Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation frequency. Twelve able-bodied volunteers participated in the study, in which they were asked to identify the stroked texture  using only electrotactile feedback. The experiments were done in three phases: (1) Training, (2) with-feedback, (3) without-feedback. Each texture was stroked 20 times each during all three phases. The results show that the participants were able to differentiate between different textures, with a median accuracy of 85%, by using only electrotactile feedback with the stimulation frequency being the only variable parameter. Every day the human hand is used to explore and interact with the surroundings. This is made possible by the delicate interaction between the sensory and motor systems in the peripheral and central nervous system. The human hand consists of 17,000 mechanoreceptors such as Meissner’s corpuscles, Merkel disks, Ruffini organs, and Pacinian corpuscles, located at different depths in the skin and they react to different stimuli .Both the spatial pattern of the object manipulated and the temporal pattern with which the object is being manipulated play a role in texture perception. The different patterns are conveyed in afferent responses. The spatial, such as gratings and Braille dots (on the order of millimeters), evoke a response of slowly adapting type I (SAI) afferents. However, in discriminating natural textures the temporal pattern is more dominant. The temporal pattern is encoded in the responses of rapidly adapting (RA) afferents and Pacinian corpuscles .The loss of a hand, through amputation, disconnects the afferent and efferent pathways from reaching their targets. In order to achieve the motor control necessary for object manipulation and object identification, the afferent pathways provide crucial information to close the loop between the hand and the brain and provide sensory feedback . In addiSeveral different commercial prosthetic hands restore some motor functions to the amputee. However, these prostheses do not provide any sensory feedback and sensory feedback has been highlighted, by prosthesis users, as one of the desired functions in prosthetic hands . It has One way to provide sensory feedback is to use transcutaneous electrical nerve stimulation (TENS), a technique that is based on high-voltage electrical pulses sent through a pair or a plurality of electrodes placed on the skin, to stimulate nerve fibers. It is commonly used to relieve pain  or proviTechnical solutions to provide sensory feedback of the force generated during a grasp are well explored, while feedback for texture perception for use in prosthetic hands is not. Interestingly, sensors used to detect texture-information are more common in self-organizing robots or robotics in applications such as health, eldercare, and manufacturing ,21,22,23It has been shown that participants who have lost sensibility in a hand can substitute it, to some extent, with auditory information . The parThe current paper contributes a simple electrotactile feedback system with a computational method to convert recorded friction sounds, arising from stroking different textures, into somatotopic electrical stimulation in real-time. Electrical stimulation was chosen because of its easy application and the control of the stimulation parameters, such as the amplitude and frequency. The friction sound of a texture was recorded with a condenser microphone and median frequency was calculated of the audio spectrum. By analyzing the accuracy for discriminating different textures with the proposed system, conclusions can be made if the system is fit to be used as a texture sensing substitute. With the proposed system the participants had an overall median accuracy of 85% in discriminating different textures.Twelve able-bodied participants, 10 males and 2 females , with no known neurological disorders participated in the study. Two participants had previous experience of electrical stimulation, however, they were not familiar with the current study protocol. The rest of the participants had llittle knowledge of electrical stimulation in general. All participants provided informed consent, and the study was approved by the Swedish Ethical Review Authority (DNR 2020-03937).Four different textures  see Fig were useAn omnidirectional electret condenser microphone with an amplifier module  a was useThe extracted signal features were sent to a custom-made electrical stimulator capable of producing biphasic charge-balanced cathodic-first current-controlled pulses of amplitudes in the range from 0.1 mA to 10 mA (steps of 0.1 mA), and frequencies of 1 to 100 Hz. The DC/DC boost switching regulator was used to generate stimulation voltage which was maximally 38 V (depending on the skin impedance and the stimulation current). The stimulation control was done by an onboard PIC18F25K22 microcontroller . The microcontroller managed generation of the stimulation patterns and communicated with both, the Teensy microcontroller and PC, enabling alteration of stimulation parameters in real-time. The electrical stimulation was delivered to the participants through self-adhesive Pals electrodes , placed on the skin over the median nerve so the sensations following stimulation, were associated with the median nerve innervated fingers  and palm area. An overview of the system can be seen in The formulation of the algorithm presented in this paper was based on several empirical pre-tests  that were used to characterize friction-based interaction between the microphone and selected textures. These pre-tests identified the audio frequency range resulting from friction with selected textures and the behavior of several signal features, such as total signal power, peak frequency, mean frequency, and median frequency, during manual stroking.http://github.com/kosme/arduinoFFT, accessed on 11 November 2020). The FFT was updated after every 128 samples, corresponding to a ∼50 Hz update rate with the sampling rate of 6 kHz. In the next step, the spectral components at 50 Hz and below 20 Hz were removed from the FFT spectrum.The first step of the algorithm that was designed and implemented, as a part of this study, was the calculation of the frequency content of the microphone signal. The FFT calculation was performed on 2048 samples of the digitalized audio signal using a modified version of the Arduino library (stim denotes the stimulation frequency (in Hz) that was sent to the stimulator, medianf denotes the median frequency signal feature, lowerB denotes the lowest median frequency that was empirically chosen as relevant, and scaling denotes the linear scaling factor used to constrain the possible median frequencies of the microphone signal into the range of stimulation frequencies produced by the stimulator. In the current study, the lowerB and scaling constants were empirically set to 50 and 10, respectively. The addition of 5 Hz was done to constrain the lower dynamics of the stimulation as extremely low frequencies would significantly reduce the information bandwidth delivered to the participant. In other words, sending a low stimulus frequency  would mean that the next stimulation pulse, and also the change of stimulation frequency, would have to wait for a long time (more than 1 s in the case of <1 Hz stimulation). This is the result of the electronic stimulator protocol which accepts updates only after producing a stimulation pulse specified