Computer Sciences Commons^™

The Downstream Effect Of Gender Bias On Academic Performance And Career Aspirations Amongst Female Students In Stem Via Gender-Professional Identity Integration (G-Pii), Chi-Ying Cheng, Shuna Shiann Khoo, Shih-Fen Cheng, Yeow Leong Lee, Vandana Ramachandra Rao

Research Collection School of Social Sciences

Background Gender imbalance in STEM, characterized by a significant underrepresentation of women, remains a significant challenge. Although gender bias is a well-known contributor to women’s attrition from STEM, the psychological mechanisms linking gender bias to departure are less well understood. Our research investigates early antecedents of attrition and the psychological processes that precede leaving the STEM pathway in tertiary education. Drawing upon identity integration research, we propose that perceived gender bias exerts undermines female STEM students’ academic performance and career aspirations by reducing their Gender-Professional Identity Integration (G-PII), a construct that captures individual differences in the perceived compatibility between a …

Metric Depth Estimation From Arbitrarily Degraded Low-Resolution Depth Prompts, Kun Wang, Yun Zhu, Pan Zhou, Na Zhao

Research Collection School Of Computing and Information Systems

We propose AdaDS, an Adaptive Diffusion Sampling framework for prompted metric depth estimation, which estimates high-resolution metric depth from an image and an arbitrarily degraded low-resolution depth prompt. This setting is commonly studied as depth super-resolution, where existing methods typically regress depth directly without an explicit prior over high-quality depth, and thus often produce artifacts under severe or unseen degradations. In contrast, AdaDS exploits the contraction property of Gaussian smoothing: as noise accumulates in the forward diffusion process, the noisy distributions of degraded prompts and their high-quality counterparts converge toward an isotropic Gaussian prior, while the prompt content is gradually …

Diffusion-Enhanced Gflownet For Solving Vehicle Routing Problems, Ni Zhang, Zhiqin Zhang, Ling Pan, Hoong Chuin Lau, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Traditional neural solvers for solving vehicle routing problems (VRPs) often suffer from limited solution diversity, motivating the development of Generative Flow Network (GFlowNet)–based models. However, the effectiveness of these models is frequently constrained by insufficient flow expansion in high-reward regions, limiting their ability to distribute probability across promising solution routes. The deeper exploration could yield superior results. Diffusion models, in contrast, provide stronger structural guidance for exploration. These two paradigms are naturally complementary: GFlowNet can supply edge-level signals for diffusion to embed, while diffusion can guide broader exploration. Leveraging this synergy, we propose Diffusion-Enhanced GFlowNet (DEG), a novel framework that …

Closing The Interactivity Gap: Towards Efficient In Situ Analysis, Bidirectional Steering, And Gaussian-Based Visualization For Large-Scale Scientific Computing, Andres Sewell

All Graduate Theses and Dissertations, Fall 2023 to Present

Scientists use supercomputers to simulate weather, blood flow, plasma, and other systems. The largest machines produce more data than can be saved to disk, so scientists are only able to analyze a fraction of what gets generated. Visualizing the data during the run slows the simulation, because the rendering shares the simulation’s graphics processing units (GPUs). Most simulation codes offer no way to interact with a run in progress. The results are often too large to visualize on a local workstation, and exploring them on the supercomputer means waiting in the queue and spending allocation time. We call these three …

Predictive Tropical Cyclogenesis Using Deep Learning: A Novel Approach To Early Warning Systems, Nikolay Todorov

Computational and Data Sciences (PhD) Dissertations

Tropical cyclogenesis (TCG) is among the least predictable stages of the tropical cyclone life cycle, yet the timing and location of genesis are exactly what early warning systems (EWS) depend on. We present a spatio-temporal deep learning framework that forecasts genesis as a normalized probability field and delivers that skill through a real-time warning pipeline. The model couples a two-layer ConvLSTM encoder with learned temporal-attention pooling over nine 6-hourly steps (0–48 h lead) and a convolutional refinement head that outputs per-cell genesis probability. It is trained on 1980–2025 ERA5 fields and Emanuel potential intensity at 0.25° and evaluated by leave-one-year-out …

