An historical overview covering the field of electroanalytical metal cations speciation in freshwaters is presented here, detailing both the notable experimental and theoretical developments. Then, a critical review of the progress in the last five years is given, underlining in particular the improvements in electrochemical setups and methodologies dedicated to field surveys. Given these recent achievements, a road map to carry out on-site dynamic metal speciation measurements is then proposed, and the key future developments are discussed. This review shows that electroanalytical stripping techniques provide a unique framework for quantitatively assessing metals at trace levels while offering access to both thermodynamic and dynamic features of metal complexation with natural colloidal and particulate ligands. Metal ions in natural waters interact with a wide assortment of dissolved inorganic and organic ligands, adsorb onto natural colloids, suspended matter and at micro-organism surfaces . In addition to their large polydispersity in size, most of these natural complexing ligands, surfaces or particles present a polyfunctional and polyelectrolytic character, thus displaying a large range of complex formation/dissociation rate constant, hence controlling the bioavailability and mobility of the metal ions .Natural waters are systems that typically never reach a state of chemical equilibrium; therefore, a correct diagnostic of the fate and environmental impact of metal complexes must consider the importance of both the reactivity and mass transport fluxes of the metal compounds and the relative time scales of these processes (dynamic speciation) . ConcernThe most frequent analysis carried out in freshwater samples is the quantification of total metal concentration, which, in practice, consists of strongly acidifying the sample, coupled or not with the destruction of the natural organic matter (NOM), and sending it to a laboratory for quantification by means of an Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) . To undeIn the “dissolved” fraction, trace metal cations are predominantly bound either by NOM, which is an ensemble of negatively charged, chemically heterogeneous and polydisperse small organic colloids , or by sOne of the major problems is that sampling and handling of natural water samples can introduce changes in the metal species distribution prior to measurement . TherefoIn the last three decades, trace metal cations field measurements have been performed using two approaches: (i) the deployment of in situ exposure devices which accumulate metal  such as the Diffusion Gradient in Thin Films (DGT)  or the PDGT techniques have gained significant preponderance for in situ exposure due to being very easy to use; nonetheless, the interpretation of the signal is quite involved . The gelIn this review, the focus will be on the stripping electroanalytical techniques for their unique ability to perform equilibrium and dynamic speciation at very low quantification limits, low cost, and portability. Stripping techniques are carried out in two steps: in the first step (deposition step), a fixed potential  is applied between the working and reference electrodes, leading to the reduction in the metal ion of interest, which becomes amalgamated at the mercury working electrode or deposited at the surface of a solid working electrode. In the second step (reoxidation step), the deposited metal is re-oxidized in a controlled way in order to quantify exactly the amount deposited during the first step .Development of field electroanalytical techniques has been led by the seawater community due to the success of CLE-CSV in measuring extremely low concentrations of metals ions . This is because the instrumentation can be easily operated in a ship, and the quality of electroanalytical measurements is improved in very high salinity and naturally pH-buffered solutions. It is worth mentioning that on-field studies using CLE-CSV has been published for freshwaters systems to detect a wide variety of metallic elements such as Pb , Se 32]32] Cu anIn summary, a complete trace metal cation speciation analysis comprises the quantification of total metal, free hydrated cation and the different complexed and adsorbed species  with additional insight into the dynamic nature of said complexes. To achieve this goal, one needs first to be able to measure trace metal cation speciation on-site with minimal interference with the matrix, and secondly, to have a proper thermodynamic and kinetic physico-chemical description of the trace metal speciation to interpret the measures correctly.The objective of this document is, therefore, to offer a critical review of both the electroanalytical field measurements  and the advances on the thermodynamic and kinetic theoretical frameworks that describe trace metal speciation in natural waters and to provide a road map to develop field trace metal cation dynamic speciation.Studying the dynamic speciation of trace metal cations is a key aspect to a better assessment of bioavailability and toxicity of metal complexes, that is, whether or not they can contribute according to the dynamic features of their binding properties, to the amount of metal internalized by a living organism. Nonetheless, since the development of the Free Ion Activity Model (FIAM) in the 1970s  and its Historically, soil scientists carried out pioneering studies on metal cation binding with NOM. Soil is a solid and complex matrix, which makes it practically impossible to directly examine metal ion interaction with soil organic matter. To carry out these studies, soil organic matter was extracted and operationally separated into different fractions according to their solubility, yielding three components: fully soluble fulvic acids (FA), humic acids (HA) that precipitated in acidic conditions, and finally, the insoluble fraction named humin. By analogy with the soil procedure, NOM from aquatic systems is isolated using an XAD-8 column (hydrophobic fraction) and separated in FA and HA by acid precipitation of the later .Understanding the reactivity of the charged humic substances toward aqueous metal cations requires a correct description of both electrostatic and chemical contributions. Currently, proton and metal cation binding to FA/HA is performed using one of the two leading semi-empirical models NICA   and WHAMAlbeit the approximations used in these models, their development has almost stopped during the last two decades after the publication of ‘’generic’’ parameters for NICA-Donnan for both proton and metal ions  in the eOnly recently, a revival of the speciation computation was observed in the literature, first by Janot et al. , who preIt is necessary to remind the reader that these thermodynamic descriptions are approximations since metal cations in freshwaters are dynamic entities that never really reach equilibrium. Nonetheless, for over two decades, between 1970 and 1990, the free ion activity model was the paradigm in the domain of trace metal cation toxicity. This status quo began to be attacked in the marine literature in the 1990s following on the work of Morel and Hudson ,51,52, wThe key issue is the contribution of the metal complexes to the biouptake or the amount of metal reduced at the electrode. The dynamic nature of a complex is expressed by the interplay between the chemical dissociation kinetics and the diffusive transport  towards the interface, traversing a so-called diffusion layer generated due to the metal concentration gradient between the bulk and the uptaking surface.ka.cL,t) or dissociation (kd) rate constants and the total concentration of ligand L . If the complexes are able to dissociate within the diffusion layer, they can contribute to the interfacial process that accumulates free metal ion and are called dynamic; otherwise, if they do not have time enough to dissociate, they cannot contribute to the metal flux and are called inert.The dynamic nature of the metal complexes is established by comparison of the time taken by the free metallic ion, M, to completely traverse said diffusion layer with the characteristic lifetime of the free metallic ion in the solution, which can be computed from the metal complex association . Both the deposition and reoxidation steps are dynamic in nature; hence, relating the voltammetric signal with the trace metal speciation in solution is a mathematically involved problem.In the 1980s and 1990s, literature reports were largely focused on signal interpretation difficulties for the deposition and reoxidation steps. One major issue was the discrimination of the metal species that contribute to the reactive transport of metal towards the electrode during the preconcentration step, which was already discussed in detail in the previous section. Another main difficulty in the interpretation of the ASV signal stems from the adsorption of organic matter at the mercury electrode surface ,75,76,77Additionally, during the reoxidation step, voltammetric stripping techniques are also subject to surface metal concentration excess , meaning®) [Numerous strategies have been developed for overcoming interference from adsorption, the most common of which involves some modification of the electrode surface, typically the application of a polymer coating  , polysty®)  or Nafio®) ). Amongs®) , an acid®) , or a co®) . This la®) ,87, wher®)  and late®) .2 from solution, hence changing the pH and unbalancing the cation speciation in solution. To address this issue, Tercier and Buffle proposed an online oxygen removal system based on the permeation of oxygen through a silicone tubing surrounded by an enzymatic cross-linked O2 scavenging gel [To carry out field electrochemical stripping measurement, another challenging task is to reduce the dissolved oxygen interference that commonly distorts electrochemical stripping techniques signals. In the laboratory, the easier option is to purge the solution with Nitrogen or Argon, while for field speciation studies, this procedure cannot be applied since degassing removes COging gel .Both the GIME and the proposed online oxygen removal were used in the development of the most successful ASV field probe named the “Voltammetric in situ profiling system” (VIP), which has been developed by Geneva University and commercialized by Idronaut for more than two decades .In the first half of the 2000s, three novel stripping techniques were introduced that significantly changed the panorama of electrochemical trace metal speciation. In 2001 and 2002, Town and Van Leeuwen introduced SCP  and SSCPE) with reoxidation time (t) that reflects the metal electrolysis from the electrode to the solution. Then, the amount of deposited metal during the preconcentration step can be computed using the Faraday law, resulting in a direct proportion between the limiting transition time (τ*) and the metal concentration in the sample [t/dE vs. E, with good discrimination regarding the capacitive charge that appears under the peak baseline and avoiding the problems of signal distortion in the voltammetric stripping techniques caused by adsorption or surface excess. Thus, SCP offers an excellent detection limit comparable to differential pulse (DP)-ASV or square-wave(SW)-ASV, is not affected by organic matter adsorption [Ed), ranging from the situation where no metal ions are reduced, passing by the Nernstian region, and ending in the diffusion-limited limit where all metal ions arriving at the electrode are reduced [SCP is similar to Jagner’s Potentiometric Stripping Analysis (PSA)  but apple sample . In SCP,sorption , and is sorption . Howeversorption . To over reduced .DML) is known [SSCP is actually the most complete technique for trace metal speciation analysis, essentially due to the analytical equation that describes the SSCP wave in the kinetic regime . By compis known . Thus, tis known .The presence of heterogeneous ligands affects the slope of the SSCP wave due to the variation of the stability constant with the metal-to-ligand ratio effective at the