Statistical Interpretation Research Articles

Edaphic properties as pieces of evidence of tailings deposit on soils

Mine tailings are one of the primary contaminant sources of heavy metals and metalloids in the soil. Besides increasing the concentration of potentially toxic elements (PTEs), tailings may modify the edaphic conditions and decrease the buffer capacity of impacted soils. The influence of tailings may reach distances far from the impoundments depending on the transport path and the specific transport mean: air, rain (runoff and infiltration), or acid mine drainage. In this study, soil samples from various horizons were collected in trial pits along a transect, at different distances from sulfide tailings. Soil analysis included texture, organic matter, alkalinity, porous space, carbonates, pH, electrical conductivity, real density, apparent density, total sulfur, main mineralogy, and total concentrations of As, Cd, Pb, Fe, and Zn. Graphical and statistical interpretation of the results showed that real density and porous space are the leading indicators of the tailings dispersion and accumulation and that pH is not a significant parameter (all values were above the neutrality) due to the limestone abundance in the area. However, Zn and Cd concentrations had an inverse relation with pH. Differences in the concentrations of PTEs between the superficial and deep layers that increased toward the tailings were also observed. Gypsum was only present in the closest samples to the tailings and may also be an indicator of tailings’ influence on soils. This study allowed us to identify general edaphic parameters as a first and quick means to determine the tailings contamination of soils.

Causal viewpoint and ensemble interpretation: from physics to the social sciences.

In this article, we assert that, for the construction of quantum physics-analogous (as opposed to quantum math-analogous) probabilistic models of the social (e.g. economics-financial) reality, the use of the notion of causality and the idea of an ensemble of similarly prepared systems in a socially analogous manner could be essential. We give plausibility arguments in favour of this assertion by considering two social situations describable in terms of discrete-time stochastic (i.e. Markov) processes. The first one is an arbitrary economics/financial context expressed as a temporal sequence of actualized social states (e.g. decisions, choices, preferences, etc.). The other one is more specific, involving a generic supply chain context. This article is part of the theme issue 'Thermodynamics 2.0: Bridging the natural and social sciences (Part 1)'.

The Socialization of Primary School Pupils through Motor Games

This paper aims to bring to the forefront the most important connecting element of society, namely socialization. Improving socialization through motor games is, perhaps, a solution to many problems, as children align emotionally, emotionally, volitionally, physically, cognitively, etc. The study was conducted on a sample of 59 students aged 6 to 9 years. The sociometric technique was used to determine negative, positive, and neutral interpersonal relationships by age level before and after the use of the chosen motor games. The index of preferred status, cohesion index, arithmetic mean and progress rate were used for statistical interpretation.

Filling the maize yield gap based on precision agriculture – A MaxEnt approach

Precision agriculture (PA) and yield gap (Yg) analysis are promising strategies to achieve the desired sustainable intensification of agricultural production systems. Current crop Yg approaches do not consider the internal field yield variability caused by soil properties. Topographic and edaphic characteristics causing consistent high and low yield patterns in time and space can be interpreted as an ecological niche and used as proxies for potential yield (Yp) and Yg. Ecological niche models (ENMs) are statistical models originally developed to forecast a species’ niche. However, its application to analyse crop yield spatio-temporal variability has never been made. This study aimed to fill this void by developing a novel approach: i) to quantify the magnitude and spatio-temporal distribution of Yp and Yg, ii) to identify the main factors that cause the Yg, and iii) to provide statistical and agronomical interpretation of the data to reduce the Yg. We performed this work using high-resolution maize yield maps from three seasons, with an ancillary dataset composed of soil electrical conductivity, soil properties and digital elevation models provided by “Quinta da Cholda”, Portugal. The yield maps were averaged, resulting in a standardised multiyear yield map. The 90th and 10th yield percentiles were interpreted as proxies for Yp and Yg, and analysed by an ENM machine learning algorithm – maximum entropy (MaxEnt). The average Yg and Yp were quantified as 1.5 and 19.1 ton/ha. Yp was characterised by having silty, richer soils and lower elevations, with several nutritional factors above the critical limits to maintain higher yields. Yg had loam soils coupled with higher relative elevations and lower nutrition content. This innovative modelling approach can efficiently manage high-dimensional spatio-temporal data to support advanced PA solutions, allowing detailed support for narrowing the Yg.

Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms (Hartnett et al 2016 Phys. Rev. Lett. 116 038701). Within the model, the state of an agent i is described by the pair of variables corresponding to its opinion and a bias ω i toward any of the opposing values of S i . Collective decision making is interpreted as an approach to the equilibrium state within the nonlinear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such a physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that, depending on the assumptions about the nature of social interactions, two different Hamiltonians can be formulated, which can be solved using different methods. In such an interpretation the temperature serves as a measure of fluctuations, not considered before in the original model. We find exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are confirmed using individual-based simulations. The simulations also allow us to study the impact of system size and initial conditions on the collective decision making in finite-sized systems, in particular, with respect to convergence to metastable states.

Estimation of projection pursuit regression via alternating linearization

The projection pursuit regression (PPR) has played an important role in statistical modeling. It can be used both as a data model for statistical interpretation and as an algorithmic model for approximating general non-parametric regressions. Existing estimation methods of PPR usually involve complicated minimization in order to achieve desired efficiency under general settings. This paper presents an algorithm by alternatively linearizing the estimation loss function, referred to as aPPR hereafter, which is easy to implement. The asymptotic theory is also established for both the PPR data model and the algorithmic model. Numerical performance of aPPR in model estimation and model interpretation is demonstrated through simulations and real data analysis.

CAPITAL SPENDING AND ECONOMIC GROWTH CORRELATION IN GEORGIA

Nino Mikeladze
 Email:ninomikeladze88@gmail.com
 Doctoral student (Master’s in Economics) Ivane Javakhishvili Tbilisi State University 
 Tbilisi, Georgia
 https://orcid.org/0009-0008-1214-7241
 Government measures during the economic policy implementation should be based on the impact on economic growth and economic development. Moreover, as it is inevitable to have government expenditures, it is crucial to analyze what kind of relationship exists between economic growth and government expenditure. According to Keynes, government expenditure is the measure of the fiscal policy, On the other hand, according to Wagner, public spending is endogenous factor of economic development. There is no common attitude towards Wagner theory. Moreover, Wagner theory is considered to be valid for the long run period and results could be more valid in terms of economic as well as statistical interpretation, when longer time-series is taken. While exploting the dependence between economic growth and government spending, we should analyze the Armey-Rahn curve as well, which explains that up to some point, when government spending is increasing, economic activity is increasing as well, but then is starts to decline. The paper examines such dependence based on Wagner’s law and Keynes’s theory for Georgia, but using only capital spending as it is perceived as one of the main sources for economic growth. All the available data is used, during 1995-2022, for capital spending growth and economic growth indicators. 
 The results show that Wagner’s law is not fulfilled meaning that economic growth is depended variable and capital spending growth is explanatory. The impact on economic growth is observed after 3 years, which makes sense if we consider the nature of capital spending. However, we can say that due to the lack of data and only two variables included in the analysis, the results might not be relevant.

Data Heterogeneity in Meta-Analyses: Statistical Methods, Interpretation, and Guidance.

Data heterogeneity determines whether sufficiently strong conclusions can be derived from synthesizing and aggregating the available literature. Multiple tools are available to calculate data heterogeneity, but each tool has pros and cons. Providing a prediction interval may be the most beneficial since it allows readers to quantify heterogeneity in a clear and clinically relevant form. However, the ultimate decision on which tool to use is left to the discretion of the researcher. This decision should be decided upon during the study inception.

