Correlation Coefficient Values Research Articles

An Estimation of Baricitinib by AQbD-driven UV Spectrophotometry Development and Validation Process

Background: Baricitinib (BCTB) is a novel Janus Kinase (JAK) 1 and 2 inhibitor used in the therapy of rheumatoid arthritis, approved by the “Food and Drug Administration” in 2018. It has significant dose-dependent effectiveness and severe side effects. Thus, it is crucial to figure out its concentration in developed dosage forms. The literature search revealed that there has only been one UV spectroscopy technique documented up to this point. Methanol was chosen as the detection medium in this approach, which is not comparable with plasma or serum. As a result, the preliminary research suggested developing a UV spectroscopic approach that can estimate BCTB concentration and compare it to its concentration in the plasma or serum. Thus, in the proposed method, 7.4 pH phosphate buffer was selected as a mobile phase. Aim: Using the Analytical Quality by Design (AQbD) methodology, a simple, robust spectrophotometric method for the detection of BCTB in API form and Niosomes drug delivery system is designed and assessed. Methods: In the AQbD approach, a face-centered CCD design of Design Expert 13 software was used to evaluate two critical method variables: scanning speed and sampling interval. The design space suitability was confirmed by standard error and overlay plots. The 2-D contour and 3-D response surface plots were used to forecast the relationship between the response variable and predictor variables. Results: Baricitinib displays an absorption maximum at 249.40 nm in saline phosphate buffer pH 7.4. The distinguished linearity of the method was obtained over a concentration of 5–30 µg/ml with a correlation coefficient (R2 ) value of 0.998. BCTB % assay was found to be near 99 %. Intraday and Interday precision were found to have % RSDs of 0.067–0.488 and 0.146–0.942, respectively. Conclusion: The established spectrophotometric technique was observed to be precise as per ICH revised guidelines ICH Q2 (R1) and Q14 for analytical method validation. Our findings are instructional for the future design and development of safe and reliable therapeutic dosage forms of BCTB for rheumatoid arthritis.

Biological activities of Hypericum spectabile extract optimized using artificial neural network combined with genetic algorithm application

Optimizing extraction conditions can help maximize the efficiency and yield of the extraction process while minimizing negative impacts on the environment and human health. For the purpose of the current study, an artificial neural network (ANN) combined with a genetic algorithm (GA) was utilized for that the extraction conditions of Hypericum spectabile were optimized. In this particular investigation, the main objective was to get the highest possible levels of total antioxidant status (TAS) for the extracts that were obtained. In addition to this, conditions of the extract that exhibited the maximum activity have been determined and the biological activity of the extract that was obtained under these conditions was analyzed. TAS values were obtained from extracts obtained using extraction temperatures of 30–60 °C, extraction times of 4–10 h, and extract concentrations of 0.25-2 mg/mL. The best model selected from the established ANN models had a mean absolute percentage error (MAPE) value of 0.643%, a mean squared error (MSE) value of 0.004, and a correlation coefficient (R) value of 0.996, respectively. The genetic algorithm proposed optimal extraction conditions of an extraction temperature of 59.391 °C, an extraction time of 8.841 h, and an extraction concentration of 1.951 mg/mL. It was concluded that the integration of ANN-GA can successfully be used to optimize extraction parameters of Hypericum spectabile. The total antioxidant value of the extract obtained under optimum conditions was determined as 9.306 ± 0.080 mmol/L, total oxidant value as 13.065 ± 0.112 µmol/L, oxidative stress index as 0.140 ± 0.001. Total phenolic content (TPC) was 109.34 ± 1.29 mg/g, total flavonoid content (TFC) was measured as 148.34 ± 1.48 mg/g. Anti-AChE value was determined as 30.68 ± 0.77 µg/mL, anti-BChE value was determined as 41.30 ± 0.48 µg/mL. It was also observed that the extract exhibited strong antiproliferative activities depending on the increase in concentration. As a result of LC-MS/MS analysis of the extract produced under optimum conditions in terms of phenolic content. The presence of fumaric, gallic, protocatechuic, 4-hydroxybenzoic, caffeic, 2-hydoxycinamic acids, quercetin and kaempferol was detected. As a result, it was determined that the H. spectabile extract produced under optimum conditions had significant effects in terms of biological activity.

