Forecasting towards Planning and Sustainable Development Based on a System Dynamic Approach: A Case Study of the Setiu District, State of Terengganu, Malaysia

In this paper, a system dynamics approach is used instead of the traditional approaches to stimulate, forecast and analyze the economic effects of an existing policy practice in Setiu Wetland. As a part of Setiu district that uphold tradition in fishery and maritime based industry, Setiu Wetland area seems to be left behind in terms of economic and livelihood. Generally, Setiu development policy consists of five sub-system including population growth, economic, residential, transportation and sub-urban sprawl. Due to their widespread population distribution, Setiu Wetland receives low urban-related progress. Hence, a forecast of 30 years from 2016 to 2046 providing a necessary insight for potential development of the Setiu Wetland region, to simulate its environment, identify gaps, propose suitable land model towards Setiu Minapolitan area (Peri-urban area) and suggest directions for future studies particularly in economic and livelihood for local authorities to develop with.

Sample Selection Model with Bootstrap (BPSSM) Approach: Case Study of the Malaysian Population and Family Survey

Heckman Sampel Selection Model (PSSM) has been adopted widely in the study of labour work. This model contains exogenous, endogenous and standard error variables. However, this model is constantly exposed to high inaccuracy of estimation result. Therefore, to obtain an accurate and precise estimation, the bootstrap approach is introduced. The bootstrap approach will be hybrid with PSSM model known as BPSSM to achieve estimation result that is more precise. Then, the BPSSM is applied to Malaysian Population and Family Survey 1994 (MPFS-1994) data. The results showed that BPSSM provide a smaller standard error and shorter confidence intervals.

The Efficiency Measurement of Indonesian Universities Based on a Fuzzy Data Envelopment Analysis

The main objective of this study is to measure the relative efficiency of Indonesian universities in 2015. There are twenty five DMUs with four inputs and five outputs that are analyzed. Due to the low number of Indonesian scientific publications, this study analyses the performance of the top 25 universities based on the Webometrics ranking as it has been used as one of the indicators of university achievements by the Higher Education of Indonesia. The Data Envelopment Analysis (DEA) is used to obtain the scores of efficiency, while the Fuzzy approach is applied to address the possibility of errors from the auditor’s assessment in determining the input and output variables correctly. The FDEA can be used in measuring the universities performances under imprecise inputs and outputs. Both the CRS (constant returns to scale) and the VRS (variable returns to scale) models are presented. The empirical results show that 36 percent of universities perform efficiently on the CRS model while 52 percent of universities have efficient performances under the VRS model. Furthermore, the well-known universities have shown relatively low scores, which indicate they need to improve their performances in publishing scientific work, as well as providing useful information to the public through the official websites. Generally, the results of the VRS model are better than the CRS model for both the DEA and the FDEA methods.

K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

Any number that can be uniquely determined by a graph is called graph invariants.During the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation analysis.In the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular graph.In this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or networks.The review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti Indices.These concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

Laplace Discrete Adomian Decomposition Method for Solving Nonlinear Integro Differential Equations

This paper proposes the Laplace Discrete Adomian Decomposition Method and its application for solving nonlinear integro-differential equations. This method is based upon the Laplace Adomian decomposition method coupled with some quadrature rules of numerical integration. Four numerical examples of integro-differential equations in both Volterra and Fredholm integrals are used to be solved by the proposed method. The performance of the proposed method is verified through absolute error measures between the approximated solutions and exact solutions. The series of experimental numerical results show that our proposed method performs in high accuracy and efficiency. The study clearly highlights that the proposed method could be used to overcome the analytical approaches in solving nonlinear integro-differential equations.

The Performance of Double Bootstrap Method for Large Sampling Sequence

Studies on the iteration procedure in double bootstrap method have given a great impact on confidence interval performance. However, the procedure was claimed to be complicated and demand intensive computer processor. Considering this problem, an alternative procedure was proposed in this research. Despite of using small sampling sequence, this research was aimed to increase the accuracy estimation using a second replication number which resulted in a large sampling sequence of double bootstrap. In this paper, the alternative double bootstrap method was hybrid onto an example model and its performance was based on Studentised interval. The performance was examined in simulation study and real sample data of sukuk Ijarah. The result showed that hybrid double bootstrap model gave more accurate estimation in terms of its shorter length when dealing with various parameter values and has shown to improve the single bootstrap estimation.

Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.

Bootstrap Approaches to Autoregressive Model on Exchange Rates Currency

The use of historical data is important in making the predictions, for instance in the exchange rate. However, in the construction of a model, extreme data or dirtiness of data is inevitable. In this study, AR model is used with the exchange rate historical data (January 2007 until December 2007) for USD/MYR and is divided into 1-, 3- and 6-horizontal months respectively. Since the presence of extreme data will affect the accuracy of the results obtained in a prediction. Therefore, to obtain a more accurate prediction results, the bootstrap approach was implemented by hybrid with AR model coins as the Bootstrap Autoregressive model (BAR). The effectiveness of the proposed model is investigated by comparing the existing and the proposed model through the statistical performance methods which are RMSE, MAE and MAD. The comparison involves 1%, 5% and 10% for each horizontal month. The results showed that the BAR model performed better than the AR model in terms of sensitivity to extreme data, the accuracy of forecasting models, efficiency and predictability of the model prediction. In conclusion, bootstrap method can alleviate the sensitivity of the model to the extreme data, thereby improving the accuracy of forecasting model which also have high prediction efficiency and that can increase the predictability of the model.

On Characterization of Poised Nodes for a Space of Bivariate Functions

There are several examples of spaces of univariate functions for which we have a characterization of all sets of knots which are poised for the interpolation problem. For the standard spaces of univariate polynomials, or spline functions the mentioned results are well-known. In contrast with this, there are no such results in the bivariate case. As an exception, one may consider only the Pascal classic theorem, in the interpolation theory interpretation. In this paper, we consider a space of bivariate piecewise linear functions, for which we can readily find out whether the given node set is poised or not. The main tool we use for this purpose is the reduction by a basic subproblem, introduced in this paper.

Hilbert Space of Probability Density Functions Based on Aitchison Geometry

The set of probability functions is a convex subset of L1 and it does not have a linear space structure when using ordinary sum and multiplication by real constants. Moreover, difficulties arise when dealing with distances between densities. The crucial point is that usual distances are not invariant under relevant transformations of densities. To overcome these limitations, Aitchison＇s ideas on compositional data analysis are used, generalizing perturbation and power transformation, as well as the Aitchison inner product, to operations on probability density functions with support on a finite interval. With these operations at hand, it is shown that the set of bounded probability density functions on finite intervals is a pre-Hilbert space. A Hilbert space of densities, whose logarithm is square-integrable, is obtained as the natural completion of the pre-Hilbert space.