Variable Model Research Articles

Linking patient safety, caring behaviours and professional self-efficacy with missed nursing care among Filipino emergency room nurses: A structural equation model study.

To investigate the interrelationships of patient safety, caring behaviours, professional self-efficacy and missed nursing care among emergency room nurses. Cross-sectional, correlational study. Filipino emergency room nurses (n = 345) participated via convenience sampling from September 2023 to January 2024. Four validated self-report scales were used to collect data and were analysed using Spearman rho, covariance-based structural equation modelling, mediation and path analyses. The emerging model of study variables displayed satisfactory fit indices. Patient safety directly influenced caring behaviours and professional self-efficacy, while negatively influencing missed nursing care. Caring behaviours directly and indirectly affected professional self-efficacy and missed nursing care, respectively. Professional self-efficacy negatively influences missed nursing care. Finally, caring behaviours and professional self-efficacy were significant mediators between the association of patient safety and missed nursing care. Caring behaviours and professional self-efficacy of emergency room nurses demonstrated mediating effects that can potentially improve patient safety practices thereby minimizing unfinished or missed nursing care. Nurses and healthcare organizations should commit to consistently maintain a workplace culture that fosters patient safety, caring behaviours and professional self-efficacy to minimize avoidable injuries and omitting nursing care tasks. STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). No Patient or Public Contribution.

A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea.

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography-mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea.

Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India.

An internal variable model of macroscopic twin boundary dynamics

Depending on whether the experiments are quasi-static or fast dynamics, the measured twin boundary velocity values range from zero to the material’s sound speed. The twin boundary velocity is not yet predicted theoretically in the continuum mechanics framework. The extension of continual description is provided in the paper by means of internal variables. It is shown that a diffusional slow motion of twin boundaries can be represented using a single internal variable. The dual internal variable technique is employed for the description of the fast dynamics of twin boundaries.

Assessing Voltage Stability in Distribution Networks: A Methodology Considering Correlation among Stochastic Variables

Distributed photovoltaic (PV) output exhibits strong stochasticity and weak adjustability. After being integrated with the network, its interaction with stochastic loads increases the difficulty of assessing the distribution network’s static voltage stability (SVS). In response to this issue, this article presents a probabilistic assessment method for SVS in a distribution network with distributed PV that considers the bilateral uncertainties and correlations on the source and load sides. The probabilistic models for the uncertain variables are established, with the correlation between stochastic variables described using the Copula function. The three-point estimate method (3PEM) based on the Nataf transformation is used to generate correlated samples. Continuous power flow (CPF) calculations are then performed on these samples to obtain the system’s critical voltage stability state. The distribution curves of critical voltage and load margin index (LMI) are fitted using Cornish-Fisher series. Finally, the utility function is introduced to establish the degree of risk of voltage instability under different scenarios, and the SVS assessment of the distribution network is completed. The IEEE 33-node distribution system is utilized to test the method presented, and the results across various scenarios highlight the method’s effectiveness.

Partisan Media, Trust, and Media Literacy: Regression Analysis of Predictors of COVID-19 Knowledge.

The COVID-19 pandemic was a devastating public health event that spurred an influx of misinformation. The increase in questionable health content was aided by the speed and scale of digital and social media and certain news agencies' and politicians' active dissemination of misinformation about the virus. The popularity of certain COVID-19 myths created confusion about effective health protocols and impacted trust in the health care and government sectors deployed to manage the pandemic. This study explored how people's information habits, their level of institutional trust, the news media outlets they consume and the technologies in which they access it, and their media literacy skills influenced their COVID-19 knowledge. We administered a web-based survey using Amazon Mechanical Turk (MTurk) to assess US adults' (n=1498) COVID-19 knowledge, media and news habits, media literacy skills, and trust in government and health-related institutions. The data were analyzed using a hierarchical linear regression to examine the association between trust, media literacy, news use, and COVID-19 knowledge. The regression model of demographic variables, political affiliation, trust in institutions, media literacy, and the preference for watching Fox or CNN was statistically significant (R2=0.464; F24,1434=51.653; P<.001; adjusted R2=0.455) in predicting COVID-19 knowledge scores. People who identified as politically conservative, watched Fox News, and reported lower levels of institutional trust and media literacy, scored lower on COVID-19 knowledge questions than those who identified as politically liberal, did not watch Fox News and reported higher levels of institutional trust and media literacy. This study suggests that the media outlets people turn to, their trust in institutions, and their perceived degree of agency to discern credible information can impact people's knowledge of COVID-19, which has potential implications for managing communication in other public health events.

