Physical Sciences and Mathematics Commons^™

Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun

Research Collection School Of Computing and Information Systems

Human pose estimation (HPE) models underperform in recognizing rare poses because they suffer from data imbalance problems (i.e., there are few image samples for rare poses) in their training datasets. From a data perspective, the most intuitive solution is to synthesize data for rare poses. Specifically, the rule-based methods apply manual manipulations (such as Cutout and GridMask) to the existing data, so the limited diversity of the data constrains the model. An alternative method is to learn the underlying data distribution via deep generative models (such as ControlNet and HumanSD) and then sample “new data” from the distribution. This works …

Cold Atmospheric Plasma Induced Degradation Of Organophosphate Pesticides On Kevlar Swatches, Ta Chun Lin, Victor Somtochukwu Mbanugo, Boluwatife Stephen Ojo, Yue-Wern Huang, Marek Locmelis, Frank Daoru Han

Biological Sciences Faculty Research & Creative Works

Cold atmospheric plasma (CAP) was evaluated for degrading organophosphate pesticide (OP) residues (acephate, malathion, and dimethoate) from Kevlar fabrics. Fourier transform-Infrared (FTIR) spectroscopy, scanning electron microscope (SEM) imaging and tensile strength testing confirmed that CAP treatment preserved Kevlar's structural integrity and mechanical properties. Results show degradation efficiency increased with higher power, shorter discharge gaps, and longer exposure duration. Hyperspectral imaging supported the detection of plasma-induced spectral changes, reinforcing the presence of reactive species. The strong oxidative potential of CAP facilitated the rapid breakdown of OPs into nontoxic byproducts. These findings demonstrate the potential of CAP as a portable, energy-efficient solution …

Metal-Free Photoredox Catalysis For The S-Trifluoromethylation Of Thiols, Raheemat Rafiu, Puspa Aryal, V. Prakash Reddy

Chemistry Faculty Research & Creative Works

The S-trifluoromethylation of thiols is accomplished using a simple and cost-effective organic catalyst (diacetyl) along with a bench-stable and environmentally friendly trifluoro methylating reagent (Langloi's reagent). Under optimized conditions (2.5 equivalents of Langlois reagent, Blue LED irradiation, 48-h reaction time at room temperature), the reaction yielded up to 81% S-trifluoro methylated product. This method's high yields, compatibility with a broad substrate scope (aliphatic, heteroaromatic, and aromatic thiols), and environmentally friendly conditions demonstrate its potential for broader synthetic applications. In contrast to traditional methods that rely on organometallic catalysts and environmentally hazardous reagents, this novel photoredox catalysis method offers a more …

Characterizing The Transient Emission Of Particles And Gases From A Single Puff Of Electronic Cigarette Smoke, Kashala Fabrice Kapiamba, Steven Achterberg, Ta Chun Lin, Philip D. Whitefield, Yue-Wern Huang, Yang Wang

Chemistry Faculty Research & Creative Works

This study employed high-time-resolution systems to examine the transient properties of aerosols and gases emitted from electronic cigarette (EC) puffs. Using a fast aerosol sizer, we measured particle size distributions (PSDs) across various EC brands (JUUL, VUSE, VOOPOO), revealing sizes ranging from 5 to 1000 nm at concentrations of 107 to 1010 cm-3. Most aerosols were found to be in the ultrafine range (below 100 nm), with JUUL-, VUSE-, and VOOPOO-producing aerosols with geometric mean sizes of 19.9, 47.3, and 29.4 nm, respectively. Applying the International Commission on Radiological Protection (ICRP) deposition model and assuming no further evolution of aerosols …

Formation Of Hydrocarbons And Carbon Oxides In Mxene Reactions With Water Under Varying Oxidative Conditions, Shuohan Huang, Guanglei Xiang, Vadym Mochalin

Chemistry Faculty Research & Creative Works

Titanium carbide / carbonitride MXenes have garnered significant attention due to their remarkable properties, versatile solution processability, and broad range of potential applications. However, when exposed to the environment, MXenes are susceptible to degradation, which ultimately leads to the formation of metal oxides, a process that may be regarded as either disadvantageous or beneficial, depending on the application of a MXene and our knowledge about the underlying mechanisms. Therefore, it is very important to understand the reactivity of MXenes in different environments and conditions. Although researchers have made efforts to understand MXene degradation in air and water, our knowledge of …

Instantaneous Hydrolysis Of Methyl Paraoxon Nerve Agent Simulant Is Catalyzed By Nontoxic Aminoguanidine Imines, Emmanuel Kingsley Darkwah, Puspa Aryal, Chi Zhang, Charles B. Musgrave, William A. Goddard, V. Prakash Reddy

