Model Results Research Articles

Internal trade and environmental policy effectiveness in developing countries: A binary endogenous growth model with regional Heterogeneity

The shift of environmental pollution from developed to developing countries is largely induced by global labor and trade trends. Unlike their developed counterparts, developing countries face more pronounced regional disparities in social development within the realm of environmental governance. This paper proposes a binary endogenous model to assess the implications of environmental policies on such developing nations with sizable regional disparities. The model enhances the precision in simulating real-world ramifications of environmental policy implementation. It reveals that the 'pollution haven' effect extends beyond just the relocation of pollutant-generating industries to less developed regions; it also leads to the shipment of products fabricated using environmentally-degrading processes from less developed to developed regions, thereby undermining environmental regulation. Furthermore, the model's results were juxtaposed with China's increasingly stringent national environmental protection policy, adopted since 2000. The study confirms that environmental degradation has a substantial spatial spillover effect positively correlated with internal trade volume. The paper's conclusions assist in offering a new evaluative framework for the ensuing examination of environmental policy practicality in developing countries. It also recommends policy-making tools for enhancing the effectiveness of environmental regulations in these regions, such as limiting internal trade of polluting products alongside environmental levies and subsidies.

Adversarial attacks and adversarial training for burn image segmentation based on deep learning.

Deep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the model's results in image segmentation and classification. Especially in medical images, due to constraints from factors like shooting angles, environmental lighting, and diverse photography devices, medical images typically contain various forms of noise. In order to address the impact of these physically meaningful disturbances on existing deep learning models in the application of burn image segmentation, we simulate attack methods inspired by natural phenomena and propose an adversarial training approach specifically designed for burn image segmentation. The method is tested on our burn dataset. Through the defensive training using our approach, the segmentation accuracy of adversarial samples, initially at 54%, is elevated to 82.19%, exhibiting a 1.97% improvement compared to conventional adversarial training methods, while substantially reducing the training time. Ablation experiments validate the effectiveness of individual losses, and we assess and compare training results with different adversarial samples using various metrics.

Applying geospatial multi-agent system to model various aspects of tuberculosis transmission

IntroductionThe paper presents epidemiological process modeling, with a focus on tuberculosis utilizing multi-agent system. Material and methodsThis study involves the development of an algorithm that harnesses the potential of artificial intelligence to create a geospatial model that highlights the different pathways of TB transmission. The modeling process itself is characterized by a series of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, propagation of the spread of infection, the evolution of disease trajectories, rigorous statistical calculations and transition to the following day. A comprehensive description of the course of active tuberculosis is presented, following the official hypothesis recommended by the World Health Organization. A comprehensive simulation, illustrating the propagation of tuberculosis in an entirely healthy environment devoid of any preventive or therapeutic measures, is presented. To ascertain the adequacy of the model and its sensitivity to the principal parameters governing the course of tuberculosis, a series of experiments were meticulously conducted, employing three distinct approximations, namely: the basic model, the model incorporating mortality factors, and the comprehensive model, encompassing all relevant aspects. ConclusionsThe model's results exhibit stability and lack of significant fluctuations. The statistical values obtained for infected, latent, and recovered individuals align well with known medical data, confirming the model's adequacy.

Prediction of monkeypox infection from clinical symptoms with adaptive artificial bee colony-based artificial neural network

AbstractIn 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%.

Efficiency improvement in energy consumption: A novel deep learning based model for leading a greener Economic recovery

This research focuses on elevating energy consumption efficiency through an innovative predictive model designed for urban industrial and residential sectors. Leveraging the Deep Q Network (DQN) framework, the study introduces the Dueling DQN architecture, a breakthrough approach that separates state value and advantage function estimations. This segregation significantly enhances learning efficiency, providing a more precise representation of the state's value and, consequently, optimizing energy consumption predictions. In tandem with architectural advancements, the research proposes a unique fuzzy Particle Swarm Optimization (PSO) approach to fine-tune DQN setting parameters during training. This integration of fuzzy logic into PSO ensures adaptability and robustness, addressing challenges associated with diverse environmental and operational conditions. For the goal of efficiency improvement in energy consumption, this study not only introduces a state-of-the-art solution for energy consumption prediction and optimization but also aligns with the broader goal of fostering a greener economic recovery. The model's promising results signify a significant step toward more sustainable and resource-efficient urban development, contributing to a resilient and environmentally conscious economic future. This research lays the foundation for a transformative approach to energy consumption, vital for steering economic recovery toward a greener and more sustainable trajectory. The simulation results advocate the high performance of the proposed deep learning prediction model.

Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers

Carbon emissions play a pivotal role in exacerbating the global warming crisis and driving climate change. Accurate and consistent projections of carbon emissions are of utmost importance for nations worldwide, as they shape emission reduction strategies and expedite the pursuit of carbon peaking and carbon neutrality goals. While previous studies have focused on hybrid methodologies for carbon emission forecasting, yielding commendable predictive performance, these approaches often overlook the significance of internal interpretability within the forecasting models. In light of this gap, this study introduces a groundbreaking and elucidating hybrid carbon emission forecasting model that amalgamates the two-stage layer decomposition method, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT). To begin with, a series of sub-sequences is derived by employing a flexible two-stage decomposition strategy, which leverages a linear-nonlinear decomposition criterion to thoroughly extract the fluctuating characteristics inherent in the carbon emission series. Subsequently, the JADE algorithm intelligently and efficiently optimizes the parameter combinations within the TFT model, ensuring both stability and reliability of the prediction framework. Empirical investigations conclusively demonstrate the remarkable applicability and efficacy of the proposed model in short-term carbon emission forecasting. By delving into the interpretability of the model's results, the study enhances the capacity of policymakers to devise well-informed strategies based on comprehensive insights gleaned from the forecasting process.

Retrospective analysis of predictive factors for AVF dysfunction in patients undergoing MHD.

To construct an early clinical prediction model for AVF dysfunction in patients undergoing Maintenance Hemodialysis (MHD) and perform internal and external verifications. We retrospectively examined clinical data from 150 patients diagnosed with MHD at Hefei Third People's Hospital from January 2014 to June 2023. Depending on arteriovenous fistula (AVF) functionality, patients were categorized into dysfunctional (n = 62) and functional (n = 88) cohorts. Using the least absolute shrinkage and selection operator(LASSO) regression model, variables potentially influencing AVF functionality were filtered using selected variables that underwent multifactorial logistic regression analysis. The Nomogram model was constructed using the R software, and the Area Under Curve(AUC) value was calculated. The model's accuracy was appraised through the calibration curve and Hosmer-Lemeshow test, with the model undergoing internal validation using the bootstrap method. There were 11 factors exhibiting differences between the group of patients with AVF dysfunction and the group with normal AVF function, including age, sex, course of renal failure, diabetes, hyperlipidemia, Platelet count (PLT), Calcium (Ca), Phosphorus, D-dimer (D-D), Fibrinogen (Fib), and Anastomotic width. These identified factors are included as candidate predictive variables in the LASSO regression analysis. LASSO regression identified age, sex, diabetes, hyperlipidemia, anastomotic diameter, blood phosphorus, and serum D-D levels as 7 predictive factors. Unconditional binary logistic regression analysis revealed that advanced age (OR = 4.358, 95% CI: 1.454-13.062), diabetes (OR = 4.158, 95% CI: 1.243-13.907), hyperlipidemia (OR = 3.651, 95% CI: 1.066-12.499), D-D (OR = 1.311, 95% CI: 1.063-1.616), and hyperphosphatemia (OR = 4.986, 95% CI: 2.513-9.892) emerged as independent risk factors for AVF dysfunction in MHD patients. The AUC of the predictive model was 0.934 (95% CI: 0.897-0.971). The Hosmer-Lemeshow test showed high consistency between the model's predictive results and actual clinical observations (χ2 = 1.553, P = .092). Internal validation revealed an AUC of 0.911 (95% CI: 0.866-0.956), with the Calibration calibration curve nearing the ideal curve. Advanced age, coexisting diabetes, hyperlipidemia, blood D-D levels, and hyperphosphatemia are independent risk factors for AVF dysfunction in patients undergoing MHD.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure–activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

Study on the Melt Rheological Characterization of Micro-Tube Gas-Assisted Extrusion Based on the Cross-Scale Viscoelastic Model.

