Penerbitan modul pembangunan kemahiran generik berlandaskan origami

Kebanyakan majikan kini memerlukan pekerja yang bukan sahaja mempunyai kemahiran teknikal, tetapi juga memerlukan kemahiran generik untuk melaksanakan tugas yang diberikan untuk meningkatkan produktiviti dan daya saing. Walau bagaimanapun, kebanyakan graduan tidak bersedia untuk memenuhi keperluan aspek pekerjaan berpusatkan kemahiran generik seperti kemahiran kepimpinan, kemahiran komunikasi, kemahiran kerja berpasukan, kemahiran menyelesaikan masalah, kemahiran keusahawanan, pemikiran kritis dan kemahiran kreatif. Kajian telah dilaksanakan untuk origami yang berkaitan dengan peningkatan kemahiran generik dan mendedahkan maklum balas positif. Kajian ini telah menyusun semua proses yang berkaitan ke dalam satu modul rujukan. Tujuan kajian ini adalah untuk menilai kesesuaian modul disusun untuk penerbitan dan pengedaran. Para responden yang dipilih untuk kajian ini adalah seramai 40 guru dari Kolej Vokasional, Lebuh Cator, Ipoh, Perak. Instrumen dipatuhi diedarkan selepas kursus demonstrasi dan perbincangan kumpulan untuk menilai tahap persepsi terhadap kesesuaian modul untuk pembangunan kemahiran generik. Maklum balas yang diperolehi melalui soal selidik ini dianalisis menggunakan SPSS versi 16 untuk min dan sisihan piawai dari maklum balas responden. Dapatan kajian menunjukkan bahawa persepsi guru-guru terhadap kesesuaian modul yang tinggi dari segi format, kandungan, bahan-bahan pembelajaran dan aktiviti latihan. Beberapa cadangan telah dibuat pada akhir penyelidikan untuk meningkatkan kualiti modul bagi membangunkan kemahiran generik pada masa akan datang

Proposed new upgrading works of Muzium Layang-Layang Pasir Gudang for Pasir Gudang Municipal Council at Bukit Layang-Layang, Jalan Bandar, 81700 Pasir Gudang, Johore / Muhamad Ezwan Che Mat Zin

This Diploma level of Interior Design final project is towards student understanding about the interior design project’s in the whole aspect that had been studied from the basic level until the design proposal presentation. The selected final project for my final semester is a proposed an upgrading work of Muzium Layang-Layang Pasir Gudang for Pasir Gudang Municipal Council at Bukit Layang-Layang, Jalan Bandar, 81700 Pasir Gudang, Johore. The museum authority itself has play a big role in providing and promoting national heritage of traditional kites making and flying toward the Malaysian citizen and foreigner. Thus it was an honor for me to get involved with the authority in making their museum as a part of my final years project. The concept and image is also apply to both interior and exterior to appeal the centre will be able to lure more tourists and visitors who were always keen in our aesthetic values in architecture. There were several researches done to succeed this tourist information centre final project, amongst them are site analysis, building analysis, interview, observation and case studies. All these studies have a purpose of identifying the existing weaknesses and also attempt to solve some of the problems that appear. These investigations will be implemented in the design process with the intention to gain an attractive design and also suitable with the Muzium Layang-Layang Pasir Gudang demands in terms of function and aesthetic value

Robust Diagnostics In Logistic Regression Model

In recent years, due to inconsistency and sensitivity of the Maximum Likelihood Estimator (MLE) in the presence of high leverage points and residual outliers, diagnostic has become an essential part of logistic regression model. High leverage points and residual outliers have huge tendency to break the covariate pattern resulting in biased parameter estimates. The identification of high leverage points and residual outliers are believed to be vital in order to improve the performance of the MLE. The presence of high leverage points and the residual outliers give adverse effect on the inferences by inducing large values to the Influence Function (IF). For the identification of high leverage points, Imon (2006) proposed the Distance from the Mean (DM) diagnostic method. The weakness of the DM method is that it tends to swamp some low leverage points even though it can identify the high leverage points correctly. Deleting the low leverage points may lead to a loss of efficiency and precision of the parameter estimates. The Robust Logistic Diagnostic (RLGD) is proposed as an alternative approach that performs well compared to the DM method. The RLGD method incorporates robust approaches and diagnostic procedures. Robust approach is firstly used to identify suspected high leverage points by computing the Robust Mahalanobis Distance (RMD) based on Minimum Volume Ellipsoid (MVE) estimator or Minimum Covariance Determinant (MCD) estimator. For confirmation, the diagnostic procedure is used to compute potential. The RLGD method ensures only correct high leverage points are identified and free from the swamping and masking effects. The performance of the RLGD method is investigated by real examples and the Monte Carlo simulation study. The real examples and the simulation results indicate that the RLGD method correctly identify the high leverage points (increase the probability of the Detection of Capability (DC)) and manage to reduce the number of swamping low leverage points (decrease the probability of the False Alarm Rate (FAR)). The Standardized Pearson Residual (SPR) only successful in identifying a single residual outlier. The SPR method is less effective when residual outliers are present in the covariates. The Generalized Standardized Pearson Residual (GSPR) proposed by Imon and Hadi (2008) is a successful method in identifying residual outliers. However, in the initial stage of the GSPR method utilizes the graphical methods which are based on the observation’s judgement and not suitable for higher dimensional covariates. The Modified Standardized Pearson Residual (MSPR) based on the RLGD method is proposed which is more reliable. The MSPR method provides an alternative method to the GSPR method that produces similar result. The attractive feature of the MSPR method is that it is easier to apply. This research also utilizes the RLGD method in bootstrap procedures. The Classical Bootstrap (CB) procedure by Random-x Re-sampling is not robust to the high leverage points. To accommodate this problem, the newly develop bootstrap procedures based on the RLGD method which are called the Diagnostic Logistic Before Bootstrap (DLGBB) and the Weighted Logistic Bootstrap with Probability (WLGBP) are proposed. In the DLGBB procedure, the high leverage points are excluded before applying the re-sampling process. Meanwhile in the WLGBP procedure, the high leverage points are attributed with low probabilities and consequently having low chances of being selected in the re-sampling process. Simulation results show that the DLGBB and the WLGBP procedures are more robust to the high leverage points compared to the CB procedure

