Maximum Coefficient Of Determination Research Articles

A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries

The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R2) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact.

Ensemble machine learning models for compressive strength and elastic modulus of recycled brick aggregate concrete

This study delivers an in-depth analysis of predicting the compressive strength and elastic modulus of recycled brick aggregate concrete (RBAC). It assembles an extensive database of 633 compression test results and develops five ensemble learning models—random forest (RF), gradient boosting regression trees (GBRT), extreme gradient boosting (XGB), light gradient boosting machine (LGBM) and stacking (St). These models are evaluated against existing empirical formulas with respect to their effectiveness. The findings reveal that machine learning (ML) models outperform existing formulas: for compressive strength prediction, traditional models achieve a maximum determination coefficient R² of 0.38, whereas ML models attain an R² range of 0.91–0.94. For elastic modulus, the highest R² values are 0.44 for traditional models and 0.97 for ML models. Notably, the LGBM and St models excel in predicting compressive strength and elastic modulus, respectively. The study also identifies critical parameters influencing RBAC’s compressive behavior and highlights their impact trends. Remarkably, while the mass-weighted water absorption of coarse aggregates and the replacement ratio of recycled brick aggregates (RBAs) have less impact on compressive strength compared to the effective water-to-cement ratio, they become more influential for elastic modulus. Additionally, the decrease in elastic modulus due to higher RBA replacement ratios or increased mass-weighted water absorption of coarse aggregates exceeds the corresponding reduction in compressive strength. This research not only deepens the understanding of RBAC’s mechanical properties but also provides valuable predictive tools for civil engineering applications.

A Comparison of Statistical Validity of In-Situ Hydraulic Conductivity Prediction Models of Rock Mass Inferred from Borehole Logs and Lugeon Test Data

In-situ hydraulic conductivity is a vital property in rock engineering for jointed rock mass. Understanding its correlation with rock mass parameters is crucial for water circulation. Therefore, a study was carried out to develop statistically significant empirical relationships between hydraulic conductivity and various rock mass parameters to estimate in-situ hydraulic conductivity from Lugeon test and various rock mass parameters obtained from borehole logs.The study initially aimed to establish a correlation between hydraulic conductivity and Rock Quality Designation (RQD). However, the outcomes were unsatisfactory, prompting further research. Later, two more robust models were developed, namely the HC-model and modified HC-model. The HC-model incorporated four rock mass parameters, including Rock Quality Designation Index, Depth Index, Gouge Content Designation Index, and Lithology Permeability Index, achieving a maximum coefficient of determination (R2) of 0.46. The modified HC-model included six parameters, encompassing fracture frequency and theoretical aperture, resulting in an improved R2 of 0.69. Both models significantly outperformed RQD-alone predictions (R2 < 0.10), highlighting the need for incorporating multiple rock mass parameters in predicting hydraulic conductivity due to a complex interplay of various factors. However, the effects of joint persistence and roughness are limiting in the present analysis.

Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

This study proposed a model that utilized machine learning algorithms to predict the yield of maize (Zea mays L.) cultivars. This will enable the selection of good cultivars with high yields that are suitable for planting in specific areas, such as a district or county. The breeding value of the cultivars and 11 types of time-series meteorological variables were selected as the input parameters of the model. The yield was set as the output parameter of the model. Random forest (RF), Levenberg–Marquardt neural network, and multilayer perceptron neural network algorithms were used to construct the model. The results showed that the RF outperformed the other algorithms in predicting the yield of maize cultivars by achieving the maximum coefficient of determination (R2) of 0.77 and minimal root-mean-square error of 320.25 kg/acre, mean absolute error of 229.84 kg/acre, and mean absolute percentage error of 7.1%. The constructed model can be used to effectively predict the yield of specific varieties to enable the selection of good varieties in specific areas, such as a district or county. A prediction of the yield of a specific maize cultivar in a particular planting environment can have considerable value. It can facilitate the objective identification of better adapting cultivars among farmers and support the precise promotion and planting of cultivars.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

Construction of a Computer Network Performance Prediction Optimization Model Based on Machine Learning Algorithms

