Digital Commons Network^™

3d Object Tracking Registration Based On Improved Rbot Method, Jiarui Zhou, Haihua Cui, Pengcheng Li, Shihao Gu, Huipu Hao, Xifu Zhao, Anan Zhao, Tao Jiang

Journal of System Simulation

Abstract: To address the limitations of region-based object tracking (RBOT) in handling isotropic objects and scenarios with similar foreground-background colors, an improved method integrating edge features is proposed. The approach employs edge detection to extract object contours and designs a region segmentation strategy incorporated into an energy function framework to optimize internal line and edge consistency, thereby enhancing adaptability in dynamic environments and improving pose estimation accuracy. Validation through augmented reality assembly experiments on an aero-engine demonstrates that the proposed method effectively reduces rotational and translational errors, achieving initialization deviations of less than 1.5° and 0.5%, respectively. For static …

3d Reconstruction For Stadium Cad Drawings Based On Graphic Element Arrangement Pattern Analysis, Shang Ma, Mengyu Zhang, Lan Zhang, Gang Yang

Journal of System Simulation

Abstract: To address the issue of the time-consuming and labor-intensive manual conversion of two-dimensional CAD design drawings of buildings into three-dimensional models, and leveraging the characteristic that stadiums contain a large number of repetitively and regularly arranged objects, this study proposes a similar graphical element detection algorithm. This algorithm detects similarities between graphical elements by constructing their bounding boxes and calculating the L2-Norm distance, identifying all graphical elements of the same type within the CAD drawing. Furthermore, a transformation sequence detection algorithm is proposed. Based on the geometric transformation relationships between graphical elements, a geometric transformation space is defined. By …

Visual Relocalization Method Combining Region Classification And Local Feature Enhancement, Yining Wang, Yanli Liu, Guanyu Xing

Journal of System Simulation

Abstract: Visual relocalization tasks have important application value in fields such as digital twin and augmented reality. The current mainstream methods still face challenges such as mismatch between coordinate regression scale and receptive field and insufficient attention to local information. A visual relocalization method that combines region classification and local feature enhancement is proposed. The coordinate regression problem in large space is transformed into a multi-region classification problem and a coordinate regression problem inside a small scene, which significantly reduces the uncertainty of coordinate regression and makes the network globally have a large receptive field. A conditioning layer using deep …

Diffusion Model For Human Motion Generation With Fine-Grained Text And Spatial Control Signals, Binze Jiang, Wenfeng Song, Xia Hou, Shuai Li

Journal of System Simulation

Abstract: To improve the accuracy, controllability, and realism of text-driven human motion generation, a novel method is proposed that integrates fine-grained textual semantics with spatial control signals. Within the diffusion model framework, both global text tokens and body-part-level local tokens are introduced. These are encoded using CLIP to obtain corresponding features, which are then fed into the motion diffusion model to enable fine control over different body parts. Spatial guidance is used to dynamically adjust joint positions during the diffusion denoising process, ensuring that the generated motion adheres to spatial constraints. Realism guidance is incorporated to enhance the naturalness and …

Virtual Reality Rehabilitation Training System Based On Multimodal Brain-Computer Interface, Jing Qu, Kaining Fang, Shantong Zhu, Lingguo Bu

Journal of System Simulation

Abstract: The aging population has led to an increasing demand for rehabilitation for cognitive and motor functions. In response to the lack of interest in traditional rehabilitation and the absence of objective physiological assessment in existing virtual reality (VR) rehabilitation systems, a VR rehabilitation training system based on multimodal brain computer interface is developed by integrating VR interaction, near-infrared brain functional imaging, and motion capture technology. An immersive cognitive-motor integrated training environment was constructed to guide users in completing upper limb tasks. By recruiting subjects and synchronously collecting brain network data and Kinect upper limb motion parameters, multimodal assessment …

