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Uncertainty in medical diagnosis is the main challenge in medical emergencies (MEs) experienced by triage nurses and physicians in the emergency department (ED). The intuitionistic fuzzy correlation coefficient (IFCC) approach is used to analyze and interpret the relationship between variables in an uncertain environment. Assorted methods that involve applying a correlation coefficient under intuitionistic fuzzy sets (IFSs) were constructed based on Pearson's correlation model with various drawbacks. In this work, we construct two new intuitionistic fuzzy correlation measures (IFCMs) based on Spearman's correlation model. It is demonstrated that the Spearman-based IFCMs are appropriate for measuring correlation coefficients without any drawbacks. In addition, we show that the Spearman-based IFCMs overcome all the shortcomings of the associated IFCC methods. Equally, the Spearman-based IFCMs satisfy the maxims of the correlation coefficient that have been delineated in the classical case of correlation coefficient. Due to the challenges that uncertainty in medical diagnosis pose to MEs and the proficiency of the IFCC approach, we discuss the application of the constructed IFCMs in a triage process for an effective medical diagnosis during an ME. The medical data for the triage process are obtained via a knowledge-based approach. Finally, comparative analyses are carried out to ascertain the validity and authenticity of the developed Spearman-based IFCMs relative to other IFCC approaches.

Abstract 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.

Background: Identification is a crucial objective in autopsies, particularly when dealing with unknown, decomposed, or mutilated bodies. Various parameters are employed to establish the identity of a human being, and among them, stature holds significant importance in forensic casework. Stature estimation is pivotal in determining identity, especially when confronted with skeletal remains, fragmented bodies resulting from mass disasters, or mutilated corpses. Objectives: The main objective of the present study was to determine the relation between foot length and the stature of individual by using correlation coefficient in both sexes. To provide a linear regression model for estimation of stature from foot length in both males and females. Methodology: This cross-sectional study was conducted on 400 medical students aged between 20-25 years, studying at Andhra Medical College, Visakhapatnam, Andhra Pradesh, India. The length of both right and left foot from the posterior most part of the heel to the longest toe is taken using measuring tool and height of the individual is measured using standard scale and both are compared. Using all the data the study aims to establish a linear regression model for estimation of stature from foot length in both male and female. Conclusion: In all the groups, a significant and positive correlation between stature and foot length was established, and regression equations were derived for both the feet. An attempt was made to study the correlation between stature and foot length separately in both the sexes. However, owing to no significant difference in the results obtained, entire study sample was taken as a single unit for deriving the regression equation. In conclusion, the individual foot length proves to be useful for stature estimation, offering crucial support for forensic experts and anthropologists.

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.

Spherical fuzzy sets are more powerful in modelling the uncertain situations than picture fuzzy sets, fermatean fuzzy sets, Pythagorean fuzzy sets, intuitionistic fuzzy sets, and fuzzy sets. In this paper, we first define the variance and covariance of spherical fuzzy sets. Then, using variance and covariance, we define the unique spherical fuzzy set correlation metric in line with the statistical coefficient of correlation. Two spherical fuzzy sets are correlated in both direction and strength using the provided measure of correlation. We discussed its many characteristics. We compared the measure of correlation with the current ones through linguistic variables. We established its validity by showing its application in bidirectional approximate reasoning. We also resolve a pattern identification issue in the spherical fuzzy environment using the provided correlation function, and we compare the results with several current measurements.

Inaccurate estimation in sustainable construction projects is a significant challenge for appraisers, particularly when data and knowledge about the projects are lacking. As a result, there is a need to use cutting-edge technology to solve the issue of estimation inaccuracy. Iraq’s productivity estimates are now made using outdated, ineffective methodologies and procedures. In addition, it is essential to implement cutting-edge, quick, precise, and adaptable technology for productivity estimation. This study’s major goal is to calculate the overall costs of sustainable buildings using the cutting-edge technique known as artificial neural networks (ANNs). For Iraq’s construction industry to handle projects successfully, ANNs must be used as a new technology, a methodology developed to estimate the overall costs of sustainable construction projects. In this study, the process of cost estimation was modeled using ANNs. Investigations of a number of examples involving the creation of ANNs have also been made, including network design and internal elements and how much they impact the effectiveness of models built using ANNs. Equations were developed to determine structural productivity. These networks were shown to have extremely strong predictive power for both accounting coefficients (R) (93.33%) and the overall costs of sustainable construction projects, with a prediction accuracy of 87.00 and 93.33%, respectively.

