High Coefficient Of Determination Research Articles

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.

Integrated Struvite Precipitation and Fenton Oxidation for Nutrient Recovery and Refractory Organic Removal in Palm Oil Mill Effluent

Anaerobically treated palm oil mill effluent (AnT-POME), containing a high concentration of ammoniacal-nitrogen (NH4+-N) and soluble chemical oxygen demand (sCOD) was subjected to sequential processes of struvite precipitation to recover NH4+-N and Fenton oxidation for sCOD removal. The optimization of treatment was conducted through response surface methodology (RSM). Under optimized struvite precipitation conditions (Mg2+/NH4+, PO43−/NH4+ molar ratios: 1; pH 8.2 ± 0.1), NH4+-N concentration decreased to 41 ± 7.1 mg L−1 from an initial 298 ± 41 mg L−1 (78.8 ± 1.6 % removal). Field emission scanning electron microscopy (FESEM) coupled with energy-dispersive X-ray spectroscopy (EDX) confirmed NH4+-N was recovered as struvite. Subsequent Fenton oxidation under the optimized conditions (H2O2 dosage: 2680 mg L−1; molar ratio of Fe2+/H2O2: 0.8; reaction time: 56 min) reduced sCOD concentration to 308 ± 46 mg L−1 from an initial 1350 ± 336 mg L−1 (76.0 ± 1.0 % removal). The transparent appearance of treated AnT-POME validated the removal of sCOD responsible for the initial brownish appearance. Models derived from RSM demonstrated significance, with high coefficients of determination (R2 = 0.99). Overall, integrated struvite precipitation and Fenton oxidation effectively removed NH4+-N and sCOD from AnT-POME, contributing to nutrient recovery and environmental sustainability.

Человеческий капитал в теории и практике экономического роста (на примере Мурманской области)

Russia’s Arctic policy focuses on making large-scale progress in the Arctic’s social and economic domains, including the development of local natural resources and the Northern Sea Route and the establishment of high-tech centers that can create global value chains. One of the primary challenges and threats to the sustainable development of the Russian Arctic regions is the mismatch between regional higher education systems and the needs of the economy. The risks are significantly exacerbated by the global economic transformation and the advent of the Fourth Industrial Revolution. Therefore, when considering the prospects for the innovation-driven development of the Arctic’s economy, a critical issue is the accumulation of highly educated human capital, where higher education plays a key role. The article explores the issues surrounding the formation and accumulation of human capital from the perspective of regional economic growth. The goal of this work is to empirically test the theory of increasing returns on investments in education. The key premise is that human capital accumulation has constant returns and can therefore grow indefinitely. This made it possible to put forward the hypothesis that the better the population’s access to higher education, the bigger the human capital and, accordingly, the higher the growth of gross output per capita. An empirical assessment was conducted to check the test model of the influence of university enrollment rates on economic growth. The results indicated a high coefficient of determination from the regression of the time series of the studied indicators. Also, the model showed the presence of conditional convergence, which means that a decrease in university enrollment rates is compensated for by an increase in the return on human capital due to an increase in the quality of education.

A New Training Load Quantification Method at Supramaximal Intensity and Its Application in Injuries Among Members of an International Volleyball Team.

