Paper Submission - NPS Australia Submission System

Effect of nanofluids on performance of a flat plate solar collector

The increasing world population has increased the demand for electricity and energy. This is putting pressure on the already depleting fossil fuel resources to keep up with the demand and that is why identifying alternative ways of producing energy, especially renewable energies, is critical moving forward into the future to produce the energy demand as well as tackle some of United Nations’ Sustainable Development Goals. This study aims to investigate the performance enhancement of various nanofluids on a flat plate solar collector via an experimental study using a flat plate solar collector test rig. Nanofluids, namely water-copper oxide, water-aluminium oxide, radiator coolant-copper oxide, and radiator coolant-aluminium oxide were prepared at a 0.1% nanoparticle volume concentration through magnetic stirring with the addition of 15% concentration of the Triton-X surfactant. All four nanofluids along with water and radiator coolant were investigated at 0.5 and 0.75 LMP flow rates. The data obtained were used for numerical calculation using MS Excel to calculate the thermal efficiency of the flat plate solar collector. The findings are that the water-aluminium oxide had the maximum energy efficiency at 54.7% and 53.7% at 0.5 and 0.75 LMP flow rates respectively. Overall, the higher flow rate returned a higher efficiency.

Hydrogen Economy: A Review on the Current Applications, Policy and Production Outlook

Hydrogen, an energy carrier, is deemed as a prospective substitute for fossil fuels. Data from different articles, published documents show that the consumption of hydrogen is increasing globally, and it has increased 23% in 2020 than 2015. Different sectors such as transport and power are expected to switch to greener and cleaner energy, such as hydrogen, because it produces zero or near zero emission. To achieve net zero goal by 2050, around 530Mt of hydrogen is needed and it has been forecasted that sectors such as transport and power will dominate the consumption of hydrogen in future. Many countries are adopting or enacting policy to incorporate hydrogen into the energy sector as a substitute of fossil fuel to curb emission. This paper briefly reviewed global hydrogen production scenario, and its applications and policy in different countries and continents/sub-continents. Literatures suggest that the electrolysis method of hydrogen production is above other techniques in terms of technology and commercial readiness level. Many countries have significantly invested on research and infrastructure development to incorporate hydrogen in their energy sector. The paper will provide an in depth understanding of the global hydrogen production scenario, and its application and formulated policy in different countries.

Gesture-Based Language: Transforming Sign Language to Readable Text

For the deaf and hard-of-hearing, sign language
is an essential mode of communication. To
some, it might be a novel way to express oneself
without using words at all. But there’s a little
thing called the language barrier that gets in the way all too often. To help address this issue, this study creates a gesture-based system to convert sign language to readable text, which can help the users a little.

A Novel Hybrid CNN-RNN Architecture for Emotion Recognition from Speech

Understanding and interpreting human emotions is crucial in Human-Computer Interaction (HCI), and Speech Emotion Recognition (SER) is central to this effort. Traditional methods have been used in SER for years, but recent advances in Deep Learning (DL) offer superior results. In this regard, this research introduces a novel hybrid architecture combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to enhance SER accuracy. The model is trained on a diverse dataset from four sources, covering seven emotional categories, and achieves an impressive testing accuracy of 93.40%. The study demonstrates that the proposed model consistently performs well across different emotion classes, with accuracies ranging from 88% to 99%. Notably, the model excels in recognizing “Female surprise” with a 99% accuracy, while “Male disgust” has the lowest accuracy at 88%. These results highlight the model’s robustness and ability to generalize across various emotions and demographic groups. This research not only sets a new benchmark in SER but also advances the development of emotionally intelligent systems, with applications in interactive voice response systems, mental health monitoring, and personalized digital assistants.

Thermal Performance of Microchannels Heat Sink with Fins on side walls

The main objective of this study is to investigate the thermal performance of straight microchannel heat sinks with fins on sidewalls. A mathematical model is developed and used to carry out the simulation-based study to examine the performance of the microchannel heat sink. From the CFD study, numerical results are obtained for different operational and geometrical conditions. The study shows that using a straight microchannel with pin fins on side walls is better at lower ranges of Reynolds number lower than 400; however, at Reynolds number higher than 400 the straight microchannel with smooth sidewalls shows better performance. Moreover, at a higher Reynolds number than 800, the pressure drop increases significantly. Furthermore, increasing the size of pin fins for both triangle and square fins enhances thermal resistance but also leads to higher pressure drop.

