Extended Model Research Articles

Growth differentiation factor-15 and 5-years cardiovascular risk in chest pain patients presenting to the emergency department: a multicenter cohort study

Abstract Background Growth differentiation factor 15 (GDF-15) has been explored as a prognostic biomarker in multiple disease entities, including ischemic heart disease and heart failure (HF), but its role as a prognostic marker in chest pain patients presenting to the emergency department (ED) is unknown. Purpose To investigate the association between GDF-15 and 5-year all-cause mortality, cardiovascular mortality, and major adverse cardiac events (MACE), and to evaluate its prognostic incremental value. Methods Patients presenting to the ED with acute chest pain were prospectively enrolled in an international multicenter diagnostic study. GDF-15 concentrations were measured in a blinded fashion upon ED presentation. Follow-ups were performed at 3 months, 1-, 2- and 5-years. MACE was defined as the composite of cardiovascular mortality, acute myocardial infarction, stroke, and HF rehospitalization. Associations between GDF-15 and each prognostic outcome were modeled using multivariable Cox proportional hazard regression fitting restricted cubic splines to continuous variables. The magnitude of the effect of each GDF-15 unit change on multivariable-adjusted models was assessed graphically using dose-response plots. To compute an HR for the dose-response plots we chose a GDF-15 value of 1200 pg/mL as a reference value as this has been chosen in previous literature1. For the restricted cubic splines three knots were used. The added prognostic value of GDF-15 was evaluated by the increase in the C-statistic between a base and an extended model and the fraction of new information (FNI) provided by GDF-15. Results A total of 4779 chest pain patients had baseline GDF-15 values available. The median age was 61 [48-74] and 33% were female. Higher GDF-15 levels were more strongly associated with heart failure, kidney dysfunction, older age, hypertension, diabetes and coronary artery disease. GDF-15 was independently associated with 5-years all-cause mortality, cardiovascular mortality and MACE (Figure). When adding GDF-15 to a rich-for-prior-information base model which included age, sex, cardiovascular risk factors, creatinine, cardiac troponin and NT-proBNP, GDF-15 provided relevant incremental prognostic discrimination for 5-years all-cause death and cardiovascular death. (Table). Conclusions In chest pain patients presenting to the emergency department, GDF-15 concentration on admission was associated with 5-years all-cause mortality, cardiovascular mortality, and MACE. In addition, it provided relevant incremental prognostic information when added on top of troponin and NT-proBNP, the two current prognostic gold standard biomarkers.FigureTable

Harnessing Available Evidence in Single-Case Experimental Studies: The Use of Multilevel Meta-Analysis.

The use of multilevel models to combine and compare the results of multiple single-case experimental design (SCED) studies has been proposed about two decades ago. Since then, the number of multilevel meta-analyses of SCED studies steadily increased, together with the complexity of multilevel models used. At the same time, many studies were done to empirically evaluate the approach in a variety of situations, and to study how the flexibility of multilevel models can be employed to account for many complexities that often are encountered in SCED research, such as autocorrelation, linear and nonlinear time trends, specific designs, external event effects, multiple outcomes, and heterogeneity. In this paper, we give a state-of-the-art of the multilevel approach, by making an overview of basic and more extended models, summarizing simulation results, and discussing some remaining issues.

Adoption of SPACE-learning management system in education era 4.0: an extended technology acceptance model with self-efficacy

The COVID-19 pandemic has caused constraints on education that takes place face-to-face; thus, the learning process has been conducted online. With this emergence, an application-based learning tool called a Learning Management System (LMS) was created to cater to the requirements of distant students. Therefore, this study explores how SPACE-LMS is used in the Teacher Professional Education Program (TPEP) and how it interacts with the Technology Acceptance Model (TAM) and self-efficacy. This study collected data from TPEP participants in the province of West Sumatra-Indonesia from the 2022 cohort, and 245 individuals actively participated. As the quantitative method, Partial Least Square-Structural Equation Modeling (PLS-SEM) analysis with SmartPLS software was used to test and predict the conceptual model. The empirical findings demonstrate that the readiness of adopting SPACE influences perceived ease of use (PEU) and perceived usefulness (PU). Self-efficacy (SE) influences PEU and intention to use (ITU); PEU influences PU; PEU influences attitudes toward users (ATT); and PU influences ATT. Interestingly, PU had no impact on the ITU. The desire of TPEP students to use the SPACE-LMS is also influenced by their sense of self-efficacy. Thus, this study has both theoretical and practical implications.

