Degree of 5-Reaches in Teaching of Control System Design

Control System Design course prepares students to design automatic systems, which is indispensable of the engineering curriculum. This paper intends to illustrate how the effective learning techniques of Lu Xun are adapted as activities in the teaching of the course. This is the first time that the content design and delivery of the course are analysed objectively. From a study population of 128 students, Activity 5-9 are found to be the most capable of engaging the students to use the 5-organs while learning the course. And the Eye, Hand and Brain are the most engaging organs. Keywords: Lu Xun; 5-reaches; Control System Design eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-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) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI3.256

Support Vector Machine with Theta-Beta Band Power Features Generated from Writing of Dyslexic Children

The classification of dyslexia using EEGrequires the detection of subtle differences between groups of children in an environment that are known to be noisy and full of artifacts. It is thus necessary for the feature extraction to improve the classification. The normal and poor dyslexic are found to activate similar areas on the left hemisphere during reading and writing. With only a single feature vector of beta activation, it is difficult to distinguish the difference between the two groups. Our work here aims to examine the classification performance of normal, poor and capable dyslexic with theta-beta band power ratio as an alternative feature vector. EEG signals were recorded from 33 subjects (11 normal, 11 poor and 11 capable dyslexics) during tasks of reading and writing words and non-words. 8 electrode locations (C3, C4, FC5, FC6, P3, P4, T7, T8) on the learning pathway and hypothesized compensatory pathway in capable dyslexic were applied. Theta and beta band power features were extracted using Daubechies, Symlets and Coiflets mother wavelet function with different orders. These are then served as inputs to linear and RBF kernel SVM classifier, where performance is measured by Area Under Curve(AUC) of Receiver Operating Characteristic (ROC) graph. Result shows the highest average AUC is 0.8668 for linear SVM with features extracted from Symlets of order 2, while 0.9838 for RBF kernel SVM with features extracted from Daubechies of order 6. From boxplot, the normal subjects are found to have a lower theta-beta ratio of 2.5:1, as compared to that of poor and capable dyslexic, ranging between 3 to 5, for all the electrodes

Etching Time on Structural and Electrical Properties of Porous Silicon SERS Substrates for Non-Invasive Dengue-NS1 Detection

Surface Enhanced Raman Spectroscopy (SERS) is a sensitive and specific analytical technique which has been explored in many applications, including disease detection. However, SERS performance is highly dependent on type of SERS substrate. This work is aimed to develop a SERS substrate that is sensitive to an early dengue virus biomarker known as Dengue virus nonstructural 1 (DENV-NS1) protein from saliva of infected patients. The new SERS substrate will allow non-invasive and rapid detection method for Dengue as early as day one of infection. Early detection of infection within the first five days is crucial to monitoring patients to help in reducing the fatality rate. Here, the electrochemical etching technique is employed to fabricate porous silicon (pSi) with variation in structural features to serve as the SERS substrate base. Variation in surface structural and electrical properties of pSi with etching time is recorded. Structural surface properties of the samples are investigated using the Field Emission Scanning Electron Microscope (FESEM) and energy-dispersive X-ray spectroscopy (EDX). While, the electrical properties are observed through I-V, resistivity and conductivity curve. From FESEM images, micro size cross-shaped porous structures are observed to have formed. Top-view reveals micro-size cross-shaped structures, while triangle-shaped structures from the cross-sectional view. The size of the structure formed increases with the etching time. Based on the structural and electrical properties an etching time between 20 to 28 minutes is found optimal for producing more uniform surface structure

Etching Time on Structural and Electrical Properties of Porous Silicon SERS Substrates for Non-Invasive Dengue-NS1 Detection

Surface Enhanced Raman Spectroscopy (SERS) is a sensitive and specific analytical technique which has been explored in many applications, including disease detection. However, SERS performance is highly dependent on type of SERS substrate. This work is aimed to develop a SERS substrate that is sensitive to an early dengue virus biomarker known as Dengue virus nonstructural 1 (DENV-NS1) protein from saliva of infected patients. The new SERS substrate will allow non-invasive and rapid detection method for Dengue as early as day one of infection. Early detection of infection within the first five days is crucial to monitoring patients to help in reducing the fatality rate. Here, the electrochemical etching technique is employed to fabricate porous silicon (pSi) with variation in structural features to serve as the SERS substrate base. Variation in surface structural and electrical properties of pSi with etching time is recorded. Structural surface properties of the samples are investigated using the Field Emission Scanning Electron Microscope (FESEM) and energy-dispersive X-ray spectroscopy (EDX). While, the electrical properties are observed through I-V, resistivity and conductivity curve. From FESEM images, micro size cross-shaped porous structures are observed to have formed. Top-view reveals micro-size cross-shaped structures, while triangle-shaped structures from the cross-sectional view. The size of the structure formed increases with the etching time. Based on the structural and electrical properties an etching time between 20 to 28 minutes is found optimal for producing more uniform surface structure

A new class of pluripotent stem cell cytotoxic small molecules

10.1371/journal.pone.0085039PLoS ONE93-POLN

Performance of Extreme Learning Machine Kernels in Classifying EEG Signal Pattern of Dyslexic Children in Writing

Dyslexia is a specific learning disability that causes leaners to have difficulties to process letters and number during reading, writing and doing mathematics. Early identification of dyslexic characteristic is crucial so that early intervention given could overcome learner difficulties. A process of writing involves areas in brain learning pathway and motor cortex. This activity could be recorded using electroencephalogram (EEG) non-invasively. Using this information, a study has been conducted to distinguish EEG signal of normal, poor and capable dyslexic children. In this work, EEG signals were recorded from eight channels; C3, C4, P3, P4, FC5, FC6, T7 and T8. The signals were extracted using discrete wavelet transform (DWT) with Daubechies wavelet family order 2, 4, 6 and 8 to acquire beta and theta band features. The coefficient of beta band power and the ratio of theta/beta band power were input features of expert learning machine (ELM) classifier. Four types of kernels namely linear, radial basis function (RBF), polynomial and wavelet were applied as output weight in connecting hidden node and the output node of ELM. Parameters were varied to optimize each kernel to obtain the best classification accuracy. Results show that db2 gives the highest classification performance for all kernel among other Daubechies family. RBF and wavelet kernel yield the highest accuracy at 89% compared with other ELM kernels. This work reveals that ELM with RBF and wavelet kernel together with beta band power and ratio of theta/beta band power extracted from db2 could distinguish normal, poor and dyslexic children during writing