High Coefficient Of Determination Research Articles

Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions

Abstract The cementitious composite’s resistance to the introduction of harmful ions is the primary criterion that is used to evaluate its durability. The efficacy of glass and eggshell powder in cement mortar exposed to 5% sulfuric acid solutions was investigated in this study using artificial intelligence (AI)-aided approaches. Prediction models based on AI were built using experimental datasets with multi-expression programming (MEP) and gene expression programming (GEP) to forecast the percentage decrease in compressive strength (CS) after acid exposure. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to examine the significance of prospective constituents. The results of the experiments substantiated these models. High coefficient of determination (R 2) values (MEP: 0.950 and GEP: 0.913) indicated statistical significance, meaning that test results and anticipated outcomes were consistent with each other and with the MEP and GEP models, respectively. According to SHAP analysis, the amount of eggshell and glass powder (GP) had the most significant link with CS loss after acid deterioration, showing a positive and negative correlation, respectively. In order to optimize efficiency and cost-effectiveness, the created models possess the capability to theoretically assess the decline in CS of GP-modified mortar across various input parameter values.

Discovery of a Ni-based superalloy with low thermal expansion via machine learning

Traditional Ni-based superalloys are widely used due to their excellent high-temperature performance; however, their high thermal expansion at elevated temperatures limits their further application in the modern aerospace industry. To discover Ni-based superalloys with low thermal expansion for high-temperature environments, such as 900 °C. In this study, we developed a Support Vector Regression (SVR) model with a high coefficient of determination (R2 = 0.84) to predict the coefficient of thermal expansion (CTE) in Ni-based superalloys. Ten samples were selected and produced from a pool of candidates generated via genetic algorithm and the SVR model, revealing that seven of them exhibited superior CTE performance. Notably, the discovered alloy with the lowest CTE (11.0 e-6 °C-1 from RT to 900 °C) presented a remarkable 16.7 % reduction compared to the best performance in existing database. The finally selected alloy demonstrated both low thermal expansion and superior mechanical properties compared to traditional superalloys with low thermal expansion.

High-throughput near-infrared spectroscopy analysis of nutritional composition in sweet potato stem tips

Leaf-vegetable sweet potato is a nutritious food source, with stem tips as the harvest organ. However, the lack of rapid quantitative approaches for quality evaluation of stem tips largely limits the genetic improvement of leaf-vegetable sweet potato. This study aimed to establish a near-infrared spectroscopy (NIRS) methodology for the rapid analysis of both proximate (cellulose, crude protein, and soluble sugar) and functional (chlorogenic acid, total flavonoid content, and total phenolic content) components in the stem tips. Leveraging a dataset of 140 representative germplasm samples, we developed six robust NIRS models through the optimization of calibration sets and variable selection. These models exhibited exceptional accuracy, with high determination coefficients on calibration (R2C = 0.95–0.98), cross-validation (R2CV = 0.93–0.96), external validation (R2V = 0.91–0.95), and the ratio of prediction to deviation (RPD = 6.78–9.72). Overall, the NIRS models developed through this research facilitate high-throughput profiling of nutritional composition in stem tips, thereby enabling the swift identification of superior germplasm suited for leaf-vegetable sweet potato production.

A new human metabolic rate sensing model optimization and wearable sensor realization

Accurate assessment of metabolic rate is crucial for predicting human thermal comfort. However, many existing instruments are bulky, invasive, and inconvenient. Additionally, wearable metabolic rate measurements have faced long-standing challenges due to a lack of a comprehensive theoretical model with easy-to-measure parameters. This paper optimizes our previously proposed theoretical approach for wearable metabolic rate measurement that incorporates heart rate, whole-body unit heat loss, skin resistance, and body muscle percentage. We have improved the evaluation approach for whole-body unit heat loss by introducing a newly designed convective heat loss coefficient from various body segments. Regarding the wearable sensor design, a heat loss measuring system with serpentine channel was verified as the most suitable design following calibration experiments. We also integrated a modified square-wave-based skin resistance design, resulting in an upgraded integrated metabolic sensor. To validate our sensor and model performance, we conducted experiments with ten subjects of both genders at three temperature conditions (22, 25 and 28 °C) and four levels of activities (rest, tiptoe heel, slight walk, and moderate walk). First, our results demonstrate a strong positive relationship between the metabolic rate obtained by our optimized sensor and Quark CPET instrument, with high coefficients of determination (highest:0.97 from the male group, 0.98 from the female group). Our approach achieved at least 96 % accuracy and less than 2.5 % uncertainty for both groups. Second and increasingly, we found that only one metabolic rate model was required across combined temperature conditions when the person was fixed. Third, the positive relationship was further improved by incorporating whole-body unit heat loss. Finally, we obtained information from different segments on skin temperatures. In summary, our study provides a powerful methodology for predicting and evaluating human metabolic rate through an innovative wearable approach. Our approach and sensor have the potential to substantially contribute to energy-saving efforts in smart buildings.