electrode surface. This feature can be exploited to obtain partially the complexation isotherm and the degree of heterogeneity , howeverThe most important interference in SCP/SSCP is the dissolved oxygen since it can chemically reoxidize the metal during the stripping step and thus decreasing the signal. This oxygen competition is especially important for mercury drop electrodes where the reoxidation currents must be very small, of the order tens of nanoamperes, to assure full metal depletion. Hence the best electrodes to carry out SCP/SSCP experiments are thin mercury film electrodes (TMFE)  due to tE1) lies within the Nernstian region for the metal ion under study and (2) the deposition time must be long enough so that equilibrium between the reduced metal at the electrode and the free metal in solution is attained.AGNES  is an eqThe reoxidation step is only used to quantify the amount of reduced metal ions during the deposition step. Different electrochemical techniques were tested for this purpose before choosing a chronopotentiometric reoxidation procedure . The freThe TMFE is the optimal platform for performing AGNES due to its fast equilibration time promoted by the large electrode area for a very small electrode volume, hence yielding significantly shorter experimental times as compared with mercury drop electrodes . RemarkaTo complete the information on trace metal speciation in natural systems, we suggest reading the following three reviews. In 2006, Sigg et al.  publishePesavento et al.  presenteCompanys et al.  reviewedRegarding in situ electroanalytical measurements, the VIP/TracMetal sensing probes are a notable achievement and represent the most advanced devices, being the only ones that are commercially available. They are mostly used in seawater or estuaries due to the larger community of electrochemical marine scientists, but there is nothing preventing their use in freshwaters. In recent years, Illuminati et al. (2019)  reportedTercier-Waeber et al. presented in 2020 the development and field validation of newly designed nanostructured gold-plated gel-integrated microelectrode arrays (Au-GIME) applied using in situ Square Wave Anodic Stripping Voltammetry (SW-ASV) to evaluate the potential bioavailable inorganic mercury (Hg(II)) species in coastal areas  and inorFollowing the development of the AuNF, Tercier-Waeber et al.  reportedLayglon et al.  reportedThe VIP/TracMetal provides nice analytical performances in seawater or estuarine conditions due to the high salinity, constant pHs and small NOM concentrations (0.5–5 mg/L). However, in freshwaters where the NOM concentrations are normally higher (5–50 mg/L), the colloidal size distribution is broader, and both salinity and pH can vary significantly. Therefore, it will be more difficult to estimate the contribution of free and complexed metal ligand in the electrochemical signal. Effectively, both the free-metal ions and smaller metal complexes will permeate the gel with diffusion coefficients equal or closer to those observed in solution, while larger colloidal or nanoparticulated complexes will either be excluded or will move through the gel with unknown diffusion coefficients, rendering the electrochemical signal interpretation more intricate.In addition to the VIP system, a few autonomous devices have been described in the literature in the last five years, mostly dedicated to seawater. Hence, we provide here the example of two devices that can also be used in freshwaters. Lv et al. described in 2017  a fully As for the field application of the new techniques , Hackel et al.  presenteChen et al.  studied An important methodological advance was reported in 2020 by Pinheiro et al. , where t3 solubility product [4 electrolyte  at different metal-to-humics concentration ratios using AGNES. The obtained results evidence an important impact of electrostatics on indium complexation by humic matter.In addition to the traditional metals of environmental interest (Cd(II), Cu(II), Pb(II), Zn(II)), there is growing interest in the environmental fate of so-called strategic metals due to their growing use in electronic devices and batteries. One such metal is Indium, which has received considerable attention recently. In 2018, Tehrani et al.  proposed product . In 2019 product  reported product  presente3 concentration instead of the free antimony. The authors validated their method by comparing their experimental values of Sb(OH)3 titration with oxalate with the values predicted with the complexation constants from the literature.AGNES has also been extended to another metal element, the Antimony, by Pla-Vilanova et al. . Due to N-nitroso-N-phenylhydroxylamine (cupferron) as the adsorbing ligand. The total Eu(III) concentration in the acceptor can then be related to free Eu(III) in the donor, i.e., the natural sample. However, free Eu(III) tends to adsorb strongly to the cation-exchange membrane; thus, at least 100 mM of Ca(II) was necessary to make adsorption of Eu(III) onto the membrane negligible, which may change the speciation in certain natural samples.Electroanalytic applications for the aqueous speciation studies of rare earth elements are scarce. Nevertheless, a recent work on the free Eu(III) determination by the coupling between electroanalytical techniques and DMT device has been proposed by Janot et al. . In thisTo complete this overview, we suggest to the readers a few tutorial reviews to help become more familiar with the use of these speciation techniques. In 2019, Borril et al.  discusseIn our point of view, the combined utilization of SCP, AGNES and SSCP during a field campaign may significantly contribute to a complete trace metal cation speciation analysis, including the quantification of total metal by SCP, free hydrated cation by AGNES and insight into the heterogeneity and dynamic nature of metal complexes by SSCP.Sample characterization by independent techniques and size separation steps may be necessary to identify the different complexed and adsorbed species . In this section, we will propose a road map for the development of trace metal cation speciation based on these concepts.Trace metal cation concentrations in freshwaters vary from the natural abundance of the metal to various degree of pollution. In non-polluted samples, some metal ions are commonly found well above the detection limits of the techniques (Cu(II), Zn(II)), while others generally lie close these limits (Pb(II)) and others below , In(III)). In pristine freshwater, trace metal ion concentrations are relatively stable over large periods of time, while in polluted samples, the values are higher or much higher than the “normal” value but may vary dramatically in short periods of time (~days) depending on the nature of the contamination (spike or continuous) or natural seasonal events (flood or low-water periods) and the associated processes such as contaminated sediment remobilization.It is pointless to carry out speciation measurements for metal ions whose total concentrations in the sample are close or below the detection limit of the analytical techniques. Thus, a fast total metal screening technique such as the one proposed by Hackel et al.  should bThe proposed dynamic speciation methodology is presented in Then the sample is filtered through a 0.45 or 0.22 µm membrane. The filtered solution contains the so-called dissolved ligand fraction, and the particulate ligand fraction is the one retained in the filter.From the filtered solution, two aliquots are taken, and the first is acidified to a pH below two, being the dissolved total metal concentration that could be measured by SCP, while in the second, the free metal concentration in the dissolved compartment could be quantified by AGNES. In the third step, as long as the total dissolved metal concentration is sufficiently above the SCP detection limit, an SSCP measurement will be carried out in the filtered sample to gain information on the heterogeneity and dynamic nature of the dissolved complexes.Finally, the fourth step consists of extracting the metal from the particles retained on the filter by means of an acid washing, followed by SCP quantification.At the end of this speciation methodology, we should be able to take stock of the quantitative metal speciation by determining the total metal ions in the pristine sample, the dissolved phase and retained in the particles, the free metal in the pristine solution and dissolved phase, and the SSCP information on heterogeneity and dynamic nature of the complexes of the dissolved phase.Just recalling here that carrying out SSCP in the pristine solution (or unfiltered solution sample) is not relevant since the larger particles and organic colloids retained in the filter will not contribute for the dynamic SSCP due to their low diffusion coefficients, as discussed in the theoretical background.always kept constant during the trace metal cation quantification. The method calibrations should be carried out at the same conductivity to minimize errors.During the field measurement, complementary parameters are needed to better understand the chemical status of metallic elements: first, pH, temperature and conductivity should be measured on-site and Since the NOM is the principal complexing agent for the metal ion, it is required to quantify the total organic carbon (TOC), dissolved organic carbon (DOC) and particulate organic carbon (POC). In case of sufficiently high NOM concentrations, it will be helpful to carry out a proton titration between pH 3 and 10.5 to obtain direct information on the carboxylic and phenolic groups abundance.Regarding the particulate fraction retained in the filter, first, it will be necessary to quantify it by weight, followed if possible by an identification of the mineral phases present, to establish the main solid components .Finally, it would be helpful to quantify the major cations and anions for a better sample description, especially for estimating the Ca(II) and Mg(II) competition with the trace metal cations.Regarding the interpretation of speciation measurements of freshwaters, one of the major questions remaining is the influence NOM adsorption at the surface of natural particles. At this point, the few experimental studies that have been carried out focus on the metal binding to humic matter adsorbed on iron oxides, which is described using an independent combination of NICA-Donnan and CD-MUSIC models . More exOne of the keys of development will be the ability to provide affordable automatic instruments to carry out metal speciation in the field, allowing the deployment of multiple setups during a field survey, thus significantly increasing the number of experimental measures either in frequency or in geographical area coverage. Only the availability of large datasets will allow determining and refining the parametrization of predictive models that represent the fundamentals for environmental risk assessment studies.Finally, it will be important to extend the proposed approaches to a larger variety of metals cations. At this point, AGNES and SSCP methodologies are available for Cd(II), Pb(II), Zn(II) and In(III) in mercury electrodes, Pb(II) in bismuth electrodes and Cu(II) in gold electrodes. Albeit these metal cations are amongst the most important toxic species in freshwaters, it will be important to also be able to measure, for instance, Hg(II) and Ag(I). There are several possible development pathways, one of the most promising being the exploitation of under-deposition potential in solid electrodes, as carried out for the Pb(II) in bismuth electrodes .The unique abilities of electroanalytical stripping techniques to perform trace metal cation dynamic speciation provide the best way to investigate in-depth and integrative analysis directly in the field. For this reason, further challenging research is still necessary to develop both the instrumentation and the theory aspects pertaining to these electrochemical methods with the aim of better understanding the metal fate and reactive transfer in environmental systems.


