MLP Artificial Neural Network Research Articles

Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning.

The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.

Improving the diagnostic performance of the neural network for COVID-19VN via weight assignment to pathological symptoms

In this article, we present how to create a database of Covid-19 diseases at National Hospital of Tropical Diseases (called the CovidVN database) and then develop a learning neural network based on this database to diagnose this disease. The CovidVN Database is built based on the processing of real diagnostic test results of Covid-19 patients with a large number of samples and in accordance with the structure of the Israeli Health System Covid-19 disease database (called COVIDIsr Database). Then two MLP Artificial Neural Networks corresponding to these two databases will be developed using the Deep Learning Toolbox of MATLAB software; the results of training these networks and their accuracy are compared with each other to assess the relative quality of the CovidVN database. Other, the paper presents the method of assigning weight corresponding to pathological symptoms for network input parameters. The results showed that the weighting of input attributes corresponding to the pathological symptoms is significant.

Comprehensive investigation of isotherm, RSM, and ANN modeling of CO2 capture by multi-walled carbon nanotube

Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe–Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe–Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg–Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.

Preoperative detection of hepatocellular carcinoma's microvascular invasion on CT-scan by machine learning and radiomics: A preliminary analysis

IntroductionMicrovascular invasion (MVI) is the main risk factor for overall mortality and recurrence after surgery for hepatocellular carcinoma (HCC).The aim was to train machine-learning models to predict MVI on preoperative CT scan. Methods3-phases CT scans were retrospectively collected among 4 Italian centers. DICOM files were manually segmented to detect the liver and the tumor(s). Radiomics features were extracted from the tumoral, peritumoral and healthy liver areas in each phase. Principal component analysis (PCA) was performed to reduce the dimensions of the dataset. Data were divided between training (70%) and test (30%) sets. Random-Forest (RF), fully connected MLP Artificial neural network (neuralnet) and extreme gradient boosting (XGB) models were fitted to predict MVI. Prediction accuracy was estimated in the test set. ResultsBetween 2008 and 2022, 218 preoperative CT scans were collected. At the histological specimen, 72(33.02%) patients had MVI. First and second order radiomics features were extracted, obtaining 672 variables. PCA selected 58 dimensions explaining >95% of the variance.In the test set, the XGB model obtained Accuracy = 68.7% (Sens: 38.1%, Spec: 83.7%, PPV: 53.3% and NPV: 73.4%). The neuralnet showed an Accuracy = 50% (Sens: 52.3%, Spec: 48.8%, PPV: 33.3%, NPV: 67.7%). RF was the best performer (Acc = 96.8%, 95%CI: 0.91–0.99, Sens: 95.2%, Spec: 97.6%, PPV: 95.2% and NPV: 97.6%). ConclusionOur model allowed a high prediction accuracy of the presence of MVI at the time of HCC diagnosis. This could lead to change the treatment allocation, the surgical extension and the follow-up strategy for those patients.

Bitterling fish optimization (BFO) algorithm

The bitterling fish is a prime example of intelligent behavior in nature for survival. The bitterling fish uses the oyster spawning strategy as their babysitter. The female bitterling fish looks for a male fish stronger than other fish to find the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fish. The bitterling fish optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfly optimization algorithm, Harris Hawks optimization algorithm, and black widow optimization algorithm, according to experiments and implementations on various benchmark functions. Data mining and machine learning are two areas where meta-heuristic techniques are frequently used. In trials, the MLP artificial neural network and a binary version of the BFO algorithm are used to lower the detection error for intrusion traffic. The proposed method's accuracy, precision, and sensitivity index for detecting network intrusion are 99.14%, 98.87%, and 98.85%, respectively, according to experiments on the NSL KDD data set. Compared to machine learning approaches like NNIA, DT, RF, XGBoot, and CNN, the proposed method is more accurate at detecting intrusion. The BFO algorithm is used for feature selection in the UNSW-NB15 dataset, and the tests showed that the accuracy of the proposed method is 96.72% in this dataset. The proposed method of the BFO algorithm is also used to improve Kmeans clustering, and the tests performed on the dataset of covid 19, diabetes, and kidney disease show that the proposed method performs better than iECA*, ECA*, GENCLUST + + (G + +) methods. Deep has KNN, LVQ, SVM, ANN, and KNN.

Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries

The Lithium-Ion Batteries (LIB) industry is rapidly growing and is expected to continue expanding exponentially in the next decade. LIBs are already widely used in everyday life, and their demand is expected to increase further, particularly in the automotive sector. The European Union has introduced a new law to ban Internal Combustion Engines from 2035, pushing for the adoption of electric vehicles and increasing the need for more efficient and reliable energy storage solutions such as LIBs. As a result, the establishment of Gigafactories in Europe and the United States is accelerating to meet the growing demand and partially reduce dependencies on China, which is currently the main producer of LIBs.
 To fully realize the potential of LIBs and ensure their safe and sustainable use, it is crucial to optimize their useful life and develop reliable and robust methodologies for estimating their state of health and predicting their remaining useful life. This requires a comprehensive understanding of LIB behavior and the development of effective prognostic and health management approaches that can accurately predict battery degradation, plan for maintenance and replacements, and improve battery performance and lifespan.
 This work, funded by the GREYDIENT project, a European consortium aiming to advance the state of the art in the grey-box approach, combines physical modeling (white box) and machine learning (black box) techniques to demonstrate the grey-box effectiveness in the Prognostic and Health Management. The grey-box approach here proposed consist in a combination of a physical battery model whose degradation parameters are estimated online at every cycle by a Multi-Layer Perceptron Particle Filter (MLP-PF).
 An electrochemical degradation model of a Lithium-Ion battery cell has been derived by use of Modelica. The model simulates the output voltage of the cell, while the degradation over time is simulate through the variation of 3 parameters: qMax (maximum number of Lithium-Ions available), R0 (Internal Resistance) and D (Diffusion Coefficient). To validate the model we resorted to the well-known NASA Battery Dataset, which has also been used to infer the optimal values of the three hidden degradation parameters at every cycle, to obtain their Run-to-Failure history. Then, the physical model is combined the MLP-PF: a MLPArtificial Neural Network is firstly trained on the Run-to-Failure degradation processes of the model parameters, allowing the propagation of the parameters in the future and the corresponding estimation of the battery Remaining Use ful Life (RUL). The MLP is then updated online by a Particle Filter every time a new measurement is available from the Battery Management System (BMS), providing flexibility to this method, needed for the electrochemical nature of the batteries, and allowing the propagation of uncertainties.

Educational Data Mining to Predict Bachelors Students’ Success

Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries’ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor’s historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network’s best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student’s previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PDF

An artificial neural network and a combined capacitive sensor for measuring the void fraction independent of temperature and pressure changes for a two-phase homogeneous fluid

Void fraction plays a vital role in diverse industries like oil, petrochemical, etc., which involve a wide range of fluids, including two-phase and three-phase fluids. Various procedures are used for the measurement of the void fraction, with one of the most popular being capacitance-based sensors. The characteristics of the fluid inside the pipe affect the output of this type of sensor, and every property, such as temperature, pressure, and density, plays a role. This paper presents the use of an Artificial Neural Network (ANN) and a combined capacitance-based sensor to measure the void fraction of a two-phase air-water homogeneous fluid. The aim is to develop a system that can predict void fraction independent of temperature and pressure changes. To achieve this, the COMSOL Multiphysics software was used to design and simulate two widely used sensors, concave and ring, to create a combined capacitance sensor. Simulations were conducted for the implemented sensor in different temperature ranges (275–370 K) and pressure ranges (1–500 Bar). After a large number of simulations and producing 3780 data from the combined sensor, they were used as inputs to train the proposed MLP ANN network. The presented model provides a new metering system that can accurately estimate the amount of the void fraction of a two-phase air-water homogeneous fluid independent of temperature and pressure changes and had a low error which means the MAE is equal to 4.868.

Determining the optimal structure for accurate estimation of the dynamic viscosity of oil-based hybrid nanofluid containing MgO and MWCNTs nanoparticles using multilayer perceptron neural networks with Levenberg-Marquardt Algorithm

In this article, the prediction of dynamic viscosity (μnf) of MgO-MWCNTs/SAE40 engine oil nanofluid (NF) using artificial neural network (ANN) is investigated in different conditions (temperatures, volume fractions (φ) and shear rates). In this research, the Multi-layer perceptron (MLP ANN with Levenberg–Marquardt (ML) algorithm is used for modeling by ANN. The optimal structure is selected from a set of 400 different ANN structures for MWCNT- MgO (20:80)/SAE40 NF, including two hidden layers with 10 neurons in the first layer and 4 neurons in the second layer. φ, shear rate and temperature are considered input parameters and predicted μnf is considered as an output parameter in ANN modeling. The results show that the optimal ANN with 8 neurons per layer has the minimum mean square error (MSE) and the maximum regression coefficient R close to 1 for predicting μnf. The range of MODs is −2% < MOD<2%. The comparison of three groups of laboratory data, calculation and ANN prediction shows that the data predicted by ANN is much more capable than the new relation and estimates the laboratory data more accurately.

A Comparison of Artificial Neural Network and Time Series Models for Timber Price Forecasting

The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m−3). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m−3. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m−3, RMSE 1.04 € m−3). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m−3, RMSE 8.09 € m−3), pine (MAE 2.21 € m−3, RMSE 2.83 € m−3), beech (MAE 2.31 € m−3, RMSE 2.70 € m−3), alder (MAE 1.88 € m−3, RMSE 2.40 € m−3), and spruce (MAE 2.44 € m−3, RMSE 2.58 € m−3), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.