by the last command. In addition, the stimulation frequency resulting from Equation  Training phase, (2) feedback phase, and (3) without feedback phase. All phases were performed with participants sitting down in a quiet room. The initial step of the protocol was the placement of the stimulation electrodes. The anode  was placed on the ventral side of the forearm, while the cathode  was placed over the median nerve, just proximal to the wrist as shown in Upon establishing the stimulation amplitude, the training phase of the experiment was initiated. In this phase, the experimenter sequentially stroked each texture for 20 cycles  with an estimated speed of 14–25 mm/s. During this phase, the participant got to watch the strokes and at the same time receive the electrotactile feedback.After finishing 20 cycles, the participant was blindfolded and acoustically insulated using headphones, and the stimulation with the feedback phase was initiated. During this phase, the experimenter stroked the different textures in a proximal to the distal direction in a randomly predefined sequence, while the participant was instructed to verbally identify the texture. Up to two additional repetitions of the same texture were allowed, if requested by the participant. Upon receiving a response from the participant, the experimenter provided verbal feedback consisting of true/false statements and the information regarding the stroked texture . This phase consisted of 20 repetitions of each texture . Upon completing the phase, a short break was taken (approximately 5 min).The final phase also comprised 20 repetitions of each texture in a new randomized order, but without feedback from the experimenter. As in the previous phase, two additional repetitions of the same texture were allowed for the participant.It should be noted that all of the strokes were subjected to variability due to the manual execution by the experimenter. Specifically, with sticky or rough surfaces, such as silicone rubber, the vibrations resulting from the friction were unpredictable. In the case of smoother surfaces, the speed and consistency of the strokes were also directly mirrored in the frequency of the stimulation.Two separation analysis was done in this study, one to analyze the consistency data and one on the following experimental data.https://scikit-learn.org/, accessed on 11 November 2020) and SciPy .A custom-made LabVIEW program  was used to record data during the evaluation stages of the algorithm development. These tests comprised stroking of each texture pseudorandomly by the experimenter and were used for further investigation of the consistency of the stroking. Two factors were explored; the consistency of the time of each stroke and the consistency of the frequency. The data analysis was performed in Python with several libraries, such as Scikit-learn  and texture (p < 0.001). The multiple comparisons test performed on textures per phase revealed there was a statistically significant difference between some of the textures, namely felt vs. silicone rubber in the with feedback condition (p < 0.001), mesh vs. silicone rubber in both feedback conditions (p = 0.016 (w/ FB), p = 0.0086 (w/o FB)), and sponge vs. silicone rubber in the no feedback condition (p = 0.043). For the other combinations there were no statistically significant differences.The accuracy of all 12 participants in identifying four different textures are shown in The experimental results can be seen in the stimuli-response confusion matrix in During the second phase (stimulation with feedback), felt was often mentioned, by the participants, as being the easiest to distinguish (85.4%) because of its higher median frequency. However, it was sometimes confused with silicone rubber (12.9%), since silicone rubber, on some occasions, gave inconsistent stimulation because of its sticky characteristics in the texture. This made the participants, on some occasions, hesitate if the stimulation stemmed from silicone rubber. Vice versa, silicone rubber was in 15% of strokes misidentified as felt. As mentioned, silicone rubber gave a low frequency at the beginning, which increases during the stroke, however, during manual stroking on few occasions the audio signal did not have this characteristic and instead was confused with the felt 15% of the time and 12.5% of the time it was misinterpreted as the sponge. In the final phase (stimulation without feedback), the performance improved for mesh, sponge, and silicone  while the ability to detect felt decreased slightly (−4.2%).The presented study was designed to assess the feasibility of developing a computational method for the direct conversion of the sound detected by a microphone, when stroking a texture, into an electrotactile stimulation pattern that could be used to distinguish between different textures. However, the microphone and the audio amplifier used in this study were regular, off-the-shelf components, not chosen for the specific purpose of measuring contact vibrations. It should also be noted that the experimental conditions in this study were designed to resemble conditions that would be expected in a real-world application of a feedback system in a hand prosthesis. Mainly, the sound was translated into the frequency of the electrical stimulation continuously, permitting variability of the feedback, in accordance with the natural variability in stroking velocity and pressure seen in a hand exploring an object.The results of the study are encouraging since, after just a brief familiarization period comprising 20 strokes of each texture which lasted approximately 5 min in total, participants were able to achieve a relatively high overall median accuracy (85%). It should be pointed out that only two participants had any previous experience with electrical nerve stimulation which makes the familiarization of only 5 min even more rigorous in the case of naive participants. These participants had to get accustomed to both, the new sensation in general and the variation of stimulation frequency due to the interaction with different textures. It should be emphasized that the experiment was designed such that the participants had their vision and hearing occluded while in a real-life exploration of textures, the vision and tactile perception plays an equal role in identifying textures . The samJamali and Sammut  used PVDThe present study could also be considered as the worst-case scenario as the feedback is directly proportional to the texture stroking dynamics which was completely governed by the experimenter, while the participant did not have any complementary information. Thus, we hypothesize that implementation of the concept of the proposed system, using a microphone as a sensor and electrical stimulation as a feedback mechanism, to provide information about a texture would significantly improve the accuracy of identifying a texture. The results support this hypothesis, where the accuracy is significantly improved between the second phase (with feedback) and final phase (without feedback) which was done in a short-term controlled experiment. Considering that the system will be used long-term and during activities of daily living, the user then would also use their natural feedback modalities, such as audio, visual, proprioceptive, or force, at his/her disposal. The user would then be able to incorporate his/her natural feedback modalities to further strengthen the internal models