Digital Commons Network^™

A Finite Element Model For Thermomechanical Stress-Strain Fields In Transversely Isotropic Strain-Limiting Materials, Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah

School of Mathematical & Statistical Sciences Faculty Publications

This paper presents a comprehensive computational framework for investigating thermo-elastic fracture in transversely isotropic materials, where classical linear elasticity fails to predict physically realistic behavior near stress concentrations. We address the challenge of unphysical strain singularities at crack tips by employing a strain-limiting theory of elasticity. This theory is characterized by an algebraically nonlinear constitutive relationship between stress and strain, which intrinsically enforces a limit on the norm of the strain tensor. This approach allows the development of very large stresses, as expected near a crack tip, while ensuring that the corresponding strains remain physically bounded. A loosely coupled system …

Pattern Formation In Quantum Hierarchical Cellular Neural Networks, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung

School of Mathematical & Statistical Sciences Faculty Publications

We present a new class of quantum neural networks (QNNs) whose states are solutions of p -adic Schrödinger equations with a non-local potential that controls the interaction between the neurons. These equations are obtained as Wick rotations of the state equations of p -adic cellular neural networks (CNNs). The p -adic CNNs arise as continuous limits of large discrete hierarchical neural networks (NNs). The CNNs are bio-inspired by the Wilson–Cowan model, which describes the macroscopic dynamics of large populations of neurons. We provide a detailed study of the discretization of the new p -adic Schrödinger equations, which allows the construction …

Life Participation In Parents Of Children With Ckd., Anastasia Hughes, Andrea Matus Gonzalez, Anita Van Zwieten, Chandana Guha, Simon Carter, Germaine Wong, Dale Coghlan, Amanda Dominello, Jonathan C. Craig, Martin Howell, Siah Kim, Karine Manera, Colm O'Reilly, Javier Recabarren Silva, Angela Rejuso, Benedicte Sautenet, Nicole Scholes-Robertson, Amanda Sluiter, Rebecca Wu, Mignon Mcculloch, Natalia Armijo, Ximena Atilano-Carsi, Patricia De La Cerda, Gabriel Alexis Gallardo Letelier, Carla Galvez, Nicole Gonzalez-Agüero, Maria Maldonado, Viola Pinto, Lilian Reyes, Lena Rodriguez, Angelica Rojo, Alejandro Sepúlveda, Luca G. Torrisi, Andrea K. Viecelli, Bashir Admani, Bethany J. Foster, Alexander J. Hamilton, Augustina Jankauskiene, Rebecca J. Johnson, Jasmijn Kerklaan, Isaac Liu, Stephen D. Marks, Cozumel S. Pruette, Franz Schaefer, Sebastian Wolfenden, Allison Jaure, Song-Kids Life Participation Workshop Investigators

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: The caregiver burden among parents of children with chronic kidney disease (CKD) is high and can impair their life participation, which may in turn have negative consequences on their children's care and outcomes. We aim to describe effects on life participation in parents of children with CKD, from the perspectives of their children, health professionals, and the parents themselves.

METHODS: We thematically analyzed data from the Standardized Outcomes in Nephrology-Children and Adolescents Initiative (SONG-Kids) life participation workshops involving children with childhood CKD, parents or caregivers, and health professionals, for content related to perspectives on life participation among parents of …

Machine Learning Local Potentials For Accelerated Electron-Phonon Interactions Calculations Within The Projector Augmented-Wave Framework, Yi Xia

Mechanical and Materials Engineering Faculty Publications and Presentations

Electron–phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning (ML) strategy that learns the self-consistent local potential (LocP) on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron–phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the …

Meeting Survivors Where They Are: Gbv Service Fit And Mismatch In Rural Communities, Ziwei Qi, Rachel Dolechek, Naishuo Sun

Social Work Faculty Publications

Gender-based violence (GBV) poses ongoing challenges in rural communities, where structural and cultural barriers often hinder effective support. This qualitative study analyzed six listening sessions and two survivor panels with over 50 participants in a Midwestern state. Using trauma-informed care and cultural humility frameworks, we conducted reflexive thematic analysis to explore service alignment with local values and survivor needs. Two themes emerged: service fits, marked by adaptive, trust-based practices; and service mismatches, involving rigid systems and cultural isolation. Findings highlight the need for survivor-led, flexible, and culturally grounded responses to enhance rural GBV service delivery and promote long-term well-being.