Modeling by statistical physics and interpretation of the olfactory process of the two enantiomers 3-mercapto-2-methylbutan-1-ol and 3-mercapto-2-methylpentan-1-ol on the OR2M3 human olfactory receptor

In the present paper, a putative adsorption process of two odorants thiols (3-mercapto-2-methylbutan-1-ol and 3-mercapto-2-methylpentan-1-ol) on the human olfactory receptor OR2M3 has been investigated via advanced models developed by a grand canonical formalism of statistical physics. For the two olfactory systems, a monolayer model with two types of energy (ML2E) has been selected to correlate with the experimental data. The physicochemical analysis of the statistical physics modeling results showed that the adsorption system of the two odorants was multimolecular. Furthermore, the molar adsorption energies were inferior to 22.7 kJ/mol, which confirmed the physisorption process of the adsorption of the two odorant thiols on OR2M3. In addition, quantitative characterizations of both odorants were determined via the olfactory receptor pore size distribution (RPSD) and the adsorption energy distribution (AED), which were spread out from 0.25 to 1.25 nm and from 5 to 35 kJ/mol, respectively. For thermodynamic characterization of the olfactory process, the adsorption entropy indicated the disorder of the adsorption systems of 3-mercapto-2-methylbutan-1-ol and 3-mercapto-2-methylpentan-1-ol on the human olfactory receptor OR2M3. Besides, the used model showed that the presence of copper ions increases the efficacy (olfactory response at saturation) of 3-mercapt-2-methylpentan-1-ol odorant activating OR2M3. The docking molecular simulation indicated that the 3-mercapto-2-methylpentan-1-ol molecule presented more binding affinities (17.15 kJ/mol) with olfactory receptor OR2M3 than 3-mercapto-2-methylbutan-1-ol (14.64 kJ/mol). On the other hand, the two estimated binding affinities of the two odorants belonged to the adsorption energies spectrum (AED) to confirm the physisorption nature of the olfactory adsorption process.

What quantum matter tells about quantisation of gravity in a statistical mechanics context

We propose a “guide” towards quantisation of gravity based on quantum matter in a statistical mechanics context. On one hand, a statistical mechanics model naturally arises from the thermodynamic interpretation of horizons in Rindler space. On the other hand, the path integral formulation of quantum field theory can be interpreted from the point of view of statistical mechanics. From these perspectives, gravity and matter are related to each other in the same way as a gas and its chemical potential are. This statistical mechanics interpretation of gravity and matter suggests that gravity should be quantised in a precise way which is determined by the quantisation of matter. Although, in a first step, quantisation of gravity applies for small perturbations of the metric with respect to the vacuum, the most central and general features of quantisation (quantisation prescription, quantum space dimensions) are supported from statistical mechanics and remain valid non-perturbatively.

Failure rates of mutation-based ctDNA assays in lung cancer treatment monitoring.

e18752 Background: Despite the wide adoption of noninvasive liquid biopsy approaches for cancer genotyping, little is known about the longitudinal performance of these assays for monitoring therapeutic responses in patients with advanced disease. Here, we report results of a pooled analysis that collects existing evidence regarding the failure rates of circulating tumor DNA (ctDNA) assays designed for therapeutic monitoring in patients with advanced or metastatic lung cancer. Methods: We searched PubMed for original studies published between February 2012 and January 2022, following these inclusion criteria: (1) Adults older than 18 years old with advanced or metastatic lung cancer treated with chemotherapy, targeted therapy, or immunotherapy; (2) Tumor-informed or plasma-only circulating tumor DNA assays based on droplet digital PCR (ddPCR) or targeted deep sequencing technologies for the detection of tumor-specific allele fractions; (3) Measurement of circulating tumor DNA levels at sequential timepoints. We defined failures as a lack of detectable mutant allele fraction (MAF) in patients with metastatic lung cancer in samples collected before treatment initiation. Two reviewers independently screened studies, extracted data, assessed the risk of bias, and evaluated the quality of evidence. Divergences were resolved by a third independent reviewer reaching a consensus. Study quality assessment covered: (1) representativeness of patient population, (2) risk of funding bias, (3) description of ctDNA analysis and outcomes, and (4) statistical quality and interpretation. Results: From 142 studies retrieved by an automated PubMed query, we extracted data from 42 studies which fulfilled the eligibility criteria - 11 registered trials, 31 prospective/retrospective cohorts. MAF levels in the circulation were measured using PCR-based assays (23/42, 55%); the remainder used NGS-based approaches (19/42, 45%). The median number of targeted genes was 1 (range 1-6) vs 73 (range 36-214), respectively. The median number of samples analyzed per study was 79 (range 3-334). On average, 31% of samples collected before treatment did not have a detectable MAF in any of the target genes analyzed, despite the high disease burden typically experienced in patients with metastatic lung cancer. Only 57% of PCR-based and 78% NGS-based studies succeeded in providing molecular response assessment metrics. We identified 10/42 studies with critical quality assessment concerns and 18 studies had funding provided by a company with investments in the commercial cfDNA assay. Conclusions: Available liquid biopsy assays that report tumor-specific mutations have a high rate of failure for measuring disease burden and response outcomes during therapy. Approaches that could be reliably used for monitoring patients with advanced cancers undergoing treatment are needed to improve patient care.

