Coefficient Of Determination Research Articles

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

Transdermal characteristic study of bovine sialoglycoproteins with anti-skin aging and accelerating skin wound healing.

Sialoglycoproteins play important roles in various biological processes, including cell adhesion, immune response, and cell signaling. Our previous studies indicated that the bovine sialoglycoproteins could be developed as a reagent against skin aging and as a new candidate for accelerating skin wound healing as well as inhibiting scar formation. However, transdermal characteristic of the bovine sialoglycoproteins is still unknown. This study investigated the transdermal permeation of the bovine sialoglycoproteins through porcine skin using the Franz diffusion cell method. Our study showed that the bovine sialoglycoproteins could penetrate through the porcine skin with a linear permeation pattern described by the regression equation N% = 11.49 t-3.858, with a high coefficient of determination (R2 = 0.9903). The histochemical results demonstrated the widespread distribution of the bovine sialoglycoproteins between the epidermal and dermal layers, which suggesting parts of the bovine sialoglycoproteins had ability to traverse the epidermal barrier. The results of the lectin microarrays indicated highly enriched glycopatterns on the bovine sialoglycoproteins, which also appeared in permeated porcine skin. The LC-MS/MS analysis further showed that the bovine sialoglycoproteins were composed of approximately 100 proteins with molecular weight ranging from 748.4 kDa to 10 kDa, and there were 23 specific bovine sialoglycoproteins with molecular weight ranging from 69.2 kDa to 10 kDa to be characterized in permeated porcine skin. Parts of the bovine sialoglycoproteins with molecular weight less than 69.2 kDa had ability to traverse the epidermal barrier. Understanding the permeation characteristics of the bovine sialoglycoproteins for developing innovative formulations with therapeutic benefits, contributing to advancements in cosmetic and dermatological fields.

Deciphering nitrogen concentrations in Metasequoia glyptostroboides: a novel approach using RGB images and machine learning

Recent advances in spectral sensing techniques and machine learning (ML) methods have enabled the estimation of plant physiochemical traits. Nitrogen (N) is a primary limiting factor for terrestrial forest growth, but traditional methods for N determination are labor-intensive, time-consuming, and destructive. In this study, we present a rapid, non-destructive method to predict leaf N concentration (LNC) in Metasequoia glyptostroboides plantations under N and phosphorus (P) fertilization using ML techniques and unmanned aerial vehicle (UAV)- based RGB (red, green, blue) images. Nine spectral vegetation indices (VIs) were extracted from the RGB images. The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine, random forest (RF), and multiple linear regression, gradient boosting regression and classification and regression trees (CART). The results show that RF is the best fitting model for estimating LNC with a coefficient of determination (R2) of 0.73. Using this model, we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P. Height, diameter at breast height (DBH), and crown width of all M. glyptostroboides were analyzed by Pearson correlation with the predicted LNC. DBH was significantly correlated with LNC under N treatment. Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient, scalable, and cost-effective method for LNC quantification. Future research can extend this approach to different tree species and different plant traits, paving the way for large-scale, time-efficient plant growth monitoring.

Flutter Optimization of Carbon/Epoxy Plates Based on a Fast Tree Algorithm

This study focuses on optimizing carbon/epoxy laminate configurations to maximize the flutter speed of composite structures using a Fast Tree Regression algorithm. Initially, a seed dataset was created, using finite element method (FEM) modal analysis for common stack-ups used in composite fins and UAV components. The FEM analysis, based on the Lanczos algorithm for extracting modal frequencies in bending and torsion, was verified through experimental modal analysis using an AS-4/3501-6 composite system. Custom software was developed to interface with the FEA modal software, enabling the generation and augmentation of laminate dataset scenarios. The seed dataset was expanded until the coefficient of determination (R2) reached at least 0.95. Various regression algorithms, including Fast Forest Regression, Fast Tree Regression, Sdca Regression, and Lbfgs Poisson Regression, were evaluated. The Fast Tree Regression algorithm was selected for further analysis due to its superior performance. This algorithm was applied to a design space of nearly 2000 potential laminate candidates, focusing on symmetric lay-ups to avoid undesirable coupling between bending and torsion in UAV and missile control surfaces. The final optimized lay-ups, exhibit the highest Delta function values (the squared difference of modal frequencies in torsion and bending), indicating the expected highest flutter speeds. The results demonstrate the efficacy of tailored composite materials in achieving specific aerodynamic performance goals.

