Model Analysis Research Articles

Utilization of Generative Artificial Intelligence in Nursing Education: A Topic Modeling Analysis

The advent of artificial intelligence (AI) has prompted the introduction of novel digital technologies, including mobile learning and metaverse learning, into nursing students’ learning environments. This study used text network and topic modeling analyses to identify the research trends in generative AI in nursing education for students and patients in schools, hospitals, and community settings. Additionally, an ego network analysis using strengths, weaknesses, opportunities, and threats (SWOT) words was performed to develop a comprehensive understanding of factors that impact the integration of generative AI in nursing education. The literature was searched from five databases published until July 2024. After excluding studies whose abstracts were not available and removing duplicates, 139 articles were identified. The seven derived topics were labeled as usability in future scientific applications, application and integration of technology, simulation education, utility in image and text analysis, performance in exams, utility in assignments, and patient education. The ego network analysis focusing on the SWOT keywords revealed “healthcare”, “use”, and “risk” were common keywords. The limited emphasis on “threats”, “strengths”, and “weaknesses” compared to “opportunities” in the SWOT analysis indicated that these areas are relatively underexplored in nursing education. To integrate generative AI technology into education such as simulation training, teaching activities, and the development of personalized learning, it is necessary to identify relevant internal strengths and weaknesses of schools, hospitals, and communities that apply it, and plan practical application strategies aligned with clear institutional guidelines.

Identifiability analysis of the fixed-effects one-parameter logistic positive exponent model.

In addition to the usual slope and location parameters included in a regular two-parameter logistic model (2PL), the logistic positive exponent (LPE) model incorporates an item parameter that leads to asymmetric item characteristic curves, which have recently been shown to be useful in some contexts. Although this model has been used in some empirical studies, an identifiability analysis (i.e., checking the (un)identified status of a model and searching for identifiablity restrictions to make an unidentified model identified) has not yet been established. In this paper, we formalize the unidentified status of a large class of fixed-effects item response theory models that includes the LPE model and related versions of it. In addition, we conduct an identifiability analysis of a particular version of the LPE model that is based on the fixed-effects one-parameter logistic model (1PL), which we call the 1PL-LPE model. The main result indicates that the 1PL-LPE model is not identifiable. Ways to make the 1PL-LPE useful in practice and how different strategies for identifiability analyses may affect other versions of the model are also discussed.

Reliability optimization through soft digital twins

This paper presents a framework for developing a “soft digital twin” (SDT) for reliability optimization through modeling and simulation. The focus is on demonstrating the effectiveness of the SDT in modeling maintenance task processes within mission-critical facilities, which is particularly essential for reliable operations in critical infrastructure such as nuclear facilities. The need for efficient completion of maintenance tasks (MTs) to ensure high reliability and minimal downtime underscores the challenge faced by managers, who desire a predictive tool akin to a “crystal ball” for optimal resource configuration in the face of uncertainties from equipment failures, staffing issues, and supply chain disruptions. The proposed SDT functions as this predictive tool, leveraging the simulation’s versatility to provide insights into resource configurations and staff planning. This work extends the prior research by incorporating additional model validation, sensitivity testing, and analysis of the impact of various resource changes on the system. It employs a combination of data-driven frameworks and stochastic modeling methods to construct an adaptive SDT capable of accommodating changes in system behavior. The work provides a framework for the construction of SDT, testing and validation of its results, and its application to a case study of a mission-critical facility focused on replication and then optimizing of two key performance indicators based on human resource mixtures. Initial findings suggest that the SDT yields reliable results comparable to retrospective test datasets, offering the potential to minimize MTs’ time in the system while maximizing throughput within specified time frames through scenario experimentation and optimization.

