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[Display omitted] • Six major global geological records are analyzed by Spearman's correlation. • Acceleration of the supercontinent cycle best fits global geological records. • The supercontinent cycle could be the major process driving plate tectonics evolution. Whether the supercontinent cycle (Sc) is the major process driving plate tectonics evolution has not been purposely investigated. If true, it would result in more geological records formed during each supercontinent assembly. In this study, a quantitative statistical approach is used to investigate this question. Six global geological datasets (GGDs) of carbonatite, zircon hafnium, kimberlite, metamorphism, monazite and zircon records are analyzed using Spearman's correlation (ρ s). Ranking the GGDs according to Spearman's method allows them to be compared. Each GGD shows moderate to very strong correlations (0.5 < ρ s < 0.9), with the metamorphism, monazite, zircon and zircon hafnium datasets displaying the most similarities. Each published supercontinent cyclicity (Sc 500 Myr, Sc 600 Myr, Sc 800 Myr and Sc acceleration) is then depicted according to its assembly timing. A second Spearman correlation performed with the four supercontinent cyclicities suggests that the Sc acceleration hypothesis best fits the geological archives (average ρ s = 0.45), potentially supporting the supercontinent cycle as the first-order mechanism of plate tectonics evolution. Conversely, constant cyclicities result in weaker correlations (average ρ s < 0.26), which could indicate significant decoupling of the geological archives and the supercontinent cycle. Moreover, Sc 600 surprisingly shows no correlation with the six GGDs (average ρ s = 0.04). The implications for the supercontinent cycle and the limits of Spearman's correlation are also discussed. [ABSTRACT FROM AUTHOR]

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute. [ABSTRACT FROM AUTHOR]

Abstract The abnormal biological activity of cytokines and their imbalance are implicated in developing rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Cytokine levels were measured in RA and SLE patients and compared to healthy controls using the Wilcoxon rank sum test and Kruskal–Wallis test. The relationship between cytokine levels and blood and clinical parameters was assessed using Spearman's correlation test. Compared to healthy controls, both RA and SLE patients exhibited elevated levels of GM-CSF, CX3CL1, IFN-α2, IL-12p70, IL-17A, TNF-α, IL-1β, and IFN-γ, which is evidence of their shared inflammatory signature. IL-2 levels were elevated exclusively in RA patients, while MCP-1 and IL-10 were uniquely increased in SLE patients. Notably, TNF-α showed the most significant increase in SLE patients. IL-4 was elevated in SLE patients with nephritis, correlating with IL-6, IL-10, sCD40L, and IL-8, suggesting B cell involvement in lupus nephritis. The negative correlation between CX3CL1 and TNF-α with HDL in RA and SLE respectively, highlights the potential association of these inflammatory markers with cardiovascular risk. These findings underscore the complex cytokine interplay in RA and SLE. CX3CL1 emerges as a potential therapeutic target for RA, while TNF-α and IL-4 show promise as therapeutic targets for SLE.

The COVID-19 pandemic, which began during early 2020, had been a worldwide problem, resulting in significant fatalities. In China, the pandemic resulted in strict lockdowns, restricted movement, and reduced transportation. This resulted in improvement of air quality in many cities in China. The objective of the study is to compare the nature of air quality pre-COVID period (2018-2019) and during COVID period (2020-2201). The following air quality parameters were investigated, air quality index (AQI), particulate matter (PM2.5 and PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). The present investigation results will augment to the current understanding on the air pollution situation during the COVID-19 pandemic in Jiangsu Province in China. The study revealed that air quality in Jiangsu Province improved during the months when COVID-19 positive cases increased. The reduction in air pollutants concentrations started during 2020 and reached a maximum during 2021. Overall the air quality index (AQI) improved by 8.2 % and air pollutant reductions achieved were, PMs (≈ 21%), SO2 (26.2 %), NO2 (13.6 %), O3 (2.4 %) and CO (10.4 %). Cities in Jiangsu Province with high air pollutant concentrations achieved a moderate reduction. The correlation between air pollutants and AQI was positive except for O3. The implications of the study are, reduction of fossil fuel powered vehicles and industrial activity can make notable positive impact on the air quality of the region.