In the micro-tube gas-assisted extrusion process, flow theories ignoring cross-scale viscoelastic variations fail to effectively characterize the rheological state of the melt. To investigate the impact of cross-scale viscoelastic variation on the quality of the micro-tube gas-assisted extrusion, a 3D multiphase flow extrusion model incorporating a double gas-assisted layer was developed. Subsequently, we modified the DCPP constitutive equations based on the cross-scale factor model. Both the traditional and gas-assisted extrusions were simulated under macroscale and cross-scale models using the Ansys Polyflow. Finally, using the established gas-assisted extrusion platform, extrusion experiments were conducted. The results indicate that, owing to the reduced melt viscosity under the cross-scale model, the deformation behavior of the melt is more pronounced than in the macroscale model. The cross-scale model's numerical results more closely match the experimental outcomes under the same parameters, thereby confirming the feasibility of the theoretical analysis and numerical simulation. Moreover, the predictive capability of the cross-scale model for the micro-tube gas-assisted extrusion is further validated through numerical and experimental analyses with varying parameters. It is demonstrated that the cross-scale viscoelastic variation is a critical factor that cannot be overlooked in the gas-assisted extrusion.

Understanding vote transfers in two-round elections without resorting to declared data. The contribution of ecological inference, consolidated with factual information from a case study of the 2014 municipal elections in Montpellier

In electoral sociology, the analysis of vote transfers has traditionally depended on individual data obtained from surveys. Because such data suffer from a significant amount of declaration and memory bias, replacing them with the electoral statistics available down to polling station level may be advantageous. Recent developments in models of ecological inference allow us to use these aggregated data to establish estimates of vote transfers while minimising the risk of ecological error. Nonetheless, the reliability of ecological inference models for estimating vote transfers has thus far received little attention in the form of empirical evaluations. The purpose of the present article is to cast light on this blind spot by analysing a model for predicting electoral volatility in a two-round election, namely the municipal election held in Montpellier in 2014. What makes this approach original is its use of observed information – the proportion of non-voters in both rounds – first to compare this data with the estimates produced by the model and then to integrate it as a modelling parameter to measure its impact on estimated vote allocation. This analysis reveals that the initial model's results are relatively reliable regarding the known parameter, although they slightly overestimate its amplitude and underestimate its variability. The model that integrates information regarding the proportion of consistent non-voters yields estimates close to those obtained using the “raw” model. In terms of interpretative capacity, the value added by integrating this additional information is, therefore, slight. However, integrating the information does make it possible to establish narrower density intervals, reducing the uncertainty associated with the interpretation of the other parameters, particularly proportions associated with candidates who received few votes in the first round.

Multivariate Assessment of Inhibitory Control in Youth: Links With Psychopathology and Brain Function.

Inhibitory control is central to many theories of cognitive and brain development, and impairments in inhibitory control are posited to underlie developmental psychopathology. In this study, we tested the possibility of shared versus unique associations between inhibitory control and three common symptom dimensions in youth psychopathology: attention-deficit/hyperactivity disorder (ADHD), anxiety, and irritability. We quantified inhibitory control using four different experimental tasks to estimate a latent variable in 246 youth (8-18 years old) with varying symptom types and levels. Participants were recruited from the Washington, D.C., metro region. Results of structural equation modeling integrating a bifactor model of psychopathology revealed that inhibitory control predicted a shared or general psychopathology dimension, but not ADHD-specific, anxiety-specific, or irritability-specific dimensions. Inhibitory control also showed a significant, selective association with global efficiency in a frontoparietal control network delineated during resting-state functional magnetic resonance imaging. These results support performance-based inhibitory control linked to resting-state brain function as an important predictor of comorbidity in youth psychopathology.

Calibration, validation and uncertainty analysis of a SWAT water quality model

Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers.