Thermal fatigue cracks occurring in pipes in nuclear power plants pose a high degree of risk. Thermal fatigue cracks are generated when the thermal fatigue load caused by local temperature gradients is repeatedly applied. The flaws are mainly found in welds, owing to the effects of stress concentration caused by the material properties and geometric shapes of welds. Thermal fatigue pipes are classified as targets of risk-informed in-service inspection, for which ultrasonic testing, a volumetric non-destructive testing method, is applied. With the advancement of ultrasonic testing techniques, various studies have been conducted recently to apply the phased array ultrasonic testing (PAUT) method to the inspection of thermal fatigue cracks occurring on pipes. A quantitative reliability analysis of the PAUT method must be performed to apply the PAUT method to on-site thermal fatigue crack inspection. In this study, to evaluate the quantitative reliability of the PAUT method for thermal fatigue cracks, we fabricated crack specimens with the thermal fatigue mechanism applied to the pipe welds. We performed a round-robin test to collect PAUT data and determine the validity of the detection performance  and the error in the sizing accuracy  evaluation. The analysis results of the POD and sizing performance of the length and depth of thermal fatigue cracks were comparatively evaluated with the acceptance criteria of the American Society of Mechanical Engineers Code to confirm the effectiveness of applying the PAUT method. Thermal fatigue cracks occur when the thermal fatigue load caused by the local temperature gradient is repeated. Thermal stratification is a phenomenon caused by density differences in a layer of hot fluid flowing over a layer of cold fluid. This thermal stratification is a problem in many piping systems used in NPP where hot and cold fluids coexist or are separated by leakage valves. The main equipment of a nuclear power plant include a pressurizer surge line, a reactor coolant system branch line, a shutdown cooling system, and a mixing tee; within the equipment, low-temperature and high-temperature fluids are mixed or coexist. Hence, such equipment operate in conditions where thermal fatigue cracks can easily form ,2,3. StuTypical non-destructive volumetric testing methods include radiographic testing (RT) and ultrasonic testing (UT). RT has an excellent recording characteristic and high accuracy in identifying volumetric flaws, such as porosity and slag inclusions. However, when applying RT, there is a risk that inspectors and power plant employees may be exposed to radiation since the inspection is carried out in a location where the radiation source is exposed. Furthermore, its application is limited during ISI because of the restriction of access to the work area and the limitation of radiation intensity . RecentlHowever, conventional UT can be subject to errors in signal analysis and determination results because of basic limitations, such as the diffraction, attenuation, refraction, reflection, dead zone, waveform variation, spurious echo, and transmission loss of ultrasound waves. Furthermore, UT has lower reliability and recordability than RT, a lack of equipment and specimen reliability, and insufficient development of inspection technology and standards. In addition, there are limitations depending on the geometry of the specimen such as taper and curvature in the pipe weld, and it is difficult for an inspector to recognize flaws and accurately determine the flaw type if there is no information on the design parameters or actual dimensions of the geometrics of the pipe . AdditioIn recent years, studies have been conducted to apply various testing methods for a more accurate evaluation of thermal fatigue cracks. These include studies on the time-of-flight diffraction (TOFD) technique, which evaluates flaws using the diffraction signals of ultrasounds ,13, and In this study, we evaluate the quantitative reliability of the PAUT method for thermal fatigue cracks. We fabricated thermal fatigue crack specimens, which simulated a nuclear plant-like environment of welded parts, and performed a round-robin test (RRT) to consider human factors. Using the PAUT data acquired through the RRT, we calculated the POD and sizing performance using statistical analyses. The reliability analysis results were comparatively evaluated using the acceptance criteria of the AMSE Code to establish a technical basis for the application of the PAUT method to pipe welds with thermal fatigue cracks.To evaluate the reliability of the PAUT method for thermal fatigue pipes, we fabricated flaw specimens in which cracks were grown under thermal fatigue load as in the nuclear power plant environment. The specimens were carbon steel butt welds of pipes of two diameters, 203.2 mm and 304.8 mm, with a thickness of 18.2 mm and 17.4 mm, respectively. We chose butt welds for the base material of the pipe welds, processed with counter bores. Thermal fatigue cracks can easily occur on a counterbore because of high stress and the geometric characteristics of the pipe [Thermal fatigue cracks were fabricated by applying tensile stress using a jig, raising the temperature to a maximum of 400 °C, holding it for approximately one minute, water-cooling to about 20 °C, and repeating it 100 times or more to grow thermal fatigue cracks. A total of 12 flaws were divided and inserted into five specimens to avoid interference between the flaws. The flaws were designed to be positioned within the weld volume corresponding to the inspected volume: a range of 1/4 in from the adjacent base material and 1/3 of the thickness at the bottom. Each flaw was designed with a different length and depth, which was an analysis condition for quantitative reliability evaluation. The length and depth of the flaw were designed to be evenly distributed between 20 mm and 40 mm and between 2 mm and 7.3 mm, respectively. We used the ZETEC TOPAZ64  and the Olympus X3(MX2)  as the inspection equipment for data acquisition in the RRT, which provide more than 32 channels. We used a probe with a 5 MHz frequency, 32 oscillators, and 0.6 mm pitch, which is widely used for pipe inspection. The wedge had a shear-wave refraction angle of 55°, with a curvature processed in such a way that the space between the inspected surfaces did not exceed 0.5 mm. An encoder-mounted automatic/semi-automatic scanner was used to acquire data. The essential variables of the scan plan for the sectorial scan were the reference point of the weld axis, weld geometrics, and sweep angle; the index offset must be set up to include all inspection volumes. Calibration of the ultrasonic beam and time-corrected gain (TCG) was performed on the equipment used for the inspection. We set the reference sensitivity to 100% of the full-screen height (FSH) for the notch of the calibration specimen, which has the same diameter and thickness as the specimens. Using the evaluation criteria of the PAUT, we performed this investigation for all indications exceeding 10% of the reference sensitivity set up previously and outlined and determined the presence or absence of flaws through signal analysis. The tip diffraction method was applied as the main measurement method to measure the height of the flaw, but when the tip signals could not be distinguished, the 3 dB drop method was applied as an alternative method. The length of the flaw was measured using the 6 dB drop method. A schematic diagram of the entire system used in the RRT is shown in To validate the prepared inspection system and procedure, we performed a round-robin test (RRT), which is typically used in reliability evaluations of non-destructive testing . We perfIn this study, the RRT was performed on three teams that met the above qualifications and each included at least one inspector with more than 10 years of experience. The RRT was performed as a blind RRT that did not disclose defect information of the test specimen in order to simultaneously evaluate the influence of the inspector’s skill level. All participating inspection teams acquired PAUT data according to the inspection equipment and procedures presented in An example of data acquired with the RRT is shown in Indicators for evaluating the reliability of the non-destructive test include the POD and sizing performance . The POD is classified into the binary POD model, which is based on hit/miss data, and the size (a) . Each mong or UT . Since sng or UT ,22. In tl errors .We verified the validity of the measured flaw size data to apply the To evaluate the normal distribution, quantile–quantile plots (QQ-plots) were calculated. A QQ-plot is a statistical analysis technique widely applied to evaluate the normality of two datasets. The difference between the actual defect size and the measured defect size is separated into the same probability interval and the percentile (z-score) of the standard normal distribution for each dataset is calculated. A QQ-plot is then obtained by plotting the difference and z-score of two datasets on the x-y plane.We calculated the QQ-plots in accordance with the RRT data, as shown in For the e values . In thisues [a^) . The noiThe standard distributions for the above variances are 5.63 mm and 1.69 mm for the flaw length and depth, respectively. We have explained the statistical basis for applying the m mh1823  and compm mh1823 . Figure In the POD curve in Based on the 80% POD analysis result of each team, every inspection team satisfies the acceptance criteria. However, when considering the 90% confidence level, the deviation is large. Even if the 80% POD is at the same level, the confidence level should be calculated to check the reliability of the POD itself. This is related to the size measurement accuracy, which will now be described.The root-mean-square error (RMSE) is a method of calculating the error between the reference data and the measurement data and is used to evaluate the size measurement accuracy in the nondestructive test. We referred to the ISI acceptance criteria of the ASME Code for the RMSE-based acceptance criteria, which are 19 mm for the flaw length and 3.175 mm for the depth; In this study, the reliability of the PAUT method for thermal fatigue cracks was quantitatively evaluated by analyzing POD and sizing performance. The specimens were fabricated by growing cracks under thermal fatigue conditions as in the nuclear power plant environment, and PAUT data for thermal fatigue cracks were collected by performing an RRT with inspectors who were qualified for standardized equipment and procedures. The measured flaw length and height for thermal fatigue cracks were verified as having a statistically high correlation with the actual flaw size. It was confirmed that sizing data can be applied as a response parameter to the


