Engineering Commons^™

The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self, Ilham Phalosa Reswara, Anggi Mayangsari

Jurnal Psikologi Sosial

Artificial intelligence systems designed around continuous affirmation and minimal friction are becoming increasingly prominent as social and relational partners in everyday life. This article examines how sustained interaction with such frictionless AI systems may reshape the developmental conditions under which social selfhood forms and is maintained. Drawing on Cooley's (1902) looking glass self, Kohut's (1971) concept of optimal frustration, and the developmental literature on social competence, the article argues that frictionless AI may distort the social mirror through which identity forms, remove the manageable frustration that appears necessary for psychological growth, weaken empathic capacity, and erode the social stakes that …

Saas As The Backbone Of Digital Transformation: How Ai Turned Cloud Software Into Intelligent Enterprise Infrastructure, Amar Fejzić

Communications of the IIMA

Software-as-a-Service (SaaS) has become one of the most consequential infrastructures of digital transformation because it lowers the cost, time, and complexity of adopting enterprise capabilities. At the same time, artificial intelligence (AI), especially generative and conversational AI, is changing SaaS from a delivery model into an intelligent operating layer that automates workflows, personalizes customer interactions, and supports data-driven decisions. This paper develops a conceptual synthesis of academic literature and public organizational cases to examine how SaaS shaped digital transformation and how AI is reshaping SaaS itself. The analysis shows that SaaS enables scalable experimentation, faster deployment, and modular integration, while …

Artificial Intelligence In Food Engineering For Food Safety, Quality Assurance, And Human Health, Ayad Abbood Abdulhasan, A. M. Mustafa, F. F. Sayyid, Marwan B. Hussein, Mohanad Muzahem Khalaf

AUIQ Technical Engineering Science

Artificial intelligence (AI) is increasingly reshaping food engineering by providing data-driven tools for safeguarding food safety, ensuring quality assurance, and improving human health outcomes. This review synthesizes current evidence on the application of machine learning, deep learning, computer vision, and the Internet of Things (IoT) across the food production continuum, from raw material inspection to consumer-facing nutrition guidance. We examine how convolutional neural networks, hyperspectral imaging, and sensor-fusion approaches enable rapid, non-destructive detection of contaminants and pathogens, and how predictive models support shelf-life estimation and quality grading. We further discuss AI-enabled traceability systems, including blockchain-integrated supply chains, and the growing …

Ask Dr. Wang: A Community Conversation On Ai At Cary Library, Stephen Yang, Zhengjia Yang, Julie Yin, Eric Gu, Jiawei Liu

Paul English Applied Artificial Intelligence (AI) Institute Publications

This report describes a community engagement project by the Youth STEAM Initiative (YSI) in conjunction with a public talk by Dr. Haixun Wang, “How AI Remembers, Imagines, and Creates,” held at the Cary Memorial Library in Lexington, Massachusetts. Prior to the talk, YSI collected questions and perspectives from community members through an online survey, gathering thirteen responses spanning students, parents, and professionals. This report summarizes the talk, themes that emerged from those responses, the community’s views on AI creativity, and reflections from the YSI team.

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

Ai Cyber First Responders: Bottleneck Analysis Of Heterogeneous Cpu–Gpu Pipelines For Security Operations Center Triage, Christine Pierce

Harrisburg University Other Works

Abstract — Modern Security Operations Centers (SOCs) must continuously process massive volumes of heterogeneous security telemetry while meeting stringent throughput, latency, and operational continuity requirements. Although transformer-based artificial intelligence has significantly improved threat detection and alert prioritization, most cybersecurity research evaluates model accuracy rather than the end-to-end behavior of AI-enabled operational pipelines. Consequently, relatively little is known about how heterogeneous CPU–GPU coordination, scheduling overhead, memory movement, and synchronization collectively influence operational SOC performance. This paper presents the AI Cyber First Responder, a heterogeneous SOC triage architecture that integrates GPU-accelerated transformer inference with CPU-based doctrine-driven reasoning to investigate end-to-end pipeline behavior …

