Search

The objective of this article is to investigate the dependencies between selected European subindices before and during the COVID-19 pandemic. Research Design & Methods: The main analysis was quantitative. We used copula entropy and Pearson’s correlation. We considered the closing prices of sectoral indices from France (CAC sectors), Germany (DAX sectors), the UK (FTSE sectors), and the US (SP sectors), along with the main indices from these countries, that is CAC40, DAX, SP500, and FTSE100 (we collected the data from the database investing.com for the period from 4 January 2017 to 30 March 2023). We performed all analyses using R along with supplementary packages. Findings: When it comes to indications of the strength of dependence before and after the event (the outbreak of the COVID-19 pandemic) in relation to mutual information (delta) and linear correlation, we saw the biggest differences for the German market. For the DAX sectors, linear correlation underestimates post-event dependencies. The dependencies for other countries were similar on average. For half of the sectors (all markets), we recorded an increase in dependence after the event. A sector where we recorded growth in all countries was the TECH sector. Implications & Recommendations: The dependence measurement using mutual information expressed in terms of copulas has many advantages. It is not limited to measuring linear correlations. It can also capture a nonlinear correlation. Furthermore, it not only measures the dependence degree, but also considers the dependence structure, which is more than a correlation. Moreover, there was no assumption about the ellipticity of marginal and joint distribution. This dependence measure even allows for the modelling of the dependence of variables with different cumulative distribution functions. Contribution & Value Added: The novelty of this article is that it compares the results of dependence measurements by linear correlations and mutual information expressed in terms of copula entropy. Considering the indices and subindices of the main European stock markets, when both measures of dependence were used, we obtained significantly different results in both subperiods under investigation (i.e. before and after March 11, 2020). [ABSTRACT FROM AUTHOR]

This study aimed to investigate the gradient properties of bamboo at the microscopic level and provide a basis for improving the utilization rate of bamboo. Using moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) as a research subject, the variation of vascular bundle area percentage, chemical content, relative crystallinity (CR), mechanical properties of different bamboo slivers, and correlation between those parameters were analyzed. From the bamboo green layer (BGL) to the bamboo yellow layer (BYL), the distribution of vascular bundles changed from dense to sparse. Cellulose and lignin mass content decreased gently, and hemicellulose mass content showed gradual increases. The CR showed an order of bamboo middle layer (BML) > BGL > BYL. The tensile modulus of elasticity, tensile strength, bending modulus of elasticity, and bending strength decreased from BGL to BYL. The order of influence degree on mechanical properties of moso bamboo was vascular bundle area, hemicellulose content, lignin mass content, density, and CR, and these factors correlated with mechanical properties at a significant level (p < 0.05). Vascular bundle area had a decisive effect on the mechanical properties of bamboo. The vascular bundle area and density were linearly correlated with mechanical properties, while the lignin mass content and CR were curve-linearly correlated with mechanical properties. [ABSTRACT FROM AUTHOR]

The article presents the worldwide harmonized light-duty test cycle results of a diesel engine passenger vehicle carried out on a chassis dynamometer. Pollutant emissions from exhaust and fuel consumption were measured. Vehicle velocity was treated as a variable determining pollutant emissions and fuel consumption because the engine operating states depend on engine velocity and load during engine operation, and these states in turn affect pollutant emissions and fuel consumption. Actions taken to reduce pollutant emissions and consumption of fuel are often in opposition to each other. Therefore, there is a need to optimize these activities. There is also a need to know the relationship between the effects of taking specific actions that serve to achieve individual goals in order to rationalize actions aimed at reducing pollutant emissions and fuel use. The originality of the article lies in the use of the correlation theory between pollutant emission and fuel consumption as well as the processes that determine them, primarily vehicle velocity, to rationalize these activities. Pearson's linear correlation theory was used to assess the relationships between individual variables. Significant differences were found in the correlation coefficients between individual variables, which confirmed the need to take integrated actions to reduce pollutant emissions and fuel consumption. [ABSTRACT FROM AUTHOR]

Linear correlated fuzzy differential equations (LCFDEs) are a valuable approach to handling physical problems, optimizations problems, linear programming problems etc. with uncertainty. But, LCFDEs employed on spaces with symmetric basic fuzzy numbers often exhibit multiple solutions due to the extension process. This abundance of solutions poses challenges in the existing literature's solution methods for LCFDEs. These limitations have led to reduced applicability of LCFDEs in dealing with such types of problems. Therefore, in the current study, we focus on establishing existence and uniqueness results for LCFDEs. Moreover, we will discuss solutions in the canonical form of LCFDEs in the space of symmetric basic fuzzy number which is currently absent in the literature. To enhance the practicality of our work, we provide examples and plots to illustrate our findings.

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b ~ σ s l o p e estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b ~ . [ABSTRACT FROM AUTHOR]

Linear correlated fuzzy differential equations (LCFDEs) are a valuable approach to handling physical problems, optimizations problems, linear programming problems etc. with uncertainty. But, LCFDEs employed on spaces with symmetric basic fuzzy numbers often exhibit multiple solutions due to the extension process. This abundance of solutions poses challenges in the existing literature's solution methods for LCFDEs. These limitations have led to reduced applicability of LCFDEs in dealing with such types of problems. Therefore, in the current study, we focus on establishing existence and uniqueness results for LCFDEs. Moreover, we will discuss solutions in the canonical form of LCFDEs in the space of symmetric basic fuzzy number which is currently absent in the literature. To enhance the practicality of our work, we provide examples and plots to illustrate our findings. [ABSTRACT FROM AUTHOR]

