Detection of internal crack growth in polyethylene pipe using guided wave ultrasonic testing

Despite the success of guided wave ultrasonic inspection for internal defect detection in steel pipes,its application on polyethylene(PE)pipe remains relatively unexplored.The growth of internal cracks in PE pipe severely affects its pressure-holding capacity,hence the early detection of internal cracks is crucial for effective pipeline maintenance strategies.This study extends the scope of guided wave-based ultrasonic testing to detect the growth of internal cracks in a natural gas distribution PE pipe.Laboratory experiments and a finite element model were planned to study the wave-crack interaction at different stages of axially oriented internal crack growth with a piezoceramic transducer-based setup arranged in a pitch-catch configuration.Mode dispersion analysis supplemented with preliminary experiments was performed to isolate the optimal inspection frequency,leading to the selection of the T(0,1)mode at 50-kHz for the investigation.A transmission index based on the energy of the T(0,1)mode was developed to trace the extent of simulated crack growth.The findings revealed an inverse linear correlation between the transmission index and the crack depth for crack growth beyond 20%crack depth.

Improving Ultrasonic Testing by Using Machine Learning Framework Based on Model Interpretation Strategy

Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.Moreover,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in UT.In this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of UT.Firstly,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space.Subsequently,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating Shapley additive explanation(SHAP),flter method,embedded method and wrapper method.The most efective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively.The proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different depths.An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments.The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent SDHs.The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT signals.On this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets.The imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,respectively.Finally,the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.

Unbound^(28)O,the heaviest oxygen isotope observed:a cutting-edge probe for testing nuclear models

The beyond-dripline oxygen isotopes^(27,28)O were recently observed at RIKEN,and were found to be unbound decaying into^(24)O by emitting neutrons.The unbound feature of the heaviest oxygen isotope,^(28)O,provides an excellent test for stateof-the-art nuclear models.The atomic nucleus is a self-organized quantum manybody system comprising specific numbers of protons Z and neutrons N.

A comparative study for determining rock joint normal stiffness with destructive uniaxial compression and nondestructive ultrasonic wave testing

Rock joints are one of the vital discontinuities in a natural rock mass.How to accurately and conveniently determine joint normal stiffness is therefore significant in rock mechanics.Here,first,seven existing methods for determining joint normal stiffness were introduced and reviewed,among which MethodⅠ(the indirect measurement method),MethodⅡ(the direct determination method),MethodⅢ(the across-joint strain gauge measurement method)and MethodⅣ(the deformation measuring ring method)are via destructive uniaxial compression testing,while MethodⅤ(the best fitting method),MethodⅥ(the rapid evaluation method)and MethodⅦ(the effective modulus method)are through wave propagation principles and nondestructive ultrasonic testing.Subsequently,laboratory tests of intact and jointed sandstone specimens were conducted following the testing requirements and pro-cedures of those seven methods.A comparison among those methods was then performed.The results show that Method I,i.e.the benchmark method,is reliable and stable.MethodⅡhas a conceptual drawback,and its accuracy is acceptable at only very low stress levels.Relative errors in the results from MethodⅢare very large.With MethodⅣ,the testing results are sufficiently accurate despite the strict testing environment and complicated testing procedures.The results from MethodⅤare greatly unstable and significantly dependent on the natural frequency of the transducers.The joint normal stiffness determined with MethodⅥis stable and accurate,although data processing is complex.MethodⅦcould be adopted to determine the joint normal stiffness corresponding to the rock elastic deformation phase only.Consequently,it is suggested that MethodsⅠ,ⅣandⅥshould be adopted for the mea-surement of joint normal stiffness.The findings could be helpful in selecting an appropriate method to determine joint normal stiffness and,hence,to better solve discontinuous rock mass problems.

Electrochemical biosensors for point-of-care testing

Point-of-care testing(POCT)is the practice of diagnosing and monitoring diseases where the patient is located,as opposed to traditional treatment conducted solely in a medical laboratory or other clinical setting.POCT has been less common in the recent past due to a lack of portable medical devices capable of facilitating effective medical testing.However,recent growth has occurred in this field due to advances in diagnostic technologies,device miniaturization,and progress in wearable electronics.Among these developments,electrochemical sensors have attracted interest in the POCT field due to their high sensitivity,compact size,and affordability.They are used in various applications,from disease diagnosis to health status monitoring.In this paper we explore recent advancements in electrochemical sensors,the methods of fabricating them,and the various types of sensing mechanisms that can be used.Furthermore,we delve into methods for immobilizing specific biorecognition elements,including enzymes,antibodies,and aptamers,onto electrode surfaces and how these sensors are used in real-world POCT settings.

