Variable Scale Research Articles

EVALUATING THE EFFICIENCY OF COUNTRIES' DIGITALIZATION IN TERMS OF FINANCIAL INCLUSION BY USING DEA

The aim of the study is to evaluate the efficiency of countries in digitalization in terms of financial inclusion, in other words, their success in digitalization. Within the scope of the study, 117 countries were grouped as high, upper-middle, lower middle and low income and subjected to Data Envelopment Analysis. In the study conducted for the year 2021, four input variables related to the utilization and access of digital financial services were identified, along with ten corresponding output variables. Efficient countries were determined in the study based on the constant and variable return scale model for output. Digital efficiency has been identified in financial inclusion was detected in 25 countries based on variable returns and 18 countries based on constant returns in high-income countries. In upper-middle-income countries, digital efficiency in financial inclusion was identified in 12 countries based on variable returns and 9 countries based on constant returns. 16 countries in the lower middle and low income categories demonstrated digital efficiency in financial inclusion using variable returns, while 9 countries detected digital efficiency using constant returns. In the study, the comprehensive analysis of the success of digitalization in financial inclusion at the level of countries divided into income groups by using data envelopment analysis reveals the originality of the study.

Algorithm for Coal Mine Rock Foreign Object Detection Based on Enhanced YOLOv8

In addressing the challenge of suboptimal detection precision in coal mine rock foreign object detection due to complex environments and variable object scales, this paper proposes a coal mine rock foreign object detection algorithm based on YOLOv8. Initially, the incorporation of the BiFormer attention mechanism is advocated to refine the backbone network, augmenting the model's attention towards pivotal information regions, consequently enhancing localization and feature extraction capabilities. Secondly, a lightweight Content-Aware Recurrent Affine Feature Extraction (CARAFE) operator is utilized within the neck architecture to effectively capture and preserve intricate features at lower hierarchical levels. Finally, Wise-IoU v3 is adopted as the bounding box regression loss for the proposed algorithm, coupled with a prudent gradient allocation approach, thereby enhancing the model's localization capabilities. Empirical findings illustrate that compared to baseline algorithms, the proposed algorithm has fewer parameters, with an average mAP improvement of 2.5%, and a detection speed increase of 2fps/s.

Salience Interest Option: Temporal abstraction with salience interest functions

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.

To behave or not (un)ethically? The meditative effect of mindfulness on statistics anxiety and academic dishonesty moderated by risk aversion

Despite the growing interest in mindfulness in higher education, the literature on its relation to decision-making under risk (i.e. academic misconduct) and statistics anxiety is scarce. The present research shall fill this gap. Based on the prospect theory, we assessed the mediating effect of mindfulness on the relationship between statistics anxiety and academic dishonesty moderated by risk aversion. Data were collected from 791 undergraduate students in six Israeli academic institutions studying for bachelor’s degrees in social sciences. Questionnaires included the following measures: risk behaviour according to the prospect theory framework, Mindful Attention Awareness Scale, Statistics Anxiety Rating Scale, Academic Misconduct Scale and sociodemographic variables. Correlations among these variables were explored. The data was analysed using Structural Equation Modelling (SEM). The results indicate that the variance in academic dishonesty is explained by students' statistics anxiety with a mediation of Mindfulness moderated by Risk Aversion. Mindfulness negatively affects Academic Dishonesty, while Risk Aversion has a significant positive effect on Mindfulness. Finally, among individuals with high statistics anxiety, Risk Averse individuals show significantly higher Mindfulness than Risk Seekers. We conclude that mindfulness-based interventions might be a constructive tool to reduce risk-taking and promote ethical decision-making among individuals who experience high levels of statistics anxiety. Furthermore, developing mindful skills may help individuals with higher anxiety levels neutralize these unwanted feelings and get along with their learning tasks. Hence, avoid academic unethical behaviours.