Chemistry Faculty Research & Creative Works

Exposure to organophosphate-based nerve agents and pesticides poses health and security threats to civilians, soldiers, and first responders. Thus, there is a need to develop effective decontamination agents that are nonhazardous to human health. To address this, we demonstrate that instantaneous hydrolysis of methyl paraoxon (Me-POX), a nerve agent simulant, can be achieved in the presence of aminoguanidine imines at pH 10: ● the pyridine-4-aldehyde aminoguanidine-imine (1) and ● the 2,3-butanedione aminoguanidine-imine (2). The hydrolysis of Me-POX under these conditions is substantially faster than that of the state-of-the-art decontaminating agent, Dekon-139 (2,3-butanedione oxime, potassium salt). Furthermore, Dekon-139 shows adverse effects …

Synthesis And Characterization Of Free Radical Scavenging Dendrimer Nanogels Via Cross-Linking Reaction-Enabled Flash Nanoprecipitation, Lin Qi, Da Huang, Huari Kou, Anna Chernatynskaya, Nuran Ercal, Hu Yang

Chemistry Faculty Research & Creative Works

This work reports the development and evaluation of dendrimer-based nanogels based on Poly amidoamine (PAMAM) dendrimer generation 5, engineered to act as a carrier with reactive oxygen species (ROS)-scavenging capabilities. We developed a cross-linking reaction-enabled flash nanoprecipitation method in which the cross-linking reaction occurs during the flash nanoprecipitation process to form a cross-linked nanostructure. Using this approach, an N-hydroxy succinimide (NHS)-functionalized ROS-responsive thioketal cross-linker (TK-NHS) was synthesized and utilized to cross-link DAB-core PAMAM dendrimer G5, resulting in the formation of G5-TK nanogels. The resulting nanogels were characterized using dynamic light scattering and transmission electron microscopy, and their cytocompatibility, irritancy, cellular …

Pla-Based Bone Tissue Engineering Scaffolds Incorporating Hydroxyapatite And Bioactive Glass Using Digital Light Processing, Engin Gepek, Fateme Fayyazbakhsh, Lev Suliandziga, Vadym Mochalin, Osman Iyibilgin, Yue-Wern Huang, Ming C. Leu

Chemistry Faculty Research & Creative Works

Bone tissue engineering (BTE) aims to repair bone defects using biocompatible materials with tailored geometries and pore structures, providing appropriate mechanical support and control over biodegradation kinetics to promote bone growth. In this study, we utilized digital light processing (DLP) 3D printing to fabricate scaffolds with varying pore sizes using polymer–ceramic slurries composed of polylactic acid (PLA) as the main polymer matrix, incorporated with hydroxyapatite (HA) and bioactive borate glass (BBG) at various ratios. We studied the effect of composition on rheological behavior, printability, mechanical properties, bioactivity, degradation rate, and biocompatibility. While HA increased viscosity and reduced printing accuracy, it …

Unveiling Pseudocapacitance: A Kinetic Treatment Of The Pseudocapacitive Biosensor, Rokas Gerulskis, Egor Baiarashov, Maryam Karimi, Wassim El Housseini, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

A kinetic framework is introduced for a pseudocapacitive potentiometric biosensor. Mathematical derivation and kinetic modeling demonstrate that experimentally observed linearity in analyte-OCP response arises from a dynamic equilibrium between competing redox reactions on a single electrode. This system can be expanded to develop a new generation of biosensors.

Mechanical And Thermal Characterization Of Additively Manufactured Carbon/Nylon 12 And Carbon/Peek Composites, Matik Heskin, Bradley Deuser, Thomas P. Schuman, K. Chandrashekhara, John Bayldon, Jeff Degrange, Steven Patterson, Neiko Levenhagen

Chemistry Faculty Research & Creative Works

This study explores additive manufacturing of carbon fiber-reinforced thermoplastic composites using the Composite-Based Additive Manufacturing (CBAM) process. Carbon/Nylon 12 and Carbon/PEEK composites were fabricated and evaluated through mechanical (compression, tensile, flexural, and impact) and thermal (DSC and TGA) tests. Carbon/PEEK exhibited superior mechanical performance, with 97.5% higher tensile strength, 79.8% higher elastic modulus, and 59.6% higher flexural strength compared to Carbon/Nylon 12. Thermal testing showed that Carbon/PEEK had higher thermal stability, beginning degradation at 350 °C versus 298 °C for Carbon/Nylon. These results indicate that CBAM-fabricated Carbon/PEEK composites are suitable for applications requiring high strength and temperature resistance.