Validated Near-Infrared Spectroscopy And Chemometric Modelling For Rapid Quantification Of Essential Oil Yield And Α/Β-Santalol In Santalum Album L., Muhammad Hassnain, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

The sandalwood industry remains constrained by destructive, time-intensive assays for essential oil (EO) yield, composition, moisture content, and wood fraction, which limit real-time decision-making. We report a unified near-infrared spectroscopy-artificial intelligence (NIRS-AI) platform for non-destructive analytics across the Santalum album L. value chain. Reflectance and transmittance spectra from solid matrices (disks, logs, chips, and powders), oils, ethanol extracts, and CID-derived emulsions were acquired using benchtop (400–2500 nm) and portable (900–1700 nm) spectrometers and calibrated against hydrodistillation, gas chromatography, extraction, and moisture assays. Advanced chemometric modelling using AI-based machine learning techniques, including regularised regression, ensemble learning, boosting algorithms, and neural networks, …

Parameter Dependent Chen-Fliess Series And Their Nonrecursive Interconnections, Natalie T. Pham

Electrical & Computer Engineering Theses & Dissertations

In control theory, a Chen-Fliess functional series is a weighted sum of iterated integrals constructed from a given set of input functions. Such series can be used to represent nonlinear input-output systems. In applications, they have been employed to characterize interconnected nonlinear systems, to solve system inversion and tracking problems, and to design predictive and adaptive controllers.

Distributed parameter systems exhibit spatial dependence along with temporal dependence. Such systems are typically represented in terms of partial differential equations. In control theory, there appears to be no existing method for representing the input-output map of a distributed system via a Chen-Fliess …

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

The Hidden Net Cost Of Data Center Construction And Operation For Household Service Pricing, Arezou Shafaghat, Mikhail Klimenko, Da Hu, Ali Keyvanfar

Faculty Articles

The rapid expansion of artificial intelligence (AI) is accelerating data center construction and creating downstream implications for households. This study examines how AI-era data center costs (comprising construction, energy, water, grid upgrades, cooling, and lifecycle management) move through service supply chains and affect household prices in healthcare, transportation, education, banking, and commerce. It also considers the productivity and welfare benefits that AI may transmit. This study identifies four pass-through channels: utility-rate socialization of energy costs, cloud-platform pricing, sectoral pass-through from AI-adopting industries, and indirect effects through supply chains and labor markets. It introduces the AI-inflated net good basket, defined as …

Artificial Intelligence Based Predictive Simulation And Decision-Support Framework For Full Scale Wastewater Treatment Systems, Muhammad Hassnain, Cameron Veal, Sarada M.W. Lee, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Full scale wastewater treatment plants (WWTPs) generate extensive sensor and laboratory datasets that remain challenging to translate into real-time operational insight, while increasing hydraulic variability, energy constraints, tightening effluent regulations, and events such as sensor faults, hydraulic shocks, and effluent-quality excursions require predictive decision-support tools beyond conventional mechanistic modelling. This study presents a full-scale, industry-deployed artificial intelligence (AI) framework integrating machine learning (ML), simulation, and operational boundary definition across a nitrogen-removal WWTP in Australia. Up to nine years of high-frequency online instrumentation data (≤419,000 records per target; 5–30 min resolution) and ~3000 laboratory samples were used to train and evaluate …

The Privacy Trap: Keeping Sensitive Data Safe In The Ai Era, Xiaohui Liang

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation examines privacy and data security risks associated with the growing use of artificial intelligence, with particular attention to the sensitive information users may disclose when interacting with AI systems. It explores how voice, text, images, and prompts can expose personally identifiable and privacy-sensitive information and introduces privacy threats associated with large language models and other AI systems. The presentation discusses approaches for identifying and protecting sensitive information, including named entity recognition, prompt modification, masking, replacement, and other privacy-preserving techniques. It also examines the trade-off between protecting privacy and maintaining the usefulness of AI-generated responses. Drawing on research in …