Kinetic modeling and optimization of the mono- and diglycerides synthesis mediated by the lipase Lipozyme® TL 100 L immobilized on clayey support.

Mono- and diglycerides play a crucial role in the food industry as multifunctional food additives and emulsifiers. Their importance stems from their unique properties, which allow them to improve the quality, texture, and stability of various food products. Here, results of the kinetic modeling of the mono- and diglycerides synthesis mediated by the lipase Lipozyme® TL 100 L immobilized on the clayey support Spectrogel® type C are reported. The support was characterized by TEM, SEM, and FTIR. Firstly, the influence of pH and lipase load on the immobilization process was analyzed, resulting in an enzymatic activity of 93.2 ± 0.7Ug-1 under optimized conditions (170.9Ug-1 of lipase and pH of 7.1). Afterward, the effects of reaction temperature and concentration of immobilized biocatalyst in the feedstock conversion were evaluated. At optimized parameters, a triglycerides conversion of 97% was obtained at 36.5°C, 7.9 vol.% of enzyme, a glycerol to feedstock molar ratio of 2:1, and 2h. The optimized conditions were used to determine the kinetic constants of the elementary reactions involved in the glycerolysis, where a fit superior to 0.99 was achieved between experimental values and predicted data.

A λ 3 mm Line Survey toward the Circumstellar Envelope of the Carbon-rich AGB Star IRC+10216 (CW Leo)

We present an unbiased λ 3 mm spectral line survey (between 84.5 and 115.8 GHz), conducted by the Purple Mountain Observatory 13.7 m radio telescope, together with updated modeling results, toward the carbon-rich asymptotic giant branch star IRC+10216 (CW Leo). A total of 75 spectral lines (96 transitions) are detected, and identified to arise from 19 molecules: C2H, l-C3H, C4H, CN, C3N, HC3N, HC5N, HCN, HNC, CH3CN, MgNC, CO, c-C3H2, SiC2, SiO, SiS, CS, C2S, C3S, and their isotopologues. Among them, one molecular emission line (H13CCCN J = 13–12) is discovered in IRC+10216 for the first time. The excitation temperature, column density, and fractional abundance of the detected species are deduced by assuming they are in local thermodynamic equilibrium. In addition, the isotopic ratios of [12C]/[13C], [32S]/[34S], [28Si]/[29Si], and [12C34S]/[13C32S] are obtained and found to be consistent with previous studies. Finally, we summarize all of the 106 species detected in IRC+10216 to date with their observed and modeled column densities for the convenience of future studies.

Interpretable zero-inflated neural network models for predicting admission counts

Abstract In this paper, we construct interpretable zero-inflated neural network models for modeling hospital admission counts related to respiratory diseases among a health-insured population and their dependants in the United States. In particular, we exemplify our approach by considering the zero-inflated Poisson neural network (ZIPNN), and we follow the combined actuarial neural network (CANN) approach for developing zero-inflated combined actuarial neural network (ZIPCANN) models for modeling admission rates, which can accommodate the excess zero nature of admission counts data. Furthermore, we adopt the LocalGLMnet approach (Richman & Wüthrich (2023). Scandinavian Actuarial Journal, 2023(1), 71–95.) for interpreting the ZIPNN model results. This facilitates the analysis of the impact of a number of socio-demographic factors on the admission rates related to respiratory disease while benefiting from an improved predictive performance. The real-life utility of the methodologies developed as part of this work lies in the fact that they facilitate accurate rate setting, in addition to offering the potential to inform health interventions.

Neural correlates of model-based behavior ininternet gaming disorder and alcohol usedisorder.