A Noncovalent Click-To-Release Strategy To Control Bond Cleavage And Prodrug Activation, Xuancheng Fu, Bowen Xu, Suman Maity, Michelle Wu, Luke G. Westbrook, James H. Henderson, Yaoying Wu, Katie A. Edwards, Atanu Acharya, Xiaoran Hu

Pharmacy Faculty Scholarship

Click-to-release chemistry enables bioorthogonal bond cleavage and controlled release via a click-type ligation reaction serving as both the trigger and means of localization. Extending this concept beyond covalent ligation reactions, we introduce a noncovalent click-to-release strategy based on cucurbit[7]uril-adamantane (CB-Ad) association. The CB host molecule forms a pre-assembled host-guest complex with a self-immolative guest (SIG) SIG1, where the masked SIG remains inert. Introduction of a high-affinity guest Ad initiates the CB-Ad noncovalent click reaction, displacing SIG1 and triggering its self-immolation and cargo release. As a proof-of-concept, we used a prototype prodrug SIG2 to demonstrate our strategy's potential for controlled therapeutic …

Defect Detection Method Based On Hierarchical Microscopic Feature Modeling And Simulation, Jing Zou, Xu Tan, Junji Mao, Haidong Gao, Jianrong Tan

Journal of System Simulation

Abstract: To address the challenge of detecting small and low-contrast defects in complex microscopic images, a defect method technology based on hierarchical microscopic feature modeling and simulation is proposed. The method is built on the RT-DETR (real-time detection transformer)framework to construct the HM-RTDETR (hierarchical microscopic RT-DETR) model. It maintains the global feature modeling ability of the Transformer and introduces a Dense O2O-Mosaic, a high-density one-to-one Mosaic augmentation strategy, to increase supervision density for small samples. A depthwise separable convolution (DWConv) module is used to enhance local detail extraction in microscopic textures, and a learnable PatchExpand module is applied …

Material Reconstruction From Single Image Combining Neural Networks With Singular Value Decomposition, Zhiqiang Li, Xukun Shen, Yong Hu, Xueyang Zhou, Yifan Chen

Journal of System Simulation

Abstract: The tabulated BRDFs (bidirectional reflectance distribution function) can realistically reproduce the surface appearance of objects. However, due to their high-dimensional characteristics and the fact that a single planar image contains limited reflectance information and small differences, methods for estimating tabulated BRDFs typically require complex equipment or the capture of multiple images. To address this issue, a method is proposed for reconstructing material properties from a single image by combining neural networks with singular value decomposition. The singular value decomposition is introduced to compress the material into a lower-dimensional space. The task of solving the tabulated BRDFs is simplified to …

Full-Body Co-Speech Gesture Generation Based On Spatial-Temporal Enhanced Generation Model, Shuozhe Zhang, Wenfeng Song, Xia Hou, Shuai Li

Journal of System Simulation

Abstract: Full-body co-speech gesture generation significantly enhances the interactivity of virtual digital humans, requiring generated gestures to not only align accurately with speech but also demonstrate realistic full-body dynamics. To address limitations of existing methods—Transformer-based approaches often overlook temporal features of action sequences, while diffusion model-based ones inadequately capture spatial correlations between body parts, a full-body action generation method integrating diffusion models, Mamba, and attention mechanisms is proposed. We introduce the spatial self-attention-temporal state space model (STMamba Layer) as the core of denoising network to extract
inter-part spatial features and intra-part temporal features, thus enhancing action quality and diversity. …

Vrbt: Vr Badminton Training With Multitask Injury Alerts Based On Lightweight 3d Skeletal Reconstruction, Yuning Zhu, Meng Yang, Tianyue Chen, Weiliang Meng

Journal of System Simulation

Abstract: To overcome the limitations of traditional badminton training, a VR training method that integrates multiple models for collaborative simulation is proposed. A "perception-decision- interaction" framework is developed within Unity, featuring diverse training modules powered by a physics engine for realistic trajectory simulation. The system employs a lightweight MHFormer for 3D pose estimation and a novel multi-task model (enhanced injury prediction system, EIPS) that combines random forest and XGBoost to jointly assess injury risk. This approach offers a solution for balancing real-time performance with accuracy in skeleton reconstruction and enables personalized training through dynamic risk assessment.