Objective: The objective of this article is to investigate the dependencies between selected European subindices before and during the COVID-19 pandemic. Research Design & Methods: The main analysis was quantitative. We used copula entropy and Pearson’s correlation. We considered the closing prices of sectoral indices from France (CAC sectors), Germany (DAX sectors), the UK (FTSE sectors), and the US (SP sectors), along with the main indices from these countries, that is CAC40, DAX, SP500, and FTSE100 (we collected the data from the database investing.com for the period from 4 January 2017 to 30 March 2023). We performed all analyses using R along with supplementary packages. Findings: When it comes to indications of the strength of dependence before and after the event (the outbreak of the COVID-19 pandemic) in relation to mutual information (delta) and linear correlation, we saw the biggest differences for the German market. For the DAX sectors, linear correlation underestimates post-event dependencies. The dependencies for other countries were similar on average. For half of the sectors (all markets), we recorded an increase in dependence after the event. A sector where we recorded growth in all countries was the TECH sector. Implications & Recommendations: The dependence measurement using mutual information expressed in terms of copulas has many advantages. It is not limited to measuring linear correlations. It can also capture a nonlinear correlation. Furthermore, it not only measures the dependence degree, but also considers the dependence structure, which is more than a correlation. Moreover, there was no assumption about the ellipticity of marginal and joint distribution. This dependence measure even allows for the modelling of the dependence of variables with different cumulative distribution functions. Contribution & Value Added: The novelty of this article is that it compares the results of dependence measurements by linear correlations and mutual information expressed in terms of copula entropy. Considering the indices and subindices of the main European stock markets, when both measures of dependence were used, we obtained significantly different results in both subperiods under investigation (i.e. before and after March 11, 2020).

IntroductionThe temperature of the earth has been rising by about 0.74 degrees Celsius over the past century. A gradual increase in the average annual temperature has been reported by many researchers worldwide, while other reports suggest a decrease in this parameter. The assumption is that there will be more areas of the world experiencing higher temperatures. The climate changes are effectively represented by temperature changes, which is considered one of the main indicators in climate studies. The chemical composition of the atmosphere has changed because of the increase in human industrial activities, so it is responsible for unprecedented changes in the global climate in the past century. The increase in greenhouse gas concentration is the cause of this change. The evidence indicates that the increase in atmospheric gas concentration has caused a significant increase in global temperature. The use of thermal data from sensors is widely used in the study of terrestrial phenomena, as indicated by many studies. The temperature of the earth's surface is directly and indirectly linked to all human activities. It is still not possible to calculate the temperature of the earth's surface with perfect and accurate methods, but some sensors with suitable temporal, spectral, and spatial performance are able to take photos of the entire surface of the Earth. The study is more important due to the fact that various species of animals, such as Jebeer (belonging to the Bovidae), are exposed to climate changes in arid and desert areas. Due to its impact on humans, other creatures, and the entire environment, it is imperative to pay attention to climate change nowadays. In this regard, the main aim of the current study is to evaluate the LST (Land Surface Temperature) trends, changes, and temperature threats of the land surface in the Central Plateau of Iran. Time series remote sensing data of the MODIS (MOD11A2) sensor and Terra satellite, in 8 days with spatial resolution of 1km from 2002 to 2018 have been used. Material and Methods The current study has been focused on the central plateau of Iran. The central plateau of Iran lies within the arid lands belt of the northern hemisphere. The current study has been attempting to extract exact information from the images by employing specific techniques. To achieve this goal, the MOD11A2 product of Terra satellite MODIS sensor, the trend of temperature changes and time series construction of the significance of Man Kendall methods and linear correlation parameters such as maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature, minimum annually temperature for daily and nightly temperature were used in TerrSet software and Earth Trends Modeler section to extract significant increasing and decreasing areas. After identifying some parts of provinces with significant temperatures based on analysis and results, we can identify the vital numerical value of the temperature in each pixel of those significant parts in the next stage. This can be achieved by utilizing the difference between the final temperature and the initial temperature. Trend analysis was used to simulate daily and nightly temperature changes for parameters of maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature and minimum annual temperature. Results and DiscussionDaily temperature data in the Central Plateau of Iran, which includes monthly minimum temperature, annual minimum temperature, monthly maximum temperature, annual maximum temperature based on monthly maximum temperature, monthly median temperature and annual maximum temperature based on monthly median temperature, common in Semnan and Isfahan provinces, showed a significant increase in linear correlation according to the results. In Isfahan province, the linear correlation decreased significantly between the maximum annual temperature based on the maximum monthly temperature and the median monthly temperature. There was no significant trend in other provinces. The linear correlation between temperature data in Isfahan and Semnan provinces, including the minimum monthly, minimum annual, maximum annual, and median monthly temperature, decreased significantly. The linear correlation between average annual temperature, average monthly temperature, maximum annual temperature determined by maximum monthly temperature, average monthly temperature, and maximum annual temperature determined by median monthly temperature increased significantly in Yazd and Isfahan provinces. No significant trends were observed in other provinces. To estimate the amount and approximate number of significant increases and decreases, simulations of temperature changes were conducted. The range and approximate range of numbers for significant increase and decrease in temperature were calculated in degrees Celsius. In all analyses, the parts with higher temperatures had a reddish color. The intensity of the red color increased as the temperature increased, and as the temperature decreased, the red color became fainter and turned blue. The central plateau of Iran recorded a maximum temperature of 44C°and a minimum temperature of -7C°according to this study. The central plateau of Iran has three main provinces, which include Isfahan, Semnan, and Yazd. Considering the temperatures mentioned for these three provinces, the temperatures obtained from this study are very similar, which means that the conducted study is approved to a large extent. Animals are considered to be at risk due to temperature changes. Future research should emphasize the impact of climate change and temperature increase on the living conditions of various animals, particularly those found on the central plateau of Iran.

