Absolute Coefficients Research Articles

A resource efficient and reliable standard setting method for OSCEs: Borderline regression method using standardized patients as sole raters in clinical case encounters with medical students

Finding a reliable, practical and low-cost criterion-referenced standard setting method for performance-based assessments has proved challenging. The borderline regression method of standard setting for OSCEs has been shown to estimate reliable scores in studies using faculty as raters. Standardized patients (SPs) have been shown to be reliable OSCE raters but have not been evaluated as raters using this standard setting method. Our study sought to find whether SPs could be reliably used as sole raters in an OSCE of clinical encounters using the borderline regression standard setting method. SPs were trained for on a five-point global rating scale. In an OSCE for medical students, SPs completed skills checklists and the global rating scale. The borderline regression method was used to create case passing scores. We estimated the dependability of the final pass or fail decisions and the absolute dependability coefficients for global ratings, checklist scores, and case pass-score decisions using generalizability theory. The overall dependability estimate is 0.92 for pass or fail decisions for the complete OSCE. Dependability coefficients (0.70–0.86) of individual case passing scores range demonstrated high dependability. Based on our findings, the borderline regression method of standard setting can be used with SPs as sole raters in a medical student OSCE to produce a dependable passing score. For those already using SPs as raters, this can provide a practical criterion-referenced standard setting method for no additional cost or faculty time.

AN ECONOMIC ANALYSIS OF THE DETERMINANTS OF DOMESTIC DEMAND FOR TEA IMPORTS IN IRAQ USING THE AUTOREGRESSIVE DISTRIBUTED LAG TECHNIQUE (ARDL) FOR THE PERIOD (1990-2020)

This study was aimed to estimate the domestic demand function on tea imports in Iraq using the Autoregressive Distributed Lag model (ARDL) for co-integration during the period 1990-2020.The data are verified for stationary by using unit root tests basis on Augmented Dickey - Fuller Test. The existence of a long-run equilibrium relationship among tea import quantity, current prices of tea and coffee buying, gross national income, and number of buyers is proved using bounds test method to co-integration. Results of estimating short and long-run models of the demand of tea imports by using the unrestricted error correction approach showed that the error correction parameter is adjusted annually by 140%. Results of estimating price elasticity of demand in the short run showed that the absolute coefficient of price elasticity of demand is less than one and more than zero, which means that tea is a necessary commodity for the Iraqi shopper. Additionally the cross elasticity coefficient had a positive sign in the short and long run, which means that coffee, is an alternative good for tea from the viewpoint of the Iraqi consumer. With regarding to the coefficient value of the income elasticity of demand, it was positive and less than one in the short and long run, which means that tea, is a necessary normal commodity for Iraqi buyers. The study recommended that it is necessary to diversify the sources of national income in the local economy by utilizing all available resources in order to reduce dependence on oil revenues, which were a major source of financing imports in the country.

Temperature Forecasting Correction Based on Operational GRAPES-3km Model Using Machine Learning Methods

Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.

Studies of structural and dielectric properties in Co0.9Zn0.1Fe2O4 ceramics

Spinel ferrites have been widely studied due to their remarkable properties such as high saturation magnetisation, large magnetic permeability, and high electrical resistivity. Among the oxides-based magneto strictive spinel ferrite, CoFe2O4 (CFO) possess the highest value of absolute magnetostriction coefficient (–110 ppm). But CFO has the disadvantage of large magnetic anisotropy and high coercivity. Therefore, doping of zinc (Zn) at Co-site of CFO is reported to reduce the magnetic coercivity and increases the resistivity (108Ω) keeping the magnetostriction coefficient with a sizable value. Also, substitution of Zn for Co in CFO reduces magnetic anisotropy. So, in view of this we have prepared Zn modified CFO compound: Co0.9Zn0.1Fe2O4 using sol-gel combustion technique. The room temperature (RT) XRD patterns indicate the formation of single-phase material without appearance of any impurity phases within the detection limit of X-ray diffractometer. The Rietveld refinement on the RT XRD pattern of CZFO sample was carried out using cubic crystal structure with space group Fd-3m. A good agreement between the experimental and the fitted data has been observed. The average crystallite size for the composition was calculated from the XRD patterns using Williamson–Hall method. Scanning electron micrographs displays homogeneous distribution of grains throughout the sample microstructure with the presence of minute amount of pores. The dielectric and electrical properties were studied as function of frequencies at different temperatures. Complex impedance spectroscopic technique study on this sample suggests existence of non-Debye type dielectric relaxation behaviour.