Confidenceai: A Wearable And Predictive Ai Ecosystem, Denzil Paul, Xuejun Kong, Raymond Wang, Flora Fleith Lopes, Zhengjia Yang, Brain Chu, Weijian Wang, Leo Chen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This group project presents ConfidenceAI, a conceptual wearable and predictive artificial intelligence ecosystem designed to help teenagers build confidence and manage stress in high-pressure situations such as classroom participation and public speaking. The system combines wrist-based biosensing with a personalized machine-learning model that uses physiological signals, including heart rate and heart rate variability, electrodermal activity, skin temperature, and movement, to learn an individual's baseline patterns and anticipate heightened stress responses. Rather than relying on universal thresholds, ConfidenceAI is designed to personalize the timing, method, and delivery of interventions based on each user's physiological patterns and prior responses. The proposed ecosystem …

Actioncaptions: Making Movies, Shows, And Daily Life Accessible For All, Haruna Hosokawa, Rhianon Gutierrez, Matt Davis, Justin Wang, Eric Gu, Arthur Miao, Andrew Duan, Perry Yu, Ziqi Yan, Susie Yan

Paul English Applied Artificial Intelligence (AI) Institute Publications

This group project presents ACTIONCAPTIONS, a proposed downloadable application or web application designed to improve access to audio content through customizable captioning. The project addresses limitations associated with existing captioning options in settings such as movie theaters, including limited device availability, cumbersome equipment, lack of portability, and the absence of live captioning capabilities. ACTIONCAPTIONS proposes a free and accessible platform that provides both live transcription and captioning for uploaded files. The application incorporates customizable settings for user preferences, including caption size, color, contrast, and language, and is designed around principles of accessibility and universal design. The project demonstrates how speech …

An Intelligent System For Managing High-Priority Vehicle Routing In Smart Cities Using Artificial Intelligence And Spatial Data Analytics, Bahjat Abdulelah Madhloom

Al-Esraa University College Journal for Engineering Sciences

In developing metropolises, there is a risk to public safety in addition to the usual traffic jams. Traffic delays can have life-altering effects every second that fire trucks and ambulances are delayed. Large cities like Baghdad, which suffer from haphazard road sealing and incompetent traffic management, are especially affected, creating daily emergency crisis problems. We create a new intelligent routing system for these top priority vehicles in order to solve this life-or-death problem. Our method uses a trained model with real-time spatial information instead of outdated static maps. A Long Short-Term Memory (LSTM) neural network at its core constantly forecasts …

Mathematical Modeling And Image Processing Algorithms In Artificial Intelligence-Based Technical Vision Systems, Erkin Uljaev, Ulmasjon Ulfat Ogli Abdixalilov

Chemical Technology, Control and Management

This paper presents a comprehensive study on the development of a mathematical model and image processing algorithms for artificial intelligence-based technical vision systems. The research focuses on analyzing the structural organization and functional components of the system, including image acquisition devices, illumination subsystems, and data processing modules that ensure stable and accurate operation. A systematic and integrated approach is applied to model the transformation of object parameters and to enhance the accuracy, robustness, and reliability of object detection processes. Particular attention is given to image preprocessing, segmentation techniques, and contour detection algorithms, as these stages play a crucial role in …

Management Of Multi-Agent Robotic Systems Using Artificial Intelligence Technologies, A.T. Hamiyev

Chemical Technology, Control and Management

This article addresses the problem of managing multi-agent robotic systems using artificial intelligence technologies. A hybrid model combining the Vision-Language-Action (VLA) architecture with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed for the synergetic control of a group of robots. The model is built on the Centralized Training with Decentralized Execution (CTDE) principle, with a Social-Force component incorporated into the reward function to automatically maintain a safe distance between agents. Experiments were conducted in the NVIDIA Isaac Sim environment using a dataset of over 10,000 trajectories and scenarios involving 4 to 16 agents. The results show a 90% …

Vibration Diagnostics For Industrial Machinery: From Signal Processing To Deep Learning, Walid M. Shewakh

