Pairs Of Variables Research Articles

A Causal Relationship between Economic Growth, FDI, and Trade Openness in India: A Linear and Non-linear Approach

The study examined the causal nexus between India’s trade openness, FDI and economic growth from 1970 to 2021. This study used the time series Linear and the Non-linear Causal Test to analyze the causal nexus among the variables. The findings suggested a bidirectional causality between each pair of variables. Conversely, the Non-linear Causality Test results revealed bidirectional non-linear causality between trade openness and economic growth and between FDI and economic development. The findings suggested that policymakers should adopt a balanced approach to promote economic development in India, where policies to enhance trade openness should consider their impact on the FDI. FDI strategies should likewise account for their effect on trade openness. Investment in infrastructure and human capital can also amplify this nexus. Keywords: Granger causality, non-linearity, policy JEL Codes: C10, E60, C65.

Light Polymerization through Glass-ceramics: Influence of Light-polymerizing Unit's Emitted Power and Restoration Parameters (Shade, Translucency, and Thickness) on Transmitted Radiant Power.

This in vitro study assessed light transmission through ceramic discs varying in shade, translucency, and thickness using light-polymerizing units with different radiant power/flux (RP) outputs. Disc-shaped specimens (0.5 mm, 1.0 mm, and 2.0 mm) were made from high and low-translucency glass-ceramic ingots (IPS e.max Press) in shades A1 and A4, totaling 60 discs. Two light-polymerizing units with different power outputs were used, and their emission spectra were verified. The transmitted RP values for each ceramic specimen were measured and irradiance and radiant energy influx were calculated. Differences between the light-polymerizing units and the influence of the three ceramic parameters were evaluated using an independent-samples t-test and three-way analysis of variance (ANOVA) tests (α = 0.05). A statistically significant difference was observed in the mean transmitted RP values between the two light-polymerizing units. Furthermore, the three-way ANOVA test showed a significant effect of shade, translucency, and thickness, as well as a significant interaction between each pair of variables and all three variables on the transmitted RP (P < 0.05). Despite the significant attenuation in the transmitted RP, especially in ceramics with higher shade chromaticity and thickness and lower translucency, the calculated minimal irradiance values for both light-polymerizing units (their emitted power ≥ 500 mW) were greater than the minimum recommended irradiance threshold (100 mW/cm2). However, the exposure duration needs to be increased to provide the resin with sufficient radiant exposure for adequate polymerization.

Sensitivity analysis of prior distributions in Bayesian graphical modeling: Guiding informed prior choices for conditional independence testing

Bayesian analysis methods provide a significant advancement in network psychometrics, allowing researchers to use the edge inclusion Bayes factor for testing conditional independence between pairs of variables in the network. Using this methodology requires setting prior distributions on the network parameters and on the network’s structure. However, the impact of both prior distributions on the inclusion Bayes factor is underexplored. In this paper, we focus on a specific class of Markov Random Field models for ordinal and binary data. We first discuss the different choices of prior distributions for the network parameters and the network structure, and then perform an extensive simulation study to assess the sensitivity of the inclusion Bayes factor to these distributions. We pay particular attention to the effect of the scale of the prior on the inclusion Bayes factor. To improve the accessibility of the results, we also provide an interactive Shiny app. Finally, we present the R package simBgms, which provides researchers with a user-friendly tool to perform their own simulation studies for Bayesian Markov Random Field models. All of this should help researchers make more informed, evidence-based decisions when preparing to analyze empirical data using network psychometric models.

Analyzing Teaching Self-Efficacy Correlates in Virtual Education: A Gender-Driven Structural Equation Modeling Approach

The study focused on the Philippines' education system, which abruptly adopted to online education due to the COVID-19 pandemic. The study used different theories to explore the relationships between online teaching self-efficacy and other affective variables. The researchers used structural equation modeling (SEM) to develop a model that showed the correlations among the identified variables in general and when grouped according to gender. The study found that only three pairs of latent variables were positively correlated at an average extent, indicating a low correlation between the variables. The findings drawn out from the final model can certainly benefit teacher-educators and institutional leaders alike who would most probably explore the idea of introducing the online modality as a supplementary requirement for the preservice teachers’ internship in the near future. Thus, institutions that offer education programs must find ways and means to motivate preservice teachers to online teaching as there are revealed to be the rudiments, based on the final model to a positive attitude and a favorable satisfaction level toward online language teaching.

