Portfolio of Compositions: Developing a personal compositional approach based on attributes of spoken language

This compositional portfolio comprises eight original works presented in musical scores. The works were written between October 2013 and April 2017 during the course of my PhD research. The compositions featured are for a variety of combinations of voices and solo instrument up to full orchestra. Audio recordings of performances are included for all except the final orchestral work. In these compositions, I introduce a new compositional idea, declaiming, which I have been developing and which is explained in this commentary. This idea is used to determine the formal structure of these pieces. Unique approaches to using this compositional idea are described in this commentary, regarding individual works

Relationship of inventory turnover and gross margin return on inventory investment (GMROII) of manufacturing in Malaysia public listed companies.

This comprehensive study delved deeply into the interplay between inventory turnover and gross margin return on inventory investment (GMROII) within the context of manufacturing sector companies listed in the Malaysia. Effective inventory management was undeniably a cornerstone of business operations, profoundly influencing financial performance and operational efficiency. As such, a nuanced understanding of the intricate relationship between inventory turnover and GMROII assumed paramount significance, offering invaluable insights for informed and strategic decision-making. Employing a rigorously quantitative approach, this research harnessed secondary data derived from the financial records of manufacturing companies spanning a defined temporal horizon. Through meticulous data analyses, the researcher endeavored to illuminate the intensity and directionality of the correlation between these pivotal variables, encapsulated within a cohort of 260 publicly listed manufacturing enterprises. The discerning findings of this study illuminated distinctive trends across various sectors. Notably, consumer products, healthcare, and technology sectors exhibited a promising convergence of favorable inventory turnover and robust GMROII. In contrast, sectors like construction and transportation manifested commendable inventory turnover, albeit with comparatively lower GMROII. Intriguingly, the property sector surfaced with relatively diminished investment potential and profitability. The culmination of a meticulous analysis of 260 publicly listed manufacturing companies in Malaysia unequivocally underscored the salient premise that augmenting inventory turnover could invariably catalyse heightened profitability. It resoundingly highlighted the pivotal role that effective inventory management assumed within the expansive landscape of the manufacturing secto

Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines

Complex Power System for Lithium Batteries and Lead-Acid Batteries

This paper discusses about the power system, and focus on complex power system applications on the public transportation. The objective of this research is to improve power system to achieve green energy applications. Energy depletion is a global problem for people who live on the Earth and we have to face this problem. So before energy depletion occurs, we should develop more energy alternatives. In this paper, we go through two experimental verification of complex power systems. The first part is to establish a platform for static electricity complex experiments. In the second part, using vehicle test platform for dynamic test. Finally, by creating a motor drive mode simulate actual conditions to enhance the accuracy of the experimental results. According to the results we can know the power system after being modified can improve overall system performance

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other models. Although great efforts have been delved into the transferability across models, surprisingly, less attention has been paid to the cross-task transferability, which represents the real-world cybercriminal's situation, where an ensemble of different defense/detection mechanisms need to be evaded all at once. In this paper, we investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, object detection, semantic segmentation, explicit content detection, and text detection. Our proposed attack minimizes the ``dispersion'' of the internal feature map, which overcomes existing attacks' limitation of requiring task-specific loss functions and/or probing a target model. We conduct evaluation on open source detection and segmentation models as well as four different computer vision tasks provided by Google Cloud Vision (GCV) APIs, to show how our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations linf=16.Comment: arXiv admin note: substantial text overlap with arXiv:1905.0333

IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future

IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future