Determining rock crack stress thresholds using ultrasonic through-transmission measurements

The crack initiation stress threshold is widely used in excavation industries as rock spalling strength when designing deep underground structures to avoid unwanted brittle failures.While various strain-based methods have been developed for the estimation of this critical design parameter,such methods are destructive and often requires subjective interpretations of the stress–strain curves,particularly in rocks with pre-existing microcracks or high porosity.This study explore the applicability of non-destructive ultrasonic through-transmission methods for determining rock damage levels by assessing the changes in transmitted signal characteristics during loading.The change in velocity,amplitude,dominant frequency,and root-mean-square voltage are investigated with four different rock types including marble,sandstone,granite,and basalt under various stress levels.Results suggest the rate of signal variations can be reliably used to estimate crack closure and crack initiation stress levels across the tested rocks before failure.Comparison of the results between the conventional techniques and the new proposed methods based on ultrasonic monitoring are further discussed.

Failure characterization of fully grouted rock bolts under triaxial testing

Confining stresses serve as a pivotal determinant in shaping the behavior of grouted rock bolts.Nonetheless,prior investigations have oversimplified the three-dimensional stress state,primarily assuming hydrostatic stress conditions.Under these conditions,it is assumed that the intermediate principal stress(σ_(2))equals the minimum principal stress(σ_(3)).This assumption overlooks the potential variations in magnitudes of in situ stress conditions along all three directions near an underground opening where a rock bolt is installed.In this study,a series of push tests was meticulously conducted under triaxial conditions.These tests involved applying non-uniform confining stresses(σ_(2)≠σ_(3))to cubic specimens,aiming to unveil the previously overlooked influence of intermediate principal stresses on the strength properties of rock bolts.The results show that as the confining stresses increase from zero to higher levels,the pre-failure behavior changes from linear to nonlinear forms,resulting in an increase in initial stiffness from 2.08 kN/mm to 32.51 kN/mm.The load-displacement curves further illuminate distinct post-failure behavior at elevated levels of confining stresses,characterized by enhanced stiffness.Notably,the peak load capacity ranged from 27.9 kN to 46.5 kN as confining stresses advanced from σ_(2)=σ_(3)=0 to σ_(2)=20 MPa and σ_(3)=10 MPa.Additionally,the outcomes highlight an influence of confining stress on the lateral deformation of samples.Lower levels of confinement prompt overall dilation in lateral deformation,while higher confinements maintain a state of shrinkage.Furthermore,diverse failure modes have been identified,intricately tied to the arrangement of confining stresses.Lower confinements tend to induce a splitting mode of failure,whereas higher loads bring about a shift towards a pure interfacial shear-off and shear-crushed failure mechanism.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

Quantitative analysis of laser-generated ultrasonic wave characteristics and their correlation with grain size in polycrystalline materials

Quantitative relationship between nanosecond pulsed laser parameters and the characteristics of laser-generated ultrasonic waves in polycrystalline materials was evaluated.The high energy of the pulsed laser with a large irradiation spot simultaneously generated ultrasonic longitudinal and shear waves at the epicenter under the slight ablation regime.An optimized denoising technique based on wavelet thresholding and variational mode decomposition was applied to reduce noise in shear waves with a low signal-to-noise ratio.An approach for characterizing grain size was proposed using spectral central frequency ratio(SCFR)based on time-frequency analysis.The results demonstrate that the generation regime of ultrasonic waves is not solely determined by the laser power density;even at high power densities,a high energy with a large spot can generate an ultrasonic waveform dominated by the thermoelastic effect.This is ascribed to the intensification of the thermoelastic effect with the proportional increase in laser irradiation spot area for a given laser power density.Furthermore,both longitudinal and shear wave SCFRs are linearly related to grain size in polycrystalline materials;however,the shear wave SCFR is more sensitive to finer-grained materials.This study holds great significance for evaluating metal material properties using laser ultrasound.

Source localization based on field signatures:Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Microstructure and forming mechanism of metals subjected to ultrasonic vibration plastic forming: A mini review

Compared with traditional plastic forming,ultrasonic vibration plastic forming has the advantages of reducing the forming force and improving the surface quality of the workpiece.This technology has a very broad application prospect in industrial manufactur-ing.Researchers have conducted extensive research on the ultrasonic vibration plastic forming of metals and laid a deep foundation for the development of this field.In this review,metals were classified according to their crystal structures.The effects of ultrasonic vibration on the microstructure of face-centered cubic,body-centered cubic,and hexagonal close-packed metals during plastic forming and the mech-anism underlying ultrasonic vibration forming were reviewed.The main challenges and future research direction of the ultrasonic vibra-tion plastic forming of metals were also discussed.