Vine Copula Model: Application to Chemical Elements in Water Samples

Copula can link the bivariate distribution function with marginal distribution functions without requiring specific information about the interdependence among random variables. There are several types of copulas, such as elliptical copulas, Archimedean copulas, and extreme value copulas. However, in multivariate modeling, each type of copula has limitations in modeling complex dependence structures in terms of symmetry and tail dependence properties. The class of vine copulas overcomes these limitations by constructing multivariate models using bivariate copulas in a tree-like structure. The bivariate copulas used in this study include the Clayton, Gumbel, Frank, Gaussian, and Student's t copula families. This study discusses the construction of vine copula models, parameter estimation, and their applications. The construction of vine copulas is done through the decomposition of conditional probability density functions and substituting bivariate copula density functions into the decomposition results. The data used in the study is the logarithm of the concentration of chemical elements in water samples in Colorado. The parameter estimation method used is pseudo-maximum likelihood with sequential estimation. Model selection is then performed using the Akaike information criterion (AIC) to determine the most suitable model. The results indicate that Caesium and Titanium have a dependency relationship with Scandium. Moreover, Scandium and Titanium exhibit the strongest dependence compared to other variable pairs.

Further results on the bivariate semi-parametric singular family of distributions

General classes of bivariate distributions are well-studied in the literature. Most of these classes are proposed via a copula formulation or extensions of some characterization properties in the univariate case. In Kundu (2022), we see one such semi-parametric family useful to model bivariate data with ties. This model is a general semi-parametric model with a baseline. In this paper, we present a characterization property of this class of distributions in terms of a functional equation. The general solution to this equation is explored. Necessary and sufficient conditions under which the solution becomes a bivariate distribution are investigated. An application of the characterization property of the proposed class for generating bivariate pairs of random variables from a member distribution is also discussed.

Investigation of the relationships between selenium fractions and soil properties by canonical correlation

The availability and distribution of an element are determined by soil conditions and the geochemical fractions. In this study, canonical correlation (CCA) was used to examine the associations between the soil parameters (X set) of the Amik plain and the fractions of selenium (Se) (Y set). A statistically significant correlation (P &amp;lt; 0.01) of 0.391 ± 0.06 was observed between the first canonical variable pair (FCVP). The clay, sand, and silt variables exhibited the greatest standardized canonical coefficients, with values of 74.55, 75.83, and 94.77, respectively. Additionally, the B3 percent had a standardized canonical coefficient of 1.029. The analysis of the FCVP showed that the original variables of the X set and/or its canonical variable (U1) had the strongest linear connection (0.770) with organic matter (OM) and the B3 percentage in the Y set (0.603). The OM (0.3012) exhibited the most substantial linear structural correlations between the canonical variables of the Y set and the original variables of the X set. However, the canonical redundancy analysis revealed that in the FCVP, the variance of the X set was described by its canonical variables to the extent of 20.97%, whereas the Y set was explained by 21.96%. Hence, it can be inferred that the use of canonical correlation is effective in elucidating the relationship between soil parameters and various components of plant nutrition.

Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding.

Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption about ψ-network dependence. Finally, we provide a consistent variance estimator.

Vocational Education and Industry Integration and Symbiosis of Subjects and Digital Drivers

Abstract Under the new round of scientific and technological revolution and industrial change, the integration of vocational education and industry is an important direction of educational development. In this paper, the structure of the learning path network of higher vocational professional courses is analyzed, and through the analysis of the elements of learning behaviors, the learning behaviors are mined by adopting sequence relations, and the behavioral structure model is established. The assessment of the learning path under the integration of industry and education courses was finished using uncertainty association coefficients and time association coefficients. After building the learning path network for vocational education, the subjective characteristics of students under vocational education were detected using isolated point analysis. The detection results show that the correlation coefficient between the first pair of typical variables is 0.488. The Wilks statistical test results for typical correlation coefficients show that the p-value of the first typical correlation coefficient is 0.098, which is less than 10%. The correlation study of the environment of integration of education and industry and the effect of integration can be simplified to study only the relationship between the first pair of typical variables. By analyzing the main characteristics, the main body for vocational education and industry integration is clarified, which creates a digital-driven impetus for the integration of industry and education.