Development of circulatory system mobile learning (CSML) for grade XI students

The utilization of communication devices such as mobile phones as a learning support tool in the development of technology 4.0 is still low. The low mobile phone utilization as a learning support tool causes the variety of learning media used by teachers to be low. The low variety of learning media results in students having difficulty mastering the concept of learning material, especially circulatory system material in class XI. The difficulty of concept mastery on circulatory system material can be overcome through the development of circulatory system mobile learning (CSML). The research objectives are: 1) Develop CSML on circulatory system material in grade XI, 2) Test the feasibility of CSML, and 3) Know the response of teachers and students to CSML. The type of research is Research and Development (R&D) using the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). Data collection uses feasibility validation questionnaires and response questionnaires. The data analyzed were the results of the feasibility assessment from media experts and material experts, and the results of responses from teachers and students. Data analysis was carried out quantitatively descriptive using a Likert scale of 1-4. The results of the study are 1) The result of developing CSML can be accessed online; 2) The results of the feasibility assessment from media experts were 82.27% and material experts were 89.59% with a very feasible category; 3) The results of teacher responses were 87.02% and student responses were 88.55% with a great category.

Structural Performance of GFRP-Wrapped Concrete Elements: Sustainable Solution for Coastal Protection

Protecting coastal regions is crucial due to high population density and significant economic value. While numerous strategies have been proposed to mitigate scouring and protect coastal structures, existing techniques have limitations. This paper introduces a novel approach, SEAHIVE®, which enhances the performance of engineered structures by utilizing hexagonal, hollow, and perforated concrete elements externally reinforced with glass fiber-reinforced polymer (GFRP). Unlike conventional steel bars, GFRP offers superior durability and requires less maintenance, making it a sustainable solution for any riverine and coastal environment. SEAHIVE® aims to provide robust structural capacity, effective energy dissipation, and preservation of natural habitats. Although some research has addressed energy dissipation and performance in riverine and coastal contexts, the structural performance of SEAHIVE® elements has not been extensively studied. This paper evaluates SEAHIVE® elements reinforced with externally bonded GFRP longitudinal strips and pretensioned GFRP transverse wraps. Testing full-size specimens under compression and flexure revealed that failure occurred when the pretensioned GFRP wraps failed in compression tests and when longitudinal GFRP strips slipped in flexure tests. Strength capacity was notably improved by anchoring the GFRP strips at both ends. These findings underscore the potential of the SEAHIVE® system to significantly enhance the durability and performance of coastal and riverine protection structures. FEM simulations provided critical insights into the failure mechanism and validated the experimental findings. In fact, by comparing FEM model results for cases before and after applying GFRP wraps under the same compression load, it was found that maximum stresses at crack locations were significantly reduced due to compression forces resulting from the presence of pretensioned GFRP wraps. Similarly, FEM model analysis on flexure samples showed that the most vulnerable regions corresponded to the locations where cracks started during testing.

Novel first-order phase transition and critical points in SU(3) Yang-Mills theory with spatial compactification

We investigate the thermodynamics and phase structure of SU(3) Yang-Mills theory on T2×R2 in Euclidean spacetime in an effective-model approach. The model incorporates two Polyakov loops along two compactified directions as dynamical variables, and is constructed to reproduce thermodynamics on T2×R2 measured on the lattice. The model analysis indicates the existence of a novel first-order phase transition on T2×R2 in the deconfined phase, which terminates at critical points that should belong to the two-dimensional Z2 universality class. We argue that the interplay of the Polyakov loops induced by their cross-term in the Polyakov-loop potential is responsible for the manifestation of the first-order transition. Published by the American Physical Society 2024

Model analysis and experiment study for effects of thermal viscous and fluid flow on ventilated acoustic metamaterials labyrinth

In many noise scenarios, ventilation is necessary. The practical realization of noise attenuation under the premise of ensuring ventilation is an urgent problem to be solved. In this paper, a ventilated acoustic metamaterial labyrinth (VAML) is proposed, and the corresponding analytical and numerical computational models are developed considering the thermal viscous effect (TVE). The loss in sound transmission of the VAML is quantitatively obtained and experimentally verified. The theoretical results are compared with the experimental results with an error of 0.6%. By comparing the transmission coefficient and the impedance at the entrance of the side branches, the mechanism of the TVE on the performance of VAML is analyzed, and it is clarified that the TVE is responsible for the sound absorption coefficient. The effects of structure parameters on the transmission coefficient of the VAML are analyzed individually. The results show that the resonant frequency of the system decreases as the length of channel 1 l1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_1$$\\end{document}, the length of channel 2 l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document}, and the width of channel d increase. By adjusting the magnitude of these three parameters, the control of the resonant frequency can be realized. Meanwhile, as l1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_1$$\\end{document}, l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document} and ventilation radius R decrease the valley of transmission coefficient decreases which means better noise reduction. Finally, the effect of flow velocity on the transmission coefficient is analyzed, revealing the mechanism of fluid action on macroscopic acoustic properties.

