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Highlights • Mechanical properties of waste papers are closely correlated with devolatilization. • Start of devolatilization caused a sudden decrease in relative storage modulus ( E ′ r). • Kinetics of storage modulus change is investigated referring to activation energy. • E ′ r can be predicted with mass loss ratio and CO 2 emission. Abstract This article details a correlation study on early devolatilization behaviors (drying & volatile emission at 25–240 °C) and the change in mechanical properties (expressed by viscoelasticity) of four types of waste papers. The early stage of devolatilization was investigated using the TG-FTIR and mechanical properties using the dynamic mechanical analysis (DMA) method. The results show that the elastic and viscous mechanical properties showed obvious correlations with free and bound water evaporation and volatile emission. The storage modulus (E ′) and loss modulus (E ″) of the papers decreased as temperature increased and at the two important temperatures, namely 100 °C, the start of bound water evaporation, and 150 °C, the start of devolatilization in TG & DTG curves, E ′ showed extrema. Especially the relative storage modulus ( E ′ r) exhibited a sudden decrease at around 150 °C, where the activation energy of thermochemical conversion jumps from low values to high values, indicating the start of devolatilization. CO 2 is the major compound of the volatiles and it is well correlated with E ′ r in exponential function model and can be used as an index for predicting the E ′ r of the waste papers in their early devolatilization stage; while during the drying stage, mass loss ratio can be used to predict E ′ r of the waste papers. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

The recently published Lethaia paper by Bergström et al. (https://doi.org/10.1111/let.12269) on the age of the Ordovician Winneshiek Shale (Iowa, USA), and the impact that formed the Decorah crater which hosts this rock unit, is an interesting scientific contribution, although there are a number of problems with the interpretations and data presentation that merit comment. Due mainly to a lack of adequate critical assessment of δ13C data and biostratigraphical control, we contend that the conclusions of Bergström et al. are poorly founded and should not be cursorily accepted and propagated in future scientific literature. [ABSTRACT FROM AUTHOR]

Abstract: A method for the comparison of paper structure using light transmission images and frequency analysis was developed. The resolution of the light transmission images and the algorithm for the feature extraction were greatly improved to enhance the visibility of peaks in the 2D power spectrum that results from frequency analysis. A comparison method based on correlation measures how well the spectra match as a function of the orientation of the paper, yielding an objective and quantitative measure of similarity between 0 and 1. A technical validation was carried out with 25 different papers showing the potential of this method with common copy papers. Finally, the method was applied in a case. [Copyright &y& Elsevier]

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures. [ABSTRACT FROM AUTHOR]

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest. [ABSTRACT FROM AUTHOR]

Recognizing actions from still images has been popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial sum–product network (SPN). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside subimages and models the correlation of subimages via extra layers of SPN. Our method is shown to be effective on two benchmark data sets. [ABSTRACT FROM PUBLISHER]

The digital correlator is one of the most crucial data processing components of a radio telescope array. With the scale of radio interferometeric array growing, many efforts have been devoted to developing a cost-effective and scalable correlator in the field of radio astronomy. In this paper, a 192-input digital correlator with six CASPER ROACH2 boards and seven GPU servers has been deployed as the digital signal processing system for Tianlai cylinder pathfinder located in Hongliuxia observatory. The correlator consists of 192 input signals (96 dual-polarization), 125-MHz bandwidth, and full-Stokes output. The correlator inherits the advantages of the CASPER system, for example, low cost, high performance, modular scalability, and a heterogeneous computing architecture. With a rapidly deployable ROACH2 digital sampling system, a commercially expandable 10 Gigabit switching network system, and a flexible upgradable GPU computing system, the correlator forms a low-cost and easilyupgradable system, poised to support scalable large-scale interferometeric array in the future. [ABSTRACT FROM AUTHOR]

AbstractThis paper proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength of both linear and non-linear dependency between variables. This statistic is derived by examining how the conditional probabilities of events differ from their corresponding marginal probabilities. The new statistic is defined in terms of the probability of acceptance commonly associated with the Accept-Reject algorithm and has a very simple formula. The simulated density of the probability of acceptance can be used to measure the degree of uncertainty of the estimated values of the new association statistic. The results from simulations as well as examples that employ real data indicate that this new association statistic is very powerful in detecting linear and non-linear associations between two random variables. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The paper focuses on the measurement of PM and other meteorological parameters in a small region of central Europe—eastern Slovakia and northeastern Hungary. Due to the increasing availability of sensors measuring not only PM, but also temperature, humidity, pressure, VOC, NOx, and CO2, new possibilities arise in terms of comparing (mainly in terms of correlation) PM and the other measured parameters, thus generating a large amount of data for evaluation. The correlations found are typical for inland conditions, thus able to map other regions of the world. The presented measurements can also be used to predict the evolution of PM with alerts for people with respiratory diseases, or in virtual reality using a digital twin of a humanoid robot. [ABSTRACT FROM AUTHOR]

In this paper, six melon varieties were compared in Hainan open field cultivation, Jilin open field and greenhouse cultivation mode, and the indexes of leaf longitudinal diameter, leaf transverse diameter, leaf area, weight of single melon,soluble solids and pulp thickness were determined. The experimental study showed that the longitudinal diameter, leaf transverse diameter, petiole length, leaf area, pulp thickness and single melon weight of melon cultivated in open land in Hainan were less than that in northern greenhouse and open field, while soluble solids were opposite. Certain differences, among which DG-2014-93, MX10 showed the best performance. The results showed that the main characters of melon, such as pulp thickness, soluble solids, weight of single melon and leaf area, were greatly influenced by the variety heredity and ecological environment, which had certain theoretical basis for the cross region introduction of melon in the future. [ABSTRACT FROM AUTHOR]