Computer networks have been widely used in critical infrastructure for businesses, schools, governments, and individuals. The development of computer networks is an irreversible historical trend. This article studies computer network performance prediction optimization models through machine learning, which can improve the intelligence and efficiency of network management. Existing research has made some progress in machine learning based network security and performance optimization. However, good results have not been achieved in real-time performance prediction and optimization. This article uses Long Short Term Memory Network (LSTM) to establish an effective predictive optimization model. Firstly, the data is processed, such as missing values, duplicates, and device time redundancy. Then, features are selected to select the corresponding input features of the model. Finally, the model is optimized to obtain the optimized model. The performance of the LSTM model is compared with the random forest (RF) and support vector machine (SVM) models. Model performance indicators include mean absolute error (MAE), coefficient of determination, F1 score, and model computation time. The experimental results show that the LSTM model has higher accuracy than RF and SVM models in all evaluation metrics. Meanwhile, the LSTM model has significant advantages in computational time compared to the other two models. The LSTM based computer network performance prediction optimization model can be optimized in terms of prediction accuracy, generalization ability, and computational efficiency. It has the ability to effectively process time series data. The LSTM model used for network performance prediction has a minimum average absolute error of 8% and a maximum coefficient of determination of 0.95. It achieves higher F1 scores and faster calculation speed. The results reflect the potential and practicality of the LSTM model in optimizing computer network performance prediction.

Detecting the Short-Term Effects of Water Stress on Radiata Pine Physiology Using Thermal Imagery

Despite the utility of thermal imagery for characterising the impacts of water stress on plant physiology, few studies have been undertaken on plantation-grown conifers, including the most widely planted exotic species, radiata pine. Using data collected from a pot trial, where water was withheld from radiata pine over a nine-day period, the objectives of this study were to (i) determine how rapidly key physiological traits change in response to water stress and (ii) assess the utility of normalised canopy temperature, defined as canopy temperature–air temperature (Tc–Ta), for detecting these physiological changes. Volumetric water content remained high in the well-watered control treatment over the course of the experiment (0.47–0.48 m3 m−3) but declined rapidly in the water stress treatment from 0.47 m3 m−3 at 0 days after treatment (DAT) to 0.04 m3 m−3 at 9 DAT. There were no significant treatment differences in measurements taken at 0 DAT for Tc–Ta, stomatal conductance (gs), transpiration rate (E) or assimilation rate (A). However, by 1 DAT, differences between treatments in tree physiological traits were highly significant, and these differences continued diverging with values in the control treatment exceeding those of trees in the water stress treatment at 9 DAT by 42, 43 and 61%, respectively, for gs, E and A. The relationships between Tc–Ta and the three physiological traits were not significant at 0 DAT, but all three relationships were highly significant from as early as 1 DAT onwards. The strength of the relationships between Tc–Ta and the three physiological traits increased markedly over the duration of the water stress treatment, reaching a maximum coefficient of determination (R2) at 7 DAT when values were, respectively, 0.87, 0.86 and 0.67 for gs, E and A. The early detection of changes in tree physiology from 1 DAT onwards suggests that thermal imagery may be useful for a range of applications in field-grown radiata pine.

ON THE PERFORMANCE OF SARIMA AND SARIMAX MODEL IN FORECASTING MONTHLY AVERAGE RAINFALL IN KOGI STATE, NIGERIA

Forecasting monthly rainfall is very important in Kogi state for better approach to flood management and also plays a pivotal role in agriculture which remains a significant factor in Nigeria’s economy. Advanced time series univariate models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) models are usually employed in modelling and forecasting rainfall in Nigeria due to their non-linear pattern and spatiotemporal variation. Few studies have attempted to investigate the influence of other climatic factors in modelling and prediction of rainfall pattern. This study examines the performance of a univariate seasonal ARIMA and seasonal ARIMA which uses monthly temperature and relative humidity as exogenous factors otherwise known as SARIMAX model in forecasting monthly average rainfall in Lokoja, the capital of Kogi state. The study uses monthly data on rainfall, temperature and relative humidity spanning from 2010 to 2022 obtained from Nigeria Meteorological Agency NiMet, Lokoja station. The series were appropriately differenced to attain stationarity. The plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to select some tentative models whose parameters would be estimated. The most suitable SARIMA model [SARIMA was chosen based on maximum Coefficient of Determination , and the minimum Akaike information criterion (AIC). However, SARIMAX model outperformed SARIMA model based on the criteria earlier highlighted. SARIMAX model was therefore recommended for modelling and forecasting monthly average rainfall in Kogi state.

Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Viscosity is an essential mud property which enables it to transport cuttings out of a well. Determining the real time value of mud viscosity at downhole conditions is complex and till date lacks a precise model. Literature is filled with models that are only applicable at surface conditions with most of the models being artificial neural network (ANN) based. Neural networks have been criticized for their long training time because they rely on a trial-and-error approach to determine their optimal architecture. The few models related to mud viscosity at downhole conditions are based on non-linear regression which are not flexible in nature. However, Multivariate Adaptive Regression Splines (MARS) has at no time been used notwithstanding some of its known benefits over ANN and other regression methods. The focus of this study is to model mud plastic viscosity at downhole conditions with the MARS method using parameters such as temperature, pressure and initial mud viscosity. MARS is a flexible non-parametric splines-based technique that extends linear regression models by including nonlinearities and interactions between predictors. An experimental database comprising 88 and 149 data points culled from literature for oil-based mud (OBM) and water-based mud (WBM) respectively were utilized for developing the models. For each mud type, the most suitable model was determined by choosing the model with the maximum coefficient of determination (R2) value and the least values of mean square error (MSE), root mean square error (RMSE), and generalized cross-validation criterion (GCV). Using these criteria, the R2, MSE and RMSE of the MARS models according to the mud type were found to be 0.64, 104.35, 10.21 and 0.71, 12.8, 3.57 for OBM and WBM respectively while the GCV values were 129.3 and 14.38 for OBM and WBM respectively. Due to lack of mud rheology data at downhole conditions, it was difficult to compare the performance of the developed models with existing models. Parametric importance analysis indicate that temperature has the highest effect on mud viscosity estimation at downhole conditions. The developed models are valid for 20 °C ≤ Temperature ≤315 °C and 0 psi ≤ Pressure ≤40,000 psi. This pioneer work on using MARS in drilling fluid property prediction points out the implementation protocol, the dataset utilized, the relative importance of input variables and the explicit model for estimating mud viscosity at downhole conditions.

Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R2) and variance coefficient (COV) of 0.99 and 1.65, respectively.

Updated Global Navigation Satellite System Observations and Attention-Based Convolutional Neural Network–Long Short-Term Memory Network Deep Learning Algorithms to Predict Landslide Spatiotemporal Displacement

Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R2) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk.

Experimental study for artificial neural network modeling on thermal and flow performances of electric traction motor with oil spray cooling

The oil spray cooling is emerging as advanced thermal management technique for next generation electric traction motor. The performance of oil spray cooling needs to be mapped and predicted based on various influential factors to develop efficient thermal management system for electric traction motors. In the present work, the thermal and flow performances are experimentally investigated and predicted using an artificial neural network (ANN) for electric traction motor with oil spray cooling. The thermal and flow performances are extracted in terms of maximum temperature, heat transfer coefficient, Nusselt number, spray pressure and spray power consumption. The effects of nozzle types, spray heights, volume flow rates and chiller temperatures are analyzed as operating factors. The ANNs are modeled considering Levenberg-Marquardt (LM) training variant, 10 hidden neurons and Tangential-sigmoidal (Tan) and Logarithmic-sigmoidal (Log) as transfer functions. The thermal and flow performances are superior for full cone followed by hollow cone and spiral nozzles in decreasing order. The higher spray height, higher volume flow rate and lower chiller temperature show optimum values of maximum temperature and heat transfer coefficient. The spray power consumption is maximum in case of higher volume flow rate, reported less variation with change in spray height and chiller temperature. The ANN model comprising of LM-Tan algorithm is recommended as the best model with prediction error within ±1% for all thermal and flow performances. The predicted results from the optimum ANN model are validated with experiments with maximum coefficient of determination (R2) and coefficient of variance (COV) as 0.99 and 3.20, respectively. The correlations for thermal and flow performances in terms of all influential factors are proposed using the optimum ANN model.

