated with infectious diseases. However, because of the complex and nonlinear interactions between climate variables and disease components, modelling their relationships have become the main challenge in climate-health studies. Hence, this study proposed a Generalized Linear Model (GLM) via Poisson and Negative Binomial to examine the effects of the climate factors on dengue incidence by considering the collinearity between variables. This study focuses on the dengue hot spots in Malaysia for the year 2014. Since there exists collinearity between climate factors, the analysis was done separately using three different models. The study revealed that rainfall, temperature, humidity, and wind speed were statistically significant with dengue incidence, and most of them shown a negative effect. Of all variables, wind speed has the most significant impact on dengue incidence. Having this kind of relationships, policymakers should formulate better plans such that precautionary steps can be taken to reduce the spread of dengue diseases

Keperluan intervensi kaunseling berasaskan pendekatan terapi bermain Adlerian terhadap kesejahteraan kendiri holistik kanak-kanak mangsa pengabaian: satu kajian kes

Pendekatan terapi bermain dalam intervensi kaunseling membantu kaunselor untuk berkomunikasi dengan klien yang sukar berkomunikasi secara lisan dalam sesi terutama klien kanak-kanak. Tujuan kajian ini adalah untuk meneroka pandangan kaunselor terhadap keperluan intervensi kaunseling berasaskan pendekatan terapi bermain dalam membantu kesejahteraan kendiri holistik kanak-kanak mangsa pengabaian. Reka bentuk kajian ini adalah kualitatif kajian kes. Peserta Kajian terdiri daripada sepuluh orang kaunselor yang dipilih melalui pensampelan bertujuan. Kriteria pemilihan peserta kajian adalah (i) kaunselor yang telah berkhidmat lebih daripada lima tahun, (ii) pengamal kreatif kaunseling terapi bermain dan (iii) mempunyai setting untuk melaksanakan terapi bermain. Analisis tematik telah digunakan untuk mendapatkan data perbincangan. Teknik pengumpulan data menggunakan kaedah temu bual mendalam secara separa berstruktur. Data yang diperoleh kemudian dianalisis kepada tema-tema menggunakan perisian Atlas.ti. Dapatan kajian mendapati terdapat keperluan intervensi kaunseling berasaskan pendekatan terapi bermain Adlerian. Pendekatan terapi bermain Adlerian membantu meningkatkan kesejahteraan holistik kanak-kanak mangsa pengabaian. Penggunaan alat-alat mainan sebagai medium komunikasi yang amat penting dalam sesi kaunseling bersama kanak-kanak. Selain itu, terdapat beberapa faktor lain yang penting bagi menentukan kejayaan intervensi adalah minat bertugas di setting kanak-kanak, memahami ciri-ciri psikologi kanak-kanak mangsa pengabaian dan kemahiran-kemahiran dalam melaksanakan terapi bermain. Dapatan kajian turut mencadangkan kit terapi bermain bagi kaunselor yang tidak mempunyai setting bilik terapi bermain yang mana kaunselor boleh membawa alat-alat mainan ke tempat intervensi. Dapatan ini dapat memberi cadangan kepada agensi yang menjalankan kaunseling terhadap kanak-kanak menggunakan pendekatan terapi bermain Adlerian dalam meningkatkan kesejahteraan kendiri holistik kanak-kanak mangsa pengabaian

The influence of climate factors on hand-foot-mouth disease: a five-state study in Malaysia

Hand, foot, and mouth disease (HFMD) has become an important public health problem worldwide due to its tendency to cause outbreaks and human death. The outbreak of HFMD with clinical and fatal complications has been noticed in the Asia Pacific region since the late 1990s. The increasing evidence of climate change effect on HFMD has motivated the need for further investigations. Numerous previous studies conducted in several countries have established a significant association between climate factors and HFMD. However, there are currently only a few studies in Malaysia addressing these issues. Therefore, this study aimed to examine the link between climate factors and the occurrences of HFMD in five states representing each region of Malaysia by using a generalized linear model approach. The weekly HFMD cases and four climate variables, including temperature, humidity, rainfall, and wind speed, were examined. The findings indicate that climate variables significantly influence HFMD in Malaysia; however, it varies between states as different states experience different climates. Additionally, the results revealed that humidity and temperature were the primary climate factors affecting the incidence of HFMD in Malaysia. This study could guide policymakers, health agencies, and local communities in determining the most effective prevention and control strategies

A systematic review of the statistical methodology used in establishing the link between climate factors and HFMD incidence

Hand, foot, and mouth disease (HFMD) is a common infectious disease caused by two main viruses, namely Coxsackievirus A16 and Human Enterovirus 71. It has been a significant public health disease and a substantial burden all over the world since 1969. Prior studies have shown that climate factors are significantly associated with HFMD cases by using various statistical methods. Therefore, this study aims to review the scientific studies related to climate and HFMD and hence, address the analytical techniques used. This study only includes quantitative studies from peer-reviewed and original papers published in international and national journals from the years 1957 to 2020. In total, there were 522 articles identified; however, there were only 29 studies that linked climate change and HFMD. Based on the articles reviewed, the modelling analysis technique, which includes the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), and the Generalized Additive Mixed Model (GAMM), represents the most popular analysis in identifying the association between HFMD and climate factors. The temperature and humidity showed the greatest impact on the occurrence of HFMD, and the suitable incubation period for all climatic factors was not more than three weeks

Exponential growth model and stochastic population models: a comparison via goat population data

A population dynamic model explains the changes of a population in the near future, given its current status and the environmental conditions that the population is exposed to. In modelling a population dynamic, deterministic model and stochastic models are used to describe and predict the observed population. For modelling population size, deterministic model may provide sufficient biological understanding about the system, but if the population numbers become small, then a stochastic model is necessary with certain conditions. In this study, both types of models such as exponential, discrete-time Markov chain (DTMC), continuous-time Markov chain (CTMC) and stochastic differential equation (SDE) are applied to goat population data of small size. Results from the simulations of stochastic realizations as well as deterministic counterparts are shown and tested by root mean square error (RMSE). The SDE model gives the smallest RMSE value which indicate the best model in fitting the data