Multidimensional Model Research Articles

Adaptive Recommendation of Teaching Content in Higher Education Using Mobile Interaction Technology

With the rapid advancement of mobile interaction technology, teaching methodologies in higher education are increasingly moving toward personalization and intelligence. The use of mobile interaction technology for adaptive recommendation of teaching content has become a critical topic for enhancing educational effectiveness. Existing research in content recommendation, primarily based on collaborative filtering algorithms, often relies on single-dimensional data applications and lacks comprehensive consideration of both location information and temporal effects. Consequently, these approaches fall short in addressing the complex requirements of dynamic learning environments. This study proposes a multi-dimensional dynamic adaptive recommendation system for teaching content based on mobile interaction technology to address the limitations of existing methods. The research encompasses location-based collaborative filtering for teaching content, time-effect-based collaborative filtering, and an integrated multi-dimensional dynamic recommendation model that considers both location and temporal factors. This study is expected to provide a more precise and dynamically adaptive solution for personalized teaching in higher education.

Assessing Nonadiabatic Dynamics Methods in Long Timescales.

Nonadiabatic dynamics simulations complement time-resolved experiments by revealing ultrafast excited-state mechanistic information in photochemical reactions. Understanding the relaxation mechanisms of photoexcited molecules finds application in energy, material, and medicinal research. However, with substantial computational costs, the nonadiabatic dynamics simulations have been restricted to ultrafast timescales, typically less than a few picoseconds, thus neglecting a wide range of photoactivated processes occurring in much longer timescales. Before developing new methodologies, we must ask: How well do the popular nonadiabatic dynamics methods perform in a long timescale simulation? In this study, we employ the multiconfiguration time-dependent Hartree (MCTDH) and its multilayer variants (ML-MCTDH), ab initio multiple spawning (AIMS), and fewest-switches surface hopping (FSSH) methodologies to simulate the excited-states dynamics of a weakly coupled multidimensional Spin-Boson model Hamiltonian designed for a long timescale decay behavior. Our study assures that despite having very different theoretical backgrounds, all the above methods deliver qualitatively similar results. While quantum dynamics would be very costly for long timescale simulations, the trajectory-based approaches are paving the way for future advancements.

Spatiotemporal Dynamics of Suspended Particulate Matter in Water Environments: A Review

Suspended particulate matter (SPM) is an indispensable component of water environments. Its fate and transport involve various physical and biogeochemical cycles. This paper provides a comprehensive review of SPM dynamics by integrating insights from biogeochemical processes, spatiotemporal observation techniques, and numerical modeling approaches. It also explores methods for diagnosing SPM-mediated biogeochemical processes, such as the flocculation kinetics test and organic matter composition analysis. Advances in remote sensing, in situ monitoring, and high-resolution retrieval algorithms are discussed, highlighting their significance in detecting and quantifying SPM concentrations across varying spatial and temporal scales. Furthermore, this review examines integrated models that incorporate population balance equations on the basis of flocculation kinetics into multi-dimensional sediment transport models. The results from this study provide valuable insights into SPM dynamics, ultimately enhancing our knowledge of SPM behavior and transport in water environments. However, uncertainties remain due to limited field data on flocculation kinetics and the need for parameter optimization in numerical models. Addressing these gaps through enhanced fieldwork and model refinement will significantly improve our ability to predict and manage SPM dynamics, which is critical for sustainable aquatic ecosystem management in an era of rapid environmental change.

Multi-Dimensional Modeling of Mixture Formation In A Hydrogen-Fueled Heavy-Duty Optical Engine With Direct Injection

Multi-Dimensional Modeling of Mixture Formation In A Hydrogen-Fueled Heavy-Duty Optical Engine With Direct Injection

Public Awareness Campaigns on Palliative Care: Applying a Multidimensional Model to Understand the Reception by the General Public.

Public awareness campaigns on palliative care aim to tackle limited public knowledge and negative perceptions of palliative care. However, little is known about their public reception. This study examined how existing campaigns are interpreted, evaluated, and engaged with by members of the general public. Three public campaigns, launched in Flanders or the Netherlands between 2020 and 2023, were discussed in 10 focus groups (total of 65 participants). The analysis was guided by Schrøder's multidimensional audience reception model. Our results show that campaigns were interpreted, evaluated, and engaged with in highly divergent ways. People with personal experiences surrounding serious illness were generally more open toward campaign messages, while a sense of personal relevance was lacking for others. Campaigns that centered on the perspectives of seriously ill individuals while portraying them in active and resilient positions were more positively evaluated. Moreover, they were more successful in broadening perceptions of palliative care beyond being hospital-based end-of-life care for severely ill and care-dependent persons. Conversely, materials that neglected the patient's voice while framing palliative care as enabling moments of joy "despite serious illness" generally fortified prior perceptions and were often rejected. Additionally, a preference for highlighting the social context surrounding the patient was repeatedly expressed. We conclude that diversified strategies, optimizing a sense of personal relevance, are needed to more effectively influence public understanding and engagement toward palliative care. Destigmatizing palliative care also involves destigmatizing persons with serious illness, and representing them with an emphasis on their agency and strength is vital to this shift.

