Pairs Of Variables Research Articles

Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding.

This article deals with the discovery of causal relations from a combination of observational data and qualitative assumptions about the nature of causality in the presence of unmeasured confounding. We focus on applications where unobserved variables are known to have a widespread effect on many of the observed ones, which makes the problem particularly difficult for constraint-based methods, because most pairs of variables are conditionally dependent given any other subset, rendering the causal effect unidentifiable. In this article, we show that under the principle of independent mechanisms, unobserved confounding in this setting leaves a statistical footprint in the observed data distribution that allows for disentangling spurious and causal effects. Using this insight, we demonstrate that a sparse linear Gaussian directed acyclic graph (DAG) among observed variables may be recovered approximately and propose a simple adjusted score-based causal discovery algorithm that may be implemented with general-purpose solvers and scales to high-dimensional problems. We find, in addition, that despite the conditions we pose to guarantee causal recovery, performance in practice is robust to large deviations in model assumptions, and extensions to nonlinear structural models are possible.

Rounding theory and method for the pitch curves of the variable center distance non-circular gear pair based on working conditions

Rounding theory and method for the pitch curves of the variable center distance non-circular gear pair based on working conditions

A Second Proof of Concept Investigation of Strengths Using the Structured Assessment of Violence Risk in Youth Tool With Justice-Involved Youth: Item Level Risk-Based Effects and Interactions.

Despite efforts to incorporate protective factors or 'strengths' in applied risk assessments for criminal reoffending, there has been limited progress towards a consensus regarding what is meant by such terms, what effects predictors can exert, or how to describe such effects. This proof of concept study was undertaken to address those issues. A structured professional judgment tool was used to create lower and higher historical/static risk groups with a sample of 273 justice-involved male youth with sexual offenses followed over a fixed 3-year period. Using risk and protective poles to create pairs of dichotomous variables from trichotomously rated risk and protective items, risk-based exacerbation and risk-based protective effects were found. These varied in terms of whether the effect on the outcome of a new violent (including sexual) offense was larger, smaller, or absent for youth at higher or lower historical/static risk. Some of these potentially dynamic dichotomous variables were shown to have a protective (or risk) effect after controlling for both historical/static risk and that same item's risk (or protective) effect. Some moderated the association between historical/static risk and recidivism, strengthening or reducing it. Terms for these effects and implications of incorporating strengths in research and applied practice were considered.

Navigating Financial Performance of the MENA Region Energy Sector: The Interplay of Working Capital and Leverage

This research delves into the influence of leverage considering potential outcomes within operational funds concerning the fiscal implications of the energy industry within the middle east and north Africa region. The research employed an empirical approach by collecting secondary data from the MENA region's energy sector between 2018 and 2022. To achieve this, the study formulated three hypotheses and utilized the empirical random panel data model. The investigative results affirm the validity of all three hypotheses. Additionally, they provide insights into the relationships among the variables in these hypotheses. Particularly noteworthy is the fact that certain results from the hypotheses contradict the expected theoretical impact. The article suggests that future researchers should delve into additional factors influencing firm performance, recognizing that diverse variables may yield varied outcomes. Broadening the study's scope by incorporating different timeframes and a more diverse array of firms could enhance the overall representativeness of research findings. Expanding this inquiry beyond the energy sector to encompass various industries might offer a comprehensive understanding of how working capital affects firm performance across distinct economic sectors. Subsequent empirical studies are advised to consider global health crises, political events, and other socio-economic developments in the MENA region. Examining events such as the COVID-19 pandemic and ongoing geopolitical conflicts like the Russian-Ukrainian and Israeli-Palestinian conflicts could illuminate how such incidents impact of the association among cash reserve, leverage, and company efficacy. To gain deeper insights into the causal distinctions among financial resources, and production form the company, researchers might investigate bidirectional results between each pair of variables using Granger causality tests. The findings from the present study are pertinent for energy sector companies, policymakers, and academia. Future research endeavors could focus on scrutinizing how the implications of operating funds on company procedures change when moderated by additional factors related to macroeconomics, providing practical insights for decision-makers.