Indirect selection for yield in sugarcane (Saccharum officinarum L.) production can be achieved by identifying and selecting for plant traits that are correlated to yield. This study aimed to identify and quantify sugarcane traits that are associated with ratooning ability to generate information for varieties' selection. Data sourced from three variety trials established at three sites, comprising eight varieties, and collected over six successive crops (plant cane and five ratoons) were used for this study. The data were subjected to linear mixed model, Spearman's correlation, and biplot analyses. Stalk height, stalk population, stalk mass, and stalk sucrose mass were significantly correlated with cane and sucrose yield across all ratoon crops, suggesting that varieties that combine these traits over multiple crops will have higher ratoon cane and sucrose yields. Significant correlation between brix and sucrose content suggested that brix can be used to indirectly select for sucrose content. Measuring brix is easier, cheaper, and faster than measuring sucrose content. Significant site and variety effects on ratoon crop yields indicated the existence of opportunities to identify good ratooning varieties that are specifically and or broadly adapted to different growing conditions. [ABSTRACT FROM AUTHOR]

The natural regeneration of vegetation in areas of marly soils is restricted due to drought and soil erosion. For the ecological restoration of eroded areas, the selection of suitable plant species is critical. The aim of this study is to assess specific plant species and their ecological characteristics for their ability to thrive under drought in eroded areas with marly soil. The study was conducted on 36 sampling locations in the marly areas of Eskisehir-Bozan, Türkiye, during the most drought-prone months, August and September, in 2011 and 2012. Vegetation sampling was conducted according to the Braun-Blanquet method. Fifteen plant taxa with the highest coverage and frequency were identified. Relationships between plant species and environmental factors were determined using Spearman’s correlation analysis. According to the results of numerical analysis, there were correlations between ecological parameters including nitrogen, phosphorus, organic matter, lime, slope, altitude and plant taxa. The resistance rate of fifteen plant taxa in marly areas is quite high even in the driest months. These plant taxa, possessing properties essential for soil protection, may be used for revegetation practices of marly areas exposed to soil erosion. This study’s findings will provide useful guidance for vegetation programs.

We propose an extension of Spearman's correlation for censored continuous and discrete data that permits covariate adjustment. Previously proposed nonparametric and semiparametric Spearman's correlation estimators require either nonparametric estimation of the bivariate survival surface or parametric assumptions about the dependence structure. In practice, nonparametric estimation of the bivariate survival surface is difficult, and parametric assumptions about the correlation structure may not be satisfied. Therefore, we propose a method that requires neither and uses only the marginal survival distributions. Our method estimates the correlation of probability‐scale residuals, which has been shown to equal Spearman's correlation when there is no censoring. Because this method relies only on marginal distributions, it tends to be less variable than the previously suggested nonparametric estimators, and the confidence intervals are easily constructed. Although under censoring, it is biased for Spearman's correlation as our simulations show, it performs well under moderate censoring, with a smaller mean squared error than nonparametric approaches. We also extend it to partial (adjusted), conditional, and partial‐conditional correlation, which makes it particularly relevant for practical applications. We apply our method to estimate the correlation between time to viral failure and time to regimen change in a multisite cohort of persons living with HIV in Latin America. [ABSTRACT FROM AUTHOR]

First we study estimation of the drift parameter in the fractional Ornstein-Uhlenbeck process whose marginal distribution is Student t -distribution. We obtain Spearman's correlation based estimator, quantile estimator and Brownian excursion based estimator of the drift parameter. Then we study method of moments estimator and quantile estimator in fractional inverse Gaussian and fractional gamma Ornstein-Uhlenbeck processes. [ABSTRACT FROM AUTHOR]

Increasing micro plastic concentration in the marine environment is a severe threat to the marine ecosystem and indirectly to human society depending on the marine environment. Also, the surface and subsurface water quality parameters are the indicators of the usability of the water sources for human activities. The primary source of micro plastic is the water river systems from land to sea. This study establishes a correlation between water quality parameters and micro plastic concentration to find the most suitable quality parameter for finding the micro plastic concentration in urban coastal estuaries of Chennai coast, Tamil Nadu, India. Twenty samples from Adyar and Cooum estuaries were collected, with ten each on August 2019 and August 2020. They are tested for temperature pH, EC, Turbidity, TDS, DO, BOD, COD, Salinity and micro plastic concentrations. Correlation tests such as Pearson's and Spearman's are performed based on the initial Normality and Homogeneity test. The results show that all the parameters are significantly not correlated with the Micro plastic Concentration, and Micro plastic Concentration is considered an independent factor. [ABSTRACT FROM AUTHOR]

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero‐inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within‐block permutation‐based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID‐19 mortality dynamics. [ABSTRACT FROM AUTHOR]

This research aims to analyze the relationship between food packaging knowledge among meatball and chicken noodle sellers in Pondok Kelapa, Duren Sawit, East Jakarta, and their behavior regarding the use of plastic for hot food. The study was conducted from March 2022 to July 2023 in Pondok Kelapa, Duren Sawit, East Jakarta. Samples were taken from meatball and chicken noodle vendors operating in the area. Data was collected through a questionnaire consisting of 17 questions on knowledge variables and 18 statements on behavior variables. Non-parametric statistical methods, specifically the chi-square test and Spearman's correlation, were used for data analysis as an alternative to parametric methods when certain assumptions were not met. The results of the chi-square test indicated a value of 0.123, which is greater than 0.05. This suggests that there is no significant relationship between knowledge about food packaging and the behavior of using plastic for hot food. Meanwhile, the Spearman's correlation coefficient was calculated as 0.154, indicating a very weak relationship between food packaging knowledge and plastic usage behavior for hot food. The study, conducted with 39 respondents, showed that 49% had sufficient knowledge about food packaging, and 77% had adequate behavior in using plastic for hot food. It is hoped that meatball and chicken noodle sellers can improve their knowledge regarding plastic packaging and opt for using safe and suitable HDPE or other plastic packaging for their products. [ABSTRACT FROM AUTHOR]