Valorizing Credibility: Trust as Paradigm and Revenue Repair in South African News Organizations 2012–2022

Several South African news organizations emerged in the 2010s determined to provide public journalism in more durable, accessible, and credible ways. Partly responding to the disruption of the news industry’s business models by platform companies, and sustained attempts to delegitimize journalism by populist political parties, these organizations articulated the case for evidence-based, verified journalism, and pioneered more transparent and multidimensional funding models. They also embraced communal copyright license regimes to ensure wider dissemination and reach for their stories and were able to reconstitute a portion of their audiences into communities of interest, committed to supporting their journalism work, sometimes through direct financial contributions. By paying more attention to these organizations’ contractual relationships with audiences, distribution partners, and philanthropic donors, we can better understand how they were able to generate more sustainable revenue streams to create both viable levels of income and greater levels of trust in their news products. This will likely remain key to the survival of public interest journalism in South Africa if laws such as Australia’s News Media Bargaining Code are inimitable in other national contexts.

Investigating numerical stability by scaling heat conduction in a 1D hydrodynamic model of the solar atmosphere

Numerical models of the solar atmosphere are widely used in solar research and provide insights into unsolved problems such as the heating of coronal loops. A prerequisite for such simulations is an initial condition for the plasma temperature and density. Many explicit numerical schemes employ high-order derivatives that require some diffusion, for example isotropic diffusion, for each independent variable to maintain numerical stability. Otherwise, significant numerical inaccuracies and subsequent wiggles will occur and grow at steep temperature gradients in the solar transition region. We tested how to adapt the isotropic heat conduction to the grid resolution so that the model is capable of resolving varying temperature gradients. Our ultimate goal is to construct an atmospheric stratification that can serve as an initial condition for multi-dimensional models. Our temperature stratification spans from the solar interior to the outer corona. From that, we computed the hydrostatic density stratification. Since numerical and analytical derivatives are not identical, the model needs to settle to a numerical equilibrium to fit all model parameters, such as mass diffusion and radiative losses. To compensate for energy losses in the corona, we implemented an artificial heating function that mimics the expected heat input from the 3D field-line braiding mechanism. Our heating function maintains and stabilises the obtained coronal temperature stratification. However, the diffusivity parameters need to be adapted to the grid spacing. Unexpectedly, we find that higher grid resolutions may need larger diffusivities --- contrary to the common understanding that high-resolution models are automatically more realistic and would need less diffusivity. Smaller grid spacing causes larger temperature gradients in the solar transition region and hence a greater potential for numerical problems. We conclude that isotropic heat conduction is an efficient remedy when using explicit schemes with high-order numerical derivatives.

Digital Twins in Structural Health Assessment and Monitoring: Applications in Historical Timber Buildings

ABSTRACT This paper explores the application of Digital Twins (DTs) for supporting the structural health monitoring (SHM) of timber buildings, with a particular emphasis on historical timber structures. It reviews technologies and methods for DT creation, examining how geospatial data and Building Information Modeling (BIM) serves as foundational elements in developing digital twins. The paper discusses integrating these elements with various data and models to enhance SHM tasks, detailing actionable DT approaches for documenting, analyzing, simulating and predicting structural behavior. The study highlights that while high-fidelity geometric models are well developed, their application for advanced SHM tasks remains limited. Integration of sensor data into BIM frameworks is promising but still faces challenges such as limited automation and difficulties in handling real-time and long-term data. Additionally, the paper notes that the implementation of DTs for the simulation of the effects of hygrothermal phenomena on the long-term behavior and durability of timber structures is underexplored. Multi-dimensional BIM models and machine and deep learning techniques in data-driven, physics-based models are promising approaches for timber cultural heritage management and beyond.

