scholarly journals Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV–Vis Spectrometry and SPSE-XGBoost Method

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4936 ◽  
Author(s):  
Fei Cheng ◽  
Chunhua Yang ◽  
Can Zhou ◽  
Lijuan Lan ◽  
Hongqiu Zhu ◽  
...  
Keyword(s):  
Metal Ions ◽  
Real Time ◽  
Metal Ion ◽  
Sulfate Solution ◽  
Gradient Boosting ◽  
Feature Subset ◽  
Full Spectrum ◽  
Ion Concentrations ◽  
Extreme Gradient Boosting

Excessive discharge of heavy metal ions will aggravate environment pollution and threaten human health. Thus, it is of significance to real-time detect metal ions and control discharge in the metallurgical wastewater. We developed an accurate and rapid approach based on the singular perturbation spectrum estimator and extreme gradient boosting (SPSE-XGBoost) algorithms to simultaneously determine multi-metal ion concentrations by UV–vis spectrometry. In the approach, the spectral data is expanded by multi-order derivative preprocessing, and then, the sensitive feature bands in each spectrum are extracted by feature importance (VI score) ranking. Subsequently, the SPSE-XGBoost model are trained to combine multi-derivative features and to predict ion concentrations. The experimental results indicate that the developed “Expand-Extract-Combine” strategy can not only overcome problems of background noise and spectral overlapping but also mine the deeper spectrum information by integrating important features. Moreover, the SPSE-XGBoost strategy utilizes the selected feature subset instead of the full-spectrum for calculation, which effectively improves the computing speed. The comparisons of different data processing methods are conducted. It outcomes that the proposed strategy outperforms other routine methods and can profoundly determine the concentrations of zinc, copper, cobalt, and nickel with the lowest RMSEP. Therefore, our developed approach can be implemented as a promising mean for real-time and on-line determination of multi-metal ion concentrations in zinc hydrometallurgy.

scholarly journals Determination of metal ion concentrations by SERS using 2,2′-bipyridyl complexes

The Analyst ◽  
2015 ◽  
Vol 140 (19) ◽  
pp. 6538-6543 ◽  
Author(s):  
Julie Docherty ◽  
Samuel Mabbott ◽  
W. Ewen Smith ◽  
John Reglinski ◽  
Karen Faulds ◽  
...  
Keyword(s):  
Metal Ions ◽  
Metal Ion ◽  
Ion Concentrations

SERS of bipyridyl complexes for the discrimination of six different metal ions.

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

Report Paper 4: Determination of Bonds between Metal Ions and Additives by Means of Surface Pressure and Infrared Spectra

1969 ◽  
Vol 184 (12) ◽  
pp. 201-206
Author(s):  
S. Friberg ◽  
H. Müller
Keyword(s):  
Aqueous Solution ◽  
Metal Ions ◽  
Infrared Spectra ◽  
Surface Pressure ◽  
Metal Ion ◽  
Specific Information ◽  
Bond Properties ◽  
Monomolecular Layers ◽  
Surface Balance

A surface balance of the Langmuir type was used to determine the effect of different metal ion specia in an aqueous solution on the surface pressure of monomolecular layers of additives. To gain more specific information about the bond properties, the monolayers were collected and the infrared spectra recorded. It is suggested that the results can be used as data when the bonds between the additives and the atom groups on a metal surface are being considered.

Theoretical Considerations and Equilibrium Conditions in Analytical Potentiometry. Computer Facilitated Mathematical Analysis of Equilibria in a Multicomponent System

1973 ◽  
Vol 51 (21) ◽  
pp. 3541-3548 ◽  
Author(s):  
Bihudhendra Sarkar ◽  
Theo P. A. Kruck
Keyword(s):  
Metal Ions ◽  
Mathematical Analysis ◽  
Metal Ion ◽  
Binary Systems ◽  
Accurate Determination ◽  
Multicomponent System ◽  
Glass Electrode ◽  
Complex Species ◽  
Ligand Coordination