Emirates Journal for Engineering Research

When machines break down unexpectedly, it costs factories a lot of money and can even put workers at risk. That is why vibration analysis has become such a big deal in predictive maintenance. By listening to how a machine vibrates, engineers can often tell something is wrong before the whole thing falls apart. This paper looks at how we have gotten better at this over the years. We started with fairly basic signal processing, things like Fourier transforms that have been around for ages, and now we are seeing some really interesting work with neural networks and deep learning. I …

Toward A Metric For Disciplinary Learning In The Age Of Ai: The Unblooms™ Metacognitive Awareness Scale And Discernment Rate In Ai-Mediated Learning, Tina R. Austin

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Telling students to “reflect on their own thinking” has become insufficient in AI-mediated learning environments. When students are rewarded for polished outputs, cognitive offloading to AI tools becomes rational, and traditional snapshot assessments (single-moment evaluations of task completion) produce false signals about whether durable learning has occurred. This problem is sharpened by the broader shift in AI learning tools toward Socratic tutors, study modes, Khan Academy's Khanmigo, and agentic systems that can shape the learner’s process over time. Lodge and Loble (2026) distinguish beneficial cognitive offloading, which frees working memory for intrinsic learning, from detrimental outsourcing, which bypasses the cognitive …

Metacognition As Disciplinary Infrastructure In Ai-Mediated Learning, Tina R. Austin, Jason Gulya, Nick Potkalitsky

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Metacognition is often presented as a response to generative AI’s disruption of teaching and learning, yet the term has become too generalized to guide practice. In AI-mediated environments, asking students merely to “reflect on your thinking” is insufficient. Because AI tools can redistribute cognitive labor, their educational value depends on how students use them and whether that use supports disciplinary forms of reasoning. This essay argues that metacognition must be understood as disciplinary infrastructure: students cannot effectively monitor their thinking without understanding the epistemological and ontological demands of the field in which they are working. Classrooms therefore become sites where …

Optimizing Human Capital In Ai-Enabled Architectures: A Systems Constraint And Capability Analysis, Jeremy A. Schlegel, Jennifer Daffinee

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Artificial intelligence (AI) comprises not only models, but full socio-technical systems involving data pipelines, instrumentation, human-machine interfaces, deployment architectures, and organizational processes for design, monitoring, and evaluation. Using a systems-oriented analytical framework, this paper argues that despite accelerating advances in AI capabilities, human capital remains the enduring and dominant system constraint. Human interfaces define throughput limits in areas such as prompt engineering, data-stream curation, adjudication of model outputs, and the orchestration of hybrid automation workflows including robotics, scraping, and digitization. Synthesizing emerging research across human-AI interaction, machine-learning lifecycle management, organizational adoption, and adult learning theory, we present a socio-technical evaluation …

Law Or Flaw: A Double – Blind Study Comparing Student Comprehension Of Real And Ai Generated Legal Case Briefs, Grant Shostak, Nick Wintz, Melissa A. Petkovsek

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In recent years, the U.S. legal system has seen an increase of legal filings using fictitious court cases or legal propositions generated by artificial intelligence (AI) (Stokel-Walker, 2026). Rules of professional responsibility require that lawyers review filings in which AI was used to ensure their accuracy (Missouri Bar, Office of Legal Ethics Counsel, 2024, Opinion No. 2024‑11) Despite this mandate, filings with fictitious cases and incorrect statements of law are being filed. Besides posing a threat to the parties to an action, such filings may set an unwarranted precedent for future cases. They also tie up court resources searching for …

A Comprehensive Survey Of Artificial Intelligence Applications In Predicting Mining-Induced Subsidence, Deformation, And Landslides: Strengths, Limitations, And Future Trends, Long Quoc Nguyen, Dung Ba Nguyen, Minh Tuyet Dang

Journal of Sustainable Mining

Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms …

Integrating Sustainability Into Engineering Education, Riley R. Moller

Master's Theses

Engineering decisions shape lasting technology that affects environmental integrity, public safety, economic stability, and social equity. As global challenges such as climate change, infrastructure vulnerability, and rapid technological advancement intensify, the responsibilities placed on engineers continue to expand. Sustainability provides a framework for addressing these interconnected pressures through systems-based design and long-term thinking. Yet, sustainability education within engineering programs remains unevenly integrated, often positioned as elective or peripheral rather than as a foundational component of professional preparation.

