Digital Commons Network^™

Optimization Of Indigo Dyeing On Modal Fabrics Using Thiourea Dioxide As An Environmentally Friendly Reducing Agent, Monthon Nakpathom, Buppha Somboon, Nootsara Narumol, Rattanaphol Mongkholrattanasit, Pisutsaran Chitichotpanya, Nattaya Vuthiganond

Journal of Metals, Materials and Minerals

This investigation examines the potential of thiourea dioxide as an environmentally reducing agent for dyeing of natural indigo on modal fabrics. Although traditional sodium dithionite is effective, its sulfur by-products generate environmental concerns. Thiourea dioxide, which provides a more environmentally friendly alternative, was studied under various dyeing conditions. In this study, six dyeing parameters, including thiourea dioxide and sodium hydroxide concentrations, reduction temperature, reduction duration, dyeing time, and reduction-oxidation cycles, were optimized to enhance color strength. Results indicated that the most favorable reduction conditions were achieved when 80 g/L of thiourea dioxide and 2 g/L of sodium hydroxide were heated …

Eco-Friendly Synthesis Of Flower-Like Hierarchical Structure Of Cuo Nps For Advancing Photocatalytic Efficiency And Antibacterial Applications, Yodchai Tangjaideborisut, Shanmugam Shanmugam, Supakorn Boonyuen, Govindasamy Siva, Prema Yugala, Joon Ching Juan, Choowin Phanawansombat, Atchariya Pitintharangkul, Seerangaraj Vasantharaj, Pornpan Pungpo, Pariya Na Nakorn

Journal of Metals, Materials and Minerals

In recent decades, environmental pollution caused by organic dyes and pathogenic microorganisms has posed significant challenges to both ecological and public health. Therefore, the development of multifunctional materials capable of simultaneously degrading dyes and exhibiting antibacterial properties is crucial for effective environmental remediation. Combining photocatalytic dye degradation with antibacterial activity offers a promising approach to address these dual concerns in a single process. The present study investigates the copper oxide nanparticles (CuNPs) were prepared by green synthetic method using bamboo extract for degradation of methylene blue and in inactivation of biological pathogens. The green synthesized CuONPs were characterized various spectroscopic …

Enhancing Efficiency And Productivity Of Microbial Fuel Cells For Optimization Of Wastewater Treatment With Bioenergy Production, Diptimayee Pradhan, Krishna Pada Bauri, Debarpita Ghosal, Satchidananda Mishra, Abhijeet Das, Aditya Raj Singh, Akhil Gedela, Kumbhakarna Mallik, Mohammed Hassan Ali Hbag

Journal of Metals, Materials and Minerals

The primary objective is to remove the pollutants from the wastewater streams and reintegrate them into the water cycle, therefore safeguarding the environment and promoting public health. The utilization of membrane fuel cells (MFCs) enables the conversion of organic molecules present in wastewater into electrical energy, while concurrently generating potable water. To optimize the process, it is essential to understand the parameters that affect the performance and the relevant techniques such as electrode materials, microbial collectives, and operating settings to improve the efficiency and performance of MFC. Thus, it aims to present novel ideas that support the real-world deployment of …

A Review On Recent Progress In Supported Magnetic Nanoparticles: From Synthetic Methods To Applications, Khushboo Dhiman, Ayashkanta Nanda, Navneet Kaur, Manvinder Kaur, Harvinder Singh Sohal, Himanshi Sharma, Meenakshi Verma

Journal of Metals, Materials and Minerals

Magnetic nanoparticles supported on various materials have recently gained significant attention due to their wide range of applications across multiple fields such as catalysis, biomedicine, environmental remediation, and magnetic separation. The support materials are essential for stabilizing magnetic nano-particles and enhancing their properties, thereby improving their performance and versatility. This review presents a comprehensive summary of recent advancements and key developments in supported magnetic nanoparticles, focusing on various support materials including silica, alumina, cellulose, charcoal, polymeric materials, and carbon nanotubes. We also discussed why we needed a shift from conventional nanoparticles to magnetic nanoparticles and from magnetic nanoparticles to supported …