Electrochemically Active Molybdenum Phosphate With Open-Framework Structure, Sutapa Bhattacharya, Milad Aghayi-Anaraki, Nikolay Gerasimchuk, Steven P. Kelley, Amitava Choudhury

Chemistry Faculty Research & Creative Works

Somewhat less investigated, lithium ion containing molybdenum phosphate compounds are synthesized via simple template free hydrothermal method. Single-crystal X-ray diffraction (SCXRD) study reveals that the compounds Li_0.89Mo₂O_1.89F_0.11(PO₄)2(H₂PO₄)·xH₂O (x ≈1) and Li₃Mo₂O₂(PO₄)₃·xH₂O (x ≈1) crystallize in monoclinic C2/m and C2/c space group, respectively. The structures are built of alternate corner-sharing of MoO₆ octahedra and PO₄ tetrahedra forming layers, which are pillared by phosphate groups creating a 3D framework with channels. These …

Atomistic Mechanisms Of Ti3alc2 Etching: Oxidation, Surface Stability, And Selectivity, Valentina Nesterova, Ana Maria Stratulat, Walter Malone, Vadym Mochalin, Majid Beidaghi, Konstantin Klyukin

Chemistry Faculty Research & Creative Works

Mechanisms governing MAX-phase etching set the properties and stability of resulting MXenes, yet the details of this complex process remain poorly understood. Combining thermodynamic analysis of surface chemistries with ab initio molecular dynamics and enhanced free-energy sampling, we directly model elementary steps at the MAX/etching solution interface. We find that purely thermodynamic analysis overestimates etching selectivity, which is governed by the kinetics of elementary reaction steps. Our calculations reveal that initial exposure to water drives localized, thermodynamically favored edge oxidation that seeds MXene-like terminations into the uppermost layers of Ti₃AlC₂ with oxygen occupying the interlayer space between …

Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin

Research Collection School Of Computing and Information Systems

The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that clients submit their queries in plaintext. Secondly, these models are susceptible to being impacted by malicious client behavior, for example, enabling clients to potentially engage in arbitrage …

The Gradient Puppeteer: Adversarial Domination In Gradient Leakage Attacks Through Model Poisoning, Kunlan Xiang, Haomiao Yang, Meng Hao, Shaofeng Li, Haoxin Wang, Zikang Ding, Wenbo Jiang, Tianwei Zhang

Research Collection School Of Computing and Information Systems

In Federated Learning (FL), clients share gradients with a central server while keeping their data local. However, malicious servers could deliberately manipulate the models to reconstruct clients' data from shared gradients, posing significant privacy risks. Although such Active Gradient Leakage Attacks (AGLAs) have been widely studied, they suffer from two severe limitations: 1) coverage: no existing AGLAs can reconstruct all samples in a batch from the shared gradients; 2) stealthiness: no existing AGLAs can evade principled checks of clients. In this paper, we address these limitations with two core contributions. First, we introduce a new theoretical analysis approach, which uniformly …

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …

A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis has emerged as a prominent research domain within the realm of natural language processing, garnering increasing attention and a growing body of literature. While numerous literature reviews have examined sentiment analysis techniques, methods, topics and applications, there remains a gap in the literature concerning thematic trends and research methodologies in sentiment analysis, particularly in the context of Chinese text. This study addresses this gap by presenting a comprehensive survey dedicated to the progression of research subjects, methods and trends in sentiment analysis of Chinese text. Employing a framework that combines keyword co-occurrence analysis with a sophisticated community detection …

Llm-Driven Apt Detection For 6g Wireless Networks: A Systematic Review And Taxonomy, Muhammed Golec, Yaser Khamayseh, Suhib Bani Melhem, Abdulmalik Alwarafy

All Works

Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. This paper presents the first systematic review and taxonomy for LLM-assisted APT detection in 6G networks. It …

Streamlining Digital Elevation Model Construction From Historical Aerial Photographs: The Impact Of Reference Elevation Data On Spatial Accuracy, Xin Hong, Christopher H. Roosevelt

All Works

This study proposes a streamlined workflow for producing historical digital elevation models (hDEMs) from scanned 1950s aerial photographs using structure-from-motion and multi-view-stereo (SfM-MVS) techniques along with co-registration methods. We also conducted a sensitivity analysis to assess the impact of DEM references with varying spatial resolutions on the SfM-MVS process and co-registration accuracy. The DEM references included a 30 m SRTM DEM (low resolution), a 12 m TanDEM-X DEM (medium resolution), and a 5 m DEM provided by the General Directorate of Mapping of the Ministry of National Defense, Republic of T & uuml;rkiye (high resolution). Results indicate that higher resolution …

Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar

All Works

Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive …

Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali

All Works

This study examines the impact of two key AI modalities–freedom of choice (FoC) and social proof (SP)–on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in …