In the scientific literature data analysis results are often presented when samples from different experiments or different conditions, technical replicates or times series are merged to increase the sample size before calculating the correlation coefficient. This way of proceeding violates two basic assumptions underlying the use of the correlation coefficient: sampling from one population and independence of the observations (independence of errors). Since correlations are used to measure and infer associations between biological entities, this has tremendous implications on the reliability of scientific results, as the violation of these assumption leads to wrong and biased results. In this technical note, I review some basic properties of the Pearson's correlation coefficient and illustrate some exemplary problems with simulated and experimental data, taking a didactic approach with the use of supporting graphical examples. [ABSTRACT FROM AUTHOR]

In the scientific literature data analysis results are often presented when samples from different experiments or different conditions, technical replicates or times series are merged to increase the sample size before calculating the correlation coefficient. This way of proceeding violates two basic assumptions underlying the use of the correlation coefficient: sampling from one population and independence of the observations (independence of errors). Since correlations are used to measure and infer associations between biological entities, this has tremendous implications on the reliability of scientific results, as the violation of these assumption leads to wrong and biased results. In this technical note, I review some basic properties of the Pearson’s correlation coefficient and illustrate some exemplary problems with simulated and experimental data, taking a didactic approach with the use of supporting graphical examples.

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman’s correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

The objective of this study was to evaluate the impact of the reduction in the number of test-day records per lactation on genetic parameters of test-day milk yield (TDMY) and the reliability of estimated breeding values for 305-day milk yield in Holstein cattle. Estimates of genetic parameters and breeding values were performed using the animal model of random regression and adjustment of the Legendre polynomial (fourth order). When comparing sires with the same number of daughters, greater reliability was found in the subpopulations with the highest number of milk test-day records per lactation per cow. It was also found that the elimination of at least one test-day record affected the reliability of estimated breeding value for 305-day milk yield in the sires, regardless of the class of number of daughters per sire. When selecting the 5% best sires and 20% best cows, the lowest order correlations were observed between the population with 10 test-day records per lactation (complete lactation) and the other subpopulations with incomplete lactations (4, 5, 6, 7, 8, 9 test-day records per lactation). The reduction in the number of test-day milk records per lactation interferes in the reliability of the estimated breeding value for sires and cows, negatively impacting the precision of selecting genetically superior animals. [ABSTRACT FROM AUTHOR]

The distribution of phytoplankton assemblages in response to physicochemical variables was assessed using Spearman's correlation and canonical correspondence analysis in four highly turbid estuaries of the Mekong River (MKR) system in Ben Tre Province, Vietnam. During two surveys (September 2017 and April 2018) at 20 sampling sites, a total of 162 species were recorded, with a dominance of diatoms (> 50%). Phytoplankton abundance varied from 3.01 × 105 to 11.85 × 105 cells/L, with the highest cell densities found at the BL2 station in the rainy season, mainly attributed to cyanobacteria during the rainy period, whereas diatoms were dominant in the dry season. Chlorophyll-a concentration in this area was low and decreased from the mouth stations. Similarity analysis distinguished the freshwater and oligohaline regions (characterized by lower phytoplankton abundance in the oligohaline areas) from the mesohaline and polyhaline zones (characterized by higher phytoplankton density in the polyhaline sections), based on the salinity gradient, which mostly explained the spatio-temporal distribution of phytoplankton. In which, freshwater and oligohaline sites were dominated by the diatom Coscinodiscus, the euglenoids Lepocinclis, and the green alga Scenedesmus in the rainy season, while mesohaline and polyhaline stations were dominated only by diatom species (Thalassionema and Skeletonema) in the dry season. High salinity and total dissolved solutes, and low nutrients are the major factors affecting phytoplankton growth and lead to low density and low primary biomass in turbid estuarine systems like the MKR estuaries. Our results further emphasize the importance of taking into account of salinity in understanding seasonal changes of phytoplankton communities, which can provide a valuable baseline data for ecological management strategies in tropical estuarine ecosystems. [ABSTRACT FROM AUTHOR]