ESTIMATION OF OIL SPILLAGE AND SALVAGE REVENUE IN KOKORI OIL FIELD USING NUMERICAL METHODS AND PYTHON ALGORITHM

The study aimed to estimate oil spillage in the Kokori Oil Field, discovered in 1958, using a linear model. The field has 23 oil wells and produced 383 million barrels of oil. A linear model was developed using Python programming, comparing it with the trapezoidal method and salvage revenue. Results showed a linear relationship between oil spill quantity, duration, and mitigation measures. This study provides a valuable model for estimating oil spillage in the Kokori oil field, emphasizing the importance of accurate estimation for environmental and economic purposes. The study presents a comprehensive model for estimating oil spillage in the Kokori oil field, emphasizing the significance of accurate estimation for environmental and economic purposes. The correlation coefficient value supports the model's sufficiency, and the calculated salvage revenue indicates a commendable projected value for the Kokori oil field at its end of use. Salvage revenue is the estimated value of an asset at the end of its useful life, which is crucial in determining the cost of goods sold and depreciation charge. It lowers the asset's cost, influences its usable life, resale value, and replacement cost. Salvage revenue is calculated by subtracting revenue generated without mitigation measures from revenue realized with mitigation measures.

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

Pengaruh Disiplin Kerja dan Lingkungan Kerja Fisik terhadap Kinerja Karyawan pada Kantor Akuntan Publik Jamaludin, Ardi, Sukimto & Rekan Jakarta Pusat

The purpose of this study was to determine Work Discipline, and Physical Work Environment on employee performance at the Jamaludin, Ardi, Sukimto & Rekan Public Accounting Firm, Central Jakarta. The method used is quantitative. Sampling technique used is saturated with a sample of 50 respondents, the test used is validity test, reliability test, classical assumption test, linear regression test, determination coefficient test, hypothesis test (t test and f test). The results of this study simultaneously have a significant effect on employee performance with regression Y = 1.672 + 0.622 X1 + 0.342 X2 , the correlation coefficient value of 0.861 means that simultaneously has a very strong level of relationship where the value is in the interval 0.80 - 1000. While the coefficient of determination (R Square) is 0.741 (74.1%). Based on this value, it shows that the percentage between the work discipline variable (X1), and the physical work environment (X2) affects employee performance (Y) by 74.1% while the rest (100%-74.1%) = 25.9% is influenced by other variables that are not included. For the hypothesis value, the value of Fcount> Ftabel or (10,357> 2,011). This is also reinforced by sig <0.05 or (0.000 <0.05) thus Ho is rejected and Ha is accepted, this shows that there is a simultaneous significant influence between the variables of Work Discipline (X1) and Physical Work Environment (X2) on Employee Performance (Y) at the Public Accounting Firm Jamaludin, Ardi, Sukimto & Partners Central Jakarta.

The Influence of Return on Asset, Debt to Total Asset Ratio, and Economic Value Added, on Company Value with Dividends as a Moderating Variable in Industrial Sector Companies

The purpose of this study is to determine the Effect of Return On Assets, Debt To Total Assets Ratio, and Economic Value Added on Company Value with Dividend Policy as a Moderating Variable in Industrial Sector Companies. The sampling technique in this study is purposive sampling. So that the number of samples obtained is 54 companies. The tests used are Normality Test, Multicollinearity Test, Heteroscedasticity Test, Autocorrelation Test, Interaction Test / MRA. The correlation coefficient value is 0.177, meaning that between ROA, DAR, EVA and Dividend Policy on company value, the relationship is low. The determination coefficient value (R²) or R square obtained is 0.031, indicating that 3.1% of the influence on company value is explained by the variables ROA, DAR, EVA and Dividend Policy. While the remaining 96.6% is influenced by other variables not examined in this study.