This paper constructs a multiple regression analysis model of the agricultural industry chain with rural environment and economic management and estimates the model parameters using the least squares method. The linear correlation between the variables is analyzed based on the variance expansion coefficient, standardization coefficient, complex correlation coefficient and other indicators, and the regression problem is analyzed for significance. Then the antidifferential Kalman filter optimization method of least squares and the threshold denoising optimization method based on wavelet decomposition and reconstruction transformation are introduced to optimize the model parameters to improve the model performance. Finally, the impact of agricultural industry chain construction on the rural environment and economic management is analyzed based on the model. The correlation coefficients between the three dimensions of agricultural product industry chain management and the performance of rural economic management are 0.318, 0.468, 0.416, and the correlation coefficients between them and the performance of rural environmental management are 0.297, 0.528, and 0.529, respectively, which indicates that rural agricultural product industry chain construction can promote rural management.

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

Maps of lunar wrinkle ridges (LWRs) were created from 70°N to 70°S and 140°E to 140°W (extracted and highlighted the major LWRs area) using automated LWRs detection process with Lunar Reconnaissance Orbiter Camera wide range angle camera and Lunar Orbiter Laser Altimeter data. Automatic detection of LWRs is challenging because the ridges are of irregular shapes and many ridges have been eroded and/or degraded over time. It’s a preliminary study of automated ridge detection from DEM data. Statistics and measurements of the extracted LWRs, including orientation, extent, length, height, and elevation offset, were performed based on the mapping of lunar ridges. The identified ridges were classified based on their orientation, distribution, direction, and each class were further divided over basalts, and nearby highlands. According to the findings, 3,375 segments with a total length of 26,455.01 km were identified, and the average elevation offset, width, and height of all the wrinkle ridges were 40.39 m, 3.47 km, and 0.29 km respectively after weighting by length. The LWRs were divided into three morphologies and distributions: parallel ridges, isolated ridges, and concentric ridges. The vast majority of LWRs were found in basalts area, with an extension into neighboring highland. The relations between the morphological parameters were further quantitatively analyzed, and a similar linear correlation between the width and height was found in each class of lunar ridges, implying that small and large ridges were formed as a continuum and that the three classes of ridges were probably formed by some common processes. Finally, the relations between the lunar wrinkle ridges and other geomorphic phenomena were analyzed, indicating that purely volcanic origin or buried premare structures are difficult to reconcile with the investigation. In addition, the consistency between the occurrence of the lunar wrinkle ridges and the thickness of lunar maria indicates that the formation of lunar wrinkle ridges is closely related to the lunar maria; nevertheless, the statistical NW direction of individual classes of LWRs also proposes the presence of an appropriate stress field during the process of their formation.

Despite the constant improvement of complex computer algorithms for the calculation of gas chromatographic retention indices (RIs), the simplest methods for their evaluation based on linear correlations between the indices of structural analogs from different taxonomic groups, RI1 ≈ aRI2 + b, remain important. It is shown that symbatic variations of the first numerical differences of retention indices, = RIn+ 1 – RIn (equivalent to the first derivatives of the retention indices with respect to the structural parameter varied in the group), are the conditions of correctness for such correlations in the simplest groups (substituted methanes). A monotonic variation of in one of the groups with the presence of extrema in the other group is an unequivocal sign of the absence of a linear correlation between retention indices. If the values of in one of the groups increase and decrease in the other, the ranking order of compounds in any one of them should be reversed. It is shown that the simplest relationship RI1 ≈ aRI2 + b is also applicable to more complex taxonomic groups (substituted ethanes, benzenes, and naphthalenes), and it allows one not only to estimate the RIs of compounds not yet characterized but also to refine known reference data. [ABSTRACT FROM AUTHOR]

The research shows the results of using a model-eliciting activities for learning correlation and linear regression. With its resolution, the participants acquired, expanded, and refined their statistical knowledge and skills, organizing, analyzing, and representing data, establishing correlations, and determining regression lines and coefficients of determination. Also, with the proposed models, they concluded, explained, and described the phenomenon. In addition to establishing the activity as a pedagogical tool and a useful element for research in virtual environments, the study shows deficiencies in the modeling of students linked to planning prior to addressing the problem. [ABSTRACT FROM AUTHOR]

Copyright of Revista Cientifica de la Facultade de Veterinaria is the property of Universidad del Zulia, Facultad de Ciencias Veterinarias 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.)

This study aimed to evaluate the influence of meteorological variables and geographic factors on the selection of soybean lines concerning grain yield in Brazil and Paraguay soybean-producing regions. The study was conducted in seven different environments: Bela Vista do Norte - PY, Palotina - PR, Mangueirinha - PR, Major Vieira - SC, Três Passos - RS, Toledo – PR, and Passo Fundo - RS. The randomized block design in an incomplete factorial scheme with six soybean genotypes (G1, G2, G3, G4, G5, and G6) was used for the experiments. The harvest occurred in the first half of March, and grain yield was measured through the total harvest of the plot and expressed in kg ha-1, with grain moisture at 13%. The climatic variables used in the study were maximum air temperature (Tmax, ºC), average air temperature (Tavg, ºC), minimum air temperature (Tmin, ºC), relative air humidity (RH, %), precipitation (Prec, mm), wind speed (WS, m/s), dew point (DP, °C), incident radiation (Rad_Inc, MJ/m²), and total radiation (RAD_OL, MJ/m²); and geographic factors were altitude (ALT), longitude (LON), and latitude (LAT). The G5 genotype with a genetic value for grain yield above the general average is the most adapted to favorable environments. Altitude had the greatest influence on the biological variability of the genotypes, with a negative correlation of moderate magnitude with grain yield. Grain yield was enhanced in environments with altitudes lower than 338 m at latitudes below 24.17S.

