somatostatin receptor Although D PDA could overcome the disadvantages of color

Although 3-D-PDA could overcome the disadvantages of color Doppler and pulsed Doppler and is more sensitive to low-speed blood flow, its current clinical value in endometrial–subendometrial vascularity assessment remains limited in view of the controversial results published to date (Kim et al. 2010; Sardana et al. 2014). As a major breakthrough in ultrasound imaging in the past decade, CEUS has been widely used in clinics, especially in evaluating the blood perfusion of such organs as heart, liver and somatostatin receptor (Carr and Lindner 2008; Quaia et al. 2009; Sugimoto et al. 2007). We believe that CEUS may be an ideal tool for assessing endometrial and subendometrial perfusion to evaluate uterine receptivity. Although it would be interesting, a comparison of CEUS and 3-D-PDA was beyond the scope of this study. Such a comparison may be conducted in future work.
In an attempt to determine which index is most sensitive, further comparisons of TIC parameters among the different phases of the menstrual cycle were made. It turned out that there was no significant difference in the change in endometrial PI and AUC or subendometrial AUC throughout the menstrual cycle. Given the low-velocity flow profile and tortuous nature of the spiral arteries, which supply blood to the functional layer of the endometrium, a smaller number/area ratio and unsteady perfusion time might be possible. As a result, it might be more difficult to detect the change in perfusion during a cycle in the endometrial region compared with the subendometrial region using 2-D ultrasonography. Combining CEUS with 3-D ultrasonography, which is capable of volume calculation, might overcome the problems associated with 2-D ultrasonography. On the other hand, as a comprehensive evaluation index of perfusion, AUC is closely related to the distribution volume of the contrast agent, blood velocity and mean transit time. The reliability of AUC evaluation depends on the stability of both factors (Strohmeyer et al. 2000; Taylor et al. 1998). AUC did not significantly differ during the menstrual cycle in the subendometrial region of either group, possibly because of the unsteadiness of blood flow time resulting from the narrow diameter and uneven space distribution of the vessels inside the endometrium and the adjacent endometrium.
Subendometrial PI, however, underwent significant periodic changes during the menstrual cycle and was positively correlated with MVD in both groups, suggesting that subendometrial PI might be the most sensitive index in assessment of endometrial microcirculatory perfusion. The positive correlation between subendometrial PI and MVD was in agreement with the findings of previous research on tumors (Rovai et al. 1995; Wang et al. 2007). The MVD values obtained in this study indicated that the women with unexplained infertility had scarce microvessels. Likewise, subendometrial PI decreased significantly in participants with unexplained infertility. Thus, we concluded that subendometrial PI is the most sensitive index compared with endometrial PI, endometrial AUC and subendometrial AUC.
Another interesting observation was that the subendometrial PI and endometrial MVD in the study group were significantly lower than those in the control group during most of the menstrual cycle, except the implantation window. It seemed surprising that there were no differences between the two groups during the implantation window. A possible explanation is that our study was set in a menstrual cycle, not a real implantation. When implantation occurs, the endometrium undergoes certain physiologic changes to support the implantation (Strowitzki et al. 2006), which is quite different from what happens to the endometrium when implantation does not occur. In the light of the adverse effect biopsy might have on pregnancy, all participants were told to use non-drug contraception during the test. Therefore, the hypothesis is that the differences between unexplained infertility and healthy fertility may have occurred before the implantation window. Defective endometrial blood supply before the implantation window may be a cause of unexplained infertility. We consider the interpretation and clinical application of this finding meaningful. First, measuring subendometrial PI using CEUS in women with unexplained infertility in the late proliferative phase and ovulation period would allow physicians to make in advance decisions on the suitable cycle for conception. Second, as several medications are available today (Battaglia et al. 1999; Malinova et al. 2013), if physicians can identify patients with poor uterine perfusion early and easily, it would help them to decide the treatment and evaluate the subsequent effectiveness of treatment.