An appropriate design of a chiller plant is crucial to guarantee highly performing solutions. However, several design variables, such as type of systems, total cooling capacity, and hydraulic arrangement, need to be considered. On the one hand, at present, different technical criteria for selecting the most suitable design variables are available. Studies that corroborate the influence of the design variables over the operational variables are missing. In order to fill this knowledge gap, this work proposes a statistical analysis of design variables in chiller plants operating in medium- and large-scale applications and evaluates their influence on energy consumption and life cycle cost (LCC) under the same thermal demand conditions. A case study involving 138 chiller plant combinations featuring different arrangements and a Cuban hotel was selected. The results suggested that the total chiller design and cooling capacity distribution among chillers have a significant influence on the energy consumption of the chiller plant with a Spearman's Rho and Kendall Tau (τ) correlation index value of −0.625 and 0.559, respectively. However, with LCC, only the cooling capacity distribution among the chillers had a certain influence with a Kendall Tau correlation index value of 0.289. As for the considered total cooling capacity, the applied statistical test showed that this design variable does not have any influence on performing the chiller plant. [ABSTRACT FROM AUTHOR]

The study investigated the changes of nucleotides, succinic acid, and free amino acids amounts in yolk and the causes leading to the changes after pickling to uncover the fundamental umami component of preserved egg yolk. The findings demonstrated that while the contents of 5′-adenosine monophosphate (AMP), 5′-cytidine monophosphate (CMP), 5′-guanosine monophosphate (GMP), 5′-uridine monophosphate (UMP), and succinic acid increased after slightly decreasing aspartic acid (Asp) content in preserved egg yolk increased gradually. The contents of 5′-inosine monophosphate (IMP) and other free amino acids were gradually decreased. Comparing the taste activity value (TAV), it was found that the single umami substance, succinic acid, played a key role in inducing the umami taste. In combination with the Spearman correlation analysis, it was shown that the proteinaceous substance, which is the most significant umami component in preserved egg yolk, tended to condense first and subsequently disintegrate in an alkaline environment. The orthogonal partial least squares analysis (OPLS) found that pH was also affected by the changes in proteinaceous substance. These findings offer suggestions for enhancing the pickling procedure and investigating the optimal pickling period for preserved eggs.

We study rank‐based approaches to estimate the correlation between two right‐censored variables. With end‐of‐study censoring, it is often impossible to nonparametrically identify the complete bivariate survival distribution, and therefore it is impossible to nonparametrically compute Spearman's rank correlation. As a solution, we propose two measures that can be nonparametrically estimated. The first measure is Spearman's correlation in a restricted region. The second measure is Spearman's correlation for an altered but estimable joint distribution. We describe population parameters for these measures and illustrate how they are similar to and different from the overall Spearman's correlation. We propose consistent estimators of these measures and study their performance through simulations. We illustrate our methods with a study assessing the correlation between the time to viral failure and the time to regimen change among persons living with HIV in Latin America who start antiretroviral therapy. [ABSTRACT FROM AUTHOR]

The adoption of blockchain technology by companies can change the way they interact with stakeholders, redefining communication strategies and other marketing processes. In this study, we investigated the relevance of blockchain applications for marketing management from the perspective of marketing-related professionals. Answers about blockchain technology application in the marketing arena were collected from the social platform Quora. The data were analyzed through text mining and Spearman's correlation coefficient to assess the degree of association, inherent intensity, and the association significance between the variables payments, supply chain, loyalty programs, digital marketing, credential management, and marketing management, using Quora-specific metrics, namely, upvotes, shares, and views. The results posit blockchain technology as being an asset for marketing, with greater relevance in supply chain and internal management among marketing operations. Professionals will be able to potentially improve internal management systems and marketing campaigns, which will enhance companies' competitive advantage. [ABSTRACT FROM AUTHOR]

Background Dental fear and anxiety are significant issues among pediatric patients, often complicating dental treatments. Various tools measure these emotional responses, including subjective scales such as the Visual Facial Anxiety Scale (VFAS) and Children Fear Scale (CFS), and objective scales such as Venham's Anxiety Scale (VAS) and Frankl Behavior Rating Scale (FBRS). This study explores the association between these measures in children subjected to brainwave entrainment (BWE) therapy. This study aimed to evaluate the association between subjective and objective fear and anxiety measures in pediatric dental patients within both the brainwave entrainment (BWE) intervention group and control group. Methods This randomized controlled trial included pediatric participants aged seven to 12 years reporting to the department for dental treatment. Participants were randomized into two following groups: an experimental group receiving BWE therapy and a control group receiving traditional behavioral management. Fear and anxiety levels were measured using subjective and objective scales before and after the intervention. Data were analyzed using Spearman's correlation to examine the associations between these scales, with statistical significance set at p<0.05. Results Post-intervention analysis revealed significant correlations between subjective and objective measures of fear and anxiety in both groups. In the BWE group (N=126), there was a moderate positive correlation between the VFAS and VAS (rho=0.540, p<0.001) and a strong negative correlation between the CFS and FBRS (rho=-0.666, p<0.001). The control group (N=126) showed stronger correlations, rho=0.778 for anxiety scales and rho=-0.817 for fear and behavior scales (p<0.001). Combined data analysis from both groups (N=252) confirmed strong correlations. Conclusion This study found a significant association between subjective and objective measures of fear and anxiety in pediatric dental patients within both the brainwave entrainment (BWE) intervention group and control group. Thereby proving that self-reporting behavioral scales are useful for quickly assessing anxiety in pediatric dental settings., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Sri Ramaswamy Memorial (SRM) Medical College Hospital Institutional Ethics Committee issued approval #3010/IEC/2021. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Shehani Areef et al.)