Addressing The Void Of Ai Policies In Education For Students With Specific Learning Disabilities, Mikyung Shin, Fatmana Deniz, Latesha Watson, Cynthia Dieterich, Kathy B. Ewoldt, Friggita Johnson, Jennifer E. Kong, Sung Hee Lee, April Whitehurst

Education Faculty Articles and Research

The purpose of this study was to identify the current state of artificial intelligence (AI) policies in U.S. education and propose actionable recommendations through large language model–based topic modeling and Delphi surveys. Out of 12 policy documents released between 2015 and 2025, only two documents (National Center for Learning Disabilities, 2024; W.A. v. Clarksville/Montgomery County School System, 2024) specifically addressed learning disabilities. Policy documents addressing topics such as AI-driven risk assessment, data protection, legal risk management, and ethical guidelines covering other disabilities and general AI in education policy were provided as baselines that could be discussed and validated through …

Statewide Sanctuary Policies And Female Homicide Rates, 2016–2021, Kaitlin M. Boyle, Sophia Shaiman, Veronica Valencia Gonzalez, Wendy Regoeczi

Faculty Publications

The current study examines whether state immigration enforcement policies, such as sanctuary policies that limit local police cooperation with federal immigration enforcement, are associated with female homicide rates in the United States (2016–2021). We examine crude female homicide rates from the National Violent Death Reporting System (N = 244 state-years), which we stratify by race and ethnicity (Hispanic of all races, non-Hispanic Black, and non-Hispanic White) and victim–suspect relationship (domestic vs. stranger homicide). State-years with policies that are more protective of immigrants were not associated with higher homicide rates for any group. Instead, state-years with these policies reported significantly lower …

Pdr-Stgcn: An Enhanced Stgcn With Multi-Scale Periodic Fusion And A Dynamic Relational Graph For Traffic Forecasting, Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu, Guanci Yang

Faculty Publications

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and …

Military Metaverse: Conceptual Connotation, Construction And Application Framework, Key Issues, Dayong Liu, Zhiming Dong, Jiancheng Gao

Journal of System Simulation

Abstract: Based on the analysis of the concept of the metaverse, the military metaverse concept model is established and compared with virtual-real fusion systems such as the digital twin battlefield, analyzing its core characteristics and construction significance. To accelerate the construction of the military metaverse, an overall logical architecture for the construction and application of the military metaverse is designed, the concept of military metaverse primitives is proposed, and the technical architecture is designed. The main application directions of the military metaverse are analyzed, and the construction stage division and overall thinking are provided. The key issues in construction and …

Virtual-Real Fusion Simulation Technology And Application Research For Industrial Control Systems Cybersecurity Of Process Manufacturing, Xinwei Wang, Jinjiang Wang, Zheng Wang, Laibin Zhang

Journal of System Simulation

Abstract: Aiming at the problem that the industrial control system in the process manufacturing industry lacks an effective attack and defense drill platform when facing network attacks, it is difficult to truly simulate the attack situation, verify the protective measures, and accurately evaluate the impact of attacks on the physical system, an industrial control cybersecurity simulation technology based on virtual-real fusion is proposed to build an efficient attack and defense drill range. The industrial control cybersecurity simulation architecture based on virtual-real fusion is designed, and the consistency analysis of virtual-real fusion data is carried out. At the same time, …

Spatio-Temporal Swin Transformer-Based Flow-Solid Coupling Interaction Sequence Image Prediction Network, Changjun Zou, Zhiyu Ge, Chenxi Zhong