Sex estimation using post-mortem computed tomographic images of the clavicle in a Malaysian population

Forensic practitioners need contemporary anthropological data for the identification of human remains. The clavicle possesses a high degree of variability in its anatomical, biomechanical, and morphological features that are sex-dependent albeit population specific. The aim of this study was to develop sex estimation models for Malaysian individuals using post-mortem computed tomographic images of the clavicle. Sample comprised scans of 2.0 mm resolution of 405 individuals (209 male; 196 female) aged between 19 to 88 years. These scans were reconstructed and visualized using Infinitt. Six clavicular measurements (i.e. maximum length, C1; midshaft circumference, C2; midshaft maximum diameter, C3; midshaft minimum diameter, C4; maximum breadth of the sternal end, C5; and maximum breadth of the acromial articular surface, C6) were obtained from these images. Data were analyzed using descriptive statistics and discriminant function analysis. Measurements taken from the images were highly precise (ICC = 0.770–0.999). There is a significant difference between all parameters and sex (p < 0.001), however none for age and ethnic group. A multivariate sex estimation model was developed: Sex = (C1*0.86) + (C2*0.236) + (C3*-0.145) + (C5*- 0.074) − 17.618; with an accuracy rate of 89.1 % and sex bias of −3.2 %. Lower accuracy rates were obtained for single variable models (61.5–83.2 %). The resultant sex discriminant models can be used for estimating sex based on the clavicle in our local forensic practice.

New latent variable model with varying-coefficients

. In this article, we develop one kind of varying-coefficients latent variable model and its corresponding estimation method based on local-polynomial theory. The model is an expansion of classical latent variable model to varying-coefficients ones, in which the relations among latent variables and observed variables are allowed to vary as smooth functions of another variable. As comparison, we use the existing quantile varying-coefficients latent variable model and its estimation method. Simulation studies and real data analysis are carried out to illustrate the performances of our proposed models and estimation methods.

Prediction of the mechanism of suicide among Minnesota residents using data from the Minnesota violent death reporting system (MNVDRS)

BackgroundSuicide remains a major public health problem, and firearms are used in approximately half of all such incidents. This study sought to predict the occurrence of suicide specifically by firearm,...

Patient-specific arterial wall generation for intracranial aneurysms with a variable and a near realistic vessel wall thickness for FSI studies

Background and ObjectiveImaging methodologies such as, computed tomography (CT) aid in three-dimensional (3D) reconstruction of patient-specific aneurysms. The radiological data is useful in understanding their location, shape, size, and disease progression. However, there are serious impediments in discerning the blood vessel wall thickness due to limitations in the current imaging modalities. This further restricts the ability to perform high-fidelity fluid structure interaction (FSI) studies for an accurate assessment of rupture risk. FSI studies would require the arterial wall mesh to be generated to determine realistic maximum allowable wall stresses by performing coupled calculations for the hemodynamic forces with the arterial walls. MethodsIn the present study, a novel methodology is developed to geometrically model variable vessel wall thickness for the lumen isosurface extracted from CT scan slices of patient-specific aneurysms based on clinical and histopathological inputs. FSI simulations are carried out with the reconstructed models to assess the importance of near realistic wall thickness model on rupture risk predictions. ResultsDuring surgery, clinicians often observe translucent vessel walls, indicating the presence of thin regions. The need to generate variable vessel wall thickness model, that embodies the wall thickness gradation, is closer to such clinical observations. Hence, corresponding FSI simulations performed can improve clinical outcomes. Considerable differences in the magnitude of instantaneous wall shear stresses and von Mises stresses in the walls of the aneurysm was observed between a uniform wall thickness and a variable wall thickness model. ConclusionIn the present study, a variable vessel wall thickness generation algorithm is implemented. It was shown that, a realistic wall thickness modeling is necessary for an accurate prediction of the shear stresses on the wall as well as von Mises stresses in the wall. FSI simulations are performed to demonstrate the utility of variable wall thickness modeling.

Label distribution feature selection based on hierarchical structure and neighborhood granularity

Label Distribution Learning (LDL) addresses label ambiguity in datasets but struggles with high-dimensional data due to irrelevant features. Label Distribution Feature Selection (LDFS) methods can effectively unravel the issues, but they often overlook the advantages of utilizing hierarchical relationships among data, which can improve feature discriminability. Furthermore, these methods inadequately consider the granulation process, directly affecting the important features’ identification. To overcome these challenges, this study proposes a novel LDFS approach incorporating hierarchical structures and neighborhood granularity. Our algorithm proceeds in three stages: initially, it forms a multi-granular representation of data to reveal hierarchical relationships; subsequently, in the granulation process, it employs a variable precision rough set model, leveraging neighborhood granularity for a nuanced feature relevance assessment; and finally, it synthesizes these findings via a fusion strategy, culminating in a hierarchical feature ranking. Extensive experiments are conducted on thirteen benchmark datasets against five different algorithms in terms of six evaluation metrics. The results show that our method outperforms competitors in about 80% of the cases, demonstrating its effectiveness and generalization.

