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The studies were carried out at the Institute of Oilseed Crops of the National Academy of Agrarian Sciences of Ukraine in 2019/2020. The material of the study was interspecific hybrids of the second generation obtained in the system of dialel crosses with the participation of two wild annual homostylic species L. angustifolium and L. hispanicum with n = 15 and three accessions of cultivated flax: L 5 (Czech Republic), L 6 (India), M 32/2 (Ukraine). Correlation analysis of habitus traits of a flax plant with the number of bolls was carried out. As a result of studies between the height of the plant and the number of stems on the plant, there is no relationship; between the height of the plant with the number of lateral shoots and the number of bolls, weak and medium correlations, and sometimes their absence, were established (r = +0.218…+0.559; r = +0.253…+0.698, respectively). Medium and strong correlations were found between the number of stems on a plant with the number of side shoots and the number of bolls (r = +0.334…+0.892; r = +0.482…+0.818). Exceptionally strong and stable over the years correlations were observed between the number of lateral shoots on the plant and the number of bolls (r = +0.700…+0.922). The main trait of the habitus of a flax plant, which significantly affects the number of bolls on them, is the number of lateral shoots. This trait can be used as a reliable criterion for selecting plants for breeding for high seed productivity. [ABSTRACT FROM AUTHOR]

Background: There exist several fMRI quality assurance measures to assess scanner stability. Because they have practical and/or theoretical limitations, a different and more practical measure for instability would be desirable. Purpose: To develop and test a sensitive, reliable and widely applicable temporal instability measure (TIM) for fMRI quality assurance. Study Type: Technical development. Phantom: Spherical gel phantom. Population: A total of 120 datasets from a local Philips scanner with two different receive‐only head coils (32ch and 8ch, 60 datasets per coil) were collected as well as 29 additional datasets with three different receive‐only head coils (20ch, 32ch, and 64ch) from two additional sites with GE (seven runs with 32ch) and Siemens scanners (seven runs with 32ch and Multiband imaging, five runs with 20ch, 32ch, and 64ch) were borrowed. Field Strength/Sequence: 2D Echo‐planar‐imaging (EPI). Assessment: A new TIM was proposed that is based on the eigenratio of the correlation coefficient matrix, where each entry of the matrix is a correlation coefficient between two time‐points of the time‐series. Statistical Tests: Nonparametric bootstrap resampling was used twice to estimate confidence intervals (CI) of the TIM values and to assess the improved sensitivity of this measure. Differences in coil performance were assessed via a nonparametric bootstrap two‐sample t‐test. P‐values <0.05 were considered significant. Results: The TIM values ranged between 60 parts‐per‐million and 10,780 parts‐per‐million across all 149 experiments. The mean CI was 2.96% and 2.16% for the 120 and 29 fMRI datasets, respectively (the repeated bootstrap analysis gave 2.9% and 2.19%, respectively). The 32ch coils of the local Philips data provided more stable measurements than the 8ch coil (observed two‐sample t‐values = 26.36, −0.2 and −6.2 for TIM, tSNR, and RDC, respectively. PtSNR = 0.58). Data Conclusion: The proposed TIM is particularly useful for multichannel coils with spatially nonuniform receive sensitivity and overcomes several limitations of other measures. As such, it provides a reliable test for ascertaining scanner stability for fMRI experiments. Evidence Level: 5. Technical Efficacy: Stage 1. [ABSTRACT FROM AUTHOR]

This article presents methods for determining the relationship between the flow and hydraulic parameters of rivers, channels, and channel processes based on correlation analysis. [ABSTRACT FROM AUTHOR]

