Physical Sciences and Mathematics Commons^™

Economic Material For Large-Scale H2 Storage And H2-Co2 Separation, Hussein R. Abid, Alireza Keshavarz, Header Jaffer, Basim K. Nile, Stefan Iglauer

Research outputs 2022 to 2026

Hydrogen is a clean fuel that can potentially completely decarbonize the energy supply chain and mitigate global warming. Hydrogen – a highly volatile gas – however, needs to be separated from CO2 during H2 production, and also from cushion gas in H2 geo-storage projects; in addition, large-scale H2 storage is a key obstacle. We thus tested and chemically upgraded common sub-bituminous coal as a material for H2-CO2 separation and H2 storage. The coal adsorbed significant amounts of H2 and CO2 and demonstrated an excellent H2-CO2 separation efficiency if chemically modified. The work presented here thus provides fundamental data required for …

The Impact Of Environmental Policy On Renewable Energy Innovation: A Systematic Literature Review And Research Directions, Hiva Rastegar, Gabriel Eweje, Aymen Sajjad

Research outputs 2022 to 2026

Renewable energy innovations are imperative to tackle the climate change crisis. However, there is a gap in the literature regarding the effectiveness of environmental policies in promoting renewable energy innovations. To bridge this gap, we have adopted a systematic literature review process covering the period from 2005 to 2023. We identified and analysed 29 articles in our final sample. Further, we employ two levels of analysis (individual-policy and policy-mix levels) for analysing the extant research. Our findings show that fiscal incentives and emissions trading policies such as the European Union (EU) Emissions Trading System (ETS) consistently promote renewable energy innovations. …

Understanding The Impact Of Environmental Impact Assessment Research On Policy And Practice, Angus Morrison-Saunders, Annette Nykiel, Nicole Atkins

Research outputs 2022 to 2026

There is an enormous and ever-growing body of environmental impact assessment (EIA) research, much of which is grounded in practice or seeks to advance it. In this paper we show how the impact of EIA research on policy and practice might be conceptualised and how to set about evidencing it. A framework is developed through literature review to account for impact in four areas pertaining to instrumental impact, conceptual impact, capacity building and knowledge brokerage and co-production. Methods for implementing the framework include citations within policy documents along with content analysis to determine influence and interviews or surveys with policy …

Unleashing The Power Of Internet Of Things And Blockchain: A Comprehensive Analysis And Future Directions, Abderahman Rejeb, Karim Rejeb, Andrea Appolloni, Sandeep Jagtap, Mohammad Iranmanesh, Salem Alghamdi, Yaser Alhasawi, Yasanur Kayikci

Research outputs 2022 to 2026

As the fusion of the Internet of Things (IoT) and blockchain technology advances, it is increasingly shaping diverse fields. The potential of this convergence to fortify security, enhance privacy, and streamline operations has ignited considerable academic interest, resulting in an impressive body of literature. However, there is a noticeable scarcity of studies employing Latent Dirichlet Allocation (LDA) to dissect and categorize this field. This review paper endeavours to bridge this gap by meticulously analysing a dataset of 4455 journal articles drawn solely from the Scopus database, cantered around IoT and blockchain applications. Utilizing LDA, we have extracted 14 distinct topics …

Analysis Of Element Yield, Bacterial Community Structure And The Impact Of Carbon Sources For Bioleaching Rare Earth Elements From High Grade Monazite, Melissa K. Corbett, April Gifford, Nick Fimognari, Elizabeth L. J. Watkin

Research outputs 2022 to 2026

Rare earth element (REE) recovery from waste streams, mine tailings or recyclable components using bioleaching is gaining traction due to the shortage and security of REE supply as well as the environmental problems that occur from processing and refining. Four heterotrophic microbial species with known phosphate solubilizing capabilities were evaluated for their ability to leach REE from a high-grade monazite when provided with either galactose, fructose or maltose. Supplying fructose resulted in the greatest amount of REE leached from the ore due to the largest amount of organic acid produced. Gluconic acid was the dominant organic acid identified produced by …