Views of Greek Teachers on the Implementation of Teaching Approaches in Online Classrooms

With the emergence of the coronavirus pandemic, education systems at international level were called upon to immediately undertake the transition from face-to-face teaching to a new digital environment. The Greek Ministry of Education has activated the implementation of Online Education - Distance Teaching (OE - DT) for students of Primary and Secondary Education. In this context, this research attempts to investigate the views of Greek teachers on the implementation of teaching approaches in online classrooms during the period of COVID-19. The research sample consists of 734 Greek teachers. The sample was selected by "cluster random sampling". A structured questionnaire with "closed" type of five-point Likert-type questions was used as a research tool for the research and data collection. Statistical analysis and interpretation of the empirical data was implemented with the statistical package SPSS 28. The main findings are: i) the views of the research subjects about the role of the teacher - regarding the interaction between the teacher - students, the use of teaching methods and techniques - in the teaching process, are approximately neutral, i.e. neither positive nor negative; ii) well over half of the teachers stated that in the case of extended use of OE - DT , the sociality of students may be affected.

Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood.

Multivariate heterogeneous responses and heteroskedasticity have attracted increasing attention in recent years. In genome-wide association studies, effective simultaneous modeling of multiple phenotypes would improve statistical power and interpretability. However, a flexible common modeling system for heterogeneous data types can pose computational difficulties. Here we build upon a previous method for multivariate probit estimation using a two-stage composite likelihood that exhibits favorable computational time while retaining attractive parameter estimation properties. We extend this approach to incorporate multivariate responses of heterogeneous data types (binary and continuous), and possible heteroskedasticity. Although the approach has wide applications, it would be particularly useful for genomics, precision medicine, or individual biomedical prediction. Using a genomics example, we explore statistical power and confirm that the approach performs well for hypothesis testing and coverage percentages under a wide variety of settings. The approach has the potential to better leverage genomics data and provide interpretable inference for pleiotropy, in which a locus is associated with multipletraits.

Longitudinal Assessment of Multimorbidity Medication Patterns among Smokers in the COPDGene Cohort.

Background and objectives: Chronic obstructive pulmonary disease (COPD) is usually comorbid with other chronic diseases. We aimed to assess the multimorbidity medication patterns and explore if the patterns are similar for phase 1 (P1) and 5-year follow-up phase 2 (P2) in the COPDGene cohort. Materials and Methods: A total of 5564 out of 10,198 smokers from the COPDGene cohort who completed 2 visits, P1 and P2 visits, with complete medication use history were included in the study. We conducted latent class analysis (LCA) among the 27 categories of chronic disease medications, excluding COPD treatments and cancer medications at P1 and P2 separately. The best number of LCA classes was determined through both statistical fit and interpretation of the patterns. Results: We found four classes of medication patterns at both phases. LCA showed that both phases shared similar characteristics in their medication patterns: LC0: low medication; LC1: hypertension (HTN) or cardiovascular disease (CVD)+high cholesterol (Hychol) medication predominant; LC2: HTN/CVD+type 2 diabetes (T2D) +Hychol medication predominant; LC3: Hychol medication predominant. Conclusions: We found similar multimorbidity medication patterns among smokers at P1 and P2 in the COPDGene cohort, which provides an understanding of how multimorbidity medication clustered and how different chronic diseases combine in smokers.

