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 forecast for sustainable critical mineral supply chain for electric vehicles manufacturing in Indonesia and Australia
This study provides insight for academics, policymakers, and industry experts on the possibility of fulfilling the required critical mineral demand for EV manufacturing in Indonesia and Australia by presenting a forecast using dynamic stock analysis for different circular economy scenarios.
Monitoring of Feedstock Materials & Smart Manufacturing Systems for Low Carbon Concrete
The enormous number of renewable energy plants to be built across Australia and Indonesia over the next 10 years will require massive volumes of readymix concrete for wind turbines and precast solar ballast blocks. While this study found there are wide job opportunities it also calls for automated manufacturing processes for the high volume and smart sensors for quality control.
Potential of seaweed in Indonesia as an alternative iodine source
The research indicates that iodine-rich seaweed consumption can tackle diseases induced by various free radicals and inflammatory agents.
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.
Green Hydrogen & Low Carbon Concrete for Circular Economy at South Sulawesi, Indonesia
At Bantaeng in South Sulawesi a new industrial scale port will be built to serve the KIBA industrial precinct where smelters produce nickel for global electric vehicle battery markets. A 1-2Mtpa low-carbon geopolymer concrete 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 thereby having a significantly 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 100-200 GWh pa to operate. This could be served by a renewable energy power station with a mix of wind turbines and solar PV farm producing green hydrogen for energy storage and electric fuel cells. In the option of PV50%+wind50%+hydrogen-storage the total cost was estimated to be $20-30M USD. If electricity is assumed $100/MWh then this is worth $10-20M USD pa and the payback is 15 years approx.
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.
Smart Farming – Using weather data to support farmers in tilling their fields most efficiently
using A.I. technology in the agricultural sector; using natural resources efficiently; sustainability
Low Carbon Concrete for Solid Gravity Energy Storage System and a Sustainable Electricity Grid
Construction of Solid Gravity Energy Storage Systems with waste-derived, low-carbon geopolymer concrete solves a major waste management problem for the growing battery minerals industry and enables a sustainable electricity grid. In addition, the SGES system can be scalable for large scale storage systems enabling large scale recycling of waste-derived materials.
Sustainable Energy for Port Construction with Low Carbon Concrete from Industrial Symbiosis at WESTPORT Kwinana & BANTAENG Sulawesi
The contribution of this paper has been to outline a vision of how a Circular Economy could be achieved that enables a new style of Regenerative Development. The limitations have been access to accurate data for energy audit of plant and thus sizing are preliminary estimates only. In future, more data needs to be collected from the various industries so that detailed Homer modelling can be undertaken for sizing the renewable energy system options and payback.
Computers, Mathematics and Engineering as Medium in Creation of Unique and Original Art
The significant contribution of this research is to bridge the gap between techniques found in computing, mathematics and engineering with conventional art techniques to create original art. This is to ensure that human-generate art is tenable in the longterm, as opposed to AI-generated art.
Study of a Hybrid Renewable Energy System to Produce Green Energy For Cooktown – a Far North Queensland
Abstract—Australia’s pledge to net zero in 2050 has seen a
dramatic increase in the use of renewable energy systems, particularly
solar photovoltaic (PV)-wind hybrids. This study aims to optimise
a solar PV-wind hybrid system to power a polymer electrolyte
membrane (PEM) electrolyser to produce green energy for Cooktown
as a case study. This study identifies the current hybrid systems within
the literature, develops a solar PV-wind hybrid system to meet load
demands and optimises the hybrid system through HOMER, reducing
Net Present Cost (NPC) and Levelised Cost of Energy (LCOE). Results
of the project identify that an NPC of AU$56.6 million and LCOE of
AU$0.55/kWh are achievable for a solar PV-wind hybrid renewable
system powering a PEM electrolyser in Cooktown, Far North
Queensland (FNQ). However, electrical output data identifies unmet
loads and capacity shortages, indicating electrical disruptions for the
PEM electrolyser regardless of implementation location. This paper
discusses economic and performance optimisation outcomes, with green
energy and fossil fuel technologies highlighted as possible solutions to
electrical shortages. The solar PV-wind hybrid is compared with its
counterparts, which identified cost differences and electrical production
ability. In conclusion, this project has developed a solar PV-wind hybrid
renewable energy system to power a PEM electrolyser. Economic
optimisation does occur through HOMER, with substantial reductions
in NPC and LCOE. However, performance issues limit the hybrid
system’s ability to meet the electrolyser’s load demands, and further
action is needed for the system to fulfil its electrical load requirements.
Title: Leveraging Web Applications for Enhanced Transportation Mobility: Integrating Taxi Booking and Volunteer Ride Services in Fiji
The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.