Comprehensive Study On The Structural And Optical Properties Of Bismuth Manganese Oxide Composite Ceramic For Optoelectronic Applications, P. P. Nayak, B. Swain, R. R. Patro, S. S. Hota, O. P. Das, S. Bhuyan

Journal of Metals, Materials and Minerals

This work assessed the potential of bismuth manganese oxide composite ceramic for optoelectronic applications by methodically examining its structural, vibrational, and optical characteristics. X-ray diffraction (XRD) spectroscopy provided the coexistence of Bi12MnO20 (sillenite-type) and Bi2Mn4O10 (mullite-type) phases, indicating a deviation from the ideal perovskite structure and suggesting a complex crystallographic nature influencing its functional properties. Fourier transform infrared (FTIR) spectroscopy confirmed the modes of vibration of Bi–O and Mn–O bonds that were contained, supporting the structural composition. UV-Vis-NIR spectroscopy demonstrated strong optical absorption in the 300 nm to 800 nm range, affirming the suitability of the material for optoelectronic applications. …

Fem Modeling And Comparative Study Of Graphite Lubricants In The Hot Forging Of Aisi 1045 Medium Carbon Steel, Raschanan Poungprasert, Nattarawee Siripath, Naiyanut Jantepa, Surasak Suranuntchai

Journal of Metals, Materials and Minerals

This study investigates the performance of water-based and oil-based graphite lubricants in the hot forging of AISI 1045 medium carbon steel, aiming to enhance product quality while minimizing die wear. Utilizing Finite Element Method (FEM) simulations and experimental analysis, we examined key factors including flow lines, dimensional accuracy, forging load, and die wear. Results indicate that oil-based graphite lubricants significantly outperform water-based options, achieving a lower average error of 0.26% in dimensional accuracy compared to 0.99% for water-based lubricants. Additionally, oil-based lubricants resulted in reduced forming loads, leading to improved energy efficiency and longer die life. Microstructural analysis revealed that …

Effect Of Compaction Pressure On The Structure And Mechanical Properties Of Functionally Graded Aluminium-Glass Microsphere (Fgagm) Foams Produced By Powder Metallurgy Process, Vicheka Moeun, Seksak Asavavisithchai

Journal of Metals, Materials and Minerals

Functionally graded aluminium-glass microsphere (FGAGM) foams were successfully fabricated via powder metallurgy. The influence of compaction pressure on the structural and mechanical properties of the foams was systematically investigated. The study observed that increasing compaction pressure enhanced the green (pre-sintering) density of FGAGM foam, while post-sintering densities exhibited a decreasing trend with higher compaction pressures. The mechanical examination indicated that mechanical properties improved significantly with higher compaction pressures. While higher compaction pressures generally improve mechanical properties, the study identified that excessive pressures could lead to the fracture of GM particles, introducing defects that compromise the foam's structural integrity. Therefore, determining …

Structural Tuning Of Bioi Toward Bi O /Agi Heterostructures Via Room-Temperature Ion Exchange For Improved Photocatalytic Activity, Varunya Atimayulerd, Sorawit Thueanbangyang, Kittiyaporn Singsumphan, Ganyaporn Wongwaen, Cheewita Suwanchawalit, Montri Aiempanakit

Journal of Metals, Materials and Minerals

In this work, a bismuth oxide/silver iodide (Bi2O3/AgI) heterojunction photocatalyst was successfully fabricated via a room-temperature ion exchange process using bismuth oxyiodide (BiOI) as a morphology-directing precursor. Building upon our previous work on solvent-modulated BiOI nanostructures, this study employs a chemical transformation route to develop interfacially connected heterostructures while preserving key morphological features. Structural, morphological, and optical characterizations, including X-ray diffraction, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, and ultraviolet–visible diffuse reflectance spectroscopy, provided supporting evidence for the successful formation of Bi2O3/AgI heterojunctions with likely close interfacial contact. The optimized Bi2O3/AgI composite with a 50:50 mass ratio …