SIZE AND WEIGHT CHARACTERISTICS OF THE BIVALVE MOLLUSC ANADARA KAGOSHIMENSIS (TOKUNAGA, 1906) - THE ALIEN SPECIES IN THE SEA OF AZOV

For the first time the research focuses on studying the size and weight characteristics of the alien species in the Sea of Azov - bivalve mollusc Anadara kagoshimensis (Tokunaga, 1906). The relationship between allometric parameters of the mollusc (size and weight) were approximated by the power function. The maximal coefficient of determination is found for the power dependence of the total weight of the anadara on the width of the shell, with minimum coefficient of determination depending on the height of the shell. The maximum coefficient of determination is obtained for the power dependence of the raw weight of soft tissues of anadara on the length of its shell, with the minimum coefficient of determination depending on the width of the shell. The ratio of dry and raw weight of soft tissues is 0.16. Under comparison of the different environmental areas of anadara in the Black, Aenean seas and the Sea of Azov, the hatchery housing of mollusks in waters with moderate salinity is found to be the most favorable condition for their maximal growth.

Ginsenoside Rg2 content prediction in Panax ginseng based on the fusion of hyperspectral wavelengths combined with chemometric analysis

The content of ginsenosides plays a crucial role in determining the quality of Panax ginseng C. A. Meyer. In order to clarify the potential applicability of hyperspectral imaging (HSI) for fast detecting the quality of ginseng, this study investigated the use of HSI combined with chemometric models to predict the ginsenoside Rg2 content. The results indicated that the prediction efficiency can be improved through complete wavelength fusion of different shots and the effective wavelength fusion obtained by different selection methods. Notably, the integrated utilization of the partial least squares regression (PLSR) algorithm and the fusion of effective wavelengths exhibited the best results in Rg2 content prediction, with the maximum coefficient of determination (R2) and relative percent deviation (RPD) values of 0.939 and 3.35, respectively, as well as the lowest root mean squared error (RMSE) value of 24.2 μg/g, indicating excellent model performance. This study provides valuable insights for the development and application of portable HSI equipment for rapid and non-destructive testing of Panax ginseng quality.

Discrete integration for measuring aerodynamic loads on trains in crosswinds − realizable strategies of discretization and discrete integration

Discrete integration occupies an important place in train aerodynamic tests in crosswinds. Improved delayed detached eddy simulations based on shear stress transport k-ω turbulence models were carried out to calculate the side force, lift, rolling moment around the lee rail, and surface pressure on bluff and streamlined vehicles. The changepoints and piecewise linearities of the pressure coefficients were evaluated, and the maximum coefficient of determination in the elements was 0.9973. A realizable strategy of the discretization based on the Lagrange rectangular elements was suggested, including the largest lengths and numbers of the elements. From this, a strategy of the discrete integration was presented to measure the aerodynamic loads, considering the real orientation of the elements. The maximum errors of the mean aerodynamic load coefficients of the bluff and streamlined vehicles were 4.1% and 2.2% (except the mean lift coefficient of the bluff vehicle), respectively. The errors were less than those in the previous studies, especially for the streamlined vehicle, which reduced by up to 8.7%. The unsteady aerodynamic loads with no delay obtained by the strategies were near to natural ones in the frequency range that people would be concerned about in crosswinds (at the Strouhal number of less than 0.4). Some suggestions were made for using the strategies in the full-scale tests and model tests, which provided a foundation for further studies of the running safety in natural crosswinds.