An exposition on the prediction of load on a Smart Grid

Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.

Air Temperature Sensor Estimation on Automatic Weather Station Using ARIMA and MLP

Surface meteorological quantities are now measured by Automatic Weather Station (AWS). AWS Serang records weather parameters minutely in Banten Province of Indonesia. Air temperature sensor is one instrument of this system. This study aims to design an air temperature sensor estimator model using ARIMA and Artificial Neural Network (ANN) as solution for avoiding loss data. Air temperature sensor on AWS Serang data in August of 2022 period is segmented into training, validating and testing sections. Based on criterion calculation, ARIMA (1,1,5) is simulated. It obtains not more than 0.12 of RMSE, 0.0520C of MAE, 0.193% of MAPE and 0.194% of SMAPE. Meanwhile, three different models of MLP ANN for air temperature estimator is also simulated. Input variables include air temperature, relative humidity and solar radiation intensity. Roy model has highest accuracy level for MLP ANN algorithm with 0.048 of RMSE, 0.0260C for MAE, 5% of MAPE and 4.83% of SMAPE. Overall, ARIMA (1,1,5) is better than Roy MLP ANN model in estimating air temperature sensor data on AWS Serang. Nonetheless, both models are properly fulfilling WMO (World Meteorological Organization) accuracy requirements for air temperature measurement.

A Proposal Phishing Attack Detection System on Twitter

The security of personal data is crucial for a company or any individual. Phishing is one of the most common and dangerous cybercrime attacks. These attacks aim to steal information used by individuals and organizations using social engineering, which is a key point for the success of the phishing attack. Even though there are several systems and solutions, the amount of personal information stolen continues to increase as cyberattacks become more difficult to detect. This paper consists of a broad review to study the work carried out in the fight against phishing and the identification of vulnerabilities in existing systems to achieve better efficiency. The authors focused on the social medium Twitter to study the phishing attacks passing through this medium, and they present their new design, which is based on new features. The classification of the approach includes 23 features and uses the MLP artificial neural network (ANN MLP) algorithm. Experiments show that the system is effective at detecting phishing sites, with a 96% success rate using recent data.

Faults and fractures detection using a combination of seismic attributes by the MLP and UVQ artificial neural networks in an Iranian oilfield

Faults and fractures play a significant role in oilfield drilling operations, hydrocarbon trapping, and reservoir development. Exploring faults quickly and accurately can help reach the target more efficiently. In this study, applicable seismic attributes were combined using a multilayer perceptron and an unsupervised vector quantizer and applied to a 3D seismic cube to identify discontinuities. First, high-probability faulted areas were picked manually on a seismic section as an input pattern for the MLP. Then, particular seismic attributes (dip-steering, similarity, coherency, curvature, instantaneous) were applied to the data. Consequently, the MLP and UVQ were used to determine the most contributed attributes. Using the MLP and UVQ, the most relevant attributes were integrated to find faults and fractures in the 3D seismic volume. In contrast to some fault-identifying methods and prior studies, this study used not just steered attributes but also compared supervised and unsupervised neural networks. Eventually, comparing the outputs of the MLP, faulted and non-faulted cubes, with the initial seismic section and the UVQ’s output revealed discrepancies. For a specific set of attributes, the MLP was obviously superior to the UVQ in terms of creating detailed outputs, analyzing time, and rendering more precise and trustworthy results.

Investigation the effects of different nanoparticles on density and specific heat: Prediction using MLP artificial neural network and response surface methodology

Increasing energy efficiency is of particular importance and therefore the use of nanofluids has been considered due to their thermophysical properties. in this study Response surface methodology (RSM) and MLP artificial neural network (ANN) models are applied for the determination of the density and specific heat capacity. Results indicate that the maximum residual value for the RSM model of density was ±1.5 and for the RSM model of specific heat was + 0.008 and − 0.006. The optimum MLP structure for density is created with 5 neurons in the first layer and 2 neurons in the second layer. For the MLP structure of specific heat, 4 neurons in the first layer and 5 neurons in the second layer are considered. The best MLP structures have MSE, MAE and R2 equal to 0.324113, 0.395038 and 0.9985 for density and 1.89E-05, 3.24E-03 and 0.9974 for thermal conductivity, respectively. The maximum influence of volume fraction of nanoparticles (φ) on the density belongs to TiN50–EG nanofluid and the minimum one belongs to Si3N420–EG nanofluid. TiN50–EG and TiN20–EG nanofluids have the minimum effect on the specific heat.

