Digital Commons Network^™

Optimization Of Air Defense And Antimissile Firepower Resource Allocation Based On Adaptive Hybrid Evolution, Wei Liu, Delong Chen, Ze Liu, Rui Wang, Kaiwen Li, Tao Zhang

Journal of System Simulation

Abstract: Air defense and antimissile firepower resource allocation is a core optimization problem in modern defense systems. Under complex conditions such as spatiotemporal constraints and firepower resource limitations, this problem involves the optimal configuration of limited interceptors and belongs to the class of NP-hard multi-constrained combinatorial optimization problems. This paper established a comprehensive mathematical model encompassing range constraints, time window constraints, feasibility matrices, and interception probability models. To address the high-dimensional nonlinearity of the problem, an adaptive hybrid evolutionary algorithm (AHEA) was proposed. The algorithm integrated problem-aware initialization, adaptive parameter control, seven specialized neighborhood search operators, and an adaptive strategy …

Mechanism Analysis Of Parasitic Torque In Electric Loading Systems, Xiaozhe Sun, Zhenyu Fu, Zhaoke Xu, Jianxin Li

Journal of System Simulation

Abstract: To address the issues of sources and mechanisms of parasitic torque in electric loading systems for aircraft actuator loads, a functional model of the electric loading system was established. Numerical simulations and Monte Carlo methods were employed to analyze the sources of parasitic torque and its primary influencing factors. The influence mechanisms and degrees of the system's external inputs and internal disturbances on parasitic torque were analyzed and validated through single-parameter and multi-parameter analyses. The results indicate that parasitic torque is predominantly influenced by factors such as loading command frequency, sensor signal bias, and loading motor parameters. In particular, …

Simulation On Water Hammer Characteristics Of Bipropellant Attitude And Orbit Control Propulsion System, Xianwei Lang, Yantao Wang, Fang Zhang, Yizhen Zu, Xinyu Zhang, Weibin Xiang

Journal of System Simulation

Abstract: To address the water hammer problem in bipropellant attitude and orbit control propulsion systems during start-up, shutdown, and periodic operation, the water hammer characteristics under different operating conditions are investigated using simulation methods. The water hammer characteristics of annular and branched propellant delivery lines are compared, and the effects of multiengine interactions under various operating conditions, as well as the influence of water hammer on engine performance, are analyzed. The results show that the attenuation rate of pressure fluctuation in the annular pipeline system is significantly higher than that in the branch pipeline system. Under the condition of multi-cycle …

Intersection Positioning Algorithm With Spatial Translation Based On Sar Scene Matching, Cheng'en Pu, Yumin Lai, Rui Shi, Yan Liao, Kezi Meng, Lifeng Qu

Journal of System Simulation

Abstract: To address the problem that the positioning precision of the inertial navigation system cannot meet the requirements of autonomous positioning when the aircraft flies, an autonomous positioning method with spatial translation based on circular scanning scene matching of SAR was proposed. The spatial translation positioning model of the aircraft was established according to the position of the ground matching points obtained by single point and single circular scanning scene matching of SAR, the oblique distance between the matching points and the aircraft, and the inertial measurement information of the aircraft. The characteristic information of the matching point sequence was …

State Monitoring Of Nuclear Power Connection Sleeve Quality Inspection Equipment Driven By Digital Twin, Yandong Nan, Jinda Zhu, Xinbin Lu, Zhiying Qin, Dandan Qi, Zhiheng Ding

Journal of System Simulation

Abstract: To address the problems of delayed state perception, single monitoring dimension, and insufficient visualization in the quality inspection equipment for nuclear power connection sleeves, a state monitoring method driven by digital twin was proposed. A digital twin-based collaborative state monitoring framework for the inspection equipment was constructed. Based on the OPC UA technology, a multi-source information interconnection model was established. A finite state machine model was employed to discretize and logically drive the inspection process, and a hierarchical verification strategy was proposed to establish a multi-dimensional motion state monitoring mechanism. A surrogate model coupling the radial basis interpolation function …

Design And Application Of Collaborative Simulation System For Satellite Constellation Flight Missions, Dong Yan, Hanzhe Yang, Fangfang Jiang, Chengbao Liu, Peng Zhang