AbstractWhen examining whether two continuous variables are associated, tests based on Pearson’s, Kendall’s, and Spearman’s correlation coefficients are typically used. This paper explores modern nonparametric independence tests as an alternative, which, unlike traditional tests, have the ability to potentially detect any type of relationship. In addition to existing modern nonparametric independence tests, we developed and considered two novel variants of existing tests, most notably the Heller-Heller-Gorfine-Pearson (HHG-Pearson) test. We conducted a simulation study to compare traditional independence tests, such as Pearson’s correlation, and the modern nonparametric independence tests in situations commonly encountered in psychological research. As expected, no test had the highest power across all relationships. However, the distance correlation and the HHG-Pearson tests were found to have substantially greater power than all traditional tests for many relationships and only slightly less power in the worst case. A similar pattern was found in favor of the HHG-Pearson test compared to the distance correlation test. However, given that distance correlation performed better for linear relationships and is more widely accepted, we suggest considering its use in place or additional to traditional methods when there is no prior knowledge of the relationship type, as is often the case in psychological research. [ABSTRACT FROM AUTHOR]

This paper investigates the correlation of ResearchGate among the faculties of sciences in the central universities of North-East India using Altmetrics approaches. As more innovation is seen and the discipline is in its early stages, the Department of Biotechnology and Microbiology is the primary study area. The study focuses primarily on faculty activities on the ResearchGate platform, which include frequency of publications, citations, research interest, number of followers and followings, and various publication formats. It has also sparked interest in determining the relationships between different ResearchGate metrics. According to the study, most faculties have good citation scores and an average number of publications. The survey analysis reveals a lack of awareness, concentration, and dedication to the development of the disciplines. The result of the investigation shall serve as a pathway for various future goals for selfimprovement. [ABSTRACT FROM AUTHOR]

In order to solve the target tracking problem of multiple formations in the context of unclear nascent target parameters, a formation target tracking algorithm with unknown nascent target-related parameters is proposed, which can effectively improve the tracking performance and thus better meet the demand. By using the Gaussian mixed probability hypothesis density (ET-GM-PHD) filtering framework, the PHD parameters are predicted and then the correlation between each Gaussian component is analyzed to filter out the optimal set of measurements and finally eliminate most of the noise disturbances. Based on the characteristic of dense distribution measured in the movement area of the formation target, the location of the formation target is found and tracked by the target detection method. Finally, the simulation experiments show that the method in this paper can track the formation target quickly and effectively with good tracking performance when the parameters related to the nascent target are unknown. [ABSTRACT FROM AUTHOR]

Secret key exchange relies on the creation of correlated signals, serving as the raw resource for secure communication. Thermal states exhibit Hanbury Brown and Twiss correlations, which offer a promising avenue for generating such signals. In this paper, we present an experimental implementation of a central broadcast thermal-state quantum key distribution (QKD) protocol in the microwave region. Our objective is to showcase a straightforward method of QKD utilizing readily available broadcasting equipment. Unlike conventional approaches to thermal-state QKD, we leverage displaced thermal states. These states enable us to share the output of a thermal source among Alice, Bob, and Eve via both waveguide channels and free space. Through measurement and conversion into bit strings, our protocol produces key-ready bit strings without the need for specialized equipment. By harnessing the inherent noise in thermal broadcasts, our setup facilitates the recovery of distinct bit strings by all parties involved. [ABSTRACT FROM AUTHOR]

In the latest years the capacity and complexity of climate and environmental modeling has increased considerably. Therefore, tools and criteria for model performance evaluation are needed to ensure that different users can benefit from model selection. Among graphical tools, Taylor's diagram is widely used to provide evaluation and comparison of model performances, with particular emphasis on climate models. Taylor's diagram accounts for different statistical features of model outputs and observations, including correlation, variability and centered root mean square error. Not included is model bias, which is an essential feature for climate model evaluations, and it is usually calculated separately to complement the information embedded in Taylor's diagram. In this paper a new diagram is proposed, referred to as Aras' diagram, which allows for visual assessments of the correspondence between model outputs and reference data in terms of total error, correlation, as well as bias and variability ratios through an easy-to-interpret two-dimensional (2D) plot, allowing for proper weighting of different model features. The strengths of the new diagram are exemplified in a case study of performance evaluation of EURO-CORDEX historical experiment over Southern Italy using E-OBS as reference dataset, for three hydrological variables (i.e. daily precipitation, daily surface minimum temperature, and daily maximum surface temperature), and four popular climate indices (i.e. total annual precipitation, annual maxima of daily precipitation, annual minima of daily minimum temperatures, and annual maxima of daily maximum temperatures). The proposed diagram shows interesting properties, in addition to those already included in Taylor's diagram, which may help promoting climate model evaluations based on their accuracy in reproducing the climatological patterns observed in time and space. [ABSTRACT FROM AUTHOR]

Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether relationships between two sets of variables are different or not across two conditions. Specifically, we assess: Do two sets of high‐dimensional variables have similar dependence relationships across two conditions? We propose a new kernel‐based test to capture the differential dependence. Specifically, the new test determines whether two measures that detect dependence relationships are similar or not under two conditions. We introduce the asymptotic permutation null distribution of the test statistic, and it is shown to work well under finite samples such that the test is computationally efficient, significantly enhancing its usability in analysing large datasets. We demonstrate through numerical studies that our proposed test has high power for detecting differential linear and non‐linear relationships. The proposed method is implemented in an R$$ \mathtt{R} $$ package kerDAA$$ \mathtt{kerDAA} $$. [ABSTRACT FROM AUTHOR]

