Digital Commons Network^™

Tensile And Fatigue Properties Of Haynes ® 233 Manufactured By Wire-Arc Additive Manufacturing, Samuel Onimpa Alfred, Frank W. Liou, Mehdi Amiri

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Haynes® 233 is a newly developed nickel-based superalloy currently in the early stages of commercial adoption. With the growing interest in fabricating large and complex components using wire-arc additive manufacturing (WAAM), this alloy presents a promising option for industrial applications. This study investigates the microstructure, tensile, and fatigue properties of heat-treated (HT) WAAM Haynes ® 233 and compares them to its wrought counterpart. Yield strength (YS), ultimate tensile strength (UTS), and fatigue strength of WAAM Haynes ® 233 are 709.4 MPa, 890.1 MPa, and 253.8 MPa, respectively. These values indicate a 63.8 % increase in YS, a 1.11 % decrease …

Multi-Period Risk-Aware Procurement Optimization Under Covid-19 Disruption, Jonathan Chase, Hoong Chuin Lau, Jinfeng Yang, Lu Liu

Research Collection School Of Computing and Information Systems

Supply chain resilience has been a topic of active research in the operations research and AI communities for several years, but the COVID-19 pandemic threw the frailties of global supply chains into sharp relief. Disruptions and delays caused by fresh outbreaks leading to lockdowns, put severe strain on supply chains in many industries. In this work we develop lockdown-resilient procurement capabilities for a global technology company. First, through analysis of lockdown data from China we develop a logarithmic regression-based lockdown prediction method to complement a supplier risk metric for conventional risks. Second, we develop a multi-period stochastic optimization model that …

Real-Time Task Scheduling Strategy For 3d Printing Cloud Platforms In Health Scenes, Jianjia He, Jian Wu, Jingran Ni, Yuning Zhang, Keng Siau

Research Collection School Of Computing and Information Systems

In health scenes, 3D Printing Cloud Platform (3DPCP) needs to cope with unpredictable fluctuations in tasks and resources, but traditional scheduling methods have problems such as incomplete consideration of factors, poor optimization, and weak dynamic adaptability, which make it difficult to meet real-time scheduling requirements. To this end, the real-time task scheduling problem of 3DPCP for health scenes is defined, a real-time task scheduling model is established, the design time of user personalized services is considered, a rescheduling scheme is designed in combination with task variations and device variations, and a scheduling strategy that incorporates dynamic mechanisms and improved multi-objective …

Integrating Demand Forecasting And Deep Reinforcement Learning For Real-Time Electric Vehicle Charging Price Optimization, Monowar Mahmud, Tarek Abedin, Md Mahfuzur Rahman, Shamiul Ashraf Shoishob, Tiong Sieh Kiong, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The rapid growth of electric vehicles (EVs) demands efficient, grid-friendly charging systems. This study introduces a dynamic pricing framework combining short-term demand forecasting and deep reinforcement learning. Using Adaptive Charging Network (ACN) data, XGBoost predicts charging demand accurately (R² = 0.84, MAE = 0.45 kW). Compared to a uniform rate applied to all charging usage, set at 0.15 USD/kWh across all hours, with no adjustment for system demand conditions or time-of-day, the optimized strategy enhanced total daily revenue by 133 % and diminished load variance by 72.37 %. The PPO agent also surpassed traditional Time-of-Use and demand-based pricing models …

Soft Sensing Of Biological Oxygen Demand In Industrial Wastewater Using Machine Learning Models, Muhammad Hassnain, Sarada M.W. Lee, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Traditional methods for determining biological oxygen demand (BOD) from industrial water resource recovery facilities (WRRFs) are time-consuming and often impractical for real-time process control. This study explores the application of machine learning (ML) and artificial intelligence (AI) models for the prediction of final effluent BOD (F-BOD) based on physicochemical and operational parameters by leveraging nineteen years of historical laboratory and instrumentation data from the WRRF of an essential oil manufacturing plant. The predictions from these models are then used to simulate the process dynamics, assessing the optimal operational boundary conditions for all input parameters at which the target (F-BOD) falls …

Recovery Of Daily Water Levels In The Sacramento-San Joaquin Delta, 1915–2023, Serena B. Lee, Steven Dykstra, Reyna Gomez‐Sanchez, Cole Wilkenson, Ricardo Estrada, Nick Mcguire, David A. Jay, Stefan A. Talke