Mother's knowledge of nutrition and protein intake in stunted children aged 12-36 months in Indonesia

The objective of the study is to analyze the effect of mothers’ knowledge before and after counseling on nutrition and protein intake in stunting toddlers aged 12-36 months. This study used a quasi-experimental design with a two-group pretest-posttest design from May to August 2022 in Batujajar and Cihampelas Subdistricts, West Bandung Regency, Indonesia. Subjects consisted of 124 mothers who had stunted toddlers and were divided into 2 groups: control and treatment, with 62 toddlers each, with simple random sampling. In the 1st and 4th months, nutritional measurements of protein intake were carried out using the Food Frequency Questionnaire using the 2007 Nutrisurvey software. The statistical analysis included a t-test (comparison) for each pair of variables and tests for independence using the correlation coefficient and chi-square. Pretest nutrition knowledge in the majority control group was in the sufficient category at 38.7%. Meanwhile, in the treatment group, it was in the good category at 54.8%. Nutrition knowledge showed that the posttest control (15.45 ± 3.55) and the treatment (18.53 ± 1.49) groups were significantly different (p &lt;0.01). Protein intake pretest and posttest (1st, 2nd, 3rd, and 4th month protein) between the control and treatment groups were also significantly different (p&lt;0.05). There was a significant posttest effect on mother's knowledge about nutrition and protein intake in stunted toddlers. Health workers are expected to continue to provide health education gradually and repeatedly to increase mothers' knowledge so the risk of stunting can be minimized.

Spatial Relationships between Topographic Variables and Their Interactions with Natural and Climatic Zonalization in Mongolia

The main objective of this investigation is to examine the spatial relationship between the topographic variables in Mongolia, which is important in the pattern of regional relief, geomorphological regionalization, and natural zonalization. For the analysis, we used seven different terrain parameters such as elevation, slope, aspect, topographic position index (TPI), topographic wetness index (TWI), latitude, and longitude. Five of seven variables were obtained from a digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) at a spatial resolution of 30 m. The location of the latitude and longitude were extracted from the central point of the gridded vector data. The correlation analysis of seven topographic variables showed that the highest positive correlation (r=0.55) was detected between elevation and slope. In contrast, the highest negative correlation (r=-0.63) was detected between elevation and longitude. The lowest correlations were detected between the aspect and the remaining six variables. The significance levels of elevation, slope, latitude, longitude, and TWI noted p &lt; 0.0001 for each pair of variables. The correlation between aspect and TPI ranged p &lt; 0.01 while elevation and aspect, latitude and TPI, and longitude and TPI ranged p &lt; 0.1, respectively. The corresponding frequency distributions are characterized by three left skew, two bimodal, and two normal distributions, respectively. We also examined the contribution of the topographic variables to natural and climatic zonalization in Mongolia. The result confirmed that topographic variables were key indicators for identifying regional differences in physical geographic regionalization, and natural and climatic zonalization. The examination of topographic variables allowed more knowledge of geographic characteristics. At the same time, the result is of great value in the investigation of natural geographic divisions, and regional nature conservation research.

Confounding Factor Analysis for Vocal Fold Oscillations.

This paper provides a methodology to better understand the relationships between different aspects of vocal fold motion, which are used as features in machine learning-based approaches for detecting respiratory infections from voice recordings. The relationships are derived through a joint multivariate analysis of the vocal fold oscillations of speakers. Specifically, the multivariate setting explores the displacements and velocities of the left and right vocal folds derived from recordings of five extended vowel sounds for each speaker (/aa/, /iy/, /ey/, /uw/, and /ow/). In this multivariate setting, the differences between the bivariate and conditional interactions are analyzed by information-theoretic quantities based on transfer entropy. Incorporation of the conditional quantities reveals information regarding the confounding factors that can influence the statistical interactions among other pairs of variables. This is demonstrated on a vector autoregressive process where the analytical derivations can be carried out. As a proof of concept, the methodology is applied on a clinically curated dataset of COVID-19. The findings suggest that the interaction between the vocal fold oscillations can change according to individuals and presence of any respiratory infection, such as COVID-19. The results are important in the sense that the proposed approach can be utilized to determine the selection of appropriate features as a supplementary or early detection tool in voice-based diagnostics in future studies.

On Exact Feature Screening in Ultrahigh-Dimensional Binary Classification

We propose a new model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification problems. With a high probability, the proposed method retains only relevant features after discarding all the noise variables. The proposed screening method is also extended to identify pairs of variables that are marginally undetectable but have differences in their joint distributions. Finally, we build a classifier that maintains coherence between the proposed feature selection criteria and discrimination method, and also establish its risk consistency. An extensive numerical study with simulated and real benchmark datasets shows clear and convincing advantages of our proposed method over the state-of-the-art methods. Supplementary materials for this article are available online.

