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The evaluation and monitoring of drought, a complex disaster, are essential issues for the planning of agricultural activities and the operation and management of water resources. In this study, the meteorological drought of the Black Sea region in northern Turkey is evaluated with the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods at 3-, 6-, and 12-month time scales. Meteorological parameters obtained between 1965 and 2019 from 28 meteorological observation stations are used to analyze the drought. Drought-wet period percentages, percentages of occurrence of drought classes, critical period, and negative peak index values are investigated. As a result of the study, it is observed that the percentage of drought occurrence at all time scales is higher than 50% at two stations in the study region, which received precipitation above the average of Turkey. The sum of the extreme drought (ED) and severe drought (SD) values throughout the region is approximately 15% for the whole area. There is a high correlation coefficient, 97.8, 97.6, and 97.3 for the 3-, 6-, and 12-month time scales, respectively, between the SPI and RDIst methods in the evaluation of meteorological drought in the Black Sea region, which is generally a rainy region. It is determined that with the increase in the time scale, the percentage of ED occurrence increased and the risk level of the region around RDI and 17089 station is the highest. It has been determined that the correlation between oscillation indices and meteorological drought indices for the Black Sea region is almost non-existent. The highest correlation value between the oscillation and meteorological drought indices in the region is obtained with the Atlantic Multidecadal Oscillation (AMO). [ABSTRACT FROM AUTHOR]

Simple Summary: The proper management of prostate cancer requires accurate assessment of patients diagnosed with prostate cancer, a common and often lethal cancer. Current conventional prostate cancer diagnosis relies on: (1) needle biopsy, which can result in side effects such as hemorrhage or infection, as well as misplacement of needles resulting in evaluation inaccuracy, (2) prostate serum antigen detection that can lead to overdiagnosis, or (3) MRI interpretations using the Prostate Imaging-Reporting and Data System (PI-RADS), which is a subjective method that depends on the experience of the radiologist's interpretation of the images. Quantitative analysis of MRI can alleviate the limitations of conventional approaches. Artificial intelligence applied to MRI requires big training datasets, and its application is restricted to patients scanned under certain restricted conditions. More recently, spectral/statistical techniques, adapted from remote sensing, have been applied and tested in spatially registered multi-parametric MRI. The new spectral/statistical techniques require limited training, are flexible regarding patient scanning conditions, and perform well relative to other techniques. This study extends spectral/statistical techniques to make them more independent of the individual radiologist by autonomously finding the appropriate tumor signature within a prostate and, therefore, providing an objective, non-invasive, accurate means for determining prostate tumor aggressiveness. Background: Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. Methods: Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. Results: The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob. Conclusions: The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness. [ABSTRACT FROM AUTHOR]

This paper presents full-scale observations of wind-induced vibrations on a 140m high cylindrical reinforced concrete tower under non-stationary and strong wind conditions. The vibration responses of the tower in two planar orthogonal axes and rotation, in conjunction with wind speed and direction data, have been utilized to extract the response characteristics. The primary focus is on discussing the correlation of wind-induced responses in three components: Along-wind, across-wind, and torsion. Throughout the varying levels of oscillation, the tower’s responses clearly exhibit correlations among these components. The correlation coefficients between along-wind and across-wind responses, as well as along-wind and torsional responses, remain negligible across the entire dataset. In contrast, during intense oscillations, there is a near-perfect correlation between the across-wind and torsional responses. Furthermore, the probability density functions (PDFs) of the response components under strong wind conditions significantly deviate from the Gaussian distribution and closely resemble the Cauchy distribution. The joint PDFs also indicate that the along-wind response is statistically independent of the other response components, while the across-wind response correlates well with the torsion, as indicated by the non-Gaussian distribution. This paper also includes distinct observations regarding dominant oscillating frequencies, torsional amplitudes, and vertical vibration amplitudes. The importance of this research lies in its examination of the relationship between different response components, a crucial aspect in understanding how structures behave under wind loading. By emphasizing the need for full-scale measurements, this study aims to provide valuable validation for previous theoretical and experimental research findings. [ABSTRACT FROM AUTHOR]

This study proposes an integrated model for assessing rainfall in Chennai using the Interval type-2 fuzzy reasoning technique and the Analytic Hierarchy Process (AHP). Interval Type-2 Fuzzy Inference System (IT2FIS) is employed to describe complex relationship between the rainfall variables and rainfall rate by considering individual weights of those variables through the aggregation of criteria weights using AHP. The suggested approach has an edge over its predecessors in terms of modeling the inter-personal and intra-personal uncertainty involved in rainfall rate classification. Finally, the developed rainfall model (RM) is applied on the historical dataset collected from Indian Meteorological Department (IMD). The actual data and the IT2FIS model’s performance are compared. Correlation coefficient (R2) and Bland–Altman Plot (BAP) have been used to evaluate the performance metrics of the proposed model. The outcome suggests that the AHP-IT2FIS model can accurately estimate rainfall over time, indicating its potential for long-term use. [ABSTRACT FROM AUTHOR]

This paper estimates a correlation model and joint distribution model of spectral accelerations of vertical ground motions based on the NGA-West2 ground motion database. The correlation model calculated via BC16 vertical GMPE in this study is compared with those published models via other vertical NGA-West2 GMPEs. It is verified that the calculated residuals of vertical ground motions at multiple periods follow the multivariate normal distributions via both a qualitative validation method and a set of quantitative validation methods. The correlation model and the joint distribution model of vertical spectral accelerations are the theoretical basis for research on the joint occurrence of spectral accelerations of vertical ground motions, and an example of applied research on joint occurrence of spectral accelerations of vertical ground motions is studied: the conditional mean spectrum of vertical ground motions. Finally, the effects of different vertical correlation models and ground motion prediction equations on vertical conditional spectra are investigated for wide application of the correlation model and the joint distribution model of spectral accelerations of vertical ground motions. [ABSTRACT FROM AUTHOR]