Civil and Environmental Engineering Faculty Publications and Presentations

This manuscript documents the data rescue, digitization, and quality assurance of archival daily maximum and minimum water levels at twenty-five sites within the Sacramento-San Joaquin Delta. The records encompass 1846 total unique years, where 915 years are newly digitized from the 1915–1985 era. The period of record for each gauge location varies from 40 to 109years (median=80 years). Quality assurance procedures and datum corrections were applied to both archival and digital records to generate a time series referenced to a common geocentric datum. Both riverine and coastal influences on mean sea level and great diurnal range are evident in the …

Freer Arrows And Why You Need Them In Haskell, Grant Vandomelen, Gan Shen, Lindsey Kupur, Yao Li

Computer Science Faculty Publications and Presentations

Freer monads are a useful structure commonly used in various domains due to their expressiveness. However, a known issue with freer monads is that they are not amenable to static analysis. This paper explores freer arrows, a relatively expressive structure that is amenable to static analysis. We propose several variants of freer arrows. We conduct a case study on choreographic programming to demonstrate the usefulness of freer arrows in Haskell.

Detection And Prevention Of Water Inrush From Seam Floor During Coal Mining Above Confined Aquifer, Weidong Pan, Yupei Deng, Houlin Du, Shiqi Liu, Mingtao Xu, Xingjie Liu

Journal of Sustainable Mining

With the increasing intensity of coal resource exploitation in China, the geological conditions of the working face are becoming more and more complex, and the bottom plate breakage (bottom bulge) and water inrush disasters caused by pressurized water mining are becoming more prominent. This article is based on the 21605 working face of Xin’an Coal Mine in Zaozhuang Mining Group. Through theoretical analysis and numerical calculations, the characteristics and scope of coal seam floor failure in the working face are obtained. Especially, the electrode cable direct current detection system designed through self-optimization was used for actual testing. Research shows that …

The Suitability Of Advanced Geospatial Technologies In Monitoring Mine Surface Displacement, Long Quoc Nguyen, Tuyet Minh Dang, Lipecki Tomasz

Journal of Sustainable Mining

This study conducts a thorough review of the current scientific literature on the application of geospatial methods in the assessment of mining-induced displacement. The scope of research included technologies for determining deformation, subsidence, and landslide in mining areas. Global Navigation Satellite Systems, Unmanned Aerial Vehicles, Terrestrial Laser Scanners, Remote Sensing, and fusion methods are approaches used to solve the research objectives. Additionally, the paper also mentions some advantages, disadvantages, and scope of application of these methods. The investigation revealed that the displacement detection method most commonly used at the moment is satellite radar interferometry.

Towards Sustainable Mining: Ghg Considerate Open Pit Long-Term Planning Using Adaptive Large Neighborhood Search Algorithm, Bahar Amirmoeini, Martin Grenon, Ali Moradi Afrapoli

Journal of Sustainable Mining

Mine planning involves the systematic design and coordination of mineral extraction from the earth’s crust, integrating exploration, production, and various engineering considerations. With increasing emphasis on environmental responsibility, the mining industry is under pressure to incorporate environmental considerations into mine planning. This paper addresses the precedence-constrained production scheduling problem (PCPSP) within the context of green long-term mining planning, aiming to optimize extraction processes while restricting carbon emission. Given the NP-hard nature of the PCPSP, this study introduces an adaptive large neighborhood search (ALNS) algorithm tailored specifically for long-term mine planning. A range of computational experiments have been carried out, including …

Economic Viability And Environmental Sustainability: A Cost-Benefit Analysis Of Green Technologies In Mineral Extraction, Tshinkobo Bukasa Orphea, Agyingi Babaca Agyingib, Xiangrui Meng

Journal of Sustainable Mining

This research offers a novel approach to comparing green technologies’ economic profitability and environmental sustainability of their mineral extraction based upon econometric and life cycle assessment methodologies. Quantitative results show attractive results with an NPV of $2,014,001 and an IRR of 17%. In the third year, the project nets at $300,000, or 63%, at a 7% discount rate. However, soil protection remains challenging, but pollution coefficients are improved, as evidenced by environmental impact assessments (EIAs). The findings in the study further underscore how regulatory frameworks and market drivers dictate the use of green technology. The economic, environmental and regulatory costs …

A Case Study Comparing The Results Of A Copper Open-Pit Mine Scheduling Considering Two Different Approaches For Spatial Interpolation Of Comminution Geometallurgical Variables, Sílvia Martins, Pedro Campos, Douglas Mazzinghy

