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.

Dynamic analysis of a novel multilink-spring mechanism for vibration isolation and energy harvesting

Due to technical limitations,existing vibration isolation and energy harvesting(VIEH)devices have poor performance at low frequency.This paper proposes a new multilink-spring mechanism(MLSM)that can be used to solve this problem.The VIEH performance of the MLSM under harmonic excitation and Gaussian white noise was analyzed.It was found that the MLSM has good vibration isolation performance for low-frequency isolation and the frequency band can be widened by adjusting parameters to achieve a higher energy harvesting power.By comparison with two special cases,the results show that the MLSM is basically the same as the other two oscillators in terms of vibration isolation but has better energy harvesting performance under multistable characteristics.The MLSM is expected to reduce the impact of vibration on high-precision sensitive equipment in some special sites such as subways and mines,and at the same time supply power to structural health monitoring devices.

Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology

The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

A Transient-Pressure-Based Numerical Approach for Interlayer Identification in Sand Reservoirs

Almost all sandstone reservoirs contain interlayers. The identification and characterization of these interlayers iscritical for minimizing the uncertainty associated with oilfield development and improving oil and gas recovery.Identifying interlayers outside wells using identification methods based on logging data and machine learning isdifficult and seismic-based identification techniques are expensive. Herein, a numerical model based on seepageand well-testing theories is introduced to identify interlayers using transient pressure data. The proposed modelrelies on the open-source MATLAB Reservoir Simulation Toolbox. The effects of the interlayer thickness, position,and width on the pressure response are thoroughly investigated. A procedure for inverting interlayer parametersin the reservoir using the bottom-hole pressure is also proposed. This method uses only transient pressuredata during well testing and can effectively identify the interlayer distribution near the wellbore at an extremelylow cost. The reliability of the model is verified using effective oilfield examples.

Understanding the Global Identification with China’s Stories: A Cross-Cultural Perspective

Cross-cultural storytelling is a primary way for humankind to seek mutual recognition of value orientations between cultures,which facilitates the ability to jointly address the problems of human existence in the context of globalization.In this study,we conducted an interview survey of 6,130 respondents who were college students or graduates from 107 countries.The results show that there were a number of cross-cultural values embodied in China’s stories seen by the respondents as part of a common vision for the future of humankind and widely identified guidance on collaborative responses to global challenges.These cross-cultural values are common prosperity,ecological harmony,individual-collective integration,the urgency of global peace,as well as respect for multicultural and indigenous development paths.

Pharmacognosy research and identification of Euphorbia prostrata Ait.

Background:Euphorbia prostrata Ait.is an annual herb widely distributed in the southern region of China with great medical values on Anti-inflammation,insect repellent,treatment of diarrhea.Despite its extensive uses as a traditional Chinese medicine,no systematic research on the identification of E.prostrata has been reported.Methods:The study aimed to establish an accurate identification system for E.prostrata through traditional pharmacognostical methods,including botanical origin,morphological characters,medicinal material characters,microscopic characters,physicochemical parameters determination,phytochemical screening,and DNA barcoding analysis.Results:Physicochemical results show that this plant likely contains flavonoids,anthraquinones,and other substances.The ITS loci of the nuclear genome and psbA-trnH loci of the chloroplast genome were selected and evaluated,which were the most variable loci.Conclusion:The findings of this study are expected to contribute to the development of species identification,as well as provide references for authenticity identification,genetic relationship analysis,and further utilization of E.prostrata.

Pharmacognostic Identification of Hedyotis auricularia and Mitracarpus villosus

[Objectives]To conduct the pharmacognostic identification of Hedyotis auricularia and Mitracarpus villosus in Guangxi and provide a scientific basis for their identification.[Methods]The characteristics of original plants were studied by origin identification method;the properties and characteristics were studied by character identification method;and the microscopic features of the roots,stems,leaves and medicinal powder of H.auricularia and M.villosus in Guangxi were studied by paraffin method and powder slicing method.[Results](i)Origin identification.H.auricularia:Leaves leathery,apex acuminate,base cuneate;petiole shorter;cyme axillary;corolla hairy at throat;fruit indehiscent at maturity;testa black after drying.M.villosus:Leaf apex short pointed,base attenuate,blade sessile;flowers small,clustered in axillary;fruits dehiscent by lid at or below middle at maturity,seeds dark brown.(ii)Character identification.Fracture surface of H.auricularia uneven,white in outer layer and sepia in inner layer.Fracture surface of M.villosus hollow,uneven and white.(iii)Microscopic identification.H.auricularia:Root phloem thick,cambium visible,duct cells quasi-polygonal,large;rays obvious.Stem transection quasi-circular square,often with non-glandular hairs on epidermis;calcium oxalate raphides present in leaf parenchymal cells.Power grayish brown,starch granules single-grained;calcium oxalate raphides frequent,calcium oxalate clustered crystals occasional;catheter spiral,rarely annular,stomata infinitive.M.villosus:Root parenchyma cells with scattered calcium oxalate raphides,calcium oxalate clustered crystals and brownish red substances visible.Stem transection quasi-square,edge angle with 4 short narrow wings.Powder brown,simple starch granules numerous,compound starch granules also present;calcium oxalate raphides numerous,calcium oxalate clustered crystals and calcium oxalate square cubic crystals also present;catheter spiral,stomata paracytic.[Conclusions]The above transaction microscopic characteristics of the roots,stems and leaves and powder characteristics can be used as the identification features of H.auricularia and M.villosus.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

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.

