Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

Identification and evaluation of shale oil micromigration and its petroleum geological significance

Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale.

Application of Next Generation Sequencing for Rapid Identification of Lactic Acid Bacteria

The rapid identification of lactic acid bacteria,which are essential microorganisms in the food industry,is of great significance for industrial applications.The identification of lactic acid bacteria traditionally relies on the isolation and identification of pure colonies.While this method is well-established and widely used,it is not without limitations.The subjective judgment inherent in the isolation and purification process introduces potential for error,and the incomplete nature of the isolation process can result in the loss of valuable information.The advent of next generation sequencing has provided a novel approach to the rapid identification of lactic acid bacteria.This technology offers several advantages,including rapidity,accuracy,high throughput,and low cost.Next generation sequencing represents a significant advancement in the field of DNA sequencing.Its ability to rapidly and accurately identify lactic acid bacteria strains in samples with insufficient information or in the presence of multiple lactic acid bacteria sets it apart as a valuable tool.The application of this technology not only circumvents the potential errors inherent in the traditional method but also provides a robust foundation for the expeditious identification of lactic acid bacteria strains and the authentication of bacterial powder in industrial applications.This paper commences with an overview of traditional and molecular biology methods for the identification of lactic acid bacteria.While each method has its own advantages,they are not without limitations in practical application.Subsequently,the paper provides an introduction of the principle,process,advantages,and disadvantages of next generation sequencing,and also details its application in strain identification and rapid identification of lactic acid bacteria.The objective of this study is to provide a comprehensive and reliable basis for the rapid identification of industrial lactic acid bacteria strains and the authenticity identification of bacterial powder.

Electroacupuncture with different waveforms for primary dysmenorrhea:A randomized controlled trial

Objective:To observe and compare the clinical effects of different electroacupuncture waveforms on primary dysmenorrhea.Methods: This was a prospective,randomized,three-group,parallel-controlled trial.Participants with primary dysmenorrhea were randomly divided into dense-sparse wave,continuous wave,and discontinuous wave groups in a 1:1:1 ratio.Two lateral Ciliao(BL 32)points were used.All three groups started treatment 3–5 days before menstruation,once a day for six sessions per course of treatment,one course of treatment per menstrual cycle,and three menstrual cycles.The primary outcome measure was the proportion with an average visual analog scale(VAS)score reduction of≥50%from baseline for dysmenorrhea in the third menstrual cycle during treatment.The secondary outcome measures included changes in dysmenorrhea VAS scores,Cox Menstrual Symptom Scale scores and the proportion of patients taking analgesic drugs.Results: The proportion of cases where the average VAS score for dysmenorrhea decreased by≥50%from baseline in the third menstrual cycle was not statistically significant(P>.05).Precisely 30 min after acupuncture and regarding immediate analgesia on the most severe day of dysmenorrhea,there was a statistically significant difference in the dense-sparse wave group compared with the other two groups during the third menstrual cycle(P<.05).Additionally,there was a statistically significant difference between the dense-sparse wave and discontinuous wave groups 24 h after acupuncture(P<.05).Conclusions: Waveform electroacupuncture can alleviate primary dysmenorrhea and its related symptoms in patients.The three groups showed similar results in terms of short-and long-term analgesic efficacy and a reduction in the number of patients taking analgesic drugs.Regarding achieving immediate analgesia,the dense-sparse wave group was slightly better than the other two groups.

Simultaneous Waveform Inverse Modelling for Litho-Fluid Prediction in an Old Marginal, “Agbbo”Field, Onshore Niger Delta, Nigeria

Simultaneous waveform inversion was used to predict lithofacies and fluid type across the field. Very often, characterizing reservoirs in terms of lithology and fluid type using conventional methods is replete with uncertainties, especially in marginal fields. An approach is employed in this study that integrated rock physics and waveform inverse modelling for lithology and fluid-type characterization to appropriately identify potential hydrocarbon saturated zones and their corresponding lithology. Seismic and well-log data were analyzed using Hampson Russel software. The method adopted includes lithofacies and fluid content analysis using rock physics parameters and seismic simultaneous inverse modelling. Rock physics analysis identified 2 broad reservoirs namely: HDZ1 and HDZ2 reservoirs. Results from the inverse modelling showed that low values of acoustic impedance from 19,743 to 20,487 (ft/s)(g/cc) reflect hydrocarbon-bearing reservoirs while medium to high values shows brine and shale respectively, with brine zone ranging from 20,487 to 22,531 (ft/s)(g/cc) and shale above 22,531 (ft/s)(g/cc). Two lithofacies were identified from inversion analysis of Vp/Vs and Mu-Rho, namely: sand and shale with VpVs 1.95 values respectively. Mu-Rho > 12.29 (GPa)(g/cc) and <12.29 (GPa) (g/cc) represent sand and shale respectively. From 3D volume, it was observed that a high accumulation of hydrocarbon was observed to be saturated at the north to the eastern part of the field forming a meandering channel. Sands were mainly distributed around the northeastern to the southwestern part of the field, that tends to be away from Well 029. This was also validated by the volume of rigidity modulus (Mu-Rho) showing high values indicating sands fall within the northeastern part of the field.