The heavy reliance on groundwater sources, the high cost and difficulties in accessing groundwater from a basement complex in Abeokuta necessitate the development of a simple prediction model. Hence, the objective of this paper was to develop a spatial model for prediction of the cost of constructing hand-dug wells in Abeokuta city, Nigeria, using geographic information systems. The static water level (SWL) of wells was measured across the city, and the digital terrain model (DTM) of the city was created in the GIS. Map algebra was applied to determine the depth and predict the overall cost of well construction. The map of the overburden removed was derived as the algebraic difference between the DTM and SWL extended down by the height of four concrete rings (OVERBURDEN = DTM – SWL+ 4 rings below the SWL). The map of the total cost of well construction in any part of the metropolis was produced by multiplying the sum of the cost of one unit of concrete ring and excavation of the depth of one concrete ring with the map of overburden. The model was validated by an empirical investigation of eight randomly selected wells. The results revealed relatively good accuracy, with correlation coefficients of 0.94, R2 values of 0.76, RMSE values of 0.27 and mean absolute percentage errors of 9.24 and 3.23 for the SWL and construction cost, respectively. The paper concludes that such spatial decision support is good for municipal water planning.

This study was conducted in the South Wollo zone to assess vegetation dynamics and their relationship with rainfall and land surface temperature. To that end, 16-day 250-meter resolution Moderate Imaging Spectro-radiometer (MODIS) Normalized difference vegetation Index (NDVI), 5-kilometer resolution MODIS Land Surface Temperature (LST), 5-kilometer CHIRPS data were used. A Mann-Kendall (MK) trend test was used to determine the trend of each dataset using seasonal and annual time series. In addition, the Pearson correlation coefficient was used to estimate the relationship between NDVI and climatic elements. The results of the study revealed that there was no significant change in the annual and seasonal rainfall and LST during the period 2000 to 2021, except LST in the autumn season (p = 0.01) and NDVI was significant only in the mean annual (p = 0.027) and summer seasons (p = 0.03). The correlation between NDVI and rainfall was positive (r = 0.84, 0.78, 0.72 & 0.79) and obtained for the summer, autumn, winter, and spring season respectively. Similarly, the correlation between NDVI and LST was negative (r= -0.79, -0.75, -0.72, and -0.78) in the summer, autumn, winter, and spring seasons respectively. The outcome of the study indicates precipitation is the most significant factor in the distribution of vegetation and LST

The complex process of decision-making is addressed in this study, especially when dealing with diverse factors and input from several specialists. In the context of m-polar interval-valued neutrosophic hypersoft sets (m-PIVNHSSs), the paper proposes innovative adaptations of the correlation coefficient (CC) and weighted correlation coefficient (WCC), drawing on correlation analysis in statistics and engineering. The goal is to improve decision-making processes in scenarios with complicated features and input from several specialists. Through defined theorems and claims, the study offers a solid mathematical framework and presents methods based on CC and WCC to address decision-making complexity. These strategies show promise for enhancing decision accuracy in circumstances involving a wide range of features and expert inputs. AHP, TOPSIS, and other strategies that are now used might also be extended, according to the research. AHP, TOPSIS, and VIKOR are three possible methodologies that might be used to the m-PIVNHSSs environment, according to the research, opening opportunities for additional breakthroughs in the decision-making sector.

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.

Aims: The surgical helmet system (SHS) was developed to reduce the risk of periprosthetic joint infection (PJI), but the evidence is contradictory, with some studies suggesting an increased risk of PJI due to potential leakage through the glove-gown interface (GGI) caused by its positive pressure. We assumed that SHS and glove exchange had an impact on the leakage via GGI. Methods: There were 404 arthroplasty simulations with fluorescent gel, in which SHS was used (H+) or not (H-), and GGI was sealed (S+) or not (S-), divided into four groups: H+S+, H+S-, H-S+, and H-S-, varying by exposure duration (15 to 60 minutes) and frequency of glove exchanges (0 to 6 times). The intensity of fluorescent leakage through GGI was quantified automatically with an image analysis software. The effect of the above factors on fluorescent leakage via GGI were compared and analyzed. Results: The leakage intensity increased with exposure duration and frequency of glove exchanges in all groups. When SHS was used and GGI was not sealed (H+S-), the leakage intensity via GGI had the fastest increase, consistently higher than other groups (H+S+, H-S+ and H-S-) after 30 minutes (p < 0.05) and when there were more than four instances of glove exchange (p < 0.05). Additionally, the leakage was strongly correlated with the duration of exposure (rs = 0.8379; p < 0.050) and the frequency of glove exchange (rs = 0.8198; p < 0.050) in H+S-. The correlations with duration and frequency turned weak when SHS was not used (H-) or GGI was sealed off (S+). Conclusion: Due to personal protection, SHS is recommended in arthroplasties. Meanwhile, it is strongly recommended to seal the GGI of the inner gloves and exchange the outer gloves hourly to reduce the risk of contamination from SHS. Cite this article: Bone Jt Open 2023;4(11):859–864.

