Investigation of the Impact, Hardness, Density and Water absorption of Polypropylene Filled Doum Palm Shell Particles Composite

Particulate fillers are used to improve the physical and mechanical properties of polymers to make it more suitable for engineering application. This study determined the impact, hardness and physical properties of polypropylene reinforced Doum palm shell particles composite; the effect of particles loading and particle size on the impact, hardness, density and water absorption properties of the composite were studied. The composites were prepared by compounding polypropylene and Doum palm shell particles using compression moulding method. The particles loading in the matrix (polypropylene) were varied from 0 - 40 wt. % at 5 wt. % intervals, after which the composites were characterized. SEM analysis was also conducted on the composites. The results showed that the addition of Doum palm shell particles stiffened the flexibility of the polymer and improved its ability to absorb and dissipate energy. Composite of 150 µm has the maximum impact strength of 4.1 kJ/m2 at 35 wt. % particles loading and 3.9 kJ/m2 at 30 wt. % particles loading for 300 µm. The hardness of the composite was improved with increase in particle loading of the composites. The hardness increases from 6.53 HRF to 9.1HRF at 35 wt. % for 150 µm particle size and 8.6 HRF for 300 µm. Density and water uptake of the composite increases with increase in particles loading and size in the composites.  The SEM images of the composites reveal a good interfacial bonding between Doum palm shell particles and the matrix. These account for the good impact and hardness properties of the composites obtained. Keywords: Impact strength, Hardness, polypropylene, Doum palm shell particles, Microstructural analysis, water absorption DOI: 10.7176/JIEA/8-1-0

Market Risk and Stock Return of Listed Financial Service Firms in Nigeria

The trading financial instruments in the capital market by Financial Service Firms (FSF) have generated return arising from changes in the prices of stock which exposed the firms to market risk. An effective market risk decision remains significant to determining stock return level realized from the volume and value of stock traded. This study examines the effect of market risk on stock return of listed FSF in Nigeria. The population of this study consists of fifty-six (56) financial service firms listed in the Nigerian Stock Exchange Market. In arriving at the sample size of twenty-nine (29) firms the purposive sampling technique and filtering criteria were employed. Data were sourced secondarily from the audited annual report of financial service firms, Nigeria Stock Exchange fact book, and other relevant financial service firms’ websites for period of twelve (12) years (2007-2018). Panel multiple regression technique of data analysis was applied using the ordinary least square estimator. The findings of the study revealed that book to market ratio as a proxy of market risk was insignificantly negative on stock return during the period under review. Net interest margin as a proxy of market risk revealed a significant positive effect on stock return during the period of review. Also, the study revealed that control variables of firm size, leverage had significant positive effects on stock return, though; the effect of monetary policy rate was positive but insignificant on stock return. The study concluded that a higher book to market ratio would reduce stock return and to a larger extent the reduction in stock return may not be affected significantly. It also concluded that a higher net interest margin would result to a higher stock return and vice versa. The study recommended that decision-makers and portfolio managers of financial service firms should employ appropriate risk strategies through derivatives, forwards, futures, swaps, options that can mitigate market risk in order to optimize return. Keywords: Financial Services Firms, Stock Returns, Market Risk, Firms Specific Risk factors, Nigerian Stock exchange. DOI: 10.7176/EJBM/13-8-10 Publication date: April 30th 202

TEEM: Online Thermal- and Energy-Efficiency Management on CPU-GPU MPSoCs

Heterogeneous Multiprocessor System-on-Chip (MPSoC) are progressively becoming predominant in most modern mobile devices. These devices are required to perform processing of applications within thermal, energy and performance constraints. However, most stock power and thermal management mechanisms either neglect some of these constraints or rely on frequency scaling to achieve energy-efficiency and temperature reduction on the device. Although this inefficient technique can reduce temporal thermal gradient, but at the same time hurts the performance of the executing task. In this paper, we propose a thermal and energy management mechanism which achieves reduction in thermal gradient as well as energy-efficiency through resource mapping and thread-partitioning of applications with online optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is experimentally appraised using different applications from Polybench benchmark suite on Odroid-XU4 developmental platform. Results show 28% performance improvement, 28.32% energy saving and reduced thermal variance of over 76% when compared to the existing approaches. Additionally, the method is able to free more than 90% in memory storage on the MPSoC, which would have been previously utilized to store several task-to-thread mapping configurations