A Cognitive Diagnostic Model of Reading Ability based on China’s Standards of English Language Ability

Traditional English reading assessments in China typically provide a single summative score, without revealing the extent to which students have mastered different aspects of reading comprehension. Cognitive Diagnostic Models (CDMs), as a type of multidimensional latent trait model, offer diagnostic insights by presenting examinees' proficiency levels across a set of predefined skills. Cognitive diagnostic assessments based on these models can deliver multidimensional, fine-grained evaluation results, thus overcoming the limitations of traditional unidimensional score-based assessments. Based on the China's Standards of English Language Ability (CSE), this study constructs a cognitive diagnostic assessment model for English reading. Using college general English reading test papers and the G-DINA model, the English reading abilities of 200 students were diagnosed, and personalized performance reports were generated. A questionnaire survey was conducted to gather students' evaluations and suggestions regarding the feedback reports. The results indicate that the cognitive diagnostic model for English reading, constructed according to the CSE, exhibits good model fit and high diagnostic reliability. The personalized performance reports visually and comprehensively present the strengths and weaknesses of the test takers' reading abilities, providing valuable guidance for students' future remedial learning. This study utilizes a multidimensional assessment approach based on cognitive diagnostic models to evaluate the English reading abilities of Chinese university students, effectively addressing the limitations of traditional assessment methods that typically offer only a single score.

Accuracy of Reaction Coordinate Based Rate Theories for Modelling Chemical Reactions: Insights From the Thermal Isomerization in Retinal.

Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studying chemical reactions remains uncertain due to the high and sharp energy barriers and complex reaction coordinates involved. This study focuses on the thermal cis-trans isomerization in retinal, employing molecular simulations and comparing rate constant estimates based on one-dimensional rate theories with those based on sampling transitions and grid-based models for low-dimensional collective variable spaces. Even though each individual method to estimate the rate passes its quality tests, the rate constant estimates exhibit considerable disparities. Rate constant estimates based on one-dimensional reaction coordinates prove challenging to converge, even if the reaction coordinate is optimized. However, consistent estimates of the rate constant are achieved by sampling transitions and by multi-dimensional grid-based models.

Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting of Multidimensional Kinetic Model with Bayesian Data Assimilation

Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting of Multidimensional Kinetic Model with Bayesian Data Assimilation

Использование комплексной дифференциации и кластеризации геолого-промысловой информации при прогнозировании продуктивности залежей Южно-Татарского свода

The problem of reliable calculation and reasonable interpretation of various productivity coefficient forecasting models is complicated by the presence of hidden patterns between geological and field parameters. The object of the study was the carbonate reservoirs of the deposits of the South Tatar arch, the data on which were obtained as a result of generalization and integration of the results of operating more than 700 wells for various purposes. Twenty geological and physical parameters were identified for the study, using which a discriminant analysis was successfully carried out. Multidimensional regression modeling was carried out for three selected groups of homogeneous objects. The simulation results have shown the efficiency of complex differentiation and clustering of geological and field information, which is based on the hierarchical structuring of parameters, which makes it possible, even with a minimum amount of data at a relevant level, to successfully identify the main trends in the influence of model parameters on the productivity coefficient, expanding the scope of their application to solve a range of basic and indirect problems of rational development of oil fields.

Prevention of Food Waste in China: Role and Impact of China's Anti-Food Waste Law.

In recent years, despite global improvements in development, food scarcity and waste remain critical issues impacting food security, human health, and environmental sustainability. China's Ministry of Agriculture and Rural Affairs reports that China's food loss and waste rate is approximately 22.7%, amounting to 460 million tons annually, with the consumption and post-harvest processing stages facing the highest losses. To address these issues, China enacted the Anti-Food Waste Law in 2021, aiming to reduce food waste through clear responsibilities, incentives, and penalties for government agencies and relevant stakeholders. While scholars note that the law represents a significant shift from moral to legal governance in tackling food waste, some argue that its provisions lack sufficient specificity. This article assesses the effectiveness of China's anti-food waste legislation using case studies and comparative analyses, highlighting the challenges in defining and implementing the law within China's cultural framework, where food signifies abundance and prosperity. Additionally, the article explores successful international practices, including the 2030 Champions Program and similar efforts in Europe and Japan, to inform China's strategy. The key recommendations for policy improvement include strengthening accountability and governance, establishing a food hierarchy for sustainable resource management, enhancing data collection on food waste, promoting food donation mechanisms, and transitioning from a solely government-led regulatory approach to a multi-dimensional co-regulation model.