Advanced Hybrid Techniques for Predicting Discharge Coefficients in Ogee-Crested Spillways: Integrating Physical, Numerical, and Machine Learning Models

Abstract The primary objective of this work was to examine the flow characteristics over an ogee spillway using both a numerical model and the Machine Learning (ML) approach. A 3D computational fluid dynamics (CFD) model was employed to simulate the flow over an ogee spillway, utilizing the Reynolds averaged Navier–Stokes equations. The simulation encompassed a wide variety of head ratios, ranging from 0.1 to 6.0, to extend the rating curve of discharge coefficient (C) and head ratio (He/H0). The formation of the negative pressure zone rapidly occurred, and the maximum velocity area developed from toe to top of the spillway surface as the head ratio increased. Then, four ML models—RF, FNN, ADB, and KNN—were utilized to estimate the discharge coefficient of the spillway. Hyperparameter tuning using the Tree-Structured Parzen Estimator (TPE) and five-fold cross-validation ensured robust model performance. The ML model's efficacy was assessed by conducting 200 random seed simulations. The RF and ADB models exhibited the highest predictive accuracy and consistency, with mean correlation coefficient (CC) values of 0.979 and 0.975, respectively. FNN and KNN also performed well but showed greater variability in their prediction. The results demonstrated reasonably good agreement between the physical, numerical, and ML models. Both numerical simulation methods and ML models, particularly RF, proved to be cost-efficient and reliable tools for designing and analyzing flow over an ogee spillway. These findings highlight the potential of integrating numerical simulations and advanced ML techniques to enhance the prediction and analysis of hydraulic structures, providing valuable insights for the design and management of spillway systems.

Income level as a differentiating factor in assessing the impact of migration on economic growth

Aim. To study the nature of the impact of migration on economic growth depending on the level of gross domestic product (GDP) per capita and the direction of migration flows.Objectives. Study of theoretical approaches to the research of the impact of labor migration on economic growth; analysis of factors that differentiate the assessment of the impact of migration flows on economic growth indicators; collection and analysis of statistical information for the development of econometric models for assessing the impact of migration on economic growth.Methods. The authors used general scientific methods of research, such as deduction, analysis, synthesis. Statistical methods of data analysis were applied, linear regression models were built.Results. The research shows that the impact of migration on economic growth in countries with different levels of GDP per capita is due to different factors. For developed economies, the positive factors of the impact of immigration on economic growth are replenishment of scarce labor resources; improvement of labor force quality due to structural changes caused by the transition of the permanent working-age population to new professional fields. For countries with a low level of GDP per capita we can identify a number of positive factors of the impact of emigration (with a negative balance of migration growth) on economic growth. In particular, the outflow of excess labor force creates conditions for reducing unemployment; remittances of migrants support effective demand in the consumer market, contribute to the development of the banking system and investment in the country of origin; the expansion of social ties leads to the development of human capital, increasing the potential for innovation.Conclusions. The value of correlation coefficients in the constructed regression models indicates that there is a weak correlation between the variable characterizing the volume and nature of migration in the country (the ratio of migration balance to population) and the variable characterizing economic growth (GDP per capita at purchasing power parity) only for the group of high-income countries. There is little statistical dependence of these variables for other groups of countries. With regard to econometric modeling of the impact of migration on economic growth, this means that it is necessary to develop separate models for each group of countries depending on the level of GDP per capita and the direction of migration flows.

The Relationship Between Service Quality And Consumer Satisfaction At UD She Water In Gunungsitoli City

The purpose of this research is to find out whether there is a relationship between service quality and consumer satisfaction and to find out how big the relationship is between service quality and consumer satisfaction at UD She Water in Gunungsitoli City. The research used in this study is quantitative research. The sample in this study was the entire population, which was 35 people. Data collection techniques. Data collection techniques used observation, questionnaires and interviews. Based on the results of the study, the researcher drew the following conclusions: 1. The implementation of proper service quality will have an impact on increasing customer satisfaction in any company including UD. She Water. 2. From the calculation of the correlation coefficient above, (rxy) item number 1 was obtained at 0.7070 and for N = 35 at a significant level of α = 5%, the data rtable = 0.334 was obtained. By referring to the valid invalid criteria, item number 1 in the questionnaire with rxy, namely 0.7070> 0.334, is concluded to be valid. 3. From the calculation of the correlation coefficient between variables X and Y, rxy = 0.7388 is obtained, then the correlation coefficient value is consulted in the r Product Moment critical value table. For N = 35 at a significance level of 5% rtable = 0.334. Thus, rxy> rtable or 0.7388> 0.334 is obtained.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Ensembling methods for protein-ligand binding affinity prediction

Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Variations of Planetary Wave Activity in the Lower Stratosphere in February as a Predictor of Ozone Depletion in the Arctic in March