Pengaruh Harga, Kualitas Pelayanan Dan Store Atmosphere Terhadap Kepuasan Pelanggan

The advent of contemporary lifestyles has precipitated a surge in the number of coffee shops, which have become pivotal sites for social interaction and work. In light of the pivotal role coffee shops play in contemporary society, it is crucial to gain insight into the factors that shape consumer satisfaction. The degree of consumer loyalty to a product, in this case coffee, is significantly influenced by the level of satisfaction experienced by the customer. The objective of this study is to gain insight into the influence of price, service quality and shop atmosphere on customer satisfaction at Sarang Kopi Susu Café in Karanggede, Boyolali. This research employs a mixed-methods approach, integrating both qualitative and quantitative techniques. The research population comprised all customers of the Sarang Kopi Susu Café. A sample of 100 respondents was selected using purposive sampling. The data were analysed using multiple linear regression analysis, a t-test, an F-test and a coefficient of determination (R²). The results of this study indicate that variable X1, namely price, has a positive but insignificant effect on customer satisfaction. Conversely, the variables of service quality and store atmosphere have a significant effect on customer satisfaction at Cafe Sarang Kopi Susu Karanggede, Boyolali.

ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION WITH LEAST ABSOLUTE DEVIATION (LAD) ESTIMATION AND M-ESTIMATION ON GRDP OF WEST JAVA PROVINCE

Geographically Weighted Regression (GWR) is an analytical method for data that contains spatial heterogeneity effects. However, parameter estimation in the GWR model has a weakness, namely it is prone to outliers and can cause the parameter estimation to be biased. This can be overcome by the Robust Geographically Weighted Regression (RGWR) method which is more robust against the presence of outliers. This method is suitable for Gross Regional Domestic Product (GRDP) data in West Java Province, which contains outliers and also has spatial effects. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of West Java Province. The purpose of this study is to compare the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation (LAD) Estimation and M-estimation and also to find out the factors that affect the Gross Regional Domestic Product (GRDP) in West Java Province in 2021 based on the model resulting from. Selection of the best model is seen based on the value of the coefficient of determination (R2) and Mean Squared of Error (MSE). The research results show that the Robust Geographically Weighted Regression (RGWR) method with M-estimation is much more effective in estimating the distribution of GRDP in West Java Province in 2021, seen from the larger coefficient of determination and the smaller Mean Square Error (MSE). The variables that have a significant influence on GRDP in West Java Province in 2021 are the variables of foreign investment and local income.

Determination of metformin from micro-liter plasma and blood glucose test strip using eco-friendly micro-extraction procedures for point-of-care diagnostics

Metformin is commonly used in the treatment of type 2 diabetes and polycystic ovary syndrome. Beyond these common applications, it also holds promise in treating chronic pain, and as an antiaging and anticancer agent. In addition, it has cardiovascular protective and neuroprotective effects. However, elevated concentrations of metformin in the plasma, such as in patients with renal impairment or with conditions that can disrupt lactate clearance, could cause metformin-associated lactic acidosis. Moreover, metformin can cause a deficiency of vitamin-B12 and folic acid and impact one-carbon metabolism negatively. This study employs micro-extraction procedures to extract metformin from the human plasma and blood glucose test strips followed by detection using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Under the optimized micro-extraction procedures and by using an eco-friendly base and solvent, the proposed method required only 10 µL of the human plasma and a small piece of the blood glucose test strip for metformin analysis. The method exhibits a linear range of 0.05–5 μg mL−1 with a coefficient of determination (r2) of 0.999 and a limit of detection of 0.01 μg mL−1. The relative standard deviation (RSD) and relative error (RE) were both below 6.7 %. The test strip is a disposable product; its reuse before disposal could increase its additional applicability. Furthermore, major blood proteins and their modifications could be identified using this tiny volume of blood by mass spectrometry after protein digestion. The proposed technique could be invaluable in point-of-care diagnostics, enabling rapid monitoring of metformin plasma concentration and major plasma proteins to help patients mitigate the risk of metformin-associated lactic acidosis. To reduce organic waste in clinical applications, micro-extraction is a simple and efficient protocol for sample preparation using minute volumes of blood samples.