Tjeerd van der Ploeg,1 René Schalk,2– 4 Robbert J J Gobbens1,2,5,6 1Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, the Netherlands; 2Tranzo, Tilburg University, Tilburg, the Netherlands; 3Human Resource Studies, Tilburg University, Tilburg, the Netherlands; 4Economic and Management Science, North West University, Potchefstroom, South Africa; 5Zonnehuisgroep Amstelland, Amstelveen, the Netherlands; 6Department Family Medicine and Population Health, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, BelgiumCorrespondence: Tjeerd van der Ploeg, Inholland University of Applied Sciences, Faculty of Health, Sports and Social Work, De Boelelaan 1109, Amsterdam, 1081 HV, the Netherlands, Tel +31 6 53519264, Email tvdploeg@quicknet.nlBackground: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands.Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error.Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets.Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.Keywords: prediction models, modeling techniques, external validation, performance, calibration, correlation coefficient, root of the mean squared error

Abstract 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 ≥ 0.1). Feature extraction is then performed using SVD on the γk ≥ 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.

The current issue of global warming is prominent, and there are many factors that affect global temperature changes. Therefore, how to correctly judge the relationship between each influencing factor and global temperature changes and accurately find out the main reasons of global temperature rise which are the problems that must be considered and solved to alleviate global warming at present. According to previous official data, this paper proposed a correlation analysis method based on principal components to comprehensively analyze the relationship between natural disaster factors, human factors, and global temperature changes, and find out the main reasons that affect global temperature rise. Compared with traditional research methods, the new method provided in this paper can still remain scientific and accurate calculation results while reducing computational dimensions. The experimental results showed that in the relationship between natural disasters and global temperature changes, the average correlation coefficient of the principal component represented by biological disasters and geological disasters was the highest at 0.6097 and a test value of p

The aim of the article is to study the demand for educational services in Ukraine under the influence of economic, political and social factors that are effective in the country. Among the main factors influencing the demand for educational services are the COVID-19 pandemic, the full-scale invasion of the russian federation on the territory of Ukraine and, as a result, migration processes, the destruction of educational infrastructure, changes in the structure of the labor market, budget constraints, reduction of funding at the expense of households, etc. The projected value of the reduction in demand for educational services (number of entrants) by 2025 based on the studied parameters is on average 10%. A correlation and regression analysis was carried out to study the demand for educational services of higher education institutions (HEIs) depending on the main demographic, economic, political and social indicators. According to the analysis of the correlation matrix, the factors that have a high level of closeness of interconnection and a direct impact on the number of entrants to HEIs are the number of school graduates, the number of population aged 16–59 years and the employment rate. Factors such as a decrease in education costs, the COVID-19 pandemic, and the occupation have an inverse impact on the number of entrants. The paper considers the study of the effectiveness of the State regulation of admission to the budget form of education in individual specialties. For this purpose, data on the results of the 2023 admission campaign from the Unified State Electronic Database on Education were used. It is found that in 2023, the highest demand is for specialties related to the social and philological direction (Psychology, Journalism), as well as those that have a well-established importance in terms of prestige and employment (Dentistry, International Relations, specialties in the IT profile, etc.). On the other hand, the lowest demand is for engineering specialties, where the competition is two applications for three budget-funded places. The article uses econometric methods of analysis of digital information, analyzes the influence model between the indicators of the passing score and the general competition. Recommendations are provided on the revision of the policy of admission and competitive selection of students in certain specialties, taking into account the existing established trends in the demand for engineering specialties and taking into account the factor of occupation of the country’s territory and, as a result, changes in the structure of production and needs in the labor market.

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network’s spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.

