High Correlation Values Research Articles

Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms

Immobilized artificial membrane chromatography (IAM) has been proposed as a more biosimilar alternative to classical lipophilicity measurement. Determination of small molecule's affinity to phospholipids can be supported for predicting their behavior in the human body. Therefore, a better understanding of the molecular interaction mechanism between small xenobiotics and phospholipids can accelerate drug discovery. Here, the quantitative structure-retention relationships (QSRR) approach was integrated with mechanistic descriptors calculated using Chemicalize software to propose an easy-to-interpretation QSRR model. Considering the heterogeneous character of the data set, locally weighted least squares kernel regression belonging to similarity-based machine learning methods have been applied. The results showed that lipophilicity, charge, and maximum projection area determine molecule binding to phospholipids. Full validation of the obtained model based on OECD recommendations has been performed and the applicability domain was defined using the probability-oriented distance-based approach. The high values of predictive squared correlation coefficient (Q2), and small root mean square error of prediction (RMSEP), 0.812 and 6.739, respectively, confirmed that the obtained QSRR model is not well-fitted to the training data but also showed prediction power. Additionally, only 1.5% of molecules from the training set and 2.8% from the validation test are outside the applicability domain, confirming great predictive abilities.

Use of tritium in characterizing the leachate migration from a landfill – Central de tratamento de resíduos sólidos (CTRS) BR040 (Belo Horizonte, Minas Gerais, Brazil)

Landfills often become a source of environmental impact due to the percolation of the leachate (liquid resulting from the decomposition of buried waste), which can eventually disperse and contaminate the soil and water bodies. This study aimed to evaluate quantitatively and spatially the leachate plume from the former Belo Horizonte landfill in the Coqueiros and Taiobas watersheds by using tritium (3H) concentration in nineteen (19) sampling points: 14 groundwater, 4 surface water and 1 leachate. Among the methodologies applied to determine the 3H content in the leachate, pre-filtration proved to be the most effective due to the ease treating of the sample, as well as the identical results obtained compared to the traditional method. The 3H contents resulted in concentrations up to thirty times higher in the leachate (=96,19 to 111,87 TU) and ten times in groundwater (PM23/3 = 45,06 TU), compared to the calculated threshold (TS) of rainfall tritium (TS_3Hrain = 3,41 TU). Temporal series results indicate no trend and no influence of seasonality regarding the entry of pluvial tritium, leachate liquid level and wells water level. The 3H contour maps shows a transport of the mixture (water and leachate) towards the Coqueiros stream, with a higher concentration of leachate in wells closer to the landfill slope (PM13, PM14, PM15, and PM23) and in the southwest margin of the Coqueiros stream (PM17). In the furthest portion of the landfill, 3H levels are higher than the base threshold only at the deepest level of PM20 (=3,90 TU), possibly due to factors such as: i) denser character of the leachate compared to groundwater; ii) greater influence of recharge for regions further away from the landfill slope; and iii) the presence of the rock about 50 m from the surface. Based on the hydrogeological conceptual model, the transport of the leachate water mixture in the groundwater water compartment of the Coqueiros watershed basin seems to follow a piston or dispersion flow, with a small Peclet number (Pd = 0,05, as an example), as indicated by the high correlation coefficient values (R2 ≈ 0,85 to 0,99) from graphic interpolation. The mixture of water and leachate results in a transient time (tt) ≈ 30 years and linear velocity (Vl) ≈ 3–14 m/year in the interpolated sections. These indicate a coherent correlation with the mean hydraulic conductivity (K ≈ 29,34 m/year), as they result in effective porosity values between (Ne) ≈ 0.16 to 0.36, which corresponds to Ne range for unconsolidated sediments, similar to the wells lithotype profiles. Time series evaluation combined with lumped parameter approach suggest to be an interesting way to better understanding the HCM, and, therefore, to determine hydrodynamic parameters using tritium as a tracer.

