This paper signifies the importance of replacing current Ordinary
Portland Cement (OPC) manufacturing processing with low carbon emission geopolymer based cements in construction industry and addressing the challenges for the supply chain in Australia.
Transforming Oil Palm Empty Fruit Bunches (OPEFBs) into Sustainable Ceramic Membranes for Microbial Fuel Cells
integration of OPEFBs for ceramic membrane in MFC to produce electricity
Development of an IoT-Enabled Biogas Digester for Optimizing Anaerobic Digestion and Methane Production
Integration of IoT to anaerobic digestion to measure operational performance
Innovative Biofilter Design with OPEFB-Activated Carbon for Sustainable Tofu Wastewater Treatment
This research provides significant contribution on valorizing waste for energy production
A Channel Selection Strategy for Energy Harvesting in Cognitive Radio IoT Networks
Energy limitation and spectrum scarcity are becoming two critical issues in the design of Internet of Things networks. Two promising technologies, cognitive radio (CR) and radio frequency (RF) energy harvesting, can be jointly used to improve spectrum and energy efficiency. Thus, energy harvesting, and cognitive radio systems are becoming more inseparable for future IoT networks. This paper analyses the effect of selecting primary user (PU) channel by the secondary users on the performance of IoT networks metrics. Furthermore, we formulate an efficient channel selection strategy that is structured on multiarmed bandit (MAB) problem. The proposed channel selection scheme is based on a distributed channel selection strategy that combines reliable reputation model and multiarmed problem policies. With the proposed channel selection scheme, the SUs finds the best available PUs channels to maximize harvested RF energy. Simulation results validate the superiority of our proposed channel selection algorithm in terms of throughput and energy harvesting rate compared to Goodput based algorithms and ultra-reliability and low latency (URLL) based algorithms. that ensures that the SU’s.
Assessment of building thermal performance with roof top greenery system and bio-phase change materials in the Australian sub-tropical climate
The incorporation of extensive rooftop greenery and bio-phase change materials as building envelopes in subtropical climates has enormous potential to counteract the adverse effects of escalating energy consumption and greenhouse gas emissions. These envelopes are capable of limiting the heat gain, reducing the cooling energy, promoting thermal comfort, and shifting the peak load throughout the day. Its design and effectiveness may differ significantly depending on location and weather. This study investigates the effects of an extensive rooftop greenery system (RTGS) and bio-phase change materials (bio-PCMs) on the thermal performance and energy consumption of buildings in the subtropical climate of Australia. Two identical shipping containers were used as experimental buildings (replicas of small offices), one equipped with RTGS and bio-PCM, whereas the other lacked this feature, that is, a reference bare roof (BR). The experimental investigations were conducted from 3rd September to 8 October 2024. While considering a typical day within the experiment duration, the data showed a temperature difference of approximately 7 °C and humidity difference of 25% at approximately 11am of that day. It was found from the experimental results that buildings with RTGS and bio-PCM as envelopes can save approximately 26.49% of energy in a typical week in September 2024, with a maximum energy saving of up to 32% experienced on a typical day. In terms of thermal comfort, the RTGS with bio-PCM maintained stable temperature and humidity levels that were favourable to standard ideal comfort zone conditions.
Reinforcement Learning Model for Real Time Voltage Control in a Hybrid Microgrid
The research contribution emphasize the capacity of reinforcement learning to revolutionize real-time voltage control in hybrid microgrids, therefore facilitating the development of greater sustainability and efficiency in energy systems.
Leveraging ChatGPT for Sponsored Ad Detection and Keyword Extraction in YouTube Videos
This study is significant for several reasons. First, it provides a scalable and automated solution for detecting and analyzing advertisements within video content, which is typically labor-intensive when done manually. Second, it offers insights into the relationship between advertisements and video content, which can have profound implications for advertisers seeking to improve targeting strategies and for content creators aiming to optimize sponsored ad placements within their videos. Third, the research lays the groundwork for future advancements in content-based advertising, where the alignment between ad messaging and content themes can be refined using advanced natural language processing (NLP).
Design and Simulation of Advanced Patch Antenna and Analysis using High Frequency Structural Simulator (HFSS)
Design a Patch Antenna, Simulation of an optimized Antenna using HFSS, A a good quality antenna modeled with good return loss
Comparative Analysis of Highly Efficient Alkaline Fuel Cell Electric Vehicles
This research offers a comparative study of two fuel cell electric vehicle (FCEV) designs: one is using an alkaline fuel cell (AFC), and the other is employing a proton exchange membrane fuel cell (PEMFC). The focus is to find superior efficiency of the AFC-powered FCEV over its PEMFC counterpart. The AFC system provides greater electrical efficiency, up to 70% under suitable conditions, compared to around 50% for the PEMFCs. This is because of its lower activation over potential at the cathode and its ability to use non-noble metal catalysts. Additionally, the AFCs benefit from faster electrode reactions and reduced costs due to the use of cheaper materials. The AFC powered FCEV is named as FCEV-A, and that for PEMFC is FCEV-P. In designing the FCEV, a dc/dc boost converter is used to make the fuel cell output voltage higher because its output voltage is not enough for the permanent magnet synchronous motor. An inverter assists to generate ac voltage for the motor. The setup is performed in MATLAB/ Simulink environment. A comparison of the FCEV-A and FCEV-P outputs reveals that the former exhibits better performance.