Application of facial expression recognition technology based on feature fusion in teaching

With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved

Computer Modeling Analysis of Electric Vehicle's Choice Behavior Considering Latent Variables

At present, there are few researches on the influence of psychological latent variables on the choice behavior of electric vehicles, and lack of consideration of individual heterogeneity. In this study, computer modeling was used to construct not only a traditional discrete choice model that takes into account individual heterogeneity, but also a hybrid choice model,which takes into account psychological latent variables are constructed to explore the mutual effects of observable personal attributes, vehicle attributes and difficult to directly observe psychological latent variables on consumers’ choice behavior of electric vehicles. The estimation results of hybrid choice model with psychological latent variables and the traditional discrete choice model without considering psychological latent variables are compared. The result show that perceived usefulness, perceived risk and purchasing attitude have a significant impact on consumers’ choice behavior of electric vehicles. Compared with traditional discrete choice model, the hybrid choice model, which takes into account psychological latent variables has significantly higher prediction accuracy and has better interpretation ability and fitting effect. The research results can provide theoretical support for further expanding the electric vehicle market and formulating relevant policieshe usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

Clinical phenotypes of comorbidities in end-stage knee osteoarthritis: a cluster analysis

ObjectivesComorbidities, as components of these heterogeneous features, often coexist with knee osteoarthritis, and are particularly prevalent in end-stage knee osteoarthritis. Here, we attempted to identify the different clinical phenotypes of comorbidities in patients with end-stage knee osteoarthritis by cluster analysis.MethodsA total of 421 inpatients diagnosed with end-stage knee osteoarthritis who underwent inpatient surgery were included in this cross-sectional study. 23 demographic, comorbidity, inflammatory immune and evaluation scale variables were collected. Systematic clustering after factor analysis and separate two-step cluster analysis were performed for individual comorbidity variables and all variables, respectively, to objectively identify the different clinical phenotypes of the study patients.ResultsFour clusters were finally identified. Cluster 1 had the largest proportion of obese patients (93.8%) and hypertension was common (71.2%). Almost all patients in cluster 2 were depressed (95.8%) and anxiety disorders (94.7%). Cluster 3 combined patients with isolated end-stage knee osteoarthritis and a few comorbidities. Cluster 4 had the highest proportion of patients with rheumatoid arthritis (58.8%).ConclusionsPatients with end-stage knee osteoarthritis may be classified into four different clinical phenotypes: "isolated end-stage knee osteoarthritis"; "obesity + hypertension"; "depression + anxiety"; and "rheumatoid arthritis", which may help guide individualized patient care and treatment strategies.

Educational technology for patients with external fixator in the lower limbs

The objective of the work is to develop and validate an educational technology in the form of a booklet for guidance on the care of orthopedic patients using external fixators in the lower limbs. This is a study of methodological, bibliographical development, with a qualitative-quantitative approach. A questionnaire containing Likert scale variables was used as an instrument for evaluating educational technology, and the Content Validity Index for each validation stage was used for analysis, considering items equal to or greater than 0.80 to be valid. Validation was carried out by 18 expert judges, among them 8 (44.44%) were nurses, 3 (16.66%) doctors, 3 (16.66%) physiotherapists and 4 (22.22%) nursing technicians , while validation by the target audience occurred with 20 patients undergoing treatment with an external fixator in the lower limbs. It is considered validated by professionals with a global Content Validity Index of 1.0 and by patients, with a global Content Validity Index of 0.98. The educational material proved to be valid in terms of appearance and content, and suitable to contribute to health promotion and use in trauma-orthopedics care practice.

A novel fault diagnosis method for permanent magnet synchronous motor based on VS-Inception

The new energy vehicle industry has an increasing demand for safety, stability, and efficiency in the operation of essential equipment, particularly in the intelligent means of fault diagnosis of motor equipment, which is rising annually. For the past several years, there has been an increasing demand for accuracy and precision in troubleshooting fault diagnosis of permanent magnet synchronous motors. This paper presents a variable-scale Inception (VS-Inception) permanent magnet motor malfunction diagnostic method on the basis of R parameters. Firstly, the Time Series Generative Adversarial Network is utilized to expand the dataset samples of the collected vibration signals, resulting in many virtual signals with motor fault characteristics. Then, the variable parameter variable scale structure of the VS-Inception model is employed to carry out the fault diagnosis of permanent magnet synchronous motors on the expanded dataset. Finally, comparison experiments of VS-Inception with the existing state-of-the-art methods and with the original method are conducted to confirm the superiority even further and stability of the VS-Inception method in the field of permanent magnet synchronous motor malfunction diagnostic.