Quantification of fat percentage in some types of meat using low-angle X-ray scattering (LAXS) and Monte Carlo simulation

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

Chitosan coated graphene oxide incorporated sodium alginate hydrogel beads for the controlled release of amoxicillin

Biopolymers are crucial in pharmaceuticals, particularly for controlled drug release. In this study, we loaded the broad-spectrum antibacterial drug amoxicillin into sodium alginate, a well-known biopolymer. Graphene oxide was incorporated into the composite, and the hydrogel beads were coated with chitosan for its mucoadhesive properties. Various composites were formulated by adjusting the weight percentage of graphene oxide (GO).The fabricated beads demonstrated controlled and sustained drug release, with 98 % of the loaded drug molecules released over 24 h at gastric pH. The antibacterial test using the disc diffusion technique confirmed the drug release, exhibiting greater effectiveness against the gram-positive bacterium S. aureus than the gram-negative bacterium E. coli. The drug release data were optimized using zero order, first order, Higuchi, and Korsmeyer-Peppas models. The experimental data were best fit to the Korsmeyer-Peppas model with a relatively higher correlation coefficient value. Biocompatibility was evaluated through a cell viability test using mouse fibroblast cell lines (L929). The MTT viability assay confirmed high levels of cytocompatibility, even at higher concentrations (100 μg/mL), with 98.15 % viable cells. These results highlight the potential of the fabricated beads as an effective amoxicillin drug delivery system with biomedical applications.

A Short-term solar irradiance forecasting modelling approach based on three decomposition algorithms and Adaptive Neuro-Fuzzy Inference System

In this study, a case study of four Indian cities i.e. Ajmer, Jaipur, Jodhpur and Kota in the state of Rajasthan are considered wherein 30 min ahead data have been obtained via the data site of the National Institute of Wind Energy and Wind Resource (NIWE) on which a proposed Global Horizontal Irradiance (GHI) prediction technique for all seasons is applied. Here, data has been pre-processed using three different signal decomposition algorithms in parallel i.e. Empirical Mode Decomposition (EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Ensemble Empirical Mode Decomposition (EEMD). Further, based on Pearson's Correlation Coefficient (PCC), corresponding IMFs & corresponding Residual obtained using the three decomposition algorithms are compared amongst each other respectively and that corresponding IMFs & corresponding Residual are chosen for signal reconstruction which are having highest correlation coefficient values. Thereafter, these selected IMFs and Residual from each algorithm are combined to form single input. In this way, three inputs formed from three decomposition algorithms based on the PCC values are fed to Adaptive Neuro-Fuzzy Inference System (ANFIS) for solar irradiance forecasting. The proposed technique shows significantly higher accurate results with less than 2% MAPE for different seasons considered at the site locations considered. Further, it terms of performance, the proposed technique is found to be independent of site location.

Mixture design optimization and machine learning-based prediction of Al-Mg alloy composite reinforced by Zn nanoparticles: A molecular dynamics study

Al-Mg alloy is widely used in various industries due to its practicality. It is crucial to achieve the highest possible tensile properties for this alloy. In this study, first, Zn nanoparticles were combined with the aluminum-magnesium alloy to modify their tensile properties. Then, a mixture design of experiment was utilized to find the best combination of Al-Mg-Zn alloy, which was validated through molecular dynamics simulation. Mechanical tests were also conducted on Al-1%Mg-1%Zn alloy at different temperatures and strain rates. The results suggested that when the temperature increases, the tensile properties decrease, while with higher strain rates they increase. Furthermore, four machine learning-based algorithms including Generalized Linear Model (GLM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF) were compared to determine the simplest and the most accurate one for the prediction of mechanical properties of Al-Mg-Zn alloy and due to the highest values of correlation coefficient, the ANN algorithm was found as the most accurate one for the prediction of mechanical properties. The GLM algorithm was also known as an appropriate algorithm to predict the mechanical properties. However, the DT and RF algorithms had low prediction precisions. Therefore, the parameters that were influential in the prediction precision of DT and RF algorithms were changed, and the best value of each parameter was obtained. It was found that the DT algorithm with a maximum depth of 6 has the highest precision prediction of mechanical properties. Furthermore, the RF algorithm had the highest accuracy for the prediction of mechanical properties with No. of trees = 4 and maximum depth = 4.