Enhancing Transient Stability in DFIG-Based Wind Energy Systems using Resistive Fault Current Limiters
This paper presents the transient stability enhancement of Doubly-Fed Induction Generator (DFIG)-based wind energy systems through the implementation of a Resistive Fault Current Limiter (RFCL). DFIG technology has gained popularity due to its ability to efficiently harness wind energy under variable conditions. However, its vulnerability to faults, particularly during symmetrical and asymmetrical disturbances, poses significant challenges to grid stability. This study investigates various internal and external control strategies, highlighting the limitations of conventional methods. The RFCL is proposed as an effective solution to mitigate fault impacts and improve system resilience. Simulation results demonstrate that integrating the RFCL significantly enhances transient stability, outperforming traditional fault current limiting approaches. This research contributes to optimizing DFIG performance and ensuring reliable wind energy generation.
Energy Management in Microgrids Using Energy Storage Systems to Enhance Reliability
This paper investigates energy management in smart microgrids by incorporating energy storage batteries to improve the operational cost efficiency and system reliability. The considered cost and reliability index are respectively the battery costs and the loss of load expectation (LOLE). Since operational indices depend on location and costs on battery capacity, the main challenge is determining the optimal capacity and installation location for batteries. To achieve both objectives, the functions are consolidated into a single overarching objective function. This problem is addressed through a novel optimization algorithm known as the Symbiotic Organisms Search (SOS) algorithm. Unlike other heuristic algorithms, the SOS algorithm requires no specific tuning parameters, allowing for faster convergence. To verify its efficiency, the algorithm’s results are compared with those of the widely recognized Genetic Algorithm (GA). A sodium-sulfur (NaS) battery is selected for this study due to its high power density, efficiency, and long life cycle. Renewable energy sources utilized in this study are in the form of wind turbines and photovoltaic (PV) cells. The proposed methodology is tested on the IEEE 33-bus system, with results confirming its practical feasibility.
Feasibility Study of Hybrid Energy System Towards Decarbonisation
To combat global greenhouse gas emissions and
ensure energy security the role of renewable energy is crucial.
Implementation of renewable energy-based hybrid energy
system at diesel-energy based remote places is vital for
decarbonization offering beneficial use of underutilised
resources. In this study a hybrid energy system is examined at
regional Australia including solar, hydrogen and bioenergy
using HOMER Pro software. To address the transportation
sector carbon emission, electric vehicle loads have been
included. Result shows, by including photovoltaic, battery, fuel
cell, and biodiesel generator with carbon-based generators can
reduce the system and the energy cost. Exclusion of carbon
based generators can provide zero carbon emission, however
with the highest cost indicating the need of cost reduction of
renewable components and renewable fuel as biodiesel and
hydrogen energy to avail the decarbonization benefits
Numerical investigation of performance, combustion and emission fuelling with hydrogen in a spark ignition engine.
Global greenhouse gas emissions resulting in
climate change are the concerns for researchers and
policymakers. As per compliance with Net Zero emissions by
2050, it is obligatory to explore sustainable fuels for internal
combustion engines. Aiming this target, a numerical model was
developed using GT-Suite software for hydrogen port injection
in a single-cylinder, four-stroke gasoline engine. The reason for
choosing hydrogen is that green hydrogen is the cleanest fuel and
can be produced from renewable sources. The model was
developed to provide performance, combustion, and emission
data for both hydrogen port injections. The performance data
included brake power, brake mean effective pressure, and brake
thermal efficiency, while the combustion data included incylinder pressure, rate of heat release, and in-cylinder
temperature. The hydrogen port injection performance,
combustion and emission data were compared with those of
gasoline port injection. Compared to gasoline port injection, no
significant variations in performance and combustion data were
observed with hydrogen port injection. However, Carbon
dioxide emissions were entirely removed with hydrogen p
Modelling and simulation of performance, combustion and emission of a diesel engine fueled with renewable dimethyl ether-ethanol blend
Due to price hikes, stringent emission regulations
(Net Zero emissions by 2050), and global climate change issues,
researchers are driven to explore sustainable fuels. This study
reports on 1-dimensional (1D) modelling and simulation of
performance, combustion and emission of a direct injection
diesel engine using dimethyl ether (DME)-ethanol blend as one
of the carbon-footprint compliance fuels. DME can be sourced
from sustainable feedstocks, which are considered one of the
cleanest fuels due to their inherent oxygen content in the
molecular structure. Like DME, bioethanol can also be sourced
from renewable feedstocks and is considered an oxygenated
fuel. For the 1D modelling and simulation, GT-Suite Software
was used. Using DME 50%+Ethanol 50% (henceforth termed
DME50), the modelling and simulation were performed for
performance, combustion and emission characteristics. The
different performance, combustion and emissions data were
compared with those of base diesel fuel. The results showed that
DME50 outperforms diesel fuel in terms of engine performance,
combustion, and emissions, including greenhouse gas emissions,
carbon dioxide (CO2) in this investigation.