The article discusses the typology of significant connections for the indicator “Group-Oriented – Self-Reliant” (“ conformism – non-conformism”) (oriented at the group – independent) within the framework of studying statistical connections with the author’s method and correlation analysis. A total of 13 significant connections were identified, two of which are moderate (the maximum is 0.41), and the rest are very weak or weak in the range from 0.19 to 0.3. It means that in terms of linear mo dels they can be ignored. At the same time, among these 13 dependencies, two strong simplest nonlinear dependencies can be distinguished which are truly crucial for the research. The strong nonlinear dependency of the variable “ Ideosyncratic type” on the variable “Group-Oriented – Self-Reliant” (“conformism – non-conformism”) was considered in detail, as well as the connection of two significant dependencies of the variables “psychopathy” and “Hermit personality type” with the same variable for comparison. All the three dependencies are similar in their linear component and within the framework of the modern approach, popular among psychologists, should be treated as a meaningful result of the study. However, in reality, only one of them is interesting to the researcher. For visual representation, graphical comparisons of the three with each other and with their regression models built on the basis of linear correlation are considered. This indicates the non-linear nature of the dependencies of the indicator “Group-Oriented – Self-Reliant” (“ conformism – non-conformism”) in the conducted psychological study.

This paper introduces a Koch fractal octagonal antenna designed for the Ultrawideband (UWB) Frequency range. The utilization of the Koch fractal in the antenna design contributes to size reduction as well as provides compactness in the antenna for UWB. Additionally, it has been noted that the Koch fractal offers wideband operation. The antenna employs copper as the conductor material, and Flame Retardant-4 (FR4) serves as the substrate. The substrate has a dielectric constant ϵr=4.4, a loss tangent tanδ=0.02, and a thickness (h) of 1.6 mm. The overall antenna's dimensions are 34.3 × 26.5 × 1.6 or electrical dimensions represent as 0.68λ0 × 0.53λ0 × 0.032λ0 . It achieves a maximum gain of 8.94 dBi at a frequency of 12.25 GHz, offering a broad bandwidth ranging from 2 GHz to 12.1 GHz.This antenna exhibits resonance at three distinct frequencies, namely 3.3 GHz, 6 GHz, and 8.6 GHz, making it highly efficient with an overall efficiency of 96.8%. The time domain characteristic is acceptable for UWB, group delay is 1.1 ns, the The proposed antenna has a high-fidelity factor of 90.2, and the correlation coefficient is 0.9. which makes the antenna a good candidate for UWB. Due to its performance characteristics, this proposed antenna is well suited for short-range wireless personal area networks (WPANs), supporting high-data-rate applications like Bluetooth and wireless USB and Wireless Body Area Network (WBAN) applications. Its proficiency also extends to industrial settings, where it helps with precision in control systems, asset tracking, and short-range sensing and radar systems. [JJCIT 2024; 10(1.000): 58-73]

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 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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This research is designed to investigate the variations in ultrasonic p-wave velocity in various coal based paste fill materials used for recovering standing pillars in closed/closing coal mines, with consideration given to the effects of numerous material-related factors. For this purpose, orthogonal tests were designed. The evaluation was performed on the effects of four variables on the ultrasonic p-wave velocities in samples, using coal grains as the primary material. These variables consisted of the coal grains' particle size (PA), high-water material content (PB), cement content (PC), and water content (PD). The experimental results show the following: (1) Ultrasonic p-wave velocity of coal based paste fill materials are measured within the range of 1.596 to 2.357 km/s, and these are classified (in descending order) as PD, PB, PC, and then PA, based on their effects on ultrasonic p-wave velocity. (2) Ultrasonic p-wave velocity is positively correlated with compressive strength and shear strength; the correlation coefficients are 0.82 and 0.69, respectively. (3) Changes in the ultrasonic p-wave velocity of coal based paste fill materials, when exposed to various factors, have been characterized by fitted formulae. It was observed that the velocity maintained a quadratic polynomial correlation with factor PB and exponential correlations with factors PA, PC, and PD. The comprehensive predictive model, reflecting the characteristics of the ultrasonic p-wave velocity in response to the combined influence of these four factors, was developed through the utilization of fitted equations pertaining to individual factor variations. Subsequently, this model underwent verification. [ABSTRACT FROM AUTHOR]