The present study attempts to spatial and temporal analysis of the reactions between urban commercial marketing and influencing economic factors in Mashhad City (Iran). For this purpose, statistical and spatial data from 18 selected commercial complexes and trading towers were collected using official documents, fieldwork observations, and surveys of some interviews for time windows of 2010 and 2020. The total annual revenue, as the commercial marketing indicator, was chosen as the dependent variable to indicate the mean productivity and profitability of shop stores in the case studies. Besides, eight factors, including commercial marketing and urban regional characteristics were chosen as independent factors. Results revealed that the mean values of commercial interest rates were recorded equal to 0.71 and 0.64 in 2010 and 2020, revealing the overall decrease in profitability of the commercial complexes within the recent decade. Despite the growing rate of construction of renewal mega projects and trading towers in the central district of Mashhad City, the marketing revenues and commercial interests had no positive relationships with city center profits in the recent decade. The land price market has severely changed the spatial structure and economy of Mashhad, such as commercial projects and marketing. It means that the main motivation for marketing in commercial complexes can be considered as regional land values instead of the traditional function of the religious city center. [ABSTRACT FROM AUTHOR]

A kernel based extreme learning machine (KELM) method was proposed for predicting joint moments of lower limb rehabilitation robots to address the problem that random initialization of input weights and bias affect the accuracy of the model when predicting lower limb joint moments by extreme learning machine (ELM). The method integrated Gaussian kernel function with ELM and used genetic algorithm ( GA) combined with particle swarm optimization ( PSO) of genetic algorithm-particle swarm optimization (GAPSO) to optimize the parameters of KELM. Firstly, the motion data of a patient with right lower limb hemiplegia walking on a treadmill at 5 different speeds of 0. 4, 0. 5, 0. 6, 0. 7 and 0. 8 m/ s were collected and preprocessed. Secondly, the KELM was optimized by GAPSO to obtain the optimal regularization coefficient C and kernel function width parameter S. The output joint moments were compared with the joints calculated by inverse biomechanical analysis. Finally, the root mean square (RMSE) and correlation coefficient (P) were used to evaluate the superiority of the algorithm. The experimental results show that the average maximum root mean square error of KELM algorithm based on GAPSO(GAPSO-KELM) optimization is 14%, 18% and 28% lower than that of PSO-KELM algorithm, KELM algorithm and ELM algorithm, respectively, and the minimum P is 0. 84 except for 0. 8 m/ s right ankle inversion, which is 0. 79. The GAPSO-KELM algorithm further improves the prediction accuracy, making it a more effective algorithm support for rehabilitation treatment. [ABSTRACT FROM AUTHOR]

In the interval-valued intuitionistic fuzzy environment, a new multi-attribute three-way decision-making model is proposed to address the problems that the relative loss function in the existing multi-attribute three-way decision-making model does not consider the degree of hesitancy, and the alternative conditional probabilities are given subjectively by the authors, which lacks objectivity. First, three types of ideal solutions are introduced, and the correlation coefficients between the evaluated values and ideal solutions are utilized to construct alternative relative loss functions. Second, the ELECTRE-I method is generalized to the interval-valued intuitionistic fuzzy environment to establish the outranking relation and a method for estimating the conditional probability of alternatives is given. Finally, the model is used to experimentally analyze examples to illustrate the effectiveness and rationality of the model. [ABSTRACT FROM AUTHOR]

To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively. [ABSTRACT FROM AUTHOR]

The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study. [ABSTRACT FROM AUTHOR]

The variation of correlation between ROTI and S 4 index is examined by using GPS L1 and L5 signals over an Indian low latitude station, Waltair (17.73° N, 83.37° E) during the period from 2015 to 2017. The effect of geomagnetic storms on the correlation between ROTI and S 4 index is the main focus of the present study. The influence of satellite elevation angle and signal frequency on the correlation between ROTI and S 4 index is also investigated during different seasons and geomagnetic conditions. The correlation is found to vary between 0.28 and 0.63 for elevation angles above 30⁰ and between 0.56 and 0.87 for the elevation angles above 50⁰. The correlation coefficient values are decreasing with solar activity from 2015 to 2017 during quiet and disturbed geomagnetic periods. From the analysis on the seasonal correlation between ROTI and S 4 index, it is found that solar activity dependent seasonal correlation is observed for both GPS L1 and L5 signals over lower elevation angle 30⁰. The correlation between ROTI and S 4 index is increasing over higher elevation angles and for lower signal frequency. [ABSTRACT FROM AUTHOR]

AbstractWe consider the estimation of Pearson correlation coefficient when two continuous variables can not be directly observed but measured with multiplicative distortion measurement errors. Different from the identifiability conditions for the distortion functions in literature, we propose a new estimation method of Pearson correlation coefficient by transforming the linear model between two variables into varying coefficient models, a moment-based estimator of the Pearson correlation coefficient is proposed. This method can deal with the non-independence condition between the confounding variable and the unobserved variables. We study the asymptotic results of these proposed estimators, and we make some comparisons among the proposed estimators through the simulation. These methods are applied to analyze a real dataset for an illustration. [ABSTRACT FROM AUTHOR]

This article compiles test results of 105 NORM-containing products categorized into bedding, clothing, and accessories for analysis using correlation coefficient from 2019 to 2022. Relationships between measurement quantities, external doses, internal doses and specific activities are investigated. Internal dose is predominantly related to the radon activity concentration for bedding products, with correlation coefficients exceeding 0.8. The coefficients decreases to 0.7 and 0.6 for clothing and accessory products, respectively. Regarding products exceed the regulatory limits, it is challenging to discern correlations between the specific activities and other measurement quantities. [ABSTRACT FROM AUTHOR]

Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Given the inherent uncertainty and ambiguity in multi-criteria decision-making (MCDM) scenarios, the concept of the fuzzy α -cut is leveraged. This allows experts to establish a broader spectrum of rankings, accommodating fluctuations in their confidence levels. To produce consistent criteria weights with the existence of outliers, the fuzzy Method based on the Removal Effects of Criteria (MEREC) is employed. To showcase the viability and effectiveness of the proposed approach, a quantitative illustration is provided through a staff performance review. In this context, the findings are compared with other MCDM methodologies, considering correlation coefficients and CPU time. The results demonstrate that the proposed technique aligns with current distance measure approaches, with all correlation coefficient values exceeding 0.9. Notably, the proposed method also boasts the shortest CPU time when compared to alternative divergence measure methodologies. As a result, it becomes evident that the proposed technique yields more sensible and practical results compared to its counterparts in this category. [ABSTRACT FROM AUTHOR]