Relationship between participation in leisure activities and the maintenance of successful aging in older Chinese adults: a 4-year longitudinal study

BackgroundIt is critical to age successfully for general health considering the aging of the global population. Successful aging is an important indicator to assess whether older individuals are experiencing a healthy life and high-quality aging. This study aimed to explore the longitudinal relationship between the frequency and number of participation in leisure activities and the maintenance of successful aging.MethodsThis longitudinal study included a total of 1,828 Chinese adults aged 60 years or older. The following six activities were gathered to reflect leisure activities: reading books or newspapers, gardening work, playing mahjong or cards, listening to radio or watching TV, raising pets or domestic animals, and engaging in social activities. We evaluated successful aging using a multidimensional model which includes the following five components: better self-reported health, good psychological status, high cognitive function, free of disability, and better physical activity level.ResultsThis study showed a retention rate of 58.4% for successful aging during a 4-year follow-up. Participants who frequently engaged in reading books or newspapers, gardening work, playing mahjong or cards, and listening to radio or watching TV, had higher rates of future successful aging maintenance (OR: 1.50, 95% CI: 1.04–2.22 to 1.89, 95% CI: 1.26–2.812) than those who never participated in these activities. Compared to those with 0 leisure activity, participants with 3 (OR: 1.78, 95% CI: 1.14–2.77) and 4–6 (OR: 1.65, 95% CI: 1.01–2.71) leisure activities had a greater probability of maintaining successful aging.ConclusionsThese findings suggest that individuals who participated more frequently in more leisure activities had a higher chance of the maintenance of successful aging in older adults. Encouraging older people to frequently participate in a greater variety of leisure activities may be an effective way to maintain successful aging over time.

The Effect of Anxiety Sensitivity to Loneliness in Hospitalized Children and Adolescents: Multidimensional Models

Introduction: Although good quality social relationships are essential for every individual’s mental and physical health and well-being, loneliness is an important yet often overlooked social determinant of child and adolescent health. Loneliness felt by a person can put them at risk of anxiety. Anxiety sensitivity is a sensation of fear that will result in physical, social, or psychological problems that later risk clinical anxiety problems. This study aims to determine the effect of anxiety sensitivity on loneliness in children and adolescents who are hospitalized in the hospital. Method: The type of research is quantitative research with an exploratory approach and a self-rating questionnaire. The research subjects were 20 respondents, who were inpatients at the West Java Provincial Mental Hospital. Anxiety was measured using the ASI - 3 (Anxiety Sensitivity Indexes - 3) questionnaire, and the UCLA Loneliness Scale 3 to assess the degree of loneliness. Data were analyzed using simple linear regression with the help of the EPI INFO 7 program. Results: The linear regression test obtained a regression coefficient of 0.786 which states the direction of the influence of variables X and Y is positive with a p-value of 0.000155 <0.05. Conclusion: There is a significant relationship between high anxiety sensitivity and feelings of loneliness in children and adolescents in inpatients. The most experienced dimension of anxiety sensitivity is about social problems while the dominant dimension of loneliness with the choice is always on the dimension of collective connectedness. Keywords: Anxiety Sensitivity, Loneliness, Children and Adolescents

Original position arguments: an axiomatic characterization

Abstract We study original position arguments in the context of social choice under ignorance. First, we present a general formal framework for such arguments. Next, we provide an axiomatic characterization of social choice rules that can be supported by original position arguments. We illustrate this characterization in terms of various well-known social choice rules, some of which do and some of which do not satisfy the axioms in question. Depending on the perspective one takes, our results can be used to argue against certain rules, against Rawlsian theories of procedural fairness, or in support of richer, multidimensional models of individual choice.

Evaluation of face validity and core concepts of a novel knowledge scale for inherited heart disease: A pilot study

AbstractThe rising demand for genetic counseling has prompted the implementation of various innovative service delivery models, such as patient webinars, videos, chatbots, and the integration of genetic testing into mainstream healthcare. To ensure patients receive adequate information for informed decision‐making, validated measures to assess these models are essential but currently limited in the setting of inherited heart disease. We aimed to develop and initiate validation of a cardiac knowledge scale, as part of the Multidimensional Model of Informed Choice measure, to assess whether patients (probands and family members) with inherited cardiomyopathies, arrhythmias, and aortopathies are provided with sufficient knowledge to make informed decisions about genetic testing. Content expert genetic counselors identified eight core concepts addressed during genetic counseling sessions; from these, eight true/false knowledge questions were created. Questions were reviewed by 22 international cardiac genetics counselors with additional changes made. Initial validation steps of the knowledge scale were conducted at two sites: the Edmonton Medical Genetic Clinic, University of Alberta Hospital in Edmonton, Canada, and the Genetic Heart Disease Clinic, Royal Prince Alfred Hospital in Sydney, Australia. Face validity was evaluated through nine patient interviews, resulting in minor revisions to four questions and major revisions to one question. An additional five patient interviews were conducted to evaluate the revised questions. The core concepts addressed in each question were further evaluated in the context of patient decision‐making about genetic testing. All participants described the eight concepts as either helpful or essential in their decision‐making process. The cardiac knowledge scale is a promising measure created to evaluate the informed choice of patients and their families affected by an inherited heart condition. The next step of validation includes trialing the cardiac knowledge scale with a real‐world sample of patients deciding about genetic testing for inherited heart disease.