Unconfined compressive strength of MICP and EICP treated sands subjected to cycles of wetting-drying, freezing-thawing and elevated temperature: Experimental and EPR modelling

Microbial-induced carbonate precipitation (MICP) and enzyme-induced carbonate precipitation (EICP) are two bio-cementation techniques, which are relatively new methods of ground improvement. While both techniques share some similarities, they can exhibit different overall behaviours due to the differences in urease enzyme sources and treatment methods. This paper presented 40 unconfined compressive strength (UCS) tests of MICP and EICP treated sand specimens with similar average calcium carbonate (CaCO3) content subjected to cycles of wetting-drying (WD), freezing-thawing (FT) and elevated temperature (fire resistance test – FR and thermogravimetric analysis – TG). The average CaCO3 content after a certain number of WD or FT cycles (ACn) and their corresponding UCS (qn) reduced while the mass loss increased. The EICP treated sand specimens appeared to exhibit a lower resistance to WD and FT cycles than MICP treated specimens possibly due to the presence of unbonded or loosely bonded CaCO3 within the soil matrix, which was subsequently removed during the wetting (during WD) or thawing (during FT) process. FR test and TG analysis showed a significant loss of mass and reduction in CaCO3 content with increased temperatures, possibly due to the thermal decomposition of CaCO3. A complete deterioration of the MICP and EICP treated sand specimens was observed for temperatures above 600 °C. The observed behaviours are complex and theoretical understanding is far behind to develop a constitutive model to predict qn. Therefore, a multi-objective evolutionary genetic algorithm (GA) that deals with pseudo-polynomial structures, known as evolutionary polynomial regression (EPR), was used to seek three choices from millions of polynomial models. The best EPR model produced an excellent prediction of qn with a minimum sum of squares error (SSE) of 2.392, mean squared error (MSE) of 0.075, root mean square error (RMSE) of 0.273 and a maximum coefficient of determination of 0.939.

Статистичний аналіз дохідності державних облігацій з використанням економетричних моделей: макроекономічні та інвестиційні аспекти

Abstract. Introduction. The yield to maturity of government bonds changes over time and depends on a number of external and internal factors. In recent years, the gap between the yield to maturity of bonds of developed countries and countries with relatively low credit ratings has been increasing. Inflation and the level of central banks key rates are important factors that cause fluctuations in the yield of government securities. The yield of government bonds is affected by the terms to maturity. Taking into account the ratio of yield and risk, the longer the maturity of government bonds, the higher the yield should be. But in practice, this dependence can be different, especially in periods of financial crises. All of the above makes relevant the task of analyzing the variation of yield to maturity of government bonds of different countries at the current stage of economic development, as well as the dependence between yield to maturity and the level of inflation on the examples of certain countries and impact on the yield to maturity of government bonds of central bank key rates. The research used such scientific methods as analysis and synthesis of results, logical-analytical methods, methods of descriptive statistics and econometric models. Purpose. The purpose of the paper are improving the approach to the analysis of the yield to maturity of government bonds of various countries and the analysis of the influence of central bank rates on the level of yield to maturity of government bonds. Results. The yield to maturity of government bonds and the rates of central banks of different countries are analyzed. The inverse relationship between the terms to maturity and the level of yield to maturity of government bonds is shown on the examples of the USA and Ukraine. For different samples, taking into account the level of the credit rating of the countries, the analysis of average yield to maturity, dispersion, standard deviation, range of variation, coefficients of oscillation and variation was performed. A high positive correlation was found between the yield to maturity of government bonds and the level of central bank rates. A regression models analysis of the influence of central bank rates on the yield to maturity of government bonds was performed. The linear regression, logarithmic regression and polynomial regression models were constructed. The most qualitative model was chosen on the basis of the coefficient of determination. Conclusions. It was found that developed countries have low (in some countries in certain periods – negative) central bank rates and a low level of yield to maturity. Countries with low credit ratings have high central bank rates and much higher yields. The examples of the USA and Ukraine show an inverse relationship between the yield to maturity and terms to maturity, which indicates the discrepancy between the level of yield and the risk of government bonds in the conditions of financial crisis. A high coefficient of variation for the entire sample of countries indicates a significant dispersion of the data. The average yield to maturity is much higher for countries with relatively low credit ratings and high yield to maturity. The standard deviation differs by more than 8 times for countries with low credit rating, and the range of variation is more than 7 times. Oscillation coefficients and coefficients of variation are also significantly higher for countries with low credit ratings and high yield. All this indicates that for countries with a high credit rating and a low yield, the variation is smaller, the population is more homogeneous, and the average value is more reliable. The linear correlation coefficient indicates a sufficiently high positive correlation between the yield to maturity of government bonds and central bank rates. The results of the regression analysis show that, of all the considered models, the influence of the central bank interest rate on the yield to maturity of government bonds with a maturity of 10 years is best described by the polynomial regression model of the third degree, which has the maximum coefficient of determination. The practical value of the methodical approach to the analysis of the yield of government bonds is that it can be used to assess the influence of various factors on the yield of bonds, as well as to analyze the dynamics of the yield indicators of certain countries' bonds.

