Digital Commons Network^™

Retraction Notice: Analysis Of Energy Sector Co2 Emanations Using Wavelet-Based Numerical Technique, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: R., Yeshwanth and S., Kumbinarasaiah (2025) ``Analysis of Energy Sector CO2 Emanations Using Wavelet-Based Numerical Technique,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 4, Article 2. DOI: https://doi.org/10.52866/2788-7421.1313.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss4/2.

Retraction Notice: Ai-Driven Flood Prediction, Monitoring, And Warning Systems: Design, Evaluation, And Simulation, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Alkharabsheh, Abdel Rahman A. and Momani, Lina M. (2025) ``AI-Driven Flood Prediction, Monitoring, and Warning Systems: Design, Evaluation, and Simulation,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 4, Article 8. DOI: https://doi.org/10.52866/2788-7421.1337.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss4/8.

Retraction Notice: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Ahmed, Abbas M. and Rashid, Tarik A. (2025) ``Adaptive Crossover and Mutation Mechanisms for Enhanced LPB Algorithm Performance,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 48. DOI: https://doi.org/10.52866/2788-7421.1323.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/48.

Retraction Notice: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Saleh, Hadeel M.; Ahmed, Sahar Hamad; and Mahmoud, Akeel Sh. (2025) ``Accurate Electrocardiogram Classification of Heart Disease Using Deep Learning Network,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 18. DOI: https://doi.org/10.52866/2788-7421.1259.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/18.

Retraction Notice: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Jassam, Israa Faisal; Mukhlif, Abdulrahman Abbas; Nafea, Ahmed Adil; Tharthar, Mustafa Adnan; and Khudhair, Ahmed Isam (2025) ``A Review of Breast Cancer Histological Image Classification: Challenges and Limitations,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 1. DOI: https://doi.org/10.52866/2788-7421.1232.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/1.

Retraction Notice: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Ibrahim, Aiesha Mahmoud; Mohammed, Mazin Abed; and Al-Boridi, Omar (2025) ``A Quantum Convolutional Neural Network Approach for Early and Accurate Diagnosis of Parkinson's Disease,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 13. DOI: https://doi.org/10.52866/2788-7421.1288.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/13.

Retraction Notice: A Novel Scheme To Optimize Lsb Steganography Based On A Logistic Chaotic Map And Genetic Algorithm, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Laila, Dena Abu; obeidt, Ibrahim Moh'd; Aljaidi, Mohammad; Almaiah, Mohammed Amin; AlBourini, Muneer; Al-Na'amneh, Qais; Samara, Ghassan; Shehab, Rami; and Momani, Khaled (2025) ``A Novel Scheme to Optimize LSB Steganography Based on a Logistic Chaotic Map and Genetic Algorithm,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 24. DOI: https://doi.org/10.52866/2788-7421.1265.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/24.

Retraction Notice: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Salih, Mahmood M.; Muhsen, Yousif Raad; Ahmed, M.A.; Ismael, Reem D.; Shuwandy, Moceheb Lazam; and Al-qaysi, Z.T. (2025) ``A Novel Benchmarking Framework for Selecting the Best Deep Learning Model Diagnosing COVID-19 Based on New Development for Dual MCDM Methods,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 2. DOI: https://doi.org/10.52866/2788-7421.1276.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/2.

Retraction Notice: A Group Decision-Making For Selecting Multi-Deep Face Recognition Models, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Alazzawi, Abdulbasit; Yas, Qahtan M.; and Albayati, Burhan (2025) ``A Group Decision-Making for Selecting Multi-Deep Face Recognition Models,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 21. DOI: https://doi.org/10.52866/2788-7421.1262. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/21.

