NPS Australia Submission System
Sustainable Energy for Port Construction with Low Carbon Concrete from Industrial Symbiosis at WESTPORT Kwinana & BANTAENG Sulawesi

At Bantaeng in South Sulawesi and Kwinana in
Western Australia new industrial scale ports will be built to serve
the industrial precincts at these locations. At both these sites a 1-
2Mtpa GPC plant is proposed for precast production of some 1,600
port modules as well as other infrastructure requiring some 750,000
cum of concrete and thereafter the plant can be repurposed for other
products for local markets such as reef modules and wall panels.
Geopolymer concrete can be the replacement for conventional
concrete and be made from waste-derived materials while having a
lower carbon footprint. The plant is designed to be operated by
renewable energy and an energy audit estimated that a 1Mtpa
geopolymer production plant needs up to 200 GWh pa to operate.
This could be served by 6-10 on-land wind turbines combined with
solar PV farm at a total cost $45-55 million USD. The electricity
generated @ say $100/MWh was worth $12-20M pa that could
result in a payback of 2-5 years. In Kwinana, planning is already
underway for a large wind farm as part of the overall
decarbonisation strategy for this industrial precinct. Feedstock
materials can be harnessed for use in the geopolymer production
plant by means of Circularity Hubs. These hubs can be established
through the KIC4 and 6-Capitals models of Industrial Symbiosis and
to optimise the proposed geopolymer plant within the industrial
precincts at Bantaeng and Kwinana. Such an approach can
contribute to Regenerative Development when both of the ports are
built.

A Novel Approach to Verification of Neural Cyber-physical Systems

The verification of Cyber-physical System (CPS),
particularly those incorporating neural networks for safety-critical
functions remains an ongoing challenge due to the lack
of advanced verification and validation frameworks. Current
methods are often limited in their scalability and ability to
comprehensively verify system-level and component-level properties,
leading to potential vulnerabilities in these systems. This
issue becomes even more pronounced when dealing with hybrid
systems that integrate physical processes and neural network-based
controllers [2]. We propose a novel verification framework
tailored for complete CPS verification using a decomposition-based
approach to address this challenge. Our method performs
sequential-distributed verification, ensuring each component
adheres to compositional Metric Interval Temporal Logic
(MITL). By applying this framework to a model (CPS), we use
backward induction to verify that component-level and system-level
properties remain within predefined operational ranges,
derived from simulation data. Implemented in MATLAB,
this approach enhances the verification process by identifying
potential failure points across subsystems, providing a scalable
solution for verifying complex hybrid systems. This method
significantly improves verification accuracy and enables precise
identification of faulty components, making it a highly effective
tool for robust system design and analysis.

Analysis of the Modeling and Biological Consequences of the Electrical Activity of the Human Brain Subjected to 5G Electromagnetic Waves Using Maxwell’s Equations

The main objective proposed in this article is to
provide explanations that can justify the validity of the results of
the studies of the interaction between electromagnetic fields and
the human body. While putting the direct applications in the
characterization and modeling of the macroscopic electrical
properties of biological environments and evaluating the effects of
fields induced by sources of electromagnetic radiation on the
human body to establish new standards on human exposure to
electromagnetic fields. To do this, we took into account, on the one
hand, the physical laws based on the Maxwell and Kirchhoff
equations, with the different physical phenomena of propagation
of a 5G electromagnetic plane wave and on the other hand, the
experimental values that can allow us to model the electrical
behavior of the human brain under the influence of 5G
electromagnetic field the Morris-Lecar model is used because it
has the ease of assimilating brain electrical activity. This model
uses the characteristic impedance of the dielectric support and
allows us to evaluate the influence of the current induced by
microwave electromagnetic waves in the brain system studied. The
results of 2D simulations obtained from computer tools
demonstrate that 5G electromagnetic waves can cause the
modification of brain rhythm, the disruption of neuronal
communication, oxidative stress and the opening of various ion
channels that govern the functionality of the brain system. This
modification can have a very significant influence on the life of
the brain’s biological tissue since electromagnetic waves can
influence the frequency and amplitude of electromagnetic signals
in the brain and this can affect cognitive functions in the brain.

Analysis of the Modeling of the Influence of Decentralized Solar Energy PV on the Intensity of Short-circuit Currents of the Power Electric System

Several advantages are linked to the integration of
renewable decentralized production sources into electrical networks,
including the reduction of line losses, etc. However, the integration
of decentralized energies, particularly solar PV, can lead to
variations in the direction or amplitude of currents in steady state,
variations in short-circuit currents, changes in voltage, variations in
measured impedances, etc. These variations can have a negative
influence on the proper functioning of the protection plan, including
protection blinding or false tripping. This article presents a
simulation model to predict the influence of the integration of
decentralized solar PV energies on the intensity of short-circuit
currents and the short-circuit power at a node of a power electrical
system. The mathematical equations developed for modeling the
energy elements of the electrical network in which the solar PV RED
is integrated were based on Kirchhoff’s laws and on the currentvoltage characteristic of the modules. The simulation model was
validated using experimental data from a grid-connected PV system
installed in DR Congo. 2D simulations based on proposed models
were developed as well as the verification of the consistency of the
different models, by comparing the fractal dimensions of the results
of our program with those of the figures obtained experimentally.
The results obtained show that the integration of PV solar generators
into the grid has a direct impact on the short-circuit current and the
short-circuit power at the connection point. The aspects developed
in this article could have direct implications in practical applications
in the engineering and design of grid-connected PV systems.

Predict diagnose diabetes using four algorithms in machine learning

Machine learning in artificial intelligence plays a very important role in various fields of life, along with data
science and data analysis. Among these roles through which machine learning can play an important role is the
role of human health in how to predict incurable diseases, such as diabetes. When learning the machine with
real data, it becomes possible to predict highly accurate results through which preventive measures can be
taken to avoid falling into such chronic diseases, and thus many people can be prevented from contracting such
chronic diseases. Future plans can also be made to avoid the spread of the disease and appropriate plans can
be made for that. By making the right decisions. Machine learning and artificial intelligence have algorithms
with specific characteristics that have the ability to predict results in advance. An example of these algorithms
is what is known as Logistic Regression, SVC, Random Forest Classifier and Gradient Boosting
Classifier.

Detection of In-Vehicle Data Falsification: A Deep Learning-Based Approach

This study’s key contributions include:

Novel application of deep learning models (DNNs and RNNs) for in-vehicle data falsification detection.
Utilization of the new CICIoV2024 dataset, providing insights into latest IoV attack scenarios.

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.