Machine Learning-Based Regression For Magnetic Field Prediction From Odmr Spectral Data, Jesse B. Hernandez

Electronic Theses, Projects, and Dissertations

Optically Detected Magnetic Resonance (ODMR) using nitrogen-vacancy (NV) centers in diamond enables sensitive, room-temperature magnetic field sensing, but real ODMR spectra are often noisy and difficult to analyze with traditional peak-fitting methods. This thesis investigates whether machine learning can reliably predict magnetic field strength directly from ODMR spectra, and compares four model families under a single regression task: a random forest, an artificial neural network (ANN), a one-dimensional convolutional neural network (1D-CNN), and a Transformer.

Training data were generated from an NV-ensemble simulation calibrated to real measurements provided by the Ulsan National Institute of Science and Technology (UNIST), spanning 0 …

A Deep Learning Based Three-Stage Fusion Neural Network With Composite Adaptive Momentum Optimizer For Bangla Cyberbully Identification, Mahin Montasir Afif, Abdullah Al Noman, Kazi Abdullah Jarif, Md Emamul Arefin Islam, Sathya Narayana Sharma K, Mahfujur Rahman, Dipta Gomes, Kazi Tanvir, Md Obaidur Rahaman, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

Cyberbullying in Bangla social media is an increasingly critical issue, posing severe risks to mental health and online safety. Despite the growing need for automated detection systems, challenges such as limited linguistic resources and the nuanced characteristics of the Bangla language hinder model performance. In this study, we propose TriB-FNN, a novel and explainable three-stage fusion neural network, designed to effectively capture cyberbully in Bangla social media texts. The model integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Units (GRUs) in a hierarchical pipeline. To optimize training, we employ a Composite Adaptive Momentum (CAM) Optimizer, …

Securing Reconfigurable Systems: Real-Time Ml-Based Hardware Trojan Detection Via Binary Fpga Bitstream Analysis, Rye C. Stahle-Smith Mr.

Caroliniana Undergraduate Research Journal

Reconfigurable computing platforms such as Field-Programmable Gate Arrays (FPGAs) are widely deployed in high-performance embedded systems and AI acceleration workloads. Their post-deployment reconfigurability, however, introduces a critical attack surface: maliciously crafted configuration bitstreams can embed hardware Trojans capable of leaking sensitive data, degrading system reliability, or silently altering computation. This paper presents an AI-based predictive modeling framework for detecting malicious FPGA configurations directly from binary bitstreams, without reliance on source code or netlists. Bitstreams sourced from state-of-the-art (SOTA) Trust-Hub benchmarks were re-engineered for the Xilinx PYNQ-Z1 platform and labeled as benign, malicious, or empty. A 256-dimensional byte-frequency feature vector is …

Comparing The Predictions Of Convolutional Neural Networks, Random Forest Transfer Learning, And Support Vector Machines In The Image Processing Of Neoplasms, Ali A. Wallam

Caroliniana Undergraduate Research Journal

This study investigated the efficacy of Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest Transfer Learning (RFTL) in the image processing of lung neoplasms for enhanced diagnostic accuracy. Leveraging the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), chest CT images were analyzed to compare the three models' performance. It was hypothesized that the most accurate predictions would result from the RFTL model because it would have the most efficient data processing. Results indicated that all three models demonstrated high accuracy, with CNNs excelling in precision, SVMs in recall, and RFTL showing a balanced performance. Precision-recall …

A Brief Review Of Classical And Quantum Relationships Between Entropy And Information, Donovan X. Chapman

PANDION: The Osprey Journal of Research and Ideas

In this review paper, different definitions of classical entropies and a quantum version of entropy, the von Neumann Entropy, are introduced. Correlations between these classical and quantum models of entropy and the relevance of each in information theory are shown. Computing and quantum teleportation are discussed with the goal of providing a pedagogical introduction to some of the applications of information theory.