Adsorption property and kinetic studies of activated carbon fibers prepared from tissues by CO2 activation

Well-developed micro and mesoporous activated carbon fibers (ACFs) with fiber structure were prepared from tissue by CO2 activation. The XRD patterns and Raman spectrum indicated ACFs had a graphitic and amorphous structure. The SEM results indicated that the sample exhibited fiber structure with lots of mesoporous, with an average pore size of 2-5 nm. The specific surface area of 1517 m2 /g, micro surface area of 412.9 m2 /g, and total pore volume of 1.194 cm3 /g were obtained at 900°C with CO2 activation for 2 hours. ACFs showed relatively high methylene blue adsorption properties with an equilibrium adsorption capacity of 526 mg/g. The kinetic model of the Pseudo-second-order equation was more suitable for MB adsorption than the Pseudo-first-order equation and Intraparticle diffusion kinetics, with a high correlation coefficient value (R&gt;0.999). The present research provided a new idea of bionics for the manufacture of ACFs and brought forward a creative prospect for achieving energy-related CO2 emissions to net-zero and mitigating global warming.

Emerging Mesoporous Polyacrylamide/Gelatin–Iron Lanthanum Oxide Nanohybrids towards the Antibiotic Drugs Removal from the Wastewater

The polyacrylamide/gelatin–iron lanthanum oxide (P-G-ILO nanohybrid) was fabricated by the free radical grafting co-polymerization technique in the presence of N,N-methylenebisacrylamide (MBA) as cross linker and ammonium persulfate (APS) as initiator. The P-G-ILO nanohybrid was characterized by the various spectroscopic and microscopic techniques that provided the information regarding the crystalline behavior, surface area, and pore size. The response surface methodology was utilized for the statistical observation of diclofenac (DF) adsorption from the wastewater. The adsorption capacity (qe, mg/g) of P-G-ILO nanohybrid was higher (254, 256, and 258 mg/g) than the ILO nanoparticle (239, 234, and 233 mg/g). The Freundlich isotherm model was the best fitted, as it gives the higher values of correlation coefficient (R2 = 0.982, 0.991 and 0.981) and lower value of standard error of estimate (SEE = 6.30, 4.42 and 6.52), which suggested the multilayered adsorption of DF over the designed P-G-ILO nanohybrid and followed the pseudo second order kinetic model (PSO kinetic model) adsorption. The thermodynamic study reveals that adsorption was spontaneous and endothermic in nature and randomness onto the P-G-ILO nanohybrids surface increases after the DF adsorption. The mechanism of adsorption of DF demonstrated that the adsorption was mainly due to the electrostatic interaction, hydrogen bonding, and dipole interaction. P-G-ILO nanohybrid was reusable for up to five adsorption/desorption cycles.

Adsorption of anionic methyl orange dye by polyaniline, poly(o‐methylaniline) and poly(o‐methoxyaniline)

AbstractThis study reports the effect of substituents in the ortho position of polyaniline on the adsorption capacity to remove the anionic dye methyl orange (MO) from an aqueous solution. The aim of this study is the synthesis of polyaniline (PANI) and its derivatives, poly‐o‐methylaniline (poly‐o‐toluidine, POT) and poly‐o‐methoxyaniline (poly‐o‐anisidine, POA) for the adsorption removal of MO dye. All polymers were obtained by oxidative polymerization of the corresponding monomers and characterized by scanning electron microscopy (SEM) and infrared spectroscopy (IR). The average particle size of the polymer was about 200 nm. The effect of various parameters such as pH, temperature, adsorption time and initial concentration was analyzed. It was found that the adsorption capacity for dye removal increases from 50.68 to 222.56 mg g−1 for PANI, from 16.89 to 66.57 mg g−1 for POT, and from 97.26 to 532.54 mg g−1 for POA with an increase in the initial dye concentration from 5 up to 50 mg L−1. For all polymers, the equilibrium state of MO adsorption was reached in 50 min. The results showed that MO adsorption on PANI, POT, and POA is well described by a pseudo second order kinetic model. Isothermal studies have shown that adsorption is in good agreement with the Langmuir isotherm model, as evidenced by higher values of correlation coefficients. Based on the data of thermodynamic studies, it was concluded that MO adsorption on PANI, POT, and POA is spontaneous and endothermic.