Visible-to-near-infrared spectroscopy for prediction of soil nitrogen mineralization after sample stratification by textural homogeneity criteria

On-time and accurate estimation of the soil nitrogen mineralization rate (SNMR) is critical for nitrogen (N) management and protecting the environment. This study evaluated the performance of a visible-to-near-infrared reflectance (vis-NIR) spectroscopy for predicting SNMR for four texture groups. A total of 62 topsoil samples were collected from 17 management zones distributed over four fields and incubated with seven destructive sampling events. Samples were analysed for total mineral N (NH4++NO3–) content and scanned using a vis-NIR sensor simultaneously at each of the seven-sampling times. Four partial least squares regression models were calibrated and validated for four textural groups (groups- 1– 4) identified over the United State Department of Agriculture (USDA) texture triangle. Prediction accuracies indicated that vis-NIR sensor was moderately to highly accurate for predicting SNMR, while observing variable accuracies across texture groups. The highest accuracy was obtained for group 1 (sandy-loam; coefficient of determination, R2 = 0.90; root mean square error, RMSE = 0.04 mg N kg−1 soil day−1), successively followed by group 2 (mostly loam; R2 = 0.80, RMSE = 0.05 mg N kg−1 soil day−1) group 4 (mostly silt; R2 = 0.66, RMSE = 0.08 mg N kg−1 soil day−1), and group 3 (silt-loam; R2 = 0.44, RMSE = 0.08 mg N kg−1 soil day−1). Variable importance in projection score revealed that the key spectral bands to predict SNMR were in 2150 – 2260 nm and 2470 – 2480 nm, resembling the key bands associated with soil organic compounds and clay minerals. In-advance texture information required for soil stratification is regarded a limitation of the proposed approach. In conclusion, vis-NIR holds potential for a rapid estimation of SNMR when samples are stratified into similar texture groups in advance, however, confirmatory research will be needed to validate the current findings for soils from different origin and under different management.

Automatic reorientation to generate short-axis myocardial PET images

BackgroundAccurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images.MethodsA total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients.ResultsOn the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications.ConclusionOur algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Weld-joint design to optimize groove weld depth and mechanical properties for welding of nozzle onto pressure vessel

Full and partial set-in weld-joints connect the nozzle to pressure vessels to prevent lamellar tearing. This investigation aims to compare full and partial set-in weld-joints through computer simulations, numerical modelling, welding experiments, nondestructive examination and destructive testing. The nozzle-to-shell opening connection’s weld-joint design model is DN20, with a 50 mm shell wall thickness. The optimal groove weld depth, at a safety factor of 3.5, ranges from 20 to 50 mm. We investigated weld-joint design variations using design-of-experiment, which included actual welding and mechanical testing. The inter-run temperature and its interaction with the heat input were found to be statistically significant. The mechanical properties of welded joint are better than those of the specified material, A516 Grade 70, which include an average impact toughness of 89.5 J at -50 °C, a microhardness of 195 Hv10, an ultimate tensile strength of 533 MPa, a yield strength of 391.5 MPa and an elongation of 56.5%. The coefficients of determination of impact toughness and microhardness, which have R2 values of 88.73% and 76.65%, respectively, conclude a successful welding experiment. The design and mechanical testing results conclude the reliability of the weld-joints.

A Dynamic Tool to Describe Lamb Growth and Its Use as a Decision Support System

A dynamic model has been developed to simulate aspects of feedlot lamb growth and body composition, including energy and protein requirements, growth rate, composition of gain, and body mass. Model inputs include initial body mass (kg), standard final mass (kg), age (days), and dietary energy concentration (Mcal·kg−1). The model was assessed as a decision support tool using a dataset of 564 individual measures of final body mass and diet energy. The simulations provide graphical and numerical descriptions of nutrient requirements, composition of gain, and estimates of animal performance over time. The model is accurate and precise, with a root mean squared error of 7.79% of the observed final body mass and a coefficient of determination of 0.89 when simulating the same variable. The model can be used as a reliable decision support tool to estimate final body mass and the days on feed required to reach a certain final mass with precision and accuracy. Moreover, the dynamic model can also serve as a learning tool to illustrate practical principles of animal nutrition, nutrient requirement relationships, and body composition changes. This model holds the potential to enhance livestock management practices and assist producers in making informed decisions about feedlot lamb production.

