Coefficient of Determination Research Papers (original) (raw)

Influence of inclined bed channel on characteristic of flow for step broad–crested weirs was investigated experimentally. Discharge coefficient, water surface profile and Froude number were calculated experimentally.The results showed... more

Influence of inclined bed channel on characteristic of flow for step broad–crested weirs was investigated experimentally. Discharge coefficient, water surface profile and Froude number were calculated experimentally.The results showed that water surface profiles were smooth and continues having a descending trend from the point of measurement taking the shape of the weir with a steep drop near the downstream face of the weir. Increasing effective head to crest height ratio (H e /Y) and channel bed slope (S o ), increased the coefficient of discharge (C D ) up to 5%. Also it was found. that for small values of (Fr 2 ), the weir performance tends to the ideal .and with increase (Fr 2 ), the discharge coefficient (C D ) decrease for all weir models, because the discharge and the velocity heads increases. Empirical equation was developed on the basis of obtained results.

Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo... more

Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo (white), Mencía, and Tempranillo (red) grape varieties based on diffuse spectroscopy measurements. To identify the most suitable spectral range for TSS prediction, the regression models were calibrated for four datasets that included the following spectral ranges: 400–700 nm (visible), 701–1000 nm (near infrared), 1001–2500 nm (short wave infrared) and 400–2500 nm (the entire spectral range). We also tested the standard normal variate transformation technique. Leave-one-out cross-validation was implemented to evaluate the regression models, using the root mean square error (RMSE), coefficient of determination (R2), ratio of performance to deviation (RPD), and the number of factors (F) as evaluation metrics. The regression models for the red varieties we...

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute... more

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the ...

The effect of systematic and continuous environmental factors on milk performance traits over standard lactations in 2805 Simmental cows was evaluated using the general linear model. The systematic factors included the effect of farm or... more

The effect of systematic and continuous environmental factors on milk performance traits over standard lactations in 2805 Simmental cows was evaluated using the general linear model. The systematic factors included the effect of farm or breeding area, calving season, year of birth, season of birth, lactation group and their interactions. The continuous factor analysed was the effect of age at first conception. The effect of farm, lactation group and calving season on standard lactation milk performance was found to be highly significant (P<0.01), excepting the effect of calving season on milk fat percent in standard lactations which showed statistical significance (P<0.05). The interactions between year and season of birth, farm and calving season, and farm and lactation group had a highly significant effect (P<0.01) on all performance traits studied. Age at first conception, as a continuous factor, had a highly significant effect (P<0.01) on milk yield and milk fat perc...

Wisnu Noviandi, 2020; Pengaruh Employee Engagement dan Disiplin Kerja Terhadap Produktivitas Kerja studi pada Karyawan Divisi Produksi PT Mustika Ratu Tbk. Skripsi, Jakarta: Program Studi S1 Manajemen, Fakultas Ekonomi, Universitas Negeri... more

Wisnu Noviandi, 2020; Pengaruh Employee Engagement dan Disiplin Kerja Terhadap Produktivitas Kerja studi pada Karyawan Divisi Produksi PT Mustika Ratu Tbk. Skripsi, Jakarta: Program Studi S1 Manajemen, Fakultas Ekonomi, Universitas Negeri Jakarta. Tujuan penelitian ini adalah: Untuk mengetahui 1) Deskripsi dari employee engagement, disiplin kerja dan produktivitas kerja pada karyawan Divisi Produksi PT Mustika Ratu Tbk, 2) Pengaruh employee engagement terhadap dan produktivitas kerja pada Divisi Produksi PT Mustika Ratu Tbk, 3) Pengaruh disiplin kerja terhadap dan produktivitas kerja pada Divisi Produksi PT Mustika Ratu Tbk, 4) Model penelitian employee engagement dan disiplin kerja dapat memprediksikan dan produktivitas kerja pada Divisi Produksi PT Mustika Ratu Tbk. Penelitian ini dilakukan kepada 126 karyawan Divisi Produksi PT Mustika Ratu Tbk. Teknik yang digunakan dalam pengumpulan data yaitu dengan metode survey dengan cara wawancara dan menyebarkan kuesioner kemudian diolah ...

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and... more

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2...

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three... more

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and c...

The estimation of compressive strength (CS) of jute fibre reinforced concrete (JFRC) is assessed with Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Random Forest (RF), and Random Tree (RT). The present... more

The estimation of compressive strength (CS) of jute fibre reinforced concrete (JFRC) is assessed with Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Random Forest (RF), and Random Tree (RT). The present study determines the best-suited model to estimate the CS of JFRC. A total of 93 experimentation observations were extracted from the literature. 70% of random data was used for training and 30% as testing subsets. Models were formulated using different input combinations i.e., aspect ratio, % of fiber, and no. of curing days to predict the CS of JFRC. Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were used to evaluate the performance of formulated models. The results showed that the RF model outperforms when compared with ANFIS, ANN, and RT models with CC (0.984, 0.912), RMSE (1.300, 2.641), and MAE (1.016, 2.162) for the training and testing stage.

Medidas precisas de volume de madeira são ferramentas importantes no planejamento do uso do recurso florestal. Neste estudo, foram investigados modelos volumétricos para a Floresta Nacional do Tapirapé-Aquirí, na Serra dos Carajás (PA),... more

Medidas precisas de volume de madeira são ferramentas importantes no planejamento do uso do recurso florestal. Neste estudo, foram investigados modelos volumétricos para a Floresta Nacional do Tapirapé-Aquirí, na Serra dos Carajás (PA), baseados numa cubagem rigorosa de 55 árvores para obter o diâmetro, altura comercial do fuste e volume sólido. Um total de 8 modelos de dupla entrada e 4 de simples entrada foram testados para o diâmetro mínimo de 14 cm. Para seleção do melhor modelo foram usadas as estatísticas do coeficiente de determinação, erro padrão da estimativa e distribuição dos resíduos. Entre os modelos de simples entrada o modelo logarítmico de Husch se ajustou melhor aos dados (R² = 0,9105) e entre os de dupla entrada o logarítmico de Schumacher & Hall se ajustou melhor (R² = 0,9942). O uso do modelo da Flona de Tapajós ou o uso do modelo de volume cilíndrico com fator de forma 0,7 subestimam a volumetria na Flona do Tapirapé. Isso enfatiza a importância de modelos volum...