Salvia Rosmarinus L.-Mediated Synthesis Of Silver Nanoparticles: Physicochemical Characterization And In Vitro Evaluation Of Antimicrobial Activity And Cytotoxicity, Raghad Ahmed Alhawi, Hawazin Ahmed Abid

Karbala International Journal of Modern Science

This study describes the biosynthesis of silver nanoparticles (AgNPs) using an aqueous Salvia rosmarinus L. (SRL) extract (Family: Lamiaceae) as a natural source of reducing and capping agents through an easy and green method assisted by blue diode laser irradiation (450 nm, 5 W, 5 min). The formation of rosemary-mediated AgNPs was verified by observing a color change in the solution and the presence of a surface plasmon resonance (SPR) band at 454 nm in the UV–Vis spectrum. XRD and FESEM revealed crystalline silver-containing nanostructures with an average estimated crystallite size of 15.53 ± 3.54 nm and particles with an …

T-Cinnamaldehyde-Driven Fe2o3 Nps: Activity Against Resistant Salmonella, Gallbladder Cancer, And Molecular Docking Focused On Bacterial Targets, Arshad Mahdi Hamad, Sarab Dalaf Khalaf, Suha Maher Abed

Karbala International Journal of Modern Science

Iron oxide nanoparticles (t-CA-Fe₂O₃ NPs) were synthesized in this study using a green preparation method with trans-cinnamaldehyde (t-CA) as a reducing agent. After observing the first sign of successful preparation, namely the color change, the properties were studied using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), ultraviolet-visible spectroscopy (UV-Vis), and Fourier transform infrared spectroscopy (FTIR) techniques. All characterization results were satisfactory, indicating the success of the preparation process and the purity of the prepared nanoparticles (NPs). The molecular docking process of several proteins important in antibiotic resistance and virulence …

Optimising Cybersecurity Application Utilisation For Digital Learning In Nigeria: An Evidence-Constrained Hybrid Agent-Based And System-Dynamics Model, Adesegun Nurudeen Osijirin, Leonard C. Anigbo, Oliver Okechukwu

Journal of Cybersecurity Education, Research and Practice

This study examines hypothetical cybersecurity-application utilisation interventions for Computer Science undergraduates in Enugu State, Nigeria. Five author-supplied datasets, each containing 364 cases, provide audited sex-specific calibration moments for a hybrid agent-based and system-dynamics model. Seven portfolios are compared over 24 months using 400 paired replications; additional experiments examine dose, effect assumptions, targeting, academic calendars, turnover and peer structure. A 1,200-point Latin-hypercube design is applied to every original scenario. Across 37,600 runs, the main-model median proportions meeting the survey utilisation criterion are 26.4%, 48.6% and 50.3% for status quo, universal combined and equity-adaptive provision. However, matching nominal dose reduces the corresponding …

From Shadow It To Shadow Ai: A Scoping Review Of How Embedded Ai And Employee Use Increase Cybersecurity And Data Privacy Risk, Jenny Ahn, Bogdan Pavlyuchenkov, Lasette Smith, Paul Wagner, Robert J. Honomichl, Ryan Straight, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

The rapid integration of artificial intelligence into enterprise software has introduced a critical governance challenge: Shadow AI, the unmanaged and often invisible adoption of AI tools and features by employees outside formal IT oversight. Unlike traditional Shadow IT, which centers on unauthorized data storage and infrastructure, Shadow AI operates at the knowledge and decision-making layer, introducing probabilistic risks including data leakage, agentic misalignment, and indirect prompt injection that existing security frameworks are ill-equipped to address. This paper presents a Systematic Literature Review (SLR) synthesizing 38 peer-reviewed and industry sources to investigate three research questions: how organizational visibility gaps into embedded …

Quantifying Attacker Payoff In Harvest Now, Decrypt Later Attacks: A Persistence-Based Framework For Trade Secret Risk, Arjun Kumar, Gene Yang, Turiya Misra, Francis Kagai

Journal of Cybersecurity Education, Research and Practice

Harvest-now, decrypt-later (HNDL) assumes that encrypted secrets stolen today will remain worth decrypting when quantum computers eventually make that possible. But what if they do not? This paper models HNDL from the attacker’s perspective by asking a different question: how much will a stolen secret still be worth when it can finally be decrypted? We develop a conditional retained-value framework combining probabilistic Q-day timing, information depreciation, and adversary-access delay. The results reveal a sharp divide. Rapidly depreciating secrets can lose much of their strategic value before quantum decryption becomes useful, while persistent secrets can retain substantial attacker payoff even under …