Grasslands have been increasingly impacted by human activities, gradually becoming one of the most threatened ecosystems globally. Advanced geographic information technology and remote sensing techniques allow for a fresh perspective on studying the response of the grassland degradation index (GDI) to climate change. This study utilized remote sensing image data of grasslands to calculate the vegetation coverage and derive the GDI for five grassland regions of China from 2001 to 2019. The results indicate that the national degradation status of grasslands remained at a level of mild degradation. The increasing trend of the GDI in some regions was effectively inhibited by regional climate change, especially in the Northeastern and Northern Plain–Mountain–Hill Grassland regions, where the GDI showed a continuous decreasing trend. GDI was strongly correlated with atmospheric pressure, precipitation, temperature, and wind speed. In the arid northern region, the increasing precipitation and decreasing temperatures predominantly contributed to the depressed GDI. In the Qinghai–Tibetan Plateau Grassland region, the instability of the GDI is attributed to fluctuating atmospheric pressure, with a correlation coefficient ranging from 0.5 to 0.8. Our findings underscore the importance of meteorological factors to evaluate and forecast grassland ecosystem stability. This understanding is vital for developing informed conservation and management strategies to address current and future climate challenges.

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 current research involves the implementation of half diallel crosses between ten genotypes of bread wheat in 2018-2019. The parents and their F1 generation, excluding reciprocals, were then cultivated in the 2019-2020 growing season at the agriculture research farm, School of Agricultural Sciences and Engineering, IFTM University, Lodhipur Rajput, Moradabad (U.P.) and R.B. (PG) College, Mudi, Agra, using a randomized complete block design with three replications. Knowledge of type of gene action controlling target traits and genetic behaviour is a basic principle for designing an appropriate breeding procedure for the purpose of genetic improvement. Hence, the success of any selection or hybridization breeding program for developing varieties depends on precise estimates of genetic variation components for traits of interest which may be additive, dominant and nonallelic interaction effects. Significant genotype means squares and its components (parents and crosses) were obtained for all traits in both locations. Genetic components analysis found that additive (D) and Dominance (H1) was significant in all the traits. The value of mean degree of dominance was greater than additive, indicating availability of over dominance for all the traits. The present investigation revealed that grain yield was positively correlated with days to maturity, plant height, number of effective tillers per plant spike length, number of spikelets per spike, days to 50% heading and yield (q ha-1). Hence, more importance should be given to these traits while exercising selection. Based on the correlation and path studies it was reported that days to ear head emergence, days to maturity, plant height, number of effective tillers per plant, number of spikelets per spike, number of grains per spike, grain yield per plant and 1000 grain weight could be used as important selection traits in order of merit to improve productivity during designing a suitable breeding programme in wheat. [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]

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]

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]

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]