Dynamics analysis of fractional-order extended neuron model under electromagnetic field and application to image encryption

Dynamics analysis of fractional-order extended neuron model under electromagnetic field and application to image encryption

An extension of the theory of planned behaviour to predict exclusive breastfeeding among Australian mother-father dyads using structural equation modelling.

The objective of this paper was to examine the predictors of breastfeeding exclusivity at two time-points (6 and 26 weeks postpartum) using a dyadic mother-father extended model of the theory of planned behaviour. A sample of 1139 first-time Australian mother-father dyads completed measures of each of the Theory of Planned Behaviour variables (intention, attitudes, subjective norms, and perceived behavioural control) and additional psychosocial and behavioural predictors of breastfeeding, including problems with breastfeeding, psychological distress, childcare stress, partner support, and the frequency and time of first public breastfeed. Confirmatory factor analyses were used to determine the factor structures of the latent constructs to be included in the structural model. Structural equation modelling was used to model pathways between variables. Breastfeeding exclusivity at 6 weeks was significantly predicted by breastfeeding self-efficacy at 6 weeks, intention to exclusively breastfeed, and frequency of public breastfeeding over the month prior, whereas childcare stress, partner support, time of first public breastfeed, and fathers' variables (attitudes and subjective norms) had little influence. Self-efficacy mediated the relationship between mothers' attitudes, psychological distress, and breastfeeding problems with breastfeeding exclusivity at 6 weeks. Breastfeeding exclusivity at 26 weeks was significantly predicted by mothers' attitudes, self-efficacy, breastfeeding exclusivity at 6 weeks, and frequency of public breastfeeding in the month prior. The results highlight the importance of breastfeeding self-efficacy and confidence to breastfeed in public as crucial constructs in promoting long-term exclusive breastfeeding and provides support for an extended theory of planned behaviour model in predicting breastfeeding outcomes.

New physics opportunities in triangle singularity

In this paper, we show that loop-induced processes involving new physics particles can readily satisfy Landau Equation and trigger triangular singularities at high-energy colliders, leading to fully visible Standard Model final states. In addition, a t-channel triangular singularity can also occur during particle scattering processes that may extend to low momentum exchange. We discuss several typical scenarios in supersymmetric and extended Higgs models, and three types of final-state kinematic features at the collider. Four-particle vertices in new physics can play a significant role in high energy diagrams. We identify an “everything on shell” triangular singularity diagram only involving bosonic couplings, which has the potential to completely avoid large virtuality suppression. We further show that such a virtuality-free diagram is missing in the Standard Model at electroweak scale, and it becomes available in new physics models.

Method for prediction magnetic silencing of uncertain energy-saturated extended technical objects in prolate spheroidal coordinate system

Aim. Development of method for prediction by energy-saturated extended technical objects magnetic silencing based on magnetostatics geometric inverse problems solution and magnetic field spatial spheroidal harmonics calculated in prolate spheroidal coordinate system taking into account of technical objects magnetic characteristics uncertainties. Methodology. Spatial prolate spheroidal harmonics of extended technical objects magnetic field model calculated as magnetostatics geometric inverse problems solution in the form of nonlinear minimax optimization problem based on near field measurements for prediction far extended technical objects magnetic field magnitude. Nonlinear objective function calculated as the weighted sum of squared residuals between the measured and predicted magnetic field COMSOL Multiphysics software package used. Nonlinear minimax optimization problems solutions calculated based on particle swarm nonlinear optimization algorithms. Results. Results of prediction extended technical objects far magnetic field magnitude based on extended technical objects spatial prolate spheroidal harmonics of the magnetic field model in the prolate spheroidal coordinate system using near field measurements with consideration of extended technical objects magnetic characteristics uncertainty. Originality. The method for prediction by extended technical objects magnetic cleanliness based on spatial prolate spheroidal harmonics of the magnetic field model in the prolate spheroidal coordinate system with consideration of magnetic characteristics uncertainty is developed. Practical value. The important practical problem of prediction extended technical objects magnetic silencing based on the spatial prolate spheroidal harmonics of the magnetic field model in the prolate spheroidal coordinate system with consideration of extended technical objects magnetic characteristics uncertainty solved. References 48, figures 2.