Potato yield modeling based on meteorological factors using discriminant analysis and artificial neural networks

ABSTRACT A reliable, pre-harvest, crop yield prediction based on meteorological factors is important to anticipate adverse effect of weather variables. Discriminant score-based regression models, MLP artificial neural network (ANN) models, and regression-ANN hybrid models were used to model potato (Solanum tuberosum L.) yield. Maximum and minimum temperatures, rainfall, and relative humidity, and their indices, were used to obtain discriminant scores for each year. These discriminant scores, along with a time variable, were used as inputs and potato yield as outputs for the development of models. A hybrid model consisting of linear and non-linear components performed better than individual models if combined linearity and nonlinearity are present in the data, else the ANN models were better than regression models. The best models can be used to obtain a reliable forecast of potato yield at 6–8 weeks before harvest using meteorological data.

Evaluation and Comparison of FFB, CFB and MLP Artificial Neural Networks for Identification of Potential Sites for Construction of Photovoltaic Solar Power Plants in East Azarbaijan Province

Due to the geographical location of Iran and having 300 sunny days, the use of solar energy in both large and small sectors contributes to sustainable energy supply.In this study, we have tried to combine the existing methods for location namely the use of spatial decision-making systems and GIS, to use new methods such as artificial neural networks to identify potential areas for the construction of solar photovoltaic (PV) power plants in East Azarbaijan province. To accomplish this, based on the criteria for the construction and location of photovoltaic solar power plants, environmental factors include solar radiation, precipitation, sundial and temperature as climate criteria, elevation and slope as physical and land use criteria, distance from roads and distance from Cities are considered as economic criteria. Based on these criteria, training data was obtained through ANP, and along with this data, and LM training algorithm was performed to train FFB, CFB, and MLP networks.Based on the MSE and RMSE evaluation criteria, the CFB network with the structure of 9,6,1 was selected as the most appropriate network and the results were obtained from this network. After preparing the final map, it was determined that solar photovoltaic power plants could be built in the province.

Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters

Abstract Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed to differentiate, between AF and non-AF, segments and ECG’s features, obtaining average accuracy of 80.67% in test set, for the 10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented to compose the network input sets, indicating a slight improvement in the model’s performance. Added to these, Shannon’s and logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.

Characterization of polyethylene terephthalate wastes/Acrylonitril-Butadiene styrene (PETW/ABS) composites with applications of artificial neural networks

During the processes done in petrochemical plants, high amount of polyethylene terephthalate (PET) as PET Waste remains. In this research in order to reuse PET and reduce the environmental problems, PET Waste in different weight percentages (0, 5, 10, 15) has been combined with ABS in different conditions of temperature, time and speed of extruder. Different tests have been performed on polymer mixture like: Impact Test, Elongation at Break, Modular Melt Flow, vicat softening temperature and Yellow Index. The results declared that in condition with constant temperature and time and speed of mixing with increasing PET Waste in ABS the number of Impact Test and Elongation at Break decreases and the number of Modular Melt Flow, vicat softening temperature and Yellow Index increases.When the percentage of PET Waste in ABS is constant, according to the change in each of the conditions of the mixing, it is possible that the amount of the change for different tests to be investigated and the laboratory data to be simulated by the MLP artificial neural networks. The results of the simulation covered the laboratory data perfectly and the information of this article can be used as a reference in order to improve the specifications of the mixed polymer according to the need of increasing polymer properties.

Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang–Mekong River by using the modified RUSLE model

Soil erosion and sediments in the Lancang-Mekong River Basin as a result of climate change and changes in land use pose a threat to the existence of the riparian people, biodiversity and ecosystems. This study seeks to assess the annual soil erosion in terms of spatial distribution and the trends of sediment yield with the climate and land changes in future scenarios in 2030 and 2040 through the modified RUSLE model. Future lands were simulated by using the MLP artificial neural network and the Markov chain analysis. The future climate was examined by using the Max Planck Institute model, which showed a corrected bias and downscaled grid size under the Representative Concentration Pathways (RCPs). The simulated land use indicated that the forest areas were converted mostly to agricultural lands and urban areas. In the future, the average rainfall under all RCP scenarios is higher than that from the historical period. The R and C factors changed constantly, thereby affecting the soil erosion rate and sediment yield. The maximum erosion was estimated at approximately 21,000 and 21,725 t/km2/y under RCP8.5 in both years. Meanwhile, the results of sediment yield in 2030 and 2040 under RCP scenarios were much higher when compared to historical sediment data around 66.3% and 71.2%, respectively. Thailand's plateau, some parts of Cambodia and Laos PDR and the Mekong Delta are vulnerable to increase soil erosion and sediment yield. Measures to address these issues need to be planned to prepare and mitigate the possible effects, especially the loss of storage capacity in dams.