Multivariate Multinomial Logit Models with Associations among Dependent Variables

The authors introduce a new group of multinomial logit models with special contrasts to identify covariate effects on multiple categorical dependent variables that are strongly associated with each other. The authors first develop the method for a case with two dependent variables and then extend the method to a case with three dependent variables. The model can account for both nominal and ordinal scales of categorical dependent variables. The authors formulate the covariate effects to represent unique effects on each dependent variable so that they become independent across different dependent variables. The application focuses on the multiplicity of occupational attainments by analyzing how gender, race, educational attainment, and parental occupation characteristics affect three distinct but nonindependent dimensions of occupations: socioeconomic status, social skill level, and math and science skill levels.

Patient-centered nutrition education improved the eating behavior of persons with uncontrolled type 2 diabetes mellitus in North Ethiopia: a quasi-experimental study.

Improving the clinical outcome of people with type 2 diabetes mellitus by modifying their eating behavior through nutrition education is an important element of diabetes self-management. Significant data from the literature supports this idea, however in the Ethiopian setting, there is a practice gap. Therefore, the purpose of this study was to assess how patient-centered nutrition education affected the eating behavior and clinical outcomes of people with uncontrolled type 2 diabetes mellitus. In this quasi-experimental trial, 178 people with uncontrolled type 2 diabetes were purposely assigned to the intervention (n = 89) or control (n = 89) arm. The intervention arm was given patient-centered nutrition education, whereas the control arm received the routine care. Eating behavior and clinical outcome indicators such as HbAc, lipid profile, anthropometric indices, and blood pressure were assessed in both groups at the start and completion of the intervention. All scale variables were tested for normality and log transformed when appropriate. The baseline characteristics of the intervention and control groups were compared using the t-test for continuous variables and the chi-square test for categorical variables. The effect of nutrition education was determined using a difference in differences (DID) approach. P < 0.05 was established as the criterion of significance. Food selection (DID = 15.84, P < 0.001), meal planning (DID = 31.11, P < 0.001), and calorie needs (DID = 37.65, P < 0.001) scores were statistically higher in the nutrition education arm. Furthermore, their overall eating behavior score (DID = 27.06, P < 0.001) was statistically greater than the controls. In terms of clinical outcomes, the overall picture reveals that the intervention did not outperform over the routine care. However, in comparison to the controls, the intervention arm showed clinically significant improvement in HbA1c (DID = -0.258, P = 0.485). Patient-centered nutrition education has resulted in positive adjustments in the eating behavior of people with uncontrolled type 2 diabetes mellitus. Furthermore, it has shown a great potential for improving their glycemic control.

Influence of barrens restoration treatments on soil carbon, nitrogen, and mercury pools and emissions

AbstractCurrently barrens communities only represent about 1% of their original area in the Great Lakes region. To maintain or restore barrens vegetation, prescribed fire is often applied to limit the regeneration of undesirable species and shrubs. Vegetation community response is a combination of direct fire effects on the vegetation vitality and the indirect effect of soil nitrogen (N) loss that favors nutrient‐poor adapted barren communities. In this study, we assessed forest floor and upper mineral soil (0–5 cm) pools of carbon (C), N, and mercury (Hg) before and after prescribed fire of the Moquah Barrens in northwest Wisconsin. Although we took measurements in four distinct cover types, we found no relationship between cover type and soil pools. Across all cover types, prescribed fire led to considerable emissions of C, N, and Hg in the forest floor but only Hg in the upper mineral soils (0–5 cm), presumably because maximum fire temperatures were met for Hg volatilization. We classified fire severity and soil surface temperatures at the quadrat scale, but no discernable relationships with emissions were observed. The lack of detectable relationships is likely the result of a mismatch between the scales of response variables and predictors. As a result, we calculated ecosystem‐scale fire emissions based on the total area burned because we could not discern other smaller scale predictors. Overall emissions from dormant, spring season prescribed fires at the Moquah Barrens were approximately 11,000 Mg (5.5 Mg ha−1) for C, 350 Mg for N (0.17 Mg ha−1), and 4,500 g for Hg (2.3 g ha−1).

