Computational Engineering Commons^™

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Jalzap: An Agile Hybrid For Ai-Accelerated Software Development, Pouya Nouri

University Honors Theses

The rapid adoption of generative AI tools allows software teams to quickly code applications, but it comes at a cost: high scope volatility, technical debt, and unrealistic expectations, which break traditional Agile frameworks. This thesis introduces JALZAP, a lightweight, hybrid Agile framework designed to serve small, high-agility teams facing compressed timelines and unpredictable schedules. This framework was evaluated over 16 weeks through a Portland State University Capstone project working for a pre-seed startup sponsor, where a six-person team built Flowmind: an AI-powered iOS task management app meant to serve users with neurodevelopmental disorders like ADD/ADHD. JALZAP implements structural boundaries, including …

Evaluating Optimal Capacity And Investment Strategies For Renewable Energy Projects: A Combined Technical And Financial Approach, Helen Josephine, Indhumathi Shanmugasundaram, Sharad Gupta, Manjari Sharma

Northeast Journal of Complex Systems (NEJCS)

The global shift toward clean energy is accelerating, and by 2050 renewable sources are expected to supply more than 85% of the world’s electricity. This transition, however, introduces new layers of complexity. Wind and solar energy behave as interconnected subsystems whose output fluctuates with weather, season, and geography. Their interaction with fixed hourly demand, capital-intensive investments, and financing structures creates a multi-dimensional system in which small changes can trigger significant operational and economic consequences.  This study presents a simulation-driven framework designed to understand and optimize this complex behaviour. The framework models hourly wind and solar generation alongside projected demand to …

Behavioral Biases As Drivers Of Complexity In Stock Markets: An Agent-Based Modeling Approach, David Joseph Dr, Alwin Joseph, Blesson James, Kajal Dass

Northeast Journal of Complex Systems (NEJCS)

By modeling financial systems as Complex Adaptive Systems, this study investigates how behavioral biases influence emergent complexity in stock markets. The study integrates heterogeneous agents, such as rational traders, herding agents, overconfident traders, and anchoring/disposition-driven investors, within a Limit Order Book framework calibrated to both U.S. and Indian market conditions using an Agent-Based Modeling (ABM) approach implemented through the high-fidelity ABIDES simulation environment. Price dynamics, volatility patterns, and liquidity structures were analyzed by Monte Carlo simulation experiments with different behavioral compositions. The results show that behavioral biases cause nonlinear price reactions, produce heavy-tailed return distributions that distort order-book complexity, and …

Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti

Library Philosophy and Practice (e-journal)

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Utilizing A Virtual Firewall Appliance For Introducing And Reinforcing The Concepts And Implementation Of Devices To Improve Security In A Computing Environment, Stanley Mierzwa, Christopher Eng

Center for Cybersecurity

The educational realm of higher education cybersecurity curriculum continues to evolve to provide more opportunities for experiential hands-on and work role-related practical applications of technology solutions. Gaining greater competencies is quickly becoming a normal requirement for such programs that are designated by the National Security Agency Center of Academic Excellence programs. The work roles of cybersecurity include a variety of knowledge, skills, and abilities, depending on the category of the activity or task. Firewalls have been a staple cybersecurity, network security, and information security device and strategy to protect organization networks and computing environments. This paper will provide details and …

Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines

Research & Publications

Cyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network's resilience and security. The …

Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati

Data Science Undergraduate Honors Theses

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …

Routing Battery-Constrained Delivery Drones In A Depot Network: A Business Model And Its Optimization-Simulation Assessment, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

This paper proposes a novel business model for on-demand package shipment services using drones, and evaluates different modeling and solution approaches for the drone routing problem that underpins the service operation. In the proposed service, customers’ shipment orders of arbitrary origins and destinations, payload weights and bid values are collected every five minutes, and available drones from multiple depots are then dispatched to fulfill a subset of these orders in a way to maximize profit. A drone path starts from a depot, serves one or more customer orders in sequence, and ends at a depot for battery recharging, which incurs …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Optimal Design And Operation Of Integrated Hydrogen Generation And Utilization Plants, Ijiwole Solomon Ijiyinka

Graduate Theses, Dissertations, and Problem Reports (ETD)

There are considerable efforts worldwide for reducing the use of fossil fuel for energy production. While renewable energy sources are being increasingly used, fossil fuel still contribute about 80% of the energy used worldwide. As a result, the level of CO₂ is still increasing fast in the atmosphere currently exceeding about 410 parts per million (ppm). For reducing CO₂ build up in the atmosphere, various approaches are being investigated. For the electric power generation sector, two key approaches are post-combustion CO₂ capture and use of hydrogen as a fuel for power generation. These two solutions can also …