ABSTRACT: This research identified the animal model that best describes the genetic and residual variations for tick counts in yearlings from a crossbred Angus-Nellore population of 6,951 animals that are progenies of 382 bulls and 6,198 cows. Genetic values were predicted by the Bayesian inference methodology. The models tested were: Traditional Animal, and Crossbred Animal with and without segregation, considering residual homoscedastic and heteroscedastic variances. The criteria of choice were the number of parameters, deviance information, and predictive order, which indicated the best fitfor the Traditional Animal model and Crossbred Animal model (with segregation), both with residual heteroscedastic Gaussian variance. The mean values of fixed genetic effects were positive and similar in the both models, indicating that animals with higher proportion of the Angus breed had greater infestation, and the Nellore breed was an important addition for resistance to ticks. The estimated genetic variation by the heteroscedastic Gaussian Animal model for the Nellore breed was 4.54-fold higher than that estimated for the Angus breed. The estimates of heritability of the different genetic groups ranged from 0.12 to 0.15 and from 0.01 to 0.35, respectively, for the Traditional Animal model and for the heteroscedastic Gaussian crossbred model. The Spearman’s rank-order correlation for the predicted genetic values was 0.94, considering all sires. However, when considering the top 10%, 20%, and 30% sires, differences in ranking were more evident (0.28 to 0.67). The Crossbred Animal model with segregation and heterogeneous residual variances was the most appropriate for genetic evaluation of tick counts on animals from Angus-Nellore crossings.

• Environmental, economic, and energy optimization of the ORC-VCR system. • A novel multi-objective optimization using the evaluation of distance from the average solution (EDAS) has been provided along with the improvements observed and comparison to grey relational analysis. • Results indicate that the isentropic efficiency of the compressor at 0.81 and of the expander at 0.86 were desirable, along with the following parametric settings. • Optimal cooling water inlet temperature at 312 K, the chilled fluid outlet temperature at 275 K. • The inlet temperature of the heat source at 392 K with R600 working fluid. The study performs multi-dimensional decision-making using the Evaluation of Distance from Average Solution (EDAS) with improved entropy weighting applied to figure out the best configuration for the ORC-VCR arrangement from a thermo-economic and environmental perspective. It is intended to maintain the operation of ORC-VCR's complete energy efficiency alongside producing minimal environmental impact. Besides EDAS, grey relational analysis has been conducted for an effective and precise determination of non-naïve operation controls. Moreover, objectively a novel cross-judgemental analysis has been implemented to measure the difference in opinion (DIO) between grey relational analysis and EDAS. The study has concluded in 30 % improvement in the desirability score of optimal operational setting for the ORC-VCR arrangement through EDAS. Judgemental analysis has proved the significance of EDAS from the perspective of a popular method like grey relational analysis, projecting a < 10 % DIO. [ABSTRACT FROM AUTHOR]

Beekeeping is an integral part of livestock-based livelihood system in tropical and subtropical regions of Ethiopia. Despite its importance, the sub-sector is challenged by humble colony strength and honey yield due to pests including wax moths. To avert these challenges different treatments were suggested by researchers regardless of economic, social and technical efficiency analysis. This participatory study is thus intended to evaluate the efficiency of treatments on infestation level with wax moths, colony strength and honey yield in Northeast Ethiopia. Un-replicated on-farm experiment was conducted on six beekeepers comprising supplementary feeding, tobacco leaf smoking, the combination and control treatments. Economic, biological and preference data were collected periodically. Profitability was estimated in partial budget, while biological and preference data were analysed using ANOVA and weighted ranking matrix, respectively. Results revealed that tobacco smoking had the lowest infected combs (0.7 ± 0.2) followed by the combination, supplementary feeding and control groups in ascending order. Supplementary feeding provided highest mean honey yield (14.7 ± 1.2) (p

The strategic planning process has been implemented into the local governance environment in the Czech Republic during last two decades. But the strategic goals that are expressed in the strategic development documents on the local level are only the part of the issues that are to be fulfilled or solved by the local government. In the everyday practical governance there is always a list of projects that appear from the actual technological, technical, public or political demand. The contemporary decision-making process is based on the personal or collective political decision or on the actual technical demand. But there are also municipalities that are already implementing strategic planning and want to avoid unsystematic interventions and decision-making processes. They try to adopt some attitudes from the corporate sphere to make the decision-making process more open and clear. This paper deals with the methodology of the mid-size city and tries to discuss it and offer some improvements. The paper’s other goals are to offer comparison of typical projects that could be found on the local level and to make a model of results provided by the different methodologies used for the decision-making process, specifically weighted sum average and analytic hierarchy process.