A Poincare Map Algorithm to Determine the Optimum MPPT for Solar Photovoltaic Panels
Despite the large uptake of solar energy around the world, low efficiency of the photovoltaic (PV) panels is their main drawback. This paper presents a novel maximum power point tracking (MPPT) for PV panels based on the Poincare Map technique that aims at increasing the efficiency of the solar panels. Using this technique, not only will make the solar panels more affordable from efficiency perspective, but also the external maloperations cannot jeopardize the accuracy of the plan, as shown by the study. Moreover, the studies show that the reliability of the plan under the standard operation of the solar panel is more than 98.94%, and is over 97.19% under external disturbances.
Wavelet-ARIMA-based Forensic Analysis of Synchrophasor Data Using Machine Learning
— Integration of distributed energy resources into
power grids fosters the development of precise monitoring,
protection, and control applications by employing immense
spatiotemporal data from micro-phasor measurement units
(µPMUs). For enhanced situational awareness, a comprehensive
methodology is required for real-time synchro phasor forensic
analysis, using advanced machine learning techniques to detect
and classify anomalies in grid events. This paper presents a twostage analytical framework that combines WaveletAutoregressive Integrated Moving Average (ARIMA)-based
analysis with a machine learning approach to enhance the
identification and classification of events by leveraging
historical frequency and spectrum data. The raw data from the
New England ISO and the European Continental Split dataset
is preprocessed in the initial phase as it includes multiple events.
The process involves Stationary Wavelet Transform (SWT) for
denoising and sliding window ARIMA model to identify the
Rolling Standard Deviation (RSD) for feature extraction and
threshold setting. The frequency excursions and oscillations are
classified based on the Synchro phasor Event Detection
Algorithm (SPEDA) as per statistical thresholds. The retrieved
features of the detected and localized events are cross-validated
using machine learning classifiers in the next stage, enhancing
the overall efficiency and effectiveness of the study. The study
will demonstrate that advanced computing facilities accelerate
sophisticated calculations and reduce model training time.
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.
DACA: A Distributed Algorithm for Task Partitioning and Offloading in Mobile Edge Computing Networks Supporting Transformer
Recent studies have explored collaborative Transformer-based inference in edge computing (EC), but they often overlook the mobility of users and edge devices, leading to potential reliability issues. This paper aims to minimize inference latency in mobile edge computing (MEC) by considering heterogeneity in mobility, computation, and communication. We propose a task partitioning model utilizing the GPipe scheme for Transformer-based inference. The task partitioning and offloading problem is then formulated with constraints on computation resources and mobility, decomposed into a bin-packing problem and an integer optimization problem. To solve these subproblems, we introduce the Distributed Aggregated Competition Algorithm (DACA). Extensive simulations and testbed experiments demonstrate the high performance of our proposed algorithm in minimizing inference latency across heterogeneous mobile edge devices and networks.
Predictive Analytics for Proactive Email Security Risk Management: A Systematic Review
In recent years, email has become an important communication tool for sharing private messages to crucial business message exchanges. However, its widespread use makes it a major target for cyber-attacks, including phishing, spam, and malware. These growing threats highlight the urgent need to investigate email security risk management to protect against attacks and maintain the integrity of communication systems. The study reviews the literature on the challenges of email security, risk management, and the role of predictive analysis in combating these threats. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), thirty-five (35) relevant peer-reviewed research articles were identified in various open research databases. This systematic literature review (SLR) also includes relevant case studies. The findings reveal that the integration of machine learning (ML), natural language processing (NLP), and real-time data analytics into email security frameworks improves threat detection and mitigation. Furthermore, these models often lack adaptability across languages and cultures. Additionally, they do not integrate well with human-centric security measures. Therefore, it is important to develop culturally adaptive predictive models, sector-specific solutions for industries such as finance and healthcare and incorporate behavioural analytics to enhance email threat detection and prevention. In other words, a comprehensive approach that combines technical advances with behavioural insights is crucial to strengthening email security and maintaining the integrity of global digital communications amid evolving cyber threats.
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 optimize 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.
Low Carbon Concrete for Solid Gravity Energy Storage System and a Sustainable Electricity Grid.