Journal of System Simulation

Abstract: To address limitations in modeling long-term dependencies and multi-scale features in fluidstructure interaction scenarios, a spatiotemporal deep learning model (SwinLSTM) integrating ConvLSTM and Swin Transformer is proposed. The model employs a gated spatiotemporal attention mechanism that dynamically embeds Swin Transformer's window-based multi-head self-attention into ConvLSTM's output gate, enabling adaptive temporal-spatial feature coupling, and designs a multi-level ConvLSTM framework to hierarchically capture complex spatiotemporal correlations. Experiments on a self-built fluid-interaction dataset show that our method achieves the highest PSNR and leading SSIM scores, with superior performance in preserving vortex details and boundary consistency. This work provides an efficient solution …

Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion, He Zhu, Feng Zhou, Mengxiao Zhu, Ju Dai

Journal of System Simulation

Abstract: To enhance the semantic discrimination capability in point cloud semantic segmentation, a 3D point cloud semantic segmentation network named PL-Mamba is proposed, which is centered on the fusion of point cloud (P) and language (L) dual modalities. This method takes PointMamba as the backbone network, leveraging its excellent long-sequence modeling and global perception capabilities. It introduces a language prompt mechanism and uses a pretrained language model BERT to encode the context of category labels, obtaining semantically rich text features. The text information serves as a language guided token and is deeply integrated with point cloud features through cross modal …

Dehpr: A Diffusion-Based End-To-End Hand Pose Reconstruction Network, Guoqiong Liao, Longjie Huang, Qingxin Li, Jiajun Zhang, Kefan Chen

Journal of System Simulation

Abstract: Traditional methods such as convolutional neural networks (CNNs) and Transformers suffer from strong dependence on large-scale annotated data and limited generalization capability when dealing with hand pose reconstruction in complex scenarios. To address these issues, a diffusion-based end-to-end hand pose reconstruction network (DEHPR) is proposed. This method employs a diffusion model to directly generate and refine 3D predictions, thereby reducing spatial uncertainties inherent in 2D-to-3D modeling paradigms. By incorporating an end-to-end framework that reprojects multiple 3D candidate predictions to select optimal joint positions, the approach ultimately produces accurate hand pose estimations. Comprehensive evaluations conducted on HO3D V2, DexYCB, …

Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement, De Zhang, Zishan Liang, Ningning Liu

Journal of System Simulation

Abstract: Crowd counting takes video surveillance data as input and can be applied to the construction of city digital twin platforms, virtual city modeling and smart city management, etc. However, when there are data domain differences between the application scenario and training scenario, counting performance often significantly decreases. A cross-domain crowd counting model based on frequency domain enhancement is proposed. To alleviate the distribution differences between domains, a frequency domain feature enhancement module and a domain invariant frequency domain adapter module are constructed: the former uses discrete cosine transform to extract key statistical features to enhance spatial representation ability, while …

Research On Real-Time Animatable Human Avatar Generation Via 3d Gaussian Splatting, Yuyou Zhong, Xukun Shen, Yong Hu

Journal of System Simulation

Abstract: Real-time animatable 3D human avatar generation technology hold significant application value in fields such as virtual reality and remote collaboration. To address the limitations of existing methods in detail modeling, real-time performance, and robustness under novel pose driving, an efficient human avatar generation and driving method based on 3D Gaussian splatting (3DGS) is proposed. This method integrates optimized parametric human reconstruction, tri-plane feature encoding, and dynamic offset prediction to achieve efficient modeling from monocular video input. By introducing a skeleton binding and visibility analysis strategy, while designing a multi-scale regularization loss to address the overfitting problem. Simulation experiments demonstrate …

Prospective Comparison Of Wbc Spect/Ct And Conventional Mri For Diagnosing Osteomyelitis And Monitoring Treatment Response In Diabetic Foot Infections, Orhan Oz, Amber M. Sherwood, Peter A. Crisologo, Amanda L. Killeen, Kavita Bhavan, Javier La Fontaine, Katie L. Rubitschung, Helena Hwang, Robert W. Haley, Lawrence A. Lavery

School of Podiatric Medicine Publications

Aims/hypothesis: Osteomyelitis is a common complication of diabetic foot ulcers and a leading cause of lower limb amputations globally. Accurate diagnosis of diabetic foot osteomyelitis (DFO) is essential for effective infection management and improving prognosis. This study aimed to compare the performance of white blood cell (WBC)-single photon emission computed tomography (SPECT)/computed tomography (CT) and conventional MRI in the initial diagnosis of DFO and in detecting residual infection during treatment monitoring, using bone biopsy as the reference standard.