Development Of A Regional Climate Change Model For Aedes Vigilax And Aedes Camptorhynchus (Diptera: Culicidae) In Perth, Western Australia, Kerry Staples, Peter J. Neville, Steven Richardson, Jacques Oosthuizen

Research outputs 2022 to 2026

Mosquito-borne disease is a significant public health issue and within Australia Ross River virus (RRV) is the most reported. This study combines a mechanistic model of mosquito development for two mosquito vectors; Aedes vigilax and Aedes camptorhynchus, with climate projections from three climate models for two Representative Concentration Pathways (RCPs), to examine the possible effects of climate change and sea-level rise on a temperate tidal saltmarsh habitat in Perth, Western Australia. The projections were run under no accretion and accretion scenarios using a known mosquito habitat as a case study. This improves our understanding of the possible implications of sea-level …

Enhancing Water Safety: Exploring Recent Technological Approaches For Drowning Detection, Salman Jalalifar, Andrew Belford, Eila Erfani, Amir Razmjou, Rouzbeh Abbassi, Masoud Mohseni-Dargah, Mohsen Asadnia

Research outputs 2022 to 2026

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection …

Performance Evaluation Of A Dual-Chamber Plant Microbial Fuel Cell Developed For Electricity Generation And Wastewater Treatment, Mahmood Golzarian, M. Ghiasvand, S. Shokri, M. Bahreini, Fatemeh Kazemi

Research outputs 2022 to 2026

Plant microbial fuel cells (PMFC) have attracted great scholarly attention as a renewable energy source. These cells have three main components: anode, cathode chambers, and a proton exchange membrane. In this study, a dual-chamber plant microbial fuel cell system was designed using Cyperus papyrus and Shewanella oneidensis. The effects of various factors, including the size of the electrodes, the distance between the electrodes, and the inoculation volume of Shewanella oneidensis, on the ability of electricity generation, were scrutinized. The results indicated that increasing the size area of the electrodes from 2 × 2 to 4 × 4 and 6 × …

Region-Specific Drivers Cause Low Organic Carbon Stocks And Sequestration Rates In The Saltmarsh Soils Of Southern Scandinavia, Carmen Leiva-Dueñas, Anna E. L. Graversen, Gary T. Banta, Jeppe N. Hansen, Marie L. K. Schrøter, Pere Masqué, Marianne Holmer, Dorte Krause-Jensen

Research outputs 2022 to 2026

Saltmarshes are known for their ability to act as effective sinks of organic carbon (OC) and their protection and restoration could potentially slow down the pace of global warming. However, regional estimates of saltmarsh OC storage are often missing, including for the Nordic region. To address this knowledge gap, we assessed OC storage and accumulation rates in 17 saltmarshes distributed along the Danish coasts and investigated the main drivers of soil OC storage. Danish saltmarshes store a median of 10 kg OC m−2 (interquartile range, IQR: 13.5–7.6) in the top meter and sequester 31.5 g OC m−2 yr−1 (IQR: 41.6–15.7). …

Malware Detection With Artificial Intelligence: A Systematic Literature Review, Matthew G. Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is …

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims' PDF documents to compromise their equipment. Conventional solutions and identification techniques are often insufficient and may only partially prevent PDF malware because of their versatile character and excessive dependence on a certain typical feature set. The primary goal of this work is to detect PDF malware efficiently in order to alleviate the current difficulties. To accomplish the goal, we first develop a comprehensive dataset of 15958 PDF samples taking into account the non-malevolent, malicious, and evasive behaviors of the …

Trends In Monitoring Of Australia’S Threatened Birds (1990–2020): Much Improved But Still Inadequate, Simon J. Verdon, Robert A. Davis, Ayesha Tulloch, Sarah M. Legge, David M. Watson, John C. Z. Woinarski, G. Barry Baker, Joris Driessen, Hayley M. Geyle, Hugh Possingham, Stephen T. Garnett