of interaction with different textures. Furthermore, with the continuous use of the proposed electrical feedback system, texture recognition would likely gradually improve because in our data there is a significant improvement in performance even between two consecutive tests. Hence, it could be argued that during long-term passive learning, the overall accuracy could be improved for the proposed system . In addiOne of the major sources of errors identified by the experimenters and participants was the inconsistency of the stimulation pattern dynamics for some of the textures. Manual stroking is prone to variations in applied force, stroking velocity, and velocity profile, all of which affect friction sound, and consequently electrical stimulation. However, all of these parameters are also subject to variation in the normal use of hands. The accuracy during the tests (which were between 70% and 89%) are the consequence of the high variability in median frequency of the audio signal resulting in the partial overlap between friction responses of other textures see a. In addDue to the omnidirectional feature of the microphone, it picks up sound with equal gain in all directions, making it susceptible to background noises which is a limitation associated with the study. However, the experiment was performed in a quiet room which eliminated most of the background noises. If the currently described system was to be used for a prosthetic hand, background noise could be removed by using a noise-canceling sub-system where an additional microphone could detect only background noise.Having in mind that other feedback modalities, such as force or hand aperture, would be prioritized in a prosthetic hand as they are coupled with the basic prosthetic hand functionality (grasping), the goal of this study was to evaluate the feasibility of using appropriate electrical stimulation for texture discrimination feedback using a microphone to pick up friction sounds of textures. As the feedback related to different sensors, such as force sensors, encoders, and microphones, would be combined within the same feedback interface , it was decided to dedicate only a single controllable stimulation parameter (frequency) out of many, such as, stimulation amplitude, frequency, pattern, and location, to texture exploration. Thus, frequency modulation of the electrical stimulation delivered to one cathode was chosen as a minimalistic setup, leaving other parameters available for other potential feedback modalities.2 in the current version) and power consumption. Therefore, this hardware setup could be implemented within common hand prostheses by (1) integrating one or several miniature microphones on the prosthesis fingertips within a silicone glove, (2) placing at least one pair of electrodes over one of the major hand nerves, and (3) embedding all necessary electronics (including the prosthesis control part) on a single printed circuit board. As the presented system is self-contained, it could be integrated with existing and future powered hand prostheses or even in cosmetic prostheses.The presented system is designed to be portable. The hardware components comprising sensor and actuator sub-systems are based on two microcontrollers, ARM Cortex M7 and PIC18F25K22. The former processor handles audio signal sampling and execution of the algorithms presented in the paper, while the later processor is responsible only for executing electrical stimulation in a time-crucial manner. Additional circuitry related to the stimulator analog output stage, consisting of the step-up converter and discrete components, has a relatively small footprint  identifying different textures already after 20 repetitions.


Purpose: The flow diversion effect of an intracranial stent is closely related to its metal coverage rate (MCR). In this study, the flow diversion effects of Enterprise and low-profile visualized intraluminal support (LVIS) stents are compared with those of a Pipeline flow diverter, focusing on the MCR change. Moreover, the changes in the flow diversion effect caused by the additional manipulations of overlapping and compaction are verified using computational fluid dynamics (CFD) analysis.Methods: CFD analysis was performed using virtually generated stents mounted in an idealized aneurysm model. First, the flow diversion effects of single Enterprise, LVIS, and Pipeline devices were analyzed. The Enterprise and LVIS were sequentially overlapped and compared with a Pipeline, to evaluate the effect of stent overlapping. The effect of compacting a stent was evaluated by comparing the flow diversion effects of a single and two compacted LVIS with those of two overlapped, uncompacted LVIS and uncompacted and compacted Pipeline. Quantitative analysis was performed to evaluate the hemodynamic parameters of energy loss, average velocity, and inflow rate.Results: Statistically significant correlations were observed between the reduction rates of the hemodynamic parameters and MCR. The single LVIS without compaction induced a reduction in all the hemodynamic parameters comparable to those of the three overlapped Enterprise. Moreover, the two overlapped, uncompacted LVIS showed a flow diversion effect as large as that induced by the single uncompacted Pipeline. Compacted stents induced a better flow diversion effect than uncompacted stents. The single compacted LVIS induced a flow diversion effect similar to that induced by the two uncompacted LVIS or single uncompacted Pipeline.Conclusions: The MCR of a stent correlates with its flow diversion effect. Overlapping and compaction can increase the MCR of an intracranial stent and achieve a flow diversion effect as large as that observed with a flow diverter. Intracranial, self-expanding stents were originally designed as scaffolding to protect aneurysmal necks against coil protrusion or migration , 2. ReceStents currently available on the market have different mechanical properties depending on their design and manufacturing methods , 8. The Each stent has a constant range of MCR that depends on its mechanical properties. However, a higher MCR can be achieved by using an adjuvant method. Overlapping multiple stents is one of the methods commonly used to increase the MCR. Previous studies have reported that sequentially placing stents across the aneurysm neck can enhance the flow diversion effect –15. AnotIn actual aneurysm treatment, stents are overlapped or compacted to induce the flow diversion effect, and sometimes these manipulations are used together. Therefore, a comprehensive understanding of the flow diversion effect induced by overlapping or compacting a stent is necessary. However, no study conducted so far has compared the effect of overlapping and compacting intracranial stents in a single configuration on the flow diversion effect. Although previous studies have demonstrated the flow diversion effect of stents that were separately overlapped or compacted, variations in the configurations used in these studies should be considered while comparing their results , 21.In this study, we compared the flow diversion effects of an Enterprise laser-cut stent  and a low-profile visualized intraluminal support (LVIS) braided stent  with that of a Pipeline flow diverter . The MCR was calculated and compared to evaluate the flow diversion effect of each stent numerically, considering additional manipulations, namely, overlapping and compaction. All the studies