The role of automated controls and streamlined compliance in managing risks in digital finance

This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.

Abstraction-based segmental simulation of reaction networks using adaptive memoization

Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time.Results We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently.Conclusion We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.

Advancements in Diffusion Models for Image Generation: A Comparative Analysis of DDPM, LDM, and DDIM

Abstract. This research provides a thorough exploration of diffusion models in image generation, comparing various methodologies to assess their efficacy and efficiency. The study begins with an introduction to foundational technologies and key concepts, progressing through an analysis of basic and advanced models, including Latent Diffusion Models (LDMs), Denoising Diffusion Implicit Models (DDIMs), and control models. The research evaluates these models based on their performance, computational efficiency, and future development potential. The review details the evolution of diffusion models from early stochastic processes to their current status as advanced generative models. Key principles, such as iterative noise addition and removal, are examined to understand the transformation from simple distributions to complex data representations. Innovations enhancing model efficiency, including advancements in score matching and neural network integration, are discussed. A thorough comparative analysis highlights the strengths and limitations of each model. The study identifies ongoing challenges such as interpretability and computational cost and proposes future research directions to address these issues. The findings aim to guide researchers and practitioners in advancing diffusion model technologies, offering insights into their impact on image generation and potential future developments.

Essential Characteristics for School Administrators in the Digital Age for Thailand

Introduction: It is well known that education and teaching practices are affected by technological developments. The ubiquitous spread of information and communication technologies in recent years has forced educational institutions to undergo a digital transformation so as to keep up with the today’s technological age. Educational activities have become sustainable in virtually all environments and under virtually all conditions, with interactive whiteboards having long since replaced the traditional blackboard in most schools. Changes emerging today include revised job definitions, changing patterns of daily life, and the desire for economic value; together they represent the primary results of this digital transformation. Objective: This study aims to explore the essential characteristic for school administrators in the digital age. Method: This is quantitative approach. The population was educational administrators in Thailand who are responsible for management in educational institutions. This Quantitative Research analyzed the data using Descriptive Statistics, Inference Statistic and Multivariate by IBM SPSS and Structural Equations Modeling (SEM) was analyzed by IBM SPSS AMOS. Results: 1) Government Policy (GP) 2) Performance Attribute (PA) and 3) Competent Innovation directly influence on Perceived Value. Moreover, Perceived Value directly influences on Performance Identification. Analysis of the SEM model was found to be accurate because the model is consistent with empirical data, harmonization index passed all criteria, Chi-Square = 173.53, df = 88 RMSEA = 0.04 and RMR = 0.03, index GIF = 0.96 and AGIF = 0.94 and R2/ df = 1.97. Conclusion: These findings can be used to develop academic fields such as research and strategic planning effectively and can be applied in educational industry as well, so making the product feature that meets the needs of the customer will result in the perception of the value of the product and making a purchase decision. This finding has benefits on human resources development and school management and school administrators as well.

Opinion response functions are key to understanding the tipping of social conventions

Abstract The extent to which committed minorities can overturn social conventions is an active area of research in the mathematical modeling of opinion dynamics. Researchers generally use simulations of agent-based models (ABMs) to compute approximate values for the minimum committed minority size needed to overturn a social convention. In this manuscript, we expand on previous work by studying an ABM’s mean-field behavior using ordinary differential equation models and a new tool, namely opinion response functions (ORFs). Using this method allows for formal analysis of the deterministic model, which can provide a theoretical explanation for observed behaviors, e.g. coexistence or overturning of opinions. In particular, ORFs are a method for characterizing equilibria in our social model. Our analysis confirms earlier numerical results and supplements them with a precise formula for computing the minimum committed minority size required to overturn a social convention.