Inclusive Education In Diverse Societies: Three Decades After The Salamanca Statement, From Disability Inclusion To The Recognition Of Student Diversity, Farida Kurniawati

Psychological Research on Urban Society

Inclusive Education in Diverse Societies: Three Decades after the Salamanca Statement, from Disability Inclusion to the Recognition of Student Diversity

Performing Proficiency: Ethnodrama As Arts-Based Research In An Esl Acting Classroom, Angel M. Morales

Journal of English Learner Education

This study explores the use of ethnodrama as an arts-based research method and pedagogical tool in English as a Second Language (ESL) education. Drawing on the work of Saldaña, Salvatore, and Denzin, the project examines how performance can communicate lived experiences, foster language development, and promote social justice in multicultural classrooms. Based on interviews and observations of ESL students in an Acting I course, the resulting ethnodrama, What?! Acting?!, presents verbatim narratives that reveal learners’ identities, struggles, and resilience. The findings highlight how theatrical practice enhances linguistic fluency, self-expression, and cultural understanding, demonstrating the transformative potential of performance in …

A Franco-American University Collaboration: Oral Assessment With Cefr And Star, Nadia Yassine-Diab, Christina Torres University Of Central Florida, Aimee Schoonmaker

Journal of English Learner Education

This article describes the collaboration between faculty at two international
universities, one in France and one in the U.S., who partnered their students to practice and assess oral skills. Using computer-mediated communication with both synchronous and asynchronous elements, the collaboration provided a valuable opportunity for both groups to engage in pedagogically meaningful and structured exchanges integrated into coursework that mirror their future professional situations (O’Dowd et. al., 2020). More specifically, each semester, the U.S. TESOL students interview and assess French speaking English language learners using the Common European Framework of Reference (CEFR) and the Situation Task Action Results (STAR) interview …

Making The Implicit Explicit For Multilingual Learners: A Language-Based Framework For Literacy Development, Luciana C. De Oliveira

Journal of English Learner Education

Literacy is fundamentally an act of meaning-making that requires bridging micro-level linguistic choices with macro-level text structures across genres. Current literacy policies dominated by Science of Reading (SoR) mandates often overemphasize isolated, word-level foundational skills. This narrow focus creates a one-size-fits-all model that reduces reading to the level of individual words, not appropriate for multilingual learners (MLs) who require access to dense, specialized disciplinary discourse. This article proposes an intentional, language-based literacy framework designed to build high-challenge, high-support classrooms for MLs without lowering academic expectations. Integrating genre-based pedagogy through the Teaching and Learning Cycle (TLC) with the mode continuum, the …

Race, Embodiment, And Professionalism: A Critical Intersectional Analysis Of Brazilian Sign Language Interpreting, Roberto César Reis Da Costa, Sheila Santos

Journal of Interpretation

This paper investigates the intersections of race, corporate professionalism, and somatic regulation within Brazilian Sign Language (Libras) translation and interpretation. While traditional translation scholarship prioritizes linguistic neutrality, this study employs an interdisciplinary critical methodology combining Critical Race Theory (CRT), Critical Discourse Analysis (CDA), and Translation and Interpreting Studies (TIS) to deconstruct how standard professional aesthetics inadvertently sustain systemic racism and Eurocentric cultural erasure. Utilizing a textual deconstruction of Article 5 of the National Libras Code of Ethics alongside an autoethnographic mapping of workplace visual ergonomics, we examine how racialized aesthetic expectations regulate and pathologize cultural markers such as braided hairstyles, …

Toward Cross-Cultural Assessment Of Career Anxiety: Psychometric Validation Of The Career Anxiety Scale In Indonesia, Arifah Wulandari, Raudha Nur Hidayah Al Jannah, Gracianus Edwin Tue P. Lejap, Elwas Berdha Krismona, Santy Andrianie, Anisah Muflihatul Latifah, Merikan Aren

Buletin Konseling Inovatif

Career anxiety has become an important psychological concern among university students amid rapid technological transformation, labor market uncertainty, and increasingly complex school-to-work transitions. However, evidence regarding the factor structure of the Career Anxiety Scale (CAS) in the Indonesian context remains limited. This study aimed to examine the factor structure and psychometric properties of the Indonesian Version of the Career Anxiety Scale (CAS-IV). A cross-sectional survey was conducted with 287 Indonesian university students aged 18–25 years. The instrument was translated in accordance with International Test Commission guidelines and evaluated using Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), inter-factor correlations, and …

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Thin Sections

No abstract provided.