Green Technology: A Systematic Review Of Ai And Iot Solutions For A Sustainable Future, Nesma Abd El-Mawla, Mohamed A. Berbar, Nawal A. El-Fishawy, Mohamed A. El-Rashidy, Mahmoud Badawy

Mansoura Engineering Journal

Green technology offers a solution to the pressing environmental crisis. It can change the structure and generation of waste so as not to harm the earth, and people can become environmentally friendly. To address complex environmental challenges like climate change and pollution, innovative Artificial Intelligence (A.I.) and Internet of Things (IoT) solutions are essential. These technologies can help optimize resource use, reduce waste, and promote sustainable development. However, it's crucial to balance economic growth, social equity, and environmental protection when implementing green technologies. This survey paper systematically examines the landscape of Green Technology, focusing on its pivotal components: Measures of …

Construction Project Performance Research: A Bibliometric, Scientometric, And Qualitative Review (1989–2023), Abdelnaser Abdelhameed, Mohamed S. Yamany, Ahmed Abdelaty, Emad Elbeltagi

Faculty Publications

Despite the significant increase in publications on construction project performance (CPP), there is a deficiency of research that rigorously assesses and synthesizes previous studies to delineate the field’s development, themes, and research gaps. This article employs quantitative and qualitative methodologies to critically evaluate studies on CPP published over the last three decades and indexed in the Scopus database. The quantitative approach includes bibliometric searches and scientometric analyses to assess the extent of research interest and achievements. The qualitative methodology aims to conduct thorough content analysis to classify existing material based on prevalent themes. The results demonstrate an exponential growth of …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Coe Annual Technical Review 2025, Jianshun Zhang, Bing Dong

SyracuseCoE

No abstract provided.

The Role Of Spatial Abilities In Stem Learning And The Influence Of Individual Differences, Styliani Malkogeorgou

Masters

Students’ decisions to pursue education and careers in Science, Technology, Engineering, and Mathematics (STEM) are shaped by an interplay of cognitive, social, and motivational factors. Spatial ability is among the most reliable predictors of STEM success, yet less is known about how it relates to students’ STEM attitudes and aspirations, and whether visuospatial working memory (VSWM) explains this relationship. This study tested the hypotheses that (a) stronger spatial abilities and VSWM would be associated with more positive STEM attitudes and stronger STEM aspirations, and (b) VSWM would mediate the relationship between spatial abilities and STEM attitudes/aspirations, while examining the influence …

Artificial Neural Network Model For Predicting Fatigue Endurance Limit Of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests, Prashanta Kumar Acharjee, Mayzan Isied, Mena I. Souliman, Sameer Jung Karkl, Tanvir Ahmed, Gokhan Saygili

Civil Engineering Faculty Publications and Presentations

Fatigue is one of the major distresses occurring in asphalt concrete pavement. Repeated traffic loading causes escalated structural damage which results in the formation of cracks. There is a strain level below which the Hot Mix Asphalt goes under fatigue failure, and it is called endurance limit. In this study, a predictive model is developed to predict endurance limit strain values by using artificial neural network. Uniaxial tension-compression fatigue test results conducted under NCHRP Project 9–44 A were utilized in the model development process. An equation is also extracted from the model along which gives the exact values as the …

Characterization Of Sediment Loads And Size Distribution In Nebraska Roadway Runoff, Bruce I. Dvorak, David M. Admiraal, Pavel Shrestha

Nebraska Department of Transportation: Research Reports

The Nebraska Department of Transportation (NDOT) must manage sediment, and pollutant loads from roadway runoff to meet stormwater regulations. The SAFL Baffle, a hydrodynamic separator used by NDOT, depends on reliable estimates of total suspended solids (TSS) and particle size distribution (PSD). However, limited data exists for Nebraska roadways. In this study stormwater runoff was monitored at four NDOT-maintained sites, two in Lincoln and two in Beatrice, over 1.5 years to characterize TSS and PSD and to evaluate SAFL Baffle performance using the SHSAM model. Results showed large variability across sites and seasons. Median TSS ranged from 158 to 580 …

A Runtime-Adaptive Transformer Neural Network Accelerator On Fpgas, Ehsan Kabir, Jason D. Bakos, David Andrews, Miaoqing Huang

Electrical Engineering and Computer Science Faculty Publications and Presentations

Transformer neural networks (TNN) excel in natural language processing (NLP), machine translation, and computer vision (CV) without relying on recurrent or convolutional layers. However, they have high computational and memory demands, particularly on resource constrained devices like FPGAs. Moreover, transformer models vary in processing time across applications, requiring custom models with specific parameters. Designing custom accelerators for each model is complex and time-intensive. Some custom accelerators exist with no runtime adaptability, and they often rely on sparse matrices to reduce latency. However, hardware designs become more challenging due to the need for application-specific sparsity patterns. This paper introduces ADAPTOR, a …

Hold Paramount The Health, Safety, And Welfare Of The Public And The Planet, Daniel B. Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

No abstract provided.