This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere in March (Tmin) and the amplitude of the planetary wave with zonal number 1 (PW1) in February were calculated. Tmin determines the conditions for the formation of polar stratospheric clouds (PSCs) following the chemical destruction of the ozone layer. The NCEP and ERA5 reanalysis data and the modern and future climate simulations of the Earth system models INM CM5 and SOCOLv4 were employed. It is shown that the maximum significant CC value between Tmin at 70 hPa in the polar region in March and the amplitude of the PW1 in February in the reanalysis data in the lower stratosphere is 0.67 at the pressure level of 200 hPa. The CCs calculated using the model data are characterized by maximum values of ~0.5, also near the same pressure level. Thus, it is demonstrated that the change in the planetary wave activity in the lower extratropical stratosphere in February can be one of the predictors of the Tmin. For further analysis of the dynamic structure in the lower stratosphere, composites of 10 seasons with the lowest and highest Tmin of the Arctic lower stratosphere in March were assembled. For these composites, differences in the vertical distribution and total ozone content, surface temperature, and residual meridional circulation (RMC) were considered, and features of the spatial distribution of wave activity fluxes were investigated. The obtained results may be useful for the development of forecasting of the Arctic winter stratosphere circulation, especially for the late winter season, when substantial ozone depletion occurs in some years.

The questions on violence (FOV) tool for interpersonal violence inquiry in Swedish healthcare settings – evaluation of content validity, face validity and test-retest reliability

BackgroundPrevious research indicates that routine inquiry or screening conducted by healthcare providers may significantly increase the identification of interpersonal violence. There is a lack of comprehensive instruments to routinely assess patients about interpersonal violence and violence against children in the household. The purpose of this study was to assess the content validity, face validity and reliability of the Questions on Violence (FOV) tool, an instrument specifically designed for routine inquiries about interpersonal violence in healthcare settings within the Swedish context.MethodsThe content validity, face validity and reliability of the FOV instrument was assessed through (1) a content validity index with six experts in the field of intimate partner violence, (2) cognitive interviews with nine patients recruited from a primary healthcare facility, and (3) an evaluation of the test-retest reliability based on responses from 37(50.0%) university students. The intraclass correlation coefficient, model 2.1, was calculated to assess the degree of correlation and agreement between the two measurements.ResultsCalculations based on the content validity index indicated that five out of seven items had excellent content validity (≥ 0.78). The average content validity index of included items was 0.88, which is slightly below the recommended threshold for excellent content validity. The results based on the cognitive interviews revealed that participants found the seven items to be relevant and easy to understand. Overall, the participants agreed that the concept of ‘close relationships’ primarily encompassed intimate partners, family members, and close friends. The value of the intraclass correlation coefficient was 0.85 (0.77–0.91; CI 95%), indicating good reliability with an interval of good to excellent test-retest reliability.ConclusionsThe results demonstrate that the seven-item FOV instrument has good content and face validity as well as good to excellent test-retest reliability. The current study provides healthcare professionals with a short yet comprehensive instrument for identifying patients who have experienced or perpetrated different forms of interpersonal violence.

DYSREGULATION OF M6A RNA METHYLATION, R-LOOPS AND THE DNA DAMAGE RESPONSE IN PAEDIATRIC HIGH-GRADE GLIOMAS

Abstract AIMS Paediatric high-grade gliomas (pHGG) are virtually incurable malignant brain tumours. The regulation of R- loops is necessary for ensuring genomic stability. The formation of R-loops at DNA damage sites promotes DNA repair via m6A methylation of the RNA strand. We aimed to investigate whether m6A methylation, R-loop homeostasis and the DNA damage response is dysregulated in pHGG and whether inhibitors, such as ATM inhibitors, could be potential therapeutics for pHGG. METHOD To assess the global levels of m6A, R-loops, yH2AX, total ATM and phosphorylated ATM in paediatric HGG cell lines compared to control human astrocytes, immunocytochemistry was initially performed. Immunocyto- chemistry was also performed to assess localisation between m6A and R-loops, R-loops and yH2AX, R-loops and total/phosphorylated ATM, m6A and total/phosphorylated ATM, and total/phosphorylated ATM and yH2AX in pHGG cell lines and human astrocytes. We are also exploring two ATM inhibitors, AZD1390 and AZD0156, and are currently investigating their effects on m6A methylation, R-loops and the DNA damage response in pHGG. As a molecular mechanism involving the tumour suppressor ARID1A was recently discovered that is crucial for maintaining genomic stability, we are also exploring the role of this protein in pHGG. We are also investigating the potential involvement of m6A readers in the pathogenesis of pHGG. RESULTS Immunocytochemistry revealed that dysregulation of global m6A, R-loop, yH2AX and ATM occurs in pHGG cell lines as higher levels of expression was generally observed compared to human astrocytes. Moreover, m6A, R-loops, yH2AX and ATM appear to colocalise as high Pearson’s correlation coefficient values were obtained. Experiments exploring ATM inhibitors, ARID1A and m6A readers in pHGG are currently in progress, however the results will be presented. CONCLUSION It appears that dysregulation of m6A methylation, R-loop homeostasis and the DNA damage response occurs in pHGG and that molecular mechanisms involving m6A methylation and R-loops could be impaired.