Semantic-Structural Decoupling: Disentangling Semantic Attention From Structural Bias In The Attention Manifold, Pengkun Jiao, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws. Specifically, MLLMs consistently exhibit disproportionate attention toward certain semantically uninformative visual tokens, a phenomenon termed "register" or "Visual Attention Sinks." While existing inference intervention methods attempt to identify these sink tokens and redistribute their attention weights, such approaches typically treat these tokens in isolation and suffer from computational inefficiency. Instead, we reframe this phenomenon as a generalized textual bias exerted over visual features that extends beyond isolated sink tokens. From this perspective, a pervasive structural bias leads to the dilution of the …

Semiconductor Supply Chain Risk Monitoring And Forecasting: A Hybrid Big Data Architecture And Empirical Validation, Ming Gong, Sangwhan Cha

Harrisburg University Other Works

Global semiconductor manufacturing networks are characterized by deep geographical concentration, high capital intensity, and acute vulnerability to regional supply disruptions. While public merchandise trade statistics provide transparent, reproducible empirical records, transforming these raw macroeconomic streams into actionable supply chain risk intelligence requires both rigorous analytical framing and scalable computational architecture. In this paper, we propose an enterprise-grade cloud data architecture designed for semiconductor risk monitoring, combining scheduled distributed batch processing on Google Cloud (Dataproc/PySpark, BigQuery) with an event-driven streaming ingestion lane (Pub/Sub, Dataflow/Apache Beam). To rigorously evaluate the underlying scoring and forecasting logic without premature distributed deployment costs, we implement …

Smart Building Iot Analytics: An Integrated Cloud Pipeline And The Consequences Of Cumulative-Meter Data Semantics For Forecasting And Anomaly Detection, Likhitha S. Gudalwar, Naga Bhavya Kore, Zeyu Wang, Sangwhan Cha

Harrisburg University Other Works

Commercial and institutional buildings account for roughly one-third of global energy demand and nearly 37% of energy-related greenhouse gas emissions. While modern smart facilities deploy dense Internet of Things (IoT) sensor meshes to monitor power draw and environmental conditions, transforming continuous, heterogeneous streams into reliable analytical intelligence requires rigorous data engineering and precise semantic modeling. In this paper, we formulate, implement, and critically audit an end-to-end cloud pipeline deployed on Google Cloud Platform (GCP). The architecture adapts the Lambda design pattern, separating bulk historical ingestion from an asynchronous real-time streaming lane (Pub/Sub, Eventarc, 2nd-generation Cloud Functions) with an explicit dead-letter …

Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories, Georgia M. Kapitsaki, Maria Papoutsoglou, Christoph Treude, Ioanna Theophilou

Research Collection School Of Computing and Information Systems

Context: Privacy legislation has impacted the way software systems are developed, prompting practitioners to update their implementations. Specifically, the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have forced the community to focus on users’ data privacy. Objectives: Relying on the vast amount of data on developer issues available in GitHub repositories, our aim is to gather empirical evidence on the issues developers of Open Source Software discuss to comply with privacy legislation. Method: We examined such discussions by mining and analyzing 32,820 issues from GitHub repositories. We partially analyzed the dataset automatically to identify …

Spatialimaginer: Towards Adaptive Visual Imagination For Spatial Reasoning, Yian Li, Yang Jiao, Bin Zhu, Tianwen Qian, Shaoxiang Chen, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge for multimodal large language models. Despite promising performance, recent multimodal large language models (MLLMs) often exhibit fragile reasoning traces in spatial intelligence tasks that involve consistent spatial state recognition. We argue that these failures stem from a mismatch between the spatial recognition mechanism and the text-only reasoning behavior of these MLLMs. Effective spatial reasoning requires low-level geometric structure to be faithfully preserved and updated throughout the reasoning process, whereas textual representations tend to abstract away precisely these critical details. …