Bouzigues, T, Maurelli, O, Imbach, F, Prioux, J, and Candau, R. A new training load quantification method at supramaximal intensity and its application in injuries among members of an international volleyball team. J Strength Cond Res 38(8): 1453-1463, 2024-The quantification of training loads (TLs) is essential for optimizing jump performance and reducing the occurrence of injuries. This study aimed to (a) propose a new method for quantifying TLs in explosive exercises, (b) determine the nature of the relationship between TLs dynamics and injury occurrence, and (c) assess a TL critical for training schedule purposes, above which the risk of injury occurrence becomes unacceptable. This study was conducted with 11 male volleyball players on a national team during a 5-month international competitive period. The proposed new method for quantifying TLs is based on a weighting factor applied to relative jumping intensities, determined by the number of sustainable jumps and their intensities measured by G-Vert accelerometer. The relationship between TLs dynamics and injury occurrence was assessed using a variable dose-response model. A high coefficient of determination was found between the maximum number of jumps possible and their intensities ( r2 = 0.94 ± 0.14, p < 0.001), indicating a strong physiological relationship between jumping intensities and the constraints imposed. The occurrence of injury was dependent on TLs dynamics for 2 players ( r2 = 0.26 ± 0.01; p < 0.001). The TL critical corresponded to 11 jumps over 80% of maximum jump height during games and approximately 130 jumps at <80% of maximal jump height. The present study proposes a new approach for quantifying supramaximal exercises and provides tools for training schedules and the prevention of volleyball injuries.

Assessment of pyrazole Schiff base's corrosion inhibition effectiveness incorporating oxadiazole moiety on mild steel in 1 M HCl: A holistic theoretical and experimental analysis

Modern industries place a high value on reasonably priced, eco-friendly products that effectively stop metal breakdown. Hence, our goal was to create an ecologically acceptable pyrazole Schiff base (PSB) that would effectively preserve mild steel (MS) during pickling and descaling procedures. We achieved this by integrating oxadiazole, which demonstrated good anti-corrosion capabilities. A complete investigation was conducted using spectroscopic analysis, electrochemical techniques, and gravimetric measurement in 1 M HCl solution. The PSB inhibitor displayed considerable corrosion inhibition on MS. PSB exhibited both cathodic and anodic properties in its dual adsorption pattern, as shown by the electrochemical investigation. Polarization resistance (Rp) values rose with the PSB concentration from 50 to 300 ppm, peaking at 300 ppm with values ranging from 31.01 to 141.21 Ω.cm². At 300 ppm and 298 K, the inhibitor effectiveness reached 90.12 %. Electrochemical testing validated the protection against mixed-type corrosion, while surface examinations (SEM, EDS, CA, AFM) showed that PSB developed a protective antioxidant layer on the MS surface. According to theoretical DFT calculations, PSB adsorbs onto the steel surface in a flat alignment. The remarkable precision of the adsorption process was validated by the high coefficient of determination (R2 = 1) for both Langmuir and other adsorption isotherms. Computational, chemical, and electrochemical analyses all support PSB's superiority as a corrosion inhibitor. The incorporation of aryl and heterocyclic rings improves the PSB structure's molecular stiffness, which facilitates effective adsorption onto the MS surface. This feature creates novel prospects for material protection and corrosion prevention, presenting PSB as a viable and eco-friendly substitute for conventional corrosion inhibitors in industrial applications.

Exploring feasibility criteria for stereotactic radiosurgical treatment of multiple brain metastases using five linac machines.

This study aimed to find descriptors that correlates with normal brain dose to determine the feasibility of performing fractionated stereotactic radiosurgery (SRS) for multiple brain metastases (BMs) using five linac machines. Thirty-two patients with 1-30 BMs were enrolled. Treatment plans were created using TrueBeam, Novalis Tx, TrueBeam Edge, Halcyon, and Tomotherapy linacs. The sum of all planning target volumes (PTVs) was defined as PTVall, and the brain region excluding PTVall was defined as normal brain. The total surface area (TSA) of the PTV was calculated from the sum of the surface areas of the equivalent spheres for each PTV. Volumes receiving more than 5, 12, and 18Gy (V5Gy, V12Gy, and V18Gy, respectively) were used for evaluation of normal brain dose. Correlations between normal brain dose and each tumor characteristic (number, PTVall, and TSA) were investigated using the Spearman rank correlation coefficient. Correlations between each characteristic and normal brain dose were statistically significant (p<0.05) across all machines. The correlation coefficients between each characteristic and V18Gy for the five machines were as follows: tumor number, 0.39-0.60; PTVall, 0.79-0.93; TSA, 0.93-0.99. The fit equations between TSA and V18Gy exhibited high coefficients of determination, ranging from 0.92 to 0.99 across five machines. This study devised fractionated SRS plans using for 1-30 BMs across five linac machines to find descriptors for determining SRS feasibility based on normal brain dose. TSA proved to be a promising descriptor of SRS feasibility for treating multiple BMs.