Uniformity of water distribution by a sprinkler irrigation system on a soccer field

Soccer field grass can be compared to highly sensitive crops regarding water deficit and must be irrigated throughout the year to provide decent conditions for use. For this, efficient irrigation systems are necessary to save water and energy, and this is possible, provided that they are evaluated. Therefore, this paper evaluated the water distribution uniformity, by different methodologies, for an irrigation system installed in a grass soccer field. Also, the influences of multiple spacings between sprinklers and meteorological factors on the final results were assessed. The irrigation system had Falcon 6504 sprinklers, operating at the pressure of 320 kPa. Six field essays were conducted considering overlaying water depths originated from the same irrigation system considering spacings between sprinklers varying from 6 m x 6 m, 6 m x 9 m, 9 m x 9 m, 9 m x 12 m, 12 m x 12 m, 12 m x 15 m, 15 m x 15 m, 15 m x 18 m, 18 m x 18 m, 18 m x 21 m, 21 m x 21 m, 21 m x 24 m, 24 m x 24 m, 24 m x 27 m, 27 m x 27 m, 27 m x 30 m and 30 m x 30 m. The uniformity coefficients used were: Christiansen uniformity coefficient (CU), distribution uniformity coefficient (DU), absolute uniformity coefficient (UA), statistical uniformity coefficient (US), Hart uniformity coefficient (UH) and HSPA standard efficiency (UHSPA). Meteorological data were obtained during the essays. Wind speed caused reduction in the length of the water jet applied by the sprinkler and also caused a bigger effect on the water distribution uniformity. Both CU and UH showed higher values compared to the other coefficients. The increase in the spacing between the sprinklers resulted in reduced water distribution coefficients. To comply with technical and economic criteria, an arrangement of 12 m x 15 m between the Falcon 6504 sprinklers, operating at a 320 kPa pressure, is recommended.

Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks.

PurposeThe record of daily quality control (QC) items shows machine performance patterns and potentially provides warning messages for preventive actions. This study developed a neural network model that could predict the record and trend of data variations quantitively.Methods and materialsThe record of 24 QC items for a radiotherapy machine was investigated in our institute. The QC records were collected daily for 3 years. The stacked long short‐term memory (LSTM) model was used to develop the neural network model. A total of 867 records were collected to predict the record for the next 5 days. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. The accuracy of the model was quantified by the mean absolute error (MAE), root‐mean‐square error (RMSE), and coefficient of determination (R 2). To validate the robustness of the model, the record of four QC items was collected for another radiotherapy machine, which was input into the stacked LSTM model without changing any hyperparameters and ARIMA model.ResultsThe mean MAE, RMSE, andR2 with 24 QC items were 0.013, 0.020, and 0.853 in LSTM, while 0.021, 0.030, and 0.618 in ARIMA, respectively. The results showed that the stacked LSTM outperforms the ARIMA. Moreover, the mean MAE, RMSE, andR2 with four QC items were 0.102, 0.151, and 0.770 in LSTM, while 0.162, 0.375, and 0.550 in ARIMA, respectively.ConclusionsIn this study, the stacked LSTM model can accurately predict the record and trend of QC items. Moreover, the stacked LSTM model is robust when applied to another radiotherapy machine. Predicting future performance record will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

Fiber reinforced polymer (FRP) serves as a prospective alternative to reinforcement in concrete slabs. However, similarly to traditional reinforced concrete slabs, FRP reinforced concrete slabs are susceptible to punching shear failure. Accounts of the insufficient consideration of impact factors, existing empirical models and design provisions for punching strength of FRP reinforced concrete slabs have some problems such as high bias and variance. This study established machine learning-based models to accurately predict the punching shear strength of FRP reinforced concrete slabs. A database of 121 groups of experimental results of FRP reinforced concrete slabs are collected from a literature review. Several machine learning algorithms, such as artificial neural network, support vector machine, decision tree, and adaptive boosting, are selected to build models and compare the performance between them. To demonstrate the predicted accuracy of machine learning, this paper also introduces 6 empirical models and design codes for comparative analysis. The comparative results demonstrate that adaptive boosting has the highest predicted precision, in which the root mean squared error, mean absolute error and coefficient of determination of which are 29.83, 23.00 and 0.99, respectively. GB 50010-2010 (2015) has the best predicted performance among these empirical models and design codes, and ACI 318-19 has the similar result. In addition, among these empirical models, the model proposed by El-Ghandour et al. (1999) has the highest predicted accuracy. According to the results obtained above, SHapley Additive exPlanation (SHAP) is adopted to illustrate the predicted process of AdaBoost. SHAP not only provides global and individual interpretations, but also carries out feature dependency analysis for each input variable. The interpretation results of the model reflect the importance and contribution of the factors that influence the punching shear strength in the machine learning model.

Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist

Daily peak load forecasting (DPLF) and total daily load forecasting (TDLF) are essential for optimal power system operation from one day to one week later. This study develops a Cubist-based incremental learning model to perform accurate and interpretable DPLF and TDLF. To this end, we employ time-series cross-validation to effectively reflect recent electrical load trends and patterns when constructing the model. We also analyze variable importance to identify the most crucial factors in the Cubist model. In the experiments, we used two publicly available building datasets and three educational building cluster datasets. The results showed that the proposed model yielded averages of 7.77 and 10.06 in mean absolute percentage error and coefficient of variation of the root mean square error, respectively. We also confirmed that temperature and holiday information are significant external factors, and electrical loads one day and one week ago are significant internal factors.

Systematic Multiscale Models to Predict the Compressive Strength of Cement Paste as a Function of Microsilica and Nanosilica Contents, Water/Cement Ratio, and Curing Ages

Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.

The structural, dielectric, electrocaloric, and energy storage properties of lead-free Ba0·90Ca0·10Zr0·15Ti0·85O3

Lead-free ferroelectric materials with Electrocaloric Effect (ECE) and recoverable energy (W rec ) represent a major breakthrough in the technological race for manufacturing low-cost and eco-friendly smart multifunctional devices. Here, guided by the benefits resulting from successive ferroelectric-ferroelectric (F–F) and ferroelectric-pseudo cubic (F-PC) transitions including broad operational temperature span, lead-free Ba 0·90 Ca 0·10 Zr 0·15 Ti 0·85 O 3 (BCZT) ferroelectric ceramic located near the phase convergence region was prepared using the conventional solid-state solution. In this study, the systematic investigation of structural and microstructural properties via X-Ray Powder Diffraction (XRPD), scanning electron microscopy (SEM), temperature-dependence Raman spectroscopy and dielectric properties confirm the formation of a pure perovskite material which undergoes consecutive R 3 c - R 3 m , R 3 m - Amm 2, Amm 2- P 4 mm (F–F) phase transitions followed by (F-PC) transition in a wide temperature range centered around room temperature (RT). Polarization-Electric field (P-E) measurements, revealed the coexistence of Positive Electro-Caloric Effect (PECE) and Negative Electro-Caloric Effect (NECE) in a wide temperature span of [-40 °C, 95 °C]. More interestingly, we reached a large absolute temperature change (ΔT) and EC coefficient (ξ) values of (5.505 °C, 1.847 KmmkV −1 ), (0.861 °C, 0.289 KmmkV −1 ) and (1.897 °C, 0.637 Kmm kV −1 ) registered respectively at about −5, 42.5 and 74.5 °C under an electric field of 29.80 kVcm −1 . This work will promote further studies on lead-free BCZT ceramics towards multifunctional environmentally-friendly material promising for solid-state cooling technology.

리튬이온 배터리의 랜덤 포레스트 기반 잔여 용량 추정 및 중요 전압 구간 선정 연구

This paper proposes random forest regression, which is an ensemble model for estimating the remaining battery capacity. The partial capacity of the set charging voltage range and the remaining capacity are used as the training data for the random forest model. The training data can be used directly without any pre-processing. When the partial capacity of a specific voltage range is entered into the trained random forest model, it estimates the remaining capacity. Furthermore, the important voltage range is extracted by selecting the voltage section that is critical for estimating the remaining capacity based on the permutation importance. The mean absolute error and coefficient of determination were used to validate the important voltage range. Validation results show that the proposed model can estimate the remaining capacity with a low error. Therefore, the proposed model can be used for online battery capacity estimation using a narrow voltage range.

Respiratory support status from EHR data for adult population: classification, heuristics, and usage in predictive modeling.