Fundamentally, drugs are substances that exhibit the capacity to alter biological functions, often serving as interventions to mitigate ailments or enhance physiological processes. However, the landscape of drug development and usage is... more

Fundamentally, drugs are substances that exhibit the capacity to alter biological functions, often serving as interventions to mitigate ailments or enhance physiological processes. However, the landscape of drug development and usage is undergoing transformation, fueled by innovative strategies that hold the promise of revolutionizing healthcare solutions. This research paper explores drug classification and repurposing through the utilization of decision tree algorithms and data analysis. Specifically, the aim is to classify drugs into three distinct types: Drug X which is most used drugs, Drug Y which is less frequently used drugs, and Drug C includes drugs like narcotics. Decision tree algorithms are employed to discern the defining attributes that categorize drugs into these types. By analyzing comprehensive datasets encompassing drug properties, interactions, and clinical outcomes, the study harnesses decision tree models to predict drug classifications accurately. This approach holds the potential to accelerate drug discovery, optimize treatment strategies, and contribute to more efficient healthcare solutions. The proposed algorithm is compared with both Naïve Bayes and K-Nearest Neighbors algorithms to prove it is more accurate. Ultimately, the significance of this paper transcends the boundaries of conventional drug development paradigms and to overcome the problem of shortage of drugs.

Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations ( C P... more

Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations ( C P ′ ) is critical for hydraulic engineers. This study proposed predictive soft computing models to estimate C P ′ on sloping channels. Therefore, three robust soft computing methods, including extreme learning machine (ELM), group method data of handling (GMDH), and M5 model tree (M5MT), were used to estimate C P ′ . The results revealed that ELM was more accurate than GMDH and M5MT methods when comparing statistical indices, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), index agreement ( I a ), and BIAS values. The performance of ELM was found to be more accurate (CC = 0.9183, RMSE = 0.0067, MAE = 0.0051, SI = 11.88%, Ia = 0.9569) when compared with the results of GMDH (CC = 0.8...

Crop simulation models have an important role in evaluating irrigation management strategies for improving agricultural water use. The aim of this study was to evaluate the AquaCrop model for ability to simulate water use and tomato... more

Crop simulation models have an important role in evaluating irrigation management strategies for improving agricultural water use. The aim of this study was to evaluate the AquaCrop model for ability to simulate water use and tomato (Lycopersicon esculentum Mill.) fruit yields under deficit irrigation conditions. A fieldexperiment was conducted at Thornpark, University of Zimbabwe Research site over four seasons (2014 and 2017). The data collected for yield and water use were used to run and evaluate the performance of AquaCrop in predicting water use efficiency and fruit yield. Four treatments defined in relation to 100% of the crop water requirement (ETc) were simulated: T1 100% ETc; T2 80% ETc; T3 60% ETc and T4 50% ETc. The model performance was satisfactory, with a good correlation between the simulated and observed soil water content (SWC) and fruit yield (FY). All the statistical indicators (The Normalised Root Mean Square Error (R2), Root Mean Square Error (RMSE), Nush Sutcl...

The process of clustering of normalized vegetation indices in five regions with a total area of 2565 hectares of the North Kazakhstan region was studied. A methodological approach to organizing the clustering process is proposed using the... more

The process of clustering of normalized vegetation indices in five regions with a total area of 2565 hectares of the North Kazakhstan region was studied. A methodological approach to organizing the clustering process is proposed using the vegetation indices NDVI, MSAVI, ReCI, NDWI and NDRE, taking into account individual characteristics in the three main phases of spring wheat development As a result of the research, vegetation indices were grouped into 3 classes using the k-means clustering method. The first cluster contained vegetation indices whose maximum values occupied about 33.98% of the total area of the study area. It was found that NDVImax located in the first cluster was positively correlated with soil-corrected vegetation indices MSAVI and crop moisture indicators NDMI (R2=0.92). The second cluster is characterized by minimum values of NDVImax coefficients at the germination, tillering and ripening phases (from 0.53 to 0.55). The lowest values of vegetation indices occup...

The quality of virgin coconut oil (VCO), together with its health functionalities, is directly related to its freshness and to the levels of free fatty acids (FFA). Thus, the goal of this work was to develop a quantitative model based on... more

The quality of virgin coconut oil (VCO), together with its health functionalities, is directly related to its freshness and to the levels of free fatty acids (FFA). Thus, the goal of this work was to develop a quantitative model based on partial least squares regression (PLSR) to predict FFA levels (%) in VCO. A total of 72 Brazilian commercial samples classified in accordance to their shelf life was analyzed. FFA levels ranged from 0.3 to 2.3%. FTIR (Fourier transform infrared spectroscopy) spectra were recorded within a range of 3100 to 680 cm−1 and submitted to mathematical preprocessing. Quantitative models were developed by partial least squares regression (PLSR). Excellent predictive results were obtained, indicating that the FFA levels in VCO could be accurately quantified by FTIR. The correlation coefficient (R) and the root mean squared error of prediction (RMSEP) were, respectively, as high as 0.994 and as low as 0.07.Practical applications: According to international stan...

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map... more

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF a...

The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using... more

The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plates were provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision, while this method...