Link Budget Design with Performance Evaluation of Tunable Impulse-Based Ultra Wideband to Support the Integration of Wireless Sensing and Identifications Infrastructures

Recently, many studies propose the use of ultra-wideband technology for passive and active radio frequency identification systems as well as for wireless sensor networks due to its numerous advantages. By harvesting these advantages of IR-UWB technology at the physical-layer design, this paper proposes that a cross layer architecture platform can be considered as a good integrator for different wireless short-ranges indoor protocols into a universal smart wireless-tagged architecture with new promising applications in cognitive radio for future applications. Adaptive transmission algorithms have been studied to show the trade-off between different specific QoS requirements, transmission rates and distances at the physical layer level and this type of dynamic optimization and reconfiguration leads to the cross-layer design proposal in the paper. Studies from both theoretical simulation and statistical indoor environments experiments are considered as a proof of concept for the proposed architecture.

Identification and Control of Typical Industrial Process

Many industrial processes such as heating furnaces have over damping dynamic characteristics. Based on an innovative impulse response model, a method of identification and control for the over damping plant is introduced in the paper. The number of parameters of the model is much less than conventional impulse response model. The model based on tuning procedure of numerical optimum PID controller parameters is presented. For an actual instance, a large scale airflow circulatory resistance furnace control system with cascades of time delays is developed. In the system, the optimum PID control is used in the inner loop. A nonlinear PI compensation control is applied in the outer loop. The coordinating control among each output is realized by a fuzzy control strategy. A process surveillance organization monitors running situation of system and tunes controller parameters.

Establishment of a molecular tool for blood meal identification in Malaysia

Objective:To establish a polymerase chain reaction(PCR) technique based on cytochrome b {cytb) gene of mitochondria DNA(mtDNA) for blood meal identification.Methods:The PCR technique was established based on published information and validated using blood sample of laboratory animals of which their whole gene sequences are available in CenBank.PCR was next performed to compile gene sequences of different species of wild rodents.The primers used were complementary to the conserved region of the cytb gene of vertebrate's mtDNA.A total of 100 blood samples,both from laboratory animals and wild rodents were collected und analyzed.The obtained unknown sequences were compared with those in the GenBank database using BLAST program to identify the vertebrate animal species.Results:Gene sequences of 11 species of wild animals caught in 9 localities of Peninsular Malaysia were compiled using the established PCR. The animals involved were Rattus(rattus) tanezumi,Rattus tiomanicus,Leopoldamys sabanus, Tupaia glis,Tupaia minor,Niviventor cremoriventor,Rhinosciurus laticaudatus,Calloseiurus caniseps,Sundamys muelleri,Rattus rajah,and Maxomys whitelwadi.The BLAST results confirmed the host with exact or nearly exact matches(>89%identity).Ten new gene sequences have been deposited in CenBank database since September 2010.Conclusions:This study indicates that the PCR direct sequencing system using universal primer sets for vertebrate cytb gene is a promising technique for blood meal identification.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

CRUST AND UPPER STRUCTURE OF QINGHAI-TIBET PLATEAU AND ITS ADJACENT REGIONS FROM SURFACE WAVEFORM INVERSION

In this paper,218 long period Rayleigh wave records from 7 seismic station of CDSN are selected.We applied a partitioned waveform inversion to these data in order to construct a 3\|D model of shear velocity down to 400km depth in the crust and upper mantle of Qinghai\|Tibet plateau and Its Adjacent Regions (22°～44°N,70°～110°E).The first step of the waveform inversion used involved the matching of the waveforms of fundamental and highermost Ravleigh waves with waveforms synthesized from stratified models;in the second stage,the 3\|D model was constructed by solve linear constrains equation. The major structural features inferred from the surface waveform inversions can be summarized as follows:(1) There is a great contrast between surface waveform through Qinghai—Thibet plateau and the others.Main frequency of the former is lower than the latter, which indicate the crust depth of Qinghai—Tibet plateau is deeper than the others. In addition,the amplitude of about 30s period and 50s period is lower than both sides,which implied these exist lower velocity layer at about 25km depth and about 50km depth in Qinghai—Tibet plateau Crust.The former is common,the latter was argued because resolution of most method can not prove it.