Research outputs 2022 to 2026

Monitoring is vital to conservation, enabling conservation scientists to detect population declines, identify threats and measure the effectiveness of interventions. However, not all threatened taxa are monitored, monitoring quality is variable, and the various components of monitoring are likely to differ in their rates of improvement over time. We assessed the presence of monitoring and monitoring quality, using a range of metrics, for all Australia’s threatened bird taxa from 1990 to 2020 (four assessments spanning 30 years). We used our assessments to understand decadal trends in the number of taxa monitored; monitoring quality; and the groups that conduct monitoring. The …

Opportunities And Challenges Posed By Disruptive And Converging Information Technologies For Australia's Future Defence Capabilities: A Horizon Scan, Pi-Shen Seet, Anton Klarin, Janice Jones, Mike Johnstone, Helen Cripps, Jalleh Sharafizad, Violetta Wilk, David Suter, Tony Marceddo

Research outputs 2022 to 2026

Introduction: The research project's objective was to conduct a comprehensive horizon scan of Network Centric Warfare (NCW) technologies—specifically, Cyber, IoT/IoBT, AI, and Autonomous Systems. Recognised as pivotal force multipliers, these technologies are critical to reshaping the mission, design, structure, and operations of the Australian Defence Force (ADF), aligning with the Department of Defence (Defence)’s offset strategies and ensuring technological advantage, especially in the Indo-Pacific's competitive landscape.

Research process: Employing a two-pronged research approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding …

Consequences Of Group-Based Misperceptions Of Climate Concern For Efficacy And Action, Zoe Leviston, Tanvi Nangrani, Samantha K. Stanley, Iain Walker

Research outputs 2022 to 2026

People tend to underestimate others’ environmental values, including when judging the values of minority-status groups. Using a large national sample (N = 5110), we test whether these misperceptions extend to concern about climate change in Australia, and differ depending on immigrant status, ethnicity, and where one is located (i.e., in or outside capital cities). We also examine the consequences of misperceptions for self-efficacy and pro-environmental behaviour. We find personal climate concern is high, but perceptions of others’ concern is lower. Immigrants and Australian-born participants have similarly high concern, but both groups underestimate how concerned immigrants are. Southern-Central-Asian identifiers are the …

A Systematic Review Of K-12 Cybersecurity Education Around The World, Ahmed Ibrahim, Marnie Mckee, Leslie F. Sikos, Nicola F. Johnson

Research outputs 2022 to 2026

This paper presents a systematic review of K-12 cybersecurity education literature from around the world. 24 academic papers dated from 2013-2023 were eligible for inclusion in the literature established within the research protocol. An additional 19 gray literature sources comprised the total. A range of recurring common topics deemed as aspects of cybersecurity behavior or practice were identified. A variety of cybersecurity competencies and skills are needed for K-12 students to apply their knowledge. As may be expected to be the case with interdisciplinary fields, studies are inherently unclear in the use of their terminology, and this is compounded in …

A Technical Perspective On Integrating Artificial Intelligence To Solid-State Welding, Sambath Yaknesh, Natarajan Rajamurugu, Prakash K. Babu, Saravanakumar Subramaniyan, Sher A. Khan, C. Ahamed Saleel, Mohammad Nur-E-Alam, Manzoore E. M. Soudagar

Research outputs 2022 to 2026

The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes …

Cyberbullying Text Identification: A Deep Learning And Transformer-Based Language Modeling Approach, Khalid Saifullah, Muhammad Ibrahim Khan, Suhaima Jamal, Iqbal H. Sarker

Research outputs 2022 to 2026

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The …

Towards Blockchain-Based Secure Bgp Routing, Challenges And Future Research Directions, Qiong Yang, Li Ma, Shanshan Tu, Sami Ullah, Muhammad Waqas, Hisham Alasmary

Research outputs 2022 to 2026

Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and traceability advantages of blockchain offer …