Otolith Biometry of Pikeperch <i>Sander lucioperca</i> from the Lakes Region of Turkey

The present study aimed to confirm the existence of relationships between the otolith size (otolith length, otolith breadht, otolith area, and otolith weight) and the size of Sander. lucioperca (L., 1758) from Eğirdir and Beyşehir Lake, Turkey. The length-weight relationships were defined as W = 0.0030L3.386 (R2 = 0.993) for females, W = 0.0032L3.309 (R2 = 0.987) for males from the Eğirdir Lake, and W = 0.0033L3.339 (R2 = 0.977) for females, W = 0.0024L3.410 (R2 = 0.976) for males from the Beyşehir Lake. No significant relations total length and otolith length (OL), otolith breadth (OB), area (A), and otolith weight (OW) were found both the sexes and all the localities. The maximum coefficient of determination were observed between total length and OL, A and OW, while the minimum was observed for OB. The results showed that can be used for the monitoring of stocks in pikeperch populations benefit otolith size.

Mass Modelling of Eggs Based on Shape Index Using Regression Analysis

This study was conducted to determine the correlation between the mass of eggs based on its geometrical dimensions characteristics such as length (L), width (W), geometric mean diameter (GMD), the first and second projection areas (PA1, PA2), criteria area (CAE), oblate spheroid volume (Vosp), measured volume (Vm) and shape index (SI). Based upon the SI, eggs were characterized as sharp (<72), normal (72 -76) and round (>76), respectively. For mass prediction, different classifications viz. dimension as 1st classification, projection area as 2nd classification, and volume as 3rd classification were considered. 1st classification (dimension), 2nd classification (projection area), and 3rd classification (volume) were considered. The analysis was executed using 33 linear regression models and the models were recommended by considering maximum coefficient of determination (R2) and minimum regression standard error (RSE). Based on the modelling analysis, 10 model equations based on the selected classifications were recommended for mass estimation. The findings of this investigation will be helpful for the researchers involved in the design and development of process equipments in the production and processing of eggs.

Artificial Intelligence-Assisted Prediction of Effluent Phosphorus in a Full-Scale Wastewater Treatment Plant with Missing Phosphorus Input and Removal Data

Although artificial intelligence (AI) such as machine learning (ML) and deep learning (DL) has been recognized as an emerging and promising tool, its application becomes challenging with incomplete data collection. Herein, in the absence of the influent phosphorus load and chemical dosage data for phosphorus removal, we employed ML/DL models to predict effluent phosphorus using nine-year data from a small-scale wastewater treatment plant. Attempts were made to select essential model input features from 42 variables by using Pearson correlation analysis to reveal internal correlations among variables. First, five ML regression models were used to predict the effluent phosphorus load, and a maximum coefficient of determination (R2) of 0.637 was achieved with the support vector machine model. Then, the DL model named long short-term memory could predict phosphorus load in one-day advance with an R2 value of 0.496. Finally, on the basis of the historical data, an anomaly alarm design was proposed to minimize the chance of exceeding the discharge permit and achieved a maximum accuracy of 79.7% to predict the phosphorus concentration after comparing seven ML classification models. This study provides an example of applying AI for process improvement and potential cost reduction with incomplete data sets.