Optically Stimulated Luminescence Dating Of Earthenware From The Nam Tok Khao Pang Archaeological Site, Kanchanaburi, Thailand, Thananan Phetkongtong, Peerasak Klubket, Santi Pailoplee, Pichet Limsuwan, Voranuch Thongpool

Journal of Metals, Materials and Minerals

The Nam Tok Khao Pang archaeological site, located in Tha Sao subdistrict, Sai Yok district, Kanchanaburi province, Thailand, is a limestone cave where numerous artifacts, including black-burnished earthenware fragments with in-curving rims, out-curving rims, and carinated bodies, have been discovered. Mineralogical and microstructural analyses using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM) identified quartz (SiO₂) as the dominant mineral phase within a partially sintered silicate matrix. The absence of vitrification and the presence of mineral impurities indicate that the earthenware was fired at temperatures between 900°C and 1000°C. To determine the age of these ceramics, …

Facile Bio-Synthesis Of Cerium Oxide Nanoparticles, Characterization And Their Photocatalytic Potential In The Degradation Of Real Tannery Wastewater, Ganeshkumar Govindasamy, A. Babu Ponnusami

Journal of Metals, Materials and Minerals

The sustainable metal oxide photocatalysts using plant extract presents a viable alternative to conventional chemical synthesis techniques. In this study, spherical shaped cerium oxide nanoparticles were synthesized using ammonium ceric nitrate and Peepal Leaves (PL) extracted and used as a photocatalyst to remove hexavalent chromium and chemical oxygen demand from actual tannery wastewater. The characterization of cerium oxide nanoparticles was carried out using UV-Visible spectroscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, Brunauer-Emmett-Teller surface area analysis, Field Emission-Scanning Electron Microscopy with energy-dispersive X-ray spectroscopy, Raman analysis, High Resolution-Transmission Electron Microscopy and Zeta potential analysis. The band gap energy of 3.25 eV was …

Methodologies For Label Free Raman Microspectroscopic Monitoring Of Viral Replication Processes In Vitro, Iqra Chaudary, Judit Barabas, Ultan F. Power, Luke O'Neill, Hugh J. Byrne, Denise Denning

Research Outputs: 2025-Present

This study demonstrates the use of Raman Spectroscopy, integrated with multivariate statistical analysis, to monitor the process of viral infection in cells, in-vitro, using the model example of Sendai Virus (SeV) infection in LLC-MK2 monkey kidney cells. A comprehensive methodology is described for determining a precise multiplicity of infection, 48 h post infection, tailored for analysis of viral-host interactions using Raman Microspectroscopy. SeV infected LLC-MK2 cells were fixed on a gold-coated glass slide for Raman spectroscopic analysis. 30-point spectra of uninfected control and 30-point spectra of SeV-infected cells were acquired, focusing randomly on the individual cells. Mean Raman spectra of …

Dogs Emotion System, Muhammad Anas Baig

ICT

For our capstone project, we built a machine learning model that can look at pictures of dogs and figure out how they’re feeling, like if they’re happy, sad, or just chill. The idea came from how important pets are in people’s lives these days and how cool it would be to actually understand their emotions better using tech. This system will allow users to upload images of dogs, which are then analysed by a trained model to classify the dog's emotional states such as happy, sad, or neutral. We followed the CRISP-DM process to build it, which basically means we …

Optimizing Lstm Neural Network For Multimodal Multivariate Footfall Prediction., Aws Al Adhami

ICT

Accurate forecasting of pedestrian activity is important for smart city planning, retail analytics, and public service management. This study presents a forecasting framework using Long Short-Term Memory (LSTM) neural networks that integrates multiple data sources, including weather, calendar variables, and engineered temporal features. The model development follows a structured pipeline from baseline univariate models to a fully optimized multivariate model using feature engineering and hyperparameter tuning techniques such as Hyperband and Bayesian optimization. The final model achieved a Mean Absolute Error of 24.05 and explained 97.10% of the variance in unseen data, effectively capturing both regular patterns and short-term fluctuations …