Experimental Investigation On Hydrogen-Rich Syngas Production Via Gasification Of Common Wood Pellet In Bangladesh: Optimization, Mathematical Modeling, And Techno-Econo-Environmental Feasibility Studies, Md Sanowar Hossain, Mujahidul Islam Riad, Showmitro Bhowmik, Barun K. Das

Research outputs 2022 to 2026

Since hydrogen produces no emissions, there is increasing interest in its production throughout the world as the need for clean and sustainable energy grows. Bangladesh has an abundance of biomass, particularly wood pellets, which presents a huge opportunity for gasification to produce hydrogen. Gasification of mahogany (Swietenia mahagoni-SM) and mango (Mangifera indica-MI) wood is performed in a downdraft gasifier to evaluate the impact of particle size, equivalence ratio, and temperature on hydrogen gas composition and gasifier performance. Under the optimal conditions determined by central composite design-response surface methodology (CCD-RSM) optimization, gasification of SM and MI wood can greatly increase hydrogen …

Gans And Synthetic Financial Data: Calculating Var*, David E. Allen, Leonard Mushunje, Shelton Peiris

Research outputs 2022 to 2026

Generative Adversarial Neural nets (GANs) are a new branch of machine learning techniques. A GAN learns to generate new data from the training data set. We examine the characteristics of the fake financial data using GANs trained on samples of daily S&P 500 and FTSE 100 index values. GANs feature two competing neural networks in a game theoretic context. The Generator net generates pseudo data that is presented to the discriminator net which then attempts to distinguish between the real and the fake data. This facilitates unsupervised learning on the dataset. The generative network generates data sets, while the discriminative …

Secrecy Rate Maximization For Active Reconfigurable Intelligent Surface Assisted Mimo Systems, Bin Gao, Jingru Zhao, Shihao Yan, Shaozhang Xiao

Research outputs 2022 to 2026

Reconfigurable intelligent surface (RIS) is a promising technology for future 6G communications and has been used to enhance secrecy performance. However, the performance improvement is restricted by the 'double fading' effect of the reflection channel link. To address this issue, we introduce an active RIS design, where the reflecting elements of RIS not only adjust the phase shift but also amplify the reflected signal through the amplifier integrated into its elements. To obtain a satisfactory solution to the non-convex problem resulting from this design, the penalty dual decomposition based alternating gradient projection (PDDAPG) method is proposed. We compare the proposed …

Detecting And Recovering From Player Preference Shifts In A Player Modeled Experience Management Environment, Anton Vinogradov

Theses and Dissertations--Computer Science

An important challenge in game design is understanding and maintaining player engagement. This is particularly crucial in both entertainment and educational games, where the player's commitment to the game directly impacts their experience and learning outcomes. However, quantifying engagement proves challenging due to the diverse interests of players. This dilemma is addressed through adaptive game design techniques where the game world is personalized to suit player preferences. In computer games, this personalization is facilitated through Experience Management and Player Modeling, where an intelligent agent gathers information on the player, including their preferences and actions, and takes actions to modify the …

Advancing Emotional Health Assessments: A Hybrid Deep Learning Approach Using Physiological Signals For Robust Emotion Recognition, Amna Waheed Awan, Imran Taj, Shehzad Khalid, Syed Muhammad Usman, Ali Shariq Imran, Muhammad Usman Akram

All Works

Emotional health significantly impacts physical and psychological well-being, with emotional imbalances and cognitive disorders leading to various health issues. Timely diagnosis of mental illnesses is crucial for preventing severe disorders and enhancing medical care quality. Physiological signals, such as Electrocardiograms (ECG) and Electroencephalograms (EEG), which reflect cardiac and neuronal activities, are reliable for emotion recognition as they are less susceptible to manipulation than physical signals. Galvanic Skin Response (GSR) is also closely linked to emotional states. Researchers have developed various methods for classifying signals to detect emotions. However, these signals are susceptible to noise and are inherently non-stationary, meaning they …