Stock Price Prediction Research Based on CNN-LSTM

In recent years, neural network technology has been widely used in the field of stock forecasting, especially in the aspect of LSTM, which has become a research hotspot because of its unique advantages in dealing with time series problems in stock forecasting. By analyzing and summarizing the recent research literature based on LSTM and its extended model, this paper aims to deeply explore the effect and potential limitations of LSTM in capturing the dynamic and nonlinear characteristics of the stock market. By constructing different combination models combining LSTM and CNN, the paper uses normalization to preprocess the split data and realize the generalization prediction of stock price. Compared with the experimental results, a suitable combination model of LSTM and CNN is found. This hybrid model can significantly improve the accuracy and stability of prediction, and also shows the advantages in multi-input data processing. The results of this study not only provide a new technical means for the direction of stock prediction, but also explore a new direction for the further application of deep learning technology in the financial field.

Electric delivery vehicle routing problem optimization model with time-varying traffic congestion

In order to plan a reasonable scheme for the fleet travel and minimize the energy consumption of electric vehicles during traffic congestion periods, while relieving the mileage anxiety of delivery personnel, the pure electric delivery vehicle routing problem considering time-varying traffic congestion was studied. Firstly , a novel mixed integer programming model (TD-EVRP) is established to step for its extended model of EVRP, which especially characterizes the time-varying traffic congestion state and measures the energy consumption of electric delivery vehicles. This model also refines its constraints including customer time window, vehicle load, and vehicle charging requirements. Secondly, a traffic congestion avoidance strategy is introduced into the model, which allows vehicles to park and charge at customer points during peak traffic periods in order to actively avoid unnecessary energy consumption caused by traffic congestion. Finally, the improved ant colony algorithm is designed to acquire the optimal solution.

Self-identity explains better breastfeeding intention of ethnic pregnant mothers of Western Nepal: Extending the theory of planned behavior.

Breastfeeding intention is one of the strongest predictors of breastfeeding behavior and practice. The Theory of Planned Behavior (TPB), with its main construct, behavioral intention, is useful to predict actual behavior. While the literature has examined the implications of other theoretical notions such as self-identity, moral norm, descriptive norm, and socio-demographic variables, their roles remain unclear. Similarly, research on ethnic and low-income mothers is even insufficient. Therefore, given the original TPB constructs, our goal was to examine the role of extra theoretical constructs and specific demographic variables, and observe whether the original model would alter. A cross-sectional analytical study was carried out among 325 pregnant mothers in six purposively selected health care facilities of Kaski and Tanahu districts, using structured pro forma, from December 2018 to November 2019. We developed stepwise multivariate logistic regression from the entered and cleaned data, observed the effects on breastfeeding intention (BFI), and checked against multiple parameters. We interpreted the model with adjusted odds ratios and β coefficients, along with the variance explained. Out of 325 pregnant women, more than half (54.8%) were primiparous, and the mean age was 25.1±5 years. All three theoretical constructs of TPB regressed the BFI significantly, with 10.7%, the breastfeeding attitude dominating (β = 0.734, p = .003), and the other two constructs-perceived breastfeeding control (β = 0.659, p = .011) and breastfeeding subjective norm (β = 0.504, p = .045). Interestingly, breastfeeding self-identity added further variance of 5.2% (β = 0.955, p < .001), followed by income, with 3.2% (β = 0.856, p = .001), both of which also tallied large effect sizes. The model parameters showed consistency and robustness. We discuss the variables of the theoretical and extended model for BFI as well as the arguments for the explained variance. Self-identity is a strong and independent predictor of breastfeeding intention and, therefore, should be considered to better prepare for breastfeeding, especially among ethnic pregnant mothers. The direct and intermediate effects of self-concept and income need further study with more robust designs.