TLC Identification of Yao Medicine Pileostegia tomentellal and Extraction Technology and Content Determination of Umbelliferone

[Objectives]To establish a TLC and content determination method of Pileostegia tomentellal,with umbelliferone as the indicator component.[Methods]TLC identification was performed by silica gel G thin layer plate with n-hexane-ethyl acetate(4:3)as the developing agent,and the plate was examined by UV lamp(365 nm).The umbelliferone content was determined by HPLC:Inertsil ODS-3 C 18 column(4.60 mm×250 mm,5μm);mobile phase acetonitrile-0.2%phosphoric acid gradient elution;detection wavelength 320 nm,flow rate 1.0 mL/min,column temperature 30℃,injection volume 10μL.[Results]The chromatogram of P.tomentellal showed the same color spot in the same position as that of reference medicinal material,and the spot was clear with good specificity.Umbelliferone showed a good linear relationship when the injection volume was 2.63-131.27μg/mL(R^(2)=0.9997).The average recovery of umbelliferone in the low,middle and high adding groups of P.tomentellal was 99.57%and the RSD was 2.15%.[Conclusions]The method can effectively identify Yao medicine P.tomentellal and accurately determine the content of umbelliferone in medicinal materials,which will provide a scientific basis for the development and utilization of medicinal resources of Yao medicine P.tomentellal.

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the dilemma of traditional particle identification methods.This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies.Multiple intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 simulation.Tree-based machine learning algorithms and deep learning algorithms e.g.TabNet show excellent performance and generalization ability.Adding additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is nonuniform.Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

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.

Thin Layer Identification of Jiedu Shengxue Granules and Determination of Notoginsenoside R1 Content

[Objectives]To establish the quality standard of hospital preparation Jiedu Shengxue granules.[Methods]Scleromitrion diffusum and Prunella vulgaris in Jiedu Shengxue granules were qualitatively identified by thin layer chromatography(TLC).A high performance liquid chromatography(HPLC)was established to determine the content of notoginsenoside R1 in the granule.[Results]The traditional Chinese medicinal materials in Jiedu Shengxue granules could be identified by TLC,and the characteristic spots were stable and clear.Notoginsenoside R1 had a good linear relationship in the range of 10.45-104.5μg/mL,with an average recovery of 98.52%and RSD=2.36%.[Conclusions]TLC and HPLC,as the quality control methods of Jiedu Shengxue granules,have high accuracy and good repeatability,which lays a foundation for the quality control of this mixture.

Non-enzymatic methods for isolation of stromal vascular fraction and adipose-derived stem cells:A systematic review

BACKGROUND Adipose-derived stem cells(ADSCs)and the stromal vascular fraction(SVF)have garnered substantial interest in regenerative medicine due to their potential to treat a wide range of conditions.Traditional enzymatic methods for isolating these cells face challenges such as high costs,lengthy processing time,and regulatory complexities.AIM This systematic review aimed to assess the efficacy and practicality of nonenzymatic,mechanical methods for isolating SVF and ADSCs,comparing these to conventional enzymatic approaches.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines,a comprehensive literature search was conducted across multiple databases.Studies were selected based on inclusion criteria focused on non-enzymatic isolation methods for SVF and ADSCs from adipose tissue.The risk of bias was assessed,and a qualitative synthesis of findings was performed due to the methodological heterogeneity of the included studies.RESULTS Nineteen studies met the inclusion criteria,highlighting various mechanical techniques such as centrifugation,vortexing,and ultrasonic cavitation.The review identified significant variability in cell yield and viability,and the integrity of isolated cells across different non-enzymatic methods compared to enzymatic procedures.Despite some advantages of mechanical methods,including reduced processing time and avoidance of enzymatic reagents,the evidence suggests a need for optimization to match the cell quality and therapeutic efficacy achievable with enzymatic isolation.CONCLUSION Non-enzymatic,mechanical methods offer a promising alternative to enzymatic isolation of SVF and ADSCs,potentially simplifying the isolation process and reducing regulatory hurdles.However,further research is necessary to standardize these techniques and ensure consistent,high-quality cell yields for clinical applications.The development of efficient,safe,and reproducible non-enzymatic isolation methods could significantly advance the field of regenerative medicine.