Canonical variables based numerical schemes for hybrid plasma models with kinetic ions and massless electrons

We study the canonical variables based numerical schemes of a hybrid model with kinetic ions and mass-less electrons. Two equivalent formulations of the hybrid model are presented with the vector potentials in different gauges and the distribution functions depending on canonical momentum (not velocity), which constitutes a pair of canonical variables with the position variable. Particle-in-cell methods are used for the distribution functions, and the vector potentials are discretized by the finite element methods in the framework of finite element exterior calculus. Splitting methods are used for the time discretizations. It is illustrated that the second formulation is numerically superior and the schemes constructed based on the anti-symmetric bracket proposed have better conservation properties and lower noise level, although the filters can be used to improve the schemes of the first formulation.

Cutaneous melanoma in older patients

BackgroundIn industrialized countries, the aging population is steadily rising. The incidence of cutaneous malignant melanoma (CMM) is highest in old people. This study focuses on the clinicopathological profile of CMM and indicators of diagnostic-therapeutic performance in older patients.MethodsThis retrospective population-based cohort study included 1,368 incident CMM, as recorded in 2017 by the Regional Veneto Cancer Registry (Northeast Italy). Older subjects were defined as ≥ 80, old as 65–79, and adults as < 65 years of age. The strength of association between pairs of variables was tested by Cramer’s-V. Using age groups as the dependent variable, ordered logistic regression was fitted using the clinicopathological CMM profiles as covariates. In each of the three age-groups, the indicators of clinical performance were computed using the Clopper-Pearson exact method.ResultsCompared to patients aged younger than 80 years (1,187), CMM in older patients (181; 13.2%) featured different CMM topography, a higher prevalence of ulcers (43.3% versus 12.7%; p < 0.001), a higher Breslow index (p < 0.001), a lower prevalence of tumor-infiltrating lymphocytes (64.4% versus 76.5%, p < 0.01), and a more advanced pTNM stage at clinical presentation (p < 0.001). Elderly patients with a positive sentinel-lymph node less frequently underwent sentinel- lymph node biopsy and lymphadenectomy (60.0% versus 94.2%, and 44.4% versus 85.5%, respectively; p < 0.001).ConclusionsIn older CMM patients, the clinicopathological presentation of CMM shows a distinctive profile. The present results provide critical information to optimize secondary prevention strategies and refine diagnostic-therapeutic procedures tailored to older patients.

Inefficacious drugs against covid-19: analysis of sales, tweets, and search engines.

Assess the correlation between the sales of two drugs with no proven efficacy against covid-19, ivermectin and chloroquine, and other relevant variables, such as Google® searches, number of tweets related to these drugs, number of cases and deaths resulting from covid-19. The methodology adopted in this study has four stages: data collection, data processing, exploratory data analysis, and correlation analysis. Spearman's method was used to obtain cross-correlations between each pair of variables. The results show similar behaviors between variables. Peaks occurred in the same or near periods. The exploratory data analysis showed shortage of chloroquine in the period corresponding to the beginning of advertising for the application of these drugs against covid-19. Both drugs showed a high and statistically significant correlation with the other variables. Also, some of them showed a higher correlation with drug sales when we employed a one-month lag. In the case of chloroquine, this was observed for the number of deaths. In the case of ivermectin, this was observed for the number of tweets, cases, and deaths. The results contribute to decision making in crisis management by governments, industries, and stores. In times of crisis, as observed during the covid-19 pandemic, some variables can help sales forecasting, especially Google® and tweets, which provide a real-time analysis of the situation. Monitoring social media platforms and search engines would allow the determination of drug use by the population and better prediction of potential peaks in the demand for these drugs.

Iterated Local Search with Linkage Learning

In pseudo-Boolean optimization, a variable interaction graph represents variables as vertices, and interactions between pairs of variables as edges. In black-box optimization, the variable interaction graph may be at least partially discovered by using empirical linkage learning techniques. These methods never report false variable interactions, but they are computationally expensive. The recently proposed local search with linkage learning discovers the partial variable interaction graph as a side-effect of iterated local search. However, information about the strength of the interactions is not learned by the algorithm. We propose local search with linkage learning 2, which builds a weighted variable interaction graph that stores information about the strength of the interaction between variables. The weighted variable interaction graph can provide new insights about the optimization problem and behavior of optimizers. Experiments with NK landscapes, knapsack problem, and feature selection show that local search with linkage learning 2 is able to efficiently build weighted variable interaction graphs. In particular, experiments with feature selection show that the weighted variable interaction graphs can be used for visualizing the feature interactions in machine learning. Additionally, new transformation operators that exploit the interactions between variables can be designed. We illustrate this ability by proposing a new perturbation operator for iterated local search.