Journal of System Simulation

Abstract: To meet the requirements of the collaborative drill for the ground operation and control system of the satellite constellation, as well as the verification and evaluation of in-orbit complex missions, the design ideas and implementation methods of the collaborative simulation system for satellite constellation flight missions were proposed. By adopting the time correction mechanism of the time-scale distributor, the time synchronization problem among the simulation nodes and the operation and control simulation system was solved. By adopting the communication technology based on improved PDXP + UDP, the problem of cross-node data interface and inter-satellite communication simulation was resolved. A …

Modeling And Simulation Of Target Characteristic Architecture Of Target Missile Based On Dodaf, Tuo Zhao, Yuqiao Liu, Yuan Ma, Yun Cheng, Shen Li

Journal of System Simulation

Abstract: In view of the problems of incomplete elements and missing architecture in the research of target characteristics of target missiles, this paper introduced the panoramic view, operational view, and capability view models in the DoDAF from the perspective of system engineering. According to the view development sequence of "panoramic description, operational decomposition, and capability matching", a target characteristic architecture of the target missile was constructed, which included target elements such as maneuvering characteristics and infrared radiation characteristics. The mapping relationship among "operational layer, capability layer, and target characteristic layer" of the target missile was obtained, and the architecture was …

Overall Design Method Of Low Earth Orbit Communication Satellite Based On Mbse, Jiace Shang, Rui Zhang, Ya Dai, Hua Zhu, Siqi Hu, Linqiang Ge, Chenguang Shi

Journal of System Simulation

Abstract: Drawing on the model-centric design philosophy of model-based systems engineering (MBSE), this study constructs a model architecture suitable for the design and analysis of low Earth orbit communication satellites. This architecture adopts a multi-dimensional matrix approach. Vertically, it traverses the mission layer, system layer, satellite general design layer, and subsystem layer, achieving top-down hierarchical decoupling. Horizontally, it establishes a comprehensive view mapping mechanism covering the domains of requirements, functions, structure, and performance. Integrating the characteristics of product development, a modeling process covering the entire lifecycle—from mission demonstration and overall design to subsystem design and integration verification—has been established. The …

Dynamic Model-Driven Verification Framework For Modular Aerial Bomb Systems, Wenlong Li, Shuhan Sang, Yusheng Liu, Haiyan He, Zan Liang, Wenqiang Yuan, Biao Niu, Weifeng Luo

Journal of System Simulation

Abstract: To address the problems of high verification costs, difficulty in covering dynamic behaviors, and lack of quantitative closed loops in the design stage of modular complex equipment, a dynamic model-driven modular system verification framework was proposed. Based on model-based systems engineering (MBSE) modeling, a structural coupling quantification model was constructed using the number of interfaces, signal interaction frequency, and dependency intensity. Dynamic tests were conducted in high-fidelity virtual simulation to collect data; performance rating for indicators such as accuracy, response, and stability, as well as system's comprehensive rating, were obtained, and the rating feedback was used for iterative optimization. …

Reading The Room: A Structural Account Of Constraint-Based Processing And Its Limits In Artificial Systems, Griselda Poe

Publications and Research

This paper does not introduce a new structure.

It makes explicit a structural relation implied but not directly stated in prior work.

Human interpersonal processing is grounded in the co-presence of internal constraint and dependency.

These do not exist as separable components.

They form a single structural condition.

This paper shows that what is commonly described as “reading the room” is not a unitary function.

It differs across configurations in how this condition is processed.

In EF configurations, the effect of this condition is expressed through translation into self-return: outputs are evaluated in terms of how they return to the …

Extreme Cf: A Structural Account Of Processing Absence And Category Absence In Cf-Foregrounded Configurations, Griselda Poe

Publications and Research

Existing frameworks of cognition and intervention assume that processing occurs, meaning is available, and evaluation can be applied.

This paper describes a configuration in which this assumption does not hold.

In CF-foregrounded processing where modulation is absent, processing occurs only when coherence is satisfied. When coherence is not satisfied, processing does not occur. Under this condition, no representation is generated, no evaluation applies, and no action selection is produced.