Data Centre Litigation And Climate Change: Can Claims Against Data Centres Resolve Tensions Between Ai Growth And Climate Goals?, Nick Scott, Heng Wang

Research Collection Yong Pung How School Of Law

As artificial intelligence (AI) has become ubiquitous, climate-related impacts of data centres used to power these technologies have emerged as a growing concern for governments, civil society groups, and the public. As a result of this, a wave of data centre litigation has started to sweep multiple jurisdictions, in particular the United States, often leveraging environmental law. However, many claims do not directly target data centres’ impacts on climate change, and the challenges of aligning AI growth with climate goals may extend far beyond energy use in this infrastructure. This article analyses two research questions: (1) how are mismatched governance …

Climate Transition Stress Testing For Corporate Credit Risk: A Replicable Data-Mining Pipeline Using Esg Risk Scores, Huynh Long Hung Nguyen, Alan @ Ali Madjelisi Megargel, Rajesh Krishna Balan, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

We present a replicable data-mining pipeline for illustrative, scenario-conditioned corporate distress analysis under feature decoupling. The pipeline links Compustat financial statements with Sustainalytics ESG Risk Rating records for 2,144 U.S. non-financial public firms, constructs a standardized Transition Risk Index (TRI), estimates a contemporaneous accounting-distress classifier, and applies transparent transition-cost sensitivities. Distress is defined as annual operating income insufficient to cover annual interest expense; interest coverage itself is excluded from the predictors. The resulting model is not a forward default forecast or a regulatory probability-of-default model. On a held-out cross-sectional test set, the logistic specification obtains AUC 0.909, PR-AUC 0.822, and …

Tiny Large Language Models For Iot Networks: Potentials And Challenges, Muhammed Golec, Suhib Bani Melhem, Yaser Khamayseh, Abdulmalik Alwarafy, Naofal Al-Dhahir

All Works

Large Language Models (LLM), which have gained great momentum in recent years, have revolutionized the field of Artificial Intelligence (AI); while their applicability for hardware-constrained Internet of Things (IoT) environments has begun to be questioned. This has led to the emergence of compact architecture and resource-efficient Tiny LLM models. This survey paper systematically examines Tiny LLMs for IoT networks and classifies existing approaches in five basic dimensions: model architectures, optimization strategies, transfer learning methods, deployment paradigms, and explainability-security integration. By applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, 139 related studies published between 2020 and 2025 …

Defense-To-Attack: Bypassing Weak Defenses Enables Stronger Jailbreaks In Vision-Language Models, Yunhan Zhao, Xiang Zheng, Yige Li, Xingjun Ma

Research Collection School Of Computing and Information Systems

Despite their superb capabilities, Vision-Language Models (VLMs) have been shown to be vulnerable to jailbreak attacks. While recent jailbreaks have achieved notable progress, their effectiveness and efficiency can still be improved. In this work, we reveal an interesting phenomenon: incorporating weak defense cues into the attack pipeline can significantly enhance both the effectiveness and efficiency of jailbreaks on VLMs. Building on this insight, we propose Defense2Attack, a novel jailbreak method that bypasses the safety guardrails of VLMs by leveraging defensive patterns to guide jailbreak prompt construction. Specifically, Defense2Attack consists of three key components: (1) a visual optimizer that embeds universal …

Keyframe Selection From Motion Capture Data With Dual-Agent Reinforcement Learning, Kun Hu, Wang, Clinton Mo, Mingyang Ma, Shaohui Mei, Zebin Chen, Zhiyong Wang

Research outputs 2022 to 2026

Animation production workflows centered around motion capture techniques require animators to edit motions based on a set of keyframes. However, most existing keyframe selection methods are optimization-based, which suffer from the issues of flexibility and efficiency. In this paper, a novel deep reinforcement learning method with dual agents are proposed for unsupervised keyframe selection. First, an S-Agent and an R-Agent evaluate the actions of selection and refinement, respectively. A deep spatio-temporal network, namely graph keyframe evaluation network (GKEN), is proposed for the agents. Then, an animation specified reward is devised based on reconstruction, which fulfills three important properties of the …