The enstrophy convection velocity (denoted by u Ω) in the evolution equation of the boundary enstrophy is proportional to skin friction (denoted by τ) in an incompressible viscous flow, which can be determined globally as an optical flow problem from a time sequence of the boundary enstrophy fields. The correlation coefficient between | u Ω | and | τ | is evaluated in a turbulent channel flow at the friction Reynolds number Re τ = 180 , which is about 0.73 in the region of interest containing near-wall strong wall-normal velocity event associated with a sweep-ejection pair) and is approximately independent of the wall-normal coordinate in the viscous sublayer. This correlation coefficient is contributed mainly by strong near-wall coherent structures with high boundary enstrophy. The statistics of the difference between the normalized | u Ω | and | τ | are discussed. The statistical correlation between u Ω and τ elucidates the connection between near-wall flow structures and skin friction. [ABSTRACT FROM AUTHOR]

Video forgery, a prevalent concern in today's digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r ¯ s metric is applied to evaluate the coherence of frame sequences. Additionally, we've harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media. [ABSTRACT FROM AUTHOR]

Background: The mechanism of lithium treatment responsiveness in bipolar disorder (BD) remains unclear. The aim of this study was to explore the utility of correlation coefficients and protein-to-protein interaction (PPI) network analyses of intracellular proteins in monocytes and CD4+ lymphocytes of patients with BD in studying the potential mechanism of lithium treatment responsiveness. Methods: Patients with bipolar I or II disorder who were diagnosed with the MINI for DSM-5 and at any phase of the illness with at least mild symptom severity and received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks were divided into two groups, responders (≥50% improvement in Montgomery-Asberg Depression Rating Scale and/or Young Mania Rating Scale scores from baseline) and non-responders. Twenty-eight intracellular proteins/analytes in CD4+ lymphocytes and monocytes were analyzed with a tyramine-based signal-amplified flow cytometry procedure. Correlation coefficients between analytes at baseline were estimated in both responders and non-responders and before and after lithium treatment in responders. PPI network, subnetwork, and pathway analyses were generated based on fold change/difference in studied proteins/analytes between responders and non-responders. Results: Of the 28 analytes from 12 lithium-responders and 11 lithium-non-responders, there were more significant correlations between analytes in responders than in non-responders at baseline. Of the nine lithium responders with pre- and post-lithium blood samples available, the correlations between most analytes were weakened after lithium treatment with cell-type specific patterns in CD4+ lymphocytes and monocytes. PPI network/subnetwork and pathway analyses showed that lithium response was involved in four pathways, including prolactin, leptin, neurotrophin, and brain-derived neurotrophic factor pathways. Glycogen synthase kinase 3 beta and nuclear factor NF-kappa-B p65 subunit genes were found in all four pathways. Conclusions: Using correlation coefficients, PPI network/subnetwork, and pathway analysis with multiple intracellular proteins appears to be a workable concept for studying the mechanism of lithium responsiveness in BD. Larger sample size studies are necessary to determine its utility. [ABSTRACT FROM AUTHOR]

Nowadays, web applications play central roles in information systems using the Internet. Then, client-side web programming using HTML, CSS, and JavaScript should be mastered first by novice students. Previously, we have presented the element fill-in-blank problem (EFP) for its self-study. An EFP instance requests to fill in the blank elements in the given source code by referring to the screenshots of the corresponding web page. The correctness of any answer is marked through string matching. However, these blanks were manually selected by considering the importance of elements and the uniqueness of their correct answers. In this paper, we propose a blank element selection algorithm to automatically generate a new EFP instance from a given source code for client-side web programming. We define the seven rules on blank element selections from the code, and implement the procedure in Python using the open source BeautifulSoup and regular expressions' ' For evaluations, we applied the algorithm to the 47 source codes used for manual generations and obtained the better EFP instances with more blanks. Besides, we verified the effectiveness by generating 10 new instances with the algorithm and assigning them to 40 students. In addition, we extended its application to three source codes for games and verified the effectiveness by assigning them to 20 students, to further validate the applicability of the algorithm in EFP instance generations. We also evaluated the relationships between the number of blanks, the number of lines in source codes, the submission times and answer rates of students to further assess the adaptability of the algorithm. These results allow us to measure the algorithm's versatility in generating a wide range of EFP instances and contributes to comprehensive understanding of instance difficulties and learning outcomes. [ABSTRACT FROM AUTHOR]

In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy. [ABSTRACT FROM AUTHOR]

The current research used a method for the quantification of fat content in many types of meat by comparing their X-ray scattering profiles measured practically and simulated profiles from a Monte Carlo simulation code. The measured X-ray scattering profile of each meat sample was compared to Monte Carlo simulated profiles to deduce the fat percentage in each sample. Two peaks were obtained from each profile. The first peak was at a momentum transfer of 1.1 nm−1 (characteristic of fat content) and the second peak was at a momentum transfer of 1.6 nm−1 (characteristic of muscle content). The correlation coefficient and least square error between measured and simulated data were calculated to obtain the percentage of fat in each sample from the best-fitted profiles. Results showed a high correlation coefficient value and low least square error of value (>0.9) and (<0.07), respectively between measured and simulated profiles. The fat content in each sample is plotted against three profile characterization parameters (the ratio of peak intensities, the ratio of area under peaks and the ratio of area under fat peak to total profile area) for measured and Monte Carlo simulated X-ray scattering profiles. Therefore, it was possible to use this Monte Carlo simulation code as a way for precis quantification of fat percentages in some types of meat. [ABSTRACT FROM AUTHOR]