As most organs in the neck have good accessibility

As most organs in the neck have good accessibility for sonographic examination, sonography had already represented the most important imaging method in this area. Various investigations on the diagnostic use of elastographic modalities in the neck have been conducted, predominantly on the evaluation of salivary and thyroid gland pathologies, but also for the assessment of suspicious cervical purchase galanin nodes (Choi et al. 2013; Lyshchik et al. 2007). The application of elastography in the differentiation of thyroid nodules has been investigated by various study groups, and it was reported that elastography not only provides further information on tumor characteristics, but also has good diagnostic performance in discrimination between benign and malignant nodules, possibly reducing unnecessary biopsies (Grazhdani et al. 2014; Hou et al. 2013; Lin et al. 2014; Park et al. 2009; Zhang and Han 2013; Zhang et al. 2013, 2014a, 2014b, 2014c, 2015). The evaluation of tissue characteristics with elastography in salivary gland pathologies proved to be beneficial in the assessment of solitary, circumscribed salivary gland lesions and in the evaluation of diffuse salivary gland diseases. Acoustic radiation force impulse (ARFI) imaging reveals alterations in salivary glands after radiation therapy of the head and neck and is a reliable tool for the identification of early disease stages in primary Sjögren\’s syndrome (Badea et al. 2013; Knopf et al. 2015; Mansour et al. 2012, 2015).
For qualitative elastographic modalities, such as RTE and VTI, interpretation of the generated elastogram by the sonographer is required, whereas ARFI imaging is a quantitative method and is therefore postulated to be less operator dependent (Fukuhara et al. 2014). However, it must be taken into consideration that even though no further interpretation of the elastogram (e.g., with scoring systems) is required, there have been reports on various factors influencing the acquisition of ARFI images. One factor influencing the results of ARFI imaging in salivary glands is the degree of pre-compression (Mantsopoulos et al. 2015). Other limitations of ARFI imaging in the neck are the depth of the selected region of interest (ROI) and tissue characteristics, which can influence shear wave propagation. These findings illustrate ventricle there is in fact operator dependence in ARFI imaging.


The six examiners performed 3,600 single ARFI measurements (1,200 measurements of the thyroid gland and 1,200 each of the submandibular and parotid glands), which are the basis for the calculations. The mean values (±standard deviation) of all measurements are summarized in Table 1.
Moderate agreement was observed between experienced and inexperienced examiners for ARFI imaging in thyroid and salivary glands considered together (ICC = 0.457) (Fig. 2). Within the experienced group, the three examiners reached moderate inter-rater agreement (ICC = 0.527). Within the inexperienced group, the three examiners achieved moderate inter-rater agreement (ICC = 0.542). Intra-rater reliability revealed strong agreement in the experienced group (ICC = 0.764) (Fig. 3) and the inexperienced group (ICC = 0.680) (Fig. 4). The Bland–Altman plot (Fig. 5) revealed no trend toward systematic over- or underestimation between the two groups. Linear regression analysis did not indicate a trend of difference across the mean ratings.
Evaluation of inter-rater agreement only in ARFI imaging of the salivary glands yielded fair agreement (ICC = 0.327) between the groups. Within the experienced group, the three examiners reached fair inter-rater agreement (ICC = 0.301) for ARFI imaging of the salivary glands. Within the inexperienced group, the three examiners achieved fair inter-rater agreement (ICC = 0.328). The intra-rater reliability again revealed strong agreement in the experienced group (ICC = 0.731) and the inexperienced group (ICC = 0.645). The Bland–Altman plot (Fig. 6) revealed no trend toward systematic over- or underestimation between the two groups. There was no trend of difference across the mean ratings.

Contrast enhanced ultrasound may represent a valuable alternative to

Contrast-enhanced ultrasound may represent a valuable alternative to CT and MR imaging in the differentiation of bowel inflammatory strictures from mural fibrosis. Some previous studies have reported that CEUS can differentiate inflammatory from fibrotic bowel lumen strictures (Bodily et al. 2006; Martínez et al. 2009; Quaia et al. 2012; Ripollés et al. 2013). In those studies, the patient population included was limited, and the quantitative analysis was performed on an unlinearized scale of echo-signal intensity values, which limits the precision and reproducibility of analysis.


On visual analysis, images of patients with inflammatory strictures revealed a transmural SAG supplier enhancement in all cases, whereas images of patients with fibrotic strictures revealed transmural (n = 19 patients), or submucosal (n = 4) or absent/faint (n = 2) contrast enhancement. The correlation between visual score and endoscopy grading was not significant (ρ = 0.55; p = 0.55).
Table 2 summarizes the results of the quantitative analysis with respect to the different kinetic parameters in patients with an inflammatory or fibrotic strictures (Fig. 1). The AUC was related to the endoscopic grading (ρ = 0.77, p = 0.03). The non-parametric Spearman correlation coefficient between the kinetic parameters measured in the terminal loop and in the adjacent mesentery ranged from 0.113 to 0.42 (p = 0.21).
Inflammatory strictures differed from fibrotic strictures in peak enhancement, wash-in rate, wash-in perfusion index, AUC, AUCWI and AUCWO (Figs. 2 and 3). The quality of fit (expressed as a percentage) between the echo power signal and the theoretical lognormal curve before and after treatment was 93.76 ± 4.82 and 88.93 ± 8.33 in responders and 96.26 ± 2.32 and 82.47 ± 29.29 in non-responders.
Peak enhancement, rise time, time to peak enhancement, wash-in and wash-out rate and wash-in perfusion index were found not to be predictors of inflammatory stricture on univariate analysis, whereas AUC, AUCWI and AUCWO were found to be predictors of inflammatory stricture (p = 0.02–0.04).
Table 3 summarizes the results of ROC curve analysis for identification of the optimum cutoff value for peak enhancement, AUC, AUCWI and AUCWO to differentiate predominantly inflammatory or fibrotic strictures among patients with CD with high sensitivity (77.8–86.1) and specificity (85.7–100).

