An increasing desire is to produce eco-friendly materials for varied engineering applications, such as natural fiber-reinforced composites (NFRCs). Although many research works on natural fiber polymer matrix composite exist, not much is known on the thermo-mechanical properties of acetic acid-treated particulate banana-sisal fiber reinforced polyester composite. Additionally, establishing the fiber constituent with a detrimental effect on thermal and mechanical properties for acetic acid-treated particulate banana-sisal reinforced polyester matrix composite is not well known. This work aims to examine the effect of banana-sisal particulate fiber on the thermal and mechanical properties of banana-sisal reinforced polyester matrix composites to address the gap. The composites were produced via the mechanical stir mix technique. Thermal, Fourier-transform infrared spectroscopy (FTIS), compressive, flexural, and impact analysis were conducted according to appropriate test standards. The results revealed that the thermal properties of the developed composites were not dependent on hybridization. Also, hybridization significantly enhanced the compressive and flexural properties, with 70B/30S and 50B/50S particulate fiber reinforced polyester matrix composite found to have the most superior compressive and flexural properties. A major contribution of this study is that the impact properties of the developed composites were dependent on the fiber composition and decreased as the sisal content percentage increased. In general, reinforced polyester matrix composite with 70B/30S particulate fiber has a preferable combination of thermal and mechanical properties.
This work investigates the effect of 3D product visualisation on online shopping behaviour. A virtual shopping interface with product categories projected in both 2D and 3D was developed and deployed. The main purpose of this system was to determine the suitability of a 3D virtual catalogue as a shopping outlet for consumers and the potential impact on consumer shopping behaviour. The virtual catalogue was implemented as a web-based interface, with products displayed with the intent of determining whether the level of presence experienced affected consumer motivations to shop. Participants completed an immersive tendency questionnaire to ascertain their alertness and levels of immersion before viewing the interface, and afterwards completed a presence questionnaire related to the viewing experience. The results showed significant correlations between individual immersive tendencies and presence experienced. In addition, items in the presence questionnaire were aligned with ease of use, interactivity and realism. This leads to a number of recommendations for the design of future virtual shopping environments and considerations for the assessment of online consumer behaviour.
Maximum power point tracking (MPPT) entails constraining photovoltaic (PV) modules to operate under a specified power condition. It has previously been shown that some meta-heuristic techniques often suffer from steady-state oscillations around maximum points and experience difficulty in adapting to environmental variations, such as irradiation and/or temperature. To address the aforementioned limitation, this work proposed an adaptable reinforcement learning (RL) technique based on a novel deep deterministic policy gradient (DDPG) agent and a reward function. The actor–network top layer uses a sigmoid activation function and the critic–network contains bottleneck layers with non-uniform nodal distributions as well as exponential linear unit (ELU) activation functions in some of the layers. The RL based on DDPG method was compared with Particle Swarm Optimization (PSO) and Perturb-and-Observe (P&O) in order to determine the optimal duty-cycle command needed for controlling the PV modules MPPT. All the investigated systems were implemented in MATLAB/Simulink. The results show that the proposed RL technique based on DDPG agent yielded superior tracking efficiency than all the other approaches. However, as the step change in irradiation at a constant temperature increases, the RL technique based on DDPG agent shows a decrease in tracking efficiency.
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