Solid Gravity Energy Storage (SGES) Systems are an innovative way to store energy by using the force of gravity. These systems can use the excess energy from solar photovoltaic power systems to lift large blocks of concrete usually around mid-day and later as the sun sets and power demand is high, the blocks are released and generate gravitational energy which is converted to electricity. Colliecrete is a low-carbon, waste-derived, geopolymer concrete developed in 2021, from the Collie power plants’ flyash, by the Mudlark geopolymer lab at Murdoch University and geopolymer precursors can come from a number of waste-derived materials. Colliecrete can be used in the blocks for SGES. In Australia, most coal power plants will shut by 2030, while in Indonesia, the expectation is to achieve carbon-neutrality by 2060. There are many methods and pathways to achieve this goal with low-carbon geopolymer concrete one of them. Geopolymer precursor material is abundant with flyash available from 200 coal-fired power stations and slag from dozens of steel mills and nickel smelters. Rice husk is disposed of in millions of tonnes by farmers across the archipelago by burning and this ash can be converted to the geopolymer activator. All these make the possibility of an enormous new geopolymer concrete industry to at least partially replace the high-carbon, Portland cement industry. Geopolymer concrete blocks in the SGES system provide long-duration energy storage, assist firming the renewables and reduce carbon emissions while creating a new industry for the energy transition.
The Advent of Metal Additive Manufacturing Technologies – Alternative Options for Small Medium Enterprises
This study aims to explore the latest development made in Metal Additive
Manufacturing Technologies including alternative options which can be readily implemented by Small Medium Enterprises. This research paper also provides a brief overview on the consideration of fused filament fabrication technique based on material extrusion for metal 3D-Printing. Other indispensable processes in this technique, namely debinding and sintering processes are further discussed.
Stacking LLM Models’ Predictions for Feature Selection in Anomaly Classification
Large language models (LLMs) are increasingly being integrated into machine learning (ML) pipelines, particularly for tasks like feature selection in supervised classification. With the growing diversity of available LLMs, their predictions often complement one another, making ensembles of LLMs a promising approach for solving various ML challenges. In this paper, we propose using stacking methods to combine the predictions of multiple LLMs. The focus of the ML task is anomaly detection, specifically identifying whether an anomaly has occurred in a system and classifying its type. The ensemble’s base models are built on feature sets selected by six different LLMs. We demonstrate that stacking LLM predictions can enhance the accuracy of individual classifiers and advocate for the use of stacking as a simple yet effective method for integrating traditional classifiers with LLMs. Additionally, we assess the impact of various base classifiers and meta-classifiers on the performance of the proposed approach.
Energy trading in a decentralized blockchain based energy network
Application of new technology (Blockchain) in peer-to-peer energy sharing in smart grids.
Machine Learning-based Active Power Loss Forecasting in Distribution Systems
This paper introduces an optimal model that utilizes machine learning algorithms to predict the
active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The model incorporates the technique of Gradient Boosting Machine Regression (GBMR). This study estimates DG location, bus voltages, DG size, and active
losses without conventional power flow calculations.
The results demonstrate that the suggested estimations of power losses and DG sizing method are effective, practical, and adaptable. The accuracy of the proposed estimation methods has been validated using R-squared and mean absolute percentage error (MAPE) metrics. In the case of fixed load, the GBMR outperforms with a very low (MAPE) (0.9281%), a root mean square error (RMSE) of 1.748, and 0.999 accuracy in predicting
active power losses. When the normalized load variation (NLV), the R-squared 0.9991 value with low MAPE (1.9815%). This approach enables grid operators to effectively manage DG unit integration by providing precise estimates and forecasts of power loss. The effectiveness of the proposed strategy is validated in the IEEE 33 bus test system using MATLAB software.
Improved Energy Management for Hybrid Systems via Dual Predator Optimization
A novel hybrid renewable energy microgrid optimization algorithm supported with wind, solar, and backup diesel generators is suggested in this research work. The proposed Dual Predator Optimization (DPO) algorithm combines the Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). This algorithm integrates with a hybrid microgrid to optimize the use of renewable resources, reduce reliance on fossil fuel, and increase the cost-effectiveness by adjusting these parameters over time. The DPO is flexible and more suitable for hybrid energy management taking into consideration the exploration (exploitation) of system-level energy behaviors simultaneously in large-scale problems. The results show that the DPO is efficient in handling hybrid systems by significantly reducing electricity costs and decreasing probabilities of non-supply. It was determined that in comparison to the current GWO and WOA, the Cost of Energy (COE) of the proposed DPO algorithm is decreased to an average of 20%, while Loss of Power Supply Probability (LPSP) increases to an average of 7.5%.
Learning through Research: The Impact of Pattern Extraction on Neural Networks Architectures
Meeting today’s learners learning styles. Demonstrate how research influences learning new concepts to the level of mastering.
Strengthening Fault Tolerance of Private/Consortium Blockchain with Trusted Execution Environment
Consensus is one of the key components of Blockchain. Common public blockchains use Proof-Of-Work or Proof-Of-Stake as their consensus protocols. In contrast, private or consortium blockchains often use Raft, which is only crash fault tolerant. It means that strong trust on node holders in private or consortium blockchains is required. To relax the strong trust requirement, we propose by taking raft as foundation and modification on raft and leveraging threshold signature and trusted execution environment to improve security. We have implemented and integrated our proposed consensus algorithm with ConsenSys Quorum. Our experiment shows that our work has slight performance degradation on blockchain compared to original Raft.