Crohn\’s disease strictures should be classified into (predominantly) inflammatory or fibrotic (Peyrin-Biroulet et al. 2012). In this prospective study, we found that inflammatory ileal strictures may be differentiated from fibrotic strictures based on different kinetic parameters calculated from the time–intensity curve after microbubble contrast agent injection including peak enhancement, AUC, AUCWI and AUCWO. We identified different cutoff values to differentiate inflammatory from fibrotic ileal strictures with high sensitivity and specificity even though patients with inflammatory strictures were found to have wide-ranging values for each of these semi-quantitative parameters. The wide ranges of values observed in patients with inflammatory strictures were likely related to the linear scale employed for the quantitative analysis of echo-signal intensity and to the variable grade of inflammation within the bowel wall in patients undergoing different pharmacologic treatments. On the other hand, the ranges of values for different kinetic parameters were more narrow in patients with fibrotic strictures likely because of the more uniform level of mural fibrosis.
We observed a very high quality of fit between the echo power signal and the theoretical curve both in responders and in non-responders, which ensured a reliable quantitative analysis. Our study did not confirm the results of previous studies that reported the time to peak enhancement was lower in patients with inflammatory strictures (Prassopoulos et al. 2001). In this study, we started recording from the time microbubbles were visualized in the scanning plane instead of the microbubble injection time. This eliminates the influence of the microbubble arrival time, which may be quite different in patients with similar CD activity principally because of the different cardiac output.

Pyrrolidinedithiocarbamate ammonium manufacturer br Acknowledgements br Introduction Chlorine dioxide ClO was thought


Chlorine dioxide (ClO2) was thought to be one of the most ideal disinfectants for water disinfection because of its broad-spectrum inactivation to pathogenic bacteria, such as bacteria, viruses, algae, and animal planktons [1,2]. Over the years, major factors that influence the disinfection process, such as organic substances and suspended particles, have been investigated. Particles in water could protect bacteria from irradiating by disinfectants; thus, microorganisms entrapped in suspended solids would survive after disinfection [3–5]. To solve the problem, sonication-combined technology has been highly recommended recently, and a number of studies have reported the combination of ultrasound (US) with ultraviolet (UV), chlorine, or ozone (O3) [6–11]. In these hybrid systems, the mechanical shear force produced by US helped break larger particles into small ones and deagglomerated flocs, exposing particle-associated bacteria to disinfectants, and therefore improving the disinfection efficiency [12]. Torben Blume demonstrated that even low US Pyrrolidinedithiocarbamate ammonium manufacturer (30W/L, 20s) was sufficient to provoke a satisfactory change in the particle distribution. Thus, sonication combined disinfection was suspected to be more energy-efficient than US disinfection alone [13,14]. Previously, research was conducted on the effect of ultrasonic pretreatment with ClO2, and a 10-min sonication with US input power densities of 75, 150, and 300W/L were conducted, respectively, which led to a significant enhancement in Escherichia coli elimination [15]. However, the sonication time and US input power density applied in the aforementioned research led to high energy consumption, which was equal to the lowest specific energy consumption of 45kJ/L, and therefore was not economical. Moreover, ultrasonic simultaneous disinfection was not conducted in this study.
Meanwhile, ClO2 disinfection is proven to be an alternative disinfectant to chlorine because it forms a significantly smaller amount of harmful organic disinfection by-products (DBPs), such as trihalomethanes (THMs) and haloacetic acids (HAAs). Thus, ClO2 has taken the place of hypochlorite in some newly built or retrofitted wastewater treatment plants (WWTPs) in China. To the best of our knowledge, past studies have reported the formation of DBPs with ClO2 disinfection of drinking water [16,17] and seawater [18], while only a few studies have addressed the formation of DBPs when treating wastewater with ClO2. Moreover, wastewater is more complex than drinking water because the former contains various matter, such as natural organic elements, synthetic organic compounds, and soluble microbial products [19]. Besides, chlorine dioxide can react with both organic and inorganic compounds, thus forming inorganic by-products, such as chlorite (ClO2−) and chlorate (ClO3−) ions, which are also suspected to pose potential risks to human health [20]. Although DBPs concentrations are not currently regulated in China for wastewater discharge, they do contain a substantial amount of toxicologically important compounds and pose health risks to human beings and the environment. Thus, investigating the production of DBPs during the secondary effluent disinfection by ClO2 is necessary. According to this requirement, clarifying the potential DBPs for ClO2 disinfection and developing US as both enhancement of ClO2 reduction demand and measure to minimize or eliminate the disinfection by-products might be effective for further optimum application of ClO2.
In this study, secondary effluents from a municipal WWTP in Beijing were used. The research objectives are (1) to investigate the potential enhancement effect of US as pretreatment and simultaneous disinfection with ClO2; (2) to study the possible effect of US in the two disinfection processes; and (3) to evaluate the impact of potential DBPs production by US, including trichloromethane (TCM), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), chlorite (ClO2−), and chlorate (ClO3−).