Comparative Analysis of Univariate and Deep Learning Models for Stock Market Prediction in Frontier Markets: A Case Study of Pakistan, Bangladesh, and Sri Lanka

Comparative Analysis of Univariate and Deep Learning Models for Stock Market Prediction in Frontier Markets: A Case Study of Pakistan, Bangladesh, and Sri Lanka

Advancements in Target Detection: A Comparative Analysis of Deep Learning Models for Real-Time Applications

Abstract. Target detection is a vital field within computer vision, playing an essential role in applications. This paper investigates the advancements and efficiencies of contemporary target detection methodologies, focusing on deep learning frameworks. Through a systematic review and evaluation of these models' architectures and performances using the Common Objects in Context (COCO) dataset, the study highlights their operational effectiveness and practical implications in real-world scenarios. A detailed comparative analysis is conducted, assessing the models based on mean Average Precision (mAP) and input dimensions to determine their suitability across various detection tasks. You Only Look Once version 3 (YOLOv3), in particular, is recognized for its ability to combine high-speed detection with significant accuracy, effectively addressing real-time processing challenges. The results confirm that YOLOv3, despite its smaller input size, performs comparably to more complex systems, demonstrating notable enhancements in design efficiency and processing speed. This research underscores the potential for future optimizations in model architectures to bridge the gap between high-speed and high-accuracy detection tasks, potentially transforming real-time applications across multiple sectors. The practical significance of this work lies in its ability to guide future developments in object detection, benefiting both academic research and industrial applications.

Tripartite Evolutionary Game and Policy Simulation: Strategic Governance in the Redevelopment of the Urban Village in Guangzhou

The scarcity of land drives urban village redevelopment projects, which involve interest redistribution among stakeholders with distinct demands. This paper utilizes evolutionary game theory and simulation methods, constructing a tripartite game model under the institutional arrangement of bottom-up with private developer funding. This study identifies the stable strategies and evolutionary trends of the tripartite interactions under four distinct scenarios and validates these strategies through simulations. The redevelopment of XC village validates the assumptions of the model and theoretical analysis, suggesting that when private developers adopt forced demolition strategies, although villagers ultimately choose to sign the contract of property exchange, it can easily lead to social conflicts. These research findings can enlighten the government to form a tripartite alliance to smooth urban village redevelopment.

Effect of Coupling Impedance on Stability Assessment of Grid-Forming Converters Under Various Grid Conditions

The phenomenon of frequency coupling is widely observed in grid-forming (GFM) converters due to the presence of asymmetrical controls and nonlinear blocks. However, the factors influencing frequency coupling have not been thoroughly explored. This paper introduces a systematic small-signal impedance model of GFM converters that intuitively reflects the factors affecting frequency coupling and provides a detailed analysis of how coupling impedance affects stability. It is demonstrated that the influencing factors of coupling impedance are an active power loop and reactive power loop. Specifically, the active power loop influences coupling impedance characteristics near the fundamental frequency, while the reactive power loop impacts the entire frequency range. This paper first reveals that the reactive power loop has a more pronounced effect on frequency coupling than the active power loop. Additionally, the variation in the steady-state operating points also affects the degree of frequency coupling of the GFM converters, primarily affecting the coupling impedance characteristics beyond the fundamental frequency, and the low operating points tend to affect system stability adversely. Finally, simulation results validate the accuracy of the mathematical model and theoretical analysis.

Processing demands modulate the activities and functional connectivity patterns of the posterior (VWFA-1) and anterior (VWFA-2) VWFA