An imbalance between model-based and model-free decision-making systems is a commonfeature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive featuresassociated with model-based and model-free systems in IGD and alcohol use disorder (AUD). Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group. The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE. The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

Reexamination of a 40Ca(n,α)37Ar cross section measurement from existing literature

Background 37Ar, produced from the neutron activation of 40Ca in sub-surface rock and soil, is important for nuclear explosion monitoring. Most of the cross section measurements done for the 40Ca(n,α) 37 Ar reaction are in the fast neutron region. A thermal cross section measurement was attempted in 1989, with the assumption that the 4-8 MeV neutrons would be completely thermalized with 8-cm of water. Methods The experimental setup was modeled using Monte Carlo N-Particle code to see if the assumptions for the neutron distribution were accurate. A model of a 3.0 MeV deuteron source was used to recreate the neutron source from the experiment. Additional modeling was done using SCALE to compare the predictions of 37Ar yields when using the measured cross section from 1989 compared to current nuclear data models. Results Modeling showed the neutron distribution was closer to 10% thermal and 90% fast as opposed to entirely thermalized. The additional modeling showed how the overestimation of the thermal cross section would affect the predicted yield of 37Ar. Conclusions Based on the modeling results, the assumption that all the 4-8 MeV neutrons are thermalized with 8-cm of water is not accurate. The high fraction of fast neutrons interacting with the Ca will result in an overestimation of the thermal cross section.

Association between depression and BMI in young adults

Globally, depression significantly affecting people's quality of life with a range of negative health outcomes. Previous studies have confirmed a link between BMI and depression. Consequently, this study focused on data spanning from 2017 to March 2020, encompassing individuals aged 18 to 30 years within the National Health and Nutrition Examination Survey (NHANES). The study included 1635 participants, with complete data and selected covariates. Then, used weighted logistic regression models to investigate the connection between depression and BMI. Given the significant influence of gender, subgroup analysis was performed to compare the varying impact of BMI between males and females. Ultimately, the analysis of model results revealed a noteworthy positive correlation between BMI and depression within the 18-30 age group, with women experiencing a more pronounced effect than men. In conclusion, reducing BMI emerges as a potential strategy to mitigate the risk of depression.

Erwerbstrajektorien von Varietätenkompetenz in der Zweitsprache: Eine temporal-relationale Perspektive auf das Aneignungsverhältnis standardsprachlicher und bairisch-dialektaler Merkmale

Abstract To date, no studies exist attempting to capture the temporal-relational emergence of dialect and standard German features in the second language (L2) across residence time in the Austro-Bavarian naturalistic context. In order to address this desideratum, 40 migrants with L2 German completed translation tasks assessing their ability to translate from standard German into dialect and vice versa. Taking a cross-sectional developmental perspective, our goal is to examine when dialect features (e. g., a-darkening, l-vocalization) manifest in participants’ multivarietal repertoires during residence in Austria over a timespan of approximately 13 years. The results of Bayesian binary logistic multilevel modeling reveal that participants quickly acquire dialect features such as negation particles (e. g., standard nicht vs. dialectal ned), fricative reduction (e. g., standard ich vs. dialectal i:), and Bavarian article use (e. g., standard das vs. dialectal des), but, even after over a decade of residence, do not produce morphological or syntactic dialectal features such as 2. person plural -s (e. g., standard braucht vs. dialectal brauchts) or relative clause connectors (e. g., standard die vs. dialectal de wos). Our results add to discussions concerning how the Austro-Bavarian naturalistic context impacts on patterns of L2 language variation and use and lend key insights into inquiries concerning which dialect features L2 learners acquire and when.

The relationship between organizational dehumanization and work engagement: the mediating effect of nurses’ work stress

BackgroundOrganizational dehumanization has detrimental consequences for nurses’ wellbeing and leads to a stressful work environment. Moreover, it is very destructive to work engagement.AimTo examine the mediating role of nurses’ work stress between organizational dehumanization and work engagement.MethodA cross-sectional research design was conducted with 245 staff nurses over a one-month period. The researchers used structured equation modeling.ResultsWork engagement and organizational dehumanization levels were both moderate. In addition, the degree of job stress among the nurses was moderate, too. The results of the structural equation modeling showed that the association between organizational dehumanization and job engagement is partially mediated by work stress.ConclusionsFor staff nurses to exhibit high levels of caring behaviors, this study emphasized the need to establish a work environment that employs tactics to improve workplace engagement and happiness. In addition to changing the organizational culture of nurses to eradicate organizational dehumanization and pressures related to the job.