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).
Machine Learning-based Active Power Loss Forecasting in Distribution Systems
This paper introduces an optimal model that utilizes machine learning algorithms to predict the
active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The model incorporates the technique of Gradient Boosting Machine Regression (GBMR). This study estimates DG location, bus voltages, DG size, and active
losses without conventional power flow calculations.
The results demonstrate that the suggested estimations of power losses and DG sizing method are effective, practical, and adaptable. The accuracy of the proposed estimation methods has been validated using R-squared and mean absolute percentage error (MAPE) metrics. In the case of fixed load, the GBMR outperforms with a very low (MAPE) (0.9281%), a root mean square error (RMSE) of 1.748, and 0.999 accuracy in predicting
active power losses. When the normalized load variation (NLV), the R-squared 0.9991 value with low MAPE (1.9815%). This approach enables grid operators to effectively manage DG unit integration by providing precise estimates and forecasts of power loss. The effectiveness of the proposed strategy is validated in the IEEE 33 bus test system using MATLAB software.
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. 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.
Leveraging Web Applications for Enhanced Transportation Mobility: Integrating Taxi Booking and Volunteer Ride Services in Fiji’s
The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.
A Reconfigurable and Efficient Architecture for Modular Polynomial Multiplier in Post-Quantum Cryptography
Quantum-resistant cryptographic algorithms have been proposed to prevent the security attacks from future Quantum Computers. The modular polynomial multiplication is the frequent and time-consuming arithmetic operation in Lattice Based Quantum-resistant Cryptography. In this paper, an efficient and reconfigurable architecture for modular polynomial multiplier is proposed in Lattice Based Post-Quantum Cryptography which can be implemented serially or parallelly depending on the application environments. The proposed modular polynomial multiplier is easily embedded in a crypto-processor to provide security services in the time of Quantum Computing.
An Interactive Learning Platform
This work helps learners engage in remote learning at their own pace whilst giving them the opportunity to also engage in in-depth assistance through the deployment of AI tools.
An Interactive Learning Platform
This work helps learners engage in remote learning at their own pace whilst giving them the opportunity to also engage in in-depth assistance through the deployment of AI tools.
An Interactive Learning Platform
This work helps learners engage in remote learning at their own pace whilst giving them the opportunity to also engage in in-depth assistance through the deployment of AI tools.
The Adoption of Internet of Things in Higher Education: Opportunities, Challenges, the Role of vision 2030 in Saudi Arabia
The research highlights the growing yet uneven adoption of IoT in Saudi universities, identifying key benefits like improved pedagogy and decision-making, while also addressing challenges such as infrastructure, financial, and cultural barriers. It provides strategic recommendations to enhance IoT integration in line with Vision 2030.
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
Quantifying the Effectiveness of Cloud and Edge Servers on Energy-Saving of Mobile Real-time Systems
Our findings provide insights into designing optimized task offloading configurations tailored to specific mobile system characteristics, balancing the benefits of cloud and edge environments.
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.
A Study on Object Detection Performance through Data Augmentation under Adverse Weather Conditions
This study compares the performance of object detection models through data augmentation with a severe weather dataset.
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.
Monitoring The Rate of Change of Vegetation Growth in Mine Rehabilitation Using Machine Learning
This study contributes to the literature on image processing, clustering, and time series forecasting in environmental monitoring. It successfully applied K-means clustering to segment HSV images, effectively tracking vegetation changes over time with high accuracy, despite challenges from lighting variations. The forecasting component, using Prophet, modelled vegetation growth in relation to mining activities through simulated scenarios, providing insights into the impact of external factors on vegetation. These findings enhance clustering techniques for image segmentation and offer a flexible method for monitoring and forecasting vegetation changes, with potential applications in ecological and environmental management.
Lung Pathology Using Artificial Intelligence Analysis Of Ultrasound Images: A Survey
The paper provides a technical survey for all AI algorithms available to be used for medical image pathology, and also provides a comparison in the performance of the different algorithms.
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.
Advanced Grid Integration of EVs with V2G Capabilities: Power Management Across Diverse Distribution Networks
This paper studies the impact of V2G technology in diverse distribution networks.
The Impact of Greenwashing on Job Applicant’s Choice of Company
The purpose of this study is to present a theoretical framework for identifying the impact of greenwashing on job applicants’ choice of company and to test its usefulness in a laboratory experiment using eye tracking. The objective of this paper is to elucidate the influence of greenwashing on decision-making processes by delineating the cognitive mechanisms underlying the evaluation of corporate information by job applicants when selecting a prospective employer. The findings of this study indicate that the presentation of environmentally-oriented information can influence the selection of prospective employers by job applicants.
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.