PWLT: Pyramid Window-based Lightweight Transformer for image classification

Recently, vision Transformers (ViTs) have achieved remarkable progress for image classification. As the computational cost of self-attention adopted in ViTs is quadratic with respect to the number of input tokens, some window-based ViTs have been proposed to solve this issue. However, these methods limit the computation of self-attention into spatial-constrained local windows, losing capability to encode image-based global interactions. Additionally, using fixed-size window always suffers the limitation of single-scale representation that is unsuitable for object recognition with variable scales. To address these problems, this paper describes a Pyramid Window-based Lightweight Transformer, namely PWLT, for image classification. Specifically, to address the need for multi-scale information, we employ windows of different sizes to encode objects with varying scales. To restore the relationships between different windows and explore global context, we introduce a dual self-attention scheme that utilizes local-to-global attention to reestablish these relationships. The extensive experiments on ImageNet-1K and CIFAR100 datasets demonstrate the effectiveness of our PWLT for image classification.

Variable-Dependent and Selective Multivariate Localization for Ensemble–Variational Data Assimilation in the Tropics

Abstract Two aspects of ensemble localization for data assimilation are explored using the simplified nonhydrostatic ABC model in a tropical setting. The first aspect (i) is the ability to prescribe different localization length scales for different variables (variable-dependent localization). The second aspect (ii) is the ability to control (i.e., to knock out by localization) multivariate error covariances (selective multivariate localization). These aspects are explored in order to shed light on the cross-covariances that are important in the tropics and to help determine the most appropriate localization configuration for a tropical ensemble–variational (EnVar) data assimilation system. Two localization schemes are implemented within the EnVar framework to achieve (i) and (ii). One is called the isolated variable-dependent localization (IVDL) scheme and the other is called the symmetric variable-dependent localization (SVDL) scheme. Multicycle observation system simulation experiments are conducted using IVDL or SVDL mainly with a 100-member ensemble, although other ensemble sizes are studied (between 10 and 1000 members). The results reveal that selective multivariate localization can reduce the cycle-averaged root-mean-square error (RMSE) in the experiments when cross-covariances associated with hydrostatic balance are retained and when zonal wind/mass error cross-covariances are knocked out. When variable-dependent horizontal and vertical localization are incrementally introduced, the cycle-averaged RMSE is further reduced. Overall, the best performing experiment using both variable-dependent and selective multivariate localization leads to a 3%–4% reduction in cycle-averaged RMSE compared to the traditional EnVar experiment. These results may inform the possible improvements to existing tropical numerical weather prediction systems that use EnVar data assimilation.

Trajectory model of adherence to cervical cancer treatment in central Mexico during the COVID-19 era

Background: Cervical Uterine Cancer is a disease that explains the vulnerability in which women find themselves in terms of reproductive health with an impact on occupational health and public health, even though in Mexico the prevalence rate is lower than the other member countries. of the OECD, its impact on Human Development and Local Development shows the importance that the disease has in communities more than in cities where prevention policies through check-ups and medical examinations seem to stop the trend, but they show the lack opportunities and capabilities of health centers in rural areas.Target: To establish the reliability, validity and correlations between the variables reported in the literature with respect to their weighting in a public hospital.Method: A non-experimental, cross-sectional, and exploratory study was carried out with a non-probabilistic selection of 104 patients from a public hospital in the State of Mexico. The Scale of Psychosocial Variables Determining Adherence to Treatment of Cervical Cancer was constructed.Results: From a structural model [χ2 = 490.330 (28 df) p = 0.000; GFI = 0.927; CFI = 0.970; RMSEA = 0.003] the fit of the trajectories of determinant relationships in which knowledge influenced treatment adherence behavior was demonstrated (β = 0.50).Conclusion: The limits of the design, sampling and analysis of the study are noted, and it is recommended to include organizational and psychological variables based on theories of organizations and theories of personality.

