Paper Submission - NPS Australia Submission System

Heat transfer enhnacment in a circular tube fitted with new twisted tape insert with rings

This study investigates the impact of using new twisted tape (TT) inserts with rings on the Nusselt number (Nu) and friction factor (f) for heat exchangers, air intercoolers and for other thermofluidics applications. The analysis compares the effects of air and water as cooling fluids. Experimental setup is used to obtain experimental data needed for a Computational Fluid Dynamics (CFD) validation. Then the CFD model is used to predict the impact of using the new TT insert with rings compared with the conventional TT without rings under various conditions which can’t be attained by the experimental facilities at the lab scale. The obtained results showed that the new proposed TT insert with rings significantly enhances heat transfer efficiency while maintaining acceptable pressure drops. The findings suggest incorporating TT with rings can optimize heat transfer performance in various heating and cooling applications. The experimental results showed that adding the rings to the TT enhances the heat transfer characteristics compared with the TT without rings, especially at larger TT pitch distances. Further, the experimental results showed that the Nu values in the case of using the TT inserts with rings significantly increased by up to 25% more than smooth pipes at low air velocities with larger pitch distance and by about 43% at higher velocities with smaller pitch distance. However, this increase in heat transfer also led to a rise in the friction factor, which went up to four times higher at low velocities and larger pitch distances and up to seven times higher at greater velocities and smaller pitch distances.

ERECT: Evidence Refinement Enhanced Complex Claim Verification with Large Language Models

The contributions of this paper are as follows:
– Introduction of ERECT model: We propose a novel evidence refinement enhanced complex claim verification model, ERECT, which effectively decomposes complex claim verification into simpler program steps and get refined evidence to support the excution of simpler program steps.
– Integration of LLM for evidence refinement: Our approach leverages large language models (LLMs) to refine evidence from a large external corpus, ensuring that the most relevant evidence is selected in evidence retrieval step. This refinement significantly enhances the precision of the claim verification process.
– Evaluation of the importance of evidence: We designed ablation experiments to test the performance of the same model under different evidence type settings, quantifying the importance of evidence accuracy in the FV task.

Design and Implementation of an AI-enabled Online Recruitment System

The recruitment process can be challenging and time-consuming for both job seekers and recruiters. To address this issue, this paper presents the design of an online recruitment system for Sultan Qaboos University (SQU) to replace the current manual and inefficient hiring process. The new system aims to modernize and accelerate recruiting through automated
screening and ranking of candidates. Core objectives include providing a user-friendly website for candidates and recruiters, seamlessly integrating artificial intelligence for qualification matching, and generating rated candidate shortlists to aid selection.

Heuristic Optimization-based Fuzzy Logic and Pitch Control of Grid-tied Wind Farms for Enhanced Wind Power Distribution

The increasing demand for renewable energy sources has driven significant advancements in solar photovoltaic (PV) technology. Stand-alone PV systems, which operate independently of the grid, are especially vital for remote areas where grid access is infeasible. This paper presents the design and implementation of a stand-alone solar PV system with battery backup, leveraging Simulink for real-time monitoring and control. The system, integrating a solar PV array and a battery storage unit connected to a constant voltage single-phase AC supply, was implemented and rigorously evaluated using MATLAB SIMULINK across seven distinct operating modes. A bidirectional DC-DC converter, functioning in buck mode for charging and boost mode for discharging, is controlled by a comprehensive Battery Management System (BMS) to optimize performance and extend battery life. Notably, the system maintained a stable DC bus voltage around 375V, with minor initial fluctuations quickly stabilized, ensuring efficient power management with an overall efficiency exceeding 90% under varying environmental conditions. The integration of multiple Maximum Power Point Tracking (MPPT) techniques further enhanced system efficiency by up to 25% during fluctuating irradiance levels. The system’s real-time response, with mode transitions occurring in under 200 milliseconds, highlights its capability for continuous and stable power delivery. The PV monitoring Dashboard feature provides real-time parameter visualization and interactive control, allowing dynamic observation of mode transitions and demonstrates the system’s capability to maintain stable operation and efficient power management under varying conditions. This study demonstrates a robust solution for stand-alone renewable energy applications, ensuring efficient energy management and prolonged battery life.

Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content

The rapid evolution of the gaming industry, driven
by technological advancements and a burgeoning community,
necessitates a deeper understanding of user sentiments, especially
as expressed on popular social media platforms like YouTube.
This study presents a sentiment analysis on video games based
on YouTube comments, aiming to understand user sentiments
within the gaming community. Utilizing YouTube API, comments
related to various video games were collected and analyzed
using the TextBlob sentiment analysis tool. The pre-processed
data underwent classification using machine learning algorithms,
including Na¨ıve Bayes, Logistic Regression, and Support Vector
Machine (SVM). Among these, SVM demonstrated superior
performance, achieving the highest classification accuracy across
different datasets. The analysis spanned multiple popular gaming
videos, revealing trends and insights into user preferences and
critiques. The findings underscore the importance of advanced
sentiment analysis in capturing the nuanced emotions expressed
in user comments, providing valuable feedback for game developers to enhance game design and user experience. Future research
will focus on integrating more sophisticated natural language
processing techniques and exploring additional data sources to
further refine sentiment analysis in the gaming domain.

Impact of Microstrain and Dislocation Density on the Quality and Properties of MAPbI3 Perovskite Films

The study aims to improve the quality of MAPbI3-based perovskite films by varying the MAI precursor concentration ratios using a sequential deposition method. The effect of microstrain and dislocation density on the film quality of MAPbI3 perovskite is investigated for various MAI precursor concentrations. However, the perovskite layer was prepared using the spin coating technique to achieve better structural properties. The main challenge is determining the optimal MAI precursor concentration ratio, which influences the final quality of the perovskite films. XRD measurements show that the crystal quality of the perovskite is improved by achieving the lowest microstrain and dislocation density. SEM results show that the perovskite material has relatively larger crystal grains, uniform surface coverage, and fewer pinholes.

Intelligent Arduino based and analogue cocoa weighing combined scale system

Falsification of analogue weighing scales has gradually decreased cocoa production in Ghana. These falsifications are due to adjustments made to the currently used analogue scale system.
The study therefore proposes an intelligent Arduino based and analogue cocoa weighing combined scale system to replace the existing analogue weighing scale approach.
This design is highly recommended for cocoa buying companies to keep their staff in check and help increase cocoa production in Ghana.

Deployment of AI Models and a TDA Mapper Algorithm to Enhance Clinical Decision-Making

This research presents a novel Clinical Decision Support System (CDSS) that integrates advanced AI technologies, including a hybrid model for COVID-19 diagnosis and a custom GPT Assistant, into a scalable, real-world tool for resource-limited healthcare settings. The CDSS enhances the quality of care by providing accurate, timely diagnostics and decision support, while also improving response efficiency through real-time monitoring and predictive analytics. The system’s adaptability, supported by open-source platforms like R Shiny, and its potential for wide adoption, particularly after a planned pilot and impact evaluation, highlight its significance in advancing healthcare delivery where resources are scarce. The deployment of this AI-driven CDSS has the potential to transform clinical decision-making, improve healthcare delivery, and enhance the overall response to public health emergencies. The structured pilot and impact evaluation will provide critical insights into the system’s effectiveness in real-world settings, paving the way for broader adoption and sustained improvements in healthcare quality and efficiency.

Topological Machine Learning: Integrating Topological Data Analysis with Machine Learning to Enhance Breast Cancer Classification

Novel TML Approach: Introduces Topological Machine Learning (TML), integrating Topological Data Analysis (TDA) with Machine Learning (ML) to enhance breast cancer classification.

Improved Accuracy: Demonstrates that TML significantly improves classification accuracy on the Wisconsin Breast Cancer (WBCD) dataset compared to traditional methods and standalone ML techniques.

Feature Evaluation: Provides a detailed assessment of topological features such as cluster means, node features, and link features, highlighting their impact on model performance.

Practical Insights: Offers valuable insights into the integration of topological features for better diagnostic accuracy, with implications for improving breast cancer classification and patient outcomes.

Determining the Colliding Vehicle in Traffic Accidents Using Hybrid Machine Learning Models

In a world rife with vehicular accidents and traffic incidents, it is known that drivers are more likely than not to shift the blame in an accident rather than admit it. Other than that, there is a noticeable lack of models in the academic sector that allow neural networks to differentiate colliding vehicles from one another and are instead fixated on tracking and detecting traffic accidents as a whole. As such, the researchers propose a way of detecting colliding vehicles and classifying both vehicles as either the ‘colliding’ vehicle or the ‘collided’ vehicle. The processes in this machine learning pipeline are split into three main parts: crash detection—to which the model would use a crash detection algorithm; footage tracking—of which the model would utilise DeepSORT; and lastly a colliding vehicle classification algorithm that uses Gated Recurrent Units (GRUs), all of which will be combined to form a novel machine learning pipeline. The model exhibits very mixed performances when detecting both Vehicle 1 and Vehicle 2 in our testing phase. When detecting Vehicle 1, the model provides a very poor recall and F1-score, meanwhile the detection of Vehicle 2 exhibits a decent amount of precision, recall, and F1-score. Overall, the model provides an accuracy of 42% with a macro average precision of 0.45, a macro average recall of 0.29, and a macro F1-score of about 0.30.

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