A method entitled "analytical potentiometry" is described which permits the accurate determination of the stability constants and of the distribution of simultaneously existing complex species in multi-component systems. A series of computer programs are introduced to facilitate the otherwise tedious mathematical analysis of data. Besides simple binary systems, this method is capable of dealing with mixed ligand coordination complexes and can be adapted to polynuclear systems. For many metal ions, there are no electrodes permitting the accurate determination of those ions over a wide enough concentration range. This method eliminates the need for metal ion specific electrodes. The concentration of free metal ions is determined indirectly by the use of a weak base and the glass electrode. These features bear great significance in resolving complex systems containing multiple species simultaneously which cannot be solved by the commonly known classical methods.

scholarly journals Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

2021 ◽  
Vol 9 ◽  
Author(s):  
Apeksha Shah ◽  
Swati Ahirrao ◽  
Sharnil Pandya ◽  
Ketan Kotecha ◽  
Suresh Rathod
Keyword(s):  
Cardiac Arrest ◽  
Real Time ◽  
Cox Regression ◽  
Risk Classification ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Time Data ◽  
Extreme Gradient Boosting ◽  
Real Time Data

Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

scholarly journals An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

Minerals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 487
Author(s):  
Maciej Rzychoń ◽  
Alina Żogała ◽  
Leokadia Róg
Keyword(s):  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Important Indicator ◽  
Upper Silesian Coal Basin ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The Impact ◽  
Partial Dependence ◽  
Individual Input

The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88.

scholarly journals Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

2020 ◽  
Vol 10 (19) ◽  
pp. 6681 ◽  
Author(s):  
Zhizhen Liu ◽  
Hong Chen ◽  
Xiaoke Sun ◽  
Hengrui Chen
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Demand Forecasting ◽  
Gradient Boosting ◽  
Intelligent Transport System ◽  
Transportation Services ◽  
Intelligent Transport ◽  
Time Prediction ◽  
Extreme Gradient Boosting ◽  
Demand Forecasting Model

The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

Effect of Metal Ions (Trivalent Chromium Ion and Copper Ion) on Light-Degradation of Silk Fabric

2011 ◽  
Vol 175-176 ◽  
pp. 209-213
Author(s):  
Hao Wang ◽  
Chen Huang
Keyword(s):  
Metal Ions ◽  
Exposure Time ◽  
Metal Ion ◽  
Breaking Strength ◽  
Copper Ions ◽  
Light Exposure ◽  
Sulfate Solution ◽  
Silk Fabric ◽  
Light Conditions ◽  
Copper Sulfate Solution

It was studied that metal ions affected light-degradation of silk fabric in this paper. The contrast of the weight and the tensile properties of silk fabric treated without or with different concentrations of metal ions (copper sulfate solution and chromium sulfate solution) by different time of light conditions were carried out. The results showed that after the treatment of silk with metal ions solution, the weight gain of the silk fabric increased with increasing the concentration of metal ions solution. There were different changes in the breaking strength of silk fabric treated with different metal ions when subjected to light exposure. In the same light conditions, comparing with the fabric without treatment, the breaking strength of the fabric with copper (ii) ions treatment increased while the breaking strength of the fabric with chromium (iii) ions treatment obviously decreased. Besides, fabric samples with copper ions treatment slowed down the decreasing rate of the breaking strength and fabric samples with chromium ions treatment accelerated the decreasing rate with increasing light exposure time. The results indicated copper (ii) ions had an inhibitory effect on light-degradation of silk fabric, and chromium (iii) ions enhanced light-degradation of silk fabric. Meanwhile, their respective function for fabric increased along with the increment of adsorption values of metal ions on silk fabrics. When being treated with the same concentration of metal ion solution, weight loss of the silk fabric improved and the breaking strength of the fabric decreased with increasing light exposure time, and the two had linear relationship.

Determination of Trace Metal Ion Concentrations in Seawater

1996 ◽  
Vol 73 (7) ◽  
pp. 671 ◽  
Author(s):  
Michael N. Quigley ◽  
Fredrick Vernon
Keyword(s):  
Trace Metal ◽  
Metal Ion ◽  
Ion Concentrations ◽  
Trace Metal Ion
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