Estimation of the coefficient of permeability as an example of the application of the Random Forest algorithm in Civil Engineering

A new world record for crude steel production was recorded in 2021, which increased by 3.8% over 2020. This also affected the amount of slag produced with this production. Total waste from industrial and construction production throughout the European Union accounts for as much as 48%. Therefore, waste management should provide for the recovery of as many resources as possible. European Union strategies in line with the circular economy objectives focus on ensuring policy coherence in the areas of climate, energy efficiency, construction and demolition waste management and resource efficiency. Slags are a material of interest to researchers in terms of their use in construction. Slags, on the one hand, are materials that are becoming better understood on the other hand, we are making sure of the heterogeneity of these materials. The characteristics of physical properties of slags are influenced by many factors, including the furnace split in which they are produced. This prompts the search for tools to help determine the parameters of slags based on already available data. The study aimed to verify the hypothesis that it is possible to determine the parameter of the filtration coefficient, relevant to applications in earth structures using the machine learning algorithm – Random Forest. In the study, two types of material were analysed: blast furnace slag and furnace slag. The results of the analysis yielded a high coefficient of determination (R2) – 0.84–0.92. This leads us to believe that the algorithm may prove useful in determining filtration parameters in slags.

Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans

Abstract This study delves into the ability of near infrared (NIR) techniques by means of a self-developed portable sensing device near-infrared reflectance spectroscopy (NIRS) i16 USK instrument to accurately predict the moisture content (MC) and chlorogenic acid (CGA) within intact coffee beans through the development of calibration models. Spectral absorbance measurements were conducted across the 1,000‒2,500 nm wavelength range. Leveraging two multivariate calibration approaches namely principal component regression and partial least square regression (PLSR) for 74 bulk coffee beans (60 g) in calibration and 36 bulk coffee beans samples in external validation. The results reveal a notably high determination coefficient (R 2) of 0.984 for MC and 0.908 for CGA in calibration using PLSR, indicating the feasibility of rapid, simultaneous, and non-destructive prediction. Furthermore, upon external validation, the PLSR model exhibited consistent predictive performance, with R 2 values for MC and CGA contents reaching 0.978 and 0.846, respectively. Consequently, these outcomes underscore NIR as an effective, concurrent, and non-invasive means to assess the quality parameters and attributes of intact coffee beans, presenting promising prospects for the advancement of coffee quality evaluation.

Optimization of Ethylene Glycol Production Process from Bio Mass (Corn Cob) Using Response Surface Methodology (RSM)

This study focuses on the increasing global demand for Monoethylene Glycol (MEG) and its production from corncob through Catalytic Hydrogenation. The process was optimized using Design of Experiment (DOE) and Response Surface Methodology (RSM). Physiochemical properties of the produced MEG were found to align with ASTM standards. Physiochemical properties of the produced Monoethylene glycol (MEG) was determined to have the following; density 2.19g/cm3, specific gravity 1.077 at 20 oC, flash point 116.20 oC and viscosity 12.32mm2/s at 40 oC, this values is in agreement with the ASTM standard values for monoethylene glycol. The study utilized a central composite design (CCD) with 21 runs to assess the impact of key parameters on biomass hydrogenation, revealing a quadratic model, also Analysis of variance (ANOVA) shows a high coefficient of determination (R2) of &gt;0.9932. Four parameters (mass ratio of binary catalyst, hydrogen pressure, temperature and mass ratio of catalyst to feedstock) were varied with two center points to determine the effects of process parameters and eventually to get optimum monoethylene glycol (MEG) yield. The optimized conditions yielded a MEG yield of 70.2wt.%, demonstrating the potential of corncob as a viable source for MEG production. This finding proved that corncob can be utilized to produce monoethylene glycol (MEG) which could be potentially used in many way.