Socio-Economic Status of Individuals in Tehran University of Medical Sciences Employees` Cohort Study Using PCA, MCA and FAMD Methods

Introduction: Determining socio-economic status (SES) can greatly help decision makers in the field of social health. Because SES can play an important role in accessing medical services or welfare amenities. We aimed to determine the SES using principal component analysis (PCA), multiple correspondence analysis (MCA), and factor analysis of mixed data (FAMD) methods. Methods: In this cross-sectional study (2023), 4448 employees aged 19 to 75 years were included to the study from Tehran University of Medical Sciences employees` cohort (TEC). Demographic variables and socioeconomic factors were considered. Considering the weaknesses of PCA and MCA methods, we calculated the SES score using PCA, MCA and FAMD methods, and the percentile of people was determined. These weaknesses include normality assumption and considering only linear relationship for PCA, inability to interpret the relationships between variables and considering each level of classification variables as a new variable for MCA. Results: We studied 4448 people (39.3% men) with mean age of 42.3 and a standard deviation of 8.7.The correlation between the percentiles obtained through PCA, MCA and FAMD methods was very high, and the highest correlation was related to the percentiles obtained through PCA and FAMD methods with a value of 0.994. The intraclass correlation coefficient value was 0.996. Also, this value was 0.996 and 0.994 in the random samples of 250 and 100 individuals from the original data, respectively. Conclusion: All of the three methods worked similarly on determining the SES and calculating the percentile of people. PCA and FAMD methods had better agreement than others. Therefore, in studies that have both quantitative and qualitative variables, the choice of analysis method depends on the opinion of the researcher.

Accuracy of Infrared Thermography in Diagnosing Breast Cancer-Related Lymphedema.

Background/Objectives: Infrared thermography (IRT) is an imaging technique used in clinical practice to detect changes in skin temperature caused by several dysfunctions, including breast cancer-related lymphedema (BCRL). Thus, the present study aimed to assess the reproducibility and accuracy of IRT in diagnosing BCRL. Methods: This cross-sectional study included participants who underwent a unilateral mastectomy and used indirect volumetry for lymphedema detection. IRT analysis was recorded in four positions, analyzing maximum, mean, and minimum temperatures, as well as the temperature differences between the upper limbs. The analysis encompassed reliability, agreement, accuracy, and the establishment of cut-off points for sensitivity and specificity. A total of 88 upper limbs were included; 176 thermograms were captured, and 1056 regions of interest were analyzed. Results: IRT presented excellent intra- and inter-rater reproducibility and reliability with excellent intraclass correlation coefficient values (0.99 to 1.00). In addition, this assessment reached a sensitivity of 85% and a specificity of 56%; the cut-off point considered a temperature difference of -0.45 °C. Conclusions: IRT was a reliable and reproducible assessment, and the temperature difference between the upper limbs evidenced moderate accuracy. Thus, IRT is recommended as a complementary technique for detecting BCRL.

Little-Cost Potentiometric and Spectrophotometric Procedures for Cephalothin Assessment in Pure and Biological Fluids.