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

We propose a signal- and sensitivity-enhanced spin-exchange-relaxation-free atomic magnetometer utilizing a multi-pass cell with a highly spatially homogeneous optical pumping scheme. The relationship between the magnetometer’s performance and the number of probe beam passes is measured. The optical rotation angle exhibits a linear correlation with the number of passes, achieving 89 mrad nT ^−1 for a quadruple-pass cell. However, the sensitivity does not increase linearly when further increasing the optical length due to the noise limit. This work provides a method to study the enhancement performance of the magnetic response and sensitivity in atomic magnetometers by using multi-pass cells.

A new improved polyurethane materials was studied to improve the strength of paper, delay the aging of paper, solve the problem of poor permeability of traditional resins to paper, and meanwhile to provide basic data for industrial design and applications, the surface tension of composite solutions consisting of nano-SiO2-based polyurethane, organo siliconbased polyurethane and epoxy cyclohexane-epichlorohydrin polyurethane dissolved in ethyl acetate had been measured at temperatures ranging from 283.15 K to 308.15 K. The surface tension was fitted with the related equation between surface tension and temperature under normal pressure. The mean relative deviations between the calculated value and the experimental value were derived. The results showed that the model estimation of the surface tension of the composite solution were in good agreement with the experimental data, the paper coated by the polyurethane materials had better mechanical property. Therefore, the prepared polyurethane material has great application prospects in the protection of paper. [ABSTRACT FROM AUTHOR]

Objective: This study aims to assess the accuracy of linear alveolar bone measurements for implant planning using cone-beam computed tomography (CBCT) by comparing three 3D imaging software: CareStream (Carestream Health, Rochester, New York, United States), RadiAnt (Medixant, Poznan, Poland), and iRYS (Cefla s.c., Imola, Italy)., Methods: Twenty-seven dry goat mandibles were used for this in-vitro study. Gutta-percha markers were placed on the mandibles, which were then scanned using a CBCT machine. Linear measurements of the alveolar bone were taken at the implant sites using CareStream, RadiAnt, and iRYS software, and compared to gold-standard measurements obtained using digital Vernier calipers. Measurements included bone height and width, and the absolute measurement errors were calculated to assess accuracy., Results: iRYS consistently provided the most accurate measurements compared to the control, especially at lower error thresholds. RadiAnt tended to overestimate, while CareStream yielded intermediate values. Across all thresholds, iRYS showed the least deviation, followed by CareStream and RadiAnt. Statistical analysis showed no significant differences between the three software programs at higher error thresholds, confirming the reliability of each for implant planning., Conclusion: All three software programs (iRYS, CareStream, and RadiAnt) offered reliable and accurate measurements for implant planning using CBCT, with iRYS being the most accurate. Clinicians can confidently use any of the three, although iRYS may be preferred for cases requiring higher precision., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: Institutional Ethics Committee of Kalinga Institute of Medical Sciences Issued protocol number KIIT/KIMS/IEC/1291/2023. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Neralla et al.)

Gluten-free products are on today’s agenda since they represent the most hastily growing segments in the market, representing an opportunity for food companies. Nevertheless, it is well-known that gluten is a crucial network structure in the wheat dough systems, which accounts for the overall desired technological features of the final bakery goods. Therefore, the absence of gluten negatively affects the characteristics of gluten-free bread, triggering a technological challenge in the manufacturing of products with resembled characteristics of wheat-derived counterparts. The search for new protein sources has been studied as an approach to circumvent the technological drawbacks of gluten removal. Dairy proteins are functional molecules that can likely be capable of building up a protein-network structure so that it would improve the technological properties of gluten-free products. In the present work, different levels of dairy product addition (10 and 20%, w/w) were used to supplement the gluten-free bread formulas, and the impact on dough rheology properties was well correlated to the bread technological quality parameters obtained. Linear correlations (R2 > 0.904) between steady shear (viscosity) and oscillatory (elastic and viscous moduli) values of the dough rheology with bread quality parameters (volume and firmness) were obtained, suggesting that the bread quality improvements are proportional to the levels of dairies added. Likewise, strong linear correlations (R2 > −0.910) between pasting properties parameters and bread staling rate supported the hypothesis that the dairies tested have a high potential to generate bread with a low staling rate, which is an advantage to extending the shelf-life. In short, results confirmed that the addition of both dairy products, as bakery ingredients, can constitute a technological advantage to improve the overall gluten-free bread quality.