Within single-layer cognitive models, EF and CF are not distinguished. Within such models, CF does not exist as a condition.

This configuration has not been represented as a category within existing …

The Digital Neuron: Neural Cellular Automata For Neural–Symbolic Translation, Nicole Assenza

SMU Data Science Review

A neural cellular automata (NCA) architecture, referred to as Pluto’s NCA, was developed to characterize bilateral communication and semantic reciprocity between symbolic representations and a spatially distributed update field. The architecture employs an encoder–automata–decoder pipeline that maps symbolic inputs into a multichannel state field and reconstructs them through agreement-driven attractor convergence within a stable semantic attractor landscape. System behavior was evaluated under controlled perturbations, including rhythmic desynchronization, graded ablations, correlated and independent noise, and percolation-based structural degradation. Quantities such as Agreement(t), internal coherence Aᵢ(t), the recovery time constant τ, and the critical percolation threshold pc were measured to assess stability, …

Leveraging Convolutional Neural Networks For Through-The-Wall Radar Imaging: Challenges, Impacts, And Future Directions, Tumaini Edgar, Abdulla F. Ally, Abdi T. Abdalla

Tanzania Journal of Engineering and Technology (TJET)

Through-the-wall radar imaging (TWRI) is an essential technology for military and rescue applications; however, its performance in detecting and visualizing high-quality images of targets behind walls is significantly degraded by multipath reflections and signal attenuation. This paper reviews the current state of TWRI and its challenges, and explores the transformative potential of deep learning, particularly convolutional neural networks (CNNs), in addressing these challenges. Peer-reviewed articles published from 2018 to 2024 were analysed to examine CNN applications in addressing TWRI challenges. The analysis reveals that using CNNs, TWRI systems can be more effective by filtering wall distortions, reducing noise, lowering computational …

Hijacking The Prompt: A Survey Of Prompt Injection Attacks, Detection, And Defense In Large Language Models, Edward J. Griggs

Cybersecurity Undergraduate Research Showcase

Prompt injection attacks, ranked the number-one vulnerability in AI systems by OWASP's 2025 Top 10 for Large Language Model Applications, remain largely unsolved, and this survey examines why. As large language models (LLMs) are deployed across enterprise workflows, agentic systems, and consumer tools, their fundamental inability to distinguish trusted instructions from untrusted user data has created a persistent and expanding attack surface. This paper presents a structured taxonomy of prompt injection attack vectors, including direct injection, indirect injection, multimodal attacks, tool and agent exploitation, hybrid chained techniques, and autonomous propagating threats. These vectors are mapped across five impact categories (data …

Negotiating Digital Identities With Ai Companions: Motivations, Strategies, And Emotional Outcomes, Renkai Ma, Shuo Niu, Lingyao Li, Alexander Hirth, Ava Brehm, Rowajana Behterin Barbie

Computer Science

AI companions enable deep emotional relationships by engaging a user's sense of identity, but they also pose risks like unhealthy emotional dependence. Mitigating these risks requires first understanding the underlying process of identity construction and negotiation with AI companions. Focusing on Character.AI (C.AI), a popular AI companion, we conducted an LLM-assisted thematic analysis of 22,374 online discussions on its subreddit. Using Identity Negotiation Theory as an analytical lens, we identified a three-stage process: 1) five user motivations; 2) an identity negotiation process involving three communication expectations and four identity co-construction strategies; and 3) three emotional outcomes. Our findings surface the …

Creating Disability Story Videos With Generative Ai: Motivation, Expression, And Sharing, Shuo Niu, Dylan Clements, Hyungsin Kim

Computer Science

Generative AI (GenAI) is both promising and challenging in supporting people with disabilities (PwDs) in creating stories about disability. GenAI can reduce barriers to media production and inspire the creativity of PwDs, but it may also introduce biases and imperfections that hinder its adoption for personal expression. In this research, we examine how nine PwD from a disability advocacy group used GenAI to create videos sharing their disability experiences. Grounded in digital storytelling theory, we explore the motivations, expression, and sharing of PwD-created GenAI story videos. We conclude with a framework of momentous depiction, which highlights four core affordances of …