Fore­casting profitability levels for different soybean (Glycine max L.) cultivation technology options

Objective of the research is to develop adequate models for forecasting the profitability of soybean seed cultivation under various technology options; to identify significant factors affecting financial performance to support effective management decisions. Methods. The use of regression analysis tools enabled the construction of stepwise models of profitability dependence on economic indicators with high coefficients of determination. Results. The dependence of economic efficiency indicators on soybean seed yield under various cultivation technology options has been determined. The research results indicate that the most effective cultivation technology was the one that combined the use of fertilizers at a dose of N15P45K60+N30 kg/ha of active ingredient, presowing seed treatment (mycorrhizal inoculant + fungicide), and foliar feeding with organo-mineral fertilizer during the budding phase, which led to the highest seed yield in the experiment, at 3.74 t/ha. Based on the conducted correlation-regression analysis and the developed mathematical models, the connection between profitability levels and the total cost of the harvest and the cost of soybean seeds was proven, both for seed treatment alone and for seed treatment combined with foliar feeding during the critical growth phases. Conclusions. The correlationregression analysis results showed that the total cost of the harvest and the cost of soybean seeds have the most significant impact on profitability. This is confirmed by the coefficients of determination, indicating that 98.2–99.8% of the total variability in profitability is due to the combined effect of the studied variables. Based on this, models were developed to describe the dependence of profitability levels on the total cost of the harvest and the cost of soybean seeds, both for seed treatment alone (mycorrhizal inoculant + fungicide) and with additional foliar feeding during critical growth and development phases.

THE ROLE OF LIMING IN COPPER ADSORPTION OF ACIDIC SOILS

Investigate the impact of varying rates of liming on the availability and sorption properties of copper in a range of acid soils with differing physico-chemical futures. Soil samples with varying acidity levels were collected from different locations in the acidic Black Sea Region of Türkiye. Out of the nine soil samples collected, four were obtained from regions where hazelnuts are grown, while the other five were obtained from areas dedicated to tea growth. The sorption properties of soils limed at 0%, 50%, and 100% of lime requirement and incubated for 12 months, then the Langmuir and Freundlich adsorption isotherms were determined by batch sorption technique. The results showed that increasing the soil pH resulted in an increase in Cu adsorption. Experimental results of Cu adsorption were explained with high coefficients of determination (93.44-98.98%) of the Langmuir model. In the three experimental groups with full, half, and no lime application, pH increased significantly to 3.95, 4.46, and 5.89, respectively, while the maximum adsorption obtained from Langmuir adsorption curves, Qmax1 1553, Qmax2 2205, and Qmax3 3902 mg kg-1, respectively. While the maximum adsorption in the tea plantation areas varied between 1491 and 1514 mg kg-1, the maximum adsorption in hazelnut cultivation areas varied between 777 and 3333 mg kg-1. These findings suggest that liming or other methods to increase soil pH could be effective soil management in reducing Cu mobility in acidic soils.