Aero‐disk is a key component of aero‐engine. Due to its complex working conditions, aero‐disk is prone to structural failures. Therefore, it is essential to analyze the reliability and importance input variables of aero‐disk. In this paper, a framework of aero‐disk reliability analysis and global sensitivity analysis (GSA) was established. First, the hazardous regions of aero‐disk were determined by finite element analysis, and the limit state function of aero‐disk was defined by the life interference model. Then D‐Vine model was utilized to establish the correlation model for aero‐disk hazardous regions to evaluate the reliability of aero‐disk. In addition, two GSA methods based on Copula and space partition were proposed to identify important input random variables considering underlying correlation, and the accuracy of the proposed method was verified by two examples. Finally, the proposed method was applied to the GSA of aero‐disk. The results show that the established framework fills in the gap of uncertainty analysis of aero‐disk, which can be extended to other engineering fields. The proposed GSA methods have both high efficiency and accuracy and can realize multi‐dimensional GSA when the correlation of input variables is considered. [ABSTRACT FROM AUTHOR]

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry. [ABSTRACT FROM AUTHOR]

Abstract: Heat management is crucial to polymer electrolyte membrane (PEM) fuel cell commercialization. Numerical modeling is often used to simulate heat transfer in the various components of the cell and specifically the gas diffusion layer (GDL). Due to the porous nature of the gas diffusion layer and its complexity of anisotropy, the effect of the structure on the thermal conductivity is usually taken into account by introducing an effective thermal conductivity. In this study, the effective thermal conductivity of carbon paper diffusion media was estimated numerically. Carbon paper is often used as the GDL in PEM fuel cells due to its ability to efficiently transport electrons, heat and gaseous species. Using the GeoDict code, a realistic three-dimensional pore morphology of carbon paper was used as the modeling domain and the governing mathematical equations were solved using the commercial software package Fluent (6.3.26) and the ThermoDict solver. The geometrical effects on the effective thermal conductivity were investigated for different geometries. It was found that the effective thermal conductivity is highly sensitive to the geometry of the porous material under investigation. The effective thermal conductivity is much larger in the in-plane direction when compared with the value in the through-plane direction. Further, the change of the effective thermal conductivity due to porosity and compression was studied. Finally, correlations for the through-plane and in-plane effective thermal conductivity were developed. [Copyright &y& Elsevier]

In this paper, we investigate the optimal reinsurance and investment problem from joint interests of the insurer and the reinsurer in the framework of the mixed leadership game, where both of them can invest their wealth in the financial market, and the liabilities are correlated with the risky asset. More specifically, we assume that the insurer is the leader to determine the amount he/she invests into the risky asset but acts as the follower to decide the optimal reinsurance retention level, while the reinsurer is the leader to set the reinsurance premium price and acts as a follower to determine his/her investment strategy. Both the insurer and the reinsurer aim to maximize the expected utility of their terminal wealth, and the explicit Stackelberg-Nash equilibrium is derived by solving the corresponding Hamilton-Jacobi-Bellman (HJB) equations. In order to better understand the effect of the mixed leadership game on the optimal reinsurance and investment problem, we also consider the optimization problem within the framework of the stochastic Stackelberg differential game shown in [Bai, Y., Zhou, Z., Xiao, H., Gao, R. & Zhong, F. (2021). A Stackelberg reinsurance-investment game with asymmetric information and delay. Optimization70(10), 2131–2168.], where the reinsurer is the leader while the insurer acts as the follower for both the reinsurance and investment strategies. Some theoretical analyses and numerical examples are provided to show the economic intuition and insights of the results. [ABSTRACT FROM AUTHOR]

We aim to evaluate the concordance level between Science and Technology in Natural Science, for this we investigated the relationship between the number of articles and patents resulting from Cerrado plant research. We found a significant concordance between scientific and technological publication patterns in Natural Sciences. Private companies, public universities, and natural person are the categories of patent assignees with more patents. Lastly, we found great importance of the Cerrado plant species to global technological development, where cosmetology is the main branch of economic bioprospecting. Therefore, it is crucial to produce studies aiming to promote both the conservation and technological development of sub-products of these plants. • There is concordance between Science and Technology in Natural Sciences. • Private companies are the main patent assignees of plant-based products. • Cosmetics is the main manufacturing segment of patents from Cerrado plants. • Cerrado plants are crucial for the global technological development. [ABSTRACT FROM AUTHOR]

Vitamin D is an indispensable fat-soluble vitamin in the human body, mainly in the form of vitamin D2 and vitamin D3,which can be synthesized by the skin and ingested in the diet. Vitamin D deficiency is currently a global public health problem, with approximately one billion people worldwide suffering from vitamin D deficiency, and pregnant women are at high risk. Gestational diabetes mellitus（GDM） refers to normal glucose metabolism before pregnancy, and abnormal glucose tolerance of different degrees occurs for the first time during pregnancy, which is one of the complications of pregnancy. Continuous studies have proved that vitamin D deficiency during pregnancy is closely related to GDM,which can affect glucose balance through various mechanisms, leading to an increased risk of GDM. In addition, GDM increases the incidence of maternal and infant complications such as hyperamniotic fluid, pregnancy-induced hypertension, premature rupture of membranes, premature delivery, macrosomia, low body weight, and teratoma, which threatens maternal and infant health. In recent years, the research on the correlation between vitamin D and GDM has been increasing, and its correlation has been paid more and more attention by more and more scholars. This paper reviewed the research progress on the correlation between vitamin D and GDM. [ABSTRACT FROM AUTHOR]