ObjectiveThis study aimed to investigate the correlation between component alignment and short-term clinical outcomes after total knee arthroplasty (TKA).Methods50 TKA patients from a regional hospital were enrolled in the study. The following component alignments were measured from radiological data acquired within 1 week after surgery: hip-knee-ankle angle (HKA), medial distal femoral angle (MDFA), medial proximal tibial angle (MPTA), femoral flexion-extension angle (FEA), tibial slope angle (TSA), femoral rotational angle (FRA) and tibial rotational angle (TRA). The Hospital for Special Surgery (HSS) knee scoring system was used to assess clinical outcomes after 1 year, with patients being divided into three groups (excellent, good and not good) according to the HSS scores. Difference analysis and linear correlation analysis were used for the statistical analysis.ResultsThe results showed significant differences in MDFA (p = 0.050) and FEA (p = 0.001) among the three patient groups. It was also found that the total HSS had only a moderate correlation with FEA (r = 0.572, p

In many real-world applications, the phenomena of interest are continuous in nature and modeled through continuous probability distributions, but their observed values are actually discrete and hence it would be more reasonable and convenient to choose an appropriate (multivariate) discrete distribution generated from the underlying continuous model preserving one or more important features. In this paper, two methods are discussed for deriving a bivariate discrete probability distribution from a continuous one by retaining some specific features of the original stochastic model, namely (1) the joint density function, or (2) the joint survival function. Examples of applications are presented, which involve two types of bivariate exponential distributions, in order to illustrate how the discretization procedures work and show whether and to which extent they alter the dependence structure of the original model. We also prove that some bivariate discrete distributions that were recently proposed in the literature can be actually regarded as discrete counterparts of well-known continuous models. A numerical study is presented in order to illustrate how the procedures are practically implemented and to present inferential aspects. Two real datasets, considering correlated discrete recurrence times (the former) and counts (the latter) are eventually fitted using two discrete analogues of a bivariate exponential distribution. [ABSTRACT FROM AUTHOR]

Recognition of a brain region's interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used. [ABSTRACT FROM AUTHOR]

Urea electrosynthesis under mild conditions starting from the adsorption of inert N2 molecules has brought out a promising alternative experimentally to conquer its huge energy consumption in industrial Haber‐Bosch process. The most crucial and inevitable reaction is the formation of urea precursor *NCON from *N2 and CO based on the pre‐selected reaction pathway, together with the following protonated processes. It is significant to comprehend their intrinsic intercorrelation and explore the principal descriptor from massive reaction data. Hereby, the authors study the dispersed dual‐metals (homonuclear MN3–MN3 moiety and heteronuclear MN3–M'N3 moiety) anchored on N‐doped graphene as electrocatalysts to synthesize urea. Based on the screened out 72 stable systems by ab initio molecular dynamics (AIMD) simulations as the database, six significant linear correlations between the computed Gibbs free energy and other important factors are achieved. Most encouragingly, the principal descriptor (ΔE(*NCONH)) is established because 72% low‐performance systems can be filtered out and its effective range (−1.0 eV < ΔEE(*NCONH) < 0.5 eV) is identified by eight optimal systems. This study not only suggests that dispersed dual‐metals via MN3 moiety can serve as promising active sites for urea production, but also identifies the principal descriptor and its effective range in high‐throughput methods. [ABSTRACT FROM AUTHOR]

Molecule rotation in the control of fungal diseases is essential to ensure sustainability and avoid the tolerance of pathogens to fungicides. Evidencing managements that promote greater efficiency in the control can contribute to increase the efficiency in the control of diseases. This work aims to reveal control alternatives for foliar diseases through different anti-fungal molecules applied in pre-flowering in the linseed crop. The experiment took place in the experimental area of Escola Fazenda, UNIJUÍ, located in the municipality of Augusto Pestana (RS). The experimental design used randomized blocks, with ten fungicide treatments in foliar application and three replications, totaling 30 experimental units. In the useful area of each experimental unit, the following characteristics were measured: plant height, plants per square meter, number of grains per plant, weight of grains per plant, grain yield, normalized green red difference index, green leaf index, spectral saturation index. The data obtained were submitted to descriptive analysis and to the assumptions of analysis of variance, such as homogeneity of residual variances by Bartlett's test and normality of errors by Shapiro Wilk. Afterwards, analysis of variance was carried out at 5% probability, with the significant variables, Tukey's test of multiple comparison of means was applied at the level of 5% of probability. In order to identify the tendency of association between the characters, Pearson's linear correlation was determined at 5% probability by t test. The active ingredient Pyraclostrobin + Fluxapyroxad can contribute favorably to the maintenance of floral units, capsules and grains per plant. The correct positioning of the use of the molecule is decisive and requires further studies in linseed culture. [ABSTRACT FROM AUTHOR]

Abstract MGMT-promoter methylation is associated with favorable outcome in glioblastoma. The aim of this study was to determine whether the absolute number of methylated Cytosine-Guanine-dinucleotide-(CpG-)sites within the DMR-2 island of the MGMT-promoter may correlate with outcome in a qualitative or quantitative fashion. In a cohort of newly diagnosed glioblastoma patients treated with stereotactic biopsy or open tumor resection plus concomitant chemoradiotherapy, we assessed MGMT-promoter methylation by methylation-specific polymerase-chain-reaction (MSP). Methylation of the CpG-sites 74–98 within the MGMT-promoter region was additionally analysed by Sanger sequencing, and the total number of methylated CpG-sites was correlated with outcome using proportional hazards models. 215 patients with glioblastoma were identified and stratified per MSP (positive: 53%, negative: 47%). Among MSP-positive tumors, hierarchical clustering identified three subgroups with different methylation rates (median: 80% vs. 52% vs. 47%), indicating a site-dependent methylation propagation. The methylation status of a given CpG-site indicated a neighborhood-dependent methylation propagation. Survival was linearly associated with the cumulative number of methylated CpG-sites. This was particularly true in patients who received at least one adjuvant cycle of temozolomide. Notably, all CpG-sites analyzed contributed similarly to effect size; this enabled a further predictive substratification of MSP-positive tumors with median OS ranging from as low as 17.1 months (