The use of remote consultation modalities has exponentially grown in the past few years, particularly since the onset of the COVID-19 pandemic. Although a huge body of the literature has described the use of phone (tele) and video consultations, very few of the studies correspond to randomized controlled trials, and none of them has assessed the safety of these consultation modalities as the primary objective. The primary objective of this trial was to assess the safety of remote consultations (both video and teleconsultation) in the follow-up of patients in the hospital setting.Multicenter, randomized controlled trial being conducted in four centers of an administrative healthcare area in Catalonia (North-East Spain). Participants will be screened from all individuals, irrespective of age and sex, who require follow-up in outpatient consultations of any of the departments involved in the study. Eligibility criteria have been established based on the local guidelines for screening patients for remote consultation. Participants will be randomly allocated into one of the two study arms: conventional face-to-face consultation (control) and remote consultation, either teleconsultation or video consultation (intervention). Routine follow-up visits will be scheduled at a frequency determined by the physician based on the diagnostic and therapy of the baseline disease (the one triggering enrollment). The primary outcome will be the number of adverse reactions and complications related to the baseline disease. Secondary outcomes will include non-scheduled visits and hospitalizations, as well as usability features of remote consultations. All data will either be recorded in an electronic clinical report form or retrieved from local electronic health records. Based on the complications and adverse reaction rates reported in the literature, we established a target sample size of 1068 participants per arm. Recruitment started in May 2022 and is expected to end in May 2024.The scarcity of precedents on the assessment of remote consultation modalities using randomized controlled designs challenges making design decisions, including recruitment, selection criteria, and outcome definition, which are discussed in the manuscript.NCT05094180. The items of the WHO checklist for trial registration are available in Additional file 1. Registered on 24 November 2021.The online version contains supplementary material available at 10.1186/s13063-023-07679-1. The ECASeT trial is sponsored by the Consorci Sanitari Alt Penedès—Garraf (CSAPG), a healthcare provider within the Catalan Health System, and funded by the Instituto de Salud Carlos III (ISCIII) through the project “PI22/01056” (co-funded by the European Union). Neither the sponsor nor the funder agency contributed to the trial design; they are not involved in trial conduct and will not be involved in the analysis of the results.The scientific committee, formed by AR-M, GC-S, RE, CT, JA, CP-L, AT, GD, ACh, MG, and JP-J, was responsible for key decisions regarding the primary endpoint and overall design. The rest of the authors made substantial contributions to the study design and manuscript preparation, as detailed in the “Declarations” section of the manuscript. The scientific committee is also in charge of trial oversight, with AR-M coordinating day-to-day issues. A professional monitor, belonging to the research support office of the Hospital acting as study sponsor, will monitor a subset of all data collected, as described in the study protocol.The debut of telemedicine into routine practice stretches back over three decades . HoweverThe concept of telemedicine encompasses multiple information and communication technologies (ICT) solutions for monitoring patients remotely . Among tThe rapid implementation of remote consultations in many healthcare systems of high-income countries has not been accompanied by high-quality evidence regarding its effectiveness and safety. Thus, although thousands of studies have described the benefits and usage of various types of remote consultation, very few correspond to randomized-controlled designs, and the assessment of potential harms of remote consultation has been typically listed as secondary—often descriptive—outcomes , 14. We Ensayo Clínico abierto y Aleatorizado para valorar la Seguridad de la Teleconsulta, frente a la consulta clínica presencial in Spanish) is aimed at testing safety of remote consultations, based on its non-inferiority compared with face-to-face consultation. The ECASeT trial will be conducted in four centers in the healthcare area of Alt Penedès-Garraf in Catalonia (North-East Spain): three secondary hospitals  and a rehabilitation center, all part of Healthcare Consortium Alt Penedès Garraf (CSAPG). All study sites belong to the public network of hospitals and primary care centers of the Catalan Health Service and provide universal healthcare to a catchment population of 247,357 inhabitants. Hospital departments from all areas  are involved in the study.The ECASeT trial  and remote consultation . All patients, irrespective of the consultation type, will receive short text message reminders a few days before the visit.Participants allocated into the control arm will be scheduled with face-to-face appointments as usual. All phone calls (except reminders for scheduled visits) performed by the physician during the study will be noted in the electronic case report form (eCRF). Per protocol, participants allocated in the control arm can receive up to 25% of physician-doctor interactions by phone .Participants allocated in the remote consultation arm will be scheduled remote visits, either phone or video consultation. In case of phone visits, the physician will call the main phone number provided by the patient and, in case of not receiving an answer, other phone numbers listed as contact numbers. In case of video consultation, the participant will receive an additional text message with the visit link. When clicking the link, a website with the video consultation system embedded will open in the default browser of the user. The video consultation platform of the physician pops up a message indicating that the patient is in the virtual waiting room. Patients will remain in the virtual waiting room until the physician starts the visit. A technical assistance is readily available for physicians who experience troubles with the system. In case the patient does not show up to the video consultation after a reasonable time, the physician calls the patient by phone. The date and hour of the phone call and the reason for the inability to connect are recorded in the eCRF.Participants allocated in the remote consultation arm that require face-to-face visits will be excluded from the per-protocol analysis, and the reason for the face-to-face visit will be recorded in the eCRF. Participants allocated in the remote consultation arm and scheduled with a phone visit cannot be visited by video, whereas those scheduled for a video consultation can undergo up to 25% of phone visits. Phone contacts and their reason in patients in the video consultation arm will be recorded in the eCRF.Non-scheduled visits will be defined as any contact between the patient and the physician that occurs before the date established in the previous follow-up visit. Non-scheduled visits will be performed according to the study arm and type of remote consultation  in which the patient has been allocated and will be considered for the analysis.All consecutive patients who visited any of the participating departments between May 2022 and May 2024 will be informed about the study design and objective and will be offered to enroll in the trial. Patients who agree to participate will provide their informed consent, which will be signed in case of a face-to-face screening visit and oral in case of a remote visit. Patients who reject enrollment will be recorded in an anonymous registry. Once signed the informed consent, the investigator will assess the eligibility criteria and record them in the eCRF.Patients will be randomly allocated in either of the study arms irrespective of the modality of the screening visit. Randomization will be balanced by medical specialty to prevent biases towards a particular diagnosis within study arms. To ensure a minimum number of participants who visited through video consultation, participants allocated to the remote consultation arm will be subsequently randomized to either video consultation or remote modality selection at physician discretion at a 3:7 ratio.Centralized randomization will be automatically conducted by the randomization module of the REDCAP system used for the eCRF. The allocation will be informed to the administrative service, which will schedule all visits indicated by the physician during the investigated period following the criteria of each study arm. The health condition for which a patient has attended the screening visit will be considered the baseline disease for the study purposes.Although study participants will be informed about the overall study objectives in the patient information sheet, they will not be aware of the study endpoints. In addition, the statisticians performing the analysis will be blinded to participant allocation. No rules for unblinding have been pre-defined