were conducted under the same experimental conditions using an idealized aneurysm model to control for variables that could affect the results. Thus, we compared the flow diversion effects of the Enterprise and LVIS stents with that of the Pipeline flow diverter, focusing on the MCR changes. Furthermore, we verified the changes in the flow diversion effect caused by the additional manipulations of overlapping and compaction using computational fluid dynamics (CFD) analysis.An idealized sidewall-type saccular aneurysm model that was minimally affected by lesion geometry was established to compare the characteristics of the stents . The idein vitro to examine the configurations of the stents in the aneurysm neck. After installing each real stent inside the silicone model, we captured the images of the stent shapes in the aneurysm neck. Based on the information obtained from the images of deployed stents, we generated virtual stents for subsequent CFD analyses.A silicone model of the ideal aneurysm was fabricated, and the real stents were deployed inside the silicone model CFD analyses were performed using an idealized aneurysm model with virtually created stents placed across the aneurysmal neck. We considered three different kinds of stents: a laser-cut stent , a braided stent , and a flow diverter . The size of each stent was set to match the size of the parent artery as much as possible. In the absence of a stent of the same size as the parent artery, an undersized stent was chosen to maximize the MCR . A stent was constructed only at the aneurysm neck to improve the efficiency of the CFD analysis . An elemectively . The pulectively . The bloTo evaluate the results of the CFD analysis quantitatively, we compared the following hemodynamic parameters: inflow rate, average velocity, and energy loss (EL). The average velocity and inflow rate into the aneurysm were calculated at plane C, which is located near the aneurysm neck . The velVm represents the volume of the model between planes A and B. ρ and A are the density and area at the inlet, respectively. vin and Pin represent the average velocity and pressure, respectively, at the inlet (plane A), and vout and Pout represent the average velocity and pressure, respectively, at the outlet (plane B). The EL indicates the amount of blood flowing into the aneurysm. We calculated the reduction rate of the EL to indicate the effect of stenting compared with the unstented case. Therefore, a higher EL reduction rate indicates less blood flow into the aneurysm.where The changes in the MCR and hemodynamic parameters caused by overlapping and compacting the stents are summarized in First, we compared the hemodynamic modifications induced by each stent . An intrThe Pipeline device  showed the most pronounced reduction rate for all the three parameters. The LVIS stent  showed a higher reduction rate for all the three parameters than the Enterprise stent  .The results of the CFD analysis for stent overlapping are shown in In terms of parameter reduction, a single LVIS stent  induced a reduction in all the hemodynamic parameters comparable to the effect of three overlapped Enterprise stents . Two uncompacted LVIS stents showed a better flow diversion effect  than a single LVIS stent. Moreover, the effect of two uncompacted LVIS stents was similar to that of a single uncompacted Pipeline device .To demonstrate the effect of stent compaction, we performed simulations in the following order: a single uncompacted LVIS stent, a single compacted LVIS stent, two uncompacted LVIS stents, two compacted LVIS stents, a single uncompacted Pipeline device, and a single compacted Pipeline device . CompactThe single compacted LVIS stent  induced a reduction in all the hemodynamic parameters comparable to the effect of the two uncompacted LVIS stents  or the single uncompacted Pipeline device . Similarly, the two compacted LVIS stents  showed a flow diversion performance comparable to that of the single compacted Pipeline device .The purpose of this study was to analyze quantitatively the flow diversion effects of stents with different MCRs and the effects of stent overlapping and compaction. We observed that (1) a single LVIS stent showed a comparable flow diversion effect to three overlapping Enterprise stents, (2) two-overlapped, uncompacted LVIS stents had a similar flow diversion performance to a single uncompacted Pipeline device, and (3) a single compacted LVIS stent and two-overlapped, uncompacted LVIS stents produced a similar performance. These findings support the following conclusions: (1) A stent with a high MCR can reduce intra-aneurysmal flow activity better than a stent with a low MCR. (2) Increasing the MCR through stent overlapping can induce a flow diversion effect as large as that induced by a flow diverter. (3) It is important to increase the MCR through stent compaction to induce a sufficient flow diversion effect. The proper compaction of a braided stent induces a better flow diversion effect compared with that induced by multiple overlapped, uncompacted stents.Intra-aneurysmal hemodynamics plays an important role in aneurysmal growth and rupture . PreviouAs the MCR of a stent is determined by its mechanical properties, such as its number, thickness, and the weave angle of the stent wire , 32, eacIn this study, we investigated the effects of overlapping LVIS stents. Overlapping two uncompacted LVIS stents induced a flow diversion effect as large as that induced by a single Pipeline device. Wang et al.  also useCompaction can also increase the MCR. A braided stent can generate various mesh densities as the wires of the stent are rearranged, making it possible to increase the MCR during stent deployment by using the push-pull technique . PreviouThis study has some limitations. First, as explained, our experiments were conducted under the assumption of an ideal condition; therefore, the results obtained in real practice may be different. We assumed an idealized sidewall-type saccular aneurysm to exclude factors other than the stent properties that affect the flow diversion effect. However, the flow diversion effects of the stents could vary in real practice depending on factors, such as the shape of the aneurysm, the geometry of the parent artery, and the degree of wall apposition between the stent and the parent artery –39. MoreWe observed that a single LVIS stent exhibited a flow diversion effect similar to that of three overlapped Enterprise stents. Compacting a single LVIS stent was as effective in terms of flow diversion as overlapping two LVIS stents, and similar results were confirmed for the Pipeline device. The MCR of a stent correlates with its flow diversion effect. Overlapping and compcation can increase the MCR of an intracranial stent and improve the flow diversion effect to match that of a flow diverter.The original contributions generated for the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.SK and HY gathered the data and drafted the manuscript in collaboration. IH assisted in the discussions and reviewed the manuscript. JO and YK conceptualized the study and supervised the process, corresponding to each field of specialty. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich sample data and a target domain with only a few or zero labeled samples and, thus, complete the transfer of knowledge by aligning the distribution between domains through methods, such as domain adaptation. Previous domain adaptation methods mostly align the features in the feature space of all categories on a global scale. Recently, the method of locally aligning the sub-categories by introducing label information achieved better results. Based on this, we present a deep fuzzy domain adaptation (DFDA) that assigns different weights to samples of the same category in the source and target domains, which enhances the domain adaptive capabilities. Our experiments demonstrate that DFDA can achieve remarkable results on standard domain adaptation datasets. In recent years, deep learning has achieved great success in computer vision  and natuTraditional machine-learning algorithms assume that the training data and test data obey the same distribution; however, due to dataset bias, the data distribution on different domains often differs. The generalization ability of the algorithm will be weakened if the statistical distribution of samples is changed. The main idea of domain adaptation  is to usThere are three common approaches to domain adaptation ,7: featuSome instances are selected from the source domain so that the probability distribution of the obtained sample subset is similar to the target sample and are then trained using traditional machine-learning methods. Apart from this, the classifier-based adaptation makes the source domain model adapt to the target by regularizing the difference between the source domain and target domain model parameters ,16,17. SPrevious feature-based domain adaptation methods have mainly used explicit distances to align feature distributions between source and target domains ,19,20 orAlthough these aforementioned methods achieved good performance, there is no sample selection process, i.e., alignment of all samples in the same category, which may affect the final results. A straightforward example is shown in For samples with the true label A in the source domain that are misclassified as B or C, it may happen to be aligned with a sample in the target domain that is also misclassified as C, but is more likely to be mismatched with several samples with the true label B. This confusion degrades the ability of domain adaptation. For more accurate alignment, we propose a fuzzy maximum mean discrepancy (FMMD), which considers the predicted information of instances in the source domain to alleviate this problem. Based on this, we design a new network architecture Deep Fuzzy Domain Adaption (DFDA) to apply FMMD, and DFDA can be easily optimized by the standard gradient descent method. The experimental results show that our method outperforms state-of-the-art metric-based approaches on benchmark datasets.Before discussing the FMMD, in this section, we introduce three related aspects of work: feature-based domain adaptation, subdomain adaptation, and maximum mean discrepancy (MMD).Research  has showThe discriminator learns to distinguish between the source domain and target domain features, and the generator learns feature representations with domain invariance to confuse the discriminator. Previous works ,25,29 haMMD reduces the mean between domains in the Reproducing Kernel Hilbert Space (RKHS), CORAL reduces the difference between the covariance matrices the two domains, and CMD   aligns tSome recent approaches have improved the performance of domain adaptation by introducing category information into the network. CDAN   conditioA joint adaptation network (JAN)  combinesInspired by the concept of the instance-based transfer method and the different above-mentioned methods, we assign different weights to samples of the same category between domains by adding the predicted value of the samples and selecting some suitable samples for fine alignment. These methods perform domain feature alignment based on the hard pseudo-labeling of samples by the classifier, which will undoubtedly lead to improper alignment due to the presence of noise in these labels.MMD calculates the mean distance of the distribution in the RKHS as the distribution discrepancy between the two domains, and its effectiveness has been proven . There iWeight MMD (WMMD)  alleviatIn the unsupervised domain adaptive scenario, we are given a source domain Deep neural networks can learn more transfer representations than traditional machine learning handcrafted features. Several popular deep transfer methods have emerged by adding adaptation layers to neural networks to align the distribution of features between domains ,40. Thesc with the same probability DSAN introduces the category information of the source domain and the predicted value information of the target domain into the network and aligns each category separately, which achieves remarkable results on both object recognition tasks and digit classification tasks. However, its assumption that every sample of the same class belongs to class k . Formally, the MMD between distributions c, respectively. Assuming that the weight of each sample in the same category is equal:DSAN introduces label information to the network and proposes the Local Maximum Mean Discrepancy (LMMD) to align the distributions of the relevant subdomains within the same category in the source and target domains.c. It is used to evaluate the quality of the features here. If the predicted value of the sample is closer to the label value, it means that it is a good sample and increases the weight of the sample. On the contrary, if the predicted value deviates from the true value, the feature weight of this sample will decrease. As in DSAN, the weights on the target domain sample are calculated using the predicted values instead of the true label.However, there are gaps in quality between samples, and the alignment of outliers can easily become confusing. In particular, when a category is too sparse in a batch, the sample weight is prone to unreasonable assignments. To address this issue, we propose the FMMD as follows:We propose a new network architecture Deep Fuzzy Domain Adaption (DFDA) in order to embed FMMD into the network. Different from the previous method that only uses the label of the source domain, the predicted information of the source domain samples are also added to the network. As shown in We analyzed the effectiveness of DFDA based on the domain adaptation theory ,41.Theorem 1.S and T, then, for any Let H be a hypothesis space of VC dimension d. Given two domains In this case, the second term, This section mainly introduces the data sets and experimental environment used in the experiment and describes in detail the analysis. We evaluate our algorithm on three widely used benchmark datasets, including the Office31, Office Home, and large-scale digital recognition datasets, and compare it with several state-of-the-art distance metric-based transfer learning models: DDC, DAN, JAN, Deep CORAL, and DSAN, to assess the effectiveness of our approach.Office31  is one oOffice Home  is much Digital recognition dataset contains three widely used benchmarks: MNIST , USPS 4 and SVHNFor the digital recognition dataset, all the images are resized to 32 × 32 as the input of the network. We use the modified LeNet  as the fFor the other two datasets, we follow the settings in DSAN and employ ResNet50 as the feature extraction network, and a bottleneck layer fcb with 256 units is added after the last average pooling layer to reduce the dimensionality. Finally, we use the output of fcb as the FMMD input. We use the pre-trained model on ImageNet to fine-tune all convolutional and pooling layers, and we train the classifier layer via backpropagation.