Brittleness index prediction in shale gas reservoirs based on efficient network models

Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China)

Application of colorimetric sensor array combined with visible near-infrared spectroscopy for the matcha classification

Based on the data of household income of Shanghai low-rent housing families, a GM(1,1) forecast model and a Back-Propagation Artificial Neural Network (BPANN) forecast model are established respectively to predict the average household income of low-rent housing families. The comparison between the GM(1,1) and the BPANN model showed that the BPANN model is better than the GM(1,1) model at the aspects of prediction accuracy and data adaptability. The BPANN model could be applied successfully to predict the average household income of Shanghai low-rent housing families in a short-term and it will provide scientific and effective basis for formulate policy on low-rent housing.

Heterocyclic amines (HAs) and N-nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP-ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat-to-lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP-ANN model. With an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP-ANN model was 5-11-2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance. The contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP-ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

In this study, the performance of an integrated desiccant air conditioning system (IDACS) activated by solar energy is evaluated by back propagation artificial neural network (BP-ANN). The IDACS consists of a liquid desiccant dehumidification cycle combined with a vapor compression refrigeration cycle. The integrated system performance is assessed utilizing the system coefficient of performance (COP), outlet dry air temperature (Tda-out), and specific moisture removal (SMR). The training of the BP-ANN is accomplished utilizing experimental results previously published. The results of the BP-ANN model revealed the high accuracy in predicting system performance parameters compared with experimental values. The BP-ANN model has shown relative errors in the trained mode for COP, Tda-out, and SMR within ±0.005%, ±0.006%, and ±0.05%, respectively. On the other side, the BP-ANN model is inspected in the predictive mode as well. The relative errors of the model for COP, Tda-out, and SMR in the predictive mode are within ±0.006%, ±0.006%, and ±0.004%, respectively. The influences of some selected parameters, namely regeneration temperature, desiccant solution temperature in the condenser and evaporator, and strong solution concentration on the system performance are examined and discussed as well.

Flavonoids, the most diverse class of plant secondary metabolites, exhibit high affinity toward the purified cytosolic NBD2(C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationships (QSARs) models were developed using back-propagation artificial neural networks (BPANN) and multiple linear regression (MLR). Molecular descriptors were calculated using PaDELDescriptor, and the number of descriptors was then reduced using a genetic algorithm (GA) and stepwise regression. The MLR (R(2)=0.855, q(2)=0.8138, Rext(2)=0.6916), 14-3-1 BPANN (R(2)=0.8514, q(2)=0.7695, Rext (2)=0.8142), 14-4-1 BPANN(R(2)=0.9199, q(2)=0.7733, Rext(2) =0.8731), and 14-5-1 BPANN (R(2)=0.8660, q(2)=0.7432, Rext(2)=0.8292) models all showed good robustness. While BPANN models exceeded significantly MLR in predictable performance for their flexible characters, could be used to predict the affinity of flavonoids for P-gp and applied in further drug screening.

Objectives: Evaluating daily functioning of people with mental illnesses with questionaires is time-consuming. Additionally, patients' familiarity with the context of function tests compromises test accuracy. Applying a computer-assisted support assessment system in this study, we intended to predict the daily functioning of the mentally ill objectively and conveniently. Methods: We collected 54 patients attending a psychiatric daycare ward at a medical center in the Taipei city. A five-fold cross-validation scheme was applied to minimize possible bias and to provide reliable estimates. We used discriminant analysis (DA) and a back-propagation neural network (BPN), to predict patients' daily functioning, according to gender, educational background, diagnosis, and age, on Chu's daily function scale. Results: Both models achieved high average overall accuracy of more than 70%. The BPN model had a high overall classification accuracy of 92.55%, 16.55% better than that of the DA model. Additionally, the discriminant function showed that young males not diagnosed with schizophrenia had better daily function. Conclusion: This study was found that the BPN as a computer-assisted assessment support system predicted daily functioning more effectively than DA. To predict daily functioning relatively more precisely, we suggest that future research need to expand the sample population and to use additional variables, such as patients' personality, family support, and living status.

Febuxostat is a well-known drug for treating hyperuricemia and gout. The published methods for determination of febuxostat in human plasma might be unsuitable for high-throughput determination and widespread application. We need to develop a highly selective, sensitive and rapid liquid chromatography-tandem mass spectrometry method. The chromatographic separation was achieved on a Hypersil Gold-C18 (2.1mm×100mm, 1.9μm) column with mobile phase A (Water containing 0.1% formic acid) and mobile phase B (acetonitrile containing 0.1% formic acid). Multiple reaction monitoring (MRM) mode was used for quantification using target ions at m/z 315.3→m/z 271.3 for febuxostat and m/z 324.3→m/z 280.3 for Febuxostat-d9 (IS). A backpropagation artificial neural network (BPANN) pharmacokinetic model was constructed by the data of bioequivalence study. After the LC-MS/MS method validated, it was successfully applied to the bioequivalence study of 30 human volunteers under fed condition. The predicted concentrations generated by BPANN model had a high correlation coefficient with experimental values. A sensitive LC-MS/MS method had been developed and validated for determination of febuxostat in healthy subjects under fed condition, and a BPANN model was developed that can be used to predict the plasma concentration of febuxostat.

As a key parameter in the adsorption process, removal rate is not available under most operating conditions due to the time and cost of experimental testing. To address this issue, evaluation of the efficiency of NH4+ removal from stormwater by coal-based granular activated carbon (CB-GAC), a novel approach, the response surface methodology (RSM), back-propagation artificial neural network (BP-ANN) coupled with genetic algorithm (GA), has been applied in this research. The sorption process was modeled based on Box-Behnben design (BBD) RSM method for independent variables: Contact time, initial concentration, temperature, and pH; suggesting a quadratic polynomial model with p-value < 0.001, R2 = 0.9762. The BP-ANN with a structure of 4-8-1 gave the best performance. Compared with the BBD-RSM model, the BP-ANN model indicated better prediction of the response with R2 = 0.9959. The weights derived from BP-ANN was further analyzed by Garson equation, and the results showed that the order of the variables’ effectiveness is as follow: Contact time (31.23%) > pH (24.68%) > temperature (22.93%) > initial concentration (21.16%). The process parameters were optimized via RSM optimization tools and GA. The results of validation experiments showed that the optimization results of GA-ANN are more accurate than BBD-RSM, with contact time = 899.41 min, initial concentration = 17.35 mg/L, temperature = 15 °C, pH = 6.98, NH4+ removal rate = 63.74%, and relative error = 0.87%. Furthermore, the CB-GAC has been characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The isotherm and kinetic studies of the adsorption process illustrated that adsorption of NH4+ onto CB-GAC corresponded Langmuir isotherm and pseudo-second-order kinetic models. The calculated maximum adsorption capacity was 0.2821 mg/g.

The present study proposes a back propagation artificial neural network (BPANN) to provide improved precision for predicting the material removal rate (MRR) in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs) in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.

Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network

Characterization of grain growth behaviors by BP-ANN and Sellars models for nickle-base superalloy and their comparisons

Rapid detection of food quality indicators using ELM and near-infrared spectroscopy.

Predicting seismic interaction effect between soil and structure group using convolutional neural network

Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index