Automatic Identification for Laparoscopic Surgical Procedure for Ligation and Online Distinction of Abnormal Manipulation for Thread Knotting

Laparoscopic surgery is a difficult surgical procedure compared with laparotomy. In particular, considerable skills and care are required for thread knotting in laparoscopic surgery. In this paper, a method for automatic identification of a laparoscopic surgical procedure for ligation and online distinction of an abnormality, defined as any unusual manipulation, in the identified surgical procedure is proposed. Ligation is divided into several individual surgical procedures, and on the basis of the threshold criteria, each surgical procedure is identified. Next, the identified surgical procedure, thread knotting, is classified as either normal or abnormal using a self-organizing map. Finally, to reduce surgical error, an abnormality warning system which warns detection of an unusual manipulation in the surgical procedure to the operator is constructed.

致麻鸭产蛋下降的副粘病毒的分离和鉴定

从浙江省某麻鸭养殖基地产蛋锐减的病鸭生殖器官分离到1株致产蛋下降、不致鸭死亡的病毒株(YH99V)。该病毒易感蛋鸭,经SPF鸡胚传至第9代时致病性突然增强,第11代出现对鸡红细胞的血凝特性,通常在42～80h致死SPF鸡胚,EF15EID50为10-4.8。经磷钨酸负染电镜观察,YH99V粒子呈现圆形、杆状形、葫芦状形等多形态,直径70～400nm不等,病毒外表有囊膜,囊膜外层有排列整齐的纤突。病毒粒子和包涵体位于细胞浆内。病毒抵抗力由弱到强依次为0.2%甲醛、24h→56℃、45min→紫外线、1h→乙醚≈氯仿≈37℃、16h→pH9→pH5→1%Try。对人眼结膜易感,鸭眼结膜不易感。接种传代细胞BHK-21、IBRS-2均能引起细胞病变。YH99V株与同样引起鸭产蛋下降的AIV、鸭源EDSV无抗原相关性,而能被禽副粘病毒型阳性血清中和。根据试验结果,初步将YH99V判定为鸭副粘病毒。

Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.

Isolation, Identification and Pathogenicity Analysis of Streptococcus suis Type 2

[Objectives]This study aimed to investigate the pathogenicity,growth characteristics and drug resistance of Streptococcus suis type 2.[Methods]Bacterial isolation and identification,biochemical experiments,determination of growth curve and correlation curve between OD 600 values and viable counts,drug susceptibility tests,pathogenicity analysis,and histopathological observations were carried out.[Results]The Streptococcus strain isolated from infected pigs was identified as Streptococcus suis type 2,which was named TA01 strain.TA01 strain reached the growth peak at 6-8 h post-incubation,and viable counts gradually declined after 8 h of incubation.The correlation equation between OD 600 values and viable counts is y=24.659 x-1.076 1,R^2=0.996 7.TA01 strain was sensitive to penicillin,erythromycin,florfenicol and oxacillin,and resistant to ciprofloxacin,polymyxin B and clindamycin.According to the results of pathogenicity analysis,all the mice in 3.6×10^9 cfu/mouse group died within 48,and these dead mice exhibited acute pyaemia septica.Based on the Reed-Muench formula,it was calculated that LD 50 of TA01 strain was 1.137×10^8 cfu/mouse.Pathological examination showed obvious blue-stained bacteria clusters,accompanied by neutrophil infiltration.[Conclusions]TA01 strain was a virulent strain of Streptococcus suis type 2.Compared with Streptococcus strains which were isolated and reported in China,TA01 strain exhibited strong virulence and rapid proliferation.

温州地区鸭疫里默氏杆菌的分离、鉴定及药敏试验

从浙江省温州地区2004年鸭场送检病死鸭的脑、心包液、脾及肝中分离到WZ1和WZ2两株细菌,将这两株细菌进行菌落形态观察、染色镜检、生化、血清鉴定及动物回归试验,证实该两株菌为鸭疫里默氏杆菌。两株细菌均对新霉素、头孢孟多、氟嗪酸及壮观霉素等抗生素敏感,对氟哌酸、氨卡青霉素、链霉素、庆大霉素、环丙沙星、氯霉素等不敏感。血清学试验表明,WZ1和WZ2两待检菌株血清型一致,但均不属于1型和2型。

Joint Design of ISAC Waveform Under PAPR Constraints

In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)constraints.We propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each subproblem.In addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest.The limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform.Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.

Microplastics Detection and Identification in the Waters along Coastal Areas of Borongan City

This study detects the presence of microplastics in the coastal areas of Borongan City, Eastern Samar, this specifically implies the microplastics present in the waters along coastal areas of Borongan City. Two (2) sampling areas were identified and selected for the presence of these microcontaminants using density separation, filtration and microscopic identification. Results reveal a total of 35 microplastics observed from the water samples collected, with this the microplastics from Baybay boulevard with an average of 0.79 microplastics per Liter, while the average microplastic contamination in Hilangagan beach resort was calculated at 0.43 microplastics per Liter. This sums up to an average of 0.49 microplastics per Liter for both sampling sites in Borongan City.

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.

Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features

Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.