Thermoelectric properties enhancement and optimization of SnTe-based material with single doping: RSM-ANN approach

Tin Telluride (SnTe) stands out among semiconductor materials, drawing attention as an environmentally friendly alternative to conventional Lead Telluride (PbTe). This study explores the novel incorporation of titanium (Ti) and zirconium (Zr) as dopant elements in SnTe-based materials, an approach that has never been previously investigated. Leveraging mathematical modeling and machine learning methodologies, we use Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) to predict thermoelectric properties of SnTe-based materials and find the optimum dopant element addition and operating temperature. Eleven samples featuring varying Ti and Zr concentrations are employed in experimental investigations, with dopant amounts ranging from 0 to 0.05 M. Characterization of each sample is conducted across a temperature spectrum from 50 to 450 °C, yielding a comprehensive dataset of 99 entries for neural network model construction. The generated RSM and ANN models demonstrate remarkable predictive accuracy for the thermoelectric properties of SnTe-based materials, showcasing coefficient of determination (R2) values spanning from 0.986 to 0.998. This study uses RSM and ANN-GA to predict an optimized zT value of 0.558. This value surpasses the highest zT value from the experiment by 3.33 %. In conclusion, this investigation underscores the potency of RSM and ANN as valuable tools for advancing research on SnTe-based thermoelectric materials, exemplifying their capacity to hasten the exhausting process of finding the optimum dopant addition and operating temperature.

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index

Grape quality is generally assessed by measuring individual indicators rather than utilizing a comprehensive metric. A flexible approach to aggregating multiple quality-related parameters of grapes is introduced in this study, and an aggregative index was proposed for the synthetical evaluation of grape quality. First, visible-near-infrared (Vis-NIR) spectral data of two grape varieties were obtained, and the physical and chemical quality indicators were measured as reference standards. The variation trends and correlations of the indicators were analyzed, and an aggregative quality indicator (AQI) was proposed using the min-max normalization and factor analysis methods. The maturity stages of grapes were categorized by analyzing the AQI values. The spectral data underwent preprocessing using multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), first-order derivative (FD), SG+FD and de-trending (DT) methods. Feature wavelengths were extracted using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the UVE-CARS algorithms. Three regression models for the AQI were established based on partial least squares regression (PLSR), support vector machine regression (SVR) and convolutional neural network (CNN). Results indicated that the PLSR combined with the FD+SG preprocessing and CARS-selected feature wavelengths achieved optimal performance for Cabernet Sauvignon grapes, with the coefficient of determination for the prediction set (Rp2)=0.972, for the calibration set (Rc2)=0.995, the root mean square error of prediction (RMSEP)=0.040 and residual predictive deviation (RPD)=6.064. In addition, for the Muscat Kyoho grape, CNN combined with DT preprocessed full-spectrum data yielded the best prediction for the AQI, with Rp2=0.989, Rc2=0.981, RMSEP=0.103 and RPD=9.482. This research demonstrated that the non-destructive prediction of grape aggregative qualities can be achieved through the combination of Vis-NIR spectroscopy and a chemometric analysis, offering a practical and comprehensive metric for determining grape maturity stages and optimal harvest times. This approach is particularly valuable for the wine industry, as it facilitates better decision-making and quality control.

Improving the quality of Glycyrrhiza stems and leaves through solid-state fermentation: flavonoid content, antioxidant activity, metabolic profile, and release mechanism

Glycyrrhiza stems and leaves (GSL) are waste products of Glycyrrhiza; however, it has been considered a potential source of flavonoid compounds. In this study, GSL was processed by solid-state fermentation (SSF) to improve the flavonoid content and antioxidant activity. Firstly, a near-infrared (NIR) prediction model for flavonoid content in fermented Glycyrrhiza stems and leaves (FGSL) was established. Next, the effects of SSF on the antioxidant activity and metabolite profile of GSL were investigated. Finally, the possible mechanism of ferment release of flavonoids was explored based on enzyme activity, thermogravimetric analysis, and FTIR spectroscopy. The results revealed that NIR spectroscopy can efficiently analyze flavonoid contents in GSL, with predicted determination coefficient (Rp2) and root mean square error (RMSEP) of 0.9874 and 0.125, respectively. SSF significantly increased the levels of flavonoids, and enhanced the scavenging activities of DPPH radical and hydroxyl radical and reducing power of FGSL. Widely targeted metabolomic analysis showed the detection of 461 differential metabolites were identified after SSF, with 141 metabolites remarkably up-regulated and 320 metabolites of FGSL down-regulated during fermentation. The main types of differential metabolites were phenolic acids and flavonoids, and the destruction of cellulose by SFF was crucial to the release of flavonoids. In conclusion, our study revealed that SSF remarkably improved the phytochemical components of FGSL by increasing enzyme activity and destroying cellulose structure, thereby contributing to the enhancement of antioxidant activity. This study provided a scientific basis for the production of high-value flavonoids from plant materials and offered a novel approach to elucidate the release and conversion of flavonoids.Graphical