Coomans plot classification of lard in the ink blends and ink extracts from printed packaging films using lard Fourier transform infrared spectral profile and multivariate analysis

Lard in blends of commercial gravure ink (ranging from 0.5 to 20%) and ink extracts from eleven commercially printed packaging films for foodstuffs was characterized using Fourier transform infrared (FTIR) spectroscopy. The FTIR spectral bands at 4000 ‒ 650 cm-1 associated with the lard fingerprint were acquired and used to classify the lard and its blends using partial least squares (PLS) regression and discriminant analysis (DA). Commercial gravure inks (also used for preparing calibration curve samples), blends of lard ranging from 0.5 to 20% in gravure inks, and commercially printed food packaging films were tested. Linear correlation of predicted and actual values of lard were determined using PLS calibration and validation models. They produced a high coefficient of determination (R2 ) of 0.943, a low root mean square error of calibration (RMSEC) of 1.674, as well as a high R2 = 0.999 and a low root mean square error of prediction (RMSEP) of 1.233, respectively. The PLS calibration was verified employing a leave-oneout cross-validation, while DA was used to classify a series of lard standard, lard-added ink, and commercial food packaging films. The Coomans plot classification of the lardadded ink and commercial food packaging films illustrated that the food packaging samples were plotted in the right and left hemispheres in the lard-added ink class. This result also demonstrated that FTIR coupled with chemometric PLS predicted the lard content in the printing inks with high overall accuracy, as indicated by a low mean difference (MD) value of 0.577 and a low standard deviation of difference (SDD) value of 0.599. The DA allowed the ink packaging samples that potentially contained lard to be distinguished from those without lard. Sample 7 (commercially printed food packaging ink) exhibits the highest possibility of containing lard.

NEW APPROACH TOWARDS THE PROBLEM OF SOMATIC CRITERIA OF SECULAR TRENDS. SEXUAL SOMATIC DIMORPHISM

Introduction. The goal of study is description of secular dynamics of sex dimorphism (SD) of height, weight, chest girth of children aged 9 years and teenagers 13 years old from Russia and neighboring countries through 1920–2010. Materials and methods. The main source of data — materials of books on physical development of children and adolescents from cities and rural regions of Russia and USSR, collected and processed according to the universal methodical standards of Research Institute of Hygiene of children and adolescents. The quantitative estimation of SD value was held using Kullback divergence. One-year age groups, contrastive in biological essence, are under discussion. The estimation of the vector and level of associations of SD of anthropometric traits and secular factor was held using correlation analysis. The analysis of local patterns of secular SD dynamics of separate traits was carried out. Results. Secular variability of height and weight SD of 9-year-old children is not significant. While secular dynamics of three body dimensions of 13-year-old teenagers is significant, the highest determination coefficient is fixed for weight. For Moscow samples the decrease of height and weight SD is fixed through the historic interval 1960s – 70s with the following recovery up to the level of 1920s in 2000s. Temporal dynamics of SD of 9-year-old children of St. Petersburg through the interval 1960s – 2000s consists of macrosomization of girls; the tendency for 13-year-old teenagers is opposite. Temporal dynamics of somatic SD of 9-year-old Russian children of Kazan reveals stability through the interval 1960th-1990th; for 13-year-old teenagers’ parameters of transversal body development demonstrate secular stability, as to height – boys compensate their lag compared to girls. For Kirovsk city (Murmansk region) through the interval 1950s – 1970s temporal fluctuations of height and weight SD of 9-year-old children are less than 0,2 standard deviations; height and weight SD of 13-year-old teenagers reveal temporal increase (0,4–0,6 standard deviations). Conclusion. The results confirm the importance of somatic SD as the criterion of secular dynamics for 13-year-old teenagers and witness, as a whole, to the temporal stability of SD during second childhood (9 years). Local differences do not contribute significantly to the intensity of secular dynamics of SD. The process of secular macrosomization of one sex as compared to the other is not the exclusive prerogative of boys and male teenagers.