Process (systematic) variations of sub-5-nm node fin field-effect transistors (FinFETs) and nanosheet field-effect transistors (NSFETs) were investigated thoroughly using fully calibrated TCAD. All the process parameters consisting of front-end- as well as middle-of-line structure were independently randomized within feasible process conditions. A novel process scheme called source/drain patterning (SDP), having a superior performance by decreasing outer fringing capacitance through the downsized source/drain (S/D) epi, has also been analyzed for the variability study. SDP FinFETs have smaller variations of threshold voltages (Vth) , OFF-state currents (IOFF) , and effective currents (Ieff) than conventional ones because the subfin leakage is effectively controlled by bottom oxide (BO) beneath the source/drain instead of high punchthrough-stopper doping. The Spearman’s correlation results between process parameters, IOFF , and Ieff , showed that the process parameters affecting the short-channel effects vary IOFF and Ieff greatly. Especially, the most critical one was the fin width (Wfin) for FinFETs. SDP NSFETs have the smallest variations of Vth , IOFF , and Ieff. The BO blocks the bottom leakage completely, and the variations of nanosheet (NS) thickness (TNS) are much smaller than those of Wfin due to the different process flows: epitaxial growth for TNS versus patterning for Wfin. Therefore, NSFETs are promising to reduce the variations of Vth , IOFF , and Ieff in the sub-5-nm node. [ABSTRACT FROM AUTHOR]

Background: Over the past few decades, DNA microarray technology has emerged as a prevailing process for early identification of cancer subtypes. Several feature selection (FS) techniques have been widely applied for identifying cancer from microarray gene data but only very few studies have been conducted on distributing the feature selection process for detecting cancer subtypes. Objective: Not all the gene expressions are needed in prediction, this research article objective is to select discriminative biomarkers by using distributed FS method which helps in accurately diagnosis of cancer subtype. Traditional feature selection techniques have several drawbacks like unrelated features that could perform well in terms of classification accuracy with a suitable subset of genes will be left out of the selection. Method: To overcome the issue, in this paper a new filter-based method for gene selection is introduced which can select the highly relevant genes for distinguishing tissues from the gene expression dataset. In addition, it is used to compute the relation between gene–gene and gene–class and simultaneously identify subset of essential genes. Our method is tested on Diffuse Large B cell Lymphoma (DLBCL) dataset by using well-known classification techniques such as support vector machine, naïve Bayes, k-nearest neighbor, and decision tree. Results: Results on biological DLBCL dataset demonstrate that the proposed method provides promising tools for the prediction of cancer type, with the prediction accuracy of 97.62%, precision of 94.23%, sensitivity of 94.12%, F-measure of 90.12%, and ROC value of 99.75%. Conclusion: The experimental results reveal the fact that the proposed method is significantly improved classification accuracy and execution time, compared to existing standard algorithms when applied to the non-partitioned dataset. Furthermore, the extracted genes are biologically sound and agree with the outcome of relevant biomedical studies. [ABSTRACT FROM AUTHOR]

Background: Jan Aushadhi, a scheme to make affordable generic drugs available to large sections of population was launched by the Government of India across the country in the year 2008. This exploratory research was conducted to study the attitude of customers (Bengaluru, India) towards acceptance of Jan Aushadhi and an attempt was made to model buying behavior in order to suggest mechanisms to speed up the acceptance. Materials and Methods: Data was collected using structured questionnaire. Z-test and Exploratory factor analysis was conducted for hypothesis testing. Discriminant analysis was conducted to model consumption. Results and Conclusion: The variables that discriminated consumers from non-consumers were ‘effectiveness’, ‘doctor’s opinion’, ‘lower price’, ‘Quality’, ‘less expensive than other branded medicines’, ‘doctor’s prescription’, ‘convenience’, ‘doctor informs’, ‘home delivery’ and ‘brand reputation’. Results found that ‘doctor’s prescription’, ‘lower price’, ‘availability of Jan Aushadhi outlet’, ‘quality of generic medicine’ and ‘recommendation from others’ had a significant influence on acceptance of Jan Aushadhi. Therefore, the study recommends the doctors to prescribe generic medicine, increase the number of Jan Aushadhi outlets and provide awareness about the quality and efficacy of Jan Aushadhi. [ABSTRACT FROM AUTHOR]