Many properties of starch-containing foods are significantly statistically correlated with various structural parameters. The significance of a correlation is judged by the p-value, and this evaluation is based on the assumption of linear relationships between structural parameters and properties. We here examined the linearity assumption to see if it can be used to predict properties at conditions that are not close to those under which they were measured. For this we used both common domesticated rices (DRs) and Australian wild rices (AWRs), the latter having significantly different structural parameters and properties compared to DRs. The results showed that (1) the properties were controlled by more than just the amylopectin or amylose chain-length distributions or amylose content, other structural features also being important, (2) the linear model can predict the enthalpy ΔHg of both AWRs and DRs from the structural parameters to some extent but is often not accurate; it can predict the ΔHg of indica rices with acceptable accuracy from the chain length distribution and the amount of longer amylose chains (degree of polymerization > 500), and (3) the linear model can predict the stickiness of both AWRs and DRs to acceptable accuracy in terms of the amount of longer amylose chains. Thus, the commonly used linearity assumption for structure-property correlations needs to be regarded circumspectly if also used for quantitative prediction.

Abstract Urea electrosynthesis under mild conditions starting from the adsorption of inert N2 molecules has brought out a promising alternative experimentally to conquer its huge energy consumption in industrial Haber‐Bosch process. The most crucial and inevitable reaction is the formation of urea precursor *NCON from *N2 and CO based on the pre‐selected reaction pathway, together with the following protonated processes. It is significant to comprehend their intrinsic intercorrelation and explore the principal descriptor from massive reaction data. Hereby, the authors study the dispersed dual‐metals (homonuclear MN3–MN3 moiety and heteronuclear MN3–M'N3 moiety) anchored on N‐doped graphene as electrocatalysts to synthesize urea. Based on the screened out 72 stable systems by ab initio molecular dynamics (AIMD) simulations as the database, six significant linear correlations between the computed Gibbs free energy and other important factors are achieved. Most encouragingly, the principal descriptor (ΔE(*NCONH)) is established because 72% low‐performance systems can be filtered out and its effective range (−1.0 eV < ΔEE(*NCONH) < 0.5 eV) is identified by eight optimal systems. This study not only suggests that dispersed dual‐metals via MN3 moiety can serve as promising active sites for urea production, but also identifies the principal descriptor and its effective range in high‐throughput methods.

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (−0.22) and between humidity and COVID-19 incidence rate (−0.15). The linear regression model showed the model linear coefficients to be 0.92 and −1.29, respectively, with the intercept being 55.64. For the test dataset, the R2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate. [ABSTRACT FROM AUTHOR]

Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production.

Background: Pulse interval is a biomarker of psychological and physiological health. Pulse interval can now be assessed using mobile phone apps, which expands researchers' ability to assess pulse interval in the real world. Prior to implementation, measurement accuracy should be established., Objective: This investigation evaluated the validity of the Light Heart mobile app to measure pulse interval and pulse rate variability in healthy young adults., Methods: Validity was assessed by comparing the pulse interval and SD of normal pulse intervals obtained by Light Heart to the gold standard, electrocardiogram (ECG), in 14 young healthy individuals (mean age 24, SD 5 years; n=9, 64% female) in a seated posture., Results: Mean pulse interval (Light Heart: 859, SD 113 ms; ECG: 857, SD 112 ms) demonstrated a strong positive linear correlation (r=0.99; P<.001) and strong agreement (intraclass correlation coefficient=1.00, 95% CI 0.99-1.00) between techniques. The Bland-Altman plot demonstrated good agreement for the mean pulse interval measured with Light Heart and ECG with evidence of fixed bias (-1.56, SD 1.86; 95% CI -5.2 to 2.1 ms), suggesting that Light Heart overestimates pulse interval by a small margin. When Bland-Altman plots were constructed for each participant's beat-by-beat pulse interval data, all participants demonstrated strong agreement between Light Heart and ECG with no evidence of fixed bias between measures. Heart rate variability, assessed by SD of normal pulse intervals, demonstrated strong agreement between techniques (Light Heart: mean 73, SD 23 ms; ECG: mean 73, SD 22 ms; r=0.99; P<.001; intraclass correlation coefficient=0.99, 95% CI 0.97-1.00)., Conclusions: This study provides evidence to suggest that the Light Heart mobile app provides valid measures of pulse interval and heart rate variability in healthy young adults., (©Stephen A Klassen, Jesica Jabbar, Jenna Osborne, Nathaniel J Iannarelli, Emerson S Kirby, Deborah D O'Leary, Sean Locke. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.08.2024.)

Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used.

MGMT-promoter methylation is associated with favorable outcome in glioblastoma. The aim of this study was to determine whether the absolute number of methylated Cytosine-Guanine-dinucleotide-(CpG-)sites within the DMR-2 island of the MGMT-promoter may correlate with outcome in a qualitative or quantitative fashion. In a cohort of newly diagnosed glioblastoma patients treated with stereotactic biopsy or open tumor resection plus concomitant chemoradiotherapy, we assessed MGMT-promoter methylation by methylation-specific polymerase-chain-reaction (MSP). Methylation of the CpG-sites 74–98 within the MGMT-promoter region was additionally analysed by Sanger sequencing, and the total number of methylated CpG-sites was correlated with outcome using proportional hazards models. 215 patients with glioblastoma were identified and stratified per MSP (positive: 53%, negative: 47%). Among MSP-positive tumors, hierarchical clustering identified three subgroups with different methylation rates (median: 80% vs. 52% vs. 47%), indicating a site-dependent methylation propagation. The methylation status of a given CpG-site indicated a neighborhood-dependent methylation propagation. Survival was linearly associated with the cumulative number of methylated CpG-sites. This was particularly true in patients who received at least one adjuvant cycle of temozolomide. Notably, all CpG-sites analyzed contributed similarly to effect size; this enabled a further predictive substratification of MSP-positive tumors with median OS ranging from as low as 17.1 months (< 18 methylated CpG-sites) to as high as 26.2 months (≥ 18 methylated CpG-sites) in the overall cohort. All in all, total number of methylated CpG-sites may correlate with outcome in a linear fashion. Such analysis may therefore add further predictive value to conventional methods of determining the MGMT-promoter status. [ABSTRACT FROM AUTHOR]