Fiber-Reinforced Foam Concrete Using Quarry Micro Fines And Sugarcane Bagasse Ash: A Box–Behnken Design Optimization And Performance Assessment, Ravindaran Thangavel, Sanjay Kumar Shukla, Mini K. Madhavan

Research outputs 2022 to 2026

Foam concrete is well-appreciated for its thermal and acoustic benefits and is prepared by introducing foam into cement slurry/mortar. The current research examines the feasibility of Quarry Micro Fines (QMF), a waste generated from the quarries during sand manufacturing, as a substitute for fine aggregate in the preparation of foam concrete. During the preparation of concrete, a portion of cement is replaced with sugarcane bagasse ash (SCBA), while polypropylene (PP) fibers are added to improve the shrinkage resistance and tensile strength of the resulting concrete. A three-factor, three-level Box–Behnken Design (BBD) in Response Surface Methodology (RSM) was used to optimize …

Generative Artificial Intelligence In Aircraft Design Optimization, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Aircraft design optimization is essential for improving aircraft performance (such as reduced fuel consumption and lowered noise), which leads to more efficient, sustainable, and affordable aircraft. Conventional aircraft design adopts physics-based simulation models, but iteratively evaluating simulation models is computationally intensive, or even practically impossible. Meanwhile, artificial intelligence (AI) emerges as a revolutionary game changer in the modern engineering industry, including aircraft design optimization. Generative AI (genAI), one of the groundbreaking AI methods, has been advancing aircraft design optimization from various aspects, including intelligent parameterization, predictive modeling, training facilitation, and constraints handling. However, there is a lack of a review …

Numerical Investigation Of A Dual-Mode Shape Memory Alloy Stent Enabling Secondary Expansion Via Focused Ultrasound: A Potential Strategy For Correcting In-Stent Restenosis, Stephen Asare, Lucinda Duncan, Josiah Owusu-Danquah, Brian L. Davis

Civil and Environmental Engineering Faculty Publications

Coronary stent implantation is an invasive procedure performed to correct atherosclerosis. The aftermath of this procedure is identified with neointimal hyperplasia, which contributes to in-stent restenosis (ISR), and thus remains a significant clinical challenge. This study introduces a novel, noninvasive conceptual approach for addressing ISR through the thermal activation of shape memory alloy (SMA) stent using focused ultrasound (FU) in a controlled manner to restore luminal patency. COMSOL Multiphysics and ABAQUS finite element software were employed to perform the numerical modeling to simulate the thermal and mechanical responses of the SMA stent integrated into the arterial wall. After 15 s …

Hybrid Data-Driven Cement-Stabilized Soil Design: An Integration Of Machine Learning, Multi-Objective Optimization, And Life Cycle Assessment, Chikezie Chimere Onyekwena, Yunli Li, Ikenna J. Okeke, Ubani Obinna Uzodimma, Monday Uchenna Okoronkwo, Wenping Wu

Chemical and Biochemical Engineering Faculty Research & Creative Works

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial …

Performance And Mechanistic Insights Into Cement Systems Modified With Wastewater-Recovered Struvite, Ugochukwu Ewuzie, Rupack R. Halder, Abdulkareem O. Yusuf, Abiodun A. Saka, Godwin I. Ogbuehi, Titus C. Egbosiuba, Damilola A. Daramola, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

Struvite, the stable hydration product and primary strength phase in magnesium ammonium phosphate cement (MAPC), derived from wastewater treatment, has recently been utilized as a sustainable additive to Portland cement (PC). However, its impacts on cement hydration kinetics, pore refinement, rheology, and the mechanisms underlying these processes have not been comprehensively studied. This study developed Portland cement-struvite (PCS) systems by replacing PC with 3–20 % struvite (ST wt.%: PCS3–PCS20) and evaluated these processes using isothermal calorimetry, 3D micro-computed tomography (μXCT), time-dependent rheometry, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), and the Krstulović-Dabić (K-D) model. The FTIR/XRD confirmed the coexistence …

Nanocatalytic Enhancement Of Local Heat Transfer In Continuous-Flow Thermal Reactors, Nasser Zouli, Nujud Maslamani, Ayman Yousef, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