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem, so it is essential to locate the source of the leak. However, due to the complexity and variability of the marine environment, the signals collected by hydrophone contain a variety of noises, which makes it challenging to extract useful signals for localization. To solve this problem, a hydrophone denoising algorithm is proposed based on variational modal decomposition (VMD) with grey wolf optimization. First, the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K and α. Afterward, the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions (IMFs). Subsequently, the number of interrelationships between each IMF and the original signal was calculated, the threshold value was set, and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction. Finally, the arrival time difference is used to locate the origin of the leak. The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig, and the effectiveness and feasibility of the method are verified. [ABSTRACT FROM AUTHOR]

The effects of Salmonella gallinarum infection on the haematology and oxidative stress (OS) in pullets were evaluated in this study. Fifty 7-week-old pullets were randomly assigned to two groups of 25 pullets per group. The infected pullets were inoculated orally with S. gallinarum (109 S. gallinarum colony forming units/mL), while uninfected pullets were the controls. Haematological and OS parameters were determined following standard protocols. There was a significant loss (p<0.05) of body weight. The packed cell volume (PCV), haemoglobin (Hb) concentration, red blood cell (RBC) count and mean corpuscular haemoglobin concentration (MCHC) were significantly lower (p<0.05). In contrast, the mean corpuscular volume (MCV) was significantly higher (p<0.05) in the infected pullets when compared to the controls. The total white blood cell (TWBC), absolute heterophil, lymphocyte, eosinophil and monocyte counts, of the infected pullets, were significantly higher than that of the controls. Serum malondialdehyde (MDA) level, glutathione peroxidase (GPx) and catalase (CAT) activities of the infected pullets were significantly higher (p<0.05) when compared to the uninfected controls. Absolute heterophil count correlated strongly, positively and significantly with MDA. An association was established between significantly elevated GPx and CAT, which have antioxidant properties, and survival/health improvement indices such as improved weight gain and self-recovery. It was concluded that S. gallinarum infection of pullets caused significant alterations in the haematology, induced OS, and stimulated the body's antioxidant defence mechanism to elaborate GPx and CAT. This may suggest the use of antioxidants in the treatment of fowl typhoid. [ABSTRACT FROM AUTHOR]

A comparative analysis of changes in the microbiological and chemical composition of water and peat in the drained and fire-event areas of the Bakchar bog (the northeastern part of the Great Vasyugan Mire) was performed during the growing season of 2022. It was revealed that the fire effect persists in the upper layer (up to 50 cm) of the post-pyrogenic area even 6 years after the fire. It manifests itself in a disruption of the seasonal dynamics of the content of a number of components (O2, CO2, , ), and in the size variance of different physiological microflora groups: ammonifying, pedotrophic, oligotrophic, hydrocarbon-oxidizing, cellulose-destructing, denitrifying, and sulfate-reducing. In addition, the correlations between the analyzed microbiological and physico-chemical parameters differ for drained and post-pyrogenic areas, which suggests the long-term pyrogenic effect on the processes of accumulation and destruction of organic matter. [ABSTRACT FROM AUTHOR]

Resistance factors (ϕ-factors) are reliability-based factors of safety used in load and resistance factor design (LRFD) of structural systems. The ϕ-factors specified in the consensus standard for the structural design of pultruded fiber-reinforced polymer (FRP) structural members and connections are generally lower than the ϕ-factors used for more conventional materials (e.g., structural steel, reinforced concrete, and wood) for comparable limit states, consequently potentially limiting the application of pultruded FRP as a structural material. This work describes a review of 350 published experimental tests of bolted connections in pultruded FRP and compares these test results with the nominal strengths from equations in the consensus standard. Subsequently, a first-order reliability method analysis—following the methodology proposed by Ellingwood for pultruded FRP composites—was performed to re-examine ϕ-factors for the most common limit states of bolted connections in pultruded FRP. All relevant limit states for bearing-type connections as defined by the consensus standard were considered in the study and the derived ϕ-factors were compared with the ϕ-factors prescribed in the consensus standard with the goal of highlighting the need for additional research. [ABSTRACT FROM AUTHOR]