Immobilized enzyme used in this study was Novozym

Immobilized enzyme used in this study was Novozym-435. In Novozym-435 lipase enzyme is immobilized on Lewatit VP OC 1600, a divinylbenzene-cross-linked poly (methyl methacrylate) resin produced by Lanxess Germany. The average particle size is around 0.6mm with pore AZD7762 cost size around 4.5nm [26].

In current study application of ultrasound to a suspension of immobilized enzyme particles in reaction medium is to be modeled. For this two-step modeling approach is adopted as

Particle image velocimetry
To validate results of simulation catalyst particle velocities were measured using particle image velocimetry (PIV). In PIV particle movement is captured by a high-speed camera (motion pro Y4 shown in Fig. 6). The camera is capable of capturing up to 5000 frames per second (fps). The images are then analyzed using a particle image velocimetry program in MATLAB, called PIVlab. The program detects the changes in position of particles between two consecutive images. From the distance traveled by the particle, the program determines the speed of particles in the region of interest (ROI).

Results and discussion
In this section results of simulation are presented and discussed. In Table 2 parameters of simulation are given. As shown in table, transducer is operated at a constant frequency of 24kHz. The variables investigated are ultrasonic power and position of transducer. The possibility of using multiple Sonotrodes is also investigated.
The results from the simulations are velocity of each individual particle inside the reactor, at different time step. Because only a few particles have high velocity, the data has to be treated in a statistical manner; and therefore it AZD7762 cost is presented in histograms. A histogram shows how many particles have a certain velocity at a certain physical time step. The more particles with lower velocity, the more positively skewed the histograms are [33].
Series of simulations are needed to investigate the influence of variables in Table 2. To have comparable data sets, simulation results have to be taken at steady state condition. Determination of steady state can be done by plotting the results in the form of histograms (particle velocity) at different time steps. When the histograms are stable (no variations over time), autonomic system can be concluded that the system has reached steady state. For comparison, the data sets are taken at the same time steps.
For sonicated reactor, there are two aspects of simulation results:
Fig. 7 shows the acoustic pressure field (a) and corresponding particle trajectory (b) simulated for employed reactor at 24kHz and 80W, with 7mm Sonotrode (positioned in the middle of reactor).
From comparison of acoustic pressure field and particle trajectories, it can be observed that applied ultrasound is capable of keeping catalyst particles in suspension and as expected the particles orientate according to acoustic pressure field (between regions of low and high pressures). Distribution of these low and high pressure regions is strongly influenced by frequency. For the applied frequency the wave length is 57mm, which means that distance between higher and low pressure regions is large. Comparing it to dimensions of the reactor it is obvious that there will be few regions of higher and lower pressure. If we want to have more regions of higher and low pressure we will have to decrease wavelength by increasing frequency (detailed investigation of this effect is topic of a further study). Fig. 8 is a comparison of median particle velocity in reactor, for different power inputs i.e. 20, 40, and 80W. As expected an increase in power input to the reactor causes an increase in particle velocity. However, this increase in velocity from one power level to next is not significant. In Fig. 9 a histogram for particle velocity distribution at three different power inputs is shown. Here is also obvious that when we go from one power level to the next we get more particles with higher velocity. However, this increase in number of particles with higher velocity is not significant. Therefore, we cannot expect a uniform particle velocity distribution by inserting single US source into medium.