Low palladium content CeO_(2)/ZnO composite for acetone sensor with sub-second response prepared by ultrasonic method

In practical applications,noble metal doping is often used to prepare high performance gas sensors,but more noble metal doping will lead to higher preparation costs.In this study,CeO_(2)/ZnO-Pd with low palladium content was prepared by ultrasonic method with fast response and high selectivity for acetone sensing.With the same amount of palladium added,the selectivity coefficient of CeO_(2)/ZnO-Pd is 1.88 times higher than that of the stirred sensor.Compared with the pure PdO-doped CeO_(2)/ZnO-PdO material,the content of Pd in CeO_(2)/ZnO-PdO is about 30%of that in CeO_(2)/ZnO-PdO,but the selectivity coefficient for acetone is 2.56 times higher.The CeO_(2)/ZnO-Pd sensor has a higher response(22.54)to 50×10^(−6) acetone at 300℃and the selectivity coefficient is 2.57 times that of the CeO_(2)/ZnO sensor.The sensor has a sub-second response time(0.6 s)and still has a 2.36 response to 330×10^(−9) of acetone.Ultrasonic doping makes Pd particles smaller and increases the contact area with gas.Meanwhile,the composition of n-p-n heterojunction and the synergistic effect of Pd/PdO improve the sensor performance.It shows that ultrasonic Pd doping provides a way to improve the utilization rate of doped metals and prepare highly selective gas sensors.

Comparative analysis of single-crater parameters in ultrasonic-assisted and unassisted micro-EDM of Ti6Al4V using discharge plasma imaging

Ultrasonic-assisted micro-electro-discharge machining(EDM)has the potential to enhance processing responses such as material removal rate(MRR)and surface finish.To understand the reasons for this enhancement,the physical mechanisms responsible for the individual discharges and the craters that they form need to be explored.This work examines features of craters formed by single discharges at various parameter values in both conventional and ultrasonic-assistedEDM of Ti6Al4V.High-speed imaging of the plasma channel is performed,and data on the individual discharges are captured in real-time.A 2D axisymmetric model using finite element software is established to model crater formation.On the basis of simulation and experimental results,a comparative study is then carried out to examine the effects of ultrasonic vibrational assistance on crater geometry.For every set ofEDM parameters,the crater diameter and depth from a single discharge are found to be higher in ultrasonic-assistedEDM than in conventionalEDM.The improved crater geometry and the reduced bulge formation at the crater edges are attributed to the increased melt pool velocity and temperature predicted by the model.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

Improving fatigue properties of normal direction ultrasonic vibration assisted face grinding Inconel 718 by regulating machined surface integrity

Fatigue properties are crucial for critical aero-engine components in extreme serviceenvironments,which are significantly affected by surface integrity(SI)indexes(especially surface topography,residual stressσ_(res),and microhardness)after machining processes.Normal-direction ultrasonic vibration-assisted face grinding(ND-UVAFG)has advantages in improving the machinability of Inconel 718,but there is a competitive relationship between higher compressiveσ_(res)and higher surface roughness R_(a)in affecting fatigue strength.The lack of a quantitative relationship between multiple SI indexes and fatigue strength makes theindeterminacy of a regulatory strategy for improving fatigue properties.In this work,a model of fatigue strength(σ_f)_(sur)considering multiple SI indexes was developed.Then,high-cycle fatigue tests were carried out on Inconel 718 samples with different SI characteristics,and the influence of ND-UVAFG process parameters on SI was analyzed.Based on SI indexes data,the(σ_f)_(sur)distribution in the grinding surface layer for ND-UVAFG Inconel 718 samples was determined using the developed model,and then the fatigue crack initiation(FCI)sites were furtherpredicted.The predicted FCI sites corresponded well with the experimental results,therebyverifying this model.A strategy for improving the fatigue life was proposed in this work,which was to transfer the fatigue source from the machined surface to the bulk material by controlling the SI indexes.Finally,a critical condition of SI indexes that FCI sites appeared on the surface or in bulk material was given by fitting the predicted results.According to the critical condition,an SI field where FCI sites appeared in the bulk material could be obtained.In this field,thefatigue life of Inconel 718 samples could be improved by approximately 140%.

Understanding the spatial interaction of ultrasounds based on three-dimensional dual-frequency ultrasonic field numerical simulation

A transient 3D model was established to investigate the effect of spatial interaction of ultrasounds on the dual-frequency ultrasonic field in magnesium alloy melt.The effects of insertion depth and tip shape of the ultrasonic rods,input pressures and their ratio on the acoustic field distribution were discussed in detail.Additionally,the spacing,angle,and insertion depth of two ultrasonic rods significantly affect the interaction between distinct ultrasounds.As a result,various acoustic pressure distributions and cavitation regions are obtained.The spherical rods mitigate the longitudinal and transversal attenuation of acoustic pressure and expand the cavitation volume by 53.7%and 31.7%,respectively,compared to the plate and conical rods.Increasing the input pressure will enlarge the cavitation region but has no effect on the acoustic pressure distribution pattern.The acoustic pressure ratio significantly affects the pressure distribution and the cavitation region,and the best cavitation effect is obtained at the ratio of 2:1(P15:P20).