Ability to Forecast standardized precipitation index in Vietnamese Mekong Delta for dry season months based on sea surface temperature

Drought has major impacts on agriculture, society, and ecosystems, so early prediction of drought plays an important role in mitigating its impacts. This study investigates the relationship between the Standardized Precipitation Index (SPI) in the Mekong Delta and global sea surface temperature (SST) in order to find potential variables for improving SPI forecast quality. The method used in determining potential predictors is based on the analysis of multiple correlation coefficients. Based on these potential predictors, SPI is predicted by stepwise regression along with the application of Leave-one-out cross-validation technique. The data used in this study is the rainfall of 15 stations in the Vietnamese Mekong Delta and the global SST from 1977 to 2020. Research results show that there is a pair of variables that have the best relationship with SPI in the study area, they are SST on Niño 3.4 region and SST in the region with latitude from 13 °N to 23 °N and longitude from 116 °E to 126 °E. When the forecast term is from 1 to 2 months, using this pair of variables gives better forecast quality than using only the predictors from the Niño indices.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19.

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Non-linear and synergistic effects of built environment factors on older adults’ walking behavior: An analysis integrating LightGBM and SHAP

Examining the relationship between the built environment and older adults’ walking behavior is of critical importance for the development of aging-friendly cities and communities. Previous studies, however, have paid limited attention to the non-linear and synergistic effects of built environment factors. To this end, based on multi-source data such as the Travel Characteristic Survey of Hong Kong and Google Street View imagery, this study integrates two advanced machine learning models—light gradient-boosting machine (LightGBM) and SHapley Additive exPlanations (SHAP)—to analyze the non-linear and synergistic effects of various built environment factors on older adults’ walking time. The results show that the effect of the built environment is largely non-linear. Critical built environment factors include access to recreational facilities and land-use mix. Access to metro and parks, however, plays a marginal role in affecting older adults’ walking. Furthermore, the synergistic effects of built environment variable pairs (e.g., access to recreational facilities and intersection density) are also identified.

Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning

We propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC), in analogy to the partial correlation coefficient in classical multivariate analysis. The construction of our new coefficient is based on the framework of multivariate regular variation and transformed-linear algebra operations. We show how this coefficient allows identifying pairs of variables that have partially uncorrelated tails given some other variables in a random vector. Unlike other recently introduced conditional independence frameworks for extremes, our approach requires minimal modeling assumptions and can thus be used in exploratory analyses to learn the structure of extremal graphical models. Similarly to traditional Gaussian graphical models where edges correspond to the nonzero entries of the precision matrix, we can exploit classical inference methods for high-dimensional data, such as the graphical Lasso with Laplacian spectral constraints, to efficiently learn the extremal network structure via the PTCC. We apply our new method to study extreme risk networks in two different datasets (extreme river discharges and historical global currency exchange data) and show that we can extract interesting extremal structures with meaningful domain-specific interpretations.

Study of the delayed pumping effect in the underground laboratory by the method of correlation analysis of radon and aeroion concentrations

Measurements and correlation analysis of radon and aeroion concentrations in the underground laboratory were carried out. For pairs of variables “pressure — radon” and “pressure — ions”, a delayed pumping effect was found, similar to that previously observed for neutrons and gamma quanta. A simple phenomenological model explaining the results is presented. In this model, the reason for the delay is the gradual accumulation of radon in the room with a decrease in atmospheric pressure. The balance of the radon accumulation rate, the time of its radioactive decay and the characteristic time of pressure variations leads to an effective delay of 2 days between atmospheric pressure variations and radon concentration. Correlation analysis for the variables “pressure — ions” indicates that ions formed in the pores of the soil are already present in the air carrying radon to the laboratory. These ions make up approximately 21% of the total number of ions in the laboratory.

Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway.

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction.

How Nutritious Are French Beans (Phaseolus vulgaris L.) from the Citizen Science Experiment?