Image Recognition Applied to Insulator Detection and Classification for Asset Management
This paper’s significant research contribution lies in the development of an original machine learning-based approach to automate insulator detection and management for overhead transmission lines. By leveraging retrained convolutional neural networks (YOLO), the study addresses challenges such as diverse image conditions and unbalanced datasets, achieving an f1-score of 97.5%. In addition to enhance the insulator detection and classification performance, we integrate our original approach seamlessly with existing asset management systems, improving real-time decision-making and reducing reliance on manual audits. This innovation significantly streamlines the maintenance and reliability of power transmission networks.
Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Model
Predicting hospital length of stay (LoS) stands as a critical factor in shaping public health strategies. This data serves as a cornerstone for governments to discern trends, patterns, and avenues for enhancing healthcare delivery. In this study, we introduce a robust hybrid deep learning model, a combination of Multi-layer Convolutional (CNNs) deep learning, Gated Recurrent Units (GRU), and Dense neural networks, that outperforms 11 conventional and state-of-the-art Machine Learning (ML) and Deep Learning (DL) methodologies in accurately forecasting inpatient hospital stay duration. Our investigation delves into the implementation of this hybrid model, scrutinising variables like geographic indicators tied to caregiving institutions, demographic markers encompassing patient ethnicity, race, and age, as well as medical attributes such as the CCS diagnosis code, APR DRG code, illness severity metrics, and hospital stay duration. Statistical evaluations reveal the pinnacle LoS accuracy achieved by our proposed model (CNN-GRU-DNN), which averages at 89% across a 10-fold cross-validation test, surpassing LSTM, BiLSTM, GRU, and Convolutional Neural Networks (CNNs) by 19%, 18.2%, 18.6%, and 7%, respectively.
Semiconductor Manufacturing Industry: Assessment, Challenges, and Future Trends
This is a review article on the state of the semiconductor industry, challenges, and future trends.
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.
Enhancing Video Compression Efficiency for Low-Bandwidth Environments with H.265/HEVC
This paper explores the functionality of the H.265/HEVC (High Efficiency Video Coding) standard in low-bandwidth scenarios. We provide an overview of H.265’s key features and mechanisms that make it suitable for lower bandwidth environments. H.265, also known as High-Efficiency Video Coding (HEVC), is renowned for delivering superior video quality at lower bitrates. We investigate the critical features of H.265 and its application in low-bandwidth scenarios, providing insights into its efficiency, performance, and practical implementation. We present experimental results
demonstrating the performance improvements and benefits of H.265 regarding video quality and bandwidth utilisation. The paper discusses potential applications and directions for optimising video compression in constrained network conditions.
Environmental Monitoring in Industry: Leveraging AI and IoT for Sustainable Solutions
1. Environmental gases monitoring in Industry
2. Levaragimg of AI and IoT Technologies
3. Deployed IoT solutions
A Fast Multi-Threshold Image Segmentation Method Using a Bayesian Forecasting Evolutionary Algorithm
The main contributions of this paper are as follows.
1. First Application of BFEA in Image Thresholding: While the Bayesian Forecasting Evolutionary Algorithm (BFEA) was originally proposed in 2014, this paper marks the first time it has been employed in the field of image thresholding. By applying BFEA to image segmentation, we introduce a novel approach that leverages the advantages of this algorithm in handling complex optimization problems within the context of image processing.
2. Adaptation from Continuous to Discrete Optimization: In its original formulation, BFEA was primarily utilized for continuous function optimization. This paper simplifies and adapts BFEA to address discrete combinatorial optimization problems. By modifying the algorithm to suit multilevel thresholding tasks, we demonstrate its versatility and ability to solve a wide range of optimization problems beyond its initial scope.
3. Improved Solution Quality through Population Initialization: One of the key enhancements in this work is the integration of a population initialization strategy with BFEA. This strategy helps prevent the algorithm from becoming trapped in local optima, thereby increasing its robustness and ensuring a more thorough exploration of the solution space. As a result, the algorithm is able to achieve more accurate and reliable results, even in complex image segmentation tasks.
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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
Intelligent Fault Diagnosis in Smart Grids: Leveraging PMU Data with VGG-Based CNN Models
The evolution of smart grid technology necessitates sophisticated methods for fault detection to ensure system reliability and efficiency. Monitoring a complex power grid with phasor measurement units (PMUs) continuously transmitting data at high velocities. Rapidly and accurately analyzing this extensive data to detect faults presents a major challenge for grid operators. This study introduces a novel approach for fault classification in smart grids by utilizing Convolutional Neural Networks (CNNs) based on the architectures of VGG16 and VGG19. VGG is capable of rapidly classifying various grid events, such as faults, generation losses, and synchronous motor switching, with high efficiency. The system detects faults swiftly, allowing operators to minimize downtime and prevent significant damage by enabling prompt responses. The study meticulously examines the performance metrics of each model, including accuracy, precision, recall, and F1 score. Evaluations reveal that the VGG16 model outperforms VGG19, achieving an impressive accuracy of 98.75% and consistent precision, recall, and F1 scores of 0.99. In contrast, the VGG19 model attained a lower accuracy of 95.00%, with slightly diminished performance metrics. These findings highlight the efficacy of advanced deep learning techniques in improving fault detection accuracy within smart grid systems, suggesting that VGG16 offers a more reliable and accurate solution compared to VGG19.