Policy on Irregular Migrants in Malaysia: An Analysis of its Implementation and Effectiveness

In the early 1970s, Malaysia began to be inundated by foreign workers, all of whom were irregular migrants. A decade later their uncontrolled entry left several negative consequences especially to the internal and border security of the country. To overcome the problems, Malaysia introduced the foreign worker policy which became fully implemented in 1992. The policy has two objectives, firstly to regulate the inflow of foreign workers; and secondly, to stem the inflow of irregular migrant workers into the country. The implementation of the policy has led to a spectacular increase in the number of legally recruited migrant workers. However, it has not been able to curb the expansion of irregular migrants; instead their number has risen in parallel with that of legally recruited ones. This report is an attempt to examine why this is so. It is based on a research carried out in 2011 among 404 irregular migrants as respondents, comprising 340 who were apprehended and housed at seven of the 17 holding depots run by the government and 64 others who are still at large

Robust logistic diagnostic for the identification of high leverage points in logistic regression model

High leverage points are observations that have outlying values in covariate space. In logistic regression model, the identification of high leverage points becomes essential due to their gross effects on the parameter estimates. Currently, the distance from the mean diagnostic method is used to identify the high leverage points. The main limitation of the distance from the mean diagnostic method is that it tends to swamp some low leverage points even though it can identify the high leverage points correctly. In this study, we propose a new diagnostic method for the identification of high leverage points. Robust approach is firstly used to identify suspected high leverage points by computing the robust mahalanobis distance based on minimum volume ellipsoid or minimum covariance determinant estimators. For confirmation, the diagnostic procedure is used by computing the group deleted potential. We called this proposed diagnostic method the robust logistic diagnostic. The performance of the proposed diagnostic method is then investigated through real examples and monte carlo simulation study. The result of this study indicates that the proposed diagnostic method ensures only correct high leverage points are identified and free from swamping and masking effects

The performance of classical and robust logistic regression estimators in the presence of outliers

It is now evident that the estimation of logistic regression parameters, using Maximum Likelihood Estimator(MLE), suffers a huge drawback in the presence of outliers. An alternative approach is to use robust logistic regression estimators, such as Mallows type leverage dependent weights estimator (MALLOWS, Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco and Yohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates the robustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. The results indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBY estimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBY estimator be employed when outliers are present in the data to obtain a reliable estimate

Modified standardized Pearson residual for the identification of outliers in logistic regression model

Detection of outlier based on standardized Pearson residuals has gained widespread use in logistic regression model in the presence of a single outlier. An innovation attempts in the same direction but dealing for a group of outliers have been made using generalized standardized Pearson residual which requires a graphical or a robust estimator to find suspected outliers to form a group deletion. In this study, an alternative measure namely modified standardized Pearson residual is derived from the robust logistic diagnostic. The weakness of standardized Pearson residuals and the usefulness of generalized standardized Pearson residual and modified standardized Pearson residual are examined through several real examples and Monte Carlo simulation study. The results of this study signify that the generalized standardized Pearson residual and the modified standardized Pearson residual perform equally good in identifying a group of outliers

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity

Synthesis of polymeric nanogel via irradiation of inverse micelles technique

Covalently cross-linked nanogels were prepared via irradiation of inverse micelles that had been prepared from radiation crosslinkable polymer, water, oil and surfactant. A mixture of polymer, water, heptane and sodium dioctyl sulfosuccinate (AOT) at certain compositions forms inverse micelles with the size ranging from 2 to 8 nm. The hydrophilic head of the surfactant facilitates encapsulation of water soluble polymer. If the entrapped polymer is radiation crosslinkable, it is expected that upon irradiation, polymerization shall take place in such small and confined space, leading to formation of nano-sized polymeric gel. Meanwhile, emulsion at 2 nm size was chosen for gamma irradiation process. The formation of the nano-sized discreet gel using irradiation of inverse micelles technique was proven at a dose as low as 5 kGy to obtain nanogel sized ~ 95 nm