Factors Influencing Safety Behavior in the Malaysian Army

Much attention has been focused on workers’ perception of workplace safety. However, relatively limited studies focus on Malaysian Armed Forces particularly the Malaysia Army. This organization experiences a significant accident rates which are not reported publicly or contributed into the SOCSO statistics. Improving occupational health and safety in the Army organization is not an easy task despite adequate safety legislation and regulative institutions. It is because the Armed Forces are not obliged to the OSHA 1994 (Act 514). This framework is a replication of Shang et.al (2009) which examined the effects of safety climate on container operation terminal employees’ perceptions of safety performance. However, the technique used to determine the perception and compliance with safety behavior among army personnel in the Malaysian Army organization is by using the Work Safety Scale (WSS) of Hayes et al.(1988). The purpose of this study is to examine whether the five critical factors of safety culture dimensions related to the safety behavior of the Malaysian Army personnel. The WSS measures five factorially distinct constructs: (a) job safety, (b) coworker safety, (c) supervisor safety, (d) management safety practices, and (e) satisfaction with the safety program. All those independent variables were measured on the perception of workplace safety towards the compliance of safety behavior as the determinants among 217 army personnel in one army unit based in Kem Terendak, Melaka. Based on the analysis there was a positive relationship between these five facets and safety behavior. It was found that satisfaction with safety programs, co-worker safety and management safety practices each made significant contributions to compliance with safety behavior, whilst job safety and supervisor safety made least contributions in the study. Results also suggest that management can enhance and refine the Army units’ safety culture by focusing especially on the variables mentioned thereby increasing and strengthening safety culture and soldiers’ safety behavior thereby reducing injuries and accident

Studies of Sn Substitution on Ca and Cu Sites of Bi-Sr-Ca-Cu-O Superconducting System

The influence of Sn substitution on Ca and Cu sites in Bi-Sr-Ca-Cu-O superconductor system simultaneously or separately have been studied using xray diffraction (XRD) method and resistance measurement technique for the structural identification and determination of critical temperature, Tc respectively. Generally, all samples displayed a normal metallic behavior above TConse,' The values of TC(R'() decreased towards Sn concentration. However, the TC(R'() value for x=0.02 sample doped simultaneously in Ca and Cu sites was observed at 104 K The critical temperature increased by 4 K compared to that of the pure sample. Sample doped with Sn, for concentration of x=0.20, at Ca site or at both Ca and Cu sites show the dominance of the 2212 and 2201 phases. Hence, altering the Ca environment favours the formation of the low Tc phases. This observation was also supported by the information obtained from the XRD patterns. ew unidentified peaks (probably impurities) and low phase peaks corresponding to 2201 phase existed for samples with Sn concentration above x=0.15. No peaks belonging to Sn02 were detected implying that Sn probably has been incorporated into the crystalline structures of the BSCCO system or formed as impurities

Multiclass support vector machines for classification of ECG data with missing values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values

Non-fiducial based ECG biometric authentication using one-class support vector machine

Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a random population using kernel-based binary and one-class Support Vector Machines (SVMs) has been considered by other biometric traits, but has been so far left aside for analysis of ECG signals. This paper investigates the effect of different parameters of data set size, labeling data, configuration of training and testing data sets, feature extraction, different recording sessions, and random partition methods on accuracy and error rates of these SVM classifiers. The experiments were carried out with defining a number of scenarios on ECG data sets designed rely on feature extractors which were modeled based on an autocorrelation in conjunction with linear and nonlinear dimension reduction methods. The experimental results show that Kernel Principal Component Analysis has lower error rate in binary and one-class SVMs on random unknown ECG data sets. Moreover, one-class SVM can be robust recognition algorithm for ECG biometric verification if the sufficient number of biometric samples is available

Feature level fusion for biometric verification with two-lead ECG signals

Electrocardiogram (ECG) is a new generation of biometric modality which has unique identity properties for human recognition. There are few studies on feature level fusion over short-term ECG signals for extracting non-fiducial features from autocorrelation of ECG windows with an identical length. In this paper, we provide an experimental study on fusion at feature extraction level by using autocorrelation method in conjunction with different dimensionality reduction techniques over vector sets with different window lengths from short and long-term two-lead ECG recordings. The results indicate that the window and recording lengths have significant effects on recognition rates of the fused ECG data sets

ECG biometric authentication based on non-fiducial approach using kernel methods

Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)

Multiclass support vector machines for classification of ECG data with missing values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values

The effect of remelting on the physical properties of borotellurite glass doped with manganese

A systematic set of borotellurite glasses doped with manganese (1–x) [(B2O3)0.3(TeO2)0.7]-xMnO, with x = 0.1, 0.2, 0.3 and 0.4 mol%, were successfully synthesized by using a conventional melt and quench-casting technique. In this study, the remelting effect of the glass samples on their microstructure was investigated through density measurement and FT-IR spectra and evaluated by XRD techniques. Initial experimental results from XRD evaluation show that there are two distinct phases of glassy and crystallite microstructure due to the existence of peaks in the sample. The different physical behaviors of the studied glasses were closely related to the concentration of manganese in each phase. FTIR spectra revealed that the addition of manganese oxide contributes the transformation of TeO4 trigonal bipyramids with bridging oxygen (BO) to TeO3 trigonal pyramids with non-bridging oxygen (NBO)

Classifications of clinical depression detection using acoustic measures in Malay speakers

Objective screening mechanism using paralinguistic cues to enhance current diagnostic on detecting depression is desirable, which resulted in the rise of research on this area. However, to date, there has been no research done using dataset of Malay speakers. This paper presented an acoustic depression detection classification using Linear and Quadratic Discriminant analysis with transition parameters and power spectral density as the acoustic features. Among the two features, power spectral density performed better, especially with the combination of band 1, 2 and 3 for both male and female data. As for the Transition parameters, we found that unvoiced feature performed best overall for both male and female

Prospective diagnostic study on the use of narrow‐band imaging on suspicious lesions during colonoscopy examination

Introduction: Colonoscopy is the gold standard to detect colorectal neoplasm. Narrow-band imaging (NBI) has a good diagnostic accuracy to differentiate between neoplastic and non-neoplastic colorectal lesions. This study explores the diagnostic validity of NBI colonoscopy as well as its associated factors related to neoplastic and non-neoplastic colorectal lesions. Methods: This study enrolled 100 patients in a single-center tertiary teaching hospital. Patients presented for screening colonoscopy, and those with suspicious colorectal lesions were included in this study. During colonoscopy, the most suspicious lesion in each patient was analyzed using the NBI system based on Sano’s classification. Each lesion was biopsied for histopathological analysis, the gold standard. Endoscopic images were captured electronically. The sensitivity, specificity, and diagnostic accuracy of NBI colonoscopy were assessed. Other associated factors related to neoplastic and non-neoplastic lesions were analyzed accordingly. Results: The sensitivity and specificity of the NBI were 88.2% and 71.9%, respectively. The area under the receiver–operator curve was 0.801, indicating that NBI has a good ability to differentiate between disease and non-disease. There are significant associations between histopathological examination outcomes and both presenting symptoms, especially weight loss, and lesion site, even after other variables were controlled (P < 0.05). Conclusion: The NBI system in colonoscopy was capable of distinguishing neoplastic from non-neoplastic colorectal lesions. It indicates an acceptable level of agreement with histopathology, the gold standard. However, the role of NBI in screening and surveillance in Malaysia still needs further evaluation and exploration