Exploring the potential of multi-source satellite remote sensing in monitoring crop nutrient status: A multi-year case study of cranberries in Wisconsin, USA

A timing and precise diagnosis of crop nutrient status is essential for optimizing management practices that promote environmentally friendly and enhanced crop yields. Although plant tissue analysis has conventionally been employed to evaluate the nutritional status of crops, this method cannot capture the spatial variability of crop nutrients. In contrast, satellite-based remote sensing can monitor the nutrient status of crops across expansive areas. This study explored the capability of multi-source satellite images (PlanetScope-4: 3 m, 4 bands; PlanetScope-8: 3 m, 8 bands; Sentinel-2: 10–60 m, 13 bands; PRISMA: 30 m, 239 bands) in mapping 12 foliar nutrients in cranberries. Three machine learning approaches, including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), were used to relate foliar nutrients to different types of satellite-derived features (SR: surface reflectance; VI: vegetation indices; TF: texture features) or their combinations (SR+VI, VI+TF and SR+VI+TF). Model performance was compared across different foliar nutrients, modelling approaches and combinations of model input features using R2 (the coefficient of determination) and RRMSE (relative root mean square error, = root mean square error/nutrient range × 100 %). Input features that were important to foliar nutrient modelling were identified. The model performance difference among nutrients was consistent between Planet-4 and Sentinel-2, as well as between Planet-8 and PRISMA. In the Planet-4 and Sentinel-2 derived models, N was best predicted (average R2 = 0.77, average RRMSE=15 %), followed by macronutrients S (0.60–0.63, 11 %), Mg (0.58–0.65, 10–11 %), Ca (0.49–0.51, 9 %), Na (0.69, 22 %), P (0.49, 9 %) and K (0.20, 8 %), and then by all micronutrients(i.e., Fe, Mn, B, Cu and Zn: R2 = 0.04–0.61; RRMSE=16–28 %). In the Planet-8 and PRISMA derived models, macronutrients (i.e., N, P, K, Mg, Ca, S and Na) had lower R2 and RRMSE (R2 = 0.06–0.59; RRMSE=7–57 %) than micronutrients (i.e., Fe, Mn, B, Cu and Zn: R2 = 0.18–0.60; RRMSE=19–66 %). The successful retrieval of foliar nutrients from satellite imagery was influenced by many factors, including the intercorrelation between nutrients and model input features, the data availability at critical growth stages, and satellite images characteristics (e.g., spatial and spectral resolutions). Except for foliar nitrogen, foliar nutrients typically do not exhibit distinct absorption features associated with C, H, N, or O molecular bonds in the 400–2500 nm range. Our results indicate that their successful retrieval can be primarily attributed to the association between foliar nutrients and other leaf components (e.g., pigments, water, and dry matter) that do display spectral features within this range. Our study demonstrated the potential of integrating multi-source satellite data for precise nutrient monitoring over large scales.

Pengaruh Brand Image dan Brand Awareness terhadap Keputusan Pembelian Body Lotion Vaseline (Studi pada Pengguna Body Lotion Vaseline di TikTok Shop)

This research aims to describe purchasing decisions, brand image, and brand awareness and analyze the influence of brand image and brand awareness on the purchasing decisions of Vaseline body lotion users at the TikTok Shop. The research uses quantitative methods with descriptive and verification approaches. Samples were taken through purposive sampling involving 100 Vaseline body lotion users at the TikTok Shop, and data was collected through a questionnaire. Data analysis includes multiple linear regression, correlation coefficient, and coefficient of determination. Descriptive analysis shows a purchasing decision score of 3,353 (very good), brand image 2,445 (good), and brand awareness 2,678 (very good). The research results show that brand image influences purchasing decisions with a correlation of 0.825 and brand awareness with a correlation of 0.813. Simultaneously, brand image and brand awareness influence purchasing decision with a simultaneous correlation of 0.935 and an influence contribution of 87.4%.