The present research was conducted to examine different parameters of variability, correlations, and path coefficients associated with yield and yield-related traits in aerobic rice. The analysis of variance indicated noteworthy variations among the genotypes for all the traits investigated. Among them, flag leaf area and alkali spreading value showed high PCV and GCV. Except for days to fifty per cent flowering, days to maturity, and amylose content, the remaining traits demonstrated substantial heritability combined with high genetic advance as percentage of mean. The correlation analysis revealed significant positive association between grain yield per plant and several traits, namely effective tillers per plant, test weight, chlorophyll content, and length breadth ratio. Traits such as test weight, effective tillers per plant, and flag leaf area showed a positive direct effect on grain yield per plant. Therefore, it is crucial to give priority to traits that exhibit significant positive correlations and substantial direct effects during selection process. This approach will yield rewarding results in the development of high-yielding cultivars suitable for aerobic conditions. [ABSTRACT FROM AUTHOR]

Background: Chickpea is a second most important pulse crop grown in 56 countries and India rank first in production which shares 61.4% of the total world chickpea production however, productivity is very low as compared to other countries. Therefore, varietal development with inherent tolerance to biotic and abiotic stress is the prime objective to improve component productivity traits to get better yield in rainfed agro-climatic conditions. Methods: Ninety germplasm accessions of chickpea along with four check viz., JG 14, JG 16, JAKI 9218 and Radhey were evaluated in augmented block design at experimental research farm of Banda University of Agriculture and Technology, Banda, Uttar Pradesh, India. Phenotypic data were subjected to study the genetic parameters and association analysis of yield and its component traits using SPAR 2.0 Package and Windostat Version 9.2. Result: The significant variation was observed for all the traits except number of secondary branches, number of pod per plant, number of seeds per pod among the genotypes. The maximum GCV and PCV was observed for height of first pod (35.28 and 39.29), followed by seed yield per plant (29.77 and 40.32) and number of primary branches (25.63 and 31.44). The high magnitude of heritability with genetic advance was estimated for seed index (96.61%), while the high genetic advance as per cent of mean was recorded for first pod height (65.27%). The positive and significant association of seed yield with number of pods per plant, seed index, number of seeds per pod, number of secondary branches, number of primary branches and height of first pod indicating the importance of these traits in selection criteria. Path analysis identified that number of seeds per pod, number of pods per plants, seed index and number of secondary branches per plant as highly desirable component for direct effect on seed yield per plant. The genotypes ICVT-181106 had highest selection indices for seed yield followed by ICVT-181107, PUSA-1053, JG-218, GNG-1999, ICVT-181102 and HC-5. Therefore, high GCV and PCV, significant positive direct and indirect correlation and high estimate of selection indices for grain yield can be directly and indirectly used for chickpea breeding program. [ABSTRACT FROM AUTHOR]

The pressure sensors have been widely used to capture the steady-state pressure data for the management of water distribution systems (WDSs). The background noise pollution in WDSs can lead to signal degradation, reducing the reliability of sensor data. Although numerous algorithms have been developed for noise removal, their optimal parameter selection is problem-dependent. The steady-state pressure sensor data are often linearly correlated, and this correlation is related to hydraulic distance. This is a key feature of WDS pressure sensor data that should be considered in noise-removal algorithms. Based on this domain knowledge, two noise-removal metrics are proposed to evaluate the performance of the noise-removal algorithm and aid in selecting the optimal parameters. In addition, four widely used noise-removal algorithms have been reviewed and used for removing noise from the pressure sensor data. The results demonstrate that the noise-removal algorithm can efficiently remove potential random noise and enhance the reliability of the sensor data. [ABSTRACT FROM AUTHOR]

Ground motion is often broken down into two horizontal components ( x, y) and one vertical component ( z) . In order to explore the feasibility of the application of Copula model in the correlation analysis of multi-dimensional ground motion parameters, 1 500 sets of measured ground motions were selected from the Pacific Engineering Seismic Research Center and 12 groups of ground motion parameters were selected from intensity, duration and spectrum to characterize and analyze the correlation between different directional components of ground motion. Firstly, Pearson linear correlation coefficients, Kendall rank correlation coefficients and Spearman rank correlation coefficients of 12 groups of ground motion parameters in the u-v ( u and v are any two components of two horizontal components and one vertical component of ground motion, u, v = x, y, z) direction were calculated. Secondly, the optimal probability models of 12 groups of ground motion parameters in x-direction, y-direction and z-direction were established based on Kolmogorov-Smirnov (K-S) test and the Bayesian information criteria ( BIC) . Finally, the optimal Copula function of ground motion parameters between u-v components was determined by BIC criterion, and the joint probability function of 12 groups of ground motion parameters between u-v components was established. The results show that the correlation of ground motion parameters in the 12 groups is good, but the correlation of peak response spectrum corresponding period parameters in the u-v direction and the correlation of arias intensity parameters in the x-z direction and y-z direction is low. The joint probability function of ground motion parameters between u-v direction components can be established accurately by Copula theory. Given the ground motion parameters of u-direction component, the Copula conditional mean and conditional random number obtained can be used to predict the ground motion parameters of vdirection component. [ABSTRACT FROM AUTHOR]

Since the range of correlation in FGM copulas is limited, we offer a new symmetric extension of it with the aim of improving the correlation coefficient range. This proposed symmetric family extends FGM copula and some of its known generalized families. The admissible range of association parameter for given FGM families will be derived and various properties of the proposed model are studied. We apply some sub-families of this new class to model a dataset of the medical science to show the superiority of presented model in comparison with the known copulas. [ABSTRACT FROM AUTHOR]

