Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.

Application of Time Serial Model in Water Quality Predicting

Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.

Optimization of the End Effect of Hilbert-Huang transform(HHT)

In fault diagnosis of rotating machinery, Hilbert-Huang transform(HHT) is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF(Intrinsic Mode Function). To counter such problems in HHT, a new method is put forward to process signal by combining the generalized regression neural network(GRNN) with the boundary local characteristic-scale continuation(BLCC).Firstly, the improved EMD(Empirical Mode Decomposition) method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the generated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain,frequency domain and related parameters of HilbertHuang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method can effectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accurately.

Tailoring the microstructure of Mg-Al-Sn-RE alloy via friction stir processing and the impact on its electrochemical discharge behaviour as the anode for Mg-air battery

Constructing the magnesium alloy with fine grains,low density of dislocations,and weak crystal orientation is of crucial importance to enhance its comprehensive performance as the anode for Mg-air battery.However,this unique microstructure can hardly be achieved with conventional plastic deformation such as rolling or extrusion.Herein,we tailor the microstructure of Mg-Al-Sn-RE alloy by using the friction stir processing,which obviously refines the grains without increasing dislocation density or strengthening crystal orientation.The Mg-air battery with the processed Mg-Al-Sn-RE alloy as the anode exhibits higher discharge voltages and capacities than that employing the untreated anode.Furthermore,the impact of friction stir processing on the electrochemical discharge behaviour of Mg-Al-Sn-RE anode and the corresponding mechanism are also analysed according to microstructure characterization and electrochemical response.

Android Malware Detection Using Local Binary Pattern and Principal Component Analysis

Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

Expanding the potential of biosensors:a review on organic field effect transistor(OFET)and organic electrochemical transistor(OECT)biosensors

Organic electronics have gained significant attention in the field of biosensors owing to their immense potential for economical,lightweight,and adaptable sensing devices.This review explores the potential of organic electronics-based biosensors as a revolutionary technology for biosensing applications.The focus is on two types of organic biosensors:organic field effect transistor(OFET)and organic electrochemical transistor(OECT)biosensors.OFET biosensors have found extensive application in glucose,DNA,enzyme,ion,and gas sensing applications,but suffer from limitations related to low sensitivity and selectivity.On the other hand,OECT biosensors have shown superior performance in sensitivity,selectivity,and signal-to-noise ratio,owing to their unique mechanism of operation,which involves the modulation of electrolyte concentration to regulate the conductivity of the active layer.Recent advancements in OECT biosensors have demonstrated their potential for biomedical and environmental sensing,including the detection of neurotransmitters,bacteria,and heavy metals.Overall,the future directions of OFET and OECT biosensors involve overcoming these challenges and developing advanced devices with improved sensitivity,selectivity,reproducibility,and stability.The potential applications span diverse fields including human health,food analysis,and environment monitoring.Continued research and development in organic biosensors hold great promise for significant advancements in sensing technology,opening up new possibilities for biomedical and environmental applications.