Understanding the evolution of moisture during isothermal dehydration of glutinous rice through global sensitivity analysis

This study examined the effect of layer thickness and temperature on the evolution of moisture during the isothermal dehydration of glutinous rice. To analyse the moisture evolution, empirical models and secondary models (SM) were employed. Global sensitivity analysis (GSA) was used to estimate the importance of the experimental parameters on the developed SM. According to the dehydration curves, the increase in temperature and reduction in the layer thickness significantly shortened (P<0.05) the dehydration time of glutinous rice. The effective diffusivity, activation energy, and Gibb-free energy were 2.0×10−12m2/s to 2.53×10−11m2/s, 43.03 kJmol−1 to 51.05 kJmol−1, and 145.10 kJmol−1 to 155.85 kJmol−1respectively. The Page and Logarithmic models are suitably considered in evaluating the dehydration kinetics of the glutinous rice with a high coefficient of determination (R2) values of >0.9989. The SM aptly described moisture evolution with higher accuracy of R2=0.9843. The GSA shows that the variation in the SM output is primarily driven by the variation in the input parameters with low reliance on its interaction with other parameters, indicating that the SM can play a significant role in the optimization of the industrial dehydration process with a wide range of applications in the agricultural production system.

Zastosowanie metod uczenia maszynowego do predykcji generowanej energii w małym systemie PV

The article presents data analysis for predicting energy production in photovoltaic (PV) power plant systems. The purpose of long-term forecasts is to determine the effectiveness of preventive actions and manage the power system effectively. Climate variables affecting the production of electricity in photovoltaic systems were analyzed. Forecasting methods using machine learning techniques such as Multi-Layer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were compared. In addition, metrics were selected to determine the quality of forecasts. Determining the quality of forecasts was based on the actual varying conditions, not on the weather forecast data. The way of data preparation to create forecasting models were presented and the models with the best metrics were selected. For this purpose, the Scikit-learn library was used to create scripts in Python. The best results were obtained for regression models: MLPRegressor, CatBoostRegressor and Support Vector Regression. Actual measurement data from a system of optimally-positioned panels with a power of 3.0 kWp were used. For the MLPRegressor model, the highest coefficient of determination 0.605 was achieved with the smallest root-mean-square error of 1.79 KWh.

ASSESSMENT OF GROWTH PATTERN IN INDIGENOUS KADAKNATH CHICKENS BY NON-LINEAR MODELS

Mathematical growth functions can explain the factors influencing an animal's growth pattern. However, there remains limited understanding of the growth curve of Kadaknath chickens, which is crucial for optimizing management and feeding practices. Therefore, this study aimed to determine the growth curve of indigenous Kadaknath chickens' body weight using four growth curve models: Logistic, Gompertz, Von Bertalanffy, and Richard. We analyzed 96 birds for this study. The models were fitted using Curve Expert and Minitab software, and we compared them based on Akaike’s information, Bayesian information, adjusted R2, and RMSE criteria. The research revealed that the Gompertz and Richard models had higher coefficients of determination (R2) and lower RMSE values in both sexes compared to other models. The age at the inflection point was estimated to be between 11 to 17.5 weeks for males and 9 to 11.4 weeks for females. According to the Richard model, adult male birds weighed 2157g, and females weighed 1297g. In the Gompertz model, male birds weighed 2186g, and females weighed 1468g. The inflection weight of birds in the Richard model was estimated to be 797.53g for males and 519.66g for females, while in the Gompertz model, it was 803.67g for males and 510g for females. In conclusion, our study determined that the Gompertz model is a reliable predictor of the body weight of male and female Kadaknath chickens from hatching to adulthood. The Richard model also demonstrated high reliability, albeit slightly behind the Gompertz model in predictive accuracy. Key words: Body weight, growth pattern, Kadaknath, Non-linear models