This paper studies two-player stochastic differential games with diffusion control on finite time horizons. The players' state processes are described in terms of controlled martingale problems, and we allow for correlation between the players' states, described by some correlation coefficient. We consider zero-sum ranking games in the sense that the criterion to optimize only depends on the difference of the two players' state processes at the finite time horizon. We explicitly compute saddle points of the game depending on the correlation coefficient. In particular, for correlation coefficients smaller than some explicit threshold, the game has a value and a saddle point (in strict controls). For correlation coefficients exceeding the threshold, we introduce the larger class of relaxed controls that can be interpreted as mixed strategies. We show that the game has a value and a saddle point in relaxed controls for all correlation coefficients. [ABSTRACT FROM AUTHOR]

【Objective】The present paper aimed to study the annual dynamic changes and correlations of mineral nutrients in three ‘Tarocco’ blood orange varieties, namely ‘Tarocco blood orange new series’, ‘Tarocco blood orange No.8’ and ‘Tarocco blood orange No.9’, to understand their nutrient absorption patterns and provide theoretical basis for scientific fertilization of blood orange.【Method】The leaves of three blood orange varieties collected in different months throughout the year were used as materials to determine the dry matter rate, total N, total P, total K, Ca, Mg, total B, Fe, Cu, Zn, Mn contents. The contents of each nutrient element in fruits were determined during the fruit ripening period, to analyze the absorption pattern and correlation between the content of nutrient elements in leaves and fruits.【Result】In the leaves, dry matter rate overall showed ‘Tarocco blood orange No.8’ ＞ ‘Tarocco blood orange No.9’ ＞‘Tarocco blood orange new series’. The changes patterns of total B, Fe and Cu were similar. The time when the Fe content of ‘Tarocco blood orange No.9’ reached its maximum value (78.30 mg/kg) was 60 days earlier than the other two varieties. The changes of total K content was opposite to that of total B and Cu, which was manifested as low at maturity stage and young fruit stage, and high at fruit enlargement period and color changing period. There were differences in the variation rules of total N, total P, Ca, Mg, Zn and Mn among the three varieties. In fruits, ‘Tarocco blood orange No.8’ had the highest dry matter rate, total N, total P, total K, Ca, Mg, total B, Cu and Mn content, while ‘Tarocco blood orange No.9’ was the lowest. Correlation analysis showed that the total N content was extremely significantly correlated with Ca, B, and Cu content in leaves (P＜0.01, the same as below), while the correlation between total P and the content of each element was not significant (P＞0.05). The dry matter rate was extremely positively correlated with the content of total P, total K, Ca, Mg, B, Cu, and Mn in fruits, while Zn content was extremely negatively correlated with dry matter rate, the contents of total P, total K, Cu, Ca, Mg, total B, and Mn. The contents of total K, Fe, Zn, and Mn in leaves were significantly or extremely negatively correlated with the contents of total P, total K, Ca, Mg, total B, Cu, and Mn content in fruits.【Conclusion】In the early stages of fruit development (flowering period to young fruit period), N, P, K compound fertilizers and Ca, B, Fe, Mn fertilizers should be mainly applied. In the late stages of fruit development (from fruit enlargement period to maturity period), attention should be paid to supplementing P and K fertilizers, as well as micro fertilizers such as Ca, Mg, Cu, Zn, and balancing the application of N fertilizer. [ABSTRACT FROM AUTHOR]

Alcohol use disorder, as the fifth largest risk factor worldwide, is an important component of stroke prevention. In recent years, a large number of epidemiological and clinical studies have shown that there is a correlation between alcohol and stroke. Alcohol can affect cardiovascular and cerebrovascular health through various ways, resulting in stroke and even death. At the same time, some studies have shown that there may be different associations between alcohol and stroke, light drinking may be a cardiovascular and cerebrovascular protective factor, and moderate and heavy drinking may induce and aggravate stroke. This paper analyzed the current prevalence of stroke complicated with alcohol use disorder, reviewed the possible pathogenesis of alcohol use disorder affecting the incidence of stroke, and summarized alcohol intake monitoring biomarkers and treatments related to alcohol use disorder, in order to further explore the correlation between stroke and alcohol use disorder and provide ideas for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Clustering and spatial representation methods are often used in combination, to analyse preference ratings when a large number of individuals and/or object is involved. When analysed under an unfolding model, row‐conditional linear transformations are usually most appropriate when the goal is to determine clusters of individuals with similar preferences. However, a significant problem with transformations that include both slope and intercept is the occurrence of degenerate solutions. In this paper, we propose a least squares unfolding method that performs clustering of individuals while simultaneously estimating the location of cluster centres and object locations in low‐dimensional space. The method is based on minimising the mean squared centred residuals of the preference ratings with respect to the distances between cluster centres and object locations. At the same time, the distances are row‐conditionally transformed with optimally estimated slope parameters. It is computationally efficient for large datasets, and does not suffer from the appearance of degenerate solutions. The performance of the method is analysed in an extensive Monte Carlo experiment. It is illustrated for a real data set and the results are compared with those obtained using a two‐step clustering and unfolding procedure. [ABSTRACT FROM AUTHOR]

In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta‐analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance–covariance matrices from series of primary studies. In this paper we discuss a unified modelling framework for multivariate meta‐analysis that also incorporates measurement error corrections. We focus on two types of effect sizes, standardized mean differences (d) and correlations (r), that are common in psychological studies. Using generalized least squares estimation, we outline estimated mean vectors and variance–covariance matrices for d and r that are corrected for measurement error. Given the burgeoning research involving multivariate outcomes, and the largely overlooked ramifications of measurement error, we advocate addressing measurement error while conducting multivariate meta‐analysis to enhance the replicability of psychological research. [ABSTRACT FROM AUTHOR]

