Digital Commons Network^™

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 …

Decades Of National Health Strategy In England On Intellectual Disability And The Drift From Evidence-Based Practice, Rohit Shankar

Peninsula Medical School

No abstract provided.

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 …

Glomalin-Related Soil Protein In Colluvial Soils As A Proxy For Reconstructing Millenary Changes In Landscape And Soil Health, Axel Werner, Lourdes López-Merino, Joeri Kaal, Cruz Ferro-Vázquez, Oscar Serrano

Research outputs 2022 to 2026

Glomalin-related soil protein (GRSP) is an operationally defined soil organic matter pool that includes a heat shock protein linked to arbuscular mycorrhizal fungi (AMF) living in symbiosis with most vascular plants and plays a key role in soil health. However, the potential of GRSP as a palaeoecological proxy remains unexplored. We analysed the GRSP content in a North-western Iberian colluvial soil profile encompassing the last 14,000 years. We aimed to 1) identify whether GRSP is preserved, and 2) investigate whether GRSP could be used as a palaeoecological proxy after comparing its record with palynological and pedoanthracological records that account for …

Multilayered Childhood Adversity And Mortality: A Population-Based Cohort Study Of 1.2 Million Individuals, Naja Hulvej Rod, Signe Kær Bennetsen, Leonie K. Elsenburg, Clive E. Sabel, David Taylor–Robinson, Dora Kovacs, Adrian G. Zucco, Tjeerd Rudmer De Vries

School of Geography, Earth and Environmental Sciences

Background: Childhood adversity is multi-layered, extending beyond the family to include broader neighbourhood and health contexts. We aimed to investigate how these multiple layers of childhood adversity relate to the risk of death in young adulthood. Methods: Children were followed from birth into young adulthood (16–42 years) using nationwide register data on multi-layered childhood adversity and mortality. Individual adversity included perinatal adversity (preterm or small-for-gestational-age) and mental and physical health-service use. Family adversity included five distinct groups using group-based multi-trajectory modelling based on 12 adversities. Neighbourhood adversity included material deprivation in small-area geographical zones. We evaluated associations of these layers …

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 …

Mgrre_Thinsections_Mgrre_11_1, Mgrre

Thin Sections

No abstract provided.

The Effect Of Project Based E-Learning Models Toward Learning Outcomes And Critical Thinking Skills Of Vocational High School Students, Made Delina Rusnawati, I Wayan Santyasa

Jurnal Pendidikan dan Pembelajaran

Learning outcomes and critical thinking skills are main competencies that students must achieve in Digital Simulation subjects. This study aimed to describe the differences in learning outcomes and critical thinking skills together between students who learned by using project-based e-learning model and students who learned by using direct e-learning in Digital Simulation subjects. This research used quasi experiment method. The research design used was a pretest-posttest control group design. The research sample consisted of 4 classes or 133 students by using a simple random sampling technique. This study used two test instruments namely, the learning outcomes test in the form …

Effects Of Differentiated Physical Education Learning On Physical Fitness And Active Lifestyle Among Junior High School Students: A Quasi-Experimental Study, Asep Akbaruddin, Sumbara Hambali, Wahyu Saputra, Mochamad Fajar Permana

Indonesian Journal of Research in Physical Education, Sport, and Health

Background: Low levels of physical fitness and physical activity among junior high school students remain a significant concern in physical education. Differentiated instruction offers a potentially effective approach by tailoring learning activities, levels of difficulty, and instructional experiences to students’ readiness, interests, and abilities. Objective: This study aimed to investigate the effects of differentiated physical education instruction on physical fitness and active lifestyles among junior high school students. Methods: A quantitative approach employing a quasi-experimental, nonequivalent control-group design was adopted. A total of 90 junior high school students participated in the study, with 45 assigned to the experimental group and …

The Effect Of Multisensory Virtual Forest Bathing On Stress Among College Students, Shinta Mustika, Tiara Ditha Puspita, Tasya Berlian Cittra Friyanti, Wilis Srisayekti, Yanti Rubiyanti, Asteria Devy Kumalasari, Yuliana Hanami

Psychological Research on Urban Society

Stress is a common psychological problem among university students and may be caused by academic, social, and environmental demands. This study aimed to examine the effect of multisensory virtual forest bathing (VFB) on reducing stress among university students. A randomized pretest-posttest waitlist-control design was employed. Participants were 58 undergraduate students from the Faculty of Psychology, Universitas Padjadjaran with moderate-to-high stress levels based on the Perceived Stress Scale (PSS-10), after outliers were removed. The experimental group received a ±9-minute 360° forest video intervention delivered through virtual reality (VR) with auditory and olfactory stimulation, while the waitlist control group received no intervention …

Understanding Gratitude And Thriving At Work Among Rescue Workers, Muhammad Faidhil Iman, Debora Eflina Purba

Psychological Research on Urban Society

Thriving at work, characterized by sustained vitality and continuous learning, is a critical psychological state for personnel navigating high-risk rescue environments. Drawing on the Socially Embedded Model of Thriving (SEMT), this study investigates gratitude at work as a key undefined personnel resource that fosters thriving at work through the mediating mechanism of meaningful work. Furthermore, we hypothesized that a toxic contextual environment, specifically abusive supervision, would act as a boundary condition hindering grateful individuals from deriving meaningfulness from their work. Using a cross-sectional design, data were collected from 238 first responders across the Fire and Rescue Department, the Disaster Management …

Mental Health As A Mediator In The Relationship Between Life Satisfaction, Resilience, And Employee Engagement Among Generation Z Employees, Ida Farida, Irfiani Triastari

Psychological Research on Urban Society

This study examined the mediating role of mental health, operationalized as lower non-specific psychological distress, in the relationships between life satisfaction, resilience, and employee engagement among Generation Z employees in Indonesia. A quantitative cross-sectional explanatory survey design was used. The sample consisted of 324 Generation Z employees selected from a larger dataset based on age cohort, full-time employment status, and voluntary participation. Data were analyzed using Structural Equation Modeling with LISREL 8.80 and Maximum Likelihood estimation. The results showed that life satisfaction was positively associated with employee engagement and with mental health as operationalized in this study. Resilience was positively …

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 …