Drilling-based measuring method for the c-φ parameter of rock and its field application

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

The influences of canopy temperature measuring on the derived crop water stress index

Crop water stress index(CWSI)is widely used for efficient irrigation management.Precise canopy temperature(T_(c))measurement is necessary to derive a reliable CWSI.The objective of this research was to investigate the influences of atmospheric conditions,settled height,view angle of infrared thermography,and investigating time of temperature measuring on the performance of the CWSI.Three irrigation treatments were used to create different soil water conditions during the 2020-2021 and 2021-2022 winter wheat-growing seasons.The CWSI was calculated using the CWSI-E(an empirical approach)and CWSI-T(a theoretical approach)based on the T_(c).Weather conditions were recorded continuously throughout the experimental period.The results showed that atmospheric conditions influenced the estimation of the CWSI;when the vapor pressure deficit(VPD)was>2000 Pa,the estimated CWSI was related to soil water conditions.The height of the installed infrared thermograph influenced the T_(c)values,and the differences among the T_(c)values measured at height of 3,5,and 10 m was smaller in the afternoon than in the morning.However,the lens of the thermometer facing south recorded a higher T_(c)than those facing east or north,especially at a low height,indicating that the direction of the thermometer had a significant influence on T_(c).There was a large variation in CWSI derived at different times of the day,and the midday measurements(12:00-15:00)were the most reliable for estimating CWSI.Negative linear relationships were found between the transpiration rate and CWSI-E(R^(2)of 0.3646-0.5725)and CWSI-T(R^(2)of 0.5407-0.7213).The relations between fraction of available soil water(FASW)with CWSI-T was higher than that with CWSI-E,indicating CWSI-T was more accurate for predicting crop water status.In addition,The R^(2)between CWSI-T and FASW at 14:00 was higher than that at other times,indicating that 14:00 was the optimal time for using the CWSI for crop water status monitoring.Relative higher yield of winter wheat was obtained with average seasonal values of CWSI-E and CWSI-T around 0.23 and 0.25-0.26,respectively.The CWSI-E values were more easily influenced by meteorological factors and the timing of the measurements,and using the theoretical approach to derive the CWSI was recommended for precise irrigation water management.

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

Measuring small longitudinal phase shifts via weak measurement amplification

Weak measurement amplification,which is considered as a very promising scheme in precision measurement,has been applied to various small physical quantities estimations.Since many physical quantities can be converted into phase signals,it is interesting and important to consider measuring small longitudinal phase shifts by using weak measurement.Here,we propose and experimentally demonstrate a novel weak measurement amplification-based small longitudinal phase estimation,which is suitable for polarization interferometry.We realize one order of magnitude amplification measurement of a small phase signal directly introduced by a liquid crystal variable retarder and show that it is robust to the imperfection of interference.Besides,we analyze the effect of magnification error which is never considered in the previous works,and find the constraint on the magnification.Our results may find important applications in high-precision measurements,e.g.,gravitational wave detection.

Digital measuring the ocular morphological parameters of guinea pig eye in vivo with Python

AIM:To quantitatively measure ocular morphological parameters of guinea pig with Python technology.METHODS:Thirty-six eyeballs of eighteen 3-weekold guinea pigs were measured with keratometer and photographed to obtain the horizontal,coronal,and sagittal planes respectively.The corresponding photo pixels-actual length ratio was acquired by a proportional scale.The edge coordinates were identified artificially by ginput function.Circle and conic curve fitting were applied to fit the contour of the eyeball in the sagittal,coronal and horizontal view.The curvature,curvature radius,eccentricity,tilt angle,corneal diameter,and binocular separation angle were calculated according to the geometric principles.Next,the eyeballs were removed,canny edge detection was applied to identify the contour of eyeball in vitro.The results were compared between in vivo and in vitro.RESULTS:Regarding the corneal curvature and curvature radius on the horizontal and sagittal planes,no significant differences were observed among results in vivo,in vitro,and the keratometer.The horizontal and vertical binocular separation angles were 130.6°±6.39°and 129.8°±9.58°respectively.For the corneal curvature radius and eccentricity in vivo,significant differences were observed between horizontal and vertical planes.CONCLUSION:The Graphical interface window of Python makes up the deficiency of edge detection,which requires too much definition in Matlab.There are significant differences between guinea pig and human beings,such as exotropic eye position,oblique oval eyeball,and obvious discrepancy of binoculus.This study helps evaluate objectively the ocular morphological parameters of small experimental animals in emmetropization research.