The Effect of Asset Structure and Dividend Policy on the Capital Structure of Infrastructure Subsector Companies Listed on the Indonesia Stock Exchange 2016-2020 Period

The purpose of this study was to determine the effect of Asset Structure and Dividend Policy on the Capital Structure ofInfrastructures Sub-sector Companies listed on the Indonesia Stock Exchange for the 2016-2020 period. The research indicatorsused are Fixed Assets Ratio, Dividend Payout Ratio and Debt to Equity Ratio. The approach used in this research is a quantitativeapproach. The population in this study were 175 financial statements from 35 infrastructures sub-sector companies listed on theIndonesia Stock Exchange and the sample used in this study were 35 financial statements from 7 companies. The data used issecondary data, namely data obtained directly in the form of financial reports from the website of each company and thewww.idx.co.id website. The data analysis technique was carried out using statistical analysis, namely classical assumption test,multiple linear regression coefficient, multiple correlation coefficient, coefficient of determination and hypothesis testing usingSPSS version 25 program. The results showed that partially Asset Structure had no significant effect on The Capital Structure ofthe infrastructures subsector companies listed on the Indonesia Stock Exchange for the 2016-2020 period, while the DividendPolicy has no significant effect on the capital structure of the infrastructures subsector companies listed on the Indonesia StockExchange for the 2016-2020 period. Simultaneously, the Asset Structure and Dividend Policy have a significant influence on thecapital structure of the infrastructures sub-sector companies listed on the Indonesia Stock Exchange for the 2016-2020 period.

Fusing Multi-Scale Functional Connectivity Patterns via Multi-Branch Vision Transformer (MB-ViT) for Macaque Brain Age Prediction

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human using chronological age (CA) as label. However, whether the BA of macaque, one of the most important animal models, can also be reliably predicted is largely unknown. To address this question, we propose a novel deep learning model called Multi-Branch Vision Transformer (MB-ViT) to fuse multi-scale (i.e., from coarse-grained to fine-grained) brain functional connectivity (FC) patterns derived from resting state functional magnetic resonance imaging (rs-fMRI) data to predict BA of macaques. The discriminative functional connections and the related brain regions contributing to the prediction are further identified based on Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our proposed model successfully predicts BA of 450 normal rhesus macaques from the publicly available PRIMatE Data Exchange (PRIME-DE) dataset with lower mean absolute error (MAE) and mean square error (MSE) as well as higher Pearson's correlation coefficient (PCC) and coefficient of determination (R2) compared to other baseline models. The correlation between the predicted BA and CA reaches as high as 0.82 of our proposed method. Furthermore, our analysis reveals that the functional connections predominantly contributing to the prediction results are situated in the primary motor cortex (M1), visual cortex, area v23 in the posterior cingulate cortex, and dysgranular temporal pole. In summary, our proposed deep learning model provides an effective tool to accurately predict BA of primates (macaque in this study), and lays a solid foundation for future studies of age-related brain diseases in those animal models.

Objective analysis of capsulorrhexis factors and posterior capsular opacification in 420 postmortem eyes

ObjectiveTo evaluate the capsulorrhexis structure in postmortem eyes and determine factors associated with posterior capsular opacification (PCO). DesignExperimental study. ParticipantsPostmortem pseudophakic human eyes (n = 420). MethodsPostmortem eyes were obtained and examined. Photographs were taken of the eyes in Miyake-Apple view and of the extracted lens-capsule complexes. PCO and Soemmering's ring (SR) were quantified using automated detector opacification software as factors of intensity and area. Miyake-Apple views and ImageJ were used to assess capsulorrhexis diameter, area of anterior capsule-optic overlap, length of the shortest anterior capsular leaflet, and area and angle of capsulorrhexis-optic nonoverlap. Linear regression analysis and Welch's t test were used to determine the relationship of these factors with PCO and SR. All analyses were repeated in sample groups specific to the 5 most common intraocular lens models. ResultsCapsule-optic overlap was positively correlated with PCO (P < 0.0001) and SR (P = 0.0016). Capsulorrhexis diameter was negatively correlated with PCO (P < 0.0001) and SR (P = 0.014). Leaflet length was positively correlated with PCO (P = 0.009). Area and angle of capsulorrhexis-optic nonoverlap were not correlated with PCO or SR. Slopes and coefficients of determination were relatively low for all significant results. ConclusionsThe pathogenesis of PCO development after cataract surgery is multifactorial. This study shows that with modern operating technology, capsulorrhexis factors have at best a modest influence on PCO formation. Factors such as time from surgery to death and intraoperative techniques such as laser capsule polishing, posterior capsule vacuuming, and cortical cleanup are likely to play a more significant role.

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75% of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results.