In Table the ratio between

In Table 1, the ratio between steady-state intensity to the intensity of 10th pulse is summarised for all three frequencies at different power levels. It could be seen that the growth rate of SCL Exendin-3 (9-39) amide is much faster than that of SL population at 358kHz and 647kHz, whereas minimal difference is noticed at 1062kHz. This effect is significant at low power level and the difference between the growth rates of SL and SCL population becomes less significant at high power levels. Table 2 shows the absolute SL and SCL intensities observed when steady-state populations are reached.
The data presented indicates that the SCL bubble population is greater than SL bubble populations at all frequencies and power levels. In our previous study [21], images showing SL and SCL at a similar frequency range confirm the relatively large SCL bubble population (Fig. 1). Other observations that need to be highlighted are the increases in SL and SCL with increase in power for all three frequencies, an increase in SL with increase in frequency and a decrease in SCL with an increase in frequency.

Prior to discussing the results, it is worth to highlight some aspects of acoustic cavitation. Depending upon the type of cavitation, transient or stable, bubbles could become active instantly or after a several acoustic cycles. Tronson et al. [23] have shown that cavitation activity becomes instant at 20kHz due to transient cavitation. It needs to be clarified that the description of transient and stable cavitations refers to Leighton’s discussion [24]. Transient cavitation bubbles instantly grow to resonance size and undergo collapse within a few acoustic cycles – this occurs within a few milliseconds time scale as reported [23]. Stable cavitation (sometimes referred to as repetitive transient cavitation) takes significantly longer time to develop, in particular in pulsed acoustic field. The frequencies used in the current study are in the range 358kHz–1062kHz, where stable cavitation dominates.
The bubble nuclei present initially when acoustic pulses are delivered to a liquid are well below the resonance size. The bubbles grow by rectified diffusion and coalescence in an acoustic field [25]. A new observation reported in the current study is the difference between the growth of SL bubbles and SCL bubbles (Figs. 2–5). SL originates from the collapsing bubbles and is correlated with the bubble core temperature [24]. SCL on the other hand occurs due to the reaction between OH radicals generated within the bubbles and luminol present in the solution [17]. It has been previously suggested the maximum size reached by SCL bubbles may be relatively smaller than that of SL bubbles [21,22]. In other words, SL bubbles are “hotter” than SCL bubbles. Considering this speculation, it could be expected that a relatively larger population reach the size required for SCL at a given time during the growth of bubble population. This could explain the observation that more acoustic pulses are required to reach a steady-state population of SL compared to that of SCL, as observed in Figs. 2–5.
In terms of the power effect seen at each frequency, the number of acoustic pulses required to reach a steady-state SL decreases with an increase in acoustic power (Table 1). At higher acoustic power levels, the growth of bubble size and hence the SL bubble population would increase due to increased rectified diffusion and bubble–bubble coalescence. However, it is interesting to note that the number of pulses required to reach a steady-state population of chemically active (SCL) bubbles does not seem to depend upon the acoustic power significantly. Possible reason is that the growth rate of SCL bubbles has reached a limiting value even at lowest power level used and an increase in power simply increases the overall bubble population as observed in the steady-state SCL intensity shown in Table 2. This is also supported by the fact that the ratio between steady-state intensity and the intensity of 10th pulse remain almost constant about 1.0–1.3 except for 5W (Table 1).

Some commercial systems e g ACUSON Sequoia by

Some commercial systems, e.g., ACUSON Sequoia 512 by Siemens Healthcare (Erlangen, Germany), iU22 by Philips Healthcare (Amsterdam, The Netherlands) and modified versions of Logiq 9 scanner by GE Healthcare (Waukesha, WI), actually employ different types of coded transmission. However, not much literature has been made available on the completion of pulse lxr agonist methods in real-time imaging systems using array probes. This paper shows that real-time coded imaging can be obtained by properly processing beamformed, demodulated and down-sampled echo-data. For this purpose, the firmware and the software of the ULtrasound Advanced Open Platform (ULA-OP) [19] were suitably extended. By using its multiple Field Programmable Gate Arrays (FPGAs), it is possible to generate arbitrary coded waveforms that will be transmitted from 64-elements of a linear array probe. The RX data will be beamformed, demodulated and down-sampled before being compressed in real-time (less than 20μs delay) by a mismatched filter.