Modeling of the North Atlantic Gyre’s Meridional Overturning Circulation with Neural Nets

This paper analyzes CNNs and spatiotemporal transformers to predict future oceanic circulation patterns and examine meridional circulation in the North Atlantic Gyre, aiming to improve existing models and provide a new tool for analysis.

Proposal for a Genetic Algorithm-Based Approach to Optimize Light Spectrum in Vertical Farming

This study introduces a novel Genetic Algorithm (GA) designed to optimize artificial lighting in vertical farms to enhance Light Use Efficiency (LUE). The proposed GA seeks to identify the optimal spectral composition of Red, Green, and Blue (RGB) LEDs, aiming to maximize crop productivity by evaluating characteristics such as height, width, fresh weight, and leaf count. The algorithm operates through ten stages, including initialization of a population, actuation of RGB values, fitness evaluation, and iterative processes of selection, crossover, mutation, and validation. By comparing RGB treatments with a reference cold white light treatment, the algorithm refines lighting conditions to improve crop performance at different growth stages. Detailed methodologies for fitness evaluation, crossover, mutation, and validation are provided, highlighting the practical steps for implementing this approach in vertical farming environments. This research aims to contribute to more energy-efficient and productive vertical farming practices, supporting the broader goal of sustainable agricultural development.

Advanced Energy Management for Homes: Optimized Control of PV, Battery, and EV Systems

The integration of renewable energy sources (RES) is essential for sustainable energy management systems (EMSs) in residential areas. However, the adoption of traditional EMSs remains constrained due to inefficiencies and limited adaptability to varying energy demands. This study presents a Solar PV/battery/EV-based EMS for home load that enhances energy efficiency, adaptability, and cost-effectiveness. The system converts solar energy into DC power using photovoltaic panels, which are then stored in a battery bank. An intelligent controller optimizes energy distribution by prioritizing essential loads and reducing reliance on grid power. The proposed advanced EMS model, developed using MATLAB Simulink optimization, demonstrates an energy efficiency ranging from 85% to 90%, resulting in expected energy savings of around 80% over traditional Home Energy Management System (HEMS) and improved user convenience by automating energy distribution. The developed model assumes that a standard residential load of around 10 kWh can be sustainably managed, ensuring uninterrupted power supply during peak hours and minimizing grid dependency.

A comprehensive study on comparison of Long short-term memory, Support Vector Machine, and their hybrid model performance using erratic cryptocurrency data

Prediction of cryptocurrency prices relatively
accurate remains a formidable challenge due to inherent
volatility associated with it and the absence of traditional
valuation metrics. This research explores the performance of
Long Short-Term Memory (LSTM), Support Vector Machine
(SVM), and a hybrid model of LSTM+SVM for this complex
task. LSTM has demonstrated potential in capturing short-term
price fluctuations, while the hybrid model aims to combine the
strengths of temporal dependencies of LSTM and pattern
recognition of SVM. To evaluate the models’ performance,
comprehensive evaluation framework has been employed,
considering generalization ability of the models, robustness,
computational efficiency, and interpretability. Historical daily
price data for five leading cryptocurrencies, Ethereum, Solana,
BNB, Tether, and Bitcoin was collected from 2020 to 2024.
This data was used to evaluate the model performance of
LSTM, SVM, and a hybrid model of them, using metrics such
as R-Square, Root Mean Square Error (RMSE), and Mean
Absolute Error (MAE). The findings from the study indicate
that LSTM generally outperformed both SVM and the hybrid
model in terms of these evaluation metrics. Moreover, the
hybrid model demonstrated competitive performance,
particularly when considering its statistical significance and
ability to generalize across different volatile conditions. While
SVM model has potential, it requires meticulous
hyperparameter tuning and feature engineering to reach optimal
performance. This research offers a comparative analysis of
machine learning models for cryptocurrency price forecasting,
detailing the strengths and limitations of LSTM, SVM, and
hybrid approaches. The insights provided are valuable for both
practitioners and researchers. Future studies could explore more
advanced hybrid architectures considering different algorithms,
incorporate additional data sources, and assess how varying
market conditions may affect model performance.

Personalized Federated Learning for Assessing Characteristic Client Data

data characterization in federated learning

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