Beekeeping is an integral part of livestock-based livelihood system in tropical and subtropical regions of Ethiopia. Despite its importance, the sub-sector is challenged by humble colony strength and honey yield due to pests including wax moths. To avert these challenges different treatments were suggested by researchers regardless of economic, social and technical efficiency analysis. This participatory study is thus intended to evaluate the efficiency of treatments on infestation level with wax moths, colony strength and honey yield in Northeast Ethiopia. Un-replicated on-farm experiment was conducted on six beekeepers comprising supplementary feeding, tobacco leaf smoking, the combination and control treatments. Economic, biological and preference data were collected periodically. Profitability was estimated in partial budget, while biological and preference data were analysed using ANOVA and weighted ranking matrix, respectively. Results revealed that tobacco smoking had the lowest infected combs (0.7 ± 0.2) followed by the combination, supplementary feeding and control groups in ascending order. Supplementary feeding provided highest mean honey yield (14.7 ± 1.2) (p < 0.05). Add to the honey yield improvement, ensuring active and strong colony with supplementary feeding is playing significant role in regulating wax moth infestation. Moreover, supplementary feeding was efficient with net benefit of ETB2811.2 (USD98.5) and 4396.9% marginal rate of return. It was also beekeepers' primary choice by the overall preference parameters. Spearman's correlation indicated that beekeepers' preference rank was coincided by 80% with actual measured rank. Therefore, supplementary feeding is suggested for further promotion in similar agro-ecologies to regulate infestation level with wax moths, colony strength and honey yield enhancement. [ABSTRACT FROM AUTHOR]

Abstract.In his seminal work, Chatterjee (2021) introduced a novel correlation measure that is distribution-free, asymptotically normal, and consistent against all alternatives. In this article, we study the probabilistic relationships between Chatterjee’s correlation and the widely used Spearman’s correlation. We show that, under independence, the two sample-based correlations are asymptotically joint normal and asymptotically independent. Under dependence, the magnitudes of two correlations can be substantially different. We establish some extreme cases featuring large differences between these two correlations. Motivated by these findings, a new independence test is proposed by combining Chatterjee’s and Spearman’s correlations into a maximal strength measure of variable association. Our simulation study and real-data application show the good sensitivity of the new test to different correlation patterns. [ABSTRACT FROM AUTHOR]

This article attempts to model interdependencies between socio-economic, energy, and environmental factors with selected data characterizing the development of the hydrogen economy. The study applies Spearman’s correlation and a linear regression model to estimate the influence of gross domestic product, population, final energy consumption, renewable energy, and CO2 emission on chosen hydrogen indicators—production, patents, energy technology research, development, and demonstration budgets. The study was conducted in nine countries selected for their actions towards a hydrogen economy based on analyses of national strategies, policies, research and development programs, and roadmaps. The results confirm the statistically significant impact of the chosen indicators, which are the drivers for the development of the hydrogen economy from 2008 to 2018. Moreover, the empirical results show that different characteristics in each country contribute to the development of the hydrogen economy vision.

In recent years, haze weather occurs frequently in Beijing, and the accumulation of air particulate matter is one of the main causes of haze weather. As an important air particulate matter, biological aerosol is harmful to human health. This study investigated the distribution of biological aerosol concentration and particle size in haze weather, and the Spearman's relationship between biological aerosol concentration and air quality index PM2.5 (AQI), ambient temperature and humidity. The correlation was studied. The particle size distribution of bio-aerosols in severe fog and haze weather in winter and summer was analyzed. The potential damage to human body was also studied. The results showed that the concentration of bio-aerosols was negatively correlated with PM2.5 (AQI) and positively correlated with ambient temperature. Environmental humidity was negatively correlated with bacterial aerosol concentration and positively correlated with fungal aerosol concentration. In winter, the maximum concentration of bacterial and fungal aerosols was detected in the range of 4.5-7.0 μm and 2.1-3.3 μm, respectively, while in summer, the highest concentration of bacterial and fungal aerosols were distributed. In the range of 3.3-4.5 μm, the results of this study will provide basic data for evaluating the hazards of bio-aerosols to human health under different haze conditions. [ABSTRACT FROM AUTHOR]

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Abstract We study locating propagation source in complex networks. We proposed an universal algorithm, which can accurately locate source of different propagation dynamics by using sparse observations. Without knowing the propagation dynamics and any parameters, we calculate Spearman centrality based on the character that positive correlation between inform time of nodes and geodesic distance between nodes and source. The algorithm have high location accuracy with low time complexity and can accurately infer initial time of source. All simulations on both model and real-world networks proved the feasibility and validity of this algorithm. Highlights • We proposed a universal source localization method for different propagation dynamics. • The proposed method can accurately locate source without knowing what kind of propagation dynamics. • We study the influence of parameters of propagation dynamics and properties of networks on locating accuracy. • The proposed method has low complexity. • We proposed a method for inferring initial time of source without knowing what kind of propagation dynamics and any parameters. [ABSTRACT FROM AUTHOR]