Effects of Particle Size and Loading on Tensile and Flexural Properties of Polypropylene Reinforced Doum Palm Shell Particles Composites

Natural Particulates (fillers) are reinforcement in composite materials. They are used in matrix for cost reduction, improved processing, density control, thermal conductivity, control of thermal expansion, flame retardancy and improved mechanical properties. Doum palm shell particles reinforced composite was prepared by compounding polypropylene matrix with 10 - 40 wt. % Doum palm shell particles at 5 wt. % intervals for 150 µm and 300 µm particles sizes using compression moulding techniques. The effect of the particles size and particles loading on tensile and flexural properties of the composite produced were investigated. The results showed that the tensile strength increased from 34.12 MPa for neat polypropylene to a maximum of 44.88 MPa at 10 wt. % Doum palm shell addition for 150 µm particle size; while it increased to a maximum of 39.62 MPa at 10 wt. % Doum palm shell addition for 300 µm particle size. Flexural strength increased from 37.91 MPa for neat polypropylene to a maximum of 57.68 MPa at 10 wt. % Doum palm shell addition for 150 µm particle sizes; however, it increased to a maximum of 49.63 MPa at 10 wt. % Doum palm shell addition for the 300 µm particle size. 150 µm particle size composite yield a better result compared to 300 µm particles size which is in agreement with the literature, the smaller the particle size the better the properties of the composite because it has better compaction, reduced porosity which give effective stress transfer between the matrix and the particles. The use of doum palm shell as fillers in composite will not only provide a renewable source of filler in polymer composite but also generate a non – food source of economic development for the famers in the rural areas

CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems

Most modern mobile cyber-physical systems such as smartphones come equipped with multi-processor systems-on-chip (MPSoCs) with variant computing capacity both to cater to performance requirements and reduce power consumption when executing an application. In this paper, we propose a novel approach to dynamic voltage and frequency scaling (DVFS) on CPU, GPU and RAM in a mobile MPSoC, which caters to the performance requirements of the executing application while consuming low power. We evaluate our methodology on a real hardware platform, Odroid XU4, and the experimental results prove the approach to be 26% more power-efficient and 21% more thermal-efficient compared to the state-of-the-art system

QUAREM: Maximising QoE Through Adaptive Resource Management in Mobile MPSoC Platforms

Heterogeneous multi-processor system-on-chip (MPSoC) smartphones are required to offer increasing performance and user quality-of-experience (QoE) , despite comparatively slow advances in battery technology. Approaches to balance instantaneous power consumption, performance and QoE have been reported, but little research has considered how to perform longer-term budgeting of resources across a complete battery discharge cycle. Approaches that have considered this are oblivious to the daily variability in the user’s desired charging time-of-day (plug-in time), resulting in a failure to meet the user’s battery life expectations, or else an unnecessarily over-constrained QoE. This paper proposes QUAREM, an adaptive resource management approach in mobile MPSoC platforms that maximises QoE while meeting battery life expectations. The proposed approach utilises a model that learns and then predicts the dynamics of the energy usage pattern and plug-in times. Unlike state-of-the-art approaches, we maximise the QoE through the adaptive balancing of the battery life and the quality of service (QoS) for the duration of the battery discharge. Our model achieves a good degree of accuracy with a mean absolute percentage error of 3.47% and 2.48% for the energy demand and plug-in times, respectively. Experimental evaluation on an off-the-shelf commercial smartphone shows that QUAREM achieves the expected battery life of the user within 20–25% energy demand variation with little or no QoE degradation. </jats:p