in the study protocol.To reduce the workload of participating investigators during visits, variables recorded in the eCRF will be limited to those that cannot be accurately retrieved from electronic health records (EHR), which will include both local hospital records and the dataset of the Catalan Health Surveillance System (CHSS), a central database of the Catalan Health System. By the time of submitting the study protocol to the ethics committee, all four study sites have separate and different EHRs. Throughout the following year, all the systems are to be unified into a single one and all the data from the different systems will be migrated. These EHR collect all the data related to the care received in the centers, clinical course, prescription, diagnostic images, laboratory tests, and administration, among others. The CHSS systematically collects and stores healthcare data on health status and resource utilization from all healthcare providers. The CHSS was originally designed for healthcare planning and has an automated validation system to identify internal inconsistencies between variables. Since the CHSS registries are used for control of capitation payments, they undergo external audits to ensure provider payment accuracy .Table The follow-up period planned by the investigator for the baseline disease will be retrieved from the local EHR. Other variables collected from EHRs at baseline will include sex, age, area of residence, the number of active prescriptions at the end of the screening visit, and the comorbidity burden, summarized using the Adjusted Morbidity Groups (AMG). Briefly, the AMG is a case-mix tool that generates a summary index of health risk by considering all chronic conditions and recent acute diagnostic codes . The tooFor homogeneity regarding the rate of preventable hospitalizations, we considered all hospitalizations due to any of the ICD-10 diagnostic codes listed as ambulatory care sensitive conditions  managed Usability will be assessed by passive and active surveillance. Study investigators will record non-attendances, as well as all technical incidences that occurred during the visit in an unstructured field of the eCRF. For active surveillance, a trained interview panel will contact by phone a subset of 100 participants randomly selected from all patients who have completed the final visit. Interviewers will administer the computer system usability questionnaire (CSUQ), a validated questionnaire aimed at measuring the users’ satisfaction with the usability of computer systems in scenario-based usability studies .The users’ satisfaction with the quality of care received will be assessed using a validated tool encompassed within the satisfaction survey plan of the Catalan Health Service . The queFigure In addition to voluntary withdrawal, the following situations will lead participants in the remote consultation arms to discontinue the study: a change in their clinical status that requires face-to-face consultation and not following the procedures for remote consultation adequately. Patients allocated in the face-to-face arm will discontinue in case their health status discourages moving to the hospital for follow-up visits. The reasons for ending the study will be recorded in the eCRF.The study will be temporarily interrupted if the local authorities or healthcare center managers decide to ban or limit face-to-face consultations because of the rapid spread of an infectious agent. This interruption can be partial in case banning face-to-face consultations does not affect all hospital services. The study will resume when face-to-face consultations are restored, and there is enough healthcare activity for adequate recruitment in all study arms. In case of a temporal interruption, the study will be automatically extended for a time that is at least equivalent to the interruption time.The independent ethics committee can interrupt the study at any time in the advent of risk for participants, and the study sponsor can do it as well because of financial issues.All data recorded in the eCRF will be stored in a REDCap platform hosted in a local server. The REDCap data will be transferred to a database, which will also contain the data extracted from the local EHR and the CHSS registries. All registries will be linked through the identification number of the Catalan Health Service.Data recorded in the eCRF will be monitored for completeness and quality by a professional monitor working at the research office of the Hospital acting as sponsor. We will schedule periodic visits to assess the adherence of investigators to the study protocol and good clinical practices. The clinical monitor will release reports at the end of each visit.A statistical analysis plan, with detailed analyses for each study outcome, will be performed after the last patient has attended the final visit and before the closure of the database.The primary objective will be assessed using a non-inferiority analysis of the cumulative incidence of complications of the baseline disease between remote consultation (video and teleconsultation arms) and face-to-face consultation, using a non-inferiority margin of 2%. The analysis will be based on a modified binomial test to assess the non-inferiority of an experimental intervention vs. a control group in a three-arm trial . The priThe primary analysis will be replicated with patients stratified according to the following characteristics: hospital department, diagnostic, disease duration (chronic vs. acute or sub-acute), anatomic-therapeutic treatment groups, and GMA health risk group. The clinical and demographic characteristics of study participants will be described and compared between excluded/withdrawn participants.The statisticians will be blinded for the allocation arm. The type I error will be set at 2.5% for the primary analysis and 5% for the rest of the analyses. All analyses will be conducted using R software (version 4.0 or higher) . Data coThe sample size was estimated based on the primary endpoint of cumulative incidence of complications related to the baseline disease. The sample size analysis was computed with R software , followiAltogether, the participating centers schedule 252,000 routine follow-up visits during a period equivalent to the study, corresponding to 121,400 different patients. Therefore, the recruitment of the estimated 2136 patients is considered within the capabilities of the participating centers.Owing to the scarcity and heterogeneity of available data about the primary endpoint, a blinded interim analysis will be conducted when 50% of the total sample has been recruited to revise the assumptions used to calculate the sample size, as proposed in the E9 Guide of the ICH on Statistical Principles for Clinical Trials .We will collect only the data necessary to achieve the study objectives. All data will be handled according to the Spanish Organic Low 03/2018 for the protection of personal data and assurance of digital rights (LOPD-GDD) and the General Data Protection Regulation 2016/679 on data protection and privacy for all individuals within the European Union and the local regulatory framework regarding data protection. No situations classified at high risk of violation of the rights and freedoms of patients according to the LOPD-GDD are foreseen. Nevertheless, considering that the study will include video conferencing, we will perform the risk analysis report requested by the LOPD-GDD. The data generated during the study will be stored for 5 years after the study ends unless the Independent Ethics Committee or the local regulatory framework establishes otherwise.The study protocol has been approved by the Independent Ethics Committee of the Bellvitge Hospital  (ref. PR215/21). All changes made to the study design after protocol approval will be notified to the Independent Ethics Committee and documented as an amendment in the ClinicalTrials.gov registry. The scientific committee and the sponsor are responsible for communicating all amendments that have been approved by the Independent Ethics Committee to the rest of the investigators, as well as, patients already enrolled in the trial if deemed necessary.When offered to enter the study, all participants will be provided with a patient information sheet . However, in our study, which encompasses many chronic conditions, the definition of the safety outcome had to include any possible complication that can be attributed to inadequate control of the baseline condition. We considered that listing all potential complications associated with the baseline conditions allowed in the trial would hamper excessively the study conduct and constrain the repertoire of possible complications. Instead, we chose a broad definition of complication . To prevThe definition of a candidate profile that is suited for remote consultation has been identified among the important challenges of remote consultation deployment . In the Aside from the little precedent for making decisions on trial design, our study is challenged by the huge sample to be enrolled for achieving enough statistical power for a primary outcome that is expected to occur in a low frequency. Although the assumptions for sample size calculation will be revised in an interim analysis, as proposed by ICH E9 guidelines , recruitIn summary, the study protocol of this RCT, which is novel in assessing the safety of remote consultation as a primary endpoint in general consultations and with powered sample size, provides design insights that will hopefully aid researchers in making decisions in future trials for assessing remote consultation.By the time of submitting the manuscript, the trial is recruiting patients. No interim analyses have been done yet.Current protocol version: 2.0 (07/02/2023).Recruitment started on November 01, 2021, and was interrupted until the protocol amendment on February 07, 2023.Expected date for ending recruitment: February 28, 2025.Additional file 1. WHO dataset ECASeT.Additional file 2. Information sheet.Additional file 3. SPIRIT Checklist.


