The Optimal Performance of Multi-Layer Neural Network for Speaker-Independent Isolated Spoken Malay Parliamentary speech

This paper describes speech recognizer modeling techniques which are suited to high performance and robust isolated word recognition in speaker-independent manner. In this study, a speech recognition system is presented, specifically for an isolated spoken Malay word recognizer which uses spontaneous and formal speeches collected from Parliament of Malaysia. Currently the vocabulary is limited to ten words that can be pronounced exactly as it written and control the distribution of the vocalic segments. The speech segmentation task is achieved by adopted energy based parameter and zero crossing rate measure with modification to better locates the beginning and ending points of speech from the spoken words. The training and recognition processes are realized by using Multi-layer Perceptron (MLP) Neural Networks with two-layer feedforward network configurations that are trained with stochastic error back-propagation to adjust its weights and biases after presentation of every training data. The Mel-frequency Cepstral Coefficients (MFCCs) has been chosen as speech extraction approach from each segmented utterance as characteristic features for the word recognizer. The MLP performance to determine the optimal cepstral orders and hidden neurons numbers are analyzed. Recognition results showed that the performance of the two-layer network increased as the numbers of hidden neurons increased. Experimental result also showed that the cepstral orders of 12 to 14 were appropriate for the speech feature extraction for the data in this study

An Evaluation of Consumers’ Perceptions Regarding “Modern Medicines” in Penang, Malaysia

The objective of this study was to evaluate consumers’ perceptions regarding “modern medicines” in Penang, Malaysia. To conduct this exploratory study, qualitative techniques were used. Consumers more than 19 years of age and could speak English, who had visited a pharmacy in the last 30 days, were included from the four major areas of Penang. Eighteen interviews were conducted until the point of saturation. The interviews were audio-taped and then transcribed verbatim for thematic content analysis. Many consumers correctly identified the major characteristics and properties of modern medicines; however, others raised doubts regarding the safety, quality and efficacy of “modern medicines”. There were many misconceptions such as “all modern medicines can cause dependence”, traditional medicines are completely “free of side-effects” and “Western medicines cure while Chinese medicines don’t”. Color was also considered a strong determinant of the safety and characteristics of a medicine. Regarding consumers’ “medicine information seeking behavior”, many consumers would seek information from doctors and pharmacists; however, there were others, who would look for books, or get it from the internet and friends. Of concern many consumers emphasized that while “self-searching for drug information” they would only look for side-effects. Misconceptions regarding medicine-taking behavior, medicine use and compliance were also identified. Though several consumers complied with the medicine-taking instructions, many reported that they would stop taking medicines, once they feel better. Though many consumers correctly identified the characteristics of “modern medicines”, misconceptions regarding "medicine information sources and “medicine-taking behavior” were rampant. The situation demands corrective actions including community-oriented educational campaigns to improve “medicine use” in the society

Initial study of electrical capacitance tomography for detecting agarwood

This study investigates the use of electrical capacitance tomography (ECT) as a non-invasive method to monitor the quantity of agarwood in a tree. Previous methods like sonic, magnetic inductive, and microwave tomography have been used, but it's unclear if voltage variations in resin regions are significant. The ECT system was developed using COMSOL Multiphysics software, modeling 8 electrodes in a 2-dimensional setup. The electrical potential distribution and performance analysis of the ECT model were evaluated for different agarwood locations, shapes, and sizes. The forward problem was solved using COMSOL, and tomogram images were obtained using a linear back-projection algorithm in MATLAB. The images closely matched the reference image based on the mean structural similarity index (MSSIM) values. This suggests the potential for accurately reconstructing agarwood images using ECT

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Harkavy's letter to Ignaz Goldziher

Studies have shown that driver drowsiness is one of the main causes of road accidents. It is estimated that 30% of road accidents are caused by driver drowsiness, which creates a need for driver drowsiness detection in modern vehicle systems. Previous works have shown the viability of using heart rate variability (HRV) for detecting the onset of driver drowsiness. HRV is obtained for electrocardiogram (ECG) signals, of which the power bands can be analysed to determine the physiological state of a person. This paper introduces a new method to detect driver drowsiness by classifying the power spectrum of a person's HRV data using Block-based Neural Networks (BbNN), which is evolved using Genetic Algorithm (GA). For most cases, regular Artificial Neural Networks (ANN) are not suitable for high speed and efficient hardware implementation. BbNNs are better candidates due to its regular block based structure, has relatively fast computational speeds, lower resource consumption, and equal classifying strength in comparison to other ANNs. Preliminary work has shown promising results with up to 99.99% classification accuracy using the proposed BbNN detection system for HRV data

Insulin Suppresses the Expression of Amyloid Precursor Protein, Presenilins, and Glycogen Synthase Kinase-3β in Peripheral Blood Mononuclear Cells

A low dose insulin infusion in type 2 diabetics significantly reduced amyloid precursor protein expression demonstrating the acute effect of insulin

Mid-infrared (MIR) Mach-Zehnder silicon modulator at 2μm wavelength based on interleaved PN junction

In this paper, a MIR silicon modulator operating at 2 μm wavelength is experimentally demonstrated. The modulator shows 9.7 GHz 3-dB electro-optic bandwidth at Vbias= -3V. We also present optical modulation at 12.5 Gb/s.</p