Comparative Evaluation Of Ai-Generated Synthetic Data And Real-World Data Performance In Predictive Analytics, Corey Louise Hughes

ICT

There are growing restraints when it comes to Real World Data (RWD), these include topics such as privacy regulations, ethical concerns, and the cost of collecting the data, and they have drove an interest in AI-generated synthetic data as a potential alternative in predictive analytics. This project examines the possibilities of synthetic data and if it can act as a reliable substitute for RWD in predictive modelling. This project uses Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic reproductive health data and evaluates its predictive performance against RWD using linear regression and key metrics, including Mean Absolute …

A Comparative Evaluation Of The Effectiveness Of Mel Frequency Cepstral Coefficients And Difference Files For Audio Effect Identification Using Convolutional Recurrent Neural Networks., Patrick Sneyd

ICT

The field of Music information retrieval (MIR) is concerned with computational systems which help humans better make sense of the processing, searching, organizing, and accessing of music-related data and takes in disciplines such as music theory, computer science, psychology, neuroscience, library science, electrical engineering and machine learning. MIR processes relating to audio classification have applications in fields such as speech recognition, automatic bandwidth allocation and Audio Database Indexing which is especially of relevance to large audio collections in broadcasting facilities, the movie industry or music content providers.

These indexing aspects of MIR have been applied to classifying audio into musical …

Cancer Registry Newsletter Vol. 06, Cancer Registry

Cancer Registry, AKUH Archives

• Messages
• Cancer Registry Team
• Cancer Committee Members
• Cancer Registry AKUH
• Our Mission
• Cancer Report
• Age-Group Distribution of Top Five Cancers
• Total Count with Gender Breakdown
• Top Ten Males 2025
• Top Ten Females 2025
• Data Release
• Top Ten Malignancies in Children (2025)
• Distribution of Malignancies According to the Geographical Area of Patients
• Poster Presentation
• Ongoing Projects
• Karachi Cancer Registry
• Implementing a Real-Time Cancer Registry Model
• Cancer Registry Website
• Quarterly Reporting of Cancer Registry Data
• National Media Highlights Scoping Review on Cancer Registries in Pakistan
• Achievements
• Activities
• Head and Neck Cancer Awareness Session 2025
• HIMS Appreciation Lunch 2025
• Independence Day Celebration
• Department …

Customer Service Support. Utilizing Machine Learning To Classify, Prioritize And Summarize Issues., Hoai Nhan Nguyen

ICT

This capstone project investigates the application of machine learning and natural language processing (NLP) to enhance customer support operations through automated ticket classification, prioritization, and summarization. Using the multilingual Customer Support Emails dataset from Kaggle, the project follows the CRISP-DM methodology, performing extensive data cleaning, preprocessing, feature engineering, and class balancing. Five machine learning models—Decision Tree, KNN, LinearSVC, Naive Bayes, and Random Forest—were evaluated using hyperparameter tuning, cross-validation, confusion matrix analysis, and learning curves. LinearSVC demonstrated the strongest performance for both queue and priority classification, achieving accuracies of 89.8% and 81.2% respectively, with consistent generalization across folds. For summarization, extractive …

Dogs Emotion System- Poster, Muhammad Anas Baig

ICT

This project is all about a deep learning-based “Dog Emotion System” that can figure out how dogs are feeling just by looking at their faces. We used a balanced set of 4,000 dog images with four different emotion categories and followed the CRISP-DM process to build it. The model was trained from scratch using a Convolutional Neural Network (CNN) without any pre-existing models. It is deployed using Steamlit, where people can upload pictures of their dogs and get their emotional state predicted in real time. The goal of this tech is to make it easier for pet owners to understand …

Comparison Of Statistical, Machine Learning, And Deep Learning Models For Time-Series Forecasting Using Weather Data From Dublin, Ireland., Erick Eduardo Rios Treviño