Changes in the Pattern of Weekdays Electricity Real Consumption during the COVID-19 Crisis

In this paper, using data from Romania, we analysed the changes in electricity consumption generated during the COVID-19 crisis, and the measures taken against the spread of the coronavirus to limit the effects of the pandemic. Using a seasonal autoregressive econometric model, we found that, beyond seasonal (weekly, monthly, quarterly, yearly) effects, the average daily electricity real consumption in Romania, during the state of the emergency period (16 March 16 to 14 May 2020) decreased by −194.8 MW (about −2.9%), compared to the historical data (2006–March 2022), and this decrease is not due to the action of some random factors, and it is not a manifestation of domain-specific seasonality. The literature discusses the hypothesis that during the pandemic time, the profile of daily electricity consumption on weekdays was close to the typical Sunday profile. We tested a similar hypothesis for Romania. As a methodology, we tried to go beyond the simple interpretation of statistics and graphics (as found in most papers) and we calculated some measures of distances (the Mahalanobis distance, Manhattan distance) and similarity (coefficient of correlation, cosines coefficient) between the vectors of daily electricity real consumptions, by hourly intervals. As the time interval, we have analysed, for Romania, the electricity real consumption over the period January 2006–March 2022, by day of the week and within the day, by hourly intervals (5911 observations). We found (not very strong) evidence supporting a hypothesis that, in the pandemic crisis, the profile of electricity consumption approaches the weekend pattern only for the state of the emergency period, and we could not find the same evidence for the state of the alert period (June 2020–March 2022). The strongest closeness is to the hourly consumption pattern of Saturday. That is, for Romania, in terms of electricity consumption, “under lockdown, every day is a Sunday” (Staffell) it is rather “under lockdown, every day is (almost) a Saturday”! During the state of the alert period, consumption returned to the pre-crisis profile. Since certain behaviours generated by the pandemic have been maintained in the medium and long term (distance learning, working from home, online sales, etc.), such studies can have policy implications, especially for setting energy policy measures (e.g., in balancing load peaks).

Mechanical behaviour of grouted sands with acrylates and polyurethane resins

Chemical grouting is a technique where solution-based binders are used to improve both mechanical and hydraulic properties of the soil or rocks. The following research study shows an extensive laboratory programme where a polyurethane (PU) and an acrylate (AC) were grouted in a tertiary sand under different water contents and relative density, with different gelling time and grouting conditions. Unconfined compressive strength, splitting tests, triaxial tests and creep tests on the grouted materials were conducted. A statistical interpretation considering the kernel probability density function was performed in order to understand the interdependency of the changing parameters. In general, PU-consolidated samples provide higher mechanical properties with respect to ACs; however, the effects and the dependency on the geotechnical base parameters are different.

ALBUMIN IN ACUTE ISCHAEMIC STROKE

Background: Serum albumin has been correlated with stroke outcome and has shown that elevated levels of serum albumin has better outcome in stroke patients. This study was done to assess albumin level in acute ischemic stroke patients at tertiary center in north India. : Ninety four patients Materials & Methods with acute stroke were included in the study. All participants were evaluated by history, examination, laboratory and radiological modalities. Blood samples for assessment of albumin was collected at the time of admission. NIHSS and GCS scoring was done to assess Stroke severity at the time of admission. modied Rankin scale (mRs) scoring was done to assess the Functional outcome at 1 week and after 3 months of stroke onset. Statistical analysis and interpretation of the data was done by using SPSS Software version 23. Among 94 patients included in the study, Mean (SD) age was 65 Result: years, 71(75.5%) were male and 23 (24.5%) were female. Hypertension was present in 52(55.3%) of the patients and 57.(60.6%) of the patients were diabetic The mean S. Albumin (g/dL) was 3.40. There was moderate negative correlation between serum albumin level and mRS score at 1 week (rho = -0.48, p = <0.001). At three months there was no correlation (rho = -0.12, p = 0.287). Patients with Conclusion: higher serum albumin level had better outcome at one week but had similar outcome at 3 month.