Introduction: Drought stress or water deficit has been known as one of the most important abiotic stresses so it considerably is decreased crop production. Barley (Hordeum vulgare L.) is the fourth most important cereal crop in the world after wheat, rice and corn. Among cereals, barley is the most tolerant crop against abiotic stresses and due to this capability is cultivated in a wide range of climates. The objective of the current study was to identify the superior drought-tolerant genotypes using grain yield and several yield-based indices of tolerance and susceptibility by applying various multivariate selection models. Materials and Methods: In the present study a set of promising genotypes of barley including 17 new genotypes along with a Jolge cultivar (as a check) was investigated through two separate experiments based on a randomized complete block design (RCBD) with three replications at the Cereal research station, Seed and Plant Improvement Institute, Karaj, Iran during two consecutive growing seasons (2019-2020 and 2020-2021) cropping seasons. After sowing, the number of irrigations was one time in autumn and five times in spring. Drought stress treatment was applied after anthesis, and irrigation was stopped for all stressed plot until seed repining stage. After collecting experimental data and estimating grain yield, several yield-based drought tolerance and susceptible indices were calculated. A heatmap-based correlation method was used to investigate association among calculated indices and grain yield data. Then three selection indices such as multi-trait genotype-ideotype distance index (MGIDI), factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP), and Smith-Hazel (SH) were exploited to identify the most tolerantgenotypes. All statistical analyses were computed using iPASTIC and R softwares. Results and Discussion: Based on combined analysis of variance for grain yield data showed significant differences for year, environment, and genotype main effects, as well as, the interaction effects for year × genotype, and year × environment × genotype. The result obtained from screening barley genotypes using drought tolerance and susceptible indices revealed good repeatability so that some of the investigated genotypes appeared in the same pattern in each year of experiments. Based on the Spearman's correlation coefficients, grain yields (Yp and Ys) positively and significantly correlated with MP, GMP, and STI indices in the first year. In the second year, a positive and significant correlation was observed between grain yields with STI, MP, GMP, and HM indices. Based on the averaged two-year data, grain yields significantly and positively correlated with HM, STI, MP, and GMP indices, supporting the repeatability of our findings. To identify the most tolerant genotypes based on multi-indices, we used three multi-trait selection indices such as MGIDI, FAIBLUP, and SH. Accordingly, genotypes numbers G7, G9, and G16 for the first year, G4, G13, and G17 for the second year, and three genotypes G7, G13, and G16 over two years were selected as superior genotypes using the MGIDI index. Based on the FAI-BLUP index, the following genotypes were identified as the most tolerant genotypes: G7, G9, and G17 in the first year; G4, G9, and G13 in the second year; G7, G13, and G16 in over two years. The result of screening genotypes using the Smith-Hazel index showed that three sets of genotypes including G4/G7/G13, G13/G14/G16, and G2/G3/G18 were identified as the high-yielding and most tolerant genotypes in each year and averaged two years, respectively. The venn-plot rendered based on three selection indices revealed that genotype numbers G7 and G13 were superior genotypes in the first and second years. Conclusion: In conclusion, our results indicated that G13 "Comp.Cr229//As46/Pro/3/Srs/4/Express/5/D10*2" with the highest grain yield in both control and drought stress conditions as well as the best ranking pattern for all drought tolerance indices can be a candidate as a superior drought-tolerant genotype for further studies before commercial introduction. [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]

In 1991, Gerstenkorn and Manko introduced a correlation coefficient similarity measure for intuitionistic fuzzy sets, aiming to investigate statistical relationships between objectives. However, their proof of the upper bound for the correlation coefficient similarity measure is overly complex and can be simplified. In 1995, Bustince and Burillo developed a similar correlation coefficient similarity measure for interval-valued intuitionistic fuzzy sets, but their proof of the upper bound closely resembles that of Gerstenkorn and Manko. Both proofs utilized the Schwarz inequality, yet failed to fully harness its power as a tool. In this paper, we begin by simplifying the intricate computations presented in the works of Gerstenkorn and Manko (1991) and Bustince and Burillo (1995). Subsequently, we demonstrate that their proofs can be directly derived by applying the Schwarz inequality. For a related transit bus model, we derive a new approximated solution that is very closed to the approximated solution of Wang et al. (2023) with the relative error less than 5%. We also consider an inventory model to show a possible new direction to locate the formulated closed-form optimal solution. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Nanchang University (Engineering & Technology) is the property of Nanchang University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Developing a novel approach to treat cancerous cells while sparing healthy cells is crucial in cancer research. Plant-synthesized using Zinc Oxide Nanoparticles nanoparticles are biocompatible and stable, making them ideal for biomedical, industrial, cell imaging, and biosensor applications. This study reported the morphological development caused by synthesized Zinc Oxide Nanoparticles (ZnO-NPs-AE) in MDA-MB 231 human breast cancer cells, and a multi-linear regression (MLR) model that predicts the morphological changes was also developed. Morphological parameters such as cell body diameter (CBD), process thickness (PT) and field diameter (FD) were analyzed after treatment with various ZnO-NPs-AE concentrations and incubation for 24 hours. Significant decrease in CBD with 25, 50, 100 and 200 μg/mL ZnO-NPs-AE (P > 0.05; n ≥ 10) and 2.5, 5 and 10 μg/mL of synthesized ZnO-NPs- AE did not show any significant effect on the CBD (P < 0.05; n ≥ 10). Similarly, a significant decrease in PT and FD was observed with increased concentration. Furthermore, the concentration of the nanoparticles and our morphometric results were considered in MLR modelling and performance efficiency was evaluated based on correlation coefficient (R), coefficient of determination (DC), mean square error (MSE) and root mean square error (RMSE). Overall, our performance efficiency results revealed the model's ability to predict morphological changes in MDA-MB 231 cells following treatment with synthesized ZnO-NPs- AE. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, spatiotemporal relationship between PM2.5 and PM10 concentrations and meteorological parameters were investigated for Silivri and Ümraniye districts in İstanbul for 2014–2020. For this purpose, hourly PM2.5 and PM10 concentrations values of two air quality monitoring stations and meteorological data (wind speed, wind direction, relative humidity, total precipitation, minimum and maximum temperature) were examined. In all seasons, while PM concentrations were lowest at 06:00 local time (LT), PM2.5 and PM10 have peak values around 09:00 and 19:00 LT both in Silivri and Ümraniye mainly due to anthropogenic activities such as vehicle exhaust emissions. In daily perspective, highest PM values were observed on Sundays in winter at Silivri. On the other side, peak PM values are shown on Fridays at Ümraniye. It was found that local emission sources during low wind speeds cause the highest PM2.5 concentrations during winter months and southerly winds exceeding 8 m/s increase the PM10 levels at Silivri and Ümraniye. The statistical analysis showed that PM2.5 and PM10 concentrations at Silivri were negatively correlated with wind speed with correlation coefficients of -0.56 (winter), -0.47 (autumn), respectively. Wind speed is negatively associated with PM2.5 (r=-0.48) and PM10 (r=-0.38) in winter season at Ümraniye. In addition to this, relative humidity showed negative relationship with PM10 (r=-0.43)in spring at Silivri, while a positive correlation was found between PM10 (r=0.40) and PM2.5 (r=0.38) measured in the summer season and the maximum temperature. In addition to the anthropogenic factors (e.g. urbanization, transportation, and industrialization) that decrease air quality of İstanbul, local meteorological variables and atmospheric transport of pollution are observed to be the other factors that contribute to air pollution. [ABSTRACT FROM AUTHOR]