Variable importance analysis of wind turbine extreme responses with Shapley value explanation

As a leading form of renewable energy, wind power is gaining prominence with its decreasing levelized cost of electricity. The wind turbine extreme analysis is crucial for the structural reliability and integrity of wind turbines. Variable importance analysis provides a quantitative assessment of how significantly each variable influences model predictions. Variance-based importance measure quantifies each input variable’s contribution to the overall variance of a model or system. Such analyses are critical for interpreting the model and selecting features, thereby facilitating informed decision-making. This study uses Shapley value explanations to conduct a variable importance analysis of three wind turbine extreme responses, blade tip-tower clearance, blade root bending moment, and tower base bending moment. Random forest-based regression models and an improved Shapley effect evaluation algorithm are employed to improve the efficiency of the numerical analysis. The findings reveal the relative importance of five inputs, two wind parameters, one aerodynamic parameter, and two structural parameters, on each wind turbine extreme response variable. Additionally, the study explores how different dependency structures and varying correlation coefficients, even within the same correlation structure, affect the outcomes of variable importance analysis.

Does small firm dynamics matter for occupation diversity and job quality? Evidence from Brazil

Recent studies have explored the role of firm dynamics and the connection to job reallocation. However, limited attention has been devoted to the role of micro and small firms in this process, particularly about the quality of the jobs generated. Therefore, this article aims to measure the impact of micro and small firm dynamics on occupation diversification in Brazil, with specific emphasis on job quality. The study used data from 558 microregions within the Brazilian industrial sector between 2003 and 2015. Panel data were applied to three econometric models: Feasible Generalized Least Squares (FGLS), Driscoll–Kraay (DK) and Instrumental Variable (IV) models for robustness analysis, including Two-Stage Least Squares (2SLS), Limited Information Maximum Likelihood (LIML), and the Generalized Method of Moments with Continuously Updating Estimators (GMM-CUE). Our main finding reveals that the dynamics of micro and small firms positively impact occupation diversity in Brazil, leading to the creation of a wider range of job types. Furthermore, the frequency of change of firms from microenterprises to small businesses increases the occupation diversity in the Brazilian industrial sector. Our findings are significant in providing policy recommendations for developing countries to achieve a more diverse labor market.

Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu etal., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal's position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

An internal state variables constitutive model for Ni-based superalloy considering the influence of second phase and its application in flexible skew rolling

Microstructure prediction and control of Ni-based alloy during plastic forming is crucial for obtaining high-performance products. Taking GH4169 alloy as an example for study, firstly, hot compression experiments were conducted using both solution treated (ST) and aging treated (AT) alloys at different true strains (0.223–0.916), temperatures (900℃-1100℃), and strain rates (0.1 s−1-10 s−1). Then, an internal state variable constitutive model was established based on experimental data. In the developed model, the evolution of dislocation density, dynamic recovery, and dynamic recrystallization (DRX) behavior are reasonably described. Specifically, by introducing the volume fraction and size of the initial δ-phase, the interaction between δ-phase and dislocations, the influence of δ-phase on DRX behavior and critical strain, and its pinning effect on grain boundaries are considered. The calculation results indicate that accurate predictions can be achieved within a large parameter range. Subsequently, the model was applied to flexible skew rolling (FSR) process, a novel plastic forming method for forming shaft parts and bars. The finite element (FE) simulations and rolling experiments were carried out. The results indicate that the FE model embedded with the constitutive model can effectively predict the forming dimensions and microstructure distribution of GH4169 alloy. The established constitutive model can provide reference for the high-temperature plastic forming process of alloys containing the second phase.