ANTIOXIDANT POTENTIAL OF CORNELIAN CHERRY (Cornus mas L.) FROM MONTENEGRO

Abstract: In this work, total phenols, flavonoids, tannins, anthocyanins, the antioxidant capacity (by the DPPH and FRAP method) were determined in the juice and pomice of cornelian cherry from the area of Montenegro. Also, content of microelements and macroelements in the juice, pomice and fruit of cornelian cherry were investigated. In the studied cornelian cherry samples, the content of phenols ranged from 159.57 to 244.93 mg GAE/100 g, flavonoids from 71.74 to 165.5 mg QE/100 g, tannins from 0.33 to 0.81 %, total anthocyanins 0.057-0.310 %. HPLC analysis identified anthocyanins: delphinidin-3-O-glucoside, cyanidin-3-O-galactoside, cyanidin-3-O-glucoside, pelargonin-3-O-glucoside and its derivative. The most abundant essential microelement in the studied cornelian cherry samples was Fe (23.44-37.69 mg/kg), followed by Zn (3.14-6.25 mg/kg), Cu (1.50-1.80 mg/kg) and Mn (0.58-0.78 mg/kg). The most abundant essential macroelement in the examined samples was K (2427.8-3276.2 mg/kg), followed by Ca (361.5-475.7), Mg (95.41-146.5 mg/kg) and Na (8.347-16.59 mg/kg). Pb, Cd and Ni were not detected in the tested samples. A high degree of correlation was obtained between the content of total flavonoids and antioxidant value measured by the FRAP method (R = 0.96), as well as between the content of total anthocyanins and antioxidant activity measured by the DPPH method (R = 0.89). Of individual anthocyanins, high correlation values were shown by cyanidin-3-O-galactoside (R = 0.96), cyanidin-3-O-glucoside (R = 0.95) and pelargonin-3-O-glucoside (R = 0.94) with antioxidant activity measured by the DPPH method.

Evaluate the use of Mehlich-III compared to Mehlich-I as a soil test in Northeast Florida

Soil laboratories throughout the United States have widely integrated the Mehlich-III (M-III), derived from Mehlich-I (M-I), methodologies to extract soil nutrients. However, there have been concerns regarding its precision. The University of Florida's Hastings Agricultural and Extension Center (HAEC) conducted a soil sampling during the spring of 2021. This research aimed to rigorously compare the extraction efficiencies of M-I and M-III, specifically focusing on critical soil nutrients like phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), boron (B), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn). From forty-six meticulously chosen fields, 276 composite soil samples derived from two distinct depth strata (0–15 ​cm and 15–50 ​cm) were analyzed using both methods. Our findings highlighted the superior extraction prowess of M-III, which showcased an efficiency almost threefold compared to M-I. Intriguingly, the correlation between M-I and M-III extraction efficiencies weakened with escalating soil nutrient concentrations. The coefficient of determination (r2) between M-I and M-III had a high correlation value and was statistically significant (p<0.05) for all macro- and micronutrients. We found that the correlation between M-I and M-III became more robust with an increase in sample size and a higher correlation coefficient. It is noteworthy to mention that the M-III method has the propensity to provide slightly inflated soil nutrient estimations. Since soil labs worldwide use M-III equations derived from M-I, this study proposes that soil labs reinvestigate and possibly adopt new correlation equations to estimate soil nutrients using M-III.

