Full Research Paper – Page 10 – NPS Australia Submission System

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.

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.

Utilizing Caesium-based Vacancy-formed Materials For All-Perovskite Tandem Solar Cells: Photovoltaic Evaluation Using SCAPS 1-D

In this study, we reported the simulation study of lead-free all-perovskite tandem solar cell comprised of varied vacancy formation perovskite structure as wide bandgap absorber layer for FAMASnGeI3. The observations revealed that all-perovskite tandem solar cell of FTO/ZnO; 400 nm/Cs2AgBiBr6; 600 nm/FAMASnGeI3; 200 nm/Cu2O; 100 nm/Au achieved a notable PCE of 22.63 % at an operating temperature of 300 K with Jsc; 27.09 mA/cm2 Voc; 1.10 V and FF; 75.68 %. These findings suggests that this cell has strong potential in converting sunlight into electrical energy and indirectly will contribute to the advancement of environmentally friendly and high-performance solar cells, promoting the broader adoption of renewable energy technologies.

Impacts of Uncoordinated Electric Ferry Charging on Distribution Network

This study examines the potential effects of uncoordinated EF (electric ferry) charging on local distribution networks, focusing on Gladstone Marina in Queensland, Australia. Using OpenDSS software, power flow analysis assesses the simulated network with BESSs (Battery Energy Storage Systems) which represent proposed charging stations.

A Channel Selection Strategy for Energy Harvesting in Cognitive Radio IoT Networks

formulated an optimal channel selection strategy based on the combination of reliable reputation model and multiarmed bandit (MAB) problem to determine an optimal channel selection policy for the SU’s. With the main goal to maximize the SUs harvested RF energy from the PUs channels during transmission.

Paper Type: Full Research Paper