The paper presents the results of systematic investigations on the gamma dose rates in air, natural radioactivity in soil and the dependence of radioactivity on the mineralogy of soils in the environs of the Belagavi, Karnataka, India. The gamma dose rates were measured using a portable survey metre and the activity in soils were measured using NaI(Tl) based gamma ray spectrometer and the mineral composition was studied using FTIR spectroscopy. The activity of 40K varies from 72.52 to 324.76 Bq kg−1 with an average of 153.17 Bq kg−1. The 226Ra activity varies from 16.53 to 70.53 Bq kg−1 with an average value of 23.88 Bq kg−1 and the activity of 232Th varies from 10.95 to 59.07 Bq kg−1 with an average value of 25.75 Bq kg−1. Elevated levels of 226Ra, 232Th and 40K in soils were observed in some part of Belagavi, which was traced to the mineralogy of the region. The higher activity of natural radionuclides poses radiological implications to the population of the region. The activity utilisation index was found to be is above the normal levels in some locations. The pattern of distribution of radionuclides in the region was studied using SPSS statistical software. The soil with clay minerals Haematite, Calcite, Kalonite, Albite and Feldspar showed higher radionuclide content. [ABSTRACT FROM AUTHOR]

Traditional periodic pattern mining ignoreds the correlation and recency of the patterns, and therefore obtains some weakly correlated and recency patterns with limited practical value. To address the issue, this paper proposed novel periodic pattern mining methods based on recency and correlation constraints named CRPFP-BFS and CRPFP-DFS. By transforming a given database into a column-based structure CRPFP-List, CRPFP-BFS and CRPFP-DFS recursively mined its data using breadth-first and depth-first search, respectively. At the same time, CRPFP-BFS and CRPFP-DFS applied four pruning strategies based on support, period, recency and correlation to reduce the search space, thereby effectively discovering correlation recency periodic patterns. By comparing with the current state-of-the-art algorithms on dense and sparse datasets, and the experimental results show that the CRPFP-BFS and CRPFP-DFS have lower memory usage, higher operating efficiencies and good scalability. Among them, CRPFP-DFS is suitable for situations with strict memory requirements, and CRPFP-BFS performs more efficiently for long transaction sparse databases. [ABSTRACT FROM AUTHOR]

Correlation, functioning as a symmetric relation, is very powerful indicator of the mutual association between two attributes. The problem of weak correlation indicates a lack of linearity in the observed range. This paper presents the precise piecewise correlation method, which overcomes the problem by determining the segments where the linear association will be present. The determination was achieved using the histogram segmentation method. The conditions of the application and analysis of the method are presented, as well as the application of the method to the representative datasets. The obtained results confirm the existence of stronger linear associations on the segments. Detected correlations reveal the strength and nature of the symmetric association between two attributes on each of the separated segments. [ABSTRACT FROM AUTHOR]

In practice, structural limit state function (LSF) is often combined with finite element (FE) models, and the corresponding time‐dependent reliability assessment (TRA) inevitably involves dealing with computational inefficiency. In this paper, a general moment‐based outcrossing rate (GMO) method is proposed to conduct TRA efficiently, with no limitation in the form of LSF. There are two innovations of GMO method: firstly, an explicit moment‐based outcrossing rate is developed; secondly, a separate algorithm is proposed that facilitates the separation of FE analysis from TRA cycle. The proposed method minimizes the computational cost associated with FE analysis and enables TRA cycle to be conducted more efficiently with the assistance of an explicit moment‐based outcrossing rate and explicit moment‐based outcrossing rate. Three numerical examples are investigated to examine the accuracy and efficiency of GMO method for TRA cycle. GMO method is found from the results that it can efficient and accurate to conduct TRA. Such improvements in computational efficiency are particularly valuable for complex structures where FE analysis is computationally expensive and time‐consuming. [ABSTRACT FROM AUTHOR]

Modern measurement systems have allowed the recording of ambient data containing oscillatory phenomena and high noise levels. The observed phenomena are associated with ambient oscillations, which can be identified for monitoring the power system's dynamic behavior. Therefore, a cross-correlation decomposition-based approach is proposed in this paper. This technique combines an extended natural excitation technique (ENEXT) with a parallel factor (PARAFAC) analysis. The ENEXT allows calculating correlation functions of the measured data, which can be employed to build a data tensor. From this data array, a set of dominant impulse responses and their spatial correlations can be extracted through the PARAFAC analysis. The cross-correlation decomposition-based approach is applied to ambient data from the IEEE 16-generator 5-area power system to characterize dominant inter-area oscillation modes. The results demonstrate that the impulse responses may be used to identify the modal parameters, and the spatial correlations may be used to identify the spatial distribution of the oscillatory modes. [ABSTRACT FROM AUTHOR]

The paper introduces a semiparametric estimator of the correlations among elliptically distributed random variables invariant to any form of heteroscedasticity, robust to outliers, and asymptotically normal. Our estimator is particularly fit for financial applications as vectors of stock returns are generally well approximated by heteroskedastic processes with elliptical (conditional) distributions and heavy tails. The superiority of our estimator with respect to Pearson's sample correlation in financial applications is illustrated using simulated data and real high-frequency stock returns. Using simple exponentially weighted moving averages, we extend our estimator to the case of time-varying correlations and compare it to the popular GARCH-DCC model. We show that the two approaches have comparable performances through simulations and a simple application. However, our estimator is extremely fast to compute, computationally robust, and straightforward to implement. [ABSTRACT FROM AUTHOR]