French beans are tender, immature, edible pods that are harvested early in the plant's growth cycle and are usually eaten cooked. The growth habits of French beans were studied for the first time in a Citizen Science experiment, and 19 pod samples were collected for further nutritional analysis. Various macronutrients (e.g., protein, ash, fat, carbohydrates, amino acids) and multi-element profiles were determined. A survey of their growing habits revealed that beans are usually planted once or twice a year in May and June at a length of 5-10 m, with a predominance of dwarf beans cultivation over climbing varieties, and pest resistance and stringless pods are the most important characteristics when deciding on a bean. Homogenised freeze-dried pod samples contained 16.1-23.1% protein, 4.5-8.2% ash, 0.1-1.1% fat, and 62.0-70.6% carbohydrates and had a caloric value of 337-363 kcal/100 g. Of the 17 free amino acids identified, 8 were essential (histidine, threonine, methionine, valine, lysine, isoleucine, leucine, phenylalanine) and 9 were non-essential (cysteine, aspartic acid, serine, glutamic acid, glycine, arginine, alanine, proline, tyrosine); meanwhile, of the 12 elements, 5 were macroelements and 7 were microelements. The predominant free amino acids were aspartic acid, glutamic acid, and serine. In the multiple comparisons (Box and Whisker plot), the parameters caloric value and iron showed the strongest response. A very strong positive significant Pearson correlation (≥0.95) was found for five pairs of variables within the free amino acids. Comparison of the nutrient data obtained in the pods showed near-perfect or high complementarity (85.2-103.4%) with the food composition databases for half of the parameters, suggesting that the home-grown French beans from the Citizen Science experiment are a highly nutritious vegetable.

Time–temperature correlations of amorphous thermoplastics at large strains based on molecular dynamics simulations

In this paper, we investigate the time–temperature correlation of amorphous thermoplastics at large strains based on coarse-grained molecular dynamics simulations. This correlation behavior is characterized by the strain hardening modulus in uniaxial tension simulations at different strain rates across the glass transition region. The temperature regime is divided into a melt zone, a glassy zone, and a transition zone between them, according to the storage modulus calculated from dynamic mechanical analysis (DMA) at small strains. In the melt zone, the existence of time–temperature superposition (TTS) at large strains is verified by constructing a master curve of the hardening modulus. The obtained shift factors are then compared to those from DMA at small strains, showing that the TTS behavior is transferable between small and large strains. In the glassy zone, the effects of time and temperature are not superposable at large strains but still can be correlated. To demonstrate this correlation behavior, we introduce a level set of the hardening modulus with a variable pair of strain rate and temperature. Pairs lying in the same level result in coincident stress–strain curves at large strains. The transferability of the correlation behavior between large and small strains is validated by comparing these stress–strain curves at small strains in the pre-yield region. In the transition zone, the correlation behavior is studied with both aforementioned methods, showing that TTS is applicable to large strains but not transferable to small strains. Finally, we propose a phenomenological constitutive model for uniaxial tension to demonstrate the time–temperature correlation at large strains, considering different constant strain rates and temperatures.

Association of Drugs for Sale on the Internet and Official Health Indicators: Darknet Parsing and Correlational Study.

Studying illicit drug circulation and its effects on population health is complicated due to the criminalization of trade and consumption. Illicit drug markets have evolved with IT, moving digital to the "darknet." Previous research has analyzed darknet market listings and customer reviews. Research tools include public health surveys and medical reports but lack neutral data on drugs' spread and impact. This study fills this gap with an analysis of the volume of drugs traded on the darknet market. We aimed to use the dark web data and officially published indicators to identify the most vulnerable regions of Russia and the correlations between the pairs of variables to measure how illicit drug trade can affect population well-being. We web-parsed the Hydra darknet drug marketplace using Python code. The dataset encompassed 3045 individual sellers marketing 6721 unique products via 58,563 distinct postings, each representing specific quantities sold in different Russian regions during 2019. In the second stage, we collected 31 variables from official sources to compare officially collected data with darknet data about amounts and types of selling drugs in every 85 regions of Russia. The health-related data were obtained from official published sources-statistical yearbooks. Maps, diagrams, correlation matrixes, and applied observational statistical methods were used. In 2019, a minimum of 124 kilograms of drugs circulated daily in small batches on the Russian darknet. Cannabis dominated the market, being 10 times more prevalent than opiates, and cannabis products' higher availability in the region is correlated with a lower incidence of opiate overdoses. The "grams of opiates in the region" variable is significantly correlated with drug overdose deaths (r=.41; P=.003), HIV-positive cases due to drug use (r=.51; P=.002), and drug court convictions in Russia (r=.39; P=.004). The study identified significant correlations between opiate sales on the darknet and higher rates of HIV among injection drug users (r=.47; P=.003). Conversely, regions with higher cannabis sales exhibited significant negative correlations with indicators of harmful drug use (r=-.52; P=.002) and its prevalence (r=-.49; P=.001). These findings suggest regional variations in drug sales on the darknet may be associated with differing public health outcomes. These indicators accurately reflect regional drug issues, though some official statistics may be incomplete or biased. Our findings point to varying levels of risk associated with different types of drugs sold on the darknet, but further research is needed to explore these relationships in greater depth. The study's findings highlight the importance of considering regional variations in darknet drug sales when developing public health strategies. The significant correlations between drug sales data and public health indicators suggest that region-specific interventions could be more effective in addressing the diverse challenges posed by illicit drug use.