−4. The learning rate is adjusted during SGD using the following formula: We follow the settings of DSAN. For each task, we use a mini-batch stochastic gradient descent (SGD) with a momentum of 0.9, and the weight attenuation coefficient is 5 × 10This progressive strategy can effectively suppress noisy activations at the early stages of training.https://github.com/jindongwang/transferlearning/ accessed on 27 September 2021) and report the average classification accuracy and standard error for three random trials. For all MMD-based approaches, we adopt a Gaussian kernel, and the bandwidth is set to the median pairwise squared distance on the training data.For a fair comparison of the above methods, the same network architecture is used on the same dataset. We implement these with publicly available code  to visualize in To demonstrate the effect of transfer learning, we utilize t-SNE (To give an overview of the results, we compared DFDA with several other MMD-based metrics in terms of the execution time and computational cost. The performance test was performed on a computer with a NVIDIA RTX3090 GPU, and the results are shown in Our approach does increase some matrix operations over the previous approach; however, these computational costs are negligible compared to the computational effort brought by ResNet50, and considerable performance gains can be obtained. The average increase in computing time per epoch is only 0.3 s, which we believe is worth the cost.The previous subdomain adaptive approach aligns all samples within the relevant subdomain without considering the quality differences of individual features. In this paper, we proposed a new method of DFDA to measure the distance between domains, which evaluates the quality of individual features in the source domain by adding predicted information of the source domain to the network. Particularly in the setting of a large batch size, this can select more suitable samples among multiple same-class samples on the source domain to match with the target domain, which effectively improved the performance of the transfer model. Compared with previous methods, our proposed DFDA achieved 1.7% and 0.8% performance improvements on the digital recognition and Office-31 datasets, respectively, and these results support the effectiveness of our proposed method.


Nondestructive evaluation of elastic properties plays a critical role in condition monitoring of thin structures such as sheets, plates or tubes. Recent research has shown that elastic properties of such structures can be determined with remarkable accuracy by utilizing the dispersive nature of guided acoustic waves propagating in them. However, existing techniques largely require complicated and expensive equipment or involve accurate measurement of an additional quantity, rendering them impractical for industrial use. In this work, we present a new approach that requires only a pair of piezoelectric transducers used to measure the group velocities ratio of fundamental guided wave modes. A numerical model based on the spectral collocation method is used to fit the measured data by solving a bound-constrained nonlinear least squares optimization problem. We verify our approach on both simulated and experimental data and achieve accuracies similar to those reported by other authors. The high accuracy and simple measurement setup of our approach makes it eminently suitable for use in industrial environments. The field of guided acoustic waves (GAWs) has attracted increasing interest in the research community over the past decade . In addiGuided acoustic waves are dispersive, i.e., their phase and group velocities depend on frequency . For parUsing the opposite approach, the dispersive nature of GAWs can be utilized to determine the elastic properties of thin structures such as sheets, plates, and tubes. The procedure first requires the measurement of group or phase velocities at multiple frequencies. A numerical model is then fitted to these measurements by optimizing the model parameters. Recent research in this area has shown that this strategy can determine the elastic properties of thin structures with remarkable accuracy ,23,24,25One of the earliest works on this subject was published by Rogers  in the mMost of the described techniques either require complicated and expensive equipment or involve accurate measurement of an additional quantity, rendering them less suitable for use outside the laboratory environment. In this work, we introduce a new simple and precise approach that requires only a pair of piezoelectric transducers used to measure the group velocities ratio of fundamental Lamb modes. A numerical model based on spectral collocation method is used to fit the measured data, and both longitudinal and transverse wave velocities and the thickness of the inspected structure are estimated by solving a bound-constrained nonlinear least squares optimization problem.In the following section, we introduce the measurement setup, the numerical model used to fit the measured data and the optimization approach used to extract the elastic properties of the inspected structure.The measurement setup involves a pair of identical piezoelectric disc transducers , attached to the surface of the test specimen at a fixed distance A typical signal recorded by the oscilloscope is demonstrated in ransform  or the sransform  may alsod between the transmitter and the receiverOnce the propagation times have been determined, group velocities d not only requires additional equipment, but also affects the measurement uncertainty of the group velocities. Our approach overcomes this issue using a simple trick: instead of estimating the velocities directly, we calculate their ratio and thus eliminate the distance d in Equation . HoweveIn this work, we employ an alternative popular method recently introduced by various authors ,31,32,33To introduce the numerical model, we consider a planar waveguide as shown in k is the wavenumber, t the time, i the imaginary number. By substituting 4) deliveN pre-assigned collocation points. This allows the evaluation of derivatives at such collocation points by multiplication with a pre-computed matrix. For bounded non-periodic domains, Chebyshev differentiation matrices are typically used [The spectral collocation method (SCM) is a numerical approach to solving differential equations. Its main idea is to expand the solution of the differential equation by a linear combination of some global polynomial basis functions that satisfy the exact solution on a set of lly used .m-th order differential operators in Equation 13) are ck values by solving the matrix eigenvalue problem  method .N required to determine the group velocities of j lowest Lamb modes with an accuracy of 10 digits can be calculated with the formula To verify our approach, we first applied it to simulated data. Guided acoustic wave signals in the frequency range 110 to 200 terature , the numThe group velocities ratios derived from the simulated acoustic signals at different frequencies as well as the corresponding solution found by optimization are shown in Since optimization results typically depend on the initial solution, or in our case on the search region specified for the solution, we repeated the optimization process multiple times with some randomly chosen upper and lower parameter bounds. In each case, the