Transient Stability Analysis of Islanded MV Microgrid under Variable Load and Fault Events
1. The authors proposed the design of an MV microgrid with traditional DG and a considerable PV plant control WECC model.
2. The proposed system has a better controller time constant, which can guarantee the effectiveness and robustness of the system.
3. The voltage profile of critical buses is improved, which results in a 1% steady-state error.
Limiting the Pollution of Batteries used in Ultra-Low Power Consumers. A Comprehensive Short Review
Detailed review of battery pollution in ultra-low power consumers
Phishing Detection Using a Convolutional Neural Network Model on Website URLs
This paper aims to contribute to the body of knowledge towards finding alternative solutions for phishing detection by developing a novel approach to convert phishing URLs to images, using text-to-image generation methods, and demonstrate the applicability of CNNs on images generated from URLs, as an effective tool to classify phishing websites.
DormGuardNet: A Lightweight Deep Learning Model for Detecting Prohibited Items in Student Dormitory Environments.
This paper investigates the critical challenge of detecting prohibited items in student dormitories, and we proposed a new deep-learning model to detect prohibited items automatically. To address the lack of an existing dataset for this task, we developed a new dataset, PISD (Prohibited Items in Student Dormitories). Our model achieved competitive performance, with the lowest GFLOPS and inference time, the highest FPS, and strong results in terms of precision, and recall highlighting its efficiency and effectiveness. This demonstrates the model’s capability to reliably detect and classify prohibited items in student dormitory environments.
Compact Voltage Doubler Rectifier for RF Energy Harvesting in Wearable Biomedical Devices
The compact voltage doubler rectifier incorporates a rectangular square wave-shaped DC filter to smooth the rectified signal. With dimensions of 33 × 13 mm, it is well-suited for wearable biomedical devices. The rectifier demonstrates high sensitivity at low input power levels, effectively balancing size and power conversion efficiency (PCE), making it ideal for WBDs where both factors are crucial. This work contributes to the advancement of efficient RF energy harvesting (RFEH) solutions for wearable biomedical applications.
An AI-Enabled Centralized Monitoring System to Predict SME Inventory Level
I. To utilize the historical data, and predict market needs in a dynamic environment to maintain inventory level. (To develop a data-driven inventory management system)
II. To observe, track, and learn about product movement by implementing an AI-powered system. (To implement an AI-powered product movement tracking system).
III. To optimize the accuracy of prediction for market demand forecasting. (To improve and refine the prediction model).
Maximum Power Penetration of Distributed Energy Resources with Optimal Sizing and Location
The motivations for incorporating renewable energy sources into power distribution networks are the diminishing availability of non-renewable energy resources, increasing demand for electricity, and the imperative for clean energy generation. It is important to improve the total capacity of distributed energy resources (DERs) that can be smoothly integrated into a specific feeder without adversely affecting voltage levels, protection mechanisms, power quality, and without requiring feeder upgrades or modifications. However, the escalating injection of DERs into the network may lead to operational challenges, including voltage fluctuations, reverse power flow, power quality issues, and thermal overloading of distribution lines, among others. This study presents an optimization technique for efficient incorporation of DERs into a distribution system. Here, a particle swarm optimization (PSO)-based algorithm is developed for the maximum penetration of DERs not for the only optimal size but also their location in the power system. We employ the Newton-Raphson load flow method to analyze power flow, considering major constraints such as overvoltage, undervoltage, and ampacity. The bus voltages were significantly improved after the penetration of three DER units in the system. The analysis is validated through MATLAB/Simulink simulation using the IEEE-33 bus distribution system as a testbed.
Analysis of Power Flow Control in Electrical Networks Considering Photovoltaic, Battery Energy Storage Systems and Electric Vehicles
Microgrids are localised power system that use local power generation to supply electricity to nearby loads. This idea has gained popularity with the development of battery energy storage system (BESS) and the emergence of renewable energy sources (RES). The integration of electric vehicles (EVs) into the grid has made it possible to integrate batteries and RES without significantly altering the system. The operation of microgrids is has discussed in this study with a special emphasis on power flow control during system disturbances and transitions. This article describes an algorithm designed to optimise the regulation of power, voltage, and frequency in a microgrid that includes EVs. The results of this study show that load control and energy distribution using simulation studies may be done effectively. This study used suggested algorithm to show stable frequency and controllable voltage dips. Additionally, this study contributes to a better knowledge of microgrid control.