The quaternion windowed linear canonical transform is a tool for processing multidimensional data and enhancing the quality and efficiency of signal and image processing; however, it has disadvantages due to the noncommutativity of quaternion multiplication. In contrast, reduced biquaternions, as a special case of four-dimensional algebra, possess unique advantages in computation because they satisfy the multiplicative exchange rule. This paper proposes the reduced biquaternion windowed linear canonical transform (RBWLCT) by combining the reduced biquaternion signal and the windowed linear canonical transform that has computational efficiency thanks to the commutative property. Firstly, we introduce the concept of a RBWLCT, which can extract the time local features of an image and has the advantages of both time-frequency analysis and feature extraction; moreover, we also provide some fundamental properties. Secondly, we propose convolution and correlation operations for RBWLCT along with their corresponding generalized convolution, correlation, and product theorems. Thirdly, we present a fast algorithm for RBWLCT and analyze its computational complexity based on two dimensional Fourier transform (2D FTs). Finally, simulations and examples are provided to demonstrate that the proposed transform effectively captures the local RBWLCT-frequency components with enhanced degrees of freedom and exhibits significant concentrations. [ABSTRACT FROM AUTHOR]

A novel technique for estimation of the wall's relative permittivity, thickness and conductivity from signals measured from the through-wall radar is presented in this paper. The retrieval process is built around an electromagnetic circuit model that represents the wall structure as a transmission line segment. The propagation of the radar signal through the wall is equivalently described as a pair of voltage and current waves that propagate along the transmission line, using analogies between the electrical parameters of the transmission line segment and the constituent parameters of the materials of the wall structure. The synthetic time domain signal obtained analytically and the real through wall imaging time domain signal were correlated to estimate the wall parameters of the layers that comprise the wall structure. The effectiveness of the proposed estimation technique has been demonstrated through simulation and experimentation on two actual building walls. A comparison with conventional methods revealed consistent results between the estimated wall parameters using the proposed technique and those obtained through traditional approaches. These findings highlight the potential of the proposed method in enhancing the accuracy of real-time target localization and tracking in through-wall imaging applications. [ABSTRACT FROM AUTHOR]

From a young age, human beings begin to ponder upon the reality of nature and the meaning of life. Finding meaning gives them a purpose and establishes a connection with their higher selves. This connection with someone much greater than themselves makes them believe in a being beyond conventional human understanding. This ability to find a purpose, enter a higher state of spiritual awareness and the capacity to experience transcendence is what we refer to as Spiritual Intelligence (S.I.). In this research paper, it has been concluded that one of the key factors that influence the S.I. of people is their well-being. The PERMA model by Martin Seligman is used to evaluate well-being. The PERMA Profiler given by Peggy Kern and a Self-Report Inventory given by David King are used to measure well-being and spiritual intelligence respectively. The scores for the well-being of high school girls (N=60) show a positive correlation of 0.4739 (p<.05) with the scores of their spiritual intelligence. This proves that higher levels of well-being will elicit higher levels of spiritual intelligence. The discussion follows that, to be more spiritually aware, one needs to have better psychological well-being; and consequently, be capable of finding the ultimate reality, meaning, and transcendence in life. [ABSTRACT FROM AUTHOR]

In a recent paper, the author examined a correlation affinity measure for selecting the coarse degrees of freedom (CDOFs) or coarse nodes (C nodes) in systems of elliptic partial differential equations (PDEs). This measure was applied to a set of relaxed vectors, which exposed the near‐nullspace components of the PDE operator. Selecting the CDOFs using this affinity measure and constructing the interpolation operators using a least‐squares procedure, an algebraic multigrid (AMG) method was developed. However, there are several noted issues with this AMG solver. First, to capture strong anisotropies, a large number of test vectors may be needed; and second, the solver's performance can be sensitive to the initial set of random test vectors. Both issues reflect the sensitive statistical nature of the measure. In this article, we derive several other statistical measures that ameliorate these issues and lead to better AMG performance. These measures are related to a Markov process, which the PDE itself may model. Specifically, the measures are based on the diffusion distance/effective resistance for such process, and hence, these measures incorporate physics into the CDOF selection. Moreover, because the diffusion distance/effective resistance can be used to analyze graph networks, these measures also provide a very economical scheme for analyzing large‐scale networks. In this article, the derivations of these measures are given, and numerical experiments for analyzing networks and for AMG performance on weighted‐graph Laplacians and systems of elliptic boundary‐value problems are presented. [ABSTRACT FROM AUTHOR]

This paper examines an emerging economy in terms of the correlation between poverty and greenhouse development as a tool contributing to higher returns of individual households that can be applied in the socio-economic system as a whole to address underdevelopment. Farming acts both as production and consumption factor. Favorable climatic and technical conditions for conducting this type of economic activity stimulate theoretical and practical scientific interest in the problem. The research target is farming as a type of economic activity, an integral element of a national economic complex. The subject of the study is greenhouse farming as a factor of the socio-economic system that affects living standards of the population. The research methodology is based on universally recognized systems concepts and phenomenological principles of scientific investigation, mathematical calculations and software data processing. The findings of this study demonstrate a high dependence of private subsidiary farms as greenhouses on the country’s population employment fluctuations. The paper presents an economic model that reflects the dependence of the unemployment poverty on factors of agricultural production, household structure and total household returns. The conducted research revealed a close relationship between intensified greenhouse farming with the level of poverty in the country [ABSTRACT FROM AUTHOR]

A system for real-time digital speckle correlation rate measurements is developed and applied on printing paper in the micro-scale. The system consists of a continuous laser and a CCD camera connected to a PC. To measure on objects in the micro-scale the system is connected to a stereomicroscope where one of the channels is used for imaging and the other channel is used for illumination of the object surface. The system is evaluated through measurements on printing paper over an area of 1 mm2 and with a spatial resolution of 30 μm on the object. The results show that the system is able to monitor the dynamics in paper during both wetting and drying in real-time over a time scale ranging from one-tenth of a second to several hours. [Copyright &y& Elsevier]