Maximising user experience through intelligent resource management in mobile systems

Mobile devices are required to deliver increasing performance and high quality of experience (QoE) to users, despite comparatively slow advances in battery technology. While the complexity of modern mobile devices has increased exponentially, both in terms of computing capabilities and applications with diverse requirements, achieving battery life goal is crucial if the users’ QoE is to be maximised. Few studies have considered longer-term resource budgeting, but they either fall short of users’ battery life expectations or an unnecessarily over-constrained QoE. This research aims at maximising the QoE across the duration of battery discharge for a user and their requirements.To achieve the stated aim, this thesis first explores how to predict plug-in times and energy demand, along with how the compute performance of a mobile device can be adapted to balance QoE and battery life requirements. This leads to the proposal of QUality of experience Adaptive REsource Management (QUAREM), an adaptive Dynamic Voltage and Frequency Scaling (DVFS) approach for maximising QoE. QUAREM learns and predicts users’ plug-in times and energy demand and exploits the DVFS via online monitoring and adaptive frequency setting to achieve the battery life. Evaluationshows an average QoE improvement while meeting daily battery life expectations.Next, the thesis explores the relationship between display brightness and how it affects user QoE, as well as how it can be adapted to further tradeoff battery life and QoE. aCADS, an adaptive brightness scaling approach that leverages insights from user perceptions of content and ambient light variations to maximise QoE, is proposed. This is achieved through the learning and classification of each sample into predefined content and ambient light clusters and the adaptive scaling of the display brightness using an energy model. Compared to state-of-the-art, aCADS improves QoE by up to 32.5 %.To effectively manage and balance battery life and QoE while accommodating diverse usage patterns beyond processing elements’ (PEs) or display subsystem’s capabilities, an adaptive brightness scaling approach along with PE’s DVFS is proposed. This is achieved with an efficient workload- and context-clustering approach and an appropriate adaptive resource evaluation and allocation. Evaluation on smartphone shows QoE and battery life improvements of 9% and 35 %, respectively, over state-of-the-art

Dataset supporting the doctoral thesis `Maximising user experience through intelligent resource management in mobile systems&#39;

This dataset contains: Dataset_Chapter3.zip&#39;: Data supporting all the figures and tables in Chapter 3 of the thesis: Adaptive DVFS management for maximising user experience. Dataset_Chapter4.zip&#39;: Data supporting all the figures in Chapter 4 of the thesis: Self-Adaptive Brightness Scaling for Maximising User Experience. Dataset_Chapter5.zip&#39;: Data supporting all the figures in Chapter 5 of the thesis: Adaptive DVFS and Display Brightness Scaling for Maximising User Experience.</span

Dataset supporting the article &#39;Maximising mobile user experience through self-adaptive content- and ambient-aware display brightness scaling&#39;

This dataset supports the publication &#39;Maximising Mobile User Experience through Self-Adaptive Content- and Ambient-aware Display Brightness Scaling&#39; to be published in the Journal of Systems Architecture.</span

Maximising mobile user experience through self-adaptive content- and ambient-aware display brightness scaling

Display subsystems have become the predominant user interface on mobile devices, serving as both input and output interfaces. For a better quality of user experience (QoE), the display subsystem is expected to provide appropriate resolution and brightness despite its impact on battery life. Existing display brightness approaches either consider content- and ambient-light in isolation or do not account for the user’s expected battery life, thereby failing to maximise the QoE. This paper proposes aCADS, a self-Adaptive Content- and Ambient-aware Display brightness Scaling in mobile devices that maximises QoE while meeting battery life expectations. The approach employs a content- and ambient lighting-aware profiler that learns and classifies each sample into predefined clusters at runtime by leveraging insights on user perceptions of content and ambient luminances variations. We maximise QoE through adaptive scaling of the display’s brightness using an energy model that determines appropriate brightness levels while meeting expected battery life. The evaluation on a commercial smartphone shows that aCADS improves QoE by up to 32.5 % compared to state-of-the-art