GröBner Bases With An Application To Tame Functions, Jessica D. Marconi

UNF Graduate Theses and Dissertations

Grobner bases are essential tools in algebraic geometry, used to simplify and solve systems of polynomial equations. These bases revolutionized computational methods in various branches of mathematics after being introduced in 1965 by Bruno Buchberger. This thesis explores the foundational concepts of Grobner bases, including their formation and properties. It also demonstrates their use in solving mathematical problems in algebraic geometry, including the ideal membership problem. As an application, we show how Grobner bases can be used to determine whether a polynomial mapping is tame. This concept is crucial for analyzing the topology near singular points and establishing whether a …

Mixed Criticality Multicore Compositional Framework, Amjad Ali, Shahid Iqbal, Imran Taj, Muhammad Fayaz, Asad Masood Khattak, Bashir Hayat

All Works

In the field of real-time systems, component-based models for mixed criticality systems (MCS) on multicore platform are gaining significant attention of the researchers from the recent past. The concept of mixed-criticality is firstly applied on single core systems but after gaining attention, it evolved to multicore platform. Initially, the tasks of MCS executes in low mode however if a task having high criticality does not its scheduling requirements complete its low mode, then the system switch to high mode. In high mode, only high critical tasks executes its high mode scheduling requirements, while all low tasks are discarded from further …

Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed

All Works

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. …

Exploring The Impact Of Conceptual Bottlenecks On Adversarial Robustness Of Deep Neural Networks, Bader Rasheed, Mohamed Abdelhamid, Adil Khan, Igor Menezes, Asad Masood Khatak

All Works

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise enhanced interpretability. This study delves into the robustness of Concept Bottleneck Models (CBMs) against adversarial attacks, comparing their original and adversarial performance with standard Convolutional Neural Networks (CNNs). The premise is that CBMs prioritize conceptual integrity and data compression, enabling them to maintain high performance under adversarial conditions by filtering out non-essential variations in input data. Our extensive evaluations across different datasets and adversarial attacks confirm that CBMs …

A Reputation-Based Aodv Protocol For Blackhole And Malfunction Nodes Detection And Avoidance, Qussai M. Yaseen, Monther Aldwairi, Ahmad Manasrah

All Works

Enhancing the security of Wireless Sensor Networks (WSNs) improves the usability of their applications. Therefore, finding solutions to various attacks, such as the blackhole attack, is crucial for the success of WSN applications. This paper proposes an enhanced version of the AODV (Ad Hoc On-Demand Distance Vector) protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs, thereby avoiding them when delivering packets. The proposed version employs a network-based reputation system to select the best and most secure path to a destination. To achieve this goal, the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast …

Designing A Haptic Boot For Space With Prompt Engineering: Process, Insights, And Implications, Mohammad Amin Kuhail, Jose Berengueres, Fatma Taher, Sana Khan, Ansah Siddiqui

All Works

The existing literature has highlighted the potential of Artificial Intelligence (AI) tools in enhancing ideation and optimizing functionality across various engineering disciplines. However, a comprehensive understanding of the impact of AI on the engineering design process, particularly in creating innovative and efficient designs, is currently lacking. This research specifically investigates the integration of AI in developing space-haptic boots by utilizing haptic technology for immersive virtual interactions. The study analyzes the role of an AI tool, ChatGPT-3.5, in the design process, starting from requirement gathering to prototyping and testing, to assess the effectiveness and challenges of AI in engineering design. We …

Saas Application Maturity Assessment Model, Saiqa Aleem, Rabia Batool, Shayma Alkobaisi, Faheem Ahmed, Asad Masood Khattak

All Works

Software-as-a-service (SaaS), as a software delivery model, has received substantial attention from software providers and users alike. In recent years, it has become one of the most promising service delivery models in cloud computing. Many existing companies are transferring their business into the SaaS delivery model. Network vendors also migrate to a SaaS business model by offering on-demand remote IT support. This increasingly competitive landscape and the variety in markets have imposed many challenges for SaaS developers and vendors and made it difficult to find a consensus on the factors contributing to the positive performance of SaaS businesses. This paper …