ICT

This study compares three time-series forecasting paradigms—statistical, machine learning, and deep learning—using ten-years of historical weather data from Dublin. The objective is to evaluate the performance of Prophet, XGBoost, and LSTM when forecasting daily solar radiation under multiple preprocessing strategies. An extensive data analysis was conducted, including descriptive statistics, inferential testing, stationarity assessment, outlier detection, feature selection, and exploratory visualisation. These steps revealed strong annual seasonality, nonlinear feature relationships, and variability across meteorological variables, informing the modelling framework and feature-engineering decisions. Four experiments were conducted using cleaned data, differenced data, log-transformed data, and cross-validation. Results show that XGBoost achieved the …

Enhancing Insider Threat Detection Through A Hybrid Approach Using Different Artificial Intelligence Techniques., Jose Roberto Da Silva Dure

ICT

Insider threats pose significant challenges in cybersecurity due to their origin from individuals with legitimate access. Traditional defenses often fail to detect malicious behavior embedded within normal activities. This study proposes a hybrid artificial intelligence framework that integrates unsupervised anomaly detection, supervised and ensemble learning, and deep learning to enhance insider threat detection. Using the CERT 4.1 dataset, features encompassing temporal, behavioral, network, and psychometric aspects were engineered. Anomaly detection models informed supervised and ensemble classifiers, while a multi-input deep learning architecture captured sequential and contextual patterns. Performance evaluation using ROC-AUC, precision, recall, F1-score, and cost-sensitive analysis demonstrates that the …

Time Series Forecasting In Financial Markets: Benchmarking The Temporal Fusion Transformer Against N-Beats., Fergus Fleury

ICT

This study compares the performance of two deep learning architectures, the Temporal Fusion Transformer (TFT) and N-BEATS, for 10-day stock price forecasting. Both models were implemented using the Darts Python library, which ensured consistent preprocessing, training, and evaluation. The dataset, sourced from Yahoo Finance, included daily equity prices, technical indicators, a market sentiment index, and earnings announcements.

TFT was applied as a multivariate model incorporating past, future, and static covariates, while N-BEATS was trained as separate univariate models with past covariates only. A rolling forecast cross-validation approach was used for evaluation. Results show that TFT consistently outperformed N-BEATS, particularly under …

Brain Tumor Classification Using Deep Learning- Poster, Rayen Bentemessek

ICT

The project presents deep learning solutions to classify brain tumors through MRI images. Two Convolutional Neural Network (CNN) models were developed, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned.

Both models were implemented following CRISP-DM methodology from data understanding to deployment, and they were evaluated using different metrics such as accuracy, precision, recall and F1-score.

Key Highlights: •The custom CNN model achieved higher accuracy but failed to locate tumors. •ResNet50 provided a good performance while balancing explainability through Grad-CAM. •Model was deployed through Gradio to demonstrate a real-world use of the solution.

Strategic Analysis Of Employment Permit Statistics And Predictive Analytics For Workforce Planning In Ireland, Amy Souza, Thaynna Vieira

ICT

By analysing historical employment permit data from Enterprise.gov.ie (Enterprise.gov.ie, 2024), this project has the aim to use Data Analytics and Machine Learning to make predictions of employment permits trends across sectors and companies, providing insights to optimize workforce planning for Recruitment Agencies and guide international job seekers requiring work visas. The insights gained are intended to enhance strategic recruitment practices and empower job seekers to make informed career decisions in Ireland’s competitive labour market.

Predicting Repeat Purchases In E-Commerce Using Interpretable Machine Learning, Zahid Bhatti

ICT

This project investigates the prediction of repeat purchase behaviour in e-commerce using machine learning, with a focus on balancing predictive accuracy and interpretability. Large volumes of transactional and behavioural data are analysed to identify customer-level features that drive loyalty and repeat purchases. Various supervised learning models, including Random Forests and Logistic Regression, are evaluated for predictive performance, while SHAP (SHapley Additive Explanations) is employed to provide both global and local interpretability. The study aims to generate actionable insights for customer relationship management and marketing strategy, demonstrating how advanced predictive models can support informed business decisions without sacrificing transparency.