Metaphors from perception and culture

Abstract Applying conceptual metaphor theory, this study aims to discuss how metaphors emerge from the interaction between perceptual experience and cultural environment, comparing English and Chinese. The kind of metaphors under study is rooted in the object image schema, particularly in its dimension in solidity with bipolar values as hard and soft. Specifically, these are primary metaphors grounded in experiential correlations in manipulating physical objects that are hard or soft. It is argued that the similarities and differences between English and Chinese in such metaphorical mappings can be accounted for by four main meaning focuses consisting in four pairs of parametric variables: more or less effort, more or less impact, more or less strength, and more or less flexibility. These parametric variables determine metaphorical mapping pathways from hard and soft as source concepts to some abstract target concepts.

Does the disposition of passive coping mediate the association between illness perception and symptoms of anxiety and depression in patients with spinal cord injury during first inpatient rehabilitation?

Purpose To examine associations between illness perception, also called illness cognitions or appraisals, disposition of passive coping, and symptoms of anxiety and depression, and to test whether passive coping mediates the associations between illness perception and symptoms of anxiety and depression. Materials and methods Longitudinal, multicentre study. Participants were inpatients of spinal cord injury (SCI) rehabilitation. Measures included the Brief Illness Perception Questionnaire (B-IPQ), the Utrecht Coping List passive coping subscale (UCL-P), and the Hospital Anxiety and Depression Scale (HADS). Mediation was tested with the PROCESS tool. Results The questionnaires were completed by 121 participants at admission and at discharge. Of them, 70% were male, 58% had a paraplegia, and 82% an incomplete lesion. Weak to strong (0.294–0.650) significant associations were found between each pair of study variables. The use of passive coping strategies mediated the associations between illness perception and symptoms of anxiety and depression. Conclusion Symptoms of anxiety and depression were more frequent in people who have a threatening illness perception combined with a lower use of passive coping strategies. Therefore, it is advised that patients are screened and treated for threatening illness perception and high use of passive coping strategies during rehabilitation after SCI.

Local confluence of conditional and generalized term rewriting systems

Reduction-based systems are used as a basis for the implementation of programming languages, automated reasoning systems, mathematical analysis tools, etc. In such inherently non-deterministic systems, guaranteeing that diverging steps can be eventually rejoined is crucial for a faithful use in most applications. This property of reduction systems is called local confluence. In a landmark 1980 paper, Gérard Huet characterized local confluence of a Term Rewriting System as the joinability of all its critical pairs. In this paper, we characterize local confluence of Conditional Term Rewriting Systems, where reduction steps may depend on the satisfaction of specific conditions in rules: a conditional term rewriting system is locally confluent if and only if (i) all its conditional critical pairs and (ii) all its conditional variable pairs (which we introduce in this paper) are joinable. Furthermore, the logic-based approach we follow here is well-suited to analyze local confluence of more general reduction-based systems. We exemplify this by (i) including (context-sensitive) replacement restrictions in the arguments of function symbols, and (ii) allowing for more general conditions in rules. The obtained systems are called Generalized Term Rewriting Systems. A characterization of local confluence is also given for them.

Thermo- and photosensitivity of Euterpe oleracea Mart. (Arecaceae) fruit pulp: Consequences for its antioxidant activity under basic storage conditions in the Amazon

Anthocyanins are chemically unstable, casting doubt on the antioxidant activity (AA) of Euterpe oleracea Mart. (Arecaceae) fruit pulp during storage. In this study, AA, total monomeric anthocyanin (TMAC), total flavonoid (TFC), and total phenolic contents (TPC) of this preparation after 24 h and 5 days at ambient temperature/ambient light-dark (AT/AL), ambient temperature/continuously dark (AT/CD), and under refrigeration/continuously dark (UR/CD) were compared to those of the fresh preparation. A FRAP and a DPPH assay, and a pH differential, an AlCl3 colorimetric, and Folin-Ciocalteu’s method were used. Associations between pairs of variables were determined by Pearson correlation coefficient (r). Student’s t test and one-way ANOVA with Tukey HSD were used to evaluate the data for statistically significant differences (p &lt; 0.05). FRAP and DPPH IC50 values of the fresh preparation were 116 ± 3 μM Fe2+ equivalents/100 μg, and 57 ± 12 μg/mL, respectively. They were hardly affected after 24 h, but had decreased 4-fold and increased 2-fold, respectively, after 5 days, the least at UR/CD. TMAC, TFC, and TPC of the fresh preparation were 21 ± 4 mg Cy3GE/mg, 25 ± 4 μM RE/100 μg, and 67 ± 2 μM GAE/100 μg. The loss of AA after 5 days was accompanied by a 3- to 5-fold TMAC and TFC degradation, and a 1.5-fold TPC decrease. AA correlated better with TMAC and TFC (r ±0.7 - ±1.0) than with TPC (r ±0.4 - ±0.9). These observations are important for communities with only basic facilities for preparing and storing E. oleracea fruit pulp.