When Generative Ai Is Intimate, Sexy, And Violent: Examining Not-Safe-For-Work (Nsfw) Chatbots On Flowgpt, Xian Li, Yuanning Han, Di Liu, Pengcheng An, Shuo Niu

Computer Science

Content Warning: This paper contains sexually explicit and violent images and text. User-created chatbots powered by generative AI offer new ways to share and interact with Not-Safe-For-Work (NSFW) content. However, little is known about the characteristics of these GenAI-based chatbots and their user interactions. Drawing on the functional theory of NSFW on social media, this study analyzes 376 NSFW chatbots and 307 public conversation sessions on FlowGPT. Findings identify four chatbot types: roleplay characters, story generators, image generators, and do-anything-now bots. AI Characters portraying fantasy personas and enabling hangout-style interactions are most common, often using explicit avatar images to invite …

Ai’S Role In Searching For Evidence: Friend And Foe, Barbara (Basia) Delawska-Elliott, Brandon Wilkinson

Publications 2026-present

No abstract provided.

Deconstructing The Black Box: An Explainability Analysis Of Deep Learning Architectures In Cytopathology, Chase A. Garrett

SACAD: Scholarly Activities

Deep learning shows strong potential in medical-image analysis, yet adoption in cyptopathology

remains limited. Cytopathology could benefit from deep learning applications by improving

diagnostic efficiency and accuracy. However deep learning comes with a notorious “black box”

that keeps the models from being transparent and trustworthy for widespread clinical adoption.

We conducted a comprehensive and comparative analysis of several deep learning architectures

for multi-class classification of acute leukemia types, ALL, AML, and normal healthy cells from

peripheral blood smear images. The models in this research include a Vision Transformer (ViT)

and a diverse selection of Convolutional Neural Network (CNN) models. The …

Pre-Experiential Constraint Reconstruction: A Structural Account Of The Prior Layer In Dialogue With Jung, Griselda Poe

Publications and Research

Experiences commonly described as pre-experiential memory—such as immediate recognition, familiarity without prior exposure, and the sense of "already knowing"—are typically interpreted as the retrieval of stored content. However, this storage-based account does not provide a structurally consistent explanation of how such content is preserved prior to experience or reactivated in a form that aligns with present input.

This paper extends the three-layer cognitive architecture developed in prior work in this series (Poe, 2026n), in which cognition operates across Core processing, Modulation, and an operationally inaccessible Prior layer that supplies constraints to all processing.

This paper proposes an alternative account in …

Dual History Enhancement With Hybrid Hypergraph-Graph Networks For Temporal Knowledge Graph Reasoning, Kailun Ye, Xiangjie Kong, Yuchao Zhang, Xuan Wang, Linan Zhu, Jiaxin Du, Guojiang Shen, Jianxin Li

Research outputs 2022 to 2026

Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual …

Does Culture Translate? A Case Study On Artificial Intelligencegenerated Text Concerning Japanese-Style Employment, Kanji Kitamura

Languages, Literatures, and Cultures Faculty Publications

Translation matters to international business when it has a discernible impact on firm performance or critical business processes. Current developments include machine translation, which is evolving in tandem with generative artificial intelligence (AI). On one hand, useful translation apps have become available to help the public communicate internationally. On the other hand, recent research concludes that even machine translation's best output still requires post-editing by a human. This argument implies that a machine-translated, grammatically accurate text may not always make sense to a human audience, possibly because of a semantic-pragmatic issue beyond the lexical level. This paper regards it as …

Statistical Inference Is Not Moral Reasoning: The Case Against Ai On Hospital Ethics Boards, Elan J. Haronian

Seaver College Research And Scholarly Achievement Symposium

As generative AI becomes more integrated in healthcare, it seems inevitable that AI will eventually be used on hospital ethics committees. However, before implementation, their roles need careful consideration. Although AI promises to reduce costs, increase efficiency, and reduce human workloads, there are important ways in which it is limited, especially when human emotion and connection are crucial, as in clinical ethics boards.