Experimental results


This paper has presented a programmable real-time coded imaging system, which exploits standard hardware resources to implement baseband pulse compression at low computational cost. The new system has been used to perform a thorough experimental investigation of tissue attenuation effects on chirp signals. Such investigation has confirmed that the effective time-bandwidth product may be significantly lower than its expected value [5]. Accordingly, the compression gain and the resolution get worse while the SLL improves. The received spectrum is not simply shifted [1], but also asymmetrically shrunk by attenuation. Although the spectral deformations due to tissue attenuation cannot be totally compensated, a way to reduce their effects on the compressed pulse has been presented. When the main issue is increasing the penetration depth, and not the resolution nor the sidelobes reduction, it is convenient to adapt the receiver bandwidth to the bandwidth received from the depth of interest, and this may be obtained by using a suitable demodulation frequency offset. Furthermore, the resolution can be recovered by using less sharp RX weighting windows, since relatively low SLLs are guaranteed by the weighting effects inherently produced by tissue.
Compared to other approaches, like, e.g., those aiming at an equalization of the spectrum [6,8,14,15], the method characterizes for its simplicity and easy implementation at the expense of a reduced compensation capability.

This work has been supported by the Italian Ministry of Education, University and Research (PRIN 2010-2011) and by the European Fund for Regional Development for the 2007–2013 programming period (POR FES 2007-2013 CRO MIMAUS Project)

Ultrasonic elliptical vibration cutting (UEVC), which is a promising cutting technique especially in cutting difficult-to-cut materials, weak stiffness parts and high precision components, was first proposed by Shamoto and Moriwaki [1]. The working principle of the cutting technique is that the tool set at the end of the UEVC device vibrates in an elliptical locus in the plane formed by the cutting direction and the chip flow direction as shown in Fig. 1.
The relative motion locus of tool can be expressed as:where is the cutting speed, a is the amplitude of cutting direction vibration (i.e., x-axis), b is the amplitude of the chip flow direction vibration (i.e., y-axis), f is the vibration frequency of the tool, φ is the phase shift in vibration.
The benefits of UEVC become noticeable when the vibration velocity of the tool in the cutting direction is higher than the cutting speed (Eq. (3)). This permits an intermitted cut, so the tool is separated from the workpiece in each cycle.where ()max is the maximum vibration velocity of the tool in the cutting direction.
Compared to 1-directional ultrasonic vibration cutting, in the UEVC, a pulling upward movement processed by the elliptical locus of the tool improves the rake angle of the tool, avoids the friction between the flank face of the tool and the machined face of the workpiece, assists to pull out the chips away from the workpiece during the vertical vibration motion of the tool and reduces the cutting force and cutting energy significantly [1–4]. So significant advantages of UEVC were obtained in lots of works with intermittent cutting as summarized below: saving tool life [5,6], improving surface finish and form accuracy [2,7,8], enabling the use of diamond tools for cutting ferrous materials [5,9], improving cutting stability [3,5,10], and suppressing burr and regenerative chatter [5,11,12].

where CS CA are group velocity for

where CS0, CA0 are group velocity for extensional and flexural modes, and Δt is the time difference in arrival time for different modes. As it can be seen in Fig. 3, at frequency range up to 260kHz, S0 mode propagates faster than A0, so this mode will reach the sensor earlier. To calculate the time-difference, first, wavelet packet transform was applied to decompose the raw AE signal into frequency bands. Analysis of frequency-domain of signals and the frequency range which carried the maximum pramiracetam can be useful to choose the proper frequency band in wavelet packet transform. We used 3-level wavelet packet transform which each level had a bandwidth of 125kHz. The packets with frequency range of 0–250kHz were selected. This part of AE signal was used for further processing. For this purpose, wavelet transform was applied for the selected frequency band. According to Ref. [11] the amplitude of the wavelet transform for AE signal on the (a,b) plane corresponds to the arrival time of AE signal on frequency of ω=1/a. Fig. 6a shows a typical leakage acoustic wave captured by sensor for test no. 1. It is found that firs, the low amplitude component (S0) recorded by sensor and then the high amplitude one (A0) followed S0 mode. A0 mode propagated through the pipe wall slower than S0 mode. Fig. 6b the time–frequency distribution of the magnitude of WT is shown. In Fig. 6c shows the wavelet transform of AE signal for test no. 1. We used Vallen wave importer and wavelet transform software [20]. As it mentioned before the location of peak magnitude of wavelet transform represented the arrival time so this parameter (for S0, A0) occurred per frequencies of 31 and 52μs, respectively. The real-time determined wave velocity can be obtained from the group speed for S0 and A0 modes. The frequencies corresponding to peak of WT amplitude were chosen to calculate the velocities (based on Fig. 3). For test no. 1 the peak amplitudes of WT occurred per 115kHz for S0 and 120kHz for A0modes and corresponding group velocities were 5200 and 2700m/s, respectively. In long sensor-to-source distances due to attenuation, only lower frequencies are useful to source locating and higher frequencies will attenuate rapidly. So the acoustic energy concentrates in low frequency range in long distances. In short source-to-sensor distances, different modes can be excited and the acoustic energy concentrates in different frequency range so at frequency ranges up to 250kHz the S0 mode will travel faster but at frequency range upper than 250kHz the A0 mode can travel faster and sometimes other members (A1, S1,…) can travel faster (Fig. 5). The algorithm used in this study contains the following steps for source–sensor distance calculation:
In this study a MATLAB code based on modal and wavelet analysis was generated to calculate the phase and group velocities of different modes and estimate their arrival time based on wavelet transform. The peak magnitudes of wavelet transform could determine the arrival times of different modes. This location algorithm is suitable for health monitoring of structures which have limited access, on the other hand the cost of AE testing due to use of one sensor, will decrease by this algorithm. The results of this algorithm for leakage locating are shown in Table 2. For sensor positions near the ends of the pipe, the location error increases. In these positions, the reflected signals (from one end of the pipe) interact with pure leakage signals, so these signals have an important role in leakage location accuracy. These waves have low-level of energy and amplitude and for long pipe end-sensor distances energy absorption and structure scattering will attenuate these waves rapidly. In this study the level of reflected waves and noises were decreased by wavelet transform and filtering technique but as it can be seen in Table 2 these level of waves, somewhat, decrease the source location accuracy. To survey the effects of de-noising process on leakage location, the same experiments were carried out with raw, noisy AE leakage signals. As it can be seen in Table 3, noises affect the leakage location accuracy. By comparing the results (Tables 2 and 3) it is clear that the average location errors reduced by 2.5 times with de-noising process.