Enhancement of adsorption capacity of Methylene blue, Malachite green, and Rhodamine B onto KOH activated carbon derived from oil palm empty fruit bunches

Enhancement adsorption capacity of Methylene blue (MeB), Malachite green (MaG), and Rhodamine B (RhoB) onto activated carbon derived from oil palm empty fruit bunches (Biochar). The activated carbon was produced by pyrolysis with impregnation in KOH solution. To enhance its efficiency, the activation process was performed twice, yielding the adsorbents 1KAC and 2KAC. The adsorption capacity and equilibrium of the three dyes onto the biochar and activated carbon samples were determined by examining the removal efficiency, capacity, kinetics and isotherm models. The pseudo-second-order model performed better than the pseudo-first-order model in terms of adsorption kinetics. The Langmuir model fits well for all three dye molecules with high correlation coefficient values, suggesting that the activated carbon surface is homogeneous and characterized by a finite number of identical sites. The maximum adsorption capacity was 1123.57, 1315.36, and 982.87 mg/g for MeB, MaG, and RhoB, respectively. This founding indicated a high potential adsorbent more than among KOH activated carbon reported in the literature. The study provides valuable insights into the use of activated carbon derived from oil palm empty fruit bunches for the removal of dyes from wastewater.

Excellent agreement of Norwegian trauma registry data compared to corresponding data in electronic patient records

BackgroundThe Norwegian Trauma Registry (NTR) is designed to monitor and improve the quality and outcome of trauma care delivered by Norwegian trauma hospitals. Patient care is evaluated through specific quality indicators, which are constructed of variables reported to the registry by certified registrars. Having high-quality data recorded in the registry is essential for the validity, trust and use of data. This study aims to perform a data quality check of a subset of core data elements in the registry by assessing agreement between data in the NTR and corresponding data in electronic patient records (EPRs).MethodsWe validated 49 of the 118 variables registered in the NTR by comparing those with the corresponding ones in electronic patient records for 180 patients with a trauma diagnosis admitted in 2019 at eight public hospitals. Agreement was quantified by calculating observed agreement, Cohen’s Kappa and Gwet’s first agreement coefficient (AC1) with 95% confidence intervals (CIs) for 27 nominal variables, quadratic weighted Cohen’s Kappa and Gwet’s second agreement coefficient (AC2) for five ordinal variables. For nine continuous, one date and seven time variables, we calculated intraclass correlation coefficient (ICC).ResultsAlmost perfect agreement (AC1 /AC2/ ICC > 0.80) was observed for all examined variables. Nominal and ordinal variables showed Gwet’s agreement coefficients ranging from 0.85 (95% CI: 0.79–0.91) to 1.00 (95% CI: 1.00–1.00). For continuous and time variables there were detected high values of intraclass correlation coefficients (ICC) between 0.88 (95% CI: 0.83–0.91) and 1.00 (CI 95%: 1.00–1.00). While missing values in both the NTR and EPRs were in general negligeable, we found a substantial amount of missing registrations for a continuous “Base excess” in the NTR. For some of the time variables missing values both in the NTR and EPRs were high.ConclusionAll tested variables in the Norwegian Trauma Registry displayed excellent agreement with the corresponding variables in electronic patient records. Variables in the registry that showed missing data need further examination.

Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600.

Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated. The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

КАРДИОВАСКУЛЯРНАЯ ПАТОЛОГИЯ И ПРОГНОЗИРОВАНИЕ ПЕРЕЛОМОВ У МУЖЧИН СТАРШЕ 45 ЛЕТ

Introduction. The combination of cardiovascular pathology with osteoporosis and fractures in men, especially at a younger age, requires the study of the causes and risk factors for the strategy of early diagnosis, treatment and prevention. Target. To study the clinical and pathogenetic relationships of cardiovascular pathology with the risk of fractures in men over 45 years of age. Materials and methods: 201 men over the age of 45 were examined, mean age 51.1 [46.1; 56.3]. The accuracy and statistical significance of the distribution of groups was assessed using discriminant analysis. High values of canonical correlation (Canonic.R=0.941) and chi-square test (χ²=630.4) with zero probability of not rejecting the null hypothesis (p=0.000) and coincidence of the results of discrimination and cluster analysis in 95% of cases made it possible to assume the reliability of the model , built on the basis of the proposed variables. Results. The frequency of risk factors in the examined patients was: smoking - 107 people. (53.2%), abdominal obesity 74 people. (36.8%); arterial hypertension - 75 people. (37.3%), hypercholesterolemia (total cholesterol&gt; 5.0 mmol / l) - 39 people. (19.4%), increased low-density lipoprotein cholesterol (LDL cholesterol)&gt; 3.0 mmol / l - 32 people. (15.9%), hypertriglyceridemia (TG&gt; 1.7 mmol / l) - 27 people. (13.4%); fasting glycemia or diabetes mellitus - 28 people. (13.9%). Among patients with fractures, men with an increased body mass index (26.5±3.3 kg/m² and 24.1±4.5 kg/m² p=0.008) and abdominal obesity (104.1±7.0 and 100.1±6.2 cm, p=0.004), vitamin D deficiency or insufficiency (37 (18.4%) versus 4 (1.9%) p=0.000) and androgen deficiency (15 (7 .4%) and 7 (3.4%) p=0.048). Fractures were significantly more often associated with diabetes mellitus (19 (9.4%) and 4 (1.9%) p=0.029), respectively. Based on the identified factors, the discriminant analysis made it possible to build a mathematical model for predicting fractures in men over 45 years of age. The canonical correlation coefficient R was 0.841. Wilkson's lambda statistics values of 0.292 indicate good discrimination (with χ² - 223.7 p&lt;0.01). The established most significant signs were assigned gradations (X1 - 6) and numerical values, where: X1 - history of fractures: no - 0, yes - 1; Х2 – age of the first fracture older than 50 years: no – 0, yes – 1; smoking: no - 0, yes - 1; diabetes mellitus: no - 0, yes - 1; walking less than 1 hour a day: no - 0, yes - 1; X6 - osteoporosis or osteopenia: no - 0, yes - 1. X1-X6 values are standardized initial data calculated by the formula: Z = (X I - X) / s, where Z - initial distribution values, X I - results dimensions of the distribution with the original dimension. The values of the constants F1 and F2 are determined: F1 = -5.99; F2 = -4.54. Having established the gradations and numerical values of the risk factors, the predictive coefficients F1 and F2 are determined according to the following formulas: F1= -5.99–0.854×X1–0.192×X2+0.855×X3–0.179×X4+8.755×X5+3.904×X6. F2= –4.54+1.24×X1+ 2.94×X2+2.165×X3+1.60×X4–0.77×X5+5.75×X6. With an absolute value of F2 greater than the absolute value of F1, a high risk is predicted, and with an F2 value less than F1, a low risk of fractures in comorbid men is predicted. Conclusions. Among men in a random sample over 45 years of age, a high incidence of cardiovascular risk factors was established. A third of the examined men had bone fractures of various localization. The mathematical model makes it possible to predict the development of fractures in men over 45 years of age, the risk of which increases in the presence of the following risk factors: a history of fractures before the age of 50, physical inactivity, smoking, the presence of diabetes mellitus and osteoporosis according to x-ray osteodensitometry.

Study of Regularized Generalized Structured Component Analysis to Overcome Multicollinearity in Component-Based SEM

Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of and indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.