Biopsy forceps are useful for measuring esophageal varices in vitro

BACKGROUND To avoid acute variceal bleeding in cirrhosis,current guidelines recommend screening for high-risk esophageal varices(EVs)by determining variceal size and identifying red wale markings.However,visual measurements of EV during routine endoscopy are often inaccurate.AIM To determine whether biopsy forceps(BF)could be used as a reference to improve the accuracy of binary classification of variceal size.METHODS An in vitro self-made EV model with sizes ranging from 2 to 12 mm in diameter was constructed.An online image-based survey comprising 11 endoscopic images of simulated EV without BF and 11 endoscopic images of EV with BF was assembled and sent to 84 endoscopists.The endoscopists were blinded to the actual EV size and evaluated the 22 images in random order.RESULTS The respondents included 48 academic and four private endoscopists.The accuracy of EV size estimation was low in both the visual(13.81%)and BF-based(20.28%)groups.The use of open forceps improved the ability of the endoscopists to correctly classify the varices by size(small≤5 mm,large>5 mm)from 71.85%to 82.17%(P<0.001).CONCLUSION BF may improve the accuracy of EV size assessment,and its use in clinical practice should be investigated.

Metrically measuring liver biopsy:A chronic hepatitis B and C computer-aided morphologic description

AIM： To describe a quantitative analysis method for liver biopsy sections with a machine that we have named ＂Dioguardi Histological Metriser＂ which automatically measures the residual hepatocyte mass （including hepatocytes vacuolization）, inflammation, fibrosis and the loss of liver tissue tectonics.METHODS： We analysed digitised images of liver biopsy sections taken from 398 patients, The analysis with Dioguardi Histological Metriser was validated by comparison with semi-quantitative scoring system.RESULTS： The method provides： （1） the metrical extension in two-dimensions （the plane） of the residual hepatocellular set, including the area of vacuoles pertinent to abnormal lipid accumulation; （2） the geo- metric measure of the inflammation basin, which distinguishes intra-basin space and extra-basin dispersed parenchymal leukoo/tes; （3） the magnitude of collagen islets, （which were considered truncated fractals and classified into three degrees of magnitude）; and （4） the tectonic index that quantifies alterations （disorders） in the organization of liver tissue. Dioguardi Histological Metriser machine allows to work at a speed of 0.1 mm^2/s, scanning a whole section in 6-8 min.CONCLUSION： The results are the first standardized metrical evaluation of the geometric properties of the parenchyma, inflammation, fibrosis, and alterations in liver tissue tectonics of the biopsy sections. The present study confirms that biopsies are still valuable, not only for diagnosing chronic hepatitis, but also for quantifying changes in the organization and order of liver tissue structure.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

PerfMon: Measuring Application-Level Performance in a Large-Scale Campus Wireless Network

WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

Positional Error Model of Line Segments with Modeling and Measuring Errors Using Brownian Bridge

Spatial linear features are often represented as a series of line segments joined by measured endpoints in surveying and geographic information science.There are not only the measuring errors of the endpoints but also the modeling errors between the line segments and the actual geographical features.This paper presents a Brownian bridge error model for line segments combining both the modeling and measuring errors.First,the Brownian bridge is used to establish the position distribution of the actual geographic feature represented by the line segment.Second,an error propagation model with the constraints of the measuring error distribution of the endpoints is proposed.Third,a comprehensive error band of the line segment is constructed,wherein both the modeling and measuring errors are contained.The proposed error model can be used to evaluate line segments’overall accuracy and trustability influenced by modeling and measuring errors,and provides a comprehensive quality indicator for the geospatial data.

Software Coupling and Cohesion Model for Measuring the Quality of Software Components

Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements.The quality attribute is a qualitative property;however,the quantitative feature is needed for software measurement,which is not considered during the development of most software systems.Many research studies have investigated different approaches for measuring software quality,but with no practical approaches to quantify and measure quality attributes.This paper proposes a software quality measurement model,based on a software interconnection model,to measure the quality of software components and the overall quality of the software system.Unlike most of the existing approaches,the proposed approach can be applied at the early stages of software development,to different architectural design models,and at different levels of system decomposition.This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes,i.e.,coupling and cohesion,for software quality measurement.In this model,the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s).The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems.The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit,which degrades their quality and the overall quality of the software system.The introduced model can help in understanding the quality of software design.In addition,it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems,which can be eliminated.

HealthMeasures患者报告结局报告清单的解读

美国匹兹堡大学和HealthMeasures指导委员会及PROMIS健康组织标准委员会共同发表了HealthMeasures患者报告结局报告清单,旨在促进不同患者报告结局测量工具的标准化报告,帮助研究人员提高工具报告质量及结果解释的准确性,进一步改善跨研究间的比较分析。本文对HealthMeasures患者报告结局报告清单的主要内容进行介绍和解读,以期为中国患者报告结局研究者提供参考和借鉴。

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.

Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

Influence of sampling on three-dimensional surface shape measurement

In order to accurately measure an object’s three-dimensional surface shape,the influence of sampling on it was studied.First,on the basis of deriving spectra expressions through the Fourier transform,the generation of CCD pixels was analyzed,and its expression was given.Then,based on the discrete expression of deformation fringes obtained after sampling,its Fourier spectrum expression was derived,resulting in an infinitely repeated"spectra island"in the frequency domain.Finally,on the basis of using a low-pass filter to remove high-order harmonic components and retaining only one fundamental frequency component,the inverse Fourier transform was used to reconstruct the signal strength.A method of reducing the sampling interval,i.e.,reducing the number of sampling points per fringe,was proposed to increase the ratio between the sampling frequency and the fundamental frequency of the grating.This was done to reconstruct the object’s surface shape more accurately under the condition of m>4.The basic principle was verified through simulation and experiment.In the simulation,the sampling intervals were 8 pixels,4 pixels,2 pixels,and 1 pixel,the maximum absolute error values obtained in the last three situations were 88.80%,38.38%,and 31.50%in the first situation,respectively,and the corresponding average absolute error values are 71.84%,43.27%,and 32.26%.It is demonstrated that the smaller the sampling interval,the better the recovery effect.Taking the same four sampling intervals in the experiment as in the simulation can also lead to the same conclusions.The simulated and experimental results show that reducing the sampling interval can improve the accuracy of object surface shape measurement and achieve better reconstruction results.

Applying the Shearlet-Based Complexity Measure for Analyzing Mass Transfer in Continuous-Flow Microchannels

Continuous-flow microchannels are widely employed for synthesizing various materials,including nanoparticles,polymers,and metal-organic frameworks(MOFs),to name a few.Microsystem technology allows precise control over reaction parameters,resulting in purer,more uniform,and structurally stable products due to more effective mass transfer manipulation.However,continuous-flow synthesis processes may be accompanied by the emergence of spatial convective structures initiating convective flows.On the one hand,convection can accelerate reactions by intensifying mass transfer.On the other hand,it may lead to non-uniformity in the final product or defects,especially in MOF microcrystal synthesis.The ability to distinguish regions of convective and diffusive mass transfer may be the key to performing higher-quality reactions and obtaining purer products.In this study,we investigate,for the first time,the possibility of using the information complexity measure as a criterion for assessing the intensity of mass transfer in microchannels,considering both spatial and temporal non-uniformities of liquid’s distributions resulting from convection formation.We calculate the complexity using shearlet transform based on a local approach.In contrast to existing methods for calculating complexity,the shearlet transform based approach provides a more detailed representation of local heterogeneities.Our analysis involves experimental images illustrating the mixing process of two non-reactive liquids in a Y-type continuous-flow microchannel under conditions of double-diffusive convection formation.The obtained complexity fields characterize the mixing process and structure formation,revealing variations in mass transfer intensity along the microchannel.We compare the results with cases of liquid mixing via a pure diffusive mechanism.Upon analysis,it was revealed that the complexity measure exhibits sensitivity to variations in the type of mass transfer,establishing its feasibility as an indirect criterion for assessing mass transfer intensity.The method presented can extend beyond flow analysis,finding application in the controlling of microstructures of various materials(porosity,for instance)or surface defects in metals,optical systems and other materials that hold significant relevance in materials science and engineering.

Measuring^(222)Rn in aquatic environment via Pulsed Ionization Chamber Radon Detector

Radon(Rn)is a naturally occurring radioactive inert gas in nature,and^(222)Rn has been routinely used as a powerful tracer in various aquatic environmental research on timescales of hours to days,such as submarine groundwater discharge.Here we developed a new approach to measure^(222)Rn in discrete water samples with a wide range of^(222)Rn concentrations using a Pulsed Ionization Chamber(PIC)Radon Detector.The sensitivity of the new PIC system is evaluated at 6.06 counts per minute for 1 Bq/L when a 500 mL water sample volume is used.A robust logarithmic correlation between sample volumes,ranging from 250 mL to 5000 mL,and system sensitivity obtained in this study strongly suggests that this approach is suitable for measuring radon concentration levels in various natural waters.Compared to the currently available methods for measuring radon in grab samples,the PIC system is cheaper,easier to operate and does not require extra accessories(e.g.,drying tubes etc.)to maintain stable measurements throughout the counting procedure.