Methods: In this prospective clinical study, 47 patients with foot wounds suspicious for DFO underwent 99mTc-WBC SPECT/CT and MRI, followed by bone …

Inverse Kinematics 3d Human Modeling Simulation Based On Multi-View Vision, Guoyu Fang, Yanze Li, Kai Chen, Xiaodong Zhao, Zizhuo Hu, Mingshi Yang, Wanqing Wu, Zichen Wang, Wenkai Guo

Journal of System Simulation

Abstract: In autonomous driving simulation and industrial virtual reality simulation, there is a high demand for accuracy and robustness in 3D human body modeling. However, current joint-based human modeling approaches suffer from issues such as continuous modeling jitter, local distortion, and poor adaptability to occlusion, which degrade model quality and limit the development of practical applications such as intelligent driving and digital factories. To address these challenges, this paper proposes a multi-view vision-based inverse kinematics 3D human modeling method using a vector quantized variational autoencoder(IK-VQ-VAE). By integrating joint training with an automatic variational gradient descent approach, the proposed method achieves …

Liahona Project Checklist And Form Samples, Yoshihiko Ariizumi

Spiritual Proficiency

Table of Contents (Page)

• Checklist for Phenomenological Reduction and Analysis of Spiritual Experiences (1)

• Spiritual Experience Phenomenological Reduction Journal Template (3)

• Daily Checklist for Spiritual Growth (4)

• 1-Minute Daily Spiritual Growth Check (5)

• Spiritual Proficiency (SP) Daily Journal (6)

• Checklist for Phenomenological Reduction and Analysis of Spiritual Experiences (Reprint) (8)

リアホナプロジェクト・チェックリストとフォームのサンプル, Yoshihiko Ariizumi

Spiritual Proficiency

目次                                                                                             ページ

霊的経験を現象学的に還元して分析するためのチェックリスト 　１

霊的経験現象学的還元ジャーナルテンプレート　　 　                  ３

霊的成長のためのデイリーチェックリスト　　 　                         ５

霊的成長・1分デイリーチェック 　                                                ６

霊的熟達（Spiritual Proficiency）デイリー・ジャーナル 　            ７

霊的経験を現象学的に還元して分析するためのチェックリスト　  ９

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks, Asaad Migot, Ahmed Saaudi, Roshan Joseph, Victor Giurgiutiu

Faculty Publications

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis …

Ethics Of Artificial Intelligence For Lawyers: I’M Sorry Dave, I’M Afraid I Can’T Do That: Competence, Confidentiality, And Communication, Cliff Mckinney

Arkansas Law Notes

In Stanley Kubrick’s 2001: A Space Odyssey, the spaceship’s onboard computer, HAL, calmly refuses to follow the astronaut’s command with the chilling words, “I’m sorry, Dave, I’m afraid I can’t do that.” HAL’s response has become a cultural shorthand for what happens when human expectations collide with machine limitations. The line endures because it captures the chilling reality that machines may appear capable, but they cannot always be trusted to act in ways humans expect or need.