Theoretical and experimental investigations on a data-driven trajectory planning scheme for dynamic shape control of piezo-actuated compliant morphing structures

Piezo-actuated compliant morphing structures can transmit motion and force via elastic deformation with lower weight, simplified design, and higher accuracy, a typical example is the variable camber wing. However, the rapid shape change of compliant structures is a challenging control problem because it often results in a high level of vibration due to the structural flexibility necessary to achieve the morphing requirement. This study presents a model-free data-driven trajectory planning approach based on spline curves and surrogate-based optimization (SBO) to obtain smooth “rest-to-rest” morphing trajectories. The macro-fiber composite (MFC) patches are used for piezoelectric actuators to generate elastic deformation. The flexible beam and variable camber wing are used for both linear numerical simulation and experimental tests with various nonlinearities and uncertainties. An optimization criterion is proposed to minimize both transient and residual vibrations during the “rest-to-rest” motion. An extraordinarily terminal displacement error function is added to eliminate the effects of the hysteresis and creep of the actuator. The control inputs, i.e., voltage profiles for the MFCs, are defined using cubic spline curves via only a few control points. Then, the optimization problem is solved by employing a Kriging surrogate model-based algorithm integrated with the expected improvement (EI) infilling-sampling criterion, which constructs an efficient algorithm similar to reinforcement learning. The optimal morphing trajectories can be highly efficiently obtained by using only 4 control points and less than 150 samplings. The experimental results of both the plate model and the variable camber wing model show that the proposed method can not only achieve a smooth dynamic morphing trajectory with minimized vibration but also automatically compensate for hysteresis and creep. The proposed method provides an effective means for the rapid realization of an optimized morphing trajectory for compliant morphing structures.

The impact of urbanisation on green total factor productivity: Exploring threshold and mediating effects of human capital.

Green development is main direction of high-quality economic development in China. Urbanisation and human capital (HC) are important factors affecting green development. This study examines effects of urbanisation and HC on green total factor productivity (GTFP) using least squares dummy variable model, based on provincial data in China from 2001 to 2019. This study then explored impact of urbanisation on GTFP through HC (educated HC, healthy HC, and HC structure), adopting mediating effect and threshold effect model. The results showed that urbanisation and three types of HC promote GTFP in China and coastal region. In inland region, urbanisation has insignificant impact on GTFP, while educated HC and HC structure improve it. The mediating analysis revealed that educated and healthy HC in China and coastal region enhance promotion effect of urbanisation on GTFP, whereas HC structure hinders it. In inland region, only healthy HC stimulates promotion effect of urbanisation on GTFP. Considering threshold effect of educated and healthy HC, promotion effect of urbanisation on GTFP is weaker than threshold value; but effect is stronger above threshold value. The promotion effect of urbanisation on GTFP is stronger when value of HC structure is below the threshold, while effect is weakened as value exceeds threshold value. Policy implications are documented to promote urbanisation in a differentiated manner, increase investment in HC, and strengthen coordination between urbanisation and HC.

Explaining the negative effects of workplace incivility on family lives: a moderated mediation model of surface acting and resource-providing variables.

The effects of workplace incivility have been understudied in educational settings. To expand incivility research to educational professions, the present research investigates whether, how, and when workplace incivility deriving from different sources (coworkers, supervisors, and outsiders) is related to work-to-family interference (WFI) of preschool teachers. Drawing on the conservation of resources theory, the present study proposes that workplace incivility and subsequent maladaptive emotion labor strategies (i.e., surface acting) jointly create a resource-depletion mechanism contributing to elevated WFI and two resource-providing variables (supervisor work-family support and psychological detachment after hours) function as potential mitigating factors to break the resource-depletion mechanism. This study used a female-dominated sample (i.e., preschool teachers) found that workplace incivility from insiders (supervisors and coworkers respectively) and external stakeholders (child's family members) all positively linked to WFI, and surface acting mediated these relationships. Moreover, moderated mediation analyses indicated that psychological detachment buffered the mediated effect of surface acting on WFI, whereas supervisor work-family support did not. Findings deepen the understanding of why and when workplace incivility influences employees' family lives, as well as point to future intervention strategies.

Moving window sparse partial least squares method and its application in spectral data

With the advancement of data science and technology, the complexity and diversity of data have increased. Challenges arise when dealing with a larger number of variables than the sample size or the presence of multicollinearity due to strong correlations among variables. In this paper, we propose a moving window sparse partial least squares method that combines the sliding interval technique with sparse partial least squares. By utilizing sliding interval partial least squares regression to identify the optimal interval and incorporating sparse partial least squares for variable selection, the proposed method offers innovations compared to traditional partial least squares (PLS) approaches. Monte Carlo simulations demonstrate its performance in variable selection and model prediction. We apply the method to seawater spectral data, predicting the concentration of chemical Oxygen demand. The results show that the method not only selects reasonable spectral wavelength intervals but also enhances predictive performance.

The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.