A nine-year (2012-2020) study of publication delay in an open access Library and Information Science journal

This study attempts to map the publication delay of articles published in an international, peer-reviewed, open access Library and Information Science journal (LIS) i.e. the DESIDOC Journal of Library and Information Technology (DJLIT). An exploratory method was applied to collect data related to the editorial timeline through archival open access to the articles published in DJLIT during the period from 2012 to 2020. Data were tabulated in an Excel spreadsheet to facilitate statistical analysis and interpretation. The study finds a mean and median waiting time of approximately 176 days and 152 days respectively for articles before they got published. About 64.57 percent of articles went through revision. The majority of articles (i.e., 40.08%) experienced a delay between 100-199 days and were closely followed by 26.11 percent of articles with a delay of up to 99 days. Only 2.43 percent of articles fell in the 400-499 days delay category before being published. A strong influence of time between submission to final revision on overall publication delay was observed. The study did not find any correlation between the number of contributing authors and increasing delay in publication and contradicts the result obtained in another study on selected information science journals. The study is expected to help researchers and authors in the field of LIS to take informed decision before submitting their manuscripts to DJLIT for publication.

A Swarming Meyer Wavelet Computing Approach to Solve the Transport System of Goods

The motive of this work is to provide the numerical performances of the reactive transport model that carries trucks with goods on roads by exploiting the stochastic procedures based on the Meyer wavelet (MW) neural network. An objective function is constructed by using the differential model and its boundary conditions. The optimization of the objective function is performed through the hybridization of the global and local search procedures, i.e., swarming and interior point algorithms. Three different cases of the model have been obtained, and the exactness of the stochastic procedure is observed by using the comparison of the obtained and Adams solutions. The negligible absolute error enhances the exactness of the proposed MW neural networks along with the hybridization of the global and local search schemes. Moreover, statistical interpretations based on different operators, histograms, and boxplots are provided to validate the constancy of the designed stochastic structure.

Alterations in serum Thyroid and Reproductive hormone levels in occupationally exposed pesticides sprayers

Background: Pesticides contain endocrine disrupting chemicals that have potential to perturb the normal thyroid and reproductive hormone axis. The aim of present study was to estimate the variations in serum tri-iodothyronine (T3) tetra-iodothyronine (T4) thyroid stimulating hormone (TSH) and testosterone levels in occupationally exposed pesticide sprayers in comparison to controls.
 Methods: For this purpose, blood samples of (n=50) pesticide sprayers were collected from District Layyah, south Punjab, Pakistan. Whereas, samples (n=25) of healthy control subjects were obtained from University of the Punjab, Lahore. Levels of T3, T4, TSH and testosterone in the serum were estimated by ELISA technique. The data obtained was analyzed by applying Un-paired Student “t” test at with significance level of P < 0.05. Graph pad prism version 6.0 software was utilized for statistical interpretation.
 Results: Pronounced (P < 0.0001) decrease of both T3 and T4, whereas, a significant increase in TSH (P = 0.005) level was evidenced in pesticide sprayers as compared to controls. Moreover, a significant decrease (P < 0.001) was noted in the level of testosterone in pesticide sprayers as compared to controls.
 Conclusion: Pertinently, the pesticides are a potent source of inducing imbalances in T3, T4, TSH, and testosterone secretion. Pesticide sprayers are, therefore, recommended to use prophylactic measures like wearing face masks and opt safer protocols in order to avoid direct hazardous exposure to endocrine disrupting chemicals.