relative error between the determined parameters and the reference was about 1%.We also applied our approach to experimental data. For this purpose, an aluminium sheet of To verify the experimental results, additional reference measurements were performed. The thickness of the aluminium sheet was measured using a micrometer screw gauge and the longitudinal wave velocity was obtained by time-of-flight measurements with an ultrasonic transducer  operating at 30 The group velocities ratios obtained from the measured acoustic signals and the corresponding solution are shown in This paper presents a method for measuring the elastic properties of thin structures such as tubes, plates and sheets, that takes advantage of the dispersive nature of guided acoustic waves. The measurement setup consists of a signal generator, a digital storage oscilloscope and a pair of piezoelectric transducers, which are used to measure the ratio of group velocities at different frequencies. The measured data is then fed into a numerical model based on the spectral collocation method in order to determine the elastic properties and the thickness of the investigated structure via optimization. Validation of the method with simulated and experimental data resulted in a measurement error of 1.1% and 3.4%, respectively, which is similar to the results reported by other authors ,23,24,25To determine the wave velocities from the signals measured with a piezoelectric receiver, the distance between the transmitter and the receiver must be known exactly. This requires additional equipment, complicates the measurement setup and raises the possibility of additional measurement uncertainties, which may have a negative impact on the accuracy of the measurement principle. Our approach overcomes this issue by determining the ratio of the group velocities, which can be calculated directly from the propagation times of the two fundamental Lamb modes, significantly simplifying the entire measurement setup.Laser ultrasonic methods for detection of guided acoustic waves usually rely on the measurement of the wave number rather than the wave velocity. This also avoids the need to determine the distance between the transmitter and receiver. However, such methods require expensive and complicated equipment and are poorly suited for the characterisation of curved or weakly reflective materials. Our approach, on the other hand, uses low-cost components and allows the characterisation of arbitrarily shaped structures and various materials, opening up a wide field of potential applications.For the determination of the propagation velocity of the two fundamental Lamb modes, a five-cycle sine burst modulated by a Hanning window is used. On the one hand, the propagation time of the wave can be determined relatively accurately using such a compact signal. On the other hand, such a signal has a much broader spectrum than signals with a higher number of cycles, which means that unwanted side effects such as dispersion become more prominent. In the application studied in this article, the Lamb waves are excited at frequencies with a relatively flat course of the frequency-dependent phase velocity, so that dispersion effects only play a minor role. The relatively small distance between transmitter and receiver also has a positive impact. However, depending on the application, such effects can have a negative impact on the measurement method, which is why the use of alternative signal patterns should be investigated in the future.For future investigations, the method could be adapted for rotationally symmetrical structures such as pipes. In this case, the measurement setup could be reduced to a single transducer that can act as both a transmitter and a receiver of circumferential Lamb waves. In this way, the measurement of wear or deposits in pipes could be provided, enabling continuous online monitoring of industrial plants. However, in contrast to many non-destructive tests performed on pipes and sheet material, the approach here is not aimed at detecting defects but at determining material properties. A defect-free part of the structure must always be selected so that the measurements are not affected by these non-representative inhomogeneities. In this context, weld metal must also be considered a defect, as the homogeneity of the material is no longer given and the evaluation of the measurement results can lead to misleading values.


In view of the poor performance of traditional feature point detection methods in low-texture situations, we design a new self-supervised feature extraction network that can be applied to the visual odometer (VO) front-end feature extraction module based on the deep learning method. First, the network uses the feature pyramid structure to perform multi-scale feature fusion to obtain a feature map containing multi-scale information. Then, the feature map is passed through the position attention module and the channel attention module to obtain the feature dependency relationship of the spatial dimension and the channel dimension, respectively, and the weighted spatial feature map and the channel feature map are added element by element to enhance the feature representation. Finally, the weighted feature maps are trained for detectors and descriptors respectively. In addition, in order to improve the prediction accuracy of feature point locations and speed up the network convergence, we add a confidence loss term and a tolerance loss term to the loss functions of the detector and descriptor, respectively. The experiments show that our network achieves satisfactory performance under the Hpatches dataset and KITTI dataset, indicating the reliability of the network. The detection of feature points and the establishment of descriptors are important steps in image matching. In computer vision-based applications such as simultaneous localization and mapping (SLAM), structure-from-motion (SFM), and image retrieval, the processing of image feature points determines the correspondence between different images. Accurate extraction of feature points can improve the matching accuracy of images. With the wide applications of computer vision and the more complex environment faced by image processing, it is particularly important to find a stable feature point detection method.At present, the processing methods for image feature points can be divided into traditional methods and deep learning-based methods. Traditional feature extraction methods are difficult to achieve satisfactory performance in challenging situations. The scale invariant feature transform (SIFT) algorithm  was scalAiming at the above issue, we propose a deep learning-based self-supervised feature point detection network with an attention mechanism that can be applied to feature extraction modules in VO. First, the feature pyramid networks (FPN)  is used The rest of the article is organized as follows. The feature point detection method based on deep learning improved the stability of feature point detection. Due to the unclear definition of feature points, adding effective labels to images became a difficulty in the detector training process. Therefore, most methods only work on local descriptors of image patches ,15. HoweThe combination of deep learning-based feature point detection methods and attention mechanisms had achieved good results. Non-local neural networks  enable aIn this work, we train in a self-supervised manner and