ObjectiveRespiratory support status is critical in understanding patient status, but electronic health record data are often scattered, incomplete, and contradictory. Further, there has been limited work on standardizing representations for respiratory support. The objective of this work was to (1) propose a practical terminology system for respiratory support methods; (2) develop (meta-)heuristics for constructing respiratory support episodes; and (3) evaluate the utility of respiratory support information for mortality prediction.Materials and MethodsAll analyses were performed using electronic health record data of COVID-19-tested, emergency department-admit, adult patients at a large, Midwestern healthcare system between March 1, 2020 and April 1, 2021. Logistic regression and XGBoost models were trained with and without respiratory support information, and performance metrics were compared. Importance of respiratory-support-based features was explored using absolute coefficient values for logistic regression and SHapley Additive exPlanations values for the XGBoost model.ResultsThe proposed terminology system for respiratory support methods is as follows: Low-Flow Oxygen Therapy (LFOT), High-Flow Oxygen Therapy (HFOT), Non-Invasive Mechanical Ventilation (NIMV), Invasive Mechanical Ventilation (IMV), and ExtraCorporeal Membrane Oxygenation (ECMO). The addition of respiratory support information significantly improved mortality prediction (logistic regression area under receiver operating characteristic curve, median [IQR] from 0.855 [0.852—0.855] to 0.881 [0.876—0.884]; area under precision recall curve from 0.262 [0.245—0.268] to 0.319 [0.313—0.325], both P < 0.01). The proposed generalizable, interpretable, and episodic representation had commensurate performance compared to alternate representations despite loss of granularity. Respiratory support features were among the most important in both models.ConclusionRespiratory support information is critical in understanding patient status and can facilitate downstream analyses.

Evaluation of gene expression programming and artificial neural networks in PyTorch for the prediction of local scour depth around a bridge pier

Local scouring around the piers of bridges has been identified as one of the main problems contributing to bridge failure globally. As such, the accurate prediction of safe scouring depths is crucial to assure safety and to develop effective maintenance routines. This study was thus intended to develop a new empirical equation and models to expect the depth of scour occurring around the pier of a bridge by utilising and assessing a variety of modelling proposals to develop the best possible performance. Three methods were used for this purpose: artificial neural networks (ANN), gene expression programming (GEP), and statistical non-linear regression (NLR). The ANN model used in this study was coded in Python, a major modern coding language, being based on the PyTorch interface. The empirical equations and models derived to predict local scour depth were all amended to incorporate the shape of the pier, flow depth, flow intensity, pier width and the attack angle based on data computed within a numerical simulation model in Flow-3D software. The performance of the functional relationship derived from the GEP approach was then compared to the results computed from both the ANN model and NLR empirical equation, with three statistical indices: root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2), as well as appropriate scatter plots used to identify the most efficient of the three models. Based on this comparison, the ANN model performed more accurately than the other two empirical equations derived from the NLR and GEP models based on its smaller values for MAE (0.012) and RMSE (0.029), and its greater R2 value (0.969). Sensitivity analysis results from the model also suggest that flow depth has the most significant impact on depth of scour predictions for compared to different input variables in this model. Based on the results, it can be concluded that the performance of the equation obtained from the ANN-based PyTorch technique is more accurate in predicting the scour depth than GEP and NLR.

Moisture Transport Coefficients Determination on a Model Pharmaceutical Tablet

In this work, a novel methodology to determine moisture transport coefficients for MMC PH101 tablets is presented. Absolute permeability, moisture diffusion, moisture transfer, and water vapor permeability coefficients were estimated on compressed powder tablets produced with different compression pressures (20 MPa to 200 MPa with an interval of 20 MPa). The ASTM D6539 standard test was used to measure the absolute permeability. The moisture transfer coefficient was determined from measured absolute permeability. The moisture diffusion coefficient was obtained with the tablet average pore radius, which was determined with the water droplet penetration method. Descriptive and phenomenological models derived from the measurements were confronted with existing and adopted models, and a good agreement was found. The obtained models are of the function of the microstructural properties of the tablet (average pore radius and average porosity). The tablet average porosity was found to be the principal parameter that governs the behavior of the moisture transport coefficients. The findings of this study might be applicable to obtain a series of input parameters for modelling software, such as COMSOL Multiphysics®, to infer delamination, sticking, and failure propensity from the effect of moisture.