Low-cost potentiometric and spectrophotometric procedures for cephalothin (CPI) determination in pure and biological fluids were investigated. The potentiometric technique is created through titration of CPI with an aqueous medium of 0.1 M NaOH at an ionic strength of μ = 0.3 M sodium chloride and room temperature by a combined glass pH electrode. Using the standard addition method, we found that the detection and quantitative limits were 0.042 mg/mL, with the standard deviation SD = 0.011, correlation coefficient R = 0.9880 (n = 5), and linear concentration ranges from 0.042 to 0.82 mg/mL. This technique was utilized to assess CPI in pure solutions, urine, and serum with suitable results. No interference was exposed in the presence of public components of the samples under study. Recovery of CPI for pure and biological fluids is in the range of 98.2-101%. Also, the spectrophotometric method has been performed through the formation of the Prussian Blue (PB) complex. The reaction between the acidic hydrolysis product of CPI (T = 60 °C) and the mixture of Fe3+ with hexacyanoferrate (III) ions (HCF(III)) was detected for the spectrophotometric determination of the drug. The maximum absorbance of the formed complex was measured at λ = 283 nm with 2.0 × 103 L mol-1 cm-1 molar absorptivity. Reaction states have been advanced to acquire the PB complex of great sensitivity and longer stability. In optimal states, the absorbent of the PB compound was attained to grow linearly with the increase in the concentration of CPI, which agrees with the correlation coefficient values. The detection and quantitative limits were 0.000036 and 0.0012 mg/mL, respectively, with the standard deviation, SD = 0.0005, correlation coefficient, R = 0.9955 (n = 5), and the linearity range of the calibration plot 0.0005-0.02 mg/mL CPI. The planned technique was positively utilized for the detection of CPI in both urine and serum models. The results fit well with the data found from the potentiometric method.

Comfort weight analysis for optimising ergonomic cabin design of a drilling rig machine using three-dimensional human simulation

Ergonomics in product design enhance user comfort, correlating with increased efficiency and usability between the user and the product. Anthropometry is an important part of creating ergonomic design that correlates with human body size. By utilising anthropometry data, this research aims to analyse the cabin's comfort in drilling rig machine by creating 3D simulations for human-machine interaction. The comfort values will be assessed using Jack software. An experimental orthogonal design is applied to generate 81 optimal combination samples. The sample with the optimal comfort value can also be obtained. Following that, the extreme learning machine and mean impact value (ELM-MIV) algorithm is employed to evaluate the comfort weight analysis of each influencing factor. The accuracy value of the prediction model assessed using mean squared error (MSE) is 0.004, and the squared correlation coefficient (R2 ) value is 0.996. It can be inferred that the prediction error is apparently minimal, indicating the reliability of ELM implementation in data training. It will optimise the redesign of a drilling rig machine cabin to solve work-related health issues and improve operator comfort and efficiency.

Hubungan Kematangan Emosi dengan Perilaku Agresif pada Siswa SMK

This study aims to determine the relationship between emotional maturity and aggressive behavior in vocational students. This research was conducted on SMK class X students. The sample in this study amounted to 135 students with probability sampling techniques. The type of research used in this study is quantitative research. The measurement tool used for data collection uses the emotional maturity scale and the aggressive behavior scale. Statistical data analysis using Pearson product moment analysis test with the help of SPSS statistics version 26. The correlation coefficient obtained is -0.426 with a significance value of 0.000 (p<0.05). These results show that the hypothesis proposed by the researcher is accepted. And the value of the negative correlation coefficient shows that the direction of the relationship between the two variables is negative, meaning that the higher the emotional maturity, the lower the aggressive behavior.

Method for enhancing fault feature signals of rolling bearings in gas turbine engines

Aiming at the problem that the weak fault signal of rolling bearings in gas turbine engines (GTEs) is affected by environmental noise, which leads to the difficulty of effective information extraction and easy to ignore, the characterization method for cyclic extraction of main bearing fault feature components in GTEs is proposed. The proposed method begins by subjecting raw vibration signals to wavelet packet decomposition and correlating correlation coefficient and kurtosis values for each node component. These values are then normalized and amalgamated into a comprehensive parameter denoted as P. Subsequently, a confidence interval is established to categorize node components into three groups: high signal-to-noise ratio signals, low signal-to-noise ratio signals, and high-noise signals. Then, the high signal-to-noise ratio signals are continuously filtered according to the feature component cyclic extraction criterion until the termination condition is reached. Finally, all high signal-to-noise ratio signals are reconstructed, followed by envelope demodulation to extract subtle bearing fault characteristics. The findings underscore the efficacy of this approach in extracting fault features within the intricate transmission path of rolling bearings, offering a robust solution for the intricate signal processing and diagnosis of complex rolling bearing faults in GTEs.