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Effective separation of coal gasification fine slag: Role of classification and ultrasonication in enhancing flotation

Effective separation of residual carbon and ash is the basis for the resource utilization of coal gasification fine slag(CGFS).The conventional flotation process of CGFS has the bottlenecks of low carbon recovery and high collector dosage.In order to address these issues,CGFS sample taken from Shaanxi,China was used as the study object in this paper.A new process of size classification-fine grain ultrasonic pretreatment flotation(SC-FGUF)was proposed and its separation effect was compared with that of wholegrain flotation(WGF)as well as size classification-fine grain flotation(SC-FGF).The mechanism of its enhanced separation effect was revealed through flotation kinetic fitting,flotation flow foam layer stability,particle size composition,surface morphology,pore structure,and surface chemical property analysis.The results showed that compared with WGF,pre-classification could reduce the collector dosage by 84.09%and the combination of pre-classification and ultrasonic pretreatment could increase the combustible recovery by 17.29%and up to 93.46%.The SC-FGUF process allows the ineffective adsorption of coarse residual carbon to collector during flotation stage to be reduced by pre-classification,and the tightly embedded state of fine CGFS particles is disrupted and surface oxidizing functional group occupancy was reduced by ultrasonic pretreatment,thus carbon and ash is easier to be separated in the flotation process.In addition,some of the residual carbon particles were broken down to smaller sizes in the ultrasonic pretreatment,which led to an increase in the stability of flotation flow foam layer and a decrease in the probability of detachment of residual carbon particles from the bubbles.Therefore,SCFGUF could increase the residual carbon recovery and reduce the flotation collector dosage,which is an innovative method for carbon-ash separation of CGFS with good application prospect.

Damage Evolution of Ballastless Track Concrete Exposed to Flexural Fatigue Loads:The Application of Ultrasonic Pulse Velocity,Impact-echo and Surface Electrical Resistance Method

In order to clarify the fatigue damage evolution of concrete exposed to flexural fatigue loads,ultrasonic pulse velocity(UPV),impact-echo technology and surface electrical resistance(SR) method were used.Damage variable based on the change of velocity of ultrasonic pulse(Du) and impact elastic wave(Di)were defined according to the classical damage theory.The influences of stress level,loading frequency and concrete strength on damage variable were measured.The experimental results show that Du and Di both present a three-stages trend for concrete exposed to fatigue loads.Since impact elastic wave is more sensitive to the microstructure damage in stage Ⅲ,the critical damage variable,i e,the damage variable before the final fracture of concrete of Di is slightly higher than that of Du.Meanwhile,the evolution of SR of concrete exposed to fatigue loads were analyzed and the relationship between SR and Du,SR and Di of concrete exposed to fatigue loads were established.It is found that the SR of concrete was decreased with the increasing fatigue cycles,indicating that surface electrical resistance method can also be applied to describe the damage of ballastless track concrete exposed to fatigue loads.

SARS-CoV-2 Pooled Testing Methodology for PCR Testing Applied in Private Laboratory in Armenia

Since the beginning of COVID-19 pandemics many countries were facing challenges with testing capacity recourse limitations. Throughout the waves of the pandemic countries were trying to address the existing constrains exploring solutions to increase the testing capacity with more cost-effective approaches. Pooled methodology was one of the methods which many have validated and used. It is evident that in case of pooled sample testing the sensitivity becomes lower, however the variation highly depends on the pool size as well as the incidence rate at the certain point. Armenia as well as many other countries has adopted regulations for mandatory COVID-19 PCR testing for all the travelers. Current study aimed to explore the efficiency of COVID-19 pooled PCR testing for nasopharyngeal swabs of individuals with no symptoms in a time period with good epidemiological state of the infection. Nasopharingeal swab samples from individuals were collected. The manual extraction of RNAs of samples was performed after pooling up to 5 samples. The pools with Cycle Threshold (CT) of < 37 were considered positive and were retested individually. In total 28,015 samples were grouped in 667 pools of which 57 were positive. The total number of positive samples was 65. The median difference (CT-pool–CT samples) was 2.4 (ranging from–3.0 to 8.9). The correlation of CT of pools and positive samples was positive. The correlation coefficient r = 0.84, P < 0.000, 95% CI range 0.7423 to 0.9243). The total economic saving when using pools compared to the individual testing was 72%. The minor difference between CT values of pools and samples can be explained by the dilution effect in the pool. However, the positive correlation between the values as well as the amount of cost saving demonstrate that pooling on nasopharyngeal samples for COVID-19 PCR testing can be a good method for efficient screening with significant resource saving. One of the most important advantages of the proposed method is the fact that samples are pooled prior extraction, which avoids the possibilities with misinterpretation of IC due to low yield of RNA in the extraction process.