Identification of Microorganisms in Poultry Farms in N’djamena and the Border Areas of Hadjer-Lamis and Chari-Baguirmi Chad

Introduction: On the outskirts of Ndjamena, semi-industrial poultry farming and traditional poultry farming are practised informally on almost all poultry farms in Chad. This type of poultry farming is faced with real health problems attributable to a lack of monitoring of the vaccination schedule, inadequate compliance with biosecurity measures and poor application of the Ichikawa rule based on the 5 M’s. Objective: The aim of this article is to identify the microorganisms responsible for contamination of poultry farms in the study area. Method: The study was carried out from 28/04/2022 to 31/01/2023 on the basis of 300 samples taken from feed, drinking water, droppings and scrapings from poultry housing surfaces in the 30 farms that served as a framework for our research. Sampling was of the simple random type, and farms were selected on the basis of the farmers’ consent. The data were recorded on pre-established survey forms. Our study was cross-sectional, descriptive and prospective. Bacteria were isolated using the reference method NF EN ISO 6579 for Salmonella spp. and cultured on the specific medium eosin methylene blue (EMB) for Escherichia coli, Pseudomonas and Citrobacter freundii. Results: The following results emerged from this study: Escherichia coli (5.33%), Pseudomonas (1.33%), Citrobacter freundii (12%) and Salmonella paratyphi (21.68%). Conclusion: Of the 300 samples analysed, 121 (40.33%) were contaminated with pathogens. This high level of contamination is a health problem. The study shows that biosecurity is less satisfactory on the farms visited. Nevertheless, farms with a very satisfactory level of biosafety ensure food safety and variety for the population.

The Moderating Effect of Organizational Identification on Occupational Stress and Insomnia Among Nurses

Objective:To explore the relationship between organizational identification,occupational stress,and insomnia among nurses and to analyze the moderating role of organizational identification between occupational stress and insomnia.Methods:Through stratified sampling,720 nurses working in a tertiary A-level hospital in Shandong Province from March to May 2021 were surveyed using an effort-reward imbalance questionnaire,an organizational identification questionnaire,and the Athens Insomnia scale.Results:Organizational identification was negatively correlated with occupational stress and insomnia(r_(1)=-0.121,P_(1)<0.01;r_(2)=-0.144,P_(2)<0.01).Occupational stress was positively correlated with insomnia(r=0.379,P<0.01).Organizational identity significantly moderated nurses’occupational stress and insomnia(β=-0.192,t=-2.558,P<0.05).Conclusion:Occupational stress and organizational identification can predict insomnia.Organizational identification could negatively mediate the relationship between occupational stress and insomnia.Improving organizational identification may help alleviate insomnia symptoms by relieving occupational stress.

Seismic Reduction and Isolation Design Strategies for Bridges in High-Intensity Earthquake Areas

High-intensity earthquakes can cause severe damage to bridges,buildings,and ground surfaces,as well as disrupt human activities.Such earthquakes can create long-distance,high-intensity surface movements that negatively impact bridge structures.This article delves into the seismic reduction and isolation design strategies for bridges in high-intensity earthquake areas.It analyzes various seismic reduction and isolation technologies and provides case studies to help relevant units understand the design strategies of these technologies.The results of this article can be used as a guideline to effectively enhance the seismic performance of bridges in high-intensity earthquake areas.

Isolation and Identification of a Specific cDNA Mapping to the Bam HI-I2 and -LFragments within the Inverted Repeats ofUnique Long Re-gion (IRL) in the Genom e ofMarek′s Disease Herpesvirus (MDV) Oncogenic Strain Beijing-1

Objective\ To understand the transcription of BamHI L DNA fragment from genome of strong virulent GA strain of Marek′s disease herpesvirus (MDV) in lymphoblastoid tumor tissue induced by oncogenic strain Beijing 1 (a specific local strain in China) of MDV. Methods\ Two oligonucleotide primers were synthesized according to the reported sequence of \%meq\% gene an ideal oncogenic candidate and our previously determined sequence of BamHI L fragment of Marek′s disease herpesvirus (MDV), respectively. Reverse transcriptase PCR(RT PCR) assay was performed by using these primers and the mRNA as a template which was isolated from visceral lymphoblastoid tumors obtained from chickens artificially infected with strain Beijing 1 of oncogenic MDV. Southern blot molecular hybridization was further carried out to detect the product of RT PCR with digoxigenin labeled nucleotide probe from BamHI I2 and L fragment in the gene library of MDV strain GA, respectively. Results\ Two probes could simultaneously hybridize this cDNA amplified by RT PCR with a length of about 730 bp. Conclusion\ It is suggested that \%meq\% transcription could extend from the right hand end of BamHI I2 to the adjacent BamHI L, and the BamHI L region was likely to be transcribed in MDV induced lymphoblastoid tumors.