Journal of Sustainable Mining

The use of geometallurgical variables to improve the accuracy obtained in mine planning is an increasingly present reality in mining. However, the way these variables are interpolated to construct the block model is still a major challenge since they are non-additive variables. In this case study, a database from a copper mine located in Brazil was used to compare mining planning by Direct Block Scheduling using two spatial interpolation approaches. In the first approach, the geometallurgy block model was produced interpolating the comminution indexes Axb and BWI from the drill holes, and the specific energy was calculated at each block. …

Impact Of Multiple Mining Subsidence On Large Diameter Steel Pipelines: The Upper Silesian Coal Basin Case Study, Piotr Kalisz, Magdalena Zięba, Marcin Grygierek

Journal of Sustainable Mining

The article concerns the impact of multiple mining subsidence on pipeline expansion capacity based on the analysis of the field research results. The article presents a case study related to a water mains system that supplies drinking water to approximately 3.5 million consumers. These pipelines are made of steel with diameters of 1600 mm and 1400 mm. The water mains partly run through mining and post-mining areas in the Upper Silesian Coal Basin in Poland. These pipelines are equipped with expansion joints to protect them against the impact of ground deformations. The field research concerned the assessment of the position …

Recovery Of Magnesium Sulfate And Calcium Sulfate From Zinc Flotation Tailing, Raquel Húngaro Costa, Jonathan Tenório Vinhal, Tatiana Scarazzato, Denise Crocce Romano Espinosa

Journal of Sustainable Mining

Resource recovery is a process that has been used to obtain products from industrial waste. Mining tailings can be used as an alternative through ore beneficiation to create by-products for this type of waste. The work herein investigated the recovery of two products from a real tailing generated in the Zn beneficiation route in the flotation step. A non-magnetic fraction was submitted to a hydrometallurgical route to produce MgSO₄ and CaSO_4. Thermodynamic simulations using FactSage software were performed to evaluate the optimal leaching conditions varying the S:L ratio, the sulfuric acid concentration, and temperature. The best modeled conditions …

Construction, Optimization, And Characterization Of An Undergraduate Cold Cathode Table-Top Electron Accelerator For Radiation Physics Education, Caitlin Balmer, Mason Skeath, Mehran M. Zaini, Peter Zencak, Erin M. Craig

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Mehran Zaini, PhD; Peter Zencak

Rising cancer cases spurred advancements in radiation therapy modalities, including electron accelerators. In this report, descriptive and diagnostic analysis was utilized to develop and characterize a functional, low energy cold cathode table-top electron accelerator for radiation physics experimentation in Central Washington University’s (CWU) undergraduate radiation lab. The device features a tungsten cathode (TC), brass anode, and copper Faraday Cup (FC) in a vacuum, enclosed by blue-tinted polyvinyl chloride (PVC). TC electron emission was facilitated by applied electric fields from input voltages of 1000 V to 5000 V. FC collected electron current in the …

Reengineering Resilience: Bio-Resilience Bonds For Financing Microbial Infrastructure And Climate Equity, Reece Buckley

COP30

This policy proposal introduces Bio-Resilience Bonds (BRBs), a performance-based financial instrument designed to monetise microbial ecosystem services as measurable climate infrastructure. Microbial ecosystems are crucial for climate resilience, yet they are often overlooked in mainstream adaptation f inance frameworks. Their ability to regulate carbon and nitrogen cycles, reduce methane emissions and enhance soil and water stability (Delgado-Baquerizo et al., 2016) makes them essential assets for climate mitigation and adaptation. With global adaptation needs exceeding £2.7 trillion (UNEP, 2024), this oversight indicates a systemic failure to recognise biology as a form of infrastructure. BRBs transform microbial outputs into localised key performance …

Fundamentals Of The New Neutrosophic Matrices, Adebisi Sunday Adesina, Ogunmuyiwa Sodiq Damilola

Neutrosophic Systems with Applications

The New Neutrosophic Matrices provides a mathematical extension of classical and fuzzy matrix theory that incorporates the element of indeterminacy alongside truth and falsity. Neutrosophic logic, pioneered by FlorentinSmarandache, provides a richer framework for dealing with uncertainty and vagueness in real-world data. This study explores the definitions, classifications, and algebraic operations onneutrosophic matrices, including addition, multiplication, scalar operations, and the formation of identities.A comparative analysis is presented to highlight the distinctions between classical, fuzzy, and neutrosophic matrices. From the concepts, potential applications could be proposed most especially, for more problem solving as well as for future exploration.Findingsaffirmthat neutrosophic matrices offer …