An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms

With the rapid advancement of science and technology, chemical and physical research has entered an era of complexity and high dimensionality, where traditional research paradigms struggle to optimize vast chemical parameter searches. The Machine Chemist platform was developed in this context, leveraging big data and intelligent models to automate chemical synthesis, characterization, and testing processes. This study aims to predict the y1, y2 and y3 properties and classes of 2,580 molecules based on their physicochemical properties and improve model accuracy through data analysis and modeling. Data preprocessing involved removing missing values, duplicates, and outliers using the quartile method, resulting in an analyzable dataset. A scatter plot of y2 and id suggested a univariate polynomial function relationship, leading to the construction of a univariate nonlinear regression model. The model achieved a high prediction accuracy, with a low root mean square error and a high coefficient of determination. Additionally, the relationship between class and y1~y3 , x1~x100 indicators was examined, revealing mostly nonlinear relationships. A random forest model was established to classify 2,580 molecules into 1 to 4 classes based on the properties of 200,000 chemical molecules. The model's performance was evaluated using decision trees, confusion matrices, precision, and recall metrics. Finally, the SHAP method assessed the impact of feature indicators on classification outcomes, contributing to the development of a reliable molecular category prediction model.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.

Statistical modeling and optimization of heavy metals (Pb and Cd) adsorption from aqueous solution by synthesis of Fe3O4/SiO2/PAM: isotherm, kinetics, and thermodynamic

In this study, magnetic material was synthesized using iron salts, then silicon-specific material was used to gain porosity, straight-chain polyacrylamide (PAM) was modified to give the surface functional properties, and the final product synthesized Fe3O4/SiO2/PAM nanocomposite material. Heavy metal (Pb and Cd) removal studies were carried out with the synthesized composite material, considering the central composite design and response surface methodology (CCD-RSM) optimization model. The effects of various parameters, for example, the initial concentration, pH, adsorbent dose, temperature and contact time, were investigated as a part of this study. To optimize these parameters, the CCD-RSM model was applied to design the experiments. Analysis of variance (ANOVA) was applied to evaluate statistical parameters and investigate interactions of variables. In the designed experimental set, the amount of adsorbent (30 mg), pH 7.0 value, temperature (40 °C), initial concentration of Pb (80 mg/L) and Cd (20 mg/L) and 90 min contact time were determined as the optimum conditions. The high coefficient of determination of both metals showed good agreement between experimental results and predicted values (R2 0.99; 0.95). TEM, SEM, XRD, FTIR, BET and Zeta potential analyses were performed to characterize the structure and morphology of the adsorbent. In Pb2+ and Cd2+ heavy metal removal studies, maximum adsorption capacities were determined as 66.54 and 13.22 mg/g, respectively. Additionally, adsorption isotherms, adsorption kinetics and thermodynamic modeling studies were conducted. Features such as large surface area and high adsorption capacity of the synthesized nanoparticles were observed. In this study, Fe3O4/SiO2/PAM demonstrated its potential as an effective adsorbent for the removal of heavy metal ions present in simulated wastewater samples. In particular, we can say that the material has a strong selectivity, as well as a high affinity for Pb(II) ions.

Performance prediction and regression analysis of scroll expander based on response surface methodology

The Organic Rankine Cycle (ORC) plays a pivotal role in the domain of renewable energy, adeptly harnessing thermal energy from sources such as solar and geothermal to generate electrical power. Central to this system is the scroll expander, whose operational efficacy directly influences the ORC's overall efficiency. This study initiates with a detailed numerical analysis of the impact of various operational parameters on both the steady and transient output performance of the scroll expander. Following this, a sophisticated performance prediction model is developed employing the response surface methodology. The model boasts high coefficients of determination, R2,values of 0.9967 and 0.9916, respectively, underscoring its superior predictive accuracy. Further, this model delves into the complex interactions among operational parameters and their consequent effects on the expander's performance. Validation of this predictive model is achieved through rigorous testing on an experimental platform designed for the ORC low-temperature waste heat power generation system. The discrepancies between predicted and actual performance measurements fall within a commendable margin of 15 %, affirming the model's robustness in forecasting scroll expander performance. The culmination of this research furnishes a novel and efficacious optimization strategy, establishing a robust theoretical foundation and offering fresh insights for the enhancement and prediction of scroll expander performance. This not only underscores the innovation of the approach but also its practical applicability in advancing the efficiency of renewable energy systems.