This installment explores three pillars of Formal Opinion 512: competence, confidentiality, and communication. These pillars focus on what lawyers need to understand about …

Changing High Water Exceedances In The Southeast U.S., Xavier Willis, Ian Kahn, Steven Meyers, Mark Luther

Making Waves REU

Sea level rise increases the likelihood that water levels will exceed flooding thresholds. This is particularly true for low-lying areas such as Florida. The rate of change of these exceedances is a topic of research, and important for planning community resilience to climate change. Data from NOAA coastal tide gauges for the Southeast U.S. were used to determine the annual 95th, 98th, and 99th percentile exceedance levels from 1975 to 2022, for each year and with Summer and Winter segments. Exceedance levels were on average 5-10% higher during Summer compared to Winter. The tide gauge station at Fernandina Beach had …

Pesticides Associated With Incident Diabetes Among Licensed Private Pesticide Applicators In The Agricultural Health Study Cohort (1993–2021), Christine G Parks, Qian Xiao, Jesse Wilkerson, Jonathan N Hofmann, Laura E Beane Freeman, Dale P Sandler

Faculty, Staff and Student Publications

Background: Growing evidence suggests pesticides may increase risk of type 2 diabetes, but data are limited on many specific chemicals.

Methods: In 29,527 private pesticide applicators in the Agricultural Health Study cohort (enrolled 1993-1997 in Iowa and North Carolina), 3,847 incident diabetes cases were identified by self-report during follow-up surveys in 1999-2003, 2005-2010, 2013-2015, and 2019-2021. We examined 50 pesticides reported at enrollment, updated in 1999-2003 or 2005-2010, prior to diabetes diagnosis or end of follow-up, using log-binomial regression to calculate relative risks (RR) and 95% confidence intervals (CI) for ever-use and intensity-weighted lifetime days (IWLD) use (tertiles, T1-3), adjusting …

The Impact Of Congenital Heart Disease On The Timing Of Alzheimer's Disease In Down Syndrome., Julianne G. Clina, Brian C. Helsel, Sigan L. Hartley, David A. White, Victoria L. Fleming-Batayneh, Benjamin Handen, Bradley Christian, Elizabeth Head, Mark Mapstone, Christy L. Hom, Beau Ances, Jeffrey Burns, H Diana Rosas, Florence Lai, Sharon Krinsky Mchale, Joseph H. Lee, Frederick A. Schmitt, Jordan Harp, Ira T. Lott, Shahid Zaman, Lauren T. Ptomey, Alzheimer Biomarker Consortium–Down Syndrome (Abc‐Ds) Investigators

Manuscripts, Articles, Book Chapters and Other Papers

INTRODUCTION: The incidence of Alzheimer's disease (AD) in Down syndrome (DS) exceeds 90%. Approximately 50% of people with DS have congenital heart disease (CHD). Having CHD increases risk for early-onset AD in populations without DS, but it is unclear if CHD influences AD in DS.

METHODS: Data from the Alzheimer Biomarker Consortium-Down Syndrome (ABC-DS) were used. Participants with CHD (n = 82, mean age = 39.9 ± 8.5 years, 97.6% White race) were age- and sex-matched to participants without CHD (n = 82, mean age = 40.5 ± 8.1 years, 98.8% White race). Cognitive assessments and Centiloid load …

Adult Advisor Efficacy To Support Lgbtq+ Youth As A Predictor Of Lgbtq+ Youth's Experiences In Gender-Sexuality Alliances (Gsas) And At School, V. Paul Poteat, Arthur Lipkin, Cayley C. Bliss, Jonquil Rumberger, Zhengyang Zhong, Robert A. Marx

Faculty Research, Scholarly, and Creative Activity

Introduction: Many LGBTQ+ youth seek support from adults at school, which can include advisors of Gender-Sexuality Alliances (GSAs; clubs affirming LGBTQ+ youth). Little research has considered how efficacious advisors feel to support LGBTQ+ GSA members, or how advisors' efficacy contributes to LGBTQ+ youth's school experiences. Methods: We examined advisors' self-efficacy to support and advocate for LGBTQ+ youth (n = 30 advisors in 30 schools/GSAs; Mage = 44.74 years, SD = 10.55; 63% LGBQ+; 90% cisgender; 77% White). Utilizing three waves of data from 310 LGBTQ+ youth in these GSAs (Mage = 15.11 years, SD = 1.60; 98% LGBQ+; 55% trans/nonbinary; …