Factors Affecting The Adoption Of Information And Communication Technologies In Africa: Literature Review, Edison Wazoel Lubua

African Conference on Information Systems and Technology

This paper synthesised the literature on the adoption of Information and Communication Technology within Africa. The purpose was to determine factors (reported by the literature) determining technology adoption and use, in Africa. The paper used the systematic literature review. The study analysed the factors descriptively. Based on the analysis, the following are the main five factors reported to affect the adoption and use of Information Technology in Africa: Lack of ICT knowledge, unreliable infrastructure, high cost of adoption, the perceived usefulness of ICT, and the perceived ease of use. The government and technology implementing organisation has the key role to …

Coastal Housing Recovery In A Postdisaster Environment: A Supply Chain Perspective, Rafael Diaz, Joshua Behr, Beatriz Acerp

VMASC Publications

In coastal cities, the damage stemming from hurricanes and severe tropical storms may reduce housing stock. Accelerating the reconstruction of housing stock becomes crucial in minimizing the time that residents are displaced. It is critical to understand how to better coordinate all available resources for an effective and balanced reconstruction where nongovernmental organizations (NGOs), government agencies, volunteers, and many other complex resources converge. This study combines Monte Carlo and beta models to assess supply, costs, and recovery times. The models capture the immediate and long-term costs of reconstruction activities under highly uncertain conditions using embedded stochastic components on projected materials, …

The Evolution Of The Internet And Social Media: A Literature Review, Charles Alves De Castro, Isobel O'Reilly Dr, Aiden Carthy

Articles

This article reviews and analyses factors impacting the evolution of the internet, the web, and social media channels, charting historic trends and highlight recent technological developments. The review comprised a deep search using electronic journal databases. Articles were chosen according to specific criteria with a group of 34 papers and books selected for complete reading and deep analysis. The 34 elements were analysed and processed using NVIVO 12 Pro, enabling the creation of dimensions and categories, codes and nodes, identifying the most frequent words, cluster analysis of the terms, and creating a word cloud based on each word's frequency. The …

Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef

Theses and Dissertations

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Mobile Application Development For University Library Services (Case Study: Library Of Uin Sunan Ampel Surabaya), Heri Cahyo Bagus, Firza Hardy Nugraha, Ilham M.Said, Yusuf Amrozy

Library Philosophy and Practice (e-journal)

This study was conducted to develop library services at UIN Sunan Ampel Surabaya by developing a system using the Rapid Application Development (RAD) method and the Mobile Library. This is done to complement the website services which can be accessed through catalog.uinsby.ac.id. Many users, especially students, experience restlessness in returning the book because there is no reminder feature for the deadline to return the book. As well as resulting in the number of fines that increase in number for each day. The purpose of this research is to make users more free to use the library services of UIN Sunan …

Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler

SMU Data Science Review

This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …

Towards A Holistic Risk Model For Safeguarding The Pharmaceutical Supply Chain: Capturing The Human-Induced Risk To Drug Quality, Heather R. Campbell

Theses and Dissertations--Pharmacy

Counterfeit, adulterated, and misbranded medicines in the pharmaceutical supply chain (PSC) are a critical problem. Regulators charged with safeguarding the supply chain are facing shrinking resources for inspections while concurrently facing increasing demands posed by new drug products being manufactured at more sites in the US and abroad. To mitigate risk, the University of Kentucky (UK) Central Pharmacy Drug Quality Study (DQS) tests injectable drugs dispensed within the UK hospital. Using FT-NIR spectrometry coupled with machine learning techniques the team identifies and flags potentially contaminated drugs for further testing and possible removal from the pharmacy. Teams like the DQS are …

Combining Bert With Contextual Linguistic Features For Identification Of Propaganda Spans In News Articles, Arjumand Younus, Muhammad Atif Qureshi

Conference papers

Recent endeavours at detection of propaganda in news articles treat this as a fine-grained problem of detecting it within fragments; and hence, transformer based embeddings perform decently in such detection. We build our propaganda detection framework on top of a transformer model simultaneously enriching it with contextual linguistic information of surrounding part-of-speech tags and LIWC categories the word itself belongs to. The evaluation outcomes being encouraging indicate a huge potential for this line of reasoning in natural language processing of news text.

Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia

SMU Data Science Review

In this paper, we introduce a proof of concept that addresses the assumption and limitation of linear local boundaries by Local Interpretable Model-Agnostic Explanations (LIME), a popular technique used to add interpretability and explainability to black box models. LIME is a versatile explainer capable of handling different types of data and models. At the local level, LIME creates a linear relationship for a given prediction through generated sample points to present feature importance. We redefine the linear relationships presented by LIME as quadratic relationships and expand its flexibility in non-linear cases and improve the accuracy of feature interpretations. We coin …

Zechipc: Time Series Interpolation Method Based On Lebesgue Sampling, Luis Miralles-Pechuán, Muhammad Atif Qureshi, Matthieu Bellucci, Brian Mac Namee

Books/Book Chapters

In this paper, we present an interpolation method based on Lebesgue sampling that could help to develop systems based time series more efficiently. Our methods can transmit times series, frequently used in health monitoring, with the same level of accuracy but using much fewer data. Our method is based in Lebesgue sampling, which collects information depending on the values of the signal (e.g. the signal output is sampled when it crosses specific limits). Lebesgue sampling contains additional information about the shape of the signal in-between two sampled points. Using this information would allow generating an interpolated signal closer to the …

Valve Health Identification Using Sensors And Machine Learning Methods, Muhammad Atif Qureshi, Luis Miralles-Pechuán, Wood, Galway Technology Park, Parkmore, Galway, Ireland, Brian Mac Namee

Books/Book Chapters

Predictive maintenance models attempt to identify developing issues with industrial equipment before they become critical. In this paper, we describe both supervised and unsupervised approaches to predictive maintenance for subsea valves in the oil and gas industry. The supervised approach is appropriate for valves for which a long history of operation along with manual assessments of the state of the valves exists, while the unsupervised approach is suitable to address the cold start problem when new valves, for which we do not have an operational history, come online.

For the supervised prediction problem, we attempt to distinguish between healthy and …

Data-Driven Predictive Maintenance Scheduling Policies For Railways, Pedro Cesar Lopes Gerum, Ayca Altay, Melike Baykal-Gürsoy

Supply Chain Management

Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the …

Generalized Relay Network Design And Collaborative Dispatching In Truckload Transportation, Amin Ziaeifar

Operations Research and Engineering Management Theses and Dissertations

The truckload industry faces a serious problem of high driver shortage and turnover rate which is typically around 100\%. Among the major causes of this problem are extended on-the-road times where drivers handle several truckload pickup and deliveries successively; non-regular schedules and get-home rates; and low utilization of drivers dedicated time. These are by-and-large consequences of the driver-to-load dispatching method, which is based on point-to-point dispatching or direct shipment from origin-to-destination, commonly employed in the industry. In this dissertation, we consider an alternative dispatching method that necessitates careful design of an underlying network. In this scheme, a truckload on its …

Machine Learning With Multi-Class Regression And Neural Networks: Analysis And Visualization Of Crime Data In Seattle, Erkin David George

Honors Projects

This article examines the implications of machine learning algorithms and models, and the significance of their construction when investigating criminal data. It uses machine learning models and tools to store, clean and analyze data that is fed into a machine learning model. This model is then compared to another model to test for accuracy, biases and patterns that are detected in between the experiments. The data was collected from data.seattle.gov and was published by the City of Seattle Data Portal and was accessed on September 17, 2018. This research will be looking into how machine learning models can be used …

A Ulysses Pact With Artificial Systems. How To Deliberately Change The Objective Spirit With Cultured Ai, Bruno Gransche

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The article introduces a concept of cultured technology, i.e. intelligent systems capable of interacting with humans and showing (or simulating) manners, of following customs and of socio-sensitive considerations. Such technologies might, when deployed on a large scale, influence and change the realm of human customs, traditions, standards of acceptable behavior, etc. This realm is known as the "objective spirit" (Hegel), which usually is thought of as being historically changing but not subject to deliberate human design. The article investigates the question of whether the purposeful design of interactive technologies (as cultured technologies) could enable us to shape modes of …

Global Aviation System: Towards Sustainable Development, Marina P. Bonser Dr.

International Journal of Aviation, Aeronautics, and Aerospace

Aviation around the world has integrated into a global system. As the integration process continues, more aspects and levels of it need to be lead towards the sustainable development of the whole system via advancing strategic management, global communication proficiency, and technological expertise. It becomes essential to enrich global language (English) proficiency with cross-cultural communication competence not only for communication in the air but also for airport security, passenger and cargo services, aircraft and equipage engineering, building, and maintenance. Nowadays lower levels of management need more advanced strategic thinking and problem solving skills, and higher levels of management need global …