Precise quantification of climate-growth relationships can make a major contribution to scientific forest management. However, whether differences in the response of growth to climate at different altitudes remains unclear. To answer this, 264 trees of Larix kaempferi from 88 plots, representing different altitudinal ranges (1000–2100 m) and tree classes were sampled and used to develop tree-ring chronologies. Tree-ring growth (TRG) was either positively (dominant) or negatively (intermediate and suppressed) correlated with climate in different tree classes at different altitudes. TRG was strongly correlated with growing season at low altitudes, but was less sensitive to climate at middle altitudes. It was mainly limited by precipitation and was highly sensitive to climate at low altitudes. Climate-growth relationships at high altitudes were opposite compared to those at low altitudes. TRG of dominant trees was more sensitive to climate change compared to intermediate and suppressed trees. Climate factors (annual temperatures; moisture, the number of frost-free days) had different effects on tree-ring growth of different tree classes along altitudinal gradients. It was concluded that the increase in summer temperatures decreased water availability, resulting in a significant decline in growth rates after 2005 at lower altitudes. L. kaempferi is suitable for planting in middle altitudes and dominant trees were the best sampling choice for accurately assessing climate-growth relationships. [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]

The degradation of Zinc Oxide Surge Arresters (ZnOSA) is commonly detected by the resistive component of its leakage current 'Ir'. The methods previously employed to extract the resistive component of the leakage current were intricate and demanded a greater number of computational steps. The objective of this paper is to propose a new technique for the age detection and degradation analysis of ZnOSA, named the 'Correlation Method', which does not require the extraction of the resistive leakage current. The correlation coefficient is calculated between the power factor (pf) and total leakage current (It) on the MATLAB software (Version R2018a). The performance of this method is analyzed experimentally by changing the voltage, ranging from 70 to 120 kV, of surge arresters. The proposed research offers improved computational efficiency and provides valuable insights into the relationship between the power factor, leakage current, and the aging process of ZnOSA. Moreover, the results of the proposed methods are compared with those of existing 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]

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]

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]

• 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]