The Actuarial Applications Of Machine Learning And Big Data In The Life Assurance Industry: Managing Customer Retention And Customer Outcomes By The Application Of Data Science., Brian Cunningham

ICT

Lapses are an issue in the insurance industry in general. They affect a company’s profitability, cash flows and solvency. High levels of lapses can cause reputational damage that could provoke a cycle of even more lapses. It is therefore incumbent on a company to do its utmost to retain the business it has written for the term it was written for.

If a company could predict which of its policies were about to lapse, it could proactively attempt to prevent them by contacting the policyholder and engaging in a discussion to ascertain the likelihood of their choosing to leave. In …

Assemble The Ensemble: A Multi Model Approach For Customer Churn Prediction In The Gambling Industry., Paul Corcoran

ICT

Churn rates are remarkably high in the gambling industry, an extremely competitive landscape coupled with a severe lack of brand loyalty among its customer base makes churn prediction one of the main problems an operator will face. This paper explores the range of possible modelling solutions with a key emphasis on ensemble learning to improve on existing methods. During this exploration, a host of modelling techniques are formulated with a focus on scalability facilitated by Apache Spark distributed computing language. Thirteen variations of models, including single classifiers and ensemble families are evaluated as to their suitability in solving the problem. …

A Comparative Analysis Of Machine Learning And Neural Network Performance In House Price Prediction: Dublin Vs. Other Irish Regions, Diarmuid Carroll

ICT

This research presents a comparative analysis of machine learning and neural network performance in predicting house prices across Ireland’s regional housing markets. It addresses important methodological challenges and offers new empirical insights into how market complexity influences algorithm accuracy and performance. Drawing on 627,294 residential transactions from the Irish Property Price Register (2012–2024), the study applies a dual validation strategy, temporal and stratified sampling, across four regional classifications: Dublin, Other Cities, the Commuter Belt, and Rural areas.

The study makes three main contributions to data analytics theory and practice. First, it identifies and resolves temporal confounding effects in algorithm evaluation. …

Predictive Analytics For Customer Churns In Financial Services., Thant Thiha

ICT

This project presents a customer churn prediction analysis in the telecommunications sector, achieving an ROC-AUC of approximately 0.86 using statistically validated features and interpretable AI models. Key churn drivers identified include the number of products held, customer age, and geographic location. Ensemble models, such as Random Forest and Gradient Boosting, provided the highest predictive performance. Ethical AI principles were applied to ensure fairness, transparency, privacy, and accountability. Business insights derived from the analysis inform targeted retention strategies, prioritising multi-product users, specific age groups, and geographic segments. Deployment recommendations include the tuned Random Forest model with ongoing monitoring, governance, and future …

Predicting Customer Churn And Enhancing Retention Strategies Through Machine Learning., Swan Saung Lwin

ICT

Customer Churn is a critical challenge faced by businesses across industries, especially in the digital market. Many companies struggle to predict customer churn accurately and have difficulties in carrying out effective retention strategies. Key challenges include ineffective traditional methods, lack of insights into impact of different services, generalized retention strategies, and the need to have cost-effective retention strategies. This project aims to predict customer churn using machine learning and identify the impact of key services offered by a telecommunication company.

Customer Response To Marketing Campaigns., Alexandru Enoiu

ICT

This project applies machine learning to predict customer responses to marketing campaigns, aiming to enhance customer engagement and optimise resource allocation. Using a dataset containing demographic, lifestyle, and purchase behaviour data, a Random Forest classifier was developed to identify potential responders to marketing initiatives. The project follows a structured methodology including data exploration, preprocessing, model building, and evaluation. By accurately predicting customer behaviour, businesses can improve campaign targeting, design personalised marketing strategies, and allocate resources more efficiently. The results provide actionable insights to support customer segmentation and strategic decision-making, promoting business growth and customer satisfaction.