The assembled beam bridge is a widespread construction, with transverse connections uniting precast beams to jointly support loads. These connections, while crucial, are the most vulnerable elements in such structures. Therefore, the development of an effective index for continuous condition assessment remains the cornerstone of structural health monitoring. The Displacement Spectrum Similarity Measure index, as per ideal conditions in numerical simulations, has shown insensitivity to short-term vehicle loads and sensitivity to transverse connection stiffness. This study extends its scope to address potential real-world interference factors, including measurement errors and deck pavement damage. Specifically, we introduce Gaussian white noise to simulate measurement errors and incorporate additional vehicle loads to emulate the impact of pavement damage. Numerical simulations confirm the robustness of the index against these challenges. Additionally, a real-time index extraction scheme is proposed and implemented on an operational bridge. The results reveal a close alignment between the transverse connection condition assessed by the index and the findings obtained through on-site surveys, thereby substantiating the viability and effectiveness of the index for real-world engineering contexts. [ABSTRACT FROM AUTHOR]

In the field of geodetic data processing, the existing literature on the treatment of the multiplicative random error model assumes that the random multiplicative error elements are independent of one another. However, there is no research exploring the correlation between these elements of the multiplicative random error. In this paper, we have developed three parameter estimation methods for the correlated observation multiplicative random error model based on existing literature research. These methods are derived using formulas for variance and correlation coefficients. The three methods are the correlated observation least squares method, the correlated observation weighted least squares method, and the correlated observation bias-corrected weighted least squares method. Additionally, the corresponding formulas for the unit weight mean square error and standard deviation are provided. The numerical simulation results demonstrate that, for the multiplicative random error model of correlated observations, the correlated observation bias-corrected weighted least squares method yields the optimal parameter estimation with higher accuracy, making it the most effective approach for solving this model. [ABSTRACT FROM AUTHOR]

High-resolution 1H nuclear magnetic resonance (1H NMR) was advanced to determine both epoxy value and iodine value (IV) of vegetable oils that occur during their epoxidation. For calibration, rather than using a series of diluted stock samples, epoxidized soybean oils with a range of IV and epoxy value synthesized using entrapped peroxophosphotungstate as the catalyst under different reaction conditions were used. During the epoxidation reaction, IV and epoxy value were tracked with respect to time using 1H NMR and conventional volumetric methods. The integrated peak area of the olefinic hydrogens (5.3–5.5 ppm) was used to calculate the absolute number of double-bonded protons. By comparison of IV obtained by 1H NMR and the traditional Wijs-cyclohexane methods, the correlation coefficient was R2 = 0.9997 for the regression equation y = 1.1314x + 1.0035, where x was the result given by 1H NMR. The average integrated peak areas related to the hydrogens of the epoxide groups located at chemical shifts of 2.9 ppm (monoepoxide) and 3.1 ppm (diepoxide) were used to determine the epoxy value. The quantification of the number of epoxides was carried out by 1H NMR and the values obtained were correlated with epoxide content determined by the hydrochloric acid/acetone method. As a consequence, the correlation coefficient was R2 = 0.9991 for the regression equation y = 0.0052x + 1.7258, where x was the average integrated peak areas related to the hydrogens of the epoxide groups by 1H NMR. The novelty of our study lies in the simultaneous correlation between IV and epoxy value obtained from conventional titrations, the integrated peak area related to olefinic protons, and the average integrated peak area located at the hydrogen of epoxide group, calculated using 1H NMR. This approach is used to monitor and optimize the epoxidation reaction. [ABSTRACT FROM AUTHOR]

In this paper, we come up with a unique technique to generate the ideal pattern diversity with the help of 24 Vivaldi antennas. With the help of a mathematical concept called golden angle, the 24 antenna elements are arranged in such a way that the radiation patterns will never overlap and the mutual coupling will be very low. It is also verified from the simulations and the measured results that the envelope correlation coefficient is less than 0.01 in the 3–4.2 GHz which is a sub‐6 GHz band for 5G. This same concept can be extended to a greater number of antennas also. [ABSTRACT FROM AUTHOR]

This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a new multiplicative fashion by an observed confounding variable, namely, a parametric distortion measurement errors model. The least squares estimation and weighted least squares estimation are used to estimate parameters in the multiplicative distortion functions. An interesting finding is that the calibrated variables obtained by using the weighted least squares estimators perform asymptotic equivalent to the calibrated variables obtained from the nonparametric kernel smoothing estimators in the literature. We then used the calibrated variables to estimate the correlation coefficient, and show that the proposed estimators of correlation coefficient are all asymptotically efficient. Moreover, we suggest an asymptotic normal approximation and an empirical likelihood-based statistic to construct the confidence intervals. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators. These methods are applied to analyze two real datasets for an illustration. [ABSTRACT FROM AUTHOR]

The problem of finding confidence intervals based on data from several independent studies or experiments is considered. A general method of finding confidence intervals by inverting a combined test is proposed. The combined tests considered are the Fisher test, the weighted inverse normal test, the inverse chi-square test and the inverse Cauchy test. The method is illustrated for finding confidence intervals for a common mean of several normal populations, common correlation coefficient of several bivariate normal populations, common coefficient of variation, common mean of several lognormal populations, and for a common mean of several gamma populations. For each case, the confidence intervals based on the combined tests are compared with the other available approximate confidence intervals with respect to coverage probability and precision. R functions to compute all confidence intervals are provided in a supplementary file. The methods are illustrated using several practical examples. [ABSTRACT FROM AUTHOR]

In this manuscript, the design of a high isolation Multiple Input Multiple Output (MIMO) Hybrid Fractal Antenna (HFA) has been presented for 5 G and other wireless standards in a wider operational frequency range from 1 to 30 GHz. The fractional ground plane is used for the assembly of the antenna in the initial phase, further, the T-stub has been exploited on the ground plane to expand the isolation between the radiating elements. The dimensions of the proposed antenna are optimised using the Particle Swarm Optimisation Gravitational Search Algorithm (PSOGSA). The performance parameters of the projected antenna have been measured and analysed. The obtained experimental results are highly correlated with simulated ones. Performance diversity parameters of designed antenna like CCL, DG, ECC, TARC, and MEG fall within working limits in the frequency range of interest. The developed antenna retains high isolation between −30 to−50 dB in the desired frequency range. The proposed model of an antenna can be useful for 5 G 3.5 GHz band, 5 G NR (New Radio) frequency bands (3.3–5.0 GHz), LTE band 46 (5.15–5.925 GHz), European Union 5 G frequency band (5.9–6.4 GHz), 5 G 26 GHz frequency band and other wireless standards in the frequency range from 1 to 30 GHz. [ABSTRACT FROM AUTHOR]