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (−0.22) and between humidity and COVID-19 incidence rate (−0.15). The linear regression model showed the model linear coefficients to be 0.92 and −1.29, respectively, with the intercept being 55.64. For the test dataset, the R2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate.

The new technological advances have brought a revolution on how economic agents interact with society and markets. Nowadays, the use of virtual currencies is more frequent in the financial transactions and bitcoin has been defined as the most important world cryptocurrency due to its high market capitalization and its technological infrastructure. Several studies have been conducted to discuss bitcoin advantages and disadvantages; however, few papers in literature have examined its connection and influence on the stock market. The objective of this paper is precisely cover this gap. Firstly, by providing tools and concepts to understand bitcoin’s dynamic, and then determining its relationship with stock market indexes. In that context, this manuscript examines the definition and function of bitcoin in the global world and its presence in Ecuador. Besides, exploratory and visual analyses are provided using the evolution of bitcoin and other market indexes. Finally, a linear correlation is computed between bitcoin, other cryptocurrencies, stock exchange indexes and commodities. The results in this study, employing visual and statistical analyses, demonstrated that bitcoin has: a strong relationship with other cryptocurrencies; a lineal correlation, not as strong as the previous one, with the main stock market indexes; and no linear correlation with commodities.

The objective of this study was to generate information through the analysis of variability and given structure of spatial dependence as well as the linear correlation of physical and chemical attributes of the soil, in order to assist in the planning of agricultural activities in six cities in the province of El Oro, Ecuador. The soil sample collections were carried out every two kilometers in the form of a grid with small changes in the field with a Dutch-type auger and GPS for georeferencing, with a depth of 0-0.20 m, thus totaling 368 points in the sample grid. Chemical and physical attributes of six municipalities in the province of El Oro were evaluated: Huaquillas, Machala, Chilla, Atahualpa, Portovelo and Las Lajas. The sampling was carried out in each city, but not between them. Distances between cities were 20,000 km to 180,000 km. Each soil attribute was subjected to an initial descriptive analysis, geostatistics and simple linear correlation matrix. Only pH and sand showed a high variation coefficient (22.02% and 25.42%) and all other attributes showed very high variation. Most of the semivariograms were adjusted to the spherical model. The longest range was indicated by magnesium (41,500 m) and the shortest by copper (7,740 m). The Mg pair versus sum of bases was the highest linear correlation among all the analyzed attributes. The pH stood out with a greater linear correlation between the other chemical attributes of the soil and its specific management zones. In view of the great taxonomic variation of the soil, climatic types, cultures used and management adopted, a specific analysis in each city is recommended for better agricultural recommendations. [ABSTRACT FROM AUTHOR]

Crystal structure and the optical spectral properties of 0.5 at.% Nd3+, 5 at.% Gd3+‐codoped Ca1‐xSrxF2 (0 ≤ x≤1) crystals grown by the temperature gradient technique (TGT) were investigated. The linear variations of the crystal structure and spectral parameters as a function of Sr2+ proportion (x) were observed and correlated with each other. The results indicated that Sr2+ ions introduced as matrix‐substituted ions could regulate the crystal structures and the spectroscopic properties. [ABSTRACT FROM AUTHOR]

• Coupling normalized abundance with ICR can get accurate abundance retrieval model. • The model can enhance the linear relationship between CRBD and mineral abundance. • The model performs better in abundance inversion on synthetic and real image data. • It has potential to extend to survey extraterrestrial minerals. Mapping or quantitatively estimating mineral content using remote sensing technology is an active way for monitoring minerals in large or inaccessible areas. Different spectral features of minerals, induced by their ionic composition, can be identified and related to mineral type or abundance. Based on the distinctive spectral absorption around 2.33 µm induced by the carbonate ion, we propose an improved continuum removal (ICR) algorithm, coupled with normalized abundance, to assess the relationship between continuum removal band depth (CRBD) and carbonate ion abundance. Through experiments using synthetic and real image data, ICR with ratio abundance normalization enhances the linear relationship between CRBD and abundance, resulting in the best performance for abundance retrieval. The synthetic data showed the lowest root mean square error of 0.04, while the lowest mean relative error for real image data reached 6.80 %. Compared it with five other algorithms, coupling normalized carbonate mineral abundance with ICR improves the accuracy of quantitative retrieval of carbonate ion. These results demonstrate that the proposed approach effectively reduces the influence of reflectance differences caused by external factors, providing a more reasonable algorithm to enhance spectral absorb features and quantitatively retrieve mineral abundance. The proposed approach has the potential to extend mineral information extraction even in the absence of sample data, and can be applied in surveys of the Moon and Mars for mineral quantitative analysis. [ABSTRACT FROM AUTHOR]

Introduction: Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. Methods: In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Results: Compared to normal subjects by using statistical tests (P

PILs are promising solvent systems for CO2 absorption and transformations. Although previously tremendous work has been paid to synthesize functionalized PILs to achieve a high-performance absorption, the underlying mechanisms are far less investigated and still not clear. In this work, a series of DBU-based PILs, i.e., [DBUH][X], with anions of various basicities were synthesized. The basicities of the anions were accurately measured in [DBUH][OTf] or extrapolated from the known linear correlations. The apparent kinetics as well as the capacities for CO2 absorption in these PILs were studied systematically. The results show that the absorption rate and capacity in [DBUH][X] are in proportional to the basicity of PIL, i.e., a more basic PIL leads to a faster absorption rate and a higher absorption capacity. In addition, the spectroscopic evidences and correlation analysis indicate that the capacity and mechanism of CO2 absorption in [DBUH][X] are essentially dictated by the basicities of anions of these PILs.