Estimating Hardening Soil-Brick model parameters for sands based on CPTU tests and laboratory experimental evidence

The Hardening Soil-Brick model for soils is designed to carry out complex numerical analyses of soil-structure interaction problems taking into account strong stiffness variation in the range of small strains. However, to calibrate its parameters advanced triaxial and oedometric tests are required. In case of uncemented sands laboratory testing is usually difficult. Therefore, to facilitate calibration procedures, a CPTU based method, enhanced by an experimental evidence derived from advanced triaxial drained and oedometric tests, has been proposed and verified. It is shown in the paper that using exclusively the CPTU test results one can calibrate most important model parameters for sands with accuracy that is sufficient for solving real life problems. The major goal of this paper is to identify correlations between all reference stiffness moduli, then verify them, and finally link with the CPTU based identification procedures. It is shown in the paper that such correlations exist and they exhibit very high coefficients of determination. Moreover, as the seismic version of the CPTU test is not often available in the practice, an enhanced procedure for identification of very small strain shear stiffness modulus has been proposed and then verified, using set of the SCPTU tests conducted in Gdańsk sands (Poland).

Microstructure Analysis, Piezoelectrical Resistivity, and Compressive Strength Concrete Incorporated with Waste Steel Slag as a Fine Aggregate Replacement

Abstract This study aims to investigate the effect of waste steel slag (SS) as partially replaced with cement and fine aggregate on conventional concrete for different mixes named M25, M35, and M47 in terms of compressive strength (CS), electrical resistivity (ER), and piezoresistivity behavior. SS is a molten mixture of silicates and oxides that solidifies upon cooling, a byproduct of the steel-making process. Before doing the design experiments, the optimum value of SS as powder and fine aggregate was determined using seven different mixes to investigate the effect of different SS sizes on the CS and piezoresistivity of normal concrete. Based on the results achieved, the optimum value and size of SS were selected to modify and investigate the effect of SS on three different mixes of conventional concrete named M25, M35, and M47 in terms of CS, ER, and piezoresistivity behavior. The resistivity of all concrete mixes was measured using four-probe from early curing to 28 days of curing time. The results demonstrated that M47 mix modified with SS has lower resistivity than the rest of the concrete mixes. The results of piezoresistivity behavior indicated that M47 mix modified with SS has a higher resistivity change while applying stress at 3 days of curing compared to the M25 and M35 concrete mix modified with SS by 44.1 % and 37.6 %, respectively. The Vipulandanan p-q model was applied to predict both ER versus time and change of resistivity versus stress for all mixes. The results demonstrated that the model predicted the change of resistivity versus applied stress with a high coefficient of determination that varied between 0.82 and 0.989, and a low root mean square error changed between 0.81 Ω.m and 7.94 Ω.m.

Raising double-muscled breed cattle and their crossbreds in the tropics: insight from growth models.

In tropical conditions, modeling the predictive parameters of live weight, including those at birth, pre-weaning, post-weaning, finishing, and maturing, and the average daily gain, is challenging. The heat load significantly influences the growth rate and final mature weights in the tropics. The study compared the growth rates of Kedah-Kelantan (KK), Brahman (BRAH), and Belgian Blue (BB) crossbred calves. The study conducted growth analysis using the non-linear regression growth models as it approximates the sex, breed, and growth physiology changes in beef cattle. It is supported by the utility of the most common growth functions (Brody, Logistic, von Bertalanffy, and Richard's model) in normal-muscled tropical breeds and double-muscled crossbred beef cattle in the tropics. The BB crossbreds outperformed the KK and BRAH breeds by 50%-100% in live weight gains under tropical conditions. The crossbreds display the double-muscled effect and highlight the advantages of heterosis, making them suitable for upgrading local herds. The study's findings on the growth characteristics of BB crossbred cattle were best described by the von Bertalanffy growth model, which had a high coefficient of determination (R2 > 0.8) and yielded estimated mature weights of 527.5 kg for males and 518.5 kg for females. According to results, raising BB crossbreds in the tropics as a solution to ensure a sustainable beef supply could yield significant growth and economic benefits.

Multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape

The performance of several heat transfer mechanisms, including solar energy sources, is being improved through optimization, which also effectively supports the most efficient feasible design. A numerical study has been done on a turbulator-enhanced flat plate solar collector (FPSC) while also introducing the utilization of a quad-lobed tube as a pioneering innovation in the system’s design. The incorporation of nanoparticles into the working fluid was implemented to enhance performance, with aluminum oxide particles selected for this purpose. Furthermore, assessing the second law and analyzing exergy generation was employed to discern irreversibility within the system. Optimization of both geometric and non-geometric elements has been accomplished through the integration of a multi-objective optimization (MOO) method with machine learning (ML). Random Forest (RF), LASSO Regression (LaR), and Support Vector Regression (SVR) are the machine learning models that were utilized in order to ascertain the correlations that exist between the system’s inputs and outputs. According to the findings, RF was found to be the most appropriate algorithm for capturing the impacts of parameter variations. This was demonstrated by the high coefficient of determination (R2) quantities that RF possessed, which were 0.95, 0.99, and 0.99 for friction factor (f), exergy loss (Xd), and second law effectiveness (ηII), respectively. However, the SVR and LaR algorithms showed significant deviations from the FPSC simulation results. The SVR algorithm achieved R2 values of 0.54 for f, 0.78 for Xd, and 0.84 for ηII, while the LaR algorithm achieved R2 values of 0.65 for f, 0.99 for Xd, and 0.98 for ηII. This research aims to find the optimal input parameters that minimize f and Xd while maximizing ηII. Therefore, to determine the Pareto optimal solutions the non-dominated sorting genetic algorithm (NSGA-II) has been utilized. The result was an illustration of the Pareto set, which stands for a collection of optimal solutions. Every solution in this collection achieved the desiredbalance, obtaining the best outcomes without compromising any objectives.

Modeling of driving factors and headcut rates of ephemeral gullies in the loess plateau of China using high-resolution remote sensing images

ABSTRACT Ephemeral gully headcut erosion contributes significantly to global land degradation and increased sediment yields, but the underlying driving factors and prediction models remain poorly understood. We conduct a comprehensive quantitative analysis of ephemeral gully headcut erosion in the Loess Plateau using an optimal parameters-based geographical detector (OPGD) model, leveraging high-resolution remote sensing images. Our findings reveal a varied ephemeral gully head advance rate spanning 0.04–5.54 m yr−1 between 2009 and 2021 (average 1.37 m yr−1), with over 58% of the erosion rates falling between 0.50 and 2.00 m yr−1. Catchment area emerges as the primary driving factor, with an explanatory power of 61%. Moreover, the interactions between catchment area and slope degree, rainfall erosivity, and fractional vegetation coverage (FVC) had explanatory powers exceeding 80%. Furthermore, we developed a robust prediction model for ephemeral gully head advance rates based on the results from the OPGD model, incorporating the FVC factor. The validation of our model yielded a high coefficient of determination (R2 = 0.92 m yr−1) and low root mean square error (RMSE = 0.31 m yr−1). Our study offers new insights into ephemeral gully headcut erosion control in the Loess Plateau and serves as a valuable reference for loess regions worldwide.

Correlation analysis of primal cuts weight, fat contents, and auction prices in Landrace × Yorkshire × Duroc pig carcasses by VCS2000.

Currently, in pork auctions in Korea, only carcass weight and backfat thickness provide information on meat quantity, while the production volume of primal cuts and fat contents remains largely unknown. This study aims to predict the production of primal cuts in pigs and investigate how these carcass traits affect pricing. Using the VCS2000, the production of shoulder blade, loin, belly, shoulder picnic, and ham was measured for gilts (17,257 pigs) and barrows (16,365 pigs) of LYD (Landrace × Yorkshire × Duroc) pigs. Single and multiple regression analysis were conducted to analyze the relationship between the primal cuts and carcass weight. The study also examined the correlation between each primal cut, backfat thickness (1st thoracic vertebra backfat thickness, grading backfat thickness, and Multi-brached muscle middle backfat thickness), pork belly fat percentage, total fat yield, and auction price. A multiple regression analysis was conducted between the carcass traits that showed a high correlation and the auction price. After conducting a single regression analysis on the primal cuts of gilt and barrow, all coefficients of determination (R2) were 0.77 or higher. In the multiple regression analysis, the R2 value was 0.98 or higher. The correlation coefficient between the carcass weights and the auction price exceeded 0.70, while the correlation coefficients between the primal cuts and the auction prices were above 0.65. In terms of fat content, the backfat thickness of gilt exhibited a correlation coefficient of 0.70, and all other items had a correlation coefficient of 0.47 or higher. The correlation coefficients between the Forequarter, Middle, and Hindquarter and the auction price were 0.62 or higher. The R2 values of the multiple regression analysis between carcass traits and auction price were 0.5 or higher for gilts and 0.4 or higher for barrows. The regression equations between carcass weight and primal cuts derived in this study exhibited high determination coefficients, suggesting that they could serve as reliable means to predict primal cut production from pig carcasses. Elucidating the correlation between primal cuts, fat contents and auction prices can provide economic indicators for pork and assist in guiding the direction of pig farming.