The fuzzy filter based on the method of areas’ ratio

The paper describes a new fuzzy filter based on the method of areas’ ratio which allows to reduce noise during filtering signals. To expand the functionality of the method of areas’ ratio two computational procedures were developed to eliminate errors inherent in classical defuzzification models, namely a narrow range of defuzzification and insensitivity of a defuzzification model. Presented various computational procedures for the fuzzy filter can change the properties of the output variable resulting. As an example, the proposed mathematical model of the Fuzzy Filter based on the Method of Areas’ Ratio illustrated its distinctive characteristics are shown. Firstly, the Fuzzy Filter based on the Method of Areas’ Ratio model has the property of continuity. Secondly, computational procedures provide an increase in the performance of the fuzzy filter. Using detailed numerically calculated Root Mean Square Error and Mean Absolute Percentage Error evaluated the proposed model of the fuzzy filter with other filters such as Kalman Filter, Fuzzy Kalman Filter, Ensemble Kalman Filter and Fuzzy Extended Kalman Filter, Basic defuzzification distributions, Fuzzy mean, Quality method, Root mean square and New weighted trapezoid median average. One of the main goals of the article was to confirm the hypothesis about the possibility of using a fuzzy filter based on the method of area’s ratio for filtering signals. As well as studies of the sensitivity of the proposed fuzzy filter is based on the Root Mean Square Error and Mean Absolute Percentage Error coefficients. These coefficients were established during the experimental studies and showed that the sensitivity of the fuzzy filter based on the method of area’s ratio is better than other filters.

Weather records from recent years performed better than analogue years when merging with real-time weather measurements for dynamic within-season predictions of rainfed maize yield

Within-season crop yield prediction with a dynamic crop model can provide valuable references for field management practices and regional food security. However, weather ensembles containing the unknown future weather conditions occurring after prediction dates are essential for such predictions using crop models. Two strategies were established for selecting analogue weather years as the target growing season based on a five-year maize experiment conducted at eight sites in the Loess Plateau of China. The first strategy tried weather data from different lengths of years ahead the planting year. The second strategy used the k-nearest neighbor (k-NN) algorithm to select analogue weather according to different combinations of weather variables with daily or accumulative values. The results showed that satisfactory predictions could be obtained after maize tasseling (about 50 d prior to maturity). The mean absolute relative error (ARE) and coefficient of variation (CV) of the daily yield predictions after tasseling were 6.6% and 5.7%, respectively, in 2010 at the Yulin site. In the leading-year strategy, the most reliable predictions were obtained by the weather data from the 10 years ahead of planting, with an overall average ARE of 11.7%. In the k-NN strategy, the most reliable predictions were obtained by using the analogue weather selected with only accumulative precipitation, with an overall average ARE of 11.5%. Additionally, both of the two optimal strategies improved the original predictions in most cases. However, the k-NN strategy was more likely to generate worse predictions in the early part of the growing season. Generally, it was more convenient to use the weather data of 10 leading years before the planting year to represent the unknown weather data after the prediction dates. This strategy provided reliable prediction accuracy without complex programming and requirement for long-term weather records.

A Hybrid Leaf Area Index Estimation Method of Dioscorea Polystachya Turczaninow Using Sentinel-2 Vegetation Indices

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dioscorea polystachya</i> Turczaninow is an herbaceous vine plant distributed in China, and its rhizome named Chinese yam is a famous traditional Chinese medicine for treating diabetes and other diseases. However, a large region monitoring method of its growth status is lacking, which is important for Chinese yam yield estimation. Therefore, this study proposed a leaf area index (LAI) estimation algorithm for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dioscorea polystachya</i> Turczaninow using a hybrid method and Sentinel-2 vegetation indices. First, two feature selection algorithms the gradient boosting regression tree (GBRT) and absolute Pearson correlation coefficient (APCC), were combined with field-measured data and radiation transfer model simulated data to generate four different feature important ranking groups. Then, a hybrid feature selection algorithm was used to determine the best feature subsets under each ranking group, and GBRT regression and least absolute shrinkage and selection operator (LASSO) were used to develop the LAI estimation models. Finally, the best LAI estimation model for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dioscorea polystachya</i> Turczaninow was determined based on validation accuracy. The results indicated that the field-measured data were more reliable than the simulated data for feature selection, and the best LAI estimation model was the LASSO model using nine selected vegetation indices, which achieved the performance with RMSE of 0.391 and MAE of 0.310. The proposed method could provide real-time LAI estimates for future Chinese yam yield prediction.