Evaluating Scalability, Extrapolation Limits, And Microgeographic Divergence: A Hybrid Batch–Streaming Architecture For Urban Crime Analytics, Yicheng Jiang, Sangwhan Cha

Harrisburg University Other Works

While municipal crime statistics frequently report broad, citywide downward trends, aggregate metrics often mask localized divergence at microgeographic scales. Concurrently, data engineering pipelines designed to ingest, process, and forecast municipal incidents must navigate fundamental trade-offs between distributed computing overhead, low-latency streaming ingestion, and the extrapolation limitations of machine learning estimators. This paper presents the design, end-to-end cloud implementation, and empirical evaluation of a dual-path batch and streaming Big Data architecture deployed over four contiguous Philadelphia neighborhoods (West Kensington, East Kensington, Fishtown, and Port Richmond). Historical incidents (309,106 records spanning 2006–2026) were processed via Apache Spark and MapReduce backends, cross-validating an …

Llm-Driven Simulation Method For Mobilization Decision-Making In Weapons And Equipment Supply Chain, Qichang Guo, Jihai Zhang, Zichu Qin

Journal of System Simulation

Abstract: A LLM-driven multi-agent simulation model for production mobilization in the weapons and equipment supply chain is developed to address the practical challenge of transitioning the supply chain from peacetime to wartime production in response to surging wartime demand. The model consists of a military demand layer, a supply chain execution layer, and a mobilization decision-making layer. An LLM is incorporated as a mobilization decision generation module to represent the dynamic regulation of production conversion and expansion under war-readiness budget and production safety constraints. The simulation results show that compared with the no-mobilization and rule-based mobilization strategies, the LLM-based strategy …

Chemical Plant Control Based On Dynamic Simulation And Reinforcement Learning, Yuxin Li, Haijie Li, Shangbo Han, Kai Gao, Qingyuan Zhang, Yanwen Xiao, Zhengcai Liu

Journal of System Simulation

Abstract: An intelligent control strategy framework combining offline training and on-site deployment for regenerative thermal oxidizer (RTO) operation control based on RL is proposed to solve the problem that fluctuations in exhaust gas concentration and flow, as well as changes in working conditions, make it difficult for RTO to maintain the system operating parameters within the economically optimal range in practical applications. In the offline phase, RL is employed to enable autonomous policy learning and global optimization. In the online phase, an adaptive policy correction mechanism based on real-time data is introduced. RL control strategy is integrated with the operation …

Five-Dimensional Digital Twin System For Closed-Loop Gasoline Blending Optimization, Yaning Fang, Renchu He

Journal of System Simulation

Abstract: An adaptive blending optimization system based on five-dimensional digital twin theory is designed and developed to address the challenges of strict nonlinear constraints and frequent raw material fluctuations. A five-dimensional model architecture is constructed to achieve virtual-real mapping and synergy throughout the blending process. A closed-loop control strategy of "online model correction and dynamic recipe optimization" is proposed. The constrained recursive least squares (RLS) algorithm is adopted to update the octane blending effect model parameters, resolving the model mismatch caused by raw material fluctuations. The linear decreasing weight particle swarm optimization algorithm (LDW-PSO) is combined to determine the optimal …

Research On Digital Shipyard Design-Procurement Coupling Based On Multi-Agent Systems, Kaitai Peng, Dingyi Feng, Wenbo Zhao, Feier Chen

Journal of System Simulation

Abstract: To address the issues of low supply chain collaboration efficiency and inadequate dynamic scheduling capability caused by the disconnection between design and procurement in digital shipbuilding, a method integrating multi-agent simulation with the Q-learning reinforcement learning algorithm is proposed, and a four-tier dynamic supply chain model coupling design and procurement is developed, covering mines, steel mills, shipyards, and shipowners. Based on the depth of collaboration, steel mills are classified into three levels, including full collaboration, partial collaboration, and noncollaboration, with differentiated state spaces and reward functions designed to simulate intelligent pricing and game behaviors under information asymmetry. Simulation results …