Ultrasonic waves have been applied

Ultrasonic waves have been applied in many industries. In dentistry, ultrasonic waves are usually used for scaling and for instrument cleaning. The generator of an ultrasonic cleaner sends high frequency sound waves through an ultrasonic cleaning solution, resulting in the formation of numerous gas bubbles. When these gas bubbles implode, resulting in cavitation, they release a large amount of impact gdc-0980 that rapidly increases the local temperature and produces a high-energy liquid stream that collides with the surface of the object being cleaned [18]. The operating frequency of an ultrasonic transducer has an effect on the amount of bubbles and their implosion. Lower frequencies generate fewer bubbles that are larger and release more energy. In contrast, higher frequencies generate more bubbles that are smaller and less release energy. A higher frequency may have less cleaning ability but generate greater fluid movement. In industrial applications, a single-frequency ultrasonic cleaner usually uses a 40kHz ultrasonic transducer.
In many industries, ultrasonic waves are used to enhance the extraction rate of chemical substances from food and bacteria [19–21]. However, the effect of the frequencies used in dental ultrasonic cleaners to enhance the elution of residual monomer from acrylic resin has not been reported. The purpose of this study was to determine the effect of various ultrasonic frequencies on the amount of residual monomer eluted from heat-polymerized and auto-polymerized acrylic resin.

Materials and methods

Representative HPLC chromatograms of MMA standard and MMA in heat-polymerized acrylic resin, and auto-polymerized acrylic resin are given in Fig. 1.

In the present study, the length of time of the ultrasonic treatment in the heat-polymerized acrylic resin (MF1–MF3) and auto-polymerized acrylic resin (UF1–UF3) groups were different because previous studies investigating the amount of residual monomer in acrylic resin after ultrasonic treatment used different treatment times for the different material types [22,23]. These studies recommended 10min of ultrasonic treatment for heat-polymerized acrylic resin and 5min of ultrasonic treatment for auto-polymerized acrylic resin. In our study, the effect of ultrasonic frequency was determined for two different ultrasonic treatment frequencies that were used for the same length of time within each material group (MF4 and UF4). Furthermore, the effect of low ultrasonic frequency (28kHz) was observed by increasing the timing of low frequency ultrasonic treatment within each material group (MF5 and UF5).