Micro gas turbines are widely used in distributed power generation systems. However, the combustion of gas turbine combustors produces a large amount of nitrogen oxides (NOx), which pollute the environment and endanger human life. To reduce environmental pollution, low-emission combustors have been developed. In recent years, there has been an increasing focus on the use of low-heat-value gas fuels, and it is necessary to study the NOx emissions from low heat value gas fuel combustors. Data-driven deep learning methods have been used in many fields in recent years. In this study, a variational autoencoder was introduced for the prediction of NOx production inside the combustor. The combustor used was a micro rich–quench–lean combustor designed by the research group using coal bed gas as a fuel. The internal NO distribution contour was obtained as the dataset using simulation methods, with a size of 60 images. The model architecture parameters were obtained through hyperparameter exploration using the grid search method. The model accurately predicted the distribution of NO inside the combustor. The method can be applied in the prediction of a wider range of parameters and offers a new way of designing combustors for the power industry. Distributed power generation is widely recognized as a practical solution to complement centralized power generation. It provides a flexible mode of power generation that can minimize the losses incurred during long-distance transmission. Micro gas turbines are an integral component of distributed power generation systems and have been extensively adopted worldwide . HoweverHistorically, combustible gases used for industrial purposes were preferred based on their high heat value due to their effortless combustibility and high combustion stability ,7,8. NatReducing pollution emissions from gas turbine units is heavily dependent on controlling the combustion process in the gas turbine combustor. To this end, research is being conducted on low-pollution combustor structures, such as the rich-burn quick-quench lean-burn (RQL) combustor , which iNOx production in a gas turbine combustor is influenced by various parameters, including jet momentum flux , combustOne of the primary objectives when designing industrial combustors is to minimize NOx production within the combustor. Achieving this objective requires a deep understanding of NOx distribution within the combustion field under varying operating conditions, which is critical when developing gas turbine combustors. Currently, experimental and numerical simulation methods dominate the study of combustors, as previously noted ,31,32. WData-driven prediction methods, commonly referred to as artificial intelligence methods, have recently surged in popularity. This approach eliminates the need for access to the combustor chemical mechanism, enabling direct prediction of the desired target using input parameters. Artificial intelligence methods offer significant advantages over traditional methods, particularly when ample data is available, as it saves valuable experimental or computational resources. The field of combustion has already shown promising research results through the application of artificial intelligence. For example, An et al.  leverageThe variational autoencoder (VAE)  is an exThe study’s main contribution is the introduction of the VAE method into the field of gas turbine combustor pollution emissions, specifically for predicting internal NOx generation. The method can be applied to predict flow fields and other aspects because it is a universal method.The self-designed low-heat-value gas RQL micro combustor for this study is shown in The flow inside the combustor is sophisticated, and in this study, the flow field could be described using the following control equations under the assumption that the flow field is continuously compressible and turbulent.φ, φΓ, and φS in the different equations are shown in The expressions The turbulence phenomenon was described utilizing the selected shear stress transport (SST) k-ω two-equation model.The combustion phenomenon was comprehensively investigated in this study, employing the flamelet-generated manifold probability density function (FGM-PDF) model. According to the FGM model, the entire flame can be segregated into numerous, easily calculable one-dimensional flames or flamelets, which facilitates the computation of chemical reaction data prior to the flow field simulation. During simulation, critical flame state quantities can be extracted directly from the pre-calculated data. To simulate the turbulent combustion field, the PDF method was utilized as a popular solution technique in the combustion field. This probabilistic statistical approach describes the combustion process considering velocity and temperature of the turbulent flow field as independent variables. Chemical components within the flow field are computed through integration operations based on these variables. The combustion mechanism was modeled using GRI-Mech 3.0. The numerical simulation was carried out utilizing Open-FOAM V9, and the computational fluid dynamics model employed the SIMPLE pressure-velocity coupling scheme.For the geometric model used in this study, six cases with different grid numbers were tested and each simulation case converged after approximately 20 h of computation. The results obtained are shown in The validation of the simulation model was achieved by performing experiments on a self-designed experimental bench. To validate the numerical model in this study, identical cases to those presented in our group’s previous publication were employed . The expAt the outlet of the micro RQL combustor, the concentration of NO exhaust gas is measured using a flue gas analyzer, namely, the DF-FGL flue gas analyzer manufactured by Nanjing Fangnuo Environmental Protection Equipment Company, Nanjing, China. The comparison results in An artificial neural network (ANN), first proposed in 1943 , is an aAn auto-encoder (AE)  is a speThe VAE is a directed generative neural network that builds from the ideas of the AE. The main structure of the VAE, like that of the AE, consists of two networks, the encoder and the decoder. Unlike the AE, the input to the VAE is not encoded as a hidden space data point, but as a continuous probability distribution, which gives the VAE decoder the ability to obtain directed generation in resampling.z. Both distributions obey a normal distribution.The goal of VAE is to maximize the marginal likelihood of the output of the reconstruction, which is obtained by summing up the two terms. The first of these terms is the expected value of the log marginal mean value of the data set, which indicates the error value between the generated data and the real data, and the second term is the Kullback–Leibler dispersion between the posterior distribution and the prior distribution associated with the latent variable In the practical application of the algorithm, the first term, i.e., reconstruction data error, could be described using mean squared error (MSE). The second term—the Kullback–Leibler dispersion—could be calculated asThe specific meanings of each letter of Formula (5) can be found in reference .To minimize the loss function, the Adam optimization algorithm is used in order to optimize the values of each weight of the neural network.The dataset used in this study is the contour of internal NO distribution of the RQL low-heat-value gas micro combustor obtained from the simulation. The operating conditions employed are shown in The general architecture of the VAE model for this study is shown in In the application of VAE models, hyperparameters can affect the performance of the prediction. These include the mini-batch size, the number of hidden layers, the kernel size, the learning rate, the latent vector dimension, the number of convolutional channels, and so on. The total number of iterations was chosen as 5000 in order to ensure that the loss function of the model cannot continue to decline, and the number of hidden convolutional layers of the encoder and decoder was chosen as three because it was found that the number of layers higher than three made the loss function significantly higher during testing. The hyperparameters were optimized using a grid search approach and the optional values of the hyperparameters are shown in Using the hyperparameters selected in the previous section, the VAE model was built and trained for 5000 epochs with a mini-batch size of 2 and an Adam optimizer. The sequential order of the training sets was randomly disordered and the results of the visual reconstruction of the two sets of data from the last mini-batch of the training set are shown in The present study presents a new data-driven technique that predicts NOx distribution inside a combustor. The proposed method utilizes a variational autoencoder to analyze the data images obtained from experiments or simulations. It is applied to anticipate the NOx distribution in a micro RQL combustor designed to operate on low-heat-value gas fuel (CBM). By generating a database of 60 images, the optimal network structure is obtained through grid search. The generator network comprises three convolutional layers and one fully connected layer. The encouraging results demonstrate an excellent agreement between the generated and actual images.The proposed method is based on the prediction of images obtained during simulation or experiment, which can be applied in similar scenarios. For instance, particle image velocimetry results can be used to foresee the flow field inside a combustor, while planar laser-induced fluorescence results can be utilized to predict the distribution of specific flame clusters. This approach effectively reduces workload through the use of existing data and newly generated data, and offers potential for providing data for the eventual design of industrial combustors.Nevertheless, the limited size of the dataset, comprising only 60 images of different operating conditions, represents a major hindrance for the proposed method. To increase precision, practical applications require larger datasets generated through the use of a wider range of working conditions and the averaging of results from multiple images. This would necessitate modifying the current neural network architecture, requiring additional simulation or experimental work. Finally, due to the difficulty in understanding deep learning models, adjusting hyperparameters through grid search, rather than employing specific network architecture modifications, is essential.


Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes. With the increasing emphasis on health awareness, people are paying more and more attention to health matters. Seeing a doctor or undergoing health check-ups has become part of daily life. However, this has also led to a shortage of medical resources due to the high demand. This research focuses on one of the most high-demand reasons for check-ups, periodontitis. Periodontitis is a type of periodontal disease . The symThe motivation behind this project is to delegate the task of identifying dental symptoms to artificial intelligence (AI). With the rapid development of AI technology, there have been numerous AI applications in recent years, such as vehicle counting , financiA CNN-based automated recognition system for FI lesions has been developed in this research, and the proposed final model can achieve an accuracy of 94%, which is a 5% increase compared to .An adaptive threshold and an adjusted segmentation line operation have been proposed in this research to enhance fault tolerance, which has proven helpful for the research process and final learning results.The model for distinguishing between single-rooted and double-rooted teeth in this research has achieved a high recognition accuracy of 97%, which enables the proper classification of the sample data contained in a single image. Additionally, the proposed model for classifying single and double-rooted teeth can help in the collection and categorization of samples for medical and AI automation applications in the future.Radiographic diagnosis is the most widely used and important means of evaluating teeth in dentistry. The use of new X-ray techniques like cone beam-computed tomography (CBCT) and magnetic resonance imaging (MRI) has the potential to enhance the accuracy of diagnosing root canal bifurcations . This meThe structure of this research is as follows: In this research, the most important areas are image preprocessing and image masking, which were the main factors affecting CNN training and validation. In the image preprocessing step, the noise in the original PA image is removed. In the meantime, the characteristics of the diseases classified in this research can be enhanced. This step is crucial to the next PA image classification step as it obtains better recognition accuracy. The overall flow chart of this research is shown in One of the focuses of this research is to locate FI in the posterior molars from PA images. However, due to issues such as the angle of the X-ray beam or lighting, distinguishing the three targets in PA images  is often challenging. Additionally, PA images frequently contain noise and distortion, which make it tedious and time-consuming for dentists to locate the targets and might cause the possibility of misdiagnosis. Therefore, the aim of this step is to standardize PA images by eliminating interfering confounding variables and enabling the clear differentiation of the three targets. The pre-processing step comprises three parts: gray-level adjustment, Gaussian high-pass filtering, and adaptive thresholding, as shown in To improve the image adjustment and the efficiency of CNN training, the original RGB images are converted to grayscale images. While RGB images have three dimensions, grayscale images have only two dimensions and are more suitable for image adjustment. Furthermore, since the colors captured by PA images are grayscale, there is no loss of information in converting to grayscale . This coThe most challenging problems in this research are the image noise on the PA image and the indistinct contours of the target disease. Cui and Zhang  used freAfter filtering out the noise and highlighting the contours of PA images, the adaptive thresholding is performed. The main goal of this step was to find a suitable threshold for the image to perform binarization, dividing the image into two parts: teeth and gums, background and diseases, and alveolar bone. The accuracy of this step affected the determination of the target object in the later steps. This research tested the fixed threshold using the Otsu algorithm, as mentioned in ; the iteTo address the issue of possible extreme values in the images, Chen et al. adjusted the grayscale image to avoid this problem . Based oZmin) in the grayscale range of 60 to 120, the maximum value (Lmax) in the grayscale range of 30 to 90, and the maximum value (Rmax) in the grayscale range of 91 to 170 was identified from the preprocessed grayscale image. Second, the midpoint gray value (Zmid) was calculated using Equation (2) and used as the initial binary threshold value (T0). In the next step, the total number of pixels in the image  and the total number of pixels from T0 to 170 (Zcheck) was calculated to obtain all parameters. Finally, three verification methods, namely checking whether the X-distance between two pairs of values  was less than 15, Equation (3), and Equation (4), were utilized to validate the results. If any of these tests failed, the process entered into an iterative calculation, either by changing the first step to find the second lowest value or by adjusting the threshold value to meet the restrictions.After solving the extreme value variations, adaptive threshold values could be calculated. First, the minimum value  and the other pixel values are set to 0 (black). The binary image result was tested to ensure that the total mean of all pixels is greater than 0.6 and less than 0.85. The binary result is shown in The purpose of this step is to separate each tooth in PA image and create a database of images for each tooth. This step can effectively improve the target object recognition and reduce the interference from non-target objects before CNN training.This research tested the segmentation method proposed in previous research . The resNeighboring teeth segmenting lines inevitably lie on the interdental space, which is black  in the binary image of PA. In addition, a PA image can have up to five teeth, so this research calculated the vertical pixel sum of each row. According to the algorithm conditions, neighboring interdental spaces must be divided by at least one tooth distance. The result is shown in Unlike the rotation algorithm in other research , insteadAfter determining the optimal rotation angle and segmentation lines, automated masking was applied to the areas outside the two segmentation lines. This isolates the target object of interest (a single tooth) from external factors that may affect the accuracy of CNN recognition and can improve the learning effectiveness of CNN in recognizing the target object. This research proposes a method expanding each cutting line outward by 1/30th of the original image width to avoid damaging the target while the misplacement of the segmentation lines occurs. This step provides some error tolerance to the process. In addition, the extended area provides surrounding information of the target object that would help with CNN training. The segmentation result is shown in To validate the effectiveness and reliability of the proposed model, this study selected 128 lesion images and 140 normal teeth images from the database, as listed in The experimental environment used in this proposal includes hardware and software specifications as shown in The randomly selected validation dataset is tested for the proposed model after the transfer learning is accomplished. The validation accuracy is then calculated and evaluated. The confusion matrix can be calculated to evaluate the quality of the trained model.The adjustment of hyper-parameters is crucial for deep learning outcomes. The best combination of parameter settings can be slowly found through the appropriate fine tuning for each training process. The most frequently adjusted parameters in this experiment are max epoch, initial learning rate, mini batch size, and learn drop period. The suggested values of hyper-parameters are shown in A.OptimizerSGDM (stochastic gradient descent with momentum) and Adam (adaptive moment estimation) are two popular optimization algorithms used in deep learning to train neural networks. Although Adam is faster than SGDM in terms of training speed, SGDM exhibits better convergence and more stable training performance. Considering the current number of images in the training set, the advantage of using Adam’s fast convergence speed is not significant and may encounter convergence issues.B.Initial Learning RateThe rate at which the gradient descends during model training is affected by the initial learning rate. A small value can cause slow convergence and make the model prone to overfitting. Conversely, a large value can cause the model to learn too quickly and fail to converge, leading to divergence. After several trials, a stable learning rate of 1e-4 was determined for GoogLeNet.C.Mini Batch SizeThe mini batch size parameter determines how many data points are used to train the neural network at once. It is essentially a subset of the training set. If the mini batch size is too large, more data need to be considered for training. This leads to a more accurate correction direction, but the training process will take longer. On the other hand, if the mini batch size is too small, the correct direction will be biased because only a small amount of data are used in each iteration. However, this allows for more frequent corrections. For example, if the mini batch size is set to 20, this means that only 20 data points are used for training at a time. The mini batch size and epoch are closely related. If there are 400 data points in total and mini batch size is set to 20, then 20 training instances comprise one epoch.This section provides an overview of the model performance in this research. To monitor the training progress, a validation set was utilized. Based on the data presented in Classifying images into molars and non-molar teeth was the first trial in this research, as illustrated in To enhance the recognition accuracy of the model on PA images for FI, the training samples were filtered to focus on the variables that could affect recognition accuracy. This approach made CNN more sensitive to the disease, more focused on the target, and resulted in a higher learning effect. In this study, image screening was performed on the training samples to improve the recognition accuracy. The results in A preprocessing step for dental images was found to be crucial in the research process. Proper preprocessing is essential for training a CNN, as raw PA images may not provide accurate information without it. However, establishing a standard processing method applicable to all images is challenging due to interference and external factors unique to each image. A lack of preprocessing can make subsequent segmentation difficult and result in lower accuracy due to potential noise in the PA images. In this study, the accuracy of the trained model was significantly improved by segmenting multiple teeth in PA images into individual tooth images before training. The adaptive threshold preprocessing method designed in this study accurately defined the cutting points during image segmentation, leading to improved segmentation accuracy. Preprocessing techniques like Gaussian high-pass filtering also reduced the inclusion of non-target regions. These improvements in segmentation accuracy enhanced symptom enhancement and overall model accuracy, highlighting the importance of preprocessing in this study. Furthermore, an automated process was developed to assist dentists in identifying bifurcations in PA images without causing visual or mental fatigue. During the CNN training phase, several mainstream image recognition models were tested, all achieving judgment accuracies above 90%. Following preprocessing and initial CNN recognition, the model effectively located teeth with potential diseases, with accuracy comparable to visual judgment. Specifically, GoogLeNet and AlexNet achieved judgment accuracies close to 95%. In comparison to the method proposed in , this stThe results indicate that the proposed model exhibits mature and successful problem-solving capabilities, producing results highly similar to human judgment. However, there is room for improvement in the model’s ability to further classify FI disease locations and enhance existing image enhancement techniques. In the future, this research aims to improve the automated process flow by integrating it within a chip, augmenting databases and developing a GUI interface. This will allow the automated process flow to be successfully integrated into clinical operations for dentists and result in a reduction in their workload and a shortening of patients’ treatment time.In general, this research study highlights the importance of preprocessing in improving the accuracy of a CNN model for detecting furcation involvements (FI) in dental images. The findings demonstrate that without proper preprocessing, raw PA images do not provide accurate information for training the model. The study proposes various preprocessing techniques, such as adaptive thresholding and Gaussian high-pass filtering, which significantly enhances the segmentation accuracy and the overall performance of the model. Additionally, an automated process was developed to assist dentists in identifying FI in PA images, offering a reliable and efficient alternative to visual judgment. The trained CNN model, particularly utilizing GoogLeNet and AlexNet architectures, achieved high accuracy in locating teeth with potential diseases, surpassing the performance of previous methods. Overall, this study provides valuable insights into the significance of preprocessing and the potential of CNN models in dental image analysis. The results contribute to the development of a high-accuracy medical assistance system, reducing the workload for dentists and improving the quality of dental care. Further research can build upon these findings to refine the model and explore additional enhancements for accurate FI detection and classification.