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 …

Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems, Long Zhang, Wenbo Huang, Xuanming Feng, Zhen Lei, Ying Wang

Journal of System Simulation

Abstract: A hierarchical evolutionary adaptive learning (HEAL) framework is proposed to address the problems in the operational simulation of intelligent equipment systems under complex adversarial environments, including fragmented hierarchical decision-making, unclear mapping between algorithmic indicators and operational effectiveness, and the lack of adaptive evolution capability of simulation models. A multi-layer learning architecture that integrates robust autonomous decision-making for single units, emergent collaboration of system networks, and game-theoretic evolution of system-of-systems strategies is constructed by following the “micro-meso-macro” system-oriented hierarchical modeling paradigm. A cross-layer closed-loop coupling among learned policies, cooperative structures, and operational effectiveness is achieved by combining bottom-up behavioral abstraction …

Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments, Dequan Li, Jiahao Jiang

Journal of System Simulation

Abstract: To address the requirements for low-latency local planning and safe obstacle avoidance in UAV forest-fire patrol under dense tree-trunk occlusion and locally invalid depth observations, this paper proposes a learning-based local trajectory planning method that fuses depth images, local occupancy distance maps, candidate-level collision-risk assessment, and pre-execution safety reranking. The method uses depth images, local occupancy distance maps, motion states, and target direction as inputs and generates 15 quintic-polynomial candidate trajectories in parallel using 3 × 5 angular motion primitives. During training, candidate safety costs, ranking supervision, and near-collision risk labels are constructed. During inference, online LiDAR point clouds …

Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm, Xuezhi Yue, Chao Xiong, Yanshen Jiang, Zhaolu Guo, Hu Peng

Journal of System Simulation

Abstract: To address prediction failure, manifold topology distortion, and insufficient adaptability in environmental response and operator configuration in existing prediction-based algorithms for dynamic multi-objective optimization under complex nonlinear environments and small-sample settings, this paper proposes a manifold alignment and dual prediction-based adaptive dynamic multi-objective evolutionary algorithm, termed MADP-DMOEA. When the environment changes, the algorithm decomposes the decision-variable manifold into center and shape components, effectively aligns manifolds at adjacent time instants via bipartite optimal matching, and identifies environmental change severity using geometric displacement. A dual prediction model is developed, in which a GRNN performs center extrapolation under severe changes, while an …

Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg, Yonghao Zhang, Changyou Xue, Xueman Fan, Zhuo Cheng

Journal of System Simulation

Abstract: A cooperative hunting method based on improved multi-agent deep deterministic policy gradient (MADDPG) is proposed to address the challenges of non-stationary environments, sparse rewards, and partial observations in the cooperative hunting of targets with unknown motion patterns by multiple unmanned surface vessels (USVs). A Markov game model for hunting is built, and a multidimensional guided reward shaping function incorporating collision avoidance, hunting success, and energy consumption penalties is designed to mitigate the sparse reward problem. ResNet residual networks are introduced to construct the Actor and Critic networks and thus enhance the representation capability of deep networks for complex state-action …

Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning, Junjie Liu, Jianlei Xu, Litao Ju, Yanhai Hu, Qinghao Zhang, Qiang Liu

Journal of System Simulation

Abstract: A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, …

Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application, Xin Zhang, Han Wang, Pengjiang Qian, Wei Fang

Journal of System Simulation

Abstract: A dynamic multi-objective optimization algorithm based on residual-driven two-stage prediction (RDTP) is proposed to address the dilemma of prediction strategies in dynamic multi-objective optimization struggling with insufficient samples in the early stage and low accuracy in the later stage. In the initial stage, if an environmental change is detected, grey prediction, which is fast and suitable for small samples, is employed to generate individuals for the new environment. A residual analysis is performed to determine whether to switch to a LSSVM to improve prediction accuracy as more samples become available. The Pareto front is clustered to select detection objects. …

Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response, Ding Ma, Tao Zhang, Bo Jiang, Yajie Liu, Shuijia Li

Journal of System Simulation

Abstract: In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rational allocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based …

Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters, Renxin Xiao, Yifan Xu, Guisheng Chen, Nan Pan

Journal of System Simulation

Abstract: A two-level optimization model integrating heterogeneous electric vehicle fleet configuration with multimodal path coordination is built to address the challenges in industrial cluster freight transportation involving electric heterogeneous fleet scheduling, such as diverse tasks and stringent constraints on energy consumption and delivery timeliness. Considering the performance coupling characteristics of heterogeneous electric fleets in terms of load-energy consumption-endurance, based on the coordination of multimodal paths and fleet configuration coding, the upper level employs the GWO to optimize task splitting and fleet configuration. The lower level adopts a multi-neighborhood SA algorithm to achieve path coordination scheduling. Simulation results show that the …

Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules, Hualan Wang, Li Wang, Zhongning Fu

Journal of System Simulation

Abstract: To address the limitations of circular RGV systems under high-density operating conditions, including a simple operational structure and limited parallel operation capability, this study investigates track structures and dispatching mechanisms for complex operational requirements. Based on the agent-based modeling method implemented on the AnyLogic platform, a simulation model of an RGV system with a nested circular track layout was developed, in which an embedded sub-loop equipped with track-switching devices is introduced into the traditional circular track structure. Operating and dispatching rules suitable for the nested circular structure are designed by incorporating the concepts of section operation and overtaking in …

Air Defense Resource Allocation And Reconstruction Method Based On Kggom, Jiaoxiong Chen, Chenglong Zhang, Yuqiang Zhao, Xianglin Li, Qiang Zhao

Journal of System Simulation

Abstract: To address the challenge that traditional solving methods cannot meet real-time requirements in the intelligent allocation and reconstruction of air defense operation resources, this paper focuses on an intelligent solving method for the dynamic allocation and reconstruction of intercept timing sensor-weapon-target. A multi-constraint model is established with damage effectiveness and operation cost as optimization objectives, and a knowledge-guided genetic optimization method (KGGOM) is proposed. Through knowledge-guided population generation and evolution, constraint-aware operators with correction mechanisms, and multi-path search combined with simulated annealing for gene correction, it improves its optimization capability and speed. Historical superior solution retention and dynamic elite …

Hybrid Jaya-Transformer-Based Task Scheduling Algorithm For Super-Agile Satellites, Haoyu Zhao, Guanghui Zhang, Lanting Hao, Meijia Guo

Journal of System Simulation

Abstract: Super-agile satellite task scheduling is characterized by complex constraints and tight coupling among task transitions. To address the relatively long online solution times of traditional heuristic algorithms, this paper establishes a super-agile satellite task scheduling model that aims to maximize the task profit ratio while considering constraints on visible time windows, attitude transition time, energy, and storage capacity. A Transformer is used to learn task requirements, visible time windows, and global correlations among tasks, and a dynamic mask and a task scheduler are incorporated to generate feasible task sequences. To enhance the model's ability to learn complex task correlations …

Aerial Refueling Scheduling Plan Generation Method For Multi-Position Task Management *, Jiachen Hu, Huanyu Li

Journal of System Simulation

Abstract: An aerial refueling scheduling plan generation method based on hierarchical hybrid strategy is proposed to address the high complexity of aerial refueling scheduling for multi-position tasks and the difficulty for traditional manual operations or single-position-oriented solutions to manage tasks effectively. A multi-level strategic framework based on fixed-rule heuristics, rolling horizon optimization, and steady-state cyclic scheduling is developed and integrated into a hierarchical hybrid strategy to balance scheduling efficiency, optimization capability, and emergency response capability. Simulation results demonstrate that the proposed method effectively generates feasible refueling scheduling plans, producing viable plans under both undisturbed rotational operations and disturbed dynamic support …

Flight Test Mission Planning Method Based On Adaptive Large Neighborhood Search, Luofu Wu, Xinyang Wu, Lunhao Ju, Guopeng Song, Rui Wang, Jiang Jiang

Journal of System Simulation

Abstract: Flight test mission planning is critical to aircraft performance evaluation, but traditional manual methods often struggle with extensive solution spaces and complex constraints. This paper constructs an overall flight test mission planning model and introduces a flight test planning algorithm (FTPA) based on adaptive large neighborhood search. FTPA utilizes Kahn topological sorting to generate an initial test-item priority sequence, which is then decoded into an initial solution, and jointly schedules flight test items and test aircraft within the adaptive large neighborhood search framework. A comparison with the CPLEX solver shows that the FTPA algorithm yields solutions with a gap …

Large Language Model-Assisted Control Framework For Mixed Remote Sensing Satellite Constellations, Guohua Wu, Ziyan You, Lei Li, Yi Gu, Shuanglin Li

Journal of System Simulation

Abstract: A LLM-assisted control framework for mixed remote sensing satellite constellations is proposed to address the problem that the control and scheduling of mixed remote sensing satellite constellations cannot process users' raw natural language observation requirements and realize full-process automation from requirement input to scheduling schemes. LLMs are utilized to enhance the construction of domain knowledge graphs, requirement understanding and task characterization, and realize intelligent generation of scheduling schemes and optimal solution of scheduling models. A scheduling scheme generation method based on the "planning and execution" dual-Agent architecture is designed. Combined with the constellation scheduling toolset, the capability boundary of …