Variable importance and scale of influence across individual scottish wildcat hybrid habitat models

Understanding the scale dependence of species-habitat relationships is an important area of research in species distribution modeling. There has been little research focused on how habitat selection may depend on individual variation among organisms, geographical location and ecological context of that location. Furthermore, little is known about the extent and drivers of heterogeneity of scale dependence among individuals of a species inhabiting different ecological contexts, and few studies have compared scale dependence and variable importance in a spatially replicated framework. Two of the most important factors for interpreting habitat relationships models include: (1) the relative importance of variables in the model and (2) the spatial scale at which each variable has the largest influence. Based on the existing evidence we hypothesize a priori that landcover variables will generally be the most important predictors, followed by topography, then soil type (which influence both vegetation and prey), Normalized Difference Vegetation Index (NDVI) as an indicator of total vegetation density and perhaps a proxy for prey density, vegetation cover and rabbit abundance. We also expected a priori that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. We expected topographical features to be selected at broad scales, as they influence broad-scale climatic and ecological conditions. We also expected that land cover classes and vegetation cover density to be selected at relatively broad scales given past research showing land cover generally influences habitat selection at relatively broad scales. We expected NDVI and soil type to be selected at finer scales, as their variation influences the distribution of resources and limiting conditions within landscapes. Finally, we expected that rabbit abundance and linear features would affect wildcat hybrid occurrence at the finest scales, given these are resources and conditions that vary over short distances and strongly influence wildcat and wildcat hybrid behavior and habitat use. Our results were consistent with the hypothesis that there may be consistency regarding which variables or variable groups are most important as predictors of wildcat hybrid occurrence in Scotland. Based on previous research we expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. Finally, our results identify a clear and consistent trend of increasing frequency of inclusion of variables at increasingly broad scales. This is a linear trend in frequency of variables retained increasing as the scale increased. This suggests a consistent and monotonic pattern of more frequent retention of variables at increasingly broad scales.

CONSUMERS’ BEHAVIOUR AND THEIR WILLINGNESS TO ACCEPT GREEN PACKAGING: A CASE STUDY IN VIETNAM

Objectives: This study specifically focuses on examining customers' purchasing behaviour and their acceptance of green packaging as a replacement for single-use plastic bags. Method: The data used in this study are primary, including survey data obtained from the respondents. We designed a questionnaire related to examining customers' purchasing behaviour and their acceptance of green packaging. We determined the observed variables (scales) of examining customers' purchasing behaviour and their acceptance of green packaging based on previous studies. Structural Equation Modelling and Willingness to Accept models were utilised to achieve the study's objectives. Results: The results demonstrate that personal attitude statistically significantly influences respondents' purchasing behaviour. Moreover, the findings indicate that the expenditure level and the types of green packaging have an impact on the respondents' willingness to accept green packaging as a substitute for single-use plastic bags. Conclusions: This article is an academic contribution that enables understanding of customers' purchasing behaviour and their acceptance of green packaging. To promote the use of green packaging, it is crucial for business planners to prioritise obtaining and maintaining necessary certifications for their green products. It is imperative for policy-makers to actively assume a crucial role in promoting the adoption of green packaging through official government channels, public institutions, and targeted communication campaigns.

Motion sensitive network for action recognition in control and decision-making of autonomous systems.