Manufacturing and Machining Performance Analysis of Al/SiC5 MMC using Soft Computing Techniques

Now a day’s, metal matrix composites (MMCs) are taking a superior position among the available material. The presented work aims to investigate the ease of machining for Aluminium based MMC (AlSiC5- with 5% Silicate). These MMCs are extensively used in space, defence, automobile, etc. To analyze the process, an effective two techniques ie. Response surface method (RSM) &amp; artificial neural networks (ANN) have been selected to analyze the process. Taguchi’s L27 plan was used for data collection. The parameters like current (AMP) and pulse on time (TON) are the basically dominant parameters, followed by voltage (VOLT) and pulse off time (TOFF). The ease of machining performance measured in terms of surface roughness (Ra). From obtained result, it is seen that the ANN &amp; RSM presents an exemplary commitment. An adequate contract was observed with the high correlation coefficient value (R2 = 0 9863) in RSM and (R2 = 0 9764).

Radiological examination of the skull of Siirt-coloured mohair goat, Romanov and Hamdani sheep.

The skull is a vitally important complex structure formed by the combination of many bones. Craniometric studies are performed with various imaging methods to determine morphometric characteristics in animals. One of these methods is radiography. In our study, skulls of 14 Hamdani sheep, 20 Siirt-coloured mohair goats and 20 Romanov sheep breeds were used. Skull sections were taken with a computed tomography device and saved in DICOM format. Nine separate measurements and six index calculations were made from the images. In the study, firstly, the gender difference was ignored and the similarities and differences between the species were recorded. In addition, gender differences were determined statistically, without making any distinction between species. Correlation analysis was performed to compare measurement parameters between animal species. As a result of the study, the highest skull length (SL) and cranial length (CL) values were determined in the Siirt-coloured mohair goat. It was determined that the difference between Romanov sheep and Siirt-coloured mohair goat in CL parameter was not statistically significant (p > 0.05). In addition, a statistically significant difference was observed between the Romanov sheep breed and the Hamdani sheep breed (p < 0.01). The external length of the cranium (ELC) measurement parameter was found to have a statistically significant difference in three animals (p < 0.01). According to the results of the correlation analysis, the highest correlation value was determined between cranial width (CW) and CL parameters (r = 0.920). When the species difference was ignored, and the statistical difference of the internal height of the cranium (IHC) parameter was found to be significant between female and male animals (p < 0.05). In addition, it was determined that SL, CW and internal cranium index (ICI) parameters had a significant difference depending on sex (p < 0.01). It is foreseen that the obtained data can be used in sciences such as zoo archaeology, anatomy, surgery and forensic medicine.

High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model

Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km2, which was close to the official data (158–180 km2), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions.

Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach

Ceramic nanoparticles have become increasingly popular owing to their wide range of engineering applications in the industry. Silica is one of the most promising nanomaterials for heat transfer applications due to its favorable thermophysical characteristics and dispersion stability. This work investigates the thermophysical properties of polydisperse SiO2 nanoparticles in a glycerol and water mixture. Aqueous glycerol nanofluid comprised 30% glycerol (30 GW) by weight, and SiO2 with mean particle sizes of 15, 50, and 100 nm were prepared for 0.5 and 1.0% volume concentrations using a two-step method. Measurements of properties are made in the temperature range of 30–100oC. The nanofluids behaved as a single-phase liquid remaining stable for four weeks. The viscosity and density of the base liquid and nanofluid decreased while the thermal conductivity increased with temperature. The base liquid-specific heat increased slightly while that of nanofluid decreased significantly with temperature. For 0.5% SiO2 concentration, the thermal conductivity and viscosity varied by 11.1% and 32%, respectively at 60oC compared to the base liquid. Correlations are developed for the estimation of nanofluid properties. An Explainable Artificial Intelligence (XAI) technique called Bayesian approach optimized Gaussian Process Regression was employed to develop a prognostic model for the thermophysical properties of test nanofluids. In addition, the model's predictability and explainability are both enhanced by the use of kernel functions, which makes it possible to include historical data in the representation of a phenomenon. The test XAI approaches were shown as robust one because of the high correlation values, which ranged from 99.68% to 99.99%, along with minimal modeling errors.