As a supporting factor, transportation is an important element of destination image that provides a base for the successful tourism industry. It is like the blood vessels of an area and is considered a determinant in developing a tourist destination. The article aims to characterize the status or problem of transportation accessibility in Kinnaur. GARMIN hand GPS (Global Positioning System) has been used to identify the damaged roads from their start to endpoints. In addition, a simple random sample technique has been used to register the opinion of 280 tourists about the transport facilities. Study results suggest that the bad condition of National Highway–22 is one of the barriers to tourism development in Kinnaur. There were no significant differences found between the selected destinations. Overall, the district headquarters, Kalpa, has been perceived significantly higher agreements by tourists. The government should ensure that the Border Road Organization, the organization entrusted with the responsibility of construction and maintenance of roads in international border areas, has sufficient resources to invest in transport development and its maintenance. [ABSTRACT FROM AUTHOR]

A sweeping computational ghost imaging (SCGI)-based encryption system is intended for increased data security and speedier data transport. SCGI is combined with steganography and cryptography processes in this system. SCGI requires fewer shots, resulting in faster image capture, transmission, encryption, and decryption. This strategy also results in smaller, more compact data packages and higher bitrates. Least significant bit (LSB) uses steganography to conceal the hidden picture. In the case of Rivest-Shamir-Adleman (RSA) encryption, public and private keys are generated via a one-way function based on bucket values. Encryption is performed on two levels, with an asymmetric approach divided into three sub-items that significantly increase encryption. Surprisingly, the method uses fewer pictures for image reconstruction, resulting in faster image reconstruction. SCGI promises applications in an extensive number of data encryption sectors since this technology leads to smaller data packages and higher bitrates. The presented approach is examined using the number of pixel change rate (NPCR), normalized root mean square (NRMS), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC), which indicates constant encryption improvement. We experimentally and situationally demonstrate our findings under eavesdropping, which prove the resistance and robustness of our methods. In optimal settings, this innovation enhances encryption by up to 90% when compared to traditional encryption methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology 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 Jordanian Journal of Computers & Information Technology is the property of Jordanian Journal of Computers & Information Technology 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.)

The purpose of this study is to develop an evaluation method for assessing the relative relationship between gear tooth shape deviations on every gear tooth using network theory. Our previous study introduced a method for representing the phase difference between each helix deviation as a network, demonstrating that it is possible to identify the relative relationship between gear tooth deviations. However, there has been no in-depth analysis of the impact of injection molding on the gear's phase difference network. In this paper, we begin by measuring the gear tooth shape deviation, calculating the correlation coefficient, and expressing it as an adjacency matrix. When the adjacency matrix was visualized and displayed as a pixel plot, a periodic pattern was formed. The relationship between the position of the gate used to inject molten resin material into the injection mold and the pixel plot were then investigated in detail, and it was confirmed that the helix deviation network of a gear manufactured with the injection molding process is useful as a new indicator for the manufacturing error of injection-molded plastic gear. [ABSTRACT FROM AUTHOR]

This study presents a model to accurately describe the nonlinear deformation pattern of rock creep damage process by incorporating energy principles. The model captures the accelerated creep deformation pattern by considering the relationship between time and creep parameters at each stage of rock creep. A nonlinear creep model based on energy conservation is developed by integrating the time-dependent creep parameters into the model. The identified parameters of the model are compared to validate its feasibility and accuracy. The correlation coefficient between the fitted curve and the test curve exceeds 0.90, confirming the validity of the nonlinear creep energy damage model. Utilizing the energy conservation law, the model effectively characterizes the damage evolution throughout the whole creep process and accurately represents the nonlinear deformation behavior during the accelerated creep stage of rocks. Compared with the Nishihara model, the model presented in this study demonstrates a better fit with the test curve, serving as a novel approach for creep modeling. [ABSTRACT FROM AUTHOR]

The complexities of the marine environment and the unique characteristics of underwater channels pose challenges in obtaining reliable signals underwater, necessitating the filtration of underwater acoustic noise. Herein, an underwater acoustic signal denoising method based on ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), permutation entropy (PE), and wavelet threshold denoising (WTD) is proposed. Furthermore, simulation experiments are conducted using simulated and real underwater acoustic data. The experimental results reveal that the proposed denoising method outperforms other previous methods in terms of signal-to-noise ratio, root mean square error, and CC. The proposed method eliminates noise and retains valuable information in the signal. [ABSTRACT FROM AUTHOR]