Background: A balloon dilatation catheter is a vital tool in percutaneous transluminal angioplasty. Various factors, including the material used, influence the ability of different types of balloons to navigate through lesions during delivery. Objective: Thus far, numerical simulation studies comparing the impacts of different materials on the trackability of balloon catheters has been limited. This project seeks to unveil the underlying patterns more effectively by utilizing a highly realistic balloon-folding simulation method to compare the trackability of balloons made from different materials. Methods: Two materials, nylon-12 and Pebax, were examined for their insertion forces via a bench test and a numerical simulation. The simulation built a model identical to the bench test’s groove and simulated the balloon’s folding process prior to insertion to better replicate the experimental conditions. Results: In the bench test, nylon-12 demonstrated the highest insertion force, peaking at 0.866 N, significantly outstripping the 0.156 N force exhibited by the Pebax balloon. In the simulation, nylon-12 experienced a higher level of stress after folding, while Pebax had demonstrated a higher effective strain and surface energy density. In terms of insertion force, nylon-12 was higher than Pebax in specific areas. Conclusion: nylon-12 exerts greater pressure on the vessel wall in curved pathways when compared to Pebax. The simulated insertion forces of nylon-12 align with the experimental results. However, when using the same friction coefficient, the difference in insertion forces between the two materials is minimal. The numerical simulation method used in this study can be used for relevant research. This method can assess the performance of balloons made from diverse materials navigating curved paths and can yield more precise and detailed data feedback compared to benchtop experiments. Peripheral artery disease (PAD) is a long-term arterial disease caused by atherosclerosis in the peripheral vessels, leading to complications in limbs such as pain, ulcers, gangrene, and reduced function . PercutaFinite element analysis (FEA) has become a powerful tool for the optimization process of coronary stents and balloon catheters. There have been many studies that have conducted experiments and simulations on balloons from various perspectives. Geith et al.  proposedHowever, to our knowledge, there have been no studies comparing simulated folding and tracking performances between two different materials. Although Sirivella et al.  successfIn this article, we performed a simulation study using the FEA method to investigate the folding of nylon and Pebax material balloons, as well as the trackability of balloon catheters passing through a 90-degree ideal blood vessel under identical conditions. In addition, a benchtop experiment was conducted to further validate the simulation results.The bench test offered clear insights into the performance of the balloon dilatation catheter within tortuous anatomical structures. The test evaluated the insertion forces of folded balloon catheters made of different materials as they navigated grooves—a simulation of the transport conditions within actual curved vessels. The quality of trackability can be evaluated based on the measured insertion force. Therefore, bench tests can provide a reference for balloon design and clinical selection .The tests were conducted using a test tracking fixture , as spec® 7033 SA 01 MED resin (a thermoplastic elastomer from Arkema). They are specially designed to meet the stringent requirements of medical applications such as minimally invasive devices, and they both exhibit good biocompatibility [Nylon-12 (polyamide) and Pebax are the two materials most commonly used in balloons ,25,26. Ttibility ; the modtibility  are showtibility .*MAT_89 used the Cowper–Symonds constitutive model . The equ−1) yield stress of the material, P and C are material constants determined by test [Taking the logarithm of both sides of the equation and simplifying it, we obtain: by test .During the process of manufacturing balloon catheters, cylindrical balloons are first compressed into a folded shape using a balloon-folding machine. Then, they are encapsulated into a smaller diameter using a balloon-crimping machine. Therefore, the balloons are in a folded state before inflation. In order to make the simulation more realistic, the crimping process, which involves the generation of pre-stress in the balloon, should be taken into account.The balloon model is derived from the Euphora balloon dilation catheter , featuring a principal diameter of 4 mm and a distal shaft diameter of 0.91 mm. The balloon’s thickness is 0.02 mm  Figure . The pleIn order to more accurately simulate the working process of a balloon-crimping machine, the simplistic cylindrical model that was previously widely used was replaced with a novel design composed of 8 plates combined in a specific formation. They formed a chamber that could be enlarged or reduced . Then, it rotated along the axis, causing the three-fold to rotate counterclockwise by 120 degrees. Given that both ends of the balloon were secured to the shaft and a specific negative pressure was exerted on the balloon’s inner surface at that moment, the balloon tightly adhered to and was fixed onto the shaft, while the three outward folds rotated with the pleating tool and attached to the shaft.At the initial moment, both ends of the balloon were fixed to the shaft. After the pleating tool compressed the balloon, a negative pressure of 6.5 atm was applied to the inner surface of the balloon , causingThe contact between the balloon and the pipeline was set to have an ideal friction coefficient of 0.02 , while tA mesh convergence study was conducted to ensure that the calculation results were not significantly affected by variations in the number of mesh elements . In thisBased on the mesh convergence study, the balloon model was discretized into four-node, fully integrated shell elements with five through-shell thickness integration points. It was discretized using 213,539 mixed shell elements with a thickness of 0.02 mm, and the shaft was discretized using 34,438 mixed solid elements. All other components were set as rigid bodies, and their mesh densities had little effect on the simulation results.The model discretization was performed using ANSA v21.0 , and the simulations were performed on 16 CPUs of an AMD EPYC 7532 (GHz) workstation using LS-DYNA Release 13 .The pleating tool compressed the balloon from its initial form a into a The stress distribution and effective plastic strain information can help us understand how the balloon behaves under different loading conditions, such as a folding state or bending state. This allows for an evaluation of the balloon’s mechanical integrity and the identification of any areas prone to excessive stress or deformation. After radially crimping the balloons to achieve the same diameters, the stress levels of the nylon balloon were found to be higher than those of the Pebax balloon. The maximum stress was located at the proximal end, with 61.9 MPa for the nylon balloon and 55 MPa for the Pebax balloon, which is consistent with the results of previous studies . The posThe insertion force between the balloon and the pipeline was monitored during the process of pushing the balloon into the pipeline . The balSimilar to the experimental results, the simulation results also exhibited a gradual increase in the force value for the initial 20 mm of the inserting displacement. In this study, the simulation was conducted specifically for the process of the balloon being inserted into the pipeline rather than the process of the balloon being pushed out of the pipeline. Therefore, unlike in the experiment, the contact force did not decrease after reaching its maximum value. In the experiment, the insertion force of the Pebax balloon was significantly lower than that of nylon. However, in the simulation, the insertion force of nylon was only slightly different and higher than Pebax’s at certain moments. The maximum insertion force of the nylon balloon in the simulation was slightly larger than the experimental value of 0.866 N.The surface energy density provides valuable information about the interaction between the balloon material and the surrounding environment. It can affect the balloon’s ability to navigate through blood vessels, interact with the vessel wall, and perform its intended functions effectively. The surface energy density of the pipeline with balloons fully inserted is shown in With the same shaft, the nylon balloon exhibited a greater insertion force than the Pebax balloon in the experiments. In the simulations, the coefficient of friction between the balloon and the pipeline was set to the same value for both cases for better variable control . The simThe experimental results seem to indicate that the stiffer balloon material has more friction during insertion and requires more insertion and retraction forces because it exerts more pressure on the wall than the softer material. However, numerical simulations revealed a negligible difference in the insertion forces between the two materials, contrary to the experimental results. This suggests that the influence of material stiffness on the balloon insertion force is minimal when surface characteristics are disregarded. Therefore, the significant disparity in the insertion force test results between the nylon and Pebax balloons in the experiment is attributed to the differences in their surface characteristics rather than nylon being harder and thus requiring greater force and Pebax being softer and thus requiring less force. This finding is counterintuitive.Both experiments and simulations demonstrated that as the balloon progresses deeper into the pipeline and subsequently retreats, the frictional force initially increases and then decreases. This suggests that the friction force during the insertion and retraction processes correlates with the length of the balloon within the pipeline. Thus, selecting balloons of different lengths may be necessary to ensure that the friction force during insertion/retraction is not too high for different patients ,37. Our After balloon folding, the deformation near the connection with the shaft is substantial. The element with the highest stress also occurs in this area, suggesting that this region dictates the overall balloon’s stress concentration level . This imWrinkling arises when the balloon undergoes compression on its concave side while passing through the pipeline instead of stretching on the convex side. If the bend only stretches the balloon on the convex side, it only changes the length of the balloon along the axial direction without negatively affecting the balloon’s deliverability. However, squeezing the balloon on the concave side will cause some folds to exceed the original diameter, resulting in an increased cross-sectional profile of the balloon during bending. This condition is unfavorable for navigating through tortuous anatomy  and can In this study, the assumption of using the same friction coefficient for both materials in contact with the pipeline is a simplified model. In reality, the contact properties between different materials may differ, which is a limitation of this study. In addition, fluid–structure interactions were neglected, as in previous studies ,40,41 thThis study simulated the folding process of two distinct balloon materials and inserted the folded balloons into a simulated pipeline to monitor the insertion forces. Experimental tests were conducted on both types of balloons to measure the actual insertion forces. Through comparing the experimental data and numerical simulation results, it was found that under the assumption of the same friction coefficient in the numerical simulation, the difference in insertion force between the two materials was insignificant. This contrasted with the experimental results in which the harder nylon balloon demonstrated a significantly higher insertion force in comparison to the Pebax balloon. This indicates that the impact of material stiffness on the balloon insertion force is minimal when the surface characteristics of the materials are not considered. Therefore, in practical production and application, for balloons with poor trackability, emphasis should be placed on improving surface smoothness, such as by adding lubricious coatings. Additionally, the stress and effective plastic strain results after folding showed that the harder nylon balloon experienced greater stress after folding, while the softer Pebax balloon experienced a higher level of effective plastic strain. This suggests that nylon balloons are likely to exert greater pressure on the curved vessel wall. This simulation of balloon folding and insertion forces offers a generalized methodology for simulating the trackability of balloons made from different materials. This would be advantageous for selecting suitable balloon materials for various scenarios and may provide valuable insights for the design of balloon structures and folding methods.