The ever-increasing number of space debris objects on the Earth's orbit presents a danger to existing functional satellites and human infrastructure. These objects need to be tracked to be documented and catalogued. The paper addresses the development of an image processing and data reduction pipeline to process space debris tracking observations from an optical passive sensor. The pipeline starts from the raw, un-calibrated camera frames and ends with the formation of "tracklets", i.e. consecutive series of celestial positions of the objects of interest and possibly an identification of these objects based on a reference catalogue. The paper is, on one hand, improving existing software modules, and, on the other hand, adding a series of new modules to the pipeline. The validation of the system's results in both astrometry and photometry and proves that it is one of the few capable of observing and processing low-Earth orbit objects. [ABSTRACT FROM AUTHOR]

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM. [ABSTRACT FROM AUTHOR]

Abstract: We report on the results of investigations into the characteristics of an air–water isothermal two-phase flow in minichannels, that is, in capillary tubes with inner diameters of 1 mm, 2.4 mm, and 4.9 mm, also in capillary rectangular channels with an aspect ratio of 1 to 9. The directions of flow were vertical upward, horizontal and vertical downward. Based on the authors 15 years of fundamental research into the gas–liquid two-phase flows in circular tubes and rectangular channels, we summarized the characteristics of the flow phenomena in a minichannel with special attention on the flow patterns, the time varying holdup and the pressure loss. The effects of the tube diameters and aspect ratios of the channels on these flow parameters and the flow patterns were investigated. Also the correlations of the holdup and the frictional pressure drop were proposed. [Copyright &y& Elsevier]

Objective: Schizophrenia patients often have personality disorders; schizophrenia patients with personality disorders are more difficult to treat and have a worse prognosis. Early identification of this group of patients and early intervention can achieve better prognosis. Therefore, it is very important to explore effective biomarkers and early diagnosis for the prognosis of schizophrenia. The primary purpose of this paper is to explore the relationship between plasma miRNA expression level and personality disorder with schizophrenia. Methods: Gene microarrays in miRNA files were employed, and the plasma of peripheral blood of 82 schizophrenic patients and 43 healthy control subjects were examined. Real-time reverse transcription polymerase chain reaction detection were performed to explore the results. Spearman correlation analysis was used to analyze the correlation between expression level of miRNAs and Personality Diagnosis Questionnaire-4 score. Results: The results showed that miR-1273d, miR-1303, miR-3064-5p, miR-3131, miR3687, miR-4428, miR-4725-3p, and miR-5096 were up-regulated in schizophrenic patients. Compared to healthy control subjects, the difference was statistically significant (P < .05). Schizophrenic patients with schizoid, paranoid, schizotypal, and obsessive compulsive traits had negative correlation with miR-1303, miR-3131, miR-4428, and miR-5096 expression level (r=−0.40 to −0.62, P < .05); there were no significant differences in the other miRNAs. Correlation with other personality traits was not significant (P > .05). The stepwise regression analysis indicated that miR-5096, miR-3131, and miR-1273d have a significant predictive effect on the schizoid trait (P < .01). MiR-4428 and miR-1303 had a significant predictive effect on the schizotypal trait (P < .01). MiR-5096, miR-4428, and miR-4725-3P had a significant predictive effect on the paranoid trait (P < .05). MiR-4428, miR-1303, and miR-5096 had a significant predictive effect on the obsessive compulsive trait (P < .05). Conclusion: The expression levels of miR-1273d, miR-1303, miR-3064-5p, miR-3131, miR3687, miR-4428, miR-4725-3p, and miR-5096 were up-regulated in the peripheral blood of patients with schizophrenia, and these miRNAs are expected to be diagnostic biomarkers for accurate diagnosis of schizophrenia. The expression levels of miR-1303, miR-3131, miR-1273d, miR-4428, miR-4725-3p, and miR-5096 have significant predictive effects on personality disorder in schizophrenia. [ABSTRACT FROM AUTHOR]

According to the collected fault information of a domestic NC machine tool, the subsystem is divided, and the reliability allocation method is improved according to the NC machine tool correlation model to allocate the target value. The membership degree set of influencing factors is established using the analytic hierarchy process （AHP), and the centrality influencing factors of fault-related data are determined using the DEMATEL decision laboratory analysis method. The membership function set is determined by combining maintainability, maintenance time, failure downtime, failure times, and complexity. Then the reliability allocation factor of the NC machine tool is calculated. The impact factor is introduced into the ARINS allocation method to improve the method so that the improved method is suitable for the system with subsystems connected in series and fault correlation. Numerical examples show that the proposed method can reduce the failure time requirement of the subsystem compared with the traditional ARINS method. At the same time, to verify the rationality of the distribution results in this paper, the Monte Carlo simulation method is used to predict the reliability of the target machine tool based on the distribution results, and the calculation results prove the feasibility of this method. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study of n-butane (R600) and isobutane (R600a) flows in adiabatic capillary tubes. In total, 48 experimental data points were gathered spanning the typical conditions of household refrigerators, with capillary inner diameters ranging from 0.53 to 0.79 mm, tube lengths ranging from 2.5 to 3.5 m, inlet pressure ranging from 2.83 to 6.05 bar, exit pressure ranging from 0.3 to 0.9 bar, inlet subcooling ranging from 4.7 to 15.2 °C, and mass flow rates ranging from 0.53 to 2.95 kg h−1. In addition, the accuracy of π-type correlations priorly developed for R600a flows was verified when used with R600 to predict the refrigerant mass flow rate. Also, it was found the mass flow rate predictions of the algebraic model developed by Hermes et al. (2010) for R600a agreed well with the R600 experimental data, with more than 90 % of data points within ± 10 % error bands. [ABSTRACT FROM AUTHOR]