An experimental study of neural estimators of the mutual information between random vectors modeling power spectrum features

Mutual information (MI) quantifies the statistical dependency between a pair of random variables and plays a central role in signal processing and data analysis. Recent advances in machine learning have enabled the estimation of MI from a dataset using the expressive power of neural networks. In this study, we conducted a comparative experimental analysis of several existing neural estimators of MI between random vectors that model power spectrum features. We explored alternative models of power spectrum features by leveraging information-theoretic data processing inequality and bijective transformations. Empirical results demonstrated that each neural estimator of MI covered in this study has its limitations. In practical applications, we recommend the collective use of existing neural estimators in a complementary manner for the problem of estimating MI between power spectrum features.

Factors affecting citizen safety of urban transportation service in Bangladesh: The case of Pabna municipality

BackgroundWith the rapid growth of cities, the extent of road accidents has increased, posing a threat to the safety of citizens. In Bangladesh, like many other countries, urban areas face a high incidence of road accidents, leading to loss of life, injuries, and economic costs. This research aims to investigate the factors affecting citizen safety of urban transportation service in Bangladesh. MethodsThis study utilized verbal interviews maintained by questionnaires to gather data on citizen perception regarding factors impacting transportation safety. The questionnaire consisted of two sections, collecting non-parametric data on travel behavior and socioeconomic factors, and parametric data on factors related to transportation safety. The dataset was subsequently analyzed using statistical devices such as descriptive statistics, Principal Component Analysis (PCA), Pearson's Correlation Matrix (PCM), and Cluster Analysis (CA). Results and conclusionThe findings of the study indicate strong significant correlations among several pairs of variables. Notably, traffic rules and enforcement, and driver travel behavior demonstrate a strong positive correlation of 0.784. Similarly, vehicle condition and safety features, and traffic rules and enforcement display a robust positive association of 0.764. PCA demonstrate 23% of the total variance, with a significant positive loading affecting citizen safety, which is influenced by traffic rules enforcement and drivers' travel behavior. The research findings emphasize the implication of traffic rule enforcement and responsible driver behavior in ensuring citizen safety. In Bangladesh, inadequate transportation regulation enforcement has led to high rates of reckless driving and traffic accidents, especially among pedestrians, cyclists, and motorcyclists.

Analysis of the Effect of the Addition of Aluminum Powder on Polyester-Glass Composite Properties in Tensile and Hardness Test

The study aimed to analyse the influence of aluminum powder on the mechanical properties of polyester-glass composites. Composite materials were made of glass fibres with random fibre direction and polyester resin with the addition of aluminum powder as a filler. The strength parameters of materials obtained during the static tensile test and hardness test were compared. The results were subjected to statistical analysis, taking into account the change in the amount of filler, microstructure was also observed using optical microscopy. An analysis of the results showed that the addition of aluminum powder amounting to 2% of the weight of the composites reduces their hardness and, at the same time, the longitudinal modulus of elasticity.The addition of aluminum powder amounting to 5% and 10% of the weight of the composites increased the hardness of the composites while reducing the Young's modulus. The values of hardness measurements were subjected to statistical analyses at significance level α = 95. The Shapiro-Wilk test confirmed that the hardness variables for all tested samples were in normal distributions, and therefore the most powerful parametric tests were used to test the differences. Using the Student's t-test, it was confirmed that between pairs of variables in configurations: a standard sample with a modified sample, for all tested samples there are significant statistical differences in the distributions of hardness values. Studies have shown that the modification of the mechanical and strength properties of composite materials, through the use of an aluminum filler, makes it possible to obtain materials with variable parameters that can be useful and attractive in many new areas, depending on the application and user requirements.