Unraveling popping quality through insights on kernel physical, agro-morphological, and quality traits of diverse popcorn (Zea mays var. everta) inbreds from indigenous and exotic germplasm

Popcorn is a specialty corn with worldwide popularity as a snack. Despite having great market demand, genetic improvement in popping quality is limited, which is caused by the limited germplasm utilization and narrow genetic base. An assortment of diverse germplasm, their effective characterization, and integration into popcorn breeding pipeline is the foundation for an efficient breeding program. Here, kernel characteristics, popping quality traits, and agro-morphological traits were evaluated across three locations on a diverse panel of 48 popcorn inbreds derived from diverse landraces and populations of exotic and indigenous origin. The variations due to genotypes, locations, and genotype × location interaction were highly significant. The popping quality traits recorded wide variation with a high coefficient of genotypic determination. The kernel dimensions, kernel density, test weight, and grain yield were negatively correlated with popping quality traits. Genotypes with rice-type kernels exhibited better popping quality than pearl-type kernels. Analysis of genotype × location (G×L) interaction identified two target locations for the key popping quality trait, popping expansion volume. PMI-PC-175, PMI-PC-187, PMI-PC-188, and PMI-PC-189 were identified as superior genotypes over checks for desirable popping quality, agronomic performance, and high grain yield. The contrasting inbreds for popping quality and flake shape (mushroom vs. butterfly) can be utilized for developing mapping populations to enhance our understanding of molecular aspects of popping quality traits. Further, the promising inbreds can be utilized in the genetic improvement of popcorn and crossed to develop superior popcorn hybrids. The results suggest a potential opportunity to establish an efficient popcorn breeding program.

Global Normalized Difference Vegetation Index forecasting from air temperature, soil moisture and precipitation using a deep neural network

The complexity of the relationship between climate variables including temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI) arises from the complex interaction between these factors. NDVI is a widely used index to analyze the characteristics of vegetation cover, including its dynamic patterns. It is a crucial parameter for examining vegetation stability, which is vital for ensuring sustainable food production. This study aims to develop a global-scale NDVI forecasting model based on deep learning algorithms that consider climate variables. The model was trained using three years of global data, including NDVI, temperature, precipitation, and soil moisture. The results of this study demonstrate the effectiveness of the deep learning model for forecasting NDVI. The model accurately predicted NDVI values, as evidenced by the high coefficient of determination (R2) values and the negligible average disparity between predicted and observed NDVI values. The study conducted an analysis of the model’s performance both temporally and spatially. The performance of the model was examined for each month and the overall performance of the model for months presented as the model’s temporal performance overall. Additionally, the model’s performance was analyzed at different latitudes, categorized as mid-latitude and low-latitude performance. The temporal analysis of the model demonstrated an overall R2 value of 0.85 and an RMSE of 0.096. Meanwhile, the spatial analysis of the model showed that it performed well at low-latitude, with an R2 value of 0.84 and an RMSE of 0.098, and at mid-latitude, with an R2 value of 0.82 and an RMSE of 0.095. This suggests that the model’s forecasted NDVI values showed a small average difference compared to actual values in both temporal and spatial analyses. Overall, the study supports the idea that deep learning models can effectively forecast NDVI using climate variables across various geographical zones and throughout different months of the year.