The ability of an intensity measure (IM) to accurately reflect the characteristics of ground motions is of utmost importance for seismic fragility assessment. Traditional investigations focused on selecting the optimal IM have primarily been conducted based on ordinary reinforced concrete (RC) structures. The direct application of IM specifically tailored for ordinary RC structures in the fragility analysis of self-centering prestressed RC (SCP-RC) structures will potentially introduce increased uncertainties of ground motions. This paper aims to conduct a comprehensive analysis to identify the optimal vector-valued IMs (vIMs) for the fragility assessment of SCP-RC structures. A total of eight vIMs were constructed by combining various scalar-valued IMs (sIMs) associated with the peak values and spectra of ground motions. A 5-story SCP-RC structure was designed to evaluate the efficiency and correlation of the established vIMs using a multi-parameter damage measure (mDM). Probabilistic seismic demand models for the eight examined vIMs were developed. A comparative analysis between the fragility surface slices obtained using the identified optimal vIM and the fragility curve derived from the corresponding sIM was performed. The results indicate that (PGV, I N p /PGV) is the most suitable vIM for accurately quantifying the fragility of SCP-RC structures. The seismic fragility surface is more efficient than fragility curve in estimating the seismic performance of SCP-RC structures. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing (HF) is the primary choice for stimulating petroleum reservoirs. Rock tensile strength and brittleness are crucial parameters required for screening candidate reservoirs and in designing successful HF operations. However, in situ and laboratory determinations of the hydraulic tensile strength (HTS) of rock can prove problematic. Alternatively, the HTS could be estimated from the rock brittleness once a reliable relationship has been established between them. Accordingly, this paper investigates the correlations between the HTS, as measured using laboratory hydraulic fracture tests, and ten strength-based brittleness indices (BIs) selected from the research literature. The primary inputs for computing these BIs are uniaxial compressive strength (UCS) and the Brazilian tensile strength (BTS), which are typically measured for most projects using standard laboratory rock mechanics tests or obtained from log data. For the purposes of this experimental investigation, intact rock core samples were obtained from a carbonate–dolomite formation in Iran, comprising eight distinct geomechanical units, with measured values of UCS, BTS, and HTS ranging 29.7–162.2, 1.93–12.23, and 7.20–20.63 MPa, respectively. The measured HTS was found to directly correlate with the UCS, BTS, and Young's modulus, and inversely correlated with the rock porosity. Seven of the ten investigated BIs correlated with the measured HTS over 69% (R2 ≥ 0.69). In particular, the BI expressions developed by Yagiz and Gokceoglu, Ghadernejad et al., and Khandelwal et al. exhibited relatively strong correlations with the measured HTS (producing R2 values of 0.94, 0.87, and 0.86, respectively), suggesting that these three HTS–BI correlations could be used to provide preliminary HTS estimates for the investigated carbonate–dolomite formation in Iran. This work adds to a database that can be expanded to include other geographical regions for providing useful information about the selection of a suitable site or reserve for conducting HF operations. [ABSTRACT FROM AUTHOR]

Infertility affects millions of people of reproductive age worldwide. Thyroid hormones and prolactin (PRL) affect reproduction and pregnancy; therefore, these two hormones influence fertility. This systematic review and meta-analysis aimed to summarise the strength of the correlation between serum PRL and thyroid stimulating hormone (TSH) in infertile women and to explore selected factors influencing the correlation. We conducted a systematic search of online databases (PubMed, Scopus, ScienceDirect, SAGE and Google Scholar) from inception until March 2021 and a manual search of the bibliographies of the included studies to identify relevant publications. The original research paper describing the correlation between PRL and TSH in reproductive-age women with infertility (primary and secondary) was included. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. A random effect model was used to estimate the pooled correlations of PRL and TSH, followed by an assessment of heterogeneity and a sensitivity analysis. From a total of 822 relevant articles identified, 11 were eligible and included in this systematic review and meta-analysis. The random effect pooled correlation estimates between PRL and TSH was 0.431 (95% CI: 0.251, 0.582), with substantial heterogeneity between the included studies (I² = 80%, τ ² = 0.067, P < 0.001). No significant publication bias was observed. Study region, types of infertility, sample size and year of the study did not influence the correlation estimates. Our results highlighted a significant positive moderate correlation between serum PRL and serum TSH in infertile women. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) panels and solar thermal collectors are two major technologies used to extract electrical and thermal energy, respectively, from solar irradiation. PV panels convert only 15–20% of incident solar radiation into electricity. The remaining radiation elevates the panel’s surface temperature, which badly affects the conversion efficiency and reduces the overall lifespan of the panel. Hence, in this paper, we investigate three cooling techniques developed in-house to maintain the PV panel’s surface temperature: passive cooling, active cooling, and combined cooling. A novel PV/T-PCM system was designed and fabricated to conduct a detailed experimental analysis of each of the cooling methods used on the performance of PV panel in the form of temperature and electrical efficiency of the panel. The temperature of the panel surface got reduced by a maximum of 36% at 1,050 W/m², and as an effect of that, the electrical efficiency was also improved by around 42% in the case of a combined cooling technique. Finally, empirical correlations were also developed to predict the electrical efficiency and PV panel surface temperature using the experimental data of present investigation considering investigated system and operating parameters. Based on the experimental results, the coefficient of determination (R²), for the correlation of electrical efficiency, was found to be 0.90 and 0.9188 for surface temperature that indicate good confidence level for both the correlations. As a result, the correlations and results are more important from an application standpoint for the design and development of the PV/T system. [ABSTRACT FROM AUTHOR]