An experimental investigation was conducted to evaluate the thermal conductivity (TC) and local heat-transfer coefficients (LHTCs) of nanofluids containing alumina (Al₂O₃), hematite (Fe₂O₃), and copper oxide (CuO) nanoparticles dispersed in deionized water. A newly developed non-invasive LHTC probe was integrated into the inner wall of the test section to enable direct quantification of interfacial heat-transfer performance. The measurements were conducted under laminar and turbulent flow conditons across Reynolds numbers ranging from 1000 to 10,000. The selected nanoparticles were chosen based on their high intrinsic thermal conductivity, cost effectiveness, and, in the case …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

Predicting Water Quality Using Quantum Machine Learning: The Case Of The Umgeni Catchment (U20a) Study Region, Jamal Al-Karaki, Muhammad Al Zafar Khan, Amjad Gawanmeh, Marwan Omar

All Works

The assessment of water quality has become increasingly vital for maintaining the ecological balance and ensuring public safety across global water systems. This study examines the application of Quantum Machine Learning (QML) techniques in a real-world setting to predict water quality in the U20A region of the Umgeni Catchment, Durban, South Africa. We implemented the Quantum Support Vector Classifier (QSVC) and Quantum Neural Network (QNN) on a field-collected dataset. Our results demonstrate that the QSVC is more practical to implement and yields superior performance, achieving 75 % accuracy with polynomial and radial basis function kernels. In contrast, the QNN encountered …

Utilization Of Plastic Waste In The Concrete Industry, Nancy Abdelmoneim Sakr

Theses and Dissertations

Plastic recycling has emerged as a crucial strategy that aligns with environmental, social and economic sustainability indicators. Currently, substantial volumes of plastic waste are either deposited in landfills or incinerated, neglecting the potential to harness its embodied energy and the energy consumed for producing virgin materials. A key advantage of plastic lies in its promising mechanical properties. Concrete mix design is fundamental to a wide range of construction applications, including brick walls, reinforced concrete slabs and concrete pavements. Despite the adoption of recycled plastic in construction materials in various countries, its widespread implementation remains limited. This is primarily due to …

Constrained Pricing In Logit-Based Revenue Management, Qian Shao, Tien Mai, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

We consider a dynamic pricing problem in network revenue management in which customer behavior is predicted by a choice model, that is, the multinomial logit model. The problem, even in the static setting (i.e., customer demand remains unchanged over time), is highly nonconcave in prices. Existing studies mostly rely on the observation that the objective function is concave in terms of purchasing probabilities, implying that the static pricing problem with linear constraints on purchasing probabilities can be efficiently solved. However, this approach is limited in handling constraints on prices, noting that such constraints could be highly relevant in some real …

Light Cone Cancellation For Variational Quantum Eigensolver In Solving Noisy Max-Cut, Xinwei Lee, Xinjian Yan, Ningyi Xie, Yoshiyuki Saito, Leo Kurosawa, Nobuyoshi Asai, Dongsheng Cai, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circuit, which the parameters are optimized using a classical optimizer. With the increasing need in solving large-scale problems in real-world applications, solving those large problems with fewer qubits and fewer gates becomes essential, so that we reduce the simulation difficulty and mitigate the effect of noise in real quantum hardware. In this study, we applied the Light Cone Cancellation (LCC) method to reduce the number of qubits and gates required in a two-local ansatz. LCC …

Assessment Of Winter Maintenance Performance Objectives Using Maintenance Decision Support System, Curtis L. Walker, Mark R. Anderson, Aemal Khattak, Thomas Kauzlarich, Abdul Farhan

Nebraska Department of Transportation: Research Reports

It is important for transportation agencies to assess their winter maintenance operations performance to identify areas for improvement and/or resource investment to maintain optimal levels of service, mobility, and safety. This study explores nine winter snowstorms in Nebraska during the 2023-2024 winter season across 20 road segments, 12 of which were along the Interstate-80 corridor. Datasets explored include meteorological information, traffic mobility insights, winter maintenance operations activities, and road conditions before, during, and after the periods of accumulating snowfall. Statistical analyses explored correlations and quantitative relationships among these datasets, while visual dashboards were developed to promote more qualitative considerations of …