The aim of this article is a political-geographical analysis of political and economic relations between the Slovak Republic and the countries of the world from 1993 to 2019. We spatially analyze more than 15,000 trips of Slovak government officials to countries around the world and their relation to foreign trade. Using the statistical method of correlation, we examine the degree of relationship between trade indicators and political interactions (represented by government visits) in more than 150 countries around the world. We cartographically define spatial concentration, especially the high direct correlation between trade and trips, and examine the factors and causes of this correlation on the example of selected countries with significant values. The countries of the Eastern Partnership, the Middle East, the Western Balkans, and East Asia have the highest correlation coefficients. Our article seeks to contribute to the discussion on the effectiveness of economic diplomacy using geographical approaches. [ABSTRACT FROM AUTHOR]

Chang, H.-K.; Chen, W.-W.; Cheng, C.-C.; Liou, J.-C., and Lin, S.-F., 2024. Assessment of tide model prediction and discrepancy in shallow waters of Taiwan to improve data fusion methods. Journal of Coastal Research, 40(1), 129–137. Charlotte (North Carolina), ISSN 0749-0208. Shallow bathymetry in the Taiwan Strait and corresponding complex hydrodynamics are key factors that make it difficult for hydrodynamic tide models to accurately calculate tide levels. Based on the Princeton Ocean Model with data assimilation from 2010 to 2014, the Ministry of Interior (MOI) developed a local tide model (MOI.18v1) for the waters around Taiwan that was imposed by harmonic analysis to have fast tide calculation at any time and at any location. This study first evaluated the prediction accuracy of the MOI.18v1 model using tide data at 22 gauge stations in Taiwan for 2016 and compared the results with those of NAO.99b. The MOI.18v1 root-mean-square error (RMSE) of residuals by subtraction with observations can reach about 13.49 to 18.26 cm, results lower than those of NAO.99b. A data fusion method is also proposed to further reduce the prediction discrepancy with RMSE of 3.67 to 6.35 cm, which means that the percentage of improvement increases the level of 25.85 to 47.08%. Data fusion can be used as a spatial correction for the predicted tide level using hydrodynamic tide models. [ABSTRACT FROM AUTHOR]

In the ABL wind tunnel, three inverted umbrella-shaped membrane structure groups were subjected to simultaneous pressure measurement model test. Time series of the fluctuating wind pressure were collected from 24 different wind angles. Upon examining the correlation coefficient of the fluctuating wind pressure between the upper and lower surfaces at the same pressure tap, it was discovered that the fluctuating wind pressure of the upwind roof exhibited asynchronous changes, which is a characteristic of a negative correlation area and has detrimental effects on the structure’s wind resistance. The local areas with non-Gaussian properties were evaluated by analyzing the probability density function of representative pressure taps. The mechanism behind non-Gaussian wind pressure was elucidated through considerations of the central limit theorem and spatial correlation of fluctuating wind pressure. This study introduces a quantitative classification criterion for identifying the non-Gaussian distribution area of fluctuating wind pressure, setting the threshold at an 80% cumulative probability for both skewness and kurtosis. The analysis showed that the steady airflow of the structure corresponds to the Gaussian distribution area, while the non-Gaussian distribution area is primarily concentrated at the front edge of the upwind roof and the rear edge of the downwind roof. To ascertain the peak factor for the non-Gaussian distribution, the improved peak factor method was utilized, providing insights into the range of peak factor in the non-Gaussian distribution area of this particular roof configuration. This information serves as a foundation for the wind resistance design of this structure. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the utility of neutrosophic logic in identifying gaps and areas for improvement in animal protection in Ecuador. The methodology employed was exploratory and relied on the experience and knowledge of experts in the field of animal protection. It was framed within a qualitative perspective, adopting a descriptive and exploratory design to comprehend and analyze deficiencies in animal protection in Ecuador. The results of this research allowed for the identification of the most significant areas for improvement in animal protection in Ecuador through the definition of the gaps with the greatest impact on the study subject. Deficiencies related to the lack of protocols for animal rescue and inadequate enforcement of sanctions stood out as the most severe and impactful on animal welfare. The use of neutrosophic logic enabled a precise evaluation and quantification of deficiencies in animal protection. Its application is recommended in future research in the field to obtain a more accurate understanding of the situation. [ABSTRACT FROM AUTHOR]

The paper presents the study of bread wheat's chlorophyll content and grain yield traits under changing environmental conditions -- optimal water supply and simulated water-deficit conditions. Selecting 15 wheat cultivars from different regions (Russia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkey, and CIMMYT) based on economically valuable characteristics became the specimens for the presented study that evaluated the SPAD indicators in correlation with the grain yield in bread wheat and determined the vital role of genotypes, environments, and genotype by environment interaction effects. The relationship between chlorophyll content and yield parameters under different growing conditions was also well-defined. The results revealed that ecosystems had more influence on the chlorophyll content than the wheat genotypes. Several wheat cultivars with soil moisture tolerance have gained identification, along with the correlation coefficient between chlorophyll content and grain yield under varied environmental conditions of water supply. The study validated the role of genotypes and environments in the manifestation of responses to stress conditions. [ABSTRACT FROM AUTHOR]

We developed an imaging technique for inspecting the inside of coal-fired boilers during operation by using a visible-light camera, which has a higher spatial resolution and depth of field than mid-infrared image sensor camera systems commonly used for imaging the inside of boilers. Two main problems are associated with the use of a visible-light camera: the low light transmittance of combustion gases in the visible spectrum and the large non-stationary variations in intensity levels. To overcome these difficulties, a narrow-band imaging technique using a bandpass filter was employed to identify the wavelength range with the highest transmittance through gases. Contrast adjustment and noise reduction via conditional averaging of the temporal images were performed during post-image processing. Results obtained from the tests of several bandpass filters indicated that bandpass filtering centered at 650 ± 5 nm is optimal for obtaining clear images. Adaptive histogram equalization was found to be more effective for contrast enhancement than other standard methods. Additionally, a clearer image can be obtained by conditionally extracting and averaging temporal images with high Pearson product-moment correlation coefficient values for the average of all images. Finally, a clear image of the inner wall of a 1000-MW-class coal-fired boiler during operation is presented to demonstrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset. [ABSTRACT FROM AUTHOR]