Prior research shows that sustainability is most frequently addressed through stand-alone courses rather than embedded across required curricula, reflecting disciplinary structures that prioritize …

Can Generative Ai Make Farming Decisions? Current Status And Future Pathways: A Case Study In Row Crop Production With Chatgpt, Nipuna Chamara, Yufeng Ge, Joe Luck, Yu Pan, Saleh Taghvaeian, Cory Walters, Christopher Proctor, Daran Rudnick, Daren Redfearn

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in …

Student Perceptions, Advantages, And Challenges Of Artificial Intelligence Tool Use In Higher Education, Olivia Lynch

Construction Management

Increasingly AI tools are being integrated into higher education and are starting to influence students’ access to information, the completion of assignments, and the use of new material. Generative AI-based tools, writing assistants, and automated research tools allow students to get explanations easily, sort out materials, or explore new areas of knowledge. While these technological advancements can improve productivity and make learning more efficient, they also raise questions of accuracy and academic integrity, as well as concerns about excessive reliance. This study focuses on how Construction Management (CM) students perceive AI tools in their academic work. An anonymous survey was …

Emerging Biomaterials For Next-Generation Wound Healing: From Infection Control To Tissue Regeneration, Unqa Mustafa, Aiman Kaleem, Syeda Zunaira Bukhari, Hamna Riaz, Maryam Iftikhar, Muhammad Usman Munir, Xianghao Xiao, Mingwu Shen, Xiangyang Shi, Mostafa Yazdi, Ayesha Ihsan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Wound healing is a multifactorial biological process that regenerates damaged tissues through a series of molecular and cellular events in a coordinated manner. An interruption in this cascade can lead to impaired healing, especially in diabetic complications, where effective treatment is crucial for controlling infections and promoting tissue regeneration. Recent advances in bioengineering and digital health have catalyzed the development of next-generation wound therapies capable of actively modulating the wound microenvironment, promoting regeneration, and enabling real-time clinical assessment. This review summarizes the fundamentals of physiological wound healing and highlights the significant developments in biomaterial dressings and scaffolds, 3D bioprinting, as …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

Ai-Augmented Financial Advisors: Comparing Ai And Human Analyst Investment Recommendations In Agreement, Performance, And Firm Size Effects, Crystal Chen

Honors College Theses

This study examines the level of agreement and performance between artificial intelligence (AI) generated investment recommendations and human analyst recommendations across U.S. publicly traded firms. Using a sample of twelve companies categorized by firm size (large, mid, and small), the study collects buy, hold, or sell recommendations from generative AI systems and human analysts. Agreement between AI-to-AI and AI-to-human recommendations is measured using Cohen’s Kappa agreement. Portfolio performance is evaluated by constructing equal-weighted portfolios for each recommendation source and size category. Risk-adjusted returns are measured using the Sharpe ratio over 1-, 2-, and 3-month periods. Furthermore, the study tests whether …

A Predictive-Correlational Study Investigating Flight Students' Perceptions Of The Use Of An Artificial Intelligence Instructor In The Simulated Flight Training Environment, Zachary L. Lamothe

Doctoral Dissertations and Projects

The purpose of this quantitative predictive-correlational study is to investigate how perceived ease of use, perceived usefulness, performance expectancy, and perceived enjoyment impact attitude towards use and the behavioral intention to use an artificial intelligence flight instructor in simulated flight training among aviation students earning an aeronautical degree at a collegiate flight training school in the Mid-Atlantic region. Aviation has benefited from different technological advances, and using artificial intelligence technology for flight training could be another improvement as it becomes increasingly sophisticated. The study adds to the literature by addressing the problem of limited information on the perception of artificial …