Previous studies have shown that the visual word form area (VWFA) has structural and intrinsic functional connectivity with both language and attention networks. Nevertheless, it is still unclear how the functional connectivity pattern of the VWFA is regulated by processing demands induced by experimental tasks, and whether processing demands differentially regulate the posterior (VWFA-1) and anterior (VWFA-2) subregions of the VWFA. To address these questions, the present study adopted two tasks varying in processing demands (i.e., verbal and non-verbal tasks), and used generalized psychophysiological interaction (gPPI) and dynamic causal modeling (DCM) analyses to explore the task-dependent functional connectivity patterns of the two subregions of the VWFA. Activation analysis revealed that the VWFA-2 showed higher activation for the verbal task than the non-verbal task, while there were no activation differences in the VWFA-1 after controlling for the stimulus driven effects. Functional and effective connectivity analyses revealed that, for both VWFA-1 and VWFA-2, the verbal task enhanced connections from VWFAs to the ventral language regions (e.g., the left orbital frontal cortex), while the non-verbal task enhanced connections from VWFAs to the dorsal visuospatial regions (e.g., the left intraparietal sulcus). Results of the present study indicate that processing demands induced by tasks modulate both the local activity and functional connectivity patterns of the VWFA, providing new insights for understanding its domain-general function.

Innovative factors affecting performance of local government organizations in Thailand

This dissertation aimed (1) to study factors that have causal influences that affect the performance of municipalities. (2) to study the causal relationship model of innovative organizations, innovative capabilities, service innovation and performance management that influences the performance of municipalities. (3) to develop a linear structural equation model of the influence of service innovation, innovation organization, innovative capabilities and performance management that affects the performance of local government organizations in the category of municipalities in Thailand. The sample group were 320 executives of local government organizations in the category of municipalities in Thailand. Sampling was done in 2 steps: stratified and systematic sampling. This study is a quantitative study, and the research tool was a structured questionnaire and data were analyzed by percentage, mean, standard deviation, skewness, kurtosis, and confirmatory component analysis and structural equation model analysis. The findings of this study demonstrate significant direct effects of innovative capabilities, service innovation and performance management on performance of local government organizations in Thailand. Moreover, it was observed that innovative organizations and innovative capabilities also exerted indirect effects on performance of local government organizations through their influence on performance management, serving as mediator variables. Through the development of a structural equation model, it was determined that innovative capabilities had the greatest overall influence on the performance of local government organizations in the category of municipalities in Thailand, followed by performance management, service innovation and innovation organization, respectively. This research study made executives of local government organizations in category of municipalities in Thailand can be used as a guideline in determining strategies for managing their organization to be able to keep up with changes in the digital world and to ensure that the performance of the local government organization achieves the specified goals in terms of both efficiency and effectiveness.

Neural Mechanism of Musical Pleasure Induced by Prediction Errors: An EEG Study

Background/Objectives: Musical pleasure is considered to be induced by prediction errors (surprise), as suggested in neuroimaging studies. However, the role of temporal changes in musical features in reward processing remains unclear. Utilizing the Information Dynamics of Music (IDyOM) model, a statistical model that calculates musical surprise based on prediction errors in melody and harmony, we investigated whether brain activities associated with musical pleasure, particularly in the θ, β, and γ bands, are induced by prediction errors, similar to those observed during monetary rewards. Methods: We used the IDyOM model to calculate the information content (IC) of surprise for melody and harmony in 70 musical pieces across six genres; eight pieces with varying IC values were selected. Electroencephalographic data were recorded during listening to the pieces, continuously evaluating the participants’ subjective pleasure on a 1–4 scale. Time–frequency analysis of electroencephalographic data was conducted, followed by general linear model analysis to fit the power-value time course in each frequency band to the time courses of subjective pleasure and IC for melody and harmony. Results: Significant positive fits were observed in the β and γ bands in the frontal region with both subjective pleasure and IC for melody and harmony. No significant fit was observed in the θ band. Both subjective pleasure and IC are associated with increased β and γ band power in the frontal regions. Conclusions: β and γ oscillatory activities in the frontal regions are strongly associated with musical rewards induced by prediction errors, similar to brain activity observed during monetary rewards.

Uphill dynamics: A spring-mass model analysis of sloped walking

This study explores the biomechanics of uphill running by adjusting and analyzing the spring- mass model. Modifications specific to slope conditions were made to the equations of motion. Despite facing challenges in code execution, an in-depth investigation of the models physical characteristics was conducted through both static and dynamic state analyses. The findings aid in the theoretical understanding of motion on sloped surfaces and provide valuable experience for subsequent research.