Representations of measures of concordance in terms of Pearson's correlation coefficient are studied. All transforms of random variables are characterized such that the correlation coefficient of the transformed random variables is a measure of concordance. Gini's gamma then is generalized and it is shown that the resulting generalized Gini's gamma can be represented as a mixture of measures of concordance that are Pearson's correlation coefficients of transformed random variables. As an application of this correlation mixture representation of generalized Gini's gamma, lower and upper bounds of the compatible set of generalized Gini's gamma, i.e., the collection of all square matrices of pairwise generalized Gini's gammas, are derived. [ABSTRACT FROM AUTHOR]

Sixty different varieties of brinjal (Solanum melongena L.) were evaluated for character association. The findings showed that the genotypic correlation was higher than the phenotypic correlation coefficient for most traits. Strong positive correlation was observed between the number of fruits per plant, branches per plant, and fruit yield per plant. Positive correlation was observed between moisture content and fruit characteristics, such as fruit volume and average fruit weight. The path coefficient analysis revealed that high positive direct effect on fruit yield was exerted by fruits per plant, followed by moisture content. [ABSTRACT FROM AUTHOR]

In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min–Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min–Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets. [ABSTRACT FROM AUTHOR]

One-shot object detection has been a highly demanded yet challenging task since the early age of convolutional neural networks (CNNs). For some newly started projects, a handy network that can learn the target's pattern using a single picture and automatically decide its architecture is needed. To specifically address a scenario in which a single or multiple targets are standing in relatively stable circumstances with hardly any training data, where the rough location of the target is required, we propose a one-shot simple target detection model that focuses on two main tasks: (1) deciding if the target is in the testing image, and (2) if yes, outputting the target's location in the image. This model requires no pre-training and decides its architecture automatically; therefore, it could be applied to a newly started target detection project with unconventionally simple targets and few training examples. We also propose an architecture with a non-training parameter-gaining strategy and correlation coefficient-based feedforward and activation functions, as well as easy interpretability, which might provide a perspective on studies in neural networks. We tested this design on the data we collected in our project, the Brown–Yosemite dataset and part of the Mnist dataset. It successfully returned the target area in our project and obtained an IOU of up to 87.04%, reached 80.28% accuracy on the Brown–Yosemite dataset with disposable networks, and obtained an accuracy of up to 89.4% on part of the Mnist dataset in the detection task. [ABSTRACT FROM AUTHOR]

In this study, we assessed the correlations between hemoglobin A1c (HbA1c) measurements obtained using three different diagnostic methods, namely reversed-phase cation-exchange chromatography, high-performance liquid chromatography, and lateral flow immunoassay (LIFA) with an AnyLab F instrument. HbA1c levels measured with the AnyLab F instrument and those measured with the HA8190V, HA8180, and D100 instruments were strongly correlated. High R-square values and low p-values indicated significant and reliable correlations, supporting the clinical interchangeability of these methods. Notably, demographic and clinical analyses revealed uniform HbA1c levels across age groups, suggesting minimal age-related variations in HbA1c levels in the cohort. This finding has implications for diabetes management strategies across different age groups, emphasizing the versatility of the AnyLab F instrument. Overall an average HbA1c level of 7.857% among diabetes mellitus-diagnosed participants suggests moderately elevated HbA1c levels, underscoring the need for improved diabetes management. Younger individuals exhibited lower HbA1c levels, potentially owing to heightened awareness and treatment plan adherence. Conversely, older adults had higher HbA1c levels, likely influenced by age-related changes and comorbidities. Larger sample sizes and a comprehensive evaluation of various measurement principles are needed to strengthen the findings herein. Additionally, exploring additional biomarkers and assessing LIFA performance in larger sample sets will advance the clinical utility of HbA1c measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a feature extraction method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) for electric shock fault‐type identification. The AVMD algorithm is utilized to adaptively decompose the electric shock signal into intrinsic mode components, each containing distinct frequency information. Subsequently, the correlation coefficient is employed to extract the intrinsic mode component with amplitudes greater than or equal to 0.1 (γk${\gamma }_k$ ≥ 0.1). Feature extraction is then performed using SVD on the γk${\gamma }_k$ ≥ 0.1 intrinsic mode component, based on its maximum singular value and singular entropy. This approach effectively overcomes the limitation of the traditional VMD that necessitates manual K value setting. Moreover, it achieves dimensionality reduction and feature extraction of the intrinsic mode components through SVD, resulting in enhanced computational efficiency and fault identification accuracy. Extensive simulations demonstrate the remarkable recognition rates of electric shock fault types in animals and plants using the proposed AVMD‐SVD method, achieving a recognition rate as high as 99.25%. Comparative performance analysis further verifies the superiority of AVMD‐SVD over similar empirical mode decomposition‐SVD feature extraction techniques. [ABSTRACT FROM AUTHOR]

A machine learning-based strain estimation method for structural members in a building is presented The relationship between the strain responses of structural members is determined using a convolutional neural network (CNN) For accurate strain estimation, correlation analysis is introduced to select the optimal CNN model among responses from multiple structural members. The optimal CNN model trained using the response of the structural member with a high degree of correlation with the response of the target structural member is utilized to estimate the strain of the target structural member The proposed correlation-based technique can also provide the next best CNN model in case of defects in the sensors used to construct the optimal CNN. Validity is examined through the application of the presented technique to a numerical study on a three-dimensional steel structure and an experimental study on a steel frame specimen. [ABSTRACT FROM AUTHOR]