Spatial-temporal modeling is crucial for action recognition in videos within the field of artificial intelligence. However, robustly extracting motion information remains a primary challenge due to temporal deformations of appearances and variations in motion frequencies between different actions. In order to address these issues, we propose an innovative and effective method called the Motion Sensitive Network (MSN), incorporating the theories of artificial neural networks and key concepts of autonomous system control and decision-making. Specifically, we employ an approach known as Spatial-Temporal Pyramid Motion Extraction (STP-ME) module, adjusting convolution kernel sizes and time intervals synchronously to gather motion information at different temporal scales, aligning with the learning and prediction characteristics of artificial neural networks. Additionally, we introduce a new module called Variable Scale Motion Excitation (DS-ME), utilizing a differential model to capture motion information in resonance with the flexibility of autonomous system control. Particularly, we employ a multi-scale deformable convolutional network to alter the motion scale of the target object before computing temporal differences across consecutive frames, providing theoretical support for the flexibility of autonomous systems. Temporal modeling is a crucial step in understanding environmental changes and actions within autonomous systems, and MSN, by integrating the advantages of Artificial Neural Networks (ANN) in this task, provides an effective framework for the future utilization of artificial neural networks in autonomous systems. We evaluate our proposed method on three challenging action recognition datasets (Kinetics-400, Something-Something V1, and Something-Something V2). The results indicate an improvement in accuracy ranging from 1.1% to 2.2% on the test set. When compared with state-of-the-art (SOTA) methods, the proposed approach achieves a maximum performance of 89.90%. In ablation experiments, the performance gain of this module also shows an increase ranging from 2% to 5.3%. The introduced Motion Sensitive Network (MSN) demonstrates significant potential in various challenging scenarios, providing an initial exploration into integrating artificial neural networks into the domain of autonomous systems.

Association of self-efficacy and faculty support on students’ readiness for practice

BackgroundThe nursing shortage is apparent again after nearly five decades of having a stable workforce. One novel development is that new Registered Nurses (RNs) are exiting their careers too early. Many of them leave the profession for multiple reasons such as burnout and family obligations, yet one reason has emerged – lack of readiness for practice, that also drives the RNs to leave the profession. PurposeThe purpose of this study was twofold — (1) to investigate the relationship between students' characteristics, their Generalized Self-Efficacy (GSE), Perceived Faculty Support (PFS), the Assessment Technologies, Inc. (ATI) Comprehensive Predictor RN Exam scores and their self-report of readiness for practice and (2) to examine which variables best explains the students' readiness for practice. MethodsA cross-sectional descriptive correlational research design with convenience sampling was utilized. Descriptive statistics were obtained to investigate the students' socio-demographic data. The Pearson r and multiple regression were used to analyze the relationships and influence between dependent variables Casey-Fink Readiness for Practice scale (CFRPS) and independent variables, students' characteristics, GSE, PFS, and ATI scores. ResultsOne hundred forty-three (143) out of 416 students in five baccalaureate schools from the western region of the United States participated in the survey. Findings showed GSE and PFS had significant positive correlations with students' readiness for practice. Further, factors of CFRPS in learning strategies, problem-solving, resourcefulness, and team player are best explained by GSE and PFS, accounting for 91 % of the variance. ConclusionStudents' GSE and PFS can contribute to their readiness for practice. Nurse educators must employ strategies that will bolster students' preparedness by improving their sense of self-efficacy and showing them the utmost faculty support.

The interplay between animal location accuracy and the decorrelation length scale of environmental variables when investigating environmental selection in marine organisms

Many large pelagic organisms appear to select specific oceanic conditions, probably due to physiological, energetic, reproductive, or other life history needs. However, prior to characterizing these dynamics and determining their underlying drivers, the selection itself must be reliably identified. Our ability to do so depends on the quality of animal locations and on the heterogeneity of the environmental conditions driving selection. To draw meaningful conclusions about environmental selection of large organisms and therefore about their basic ecology, distribution, and ultimately their potential exploitation and conservation, limits of selection detectability must be established. Here, we investigated how animal location accuracy and environmental variable decorrelation length scales impact the ability to detect environmental selection by marine organisms. We created synthetic tracks cuing on environmental variables potentially relating to life history demands, like sea surface temperature, chlorophyll a concentration, and Lagrangian coherent structures. By artificially imposing different animal selection strengths and location accuracies, we assessed how well environmental selection can be detected statistically in different cases. We found medium or strong selection to be reliably detected, even with relatively small samples and large position uncertainty, while weak selection presented similarly to no selection, especially for large sample sizes and position uncertainties. We therefore recommend using a selection strength threshold, which can significantly reduce the number of false positives while only increasing the risk of false negatives in cases of very weak selection, which are also less meaningful ecologically. We provide criteria to use when assessing confidence in environmental selection results for real marine organisms.