Integrating Decision Matrix and Mind Mapping for Optimal Residential Battery Storage Solutions
This paper is important because it simplifies how consumers choose residential battery energy storage systems. By introducing a practical framework that combines decision tools like decision matrices and mind mapping, it helps individuals evaluate key factors such as costs, payback periods, tariffs, and energy use patterns. Applied to the Australian market, it shows how consumers can balance financial returns with operational efficiency while considering uncertainties like battery degradation and policy changes. This research enhances decision-making and promotes energy sustainability by encouraging informed adoption of residential battery systems.
A conceptual model for cloud ERP adoption in SMEs in New Zealand: A case study of a retail company
The insights of this study can assist SME decision makers in adopting cloud ERP in their organisations. Further, the integration of TOE and UTAUT frameworks in a theoretical model represents an innovative approach, making a significant contribution to the existing body of knowledge.
Eve-Teasing Detection from Video Footage using Computer Vision and Artificial Intelligence
Eve teasing, a form of public harassment and assault
on women, is a significant issue that causes severe distress,
particularly among young women and girls. The rising incidence
of eve teasing in Bangladesh has led to severe crimes such as
rape and murder, with many offenders escaping due to a lack of
evidence and effective monitoring. This paper presents a novel
approach using computer vision and machine learning methods
to detect eve teasing from video material in various situations.
Our proposed solution combines gender detection, expression
analysis, and gesture recognition to identify behaviors indicative
of eve teasing. The system integrates male-female identification,
human behavior detection, and CCTV-based monitoring or video
footage analysis to identify such critical incidents. Additionally,
the approach includes determining the participants involved in
the scenario to provide comprehensive evidence of harassment.
By enabling more accurate detection and verification of eve
teasing in real time, our method offers a promising tool to
help victims prove harassment and support law enforcement in
apprehending offenders, thereby contributing to a safer public
environment.
Privacy preserving medical image classification and steganography using deep learning architecture: A pipeline
This study addresses significant data privacy issues in telemedicine by proposing a secure method for transmitting sensitive medical information over unsecured networks. The novel pipeline combines state-of-the-art image classification with image steganography to integrate patient diagnosis and personal information into medical images, ensuring data privacy and protection from unauthorized access. The approach uses fine-tuned convolutional models for classification and an encoder-decoder architecture for steganography, safeguarding the transmission of medical data while maintaining image quality.
Securing Electric Vehicle Charging Infrastructure: Attack Identification Using Machine Learning
1. Conducted multi-class classification tasks to identify distinct attack types using various machine learning algorithms.
2. Evaluated the use of Hardware Performance Counters (HPC) and kernel events as features, both individually and in combination, to compare and analyze the performance of these algorithms.
3. Extensive experiments using ten-fold cross-validation demonstrated that Random Forest based machine learning model achieved the highest overall accuracy of 93.4%.
4. Attack classes comprising more than 20% of the samples attain nearly 100% accuracy, while classes with less than 3% samples tend to underperform.
Generation Expansion Planning Model Towards Decarbonization: Assessing the Dunkelflaute
This paper explores the required capacity of renewable and storage resources for an isolated grid in long-term planning. The study also examines the impact of Dunkelflaute events on capacity planning and demonstrates how a balanced mix of variable renewable energy can mitigate network challenges.
Unsupervised Symbolization with Adaptive Features for LoRa-based Localization and Tracking
A novel adaptive feature extraction technique is proposed in partitioning
to overcome the problems of over-tracking and under-tracking. Mean spectral kurtosis analysis is performed across several partitioning techniques to assess their symbolization effectiveness. This enables the selection of the most appropriate partitioning technique. This enhances the localization and tracking of target objects by focusing on robustness to noise and multipath effects. The proposed method learns and estimates the distance range simultaneously, thereby eliminating the need for a separate offline training phase and the storage of reference coordinates. Experimental results using LoRa highlight the proposed method’s efficacy in real-time localization, tracking, and superiority over the state-of-the-art method.
PREDICTING THE CUSTOMER BEHAVIOR UTILIZING TREE BASED MACHINE LEARNING ALGORITHMS
The goal of this project is to predict customer behavior from a large real-world e-commerce dataset using tree-based machine learning modeling techniques that will employ decision tree, random forest, and gradient boosting. Each of the models will be evaluated and compared to determine which of the three is the best model for predicting customer behavior.
PREDICTING THE CUSTOMER BEHAVIOR UTILIZING TREE BASED MACHINE LEARNING ALGORITHMS
The goal of this project is to predict customer behavior from a large real-world e-commerce dataset using tree-based machine learning modeling techniques that will employ decision tree, random forest, and gradient boosting. Each of the models will be evaluated and compared to determine which of the three is the best model for predicting customer behavior.
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.