Asymptotically Correct Person Fit z-Statistics For the Rasch Testlet Model.

A well-known person fit statistic in the item response theory (IRT) literature is the statistic (Drasgow et al. in Br J Math Stat Psychol 38(1):67-86, 1985). Snijders (Psychometrika 66(3):331-342, 2001) derived , which is the asymptotically correct version of when the ability parameter is estimated. However, both statistics and other extensions later developed concern either only the unidimensional IRT models or multidimensional models that require a joint estimate of latent traits across all the dimensions. Considering a marginalized maximum likelihood ability estimator, this paper proposes and , which are extensions of and , respectively, for the Rasch testlet model. The computation of relies on several extensions of the Lord-Wingersky algorithm (1984) that are additional contributions of this paper. Simulation results show that has close-to-nominal Type I error rates and satisfactory power for detecting aberrant responses. For unidimensional models, and reduce to and , respectively, and therefore allows for the evaluation of person fit with a wider range of IRT models. A real data application is presented to show the utility of the proposed statistics for a test with an underlying structure that consists of both the traditional unidimensional component and the Rasch testlet component.

Development of Targeted Safety Hazard Management Plans Utilizing Multidimensional Association Rule Mining

Investigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approach mainly focuses on hazard description data, often underutilizing critical information such as hazard time and location. To address this, we integrate topic mining with association rule mining to uncover intrinsic association patterns among various attributes of mine safety hazards. Through a systematic analysis of standardized mining hazard attributes, five key analytical dimensions were identified: Hazard Type, Level, Time, Location, and Responsible Units. A topic mining model, utilizing the Biterm Topic Model, was constructed to reduce dimensionality and aggregate hazard description data. Evaluation indicators such as Standard Lift and Difference Degree were proposed, resulting in a multidimensional association rule mining model for mining safety hazards. In this research, 1,387 valid rules were extracted based on hazard inspection data from an underground gold mine in China. The analysis revealed relatively strong associations between hazard location and hazard type, responsible unit, as well as hazard level, with association degrees of 1.934, 1.412, and 1.240, respectively. Additionally, 15 rules with a high degree of differentiation were identified to explore interesting correlations among different attributes. Based on this, corresponding control measures and improvement plans were developed for 19 locations. The results demonstrate that a multidimensional partition-based association rule mining approach for mining safety hazards can significantly enhance the specificity of safety training and improve the efficiency of safety hazard investigation.

Cooper 등의 다차원 정책분석 모형을 활용한「고등·평생교육지원 특별회계」정책 분석

This study aims to comprehensively understand and systematically analyze the policy-making process of the 'Special Account for Supporting Higher and Lifelong Education' by taking a multi-dimensional approach. To achieve the research objectives, various materials such as national laws and regulations, government policy documents, press releases, research papers, and internet portal articles were collected. The multidimensional policy analysis model of Cooper et al. was utilized to analyze it in normative, structural, constituent, and technical dimensions. First, at the normative level, the Special Account was promoted with the goals of optimizing education finance and supporting university innovation. Second, structurally, the Special Account was influenced by institutional structures such as tuition freezes and the vulnerability of higher education financing. Third, in terms of its constituent dimension, the policy-making process of the Special Account revealed interactions among the central and local governments, higher education institutions, and elementary, secondary, and middle education institutions. Fourth, the technical dimension discussed the adoption of a temporary operation and funding method as a compromise, and it highlighted that the government's vision was being realized through the budget size and utilization process. Additionally, the urgency of policy evaluation for securing sustainable higher education funding was emphasized. Based on the findings, it is suggested that establishing a long-term financing measure, such as a Higher Education Finance Grant, is essential to enhance the stability and predictability of higher education funding.