Generalized transport costs in intermodal shipping: the context of the Northeast Passage

Intermodality is regarded as one way of achieving more sustainable transport solutions. To make intermodal transport the preferred solution among shippers, it must be attractive according to the concept of generalized transport costs. An extended model for generalized transport costs is developed which includes elements important for comparing maritime transport solutions in the Artic. This framework forms the basis for a principal discussion on the conditions that make one transport solution preferable to another within the context of maritime logistics. This model is then applied to the context of the Northeast Passage to discuss the necessary requirements for making an intermodal transport solution attractive relative to the current main route from Northeast Asia to Northwest Europe through the Suez Canal. Even though intermodality could be preferable in principle, current solutions cannot compete with either Arctic routes using unimodal solutions with high ice-class vessels or with the Suez route. Due to uncertainty and limited empirical evidence, a sensitivity analysis is conducted, focusing on the variables with the greatest impact on the result. Policymakers and stakeholders can consider the findings to improve transport competitiveness via the Northeast Passage.

Extending the C/C++ Memory Model with Inline Assembly

Programs written in C/C++ often include inline assembly: a snippet of architecture-specific assembly code used to access low-level functionalities that are impossible or expensive to simulate in the source language. Although inline assembly is widely used, its semantics has not yet been formally studied. In this paper, we overcome this deficiency by investigating the effect of inline assembly on the consistency semantics of C/C++ programs. We propose the first memory model of the C++ Programming Language with support for inline assembly for Intel's x86 including non-temporal stores and store fences. We argue that previous provably correct compiler optimizations and correct compiler mappings should remain correct under such an extended model and we prove that this requirement is met by our proposed model.

Optimal control strategies for infectious disease management: Integrating differential game theory with the SEIR model

The rapid spread of infectious diseases poses a critical threat to global public health. Traditional frameworks, such as the Susceptible–Exposed–Infectious–Recovered (SEIR) model, have been crucial in elucidating disease dynamics. Nonetheless, these models frequently overlook the strategic interactions between public health authorities and individuals. This research extends the classic SEIR model by incorporating differential game theory to analyze optimal control strategies. By modeling the conflicting objectives of public health authorities aiming to minimize infection rates and intervention costs, and individuals seeking to reduce their infection risk and inconvenience, we derive a Nash equilibrium that provides a balanced approach to disease management. Using Picard’s iterative method, we solve the extended model to determine dynamic, optimal control strategies, revealing oscillatory behavior in public health interventions and individual preventive measures. This comprehensive approach offers valuable insights into the dynamic interactions essential for effective infectious disease control.

Explaining preservice teachers' intention and behavior to use technology‐enabled learning in China: A multi‐group analysis across experiences

AbstractThis study examines the development of preservice teachers' intention and behavior to use of Technology‐Enabled Learning (TEL) within the Theory of Planned Behavior (TPB) framework. Despite growing interest, the effective integration of TEL among preservice teachers remains a challenge. Engaging 1023 participants ‐ senior undergraduates with practical TEL experience in real classroom settings ‐ the study employed structural equation modeling (SEM) to analyze the relationships between key factors ‐ experience, subjective norms, attitudes, and perceived behavioral control ‐ and their influence on the intention and behavior to use of TEL. The findings revealed that behavioral intention did not significantly influence the actual use of TEL, whereas perceived behavioral control acted both as a determinant of TEL use and as a mediator in the relationship between subjective norms and TEL usage. Additionally, while subjective norms showed minimal impact on behavioral intention, attitudes and perceived behavioral control emerged as crucial antecedent variables and mediators. The multi‐group analysis using SEM revealed that preservice teachers' experience with TEL moderated the first half of the mediating pathway between subjective norms, perceived behavioral control and the actual use of TEL. Notably, multi‐group analysis indicated that as preservice teachers gained more experience with TEL, the influence of subjective norms on perceived behavioral control weakened. This study contributes to a deeper understanding of the TPB framework and its extended models in the context of TEL in teacher education, offering valuable insights for developing more effective TEL training and guidance strategies, ultimately aiming to enhance preservice teachers' proficiency and engagement with TEL.

Investigating the Behavioral Intention of Smart Home Systems among Older People in Linyi City

Background: With an aging population and the continuous advancement of smart technology, the Chinese government is exploring smart elderly care models to address the challenges posed by aging. Although smart home systems are viewed as a promising solution, their adoption rate among older people remains low. Objectives: This study aimed to investigate the factors influencing the behavioral intention to use smart home systems among older people in Linyi City, Shandong Province, China. Methods: A literature review revealed a lack of quantitative research on older people’s behavioral intention toward smart home systems based on the Innovation Diffusion Theory. This study developed an extended model based on the Innovation Diffusion Theory, Technology Acceptance Model, and external variables, incorporating eight variables: intergenerational technical support, perceived cost, self-reported health conditions, compatibility, observability, trialability, perceived usefulness, perceived ease of use, and behavioral intention. Results: Analysis of 387 valid questionnaires showed that compatibility and trialability significantly and positively affect perceived ease of use, while self-reported health conditions, perceived ease of use, and perceived usefulness have significant effects on behavioral intention. In addition, perceived cost had a negative influence on behavioral intention. Contributions/Significance: These findings highlight the importance of considering these factors in the design of smart home systems to improve user experience and provide valuable practical guidance to smart home system developers, R&amp;D institutions, and policymakers.