Inclusion of concomitant variable(s) in an analysis of variance (ANOVA) model is an indication that the model is of ANCOVA model provided that there is correlation between the concomitant and response variable. This study employ numerical illustrations of the analysis of covariance of models of Sudoku square with no treatment effects on concomitant variable- Result of the illustration, showed that error variance from the ANCOVA Sudoku square models reduced 12.7761 to 5.5152 for model I; 13.6898 to 6.4690 for model II; 15.7926 to 4.8160 for model III and 16.5152 to 4.9226 for model IV respectively in which the concomitant variable had justified its inclusion in the models. The results showed that adjusted treatment effect is similar across the four ANCOVA models, the correlation coefficient for each of the ANCOVA model is highly positively and the error mean square obtained for ANCOVA models are less than the values of error mean square obtained for ANOVA models of Sudoku square. [ABSTRACT FROM AUTHOR]

Gas extraction quantity from coal seam is the key index to evaluating the effect of eliminating coal and gas outbursts. The prerequisite for increasing the gas extraction quantity and improving the effect of gas control is to make clear the influencing factors of gas extraction. The main factors affecting gas extraction are permeability, gas pressure, and buried depth of coal seam in this paper. The qualitative and quantitative analyses of gas extraction quantity by three factors are carried out according to the test data. The research results show that: (1) In the areas with high permeability and gas pressure, the gas extraction volume shows random characteristics, indicating clear regional differences. (2) The increase of buried depth of coal seam will reduce the fluctuation of gas extraction quantity, resulting in a lower extreme value of gas extraction quantity. The possibility of gas extraction at a lower level will be significantly increased. (3) The correlation analysis shows the change law of the positive correlation coefficient between gas extraction quantity, gas pressure, and permeability, and shows the opposite trend with the increase of the buried depth of coal seam. The results show an important guiding significance for improving the efficiency of gas control. [ABSTRACT FROM AUTHOR]

Track geometry inspection data have long endured positional errors and shifts, hindering the quality evaluation, deterioration analysis and maintenance work of railway track. Besides, vehicle response inspection data are also typically misaligned or shifted from each other, but it tends to be ignored by people. In light of this situation, a position calibration method on the basis of a simple similarity metric, i.e. correlation coefficient, is proposed to tackle the positional errors and shifts among multisource track inspection data concurrently. For the convenience of practical implementation, a scheme containing three parts is designed to fulfil the purpose and a two-step procedure is adopted by all the three parts. According to the performance test on a dataset containing five inspection runs of a high-speed railway in China, the positional error between inspection data and actual locations can be greatly diminished, and the data inspected at different times and from different sources are well aligned with each other, with a time consumption of 0.66 s on average for a 1 km segment. [ABSTRACT FROM AUTHOR]

Due to the high genetic diversity in the apricot population of Iran, the first step in cultivar breeding is the pomological evaluation of this population and the identification of superior genotypes. Therefore, in order to identify superior apricot genotypes in the Karaj climate, the quantitative and qualitative traits of 29 apricot genotypes were evaluated based on the Randomized Complete Block Design (RCBD) during two consecutive years (2016-2017). The results showed a high genetic diversity in the studied apricot population in terms of morphological and pomological characteristics. The highest fruit weight was recorded in the Shahroud -43 (51.57 g) and Shahroud - 50 (50.85 g), while the lowest fruit weight was recorded in the Shahroud -42 (15.77 g) and Shahroud -48a (13.90 g) genotypes, respectively. The genotypes Shahroud -50, Shahroud -32, Shahroudi, and Shahroud -15 showed the longest fruit length. In addition, the highest amount of total soluble solids was measured in the Shahroud -13, Nasiri and Ghorban-Maraghe genotypes. Both Moorpark and Shams cultivars also had the highest pH of fruit juice. Based on the obtained results Shahroud -39, Shahroud - 50, Shahroud -58, Shahroud -49 and Shahroudi cultivar and genotypes were introduced as suitable genotypes for use as fresh fruits. Also, Shahroud -13, Nasiri, Ghorban-Maraghe, Shahroud -48, Shahroud -37 and Shahroud -43 are suitable genotypes and cultivars for both fruit processing and fresh fruits, due to high sugar content and other desirable characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Acta Geoscientica Sinica is the property of Acta Geoscientica Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)