In this paper, I highlight several problems preventing AI from being useful on hospital ethics boards. These include issues of opaque reasoning (the “black box” problem), liability, transparency, privacy, and consent. While there are proposed frameworks for …

Older Adults And Emerging Technology Fraud In The Ai Deepfake Era, Thiago Neves

Research Days

Artificial intelligence has accelerated faster than society's ability to adapt, leaving older adults extremely vulnerable to AI-generated fraud. Americans over age 60 lost $4.9 billion to scams in 2024, 43% more than the previous year. In this research, I investigate how digital illiteracy, combined with AI-generated deepfakes, creates this crisis. Older adults struggle with three principal vulnerabilities: distinguishing legitimate sites from scams, judging whether online information is truthful, and understanding how algorithms use their data. AI weaponizes these gaps through voice clones, synthetic video calls, and personalized phishing emails that avoid the trust cues seniors tend to rely on. I …

The Core-Modulation Architecture (Cma): A Structural Overview Of Hallucination As Structural Mismatch, Griselda Poe

Publications and Research

Hallucination is defined not as factual error but as a structural failure of alignment across target, layer, and constraint.

Within the Core-Modulation Architecture (CMA), cognition proceeds through layered processing and requires layer-specific termination conditions. Hallucination arises when Modulation-level termination is registered as completion while Core-level resolution has not occurred, producing structurally ungrounded but locally coherent outputs.

Detection is therefore structural rather than content-based, focusing on layer mismatch and termination failure.

This document presents a minimal structural account of hallucination within the CMA framework.

Advice For Incorporating Ai Tools Into Your Legal Practice, Celia Bigoness, Robert A. Mackenzie, David J. Reiss

Cornell Law Faculty Publications

We have been speaking with many lawyers and law students about using generative artificial intelligence (AI) tools in their legal practice. We are struck by the fact that many of them have not been experimenting much, if at all, with the tools that are available to them - although many acknowledge that their clients are increasingly integrating generative AI into their businesses. We have been integrating a lot of these tools into our own professional lives, and here are some tips to help lawyers and law students get comfortable with AI tools that can help them, in big ways and …

Motivation Without Borders: Applying The Octalysis Framework To Global Faculty And Student Engagement In Ai Era, Harika Rao

Faculty and Staff Publications & Presentations

No abstract provided.

A Comprehensive Survey Of Agentic Ai: Design Principles, Security Risks, And Ethical Consideration, Md Shaba Sayeed

ATU Scholars Symposium

In the past several years, the world has managed to transition away from simple automation to independent AI systems. Agentic AI is an agent that can work independently, carrying out all essential plans and implementations without any kind of supervision from a human being. This review has tried to demonstrate the transformative impact that Agentic AI brings to contemporary models of intelligence by means of synthesis of perception, reasoning, and goal. We utilized the phrases Agentic AI, autonomous AI, multi agent systems as keywords in Google Scholar, ScienceDirect, arXiv, and other digital libraries. We have used these 38 main papers …

Dermal: A Multi-Input Deep Learning Model For Improving Access To Dermatological Screening, Aubreye Freeman

ATU Scholars Symposium

According to the World Health Organization's press release on December 12, 2024, global healthcare spending is dropping significantly, leaving a large percentage of the world without proper healthcare. In an attempt to alleviate this problem, with respect to the field of dermatology, we created a deep learning model, Dermatology Enhanced by Recognition and Machine Aided Learning (DERMAL), to assist in diagnosing skin conditions. DERMAL was trained on a portion of the Google and Stanford Medicine's SCIN dataset, which has more than 10,000 images of various skin conditions. The 9 most common skin conditions of the dataset were selected as the …

Llm-Based Stock Sentiment And Market Intelligence Platform, Joshua Thrower, Andrew Pinkerton, Ian Duggan, Wyatt Lester

ATU Scholars Symposium

Financial markets increasingly react to social media discourse, yet investors lack tools to translate this unstructured commentary into measurable indicators. Platforms such as YouTube host extensive discussions about publicly traded equities, but extracting reliable sentiment trends from high-volume, noisy comment streams remains technically challenging. This project develops a stock sentiment and market intelligence platform that transforms YouTube comment data into aggregated sentiment indicators aligned to specific equities. Comments are mapped to equities using ticker specific keyword identification combined with contextual filtering to reduce false associations from ambiguous or off-topic mentions. The system assigns numerical sentiment scores to individual comments and …