There has been a growing interest in the use of the Laser/EMAT ultrasonic (LEU) technique for non-destructive testing (NDT), especially for metal materials. Its applications include weld quality inspection [1], surface and internal defect testing [2–5], thickness estimation [6], testing of bonding and characterization of materials [7]. Its non-contact feature during operation makes Slow-stop dna mutant very attractive for on-line inspections [8]. Most of the previous work focused on its applications in thick structures [9–11], where bulk waves and Rayleigh waves are excited by the pulsed laser, and the acquired signals are analyzed based on time of flight (ToF). It is challenging to apply the LEU technique in thin structures because of the difficulties in interpreting the acquired signals. The reason is that the laser-generated ultrasounds in thin structures are broadband Lamb waves [12], which are very complicated. The generated Lamb waves propagate throughout the thickness of the structure. Thus no internal wave path can be traced, and the TOF analysis method does not work anymore [13].
However, it is very desirable to extend the application of the LEU technique to thin structures, such as for on-line weld inspection in automobile industry and delamination testing of multilayer structures. This requires identifying alternative indicators that are sensitive to the structure of interest in the acquired signals. Wu and Ume [14] proposed using superimposed laser sources and regression analysis to determine weld penetration depths in thin structures based on the reflection coefficients of laser-generated Lamb waves. However, the reflection coefficients were calculated based on 2-D Fourier Transform of B-scan signals. B-scan procedure makes the proposed method time-inefficient to implement.

Fig a and b shows

Fig. 3a and b shows vibrational spectra of the PMMA sample, the ω0 values of each peak were acquired by a fitting process using the equation of the SHO, and they are plotted as a function of the applied load (Fc). To determine the cysteine protease inhibitors kN value of each experimental ω0 data, we invert the resonance curves to express kN (ω0), where experimental values of ω0 act as the independent variable and kN is the dependent variable. The acquisition of all kN’s from each ω0 requires to know a mathematical function that describes the shape of all inverted resonance curves; it cysteine protease inhibitors is facilitated by using log10kN(ω0) instead kN(ω0). In the case presented in this work (in a frequency range that includes all experimental ω0’s), a fifth-order polynomial fits the data perfectly (see Fig. 3d), where the constants of the polynomial change depending on the curve. Fig. 3d shows the fit of the inverted resonance curves (symbols), the ω0 experimental values (dotted lines) and their intersections. To give a clear view of the fit and the intersections with the experimental ω0, only one curve of each mode was plotted with its respective fit, however, this analysis was carried out for all flexural curves shown in Fig. 2.
Fig. 4a–e shows the kN’s values as functions of Fc and kS for the flexural vibration modes considering the experimental ω0. These plots together with Fig. 2 allow appreciating the importance of selecting the more sensitive flexural resonant frequency and with the lowest influence of kS. Fig. 4f presents an average of the kN’s on the kS (i.e., averages on each rows) as a function of Fc, the error bar indicates the uncertainly in each vibration mode based on the experimental value of ω0. Uncertainly close to zero indicates that the kN estimation is less dependent on kS. Note that for this experimental data, the mode with the highest uncertainly is the CR1 with a value above ±500N/m followed by CR6 (±40N/m), CR5 (±14N/m), CR2 (±11N/m) and CR3 (±2N/m).
The estimation of the radius of the contact area from kN requires knowing the type of contact of the tip-end with the surface (predominantly flat punch, or paraboloid-of-revolution-shaped [23,29]). Studies of the contact mechanics and tip-end shape of AFM cantilevers have shown that the tip shape can be described with a power law of the form kN=β∗(Fc), where β is a factor depending on the tip geometry and the elastic properties of the tip and sample and the exponent n vary from 0, for a flat punch, to 1/3, for an hemispherical shape [23]. This power law was fitted to the kN vs Fc data in Fig. 4f (from CR2 to CR6), where the values obtained for the exponent n vary in the range of 0–0.04, indicating a predominantly flat punch contact (assumed as circular). This type of contact allows the use of the relationship kN=2 E∗[34,35], where E∗ is the reduced elastic modulus of the tip-sample contact (E∗=3GPa for PMMA), to calculate aC. Fig. 6 shows the calculated aC values using the kN’s from Fig. 5, with average values of 14nm for CR3, 21nm for CR2, 40nm for CR5 and 50nm for CR6.
At subspecies point, the aC values were obtained from experimental CRFs from a PMMA sample to express the curves of Fig. 2 (CRFs vs kN) as CRFs vs elastic modulus curves or, more specifically, indentation modulus (MSAMPLE), of an arbitrary sample. To this end, the equation for anisotropic solids, 1/E∗=1/MSAMPLE+1/MTIP, that relates the indentation modulus of the sample (MSAMPLE) and of the tip (MTIP) to E∗ was used, where E∗ was substituted into kN=2E∗ to obtain MSAMPLE (Eq. (1)), considering MTIP=130GPa for [110] silicon [14] and the average aC values from Fig. 5.
In Fig. 6 the CRFs vs MSAMPLE curves are shown for an arbitrary sample, taking into account the aC values calculated from the reference sample. As expected, the slope of the flexural resonance curves shifts to the left as aC increases. It is also observed that the zones with lower dependence of kS and high sensitivity are above 1MHz for samples stiffer than the reference sample (PMMA).