• A DC-Gaussian process regression model is used to the prediction of Dst index. • DC-GPR model has the best stability among the three models among DC-GPR, DC-SVM, and DC-NN models • DC-GPR model exhibits the better forecasting performance than DC-SVM and DC-NN models in Dst forecasting. In this paper we apply a class of Bayesian machine learning model, Gaussian Process Regression, to the prediction of Dst index by using 80 intense geomagnetic storms data (Dst ⩽ - 100 nT) from 1995 to 2014. The purpose of this paper is to compare the performance of Gaussian process regression model with Support Vector Machine model combined together with Distance Correlation (DC-SVM) and Neural Network model combined together with Distance Correlation (DC-NN) (Lu et al., 2016). For comparison, we estimate the correlation coefficients (CC), the RMS errors, the absolute value of difference in minimum Dst (Δ Dst min ) and the absolute value of difference in minimum time (Δ t Dst ) between observed Dst and predicted one.In order to compare the prediction effects and the generalizability of the three models to magnetic storm events, we combined 70 intense magnetic storm events and 10 super large magnetic storm events into one group. It is shown that DC-GPR model exhibits the better forecasting performance than by DC-SVM model and DC-NN model in magnetic storm. The CC, the RMS errors, the Δ Dst min , and the Δ t Dst of GPR are 0.65, 35.45 nT , 16.12 nT and 1.16 h , respectively. [ABSTRACT FROM AUTHOR]

Objective：To improve the pre-processing laboratory test method for the severe acute respiratory syndome, coronavirus (SARS-CoV-2) specimens, and to evaluate its effectiveness. Methods：A total of 90 SARS-CoV-2 negative specimens and 30 SARS-CoV-2 positive specimens were collected. The specimens were divided into traditional group, modified 1 group, and modified 2 group, according to the test method. The specimens in traditional group were inactivated by using the guanidine salts and a 56 ℃, 30 minute water bath, followed by single tube oscillation, specimen de-identification, and single-handed lid opening for testing. The specimens in modified 1 group were inactivated by using the guanidine salts, followed by single tube oscillation, specimen de-identification, and single-handed lid opening for testing. The specimens in modified 2 group were inactivated by using the guanidine salts, followed by batch oscillation in vertical, horizontal, and 180-degree angle, specimen de-identification, and single-handed lid opening for testing. Real-time fluorescence quantitative PCR (RT-qPCR) method was used to detect the cycle threshold (Ct) values and detection time of the specimens in various groups； Spearman correlation analysis was used to analyze the correlation of Ct values between various groups； Blank-Altman method was used to perform the consistency test； the detection time of SARS-CoV-2 of the specimens in various groups was recorded. Results：The Ct values of the specimens in modified 1 group were strongly correlated with those in traditional group (P<0. 01), and the correlation coefficient(r)= 0. 975 4(0. 966 2－0. 982 1). The Ct values of the specimens in modified 2 group were strongly correlated with those in traditional group (P<0. 01), r=0. 986 2(0. 980 9－0. 990 0). The inter-group deviation of detecting SARS-CoV-2 negative specimens between modified 1 group and traditional group was 0. 158 2 (－0. 613 7－0. 930 1), and the inter-group deviation of detecting SARS-CoV-2 positive specimens was 0. 117 0(-0. 403 1－0. 637 1), indicating good consistency. The inter-group deviation between modified 2 group and traditional group for detecting SARS-CoV-2 negative specimens was 0. 015 7(-0. 550 4－ 0. 581 8), and for detecting SARS-CoV-2 positive specimens was 0. 022 7(-0. 454 1－0. 499 4), which indicated the good consistency. The detection time of 90 SARS-CoV-2 specimens in modified 1 group was 15 min, while the detection time of 90 SARS-CoV-2 specimens in modified 2 group was 10 min and the detection time of 90 SARS-CoV-2 specimens in traditional group was 45 min. Compared with traditional group, the detection time in modified 1 group was reduced by 67%and the detection time in modified 2 group was reduced by 11% . Conclusion：The modified SARS-CoV-2 specimen testing method shows strong correlation and good consistency with the traditional testing method. It can save the detection time and improve the efficiency. [ABSTRACT FROM AUTHOR]

The paper presents the dynamics of the hydrothermal coefficients characterizing agroclimatic resources on the territory of Siberia within the coordinates of 50°–65° N, 60°–120° E during modern climate change. The intensity, frequency, and duration of adverse weather phenomena for agriculture (the atmospheric droughts and moisture excess conditions) are analyzed. The Ped' drought index () and the standardized precipitation evapotranspiration index (SPEI) calculated from the data of 134 weather stations and the ERA5 reanalysis are used as an indicator. In Eastern Siberia and in the west of the analyzed territory, there is an increase in aridity (a trend is 0.2–0.5 per decade). Northern regions of Kazakhstan, the Sayan Mountains, and the West Siberian Plain were characterized by a relative stability of hydrothermal conditions in 1950–2020 (statistically significant changes are not revealed). A direct linear dependence of hydrothermal conditions on large-scale circulation processes have been not detected for most cases. The greatest number of statistically significant correlation coefficients has been obtained for the drought index and the SCAND (Scandinavia teleconnection pattern) index. [ABSTRACT FROM AUTHOR]

The price–volume nexus is essential for understanding and discriminating the structure of the carbon market. The current research investigates the price–volume dependences in the European carbon market and Chinese national carbon markets through fractal methods. The results demonstrate that prices have close connections with volumes at multi-time scales in both markets, and that the price–volume correlation coefficient series in the Chinese carbon market have more intense volatility, implying that it is more difficult for Chinese investors to reach consensus on prices at different time scales than European investors. In addition, there are bidirectional price–volume transmissions in both markets. Interestingly, in most cases, the influence from volume to price is much more apparent than that from price to volume in both markets, indicating that volume acts a more important role in the price–volume transmission. Finally, the European price–volume dependence has lower risk and higher efficiency than the Chinese price–volume dependence, which is due to the fact that the European market is more mature than the Chinese market. This study has both theoretical and practical applications for policymakers and investors to make optimum decisions relating to policy formation and risk management in the carbon market. [ABSTRACT FROM AUTHOR]