The present study was undertaken to estimate effect of various genetic and non-genetic factors on persistency of milk production and to identify the most appropriate persistency method that fits best in our environment. In the present study, effects of different non-genetic factors, viz. year, season, days to attain peak yield, and genetic group based on the level of exotic inheritance on persistency of milk yield in crossbred cattle were studied. Data comprised of 686 first lactation daily milk yield records of crossbred cattle that were maintained at GADVASU dairy farm over a period of 25 years from 1991 to 2015 were utilized to calculate persistency coefficients by four methods, viz., Ludwick and Peterson method (P1), Mahadevan method (P2), ratio method (P3), and Prasad et al. method (P4). Overall least squares means for persistency by Ludwick and Peterson method (P1), Mahadevan method (P2), ratio method (P3), and Prasad et al. method (P4) were 0.896 ± 0.096, 1.385 ± 0.224, 187.207 ± 26.398, and 0.621 ± 0.098, respectively. Effect of sires was significant (P < 0.05) on P2 and P4 methods. Effect of genetic group on all four methods was non-significant. Period of calving had significant (P < 0.01) effect on persistency of milk yield (P2, P3, and P4 methods). Effect of season of calving on persistency of milk yield was found to be significant in all estimates obtained by the four methods. Summer and autumn calvers were most persistent whereas spring and winter calvers were least persistent for (P2, P3, and P4 methods). Persistency of milk yield was significantly (P < 0.05) affected by days to attain peak yield in P1 and P2 methods. Maximum persistency was obtained in animals attaining peak at 41-57 days of lactation and minimum in < 41 days for Mahadevan method and ratio method. The highest heritability of persistency and minimum value of standard error was estimated as 0.275 ± 0.11 for the Mahadevan method followed by the Prasad method (0.197 ± 0.10) by half sib correlation method. The maximum coefficient of variation which indicates available variability was estimated as 20.788% for persistency by the Mahadevan method followed by 18.969% for the Prasad method. The highest correlation was also observed between P1 and P3 methods by Spearman’s and Pearson’s correlation for least squares breeding value of the sires. On the basis of heritability, standard error of heritability, and coefficient of variation, it can be concluded that the Mahadevan method followed by the Prasad method suits best to our environment for animals in first lactation as well as they can be utilized for effective selection for higher persistency in crossbred animals of Punjab. [ABSTRACT FROM AUTHOR]

Spatio‐temporal variability in the observed diurnal temperature range (DTR) for the recent 30‐year period (1986–2015) is examined from a total of 18 weather stations in Hindukush Karakoram Himalaya region of northern Pakistan (HKNP). The daily maximum and minimum temperature data are used to compute the regional DTR, whereas observed total cloud cover (TCC) is used to assess its possible relationship with regional DTR, both on seasonal and annual basis. The regional mean DTR is 13.27 °C on annual basis, with a maximum in autumn (14.63 °C) and minimum in winter (11.81 °C). On annual basis, the regional DTR has increased significantly at a rate of .34 °C/decade, during the 30‐year study period at p ≤ .05, based on Mann–Kendall test. On seasonal basis, the DTR displays an increasing trend in all four seasons with largest significant increase in the winter season at a rate of .32 °C/decade. The DTR is positively correlated with maximum temperature of the region on seasonal and annual basis. Strong negative correlation is found between the DTR and observed TCC in all seasons, indicating that variability in TCC has an impact on the variation of DTR in this region. The statistically significant increasing DTR trend along with statistically significant decreasing trend of TCC in spring season suggests an early melt of snow and ice covers in the region, consequently changing the hydrological cycle of the region that demands a better water resource management in the HKNP. [ABSTRACT FROM AUTHOR]

This study explored the potential for predicting the quantities of microplastics (MPs) from easily measurable parameters in peatland sediment samples. We first applied correlation and Bayesian network analysis to examine the associations between physicochemical variables and the number of MPs measured from three districts of the Long An province in Vietnam. Further, we trained and tested three machine learning models, namely Least-Square Support Vector Machines (LS-SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) to predict the composite quantities of MPs using physicochemical parameters and sediment characteristics as predictors. The results indicate that the quantity of MPs and characteristics such as color and shape in the samples were mostly influenced by pH, TOC, and salinity. All three predictive models demonstrated considerable accuracies when applied to the testing dataset. This study lays the groundwork for using basic physicochemical variables to predict MP pollution in peatland sediments and potentially locations and environments. [Display omitted] • Machine learning shows potential for predicting MPs from sediment characteristics. • Bayesian analysis reveal direct relations between peatland sediment properties and MPs. • Soil pH, TOC, and salinity found to be important predictors of MPs in peatland areas. [ABSTRACT FROM AUTHOR]