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Contextual learning combines examples taken from everyday experience. The purpose of this research is to identify whether learning contextually will improve students' achievement. A quasi-experimental design used. The engineering students were divided into contextual and non-contextual groups. The Neuro-Linguistics Programming (NLP) VAKD Preferred Representational Systems Test shows, majority of both groups use auditory digital learning styles. There is a significant difference at a significance level of .05, in the achievement test where the contextual groups performed better. The majority of the students in both groups are auditory digital learning styles, learning statistics contextually is an effective method for engineering students. Keywords: NLP VAKD, learning styles, statistics, contextual eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI4.2908

The disease dengue is associated with both mesoscale and synoptic scale meteorology. Previous studies for south-east Asia have found very limited association between synoptic variables and the reported dengue cases. It will immensely beneficial to establish more clear association with rate of cases and the most relevant meteorological variables in order to institute an early warning system.A rigorous Bayesian modelling framework is provided to identify the most important co-variates and their lagged effects for developing an early warning system in the Central Region of Malaysia.Along with other mesoscale environmental measurements, we also examine multiple synoptic scale Niño indices which are related to the phenomenon of El Niño Southern Oscillation and an unobserved variable derived from reanalysis data. A probabilistic early warning system is built based on a Bayesian spatio-temporal hierarchical model.Our study finds a 46.87% of increase in dengue cases due to one degree increase in the central equatorial Pacific sea surface temperature with a lag time of six weeks. We discover the existence of a mild association between the rate of cases and a distant lagged cooling effect related to a phenomenon called El Niño Modoki. These associations are assessed by using a Bayesian spatio-temporal model in terms of estimated out-of-sample predictive accuracy.With the novel early warning system presented, our results show that the synoptic meteorological drivers can enhance short-term detection of dengue outbreaks and these can also potentially be used to provide longer-term forecasts.Practical ImplicationsIn 2019, it was reported that there is a severe dengue upsurge in Malaysia. Reported cases rose over 60% from 80,615 in the 2018 to 130,101 with 182 deaths (Rahim et al., 2021). The disease has been described as a silent killer that the infection rate once surpassed that of COVID-19. There is a need of an early warning system to alert the authority in order to identify relevant risk factors and the forthcoming outbreak hot-spots. The Bayesian hierarchical spatial dynamic model componentises different aspects of dengue dynamics into one unified model. Its flexibility allows us to regularly review the disease dynamic under changing environment and transmission mechanism such as the implementation of the Movement Control Orders (MCO) during COVID-19. Practically, this prototype model should be run at least once a week to generate forecasts which is fed with the dengue cases data from weekly press release and meteorological information from publicly available sources. By assessing the probability estimates, the alert has its intrinsic meaning and the sensitivity can be adjusted effortlessly.HighlightsEl Niño Southern Oscillation is a crucial driver to dengue outbreaks in Malaysia.A few different climate oscillations affect the dengue transmission pattern.Bayesian spatial dynamic model helps the development of early warning system.The model components can be added or modified under the hierarchical Bayes framework.

This study attempts to obtain the best fitted model among two clusters which describe the relationship between dengue incidence rate (DIR) and relevant covariates such as climatic and non-climatic variables. The significant variables include amount of rainfall and number of rainy days with lag 0 until 3 months, number of locality and population density. Fuzzy C-Means clustering (FCM) was applied in clustering DIR data based on the value of membership function. The boundary of membership function has been set as 0.5. There are two clusters identified in this study with Cluster 1 consist of 569 data and Cluster 2 consist of 43 data. Then, this study developed models to predict future dengue incidences in Selangor by using negative binomial Generalised Additive Model (GAM). The result shows that the model able to be one of tools for future development in controlling and reducing the number of dengue cases particularly in Selangor, Malaysia as well as other states.

The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors.

Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other models.

This study presents the used of Generalised Additive Model (GAM) in modelling Dengue Incidence Rate (DIR) with adopted clustering technique for districts in Selangor. This study identified a pattern for monthly observed dengue count and successfully select variables includes number of rainy days and amount of rainfall with time lags, number of locality and population density which significant to DIR in Selangor. Besides, this study found the districts divided into two clusters based on the value of mean DIR from January 2010 to August 2015. The first cluster consist of 6 districts of Selangor with value of mean DIR from 0 to 200 cases per 100,000 population. Meanwhile, there are 3 districts classified in the second cluster with value of mean DIR from 200 to 500 cases per 100,000 population. The Negative Binomial GAM then adopted in this study to able to handle the presence of overdispersion. In conclusion, clustering technique is one of the effective techniques to identify the different district with the higher potential of dengue risk.

A road traffic accident resulted from the combination of factors related to the few components of the system involving environment, roads, road users, vehicles and the interaction between those systems. Road traffic accident (RTA) in Malaysia recorded as the highest fatality rate (per 100,000 population) among the ASEAN countries. In 2016, more than half of million cases accident recorded with more than 7,000 people were killed. Therefore, the RTA is one of the most critical issue in Malaysia even become the worldwide burden to authority. Generally, driving is a complex process which involves movement of a vehicle by either a computer or human controller. However, failure to control and coordinate will contribute to an accident. The objective of this study is to identify the pattern of accident in Johor Malaysia and to examine the relationship between the number of accident and the types of vehicles and roads. The results could help the government to recognise the different patterns, types of vehicles and roads that show major factors in the increasing of road traffic accident in Malaysia.

Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non-climatic factors. The data used is monthly DIR for 12 states of Malaysia from 2001 to 2009. In this study, we considered an annual trend, seasonal effects, population, population density and lagged DIR, rainfall, temperature, number of rainy days and El Nino-Southern Oscillation (ENSO). The population density is found to be positively related to monthly DIR. There are generally weak relationships between monthly DIR and climate variables. A negative binomial GAM shows that there are statistically significant relationships between DIR with climatic and non-climatic factors. These include mean rainfall and temperature, the number of rainy days, sea surface temperature and the interaction between mean temperature (lag 1 month) and sea surface temperature (lag 6 months). These also apply to DIR (lag 3 months) and population density.

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy cmeans cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

The effects of nanoparticles such as Fe 3O4,TiO2, and Cu on blood flow inside a stenosed artery are studied. In this study, blood was modelled as non-Newtonian Bingham plastic fluid subjected to periodic body acceleration and slip velocity. The flow governing equations were solved analytically by using the perturbation method. By using the numerical approaches, the physiological parameters were analyzed, and the blood flow velocity distributions were generated graphically and discussed. From the flow results, the flow speed increases as slip velocity increases and decreases as the values of yield stress increases.

Many researches has been done on the lateral collapse of tube. However, the previous researches only focus on cylindrical and square tubes. Then a research has been done discovering the collapse behaviour of hexagonal tube and the mathematic model of the deformation behaviour had been developed [8]. The purpose of this research is to study the lateral collapse behaviour of symmetric octagonal tubes and hence to develop a mathematical model of the collapse behaviour of these tubes. For that, a predictive mathematical model was developed and a finite element analysis procedure was conducted for the lateral collapse behaviour of symmetric octagonal tubes. Lastly, the mathematical model was verified by using the finite element analysis simulation results. It was discovered that these tubes performed different deformation behaviour than the cylindrical tube. Symmetric octagonal tubes perform 2 phases of elastic - plastic deformation behaviour patterns. The mathematical model had managed to show the fundamental of the deformation behaviour of octagonal tubes. However, further studies need to be conducted in order to further improve on the proposed mathematical model.

In this paper, we apply Adomian Decomposition Method (ADM) as numerically analyse linear second-order Fredholm Integro-differential Equations. The approximate solutions of the problems are calculated by Maple package. Some numerical examples have been considered to illustrate the ADM for solving this equation. The results are compared with the existing exact solution. Thus, the Adomian decomposition method can be the best alternative method for solving linear second-order Fredholm Integro-Differential equation. It converges to the exact solution quickly and in the same time reduces computational work for solving the equation. The result obtained by ADM shows the ability and efficiency for solving these equations.

The palm oil industry has been an important component of the national economy especially the agriculture sector. The aim of this study is to identify the pattern of import of palm oil products, to model the time series using Box-Jenkins model and to forecast the monthly import of palm oil products. The method approach is included in the statistical test for verifying the equivalence model and statistical measurement of three models, namely Autoregressive (AR) model, Moving Average (MA) model and Autoregressive Moving Average (ARMA) model. The model identification of all product import palm oil is different in which the AR(1) was found to be the best model for product import palm oil while MA(3) was found to be the best model for products import palm kernel oil. For the palm kernel, MA(4) was found to be the best model. The results forecast for the next four months for products import palm oil, palm kernel oil and palm kernel showed the most significant decrease compared to the actual data.

Boarding school student needs to eat well balanced nutritious food which includes proper calories, vitality and supplements for legitimate development, keeping in mind the end goal is to repair and support the body tissues and averting undesired ailments and disease. Serving healthier menu is a noteworthy stride towards accomplishing that goal. Be that as it may, arranging a nutritious and adjusted menu physically is confounded, wasteful and tedious. This study intends to build up a scientific mathematical model for eating routine arranging that improves and meets the vital supplement consumption for boarding school student aged 13-18 and in addition saving the financial plan. It likewise gives the adaptability for the cook to change any favoured menu even after the ideal arrangement has been produced. A recalculation procedure will be performed in view of the ideal arrangement. The information was gathered from the the Ministry of Education and boarding schools' authorities. Menu arranging is a notable enhancement issue and part of well-established optimization problem. The model was fathomed by utilizing Binary Programming and "Delete-Reshuffle-Reoptimize Algortihm (DDRA)".

Words are symbols representing certain aspects of mathematics. The main purpose of this study is to gain insight into students’ symbolic manipulation in calculus among UTHM students. This study make use the various methods in collecting data which are documentation, pilot study, written test and follow up individual interviews. Hence, the results analyzed and interpreted based on action-process-object-schema framework which is based on Piaget’s ideas of reflective abstraction, the concept of relational and instrumental understanding and the zone of proximal development idea. The students’ reply in the interview session is analyzed and then the overall performance is discussed briefly to relate with the students flexibility in symbolic manipulation in linking to the graphical idea, the students interpretation towards different symbolic structure in calculus and the problem that related to overgeneralization in their calculus problems solving.

RSA [1] is one of the mostly used cryptosystem in securing data and information. Though, it has been recently discovered that RSA has some weaknesses and in advance technology, RSA is believed to be inefficient especially when it comes to decryption. Thus, a new algorithm called Multi prime RSA, an extended version of the standard RSA is studied. Then, a modification is made to the Multi prime RSA where another keys is shared secretly between the receiver and the sender to increase the securerity. As in RSA, the methodology used for modified Multi-prime RSA also consists of three phases; 1. Key Generation in which the secret and public keys are generated and published. In this phase, the secrecy is improved by adding more prime numbers and addition of secret keys. 2. Encryption of the message using the public and secret keys given. 3. Decryption of the secret message using the secret key generated. For the decryption phase, a method called Chinese Remainder Theorem is used which helps to fasten the computation. Since Multi prime RSA use more than two prime numbers, the algorithm is more efficient and secure when compared to the standard RSA. Furthermore, in modified Multi prime RSA another secret key is introduced to increase the obstacle to the attacker. Therefore, it is strongly believed that this new algorithm is better and can be an alternative to the RSA.