Comparing inflationary models in extended Metric-Affine theories of gravity

We study slow-roll inflation as a common feature in Metric-affine Theories of Gravity. In particular, we take into account extended metric, teleparallel and symmetric-teleparallel theories of gravity, based on different geometric invariants, discussing analogies and differences. The analysis for each model is performed in two approaches. First, we focus on the potential-slow-roll approach by studying the reconstructed potentials for different forms of the extended models related to the considered gravitational theory in the Einstein frame. Secondly, we investigate the Hubble-slow-roll approach for some conventional inflationary potentials related to the specific extended model in the Jordan frame. We compare all results with cosmic microwave background anisotropy observations coming from Planck 2018 and BICEP2/Keck array satellites in order to find the observational constraints on the parameters space of the models as well as their prediction from the spectral parameters. Eventually, we attempt to present a qualitative comparison between three classes of considered modified gravities using the obtained inflationary results. The aim is to select, in principle, cosmological signatures capable of discriminating among concurrent models in view to point out the representation of gravity, according with geometric invariants, which better addresses the early universe dynamics.

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study

BackgroundHospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of natural language processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes. ObjectiveThis study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model. DesignA retrospective observational cohort study. Setting(s)This study was conducted at Ajou University School of Medicine in South Korea. ParticipantsData from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized. MethodsTwo distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC. ResultsAmong 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672–0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768–0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model. ConclusionsThis study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

The Impact of Perceived Risk to AI Music on the General Public's Music Listening Behavioral Intention: Using Extended MGB Model

The Impact of Perceived Risk to AI Music on the General Public's Music Listening Behavioral Intention: Using Extended MGB Model

Approaches for predicting the soil-water characteristic curves of compacted quartz sand based on particle packing theory

The soil-water characteristic curve (SWCC) is an important basis for describing hydraulic properties, which is closely related to environmental problems such as the migration of toxic substances from groundwater and soil. The SWCC models vary considerably with respect to the void ratio or bulk density. However, the void ratio and bulk density of the same soil change at different depths or positions within a flat space. Accurate simulation or calculation requires a precise SWCC, but measuring every SWCC under each density or void ratio condition is difficult. In this study, two particle packing models, the Kwan model and the modified linear packing density model (MLPDM), were introduced into the van Genuchten (VG) model to predict the SWCC for soil at the minimum void ratio (the maximum dry density). Then, the prediction method of the SWCCs for the same soil with different densities or void ratios was given. A series of laboratory tests using quartz sand mixtures were conducted to verify the accuracy of two extended models (the K-VG model and the M-VG model). For the K-VG model, the average RMSE was 0.020. For the M-VG model, the average RMSE was 0.017. In general, the SWCCs predicted by the two extended models were consistent with the measured values.

Nitrogen accountancy in space agriculture.

Food production and pharmaceutical synthesis are posited as essential biotechnologies for facilitating human exploration beyond Earth. These technologies not only offer critical green space and food agency to astronauts but also promise to minimize mass and volume requirements through scalable, modular agriculture within closed-loop systems, offering an advantage over traditional bring-along strategies. Despite these benefits, the prevalent model for evaluating such systems exhibits significant limitations. It lacks comprehensive inventory and mass balance analyses for crop cultivation and life support, and fails to consider the complexities introduced by cultivating multiple crop varieties, which is crucial for enhancing food diversity and nutritional value. Here we expand space agriculture modeling to account for nitrogen dependence across an array of crops and demonstrate our model with experimental fitting of parameters. By adding nitrogen limitations, an extended model can account for potential interruptions in feedstock supply. Furthermore, sensitivity analysis was used to distill key consequential parameters that may be the focus of future experimental efforts.