Impacto De La Inteligencia Artificial En La Educación Superior. Guía Reflexiva, Jairo Eduardo Márquez Díaz

Ingeniería

La inteligencia artificial (IA) está revolucionando la educación superior en diversas formas como, por ejemplo, la personalización del aprendizaje, la creación de tutorías inteligentes y el análisis de aprendizaje. Este libro se presenta como una herramienta valiosa para todos aquellos interesados en comprender y aprovechar las oportunidades que la ia ofrece en el campo de la educación superior. Con un enfoque equilibrado y exhaustivo, esta publicación pretende servir como una guía integral para profesores y estudiantes que buscan entender cómo la ia está transformando la enseñanza y el aprendizaje en la actualidad. A lo largo de sus páginas, aborda diversos …

Stabilized Weak-Gradient Discontinuous Finite Elements With Optimal Error Estimates For Second-Order Elliptic Pdes, Aymen Laadhari

Mathematical Modelling and Numerical Simulation with Applications

This work introduces an accurate finite element approach employing a new stabilized discrete weak gradient, designed for second-order elliptic problems on arbitrary conforming meshes. We formulate the approach within a discontinuous Galerkin framework and derive a consistent and coercive bilinear form. Appropriate error analysis on a model problem confirms optimal convergence. Building on the core analysis, we extend the method to more challenging settings, including time-dependent heterogeneous scenarios and a biophysically realistic optimal-control model of photobleaching in the budding yeast cell. We further illustrate the versatility of the weak-gradient construction by applying it to an unsteady level-set equation relevant to …

Durable Low-Friction Graphite Coatings Enabled By A Polydopamine Adhesive Underlayer, Adedoyin Ayomide Abe, Fernando Maia De Oliveira, Deborah Okyere, Mourad Benamara, Jingyi Chen, Yuriy I. Mazur, Min Zou

Mechanical Engineering Faculty Publications and Presentations

This study investigates the tribological performance and wear mechanisms of graphite and polydopamine/graphite (PDA/graphite) coatings on stainless steel under dry sliding conditions. While graphite is widely used as a solid lubricant, its poor adhesion to metal substrates limits long-term durability. Incorporating an adhesion-promoting PDA underlayer significantly improved coating lifetime and wear resistance. Tribological testing revealed that PDA/graphite coatings maintained a coefficient of friction (COF) below 0.15 for over seven times longer than graphite-only coatings. High-resolution scanning electron microscopy, SEM, and profilometry showed that PDA improved coating adhesion and suppressed lateral debris transport, confining wear to a narrow zone. Surface and …

Drawing On Uncertainty Methodologies Of Neutrosophic Hypersoft Sets In Cognitive Computing-Driven Healthcare Systems, Mona Mohamed, Nurhan Alaa

Neutrosophic Systems with Applications

A new paradigm called cognitive computing simulates human reasoning and decision-making through integrating advanced techniques such as artificial intelligence (AI) and natural language processing (NLP). Cognitive computing systems, in contrast to traditional systems, can handle both structured and unstructured data, adjust to new information, and offer context-sensitive insights. This study examines how cognitive computing improves decision-making, personalization, and human-machine collaboration in various fields. Cognitive computing in the healthcare sector processes clinical notes, imaging data, and electronic health records to help physicians with diagnosis, treatment planning, and patient engagement. This study examines key applications, including their role in diagnostic support, where …

Neutrosophic Set Model For Controlling Electronic Waste Requirements Management Policies To Minimize Ecological Impact And Improving Resilience And Sustainability, Mohamed Abouhawwash, Nitin Mittal, Sudeep Tanwar

Neutrosophic Systems with Applications

The growing issue of electronic waste (e-waste) necessitates management approaches that promote sustainability and resilience while reducing environmental effects, particularly considering global disruptions and pressure on manufacturers to implement extended producer responsibility laws. There is a research gap in our knowledge of the link between sustainability and resilience since most of the literature currently available on e-waste management focuses on either operational efficiency or sustainability. This study proposes multi-criteria decision making (MCDM) methodology for controlling electronic waste requirements management policies to minimize ecological impact and improving resilience and sustainability. We use the EDAS methodology to rank the alternatives. The criteria …

Application Of Dematel Based On Bipolar Neutrosophic Sets For Sustainable Agriculture Practices, Lazim Abdullah, Nor Liyana Amalini Binti Mohd Kamal