Mathematical Modeling of COVID-19 Cases and Deaths and the Impact of Vaccinations during Three Years of the Pandemic in Peru.

The COVID-19 pandemic has caused widespread infections, deaths, and substantial economic losses. Vaccine development efforts have led to authorized candidates reducing hospitalizations and mortality, although variant emergence remains a concern. Peru faced a significant impact due to healthcare deficiencies. This study employed logistic regression to mathematically model COVID-19's dynamics in Peru over three years and assessed the correlations between cases, deaths, and people vaccinated. We estimated the critical time (tc) for cases (627 days), deaths (389 days), and people vaccinated (268 days), which led to the maximum speed values on those days. Negative correlations were identified between people vaccinated and cases (-0.40) and between people vaccinated and deaths (-0.75), suggesting reciprocal relationships between those pairs of variables. In addition, Granger causality tests determined that the vaccinated population dynamics can be used to forecast the behavior of deaths (p-value < 0.05), evidencing the impact of vaccinations against COVID-19. Also, the coefficient of determination (R2) indicated a robust representation of the real data. Using the Peruvian context as an example case, the logistic model's projections of cases, deaths, and vaccinations provide crucial insights into the pandemic, guiding public health tactics and reaffirming the essential role of vaccinations and resource distribution for an effective fight against COVID-19.

A Climatological Survey of Corsica for Power System Analyses

Climate and climate change can impact present and future energy production and demand. In light of this issue, this paper conducts climatological analyses focused on the following meteorological quantities: shortwave downward irradiance (SDI), precipitation (Pr), relative humidity (RH), air temperature (Ta), 10 m wind speed (v10), and diurnal temperature range (DTR) for four locations in Corsica. The climate analyses of these atmospheric variables consist of three parts: (1) analysis of the historical trends; (2) correlation analysis; and (3) analysis of climate projections for the decades to come. It is observed that climate change is causing alterations in the trends of Pr, RH, Ta, v10, and DTR. The correlation analysis reveals a positive correlation for the Ta-SDI and v10-Pr pairs (both annually and seasonally), and a negative correlation for Ta-RH (annually and in summer). For the other variable pairs, the sign of the correlations varies depending on the time period and site considered. The trends in the projections from the multi-model ensemble simulations are consistent or inconsistent with each other depending on the time period (annual or seasonal) considered. The observed historical trends suggest that medium-term planning of the Corsican electric power system should already consider ongoing climate change. The correlation analysis provides insights into the combined effect of different atmospheric variables on electrical power systems (EPSs). Climate projections suggest studying long-term planning that is a compromise among the different (but equally likely) outputs of different climate models.

Transfer of statistical learning from passive speech perception to speech production

Communicating with a speaker with a different accent can affect one’s own speech. Despite the strength of evidence for perception-production transfer in speech, the nature of transfer has remained elusive, with variable results regarding the acoustic properties that transfer between speakers and the characteristics of the speakers who exhibit transfer. The current study investigates perception-production transfer through the lens of statistical learning across passive exposure to speech. Participants experienced a short sequence of acoustically variable minimal pair (beer/pier) utterances conveying either an accent or typical American English acoustics, categorized a perceptually ambiguous test stimulus, and then repeated the test stimulus aloud. In the canonical condition, /b/–/p/ fundamental frequency (F0) and voice onset time (VOT) covaried according to typical English patterns. In the reverse condition, the F0xVOT relationship reversed to create an “accent” with speech input regularities atypical of American English. Replicating prior studies, F0 played less of a role in perceptual speech categorization in reverse compared with canonical statistical contexts. Critically, this down-weighting transferred to production, with systematic down-weighting of F0 in listeners’ own speech productions in reverse compared with canonical contexts that was robust across male and female participants. Thus, the mapping of acoustics to speech categories is rapidly adjusted by short-term statistical learning across passive listening and these adjustments transfer to influence listeners’ own speech productions.