Certain Knowledge Of Administrative Decisions Issued By Artificial Intelligence Systems In The Public Sector: A Comparative Analysis, Nayel Musa Alomran, Odai Mohammad Alheilat

All Works

This paper aims to elucidate the matter of certain knowledge pertaining to administrative decisions, those issued by artificial intelligence. It also clarifies the position of the administrative judiciary with regard to the adoption of this presumption and through a comparative analysis of administrative judicial applications in Jor-dan, Egypt, and Morocco. The text addresses the most significant evidence for achieving certain knowledge of an administrative decision – and thereby initiating the appeal period against the appellant. The question at hand is whether the administrative judiciary applies the traditional theory of certain knowledge to its counterpart issued by artificial intelligence systems, especially …

Performance Based Scheduling In Distributed Mixed Criticality Systems, Amjad Ali, Saud Wasly, Asad Masood Khattak, Ihsan Ali, Shahid Iqbal, Bashir Hayat

All Works

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores …

Automated Classification Of Cardiac Anomalies Through The Analysis Of Electrocardiogram Signals Using Convolutional Neural Networks., Yhosely Isset Villegas Baldiviezo

ICT

Electrocardiography (ECG) is a widely used non-invasive method for monitoring cardiac activity and detecting heart abnormalities. However, manual interpretation can be time-consuming and prone to human error, particularly for non-specialists. This study investigates the use of deep learning techniques, specifically convolutional neural networks (CNNs), to automate ECG signal classification using a large-scale dataset. A classical machine learning model is also included as a baseline for comparison. The aim is to evaluate the potential of deep learning as a clinical decision-support tool that can assist healthcare professionals in improving diagnostic accuracy and supporting early detection of cardiac conditions.

Neural Networks Activation Functions And Hybrid Activations Functions Accuracy And Loss Comparison On Small Dataset Against Large Datasets For Classification Problems., Antonio Felipe Cora Martins

ICT

Even on the era of Big Data, small datasets are the reality of many companies and sectors. Many datasets in rare disease diagnosis, custom manufacturing, military sciences, bioengineering, and disaster events are commonly limited in size, making machine learning predictive modelling difficult. Being the Activation Function choice crucial for Neural Networks learning, it raises the question of their effectiveness in such scenarios. This study compares five standard (single) activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU) and two hybrid variants (one a mix of ReLU plus Tanh and a Learnable Activation Function with a trainable weight (alpha) that balances ReLU …

Predicting Early Hospital Readmissions For Diabetic Patients Using Machine Learning, Amanda Ferraz, Leonardo Oliveira

ICT

This project applies machine learning to predict whether diabetic patients will be readmitted to a hospital within 30 days of discharge. Early readmissions are a costly and critical issue in healthcare, often signalling gaps in post-discharge care and risk management. Diabetic patients face unfair high readmission rates compared to the general population. According to the CDC Diabetes Report Card 37.3 million people in the U.S. or 11.3% of the population had diabetes as of 2019 (CDC, 2021). Our goal here is to develop a binary classification model capable of flagging high risk patient (< 30-day readmission) based on their clinical, demographic, and administrative data. This lets healthcare institutions to take measures,

European Air Pollution And The Proposed Timelines Of Implementing The World Health Organization 2021 Air Quality Guidelines Ca3., Lukia Hartin

ICT

This research examines Ireland’s air pollution trends and evaluates whether current reductions in PM2.5, PM10, and NO₂ are sufficient to meet the WHO 2021 Air Quality Guidelines by 2040. Using four years of EPA-validated pollutant data (2020–2023), alongside Building Energy Rating (BER) and national transport datasets, the study applies the CRISP-DM methodology to guide analysis, preprocessing, modelling, and evaluation. Extensive data cleaning and alignment were required due to inconsistent station coverage, varying formats, and missing values. Forecasting models—including Random Forest, Gradient Boosting, SVR, and linear regression—were assessed using MSE, R², and trend significance to project pollutant levels across different Irish …

Analysis Of The Patterns And Features Importance In Predicting Demand For Sustainable Urban Mobility. A Comparative Analysis Of Machine Learning Models Using The Dublin Bikes Dataset., Wojciech Sochanski

ICT

This study explores the application of machine learning models to predict demand in the Dublin bike-sharing scheme. A comprehensive literature review examines prior work on sustainable urban mobility, the evolution of bike-sharing schemes, the application of machine learning in predictive analytics and relevant case studies. Secondary data were gathered from publicly available sources, pre-processed and merged into a single, unified dataset. Exploratory data analysis was conducted before preparing the data for modelling to assess the data quality, with a focus on missing values.

Four predictive machine learning models – Linear Regression (LR), Decision Tree (CART), Random Forest (RF) and Gradient …

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 …