Neutrosophic Systems with Applications

The development of natural capital is a fundamental objective within sustainable agricultural systems, where the optimization of both crop and livestock production is vital to addressing global food demands. Despite this imperative, major agricultural sectors such as paddy and rubber production, often fall short of satisfying consumption needs. This study aims to identify and prioritize the most influential criteria for sustainable agriculture using the Bipolar Neutrosophic Set-based Decision-Making Trial and Evaluation Laboratory (BNS-DEMATEL) method. Expert evaluations were elicited from five agricultural specialists using linguistic assessments to analyze the performance and interdependencies among sustainability criteria. Computational analyses were conducted using MATLAB …

Evaluating And Ranking Genai Chatbots Under Uncertainty: A Type-2 Neutrosophic Rancom–Marcos Mcdm Framework, Hend Ahmed, Abduallah Gamal

Neutrosophic Systems with Applications

Owing to integrate GenAI chatbots to enhance productivity across various tasks, this research presents T2NN-RANCOM-MARCOS multi-attribute decision-making model, which employs Type-2 Neutrosophic Number (T2NN) to handle uncertain data, the RANCOM method, distinguished by its easy, highly repeatable, less time consuming, more appropriate to deal with problems exceeds 5 criteria with expert errors to assign subjective weights to criteria and MARCOS method to evaluate and rank eight GenAI chatbots against six main criteria are included 23 sub-criteria: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, Trust and Confidence and Economic are primarily derived from QUEST evaluation …

Use Matters: How Different Ways Of Using Chatgpt Drive Ai Acceptance And Solutionism, Florian Golo Flaßhoff, Fabian Anicker, Frank Marcinkowski

Human-Machine Communication

Artificial intelligence is central to solutionism—the vision of a world where all major problems are solved through technology. This study theorizes about how human–AI communication shapes attitudes toward AI and influences the formation of public opinion, sparking solutionist imaginaries. We empirically examine the attitude formation resulting from the non-simulated use of an unmanipulated conversational model in a controlled laboratory experiment. Using a between-subjects design, participants engaged in three semi-structured 20-minute sessions with ChatGPT, providing a novel perspective on the effects of its use. The findings reveal that mere use of ChatGPT causally increases AI acceptance; however, its impact significantly depends …

Generative Ai: Another Chapter Of Human-Machine Communication, Seungahn Nah, Patric R. Spence

Human-Machine Communication

This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

Retracted: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Abbas M. Ahmed, Tarik A. Rashid

Iraqi Journal for Computer Science and Mathematics

This study proposes a more effective concept of Learner Performance-based Behavior (LPB). It is a new metaheuristic algorithm based on how the university admission process is done for high school students in various departments. The adaptive crossover and mutation methods were incorporated into the LPB algorithm as part of an investigation. The goal is to enhance convergence and significantly improve the quality of the solutions. The aLPB (adaptive-learner performance-based Behaviour) method stands out because it sets the crossover and mutation parameters based on the performance of the parent solutions. Thus, the proposed technique achieves a balance between exploration and exploitation …

Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …

Multi Features Data Clustering Using Novel Statistical Method With Application To Color Images, Husham Y. A. Alameen, Ali Karah Bash

Iraqi Journal for Computer Science and Mathematics

The efficiency and performance of the color image clustering algorithms are determined by various factors, including accuracy, data size, speed, and reliability (the absence of randomness in the results). Some applications, like microscopes analyzing images of biological objects or telescopes observing planetary motion prioritize accuracy over execution time. In contrast, surveillance cameras and moving object tracking prioritize speed and reliability over accuracy. This study introduces a novel algorithm that balances these four factors by clustering data with multiple features linked through specific relationships. The proposed algorithm has been practically applied to RGB color images. Traditional clustering methods, such as K-means, …

A New Approach For Multiprocessor System-On-Chip Application Scheduling In Multi-Objective Flow Shops, Tahani Jabbar Khraibet, Bayda Atiya Kalaf, Ahmed Abbas Jasim

Iraqi Journal for Computer Science and Mathematics

The flow shop scheduling problem in Multiprocessor-System-on-Chip (MPSoC) architectures presents challenges for traditional optimization algorithms, especially when addressing multiple conflicting objectives. Hence, advanced optimization approaches are required to tackle these objectives simultaneously. Therefore, this research aims to propose and evaluate a new optimization approach based on the integration of the Fire Hawk Optimizer with the Smart Battery Scheduling Algorithm (FHO-SBSA) to address the multi-objective (make span, CPU time, global average delay, network throughput, and total energy consumption) flow shop scheduling problem in MPSoC systems. To evaluate the performance of the FHO-SBSA optimization approach, two benchmark applications were selected, with ten …