The α-H acidity is an important chemical property of ketones that has attracted much research interest. Theoretical prediction of pKa for ketone α-H is significant. In this work, we theoretically studied the nuclear shielding of various α-Hs in a set of ketones and that of the corresponding enolic hydroxyl Hs in tautomeric enol forms. It has been demonstrated through linear regression analyses that the pKa values of these ketones correlate with both sets of the calculated nuclear shielding values. The correlation coefficient R2 of the linear correlation relationship is 0.90. The present work has provided a new approach to computationally evaluating the acidity of α-Hs in ketones, enabling us to semi-empirically predict the ketone α-H acidity from the calculated nuclear shielding values. [ABSTRACT FROM AUTHOR]

Abstract In Malaysia, municipal solid waste (MSW) management has been one of the main issues that closely related to the environmental pollutions and human health risks. One of the promising method to utilize MSW as a fuel is gasification. However, MSW possess some drawbacks in terms of its properties such as high moisture content and low high heating value (HHV) which lead to the reduction of gasifier efficiencies and the quality of product gas produced. In order to overcome the problem, torrefaction process can be employed for upgrading the properties of MSW. Although torrefaction process has been widely implemented but correlation analysis of the quality of torrefied product is rather limited. This correlation analysis is indeed important as a tool to assess the suitability of biomass in torrefaction process. This is due to the fact that although the torrefaction process is able to increase some of the properties, the increment for certain biomass is deemed low and insignificant. Therefore, there is a need for a simple correlation analysis to help in the biomass selection for torrefaction purposes. In this work linear correlation models for MSW involving food waste and wood waste have been developed as a function of mass loss in order to predict the ultimate analysis of C, H, and O elements, proximate analysis of ash content, volatile matter (VM) and fixed carbon (FC) as well as high heating value (HHV) and energy yield (EY). In this study high coefficient determination was achieved using the developed linear correlation models (R2 > 0.87) for predicting H element, O element, VM, FC, HHV and EY. This suggests that the linear correlation models can be used to predict of the MSW characteristics after torrefaction process. [ABSTRACT FROM AUTHOR]

The aim of this work was to investigate the changes of the content of polyphenols in fruits of Capsicum chinense Jacq. at different harvest times and their correlation with the antioxidant activity. Habanero pepper plants grown in black soil (Mayan name: Box lu’um) and harvested at 160, 209, 223, 237 and 252 post-transplant days (PTD) were analyzed. The results indicated that subsequent harvesting cycles decreased the content of total polyphenols, catechin, chlorogenic acid and ellagic acid, while the content of gallic and protocatechuic acid increased. The antioxidant activity determined by DPPH (2,2-Diphenyl-1-picrylhydrazyl) radical scavenging and ABTS (2,2′-azino-di-3-ethylbenzthiazoline sulfonic acid) assay decreased through the harvest days. Linear correlation analysis between total polyphenol content and antioxidant activity in peppers resulted in a correlation of r2DPPH = 0.8999 and r2ABTS = 0.8922. Additionally, a good correlation of the antioxidant activity was found with catechin (r2DPPH = 0.8661 and r2ABTS = 0.8989), chlorogenic acid (r2DPPH = 0.8794 and r2ABTS = 0.8934) and ellagic acid (r2DPPH = 0.8979 and r2ABTS = 0.9474), indicating that these polyphenols highly contributed to the antioxidant activity in Habanero peppers. This work contributes to understanding the changes that take place during the development of Capsicum chinense, indicating that fruit harvested at earlier PTD showed the highest concentrations of total polyphenols and antioxidant activity, obtaining the best results at 160 PTD.

Restoration, preservation and sustainability of agricultural resources including soil demands biologically and economically viable farming practices. For boosting physico-chemical properties of soil from 2012 to 2015, three legumes including cowpea, cluster bean and soybean were intercropped with sorghum in 40-60, 75-25 and 100-100 seed blending ratios, while pure stand of sorghum was kept for comparison. Correlation studies were also conducted to determine linear or inverse relationship between physiological and agronomic attributes with green forage yield of sorghum. Results revealed that intercropping systems were not effective in significantly improving the physical properties of soil, however soybean and sorghum mixed seeded crop in 100-100 seed ratioresulted in the highest total and available nitrogen especially during last two years of the study and it was followed by mixed seeded crop of cluster bean and sorghum (100-100 seed ratio), while pure stand of sorghum appeared to be the most exhaustive farming system. Correlation analysis depicted a linear relationship between all agronomic attributes (seedling count, plant population at harvest, plant height, stem diameter, number of leaves and leaf area per plant, fresh and dry weights per plant) and physiological growth parameters (leaf area index and leaf area duration, crop growth rate and net assimilation rate) with forage yield of sorghum. Thus, legumes inclusion was found to be effective in restoring soil fertility, while long term legumes inclusion by following rotation may be suggested.