Machine Learning Methods for Spaceborne GNSS-R Sea Surface Height Measurement From TDS-1

Sea surface height (SSH) retrieval based on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) usually uses the GNSS-R geometric principle and delay-Doppler map (DDM). The traditional method condenses the DDM information into a single scalar measure and requires error model correction. In this article, the idea of using machine learning methods to retrieve SSH is proposed. Specifically, two widely-used methods, principal component analysis combined with support vector regression (PCA-SVR) and convolution neural network (CNN), are used for verification and comparative analysis based on the observation data provided by Techdemosat-1 (TDS-1). According to the DDM inversion method, ten features from TDS-1 Level 1 data are selected as inputs; The SSH verification model based on the Danmarks Tekniske Universitet (DTU) 15 ocean wide mean SSH model and the DTU global ocean tide model is used as output verification of SSH. For the hyperparameters in the machine learning model, a grid search strategy is used to find the optimal values. By analyzing the TDS-1 data from 31 GPS satellites, the mean absolute error (MAE), root-mean-square error (RMSE) and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of the PCA-SVR inversion model are 0.61 m, 1.72 m, and 99.56%, respectively; and the MAE, RMSE, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the CNN inversion model is 0.71 m, 1.27 m, and 99.76%, respectively. In addition, the time required to train the PCA-SVR and CNN inversion models is also analyzed. Overall, the technique proposed in this article can be confidently applied to SSH inversion based on TDS-1 data.

Prediction of floodwater impacts on vehicle blockages at bridges using artificial neural network

During extreme flood events, various debris like floating vehicles can block the bridges in urban rivers and floodplains. Blockage of vehicles can influence the floodwater hydrodynamics and potentially on the flood risk implications. Such obstructions often raise upstream water levels with back water effects, causing more water to be redirected into nearby metropolitan areas. This study attempts at evaluating artificial neural network (ANN) model in predicting the variations in floodwater depths and velocities along the channel centreline based on the changes in flowrate and distances from the inlet. The floodwater depth and velocity variations were obtained for three different types of bridges at specified sites along the channel centreline with three incoming discharges. A multilayer feedforward neural network (FFNN) model was used to investigate the effects of discharge (Q) and distance, on depth variation rate (D) or velocity (V). Additionally, a comparison study was done between 2 input 1 output and 2 input 2 output i.e. single output (depth variation rate (D) or velocity (V) versus multi-output depth variation rate (D) and velocity (V) for all the three models of bridges that are blocked by vehicles. The study has applied 12 training algorithms (TA) in the ANN modelling to optimize the TA that is most suitable for the dataset of three different bridges. The optimization is based on the performance criterion namely regression (R), mean squared error (MSE), mean absolute error (MAE), mean absolute percentage (MAPE), accuracy and coefficient of determinant (R2). Bayesian regularization backpropagation (BR) training algorithm gives a highest accuracy when compared in all three bridges. The scenario 2 input 2 output gave greatest accuracy results compared to 2 input 1 output. The findings showed a reliable estimation of significant impacts on the flow propagations and the hydrodynamic processes along rivers and floodplains. This study can help the decision makers in effective river and floodplain management practices.

AGROBIOLOGICAL VALUE OF SOME WHITE GRAPE VARIETIES CREATED AT RESEARCH AND DEVELOPMENT STATION FOR VITICULTURE AND VINIFICATION ODOBEȘTI, VRANCEA COUNTY, ROMANIA

Vine varieties react differently to the influence of environmental factors by decreasing or significantly intensifying the vigour of shoot growth, a biological reaction that also affects fertility and productivity. This article presents data on some agrobiological properties (vegetative growth of shoots, fertility and productivity) of some white wine varieties, created at the Research and Development Station for Viticulture and Vinification (R.D.S.V.V.) Odobești, Vrancea County, Romania) - Şarba, Băbească gri, Mioriţa and Vrancea, in the period 2020 - 2021. The data obtained were compared with the control variety Fetească regală. In Odobeşti vineyard, 2020 was much warmer than normal, characterized by a severely deficient rainfall regime resulting in atmospheric drought, followed by the gradual development of pedological drought. The vigour of shoots was negatively influenced by the climatic conditions of the vegetation period in 2020, the highest values being recorded for the varieties Șarba (159.6 cm) and Vrancea (141.5 cm), which showed significant positive differences, compared to the control variety. The fertility of the buds was lower in 2020, compared to 2021, the Vrancea variety being distinguished by higher potential fertility than the varieties Șarba, Băbească gri and Miorița, but lower potential fertility than the control variety. The absolute fertility coefficient (Cfa) had superunitary values for all varieties (1.07 - 1.50), and the relative fertility coefficient (Cfr) had subunitary values, except for the Vrancea variety (1.32). The varieties Șarba, Băbească gri and Miorița achieved an absolute productivity index (Ipa) superior to the control variety Fetească regală due to the higher average weight of the grapes, and the relative productivity index (Ipr) ranged from 199 for the Vrancea variety to 182 for the variety Șarba.