add a dual attention network. First, the feature map is obtained through the FPN structure. Then, feature dependencies are captured through a spatial attention mechanism and a channel attention mechanism. Finally, the detectors and descriptors are trained using the weighted feature maps. The descriptor output by our network is 256-bit, which is the same as the traditional ORB feature descriptor format and can be easily replaced.We design a self-supervised feature extraction network with a dual attention mechanism. The network framework adopts the current mainstream feature extraction network structure, as shown in 2 is weighted by the dual attention network to obtain the feature map P3. The network can not only realize the sharing of parameters at runtime but also realize the simultaneous operation of multiple tasks by different decoders working at the same time.The network is designed as a common encoder, feature pyramid structure, dual attention network, and two different decoders. Among them, the feature map PBackBone: The backbone of the network adopts the VGG  structurFeature pyramid structure: First, we reduce the dimension of feature map F3 to 64 channels and obtain feature map Attention network: In order to enhance the global features of the feature maps after multi-scale fusion, we model the dependencies on the spatial and channel dimensions of Position and YPosition of the feature points through Detector head: Conv1 contains two Descriptor head: Similar to the processing process of the detector, the input feature map The location attention module adds contextual information to local features to enhance their representation. As shown in The channel attention module improves feature representation by establishing interdependencies between channels. Unlike the positional attention module, the channel attention module utilizes the feature map The overall loss computation consists of the detector loss The loss calculation of the detector is divided into two parts. The first part is to calculate the scores of the two frames of images after the homography transformation. The second part is to calculate the error and confidence between the predicted value of the feature point and the true value. The loss of the detector is calculated as follows:Feature point detection can be regarded as a binary classification problem. Most of the previous work is based on the cross-entropy loss function. However, it is easy to deviate when the sample distribution is not balanced. Therefore, we apply the loss function focal loss  during mIn order to further improve the estimation accuracy of feature points, we are inspired by the literature , adding The error loss of the feature point coordinate value includes error accumulation, error mean and confidence. The error accumulation part is to ensure the overall accuracy of feature point prediction. The error mean and the confidence loss term are to ensure the stability of the accuracy of the extracted feature points of the trained network.The two image frames for which the descriptor loss is calculated are the image pairs after homography transformation. We obtain the final descriptor loss by computing the homography corresponding point pairs between the two images. At the same time, in order to improve the network training accuracy and the network convergence speed, we set the error loss term of the descriptor. The descriptor from the original image is Since the number of corresponding points between frames is significantly less than the number of non-corresponding points, we reduce the impact of the high loss of non-corresponding points on the overall descriptor loss function by setting a modulation factor Our experiments are carried out under the pytorch  frameworThe descriptor size is set to 256 bits in our experiments. We found that the descriptor loss is much larger than the detector loss during network training. Therefore, we balance the two-part loss by the weight parameter to make the network converge correctly. First, we set the initial values to meter in ,21. Duriment and , we set ience of ,19 and oThe ability of our network and other algorithms to extract feature points in different environments is tested on the Hpatches dataset. The data in We tested the running time of different algorithms under Opencv. We select the 01~10 image sequence with ground truth values from the odometry dataset under the KITTI dataset for testing. The feature point extraction ability and the estimation accuracy of the camera trajectory of our network and the three traditional algorithms of SIFT, ORB, and FAST and the deep learning based SuperPoint algorithm are evaluated in different scenarios.It should be noted that we improve the detection accuracy by refining the processing of feature maps while increasing the computational time of the network. Although we take into account both accuracy and efficiency when designing the network, we still have a gap compared with traditional feature point detection methods.In this paper, we propose a new self-supervised feature extraction network to address the poor performance of traditional feature point detection methods in low-texture situations. First, we achieve feature map fusion through the FPN to enhance the multi-scale detection of the network. Then, in order to improve the accuracy of feature point detection in the network, we propose to use a dual attention mechanism to achieve spatial weighting and channel weighting for a fused feature map. Finally, we set the descriptor output channel to 256 to facilitate subsequent transplantation and design the loss function to improve the prediction accuracy of feature point locations and speed up network convergence. Experimental results show that our proposed network is effective and accurate.In future work, our research direction is to further optimize the network structure to improve the real-time performance of the network under the condition of ensuring accuracy.


A multimodality approach in breast imaging is a unique solution to guarantee to the patient a complete diagnosis , the besIn this Special Issue, we have presented different topics of the breast diagnosis, including new methods in experiments and information on less frequent cases  ,4.The purpose of the issue is to analyze the state of the art of breast imaging for subspecialized radiologists, but also for non-dedicated medical staff, in order to strengthening the concept that breast imaging is a specific area that requires specific training .Some authors published manuscript regarding digital breast tomosynthesis (DBT) in the analysis of nipple sparing. Even though magnetic resonance is the most used method in these cases, DBT-galactography increases the sensitivity and specificity of lesion detection by improving the image quality .Another interesting article is about the radiomic analysis of features, which results from contrast-enhanced spectral mammography in the differential diagnosis between benign and malignant lesions ,8.In addition to radiomic analysis, some authors have defined a radiomics score based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer .DWI is used simultaneously with multi-slice (SMS) imaging (SMS rs-EPI) for the differentiation of breast malignant and benign lesions on a 3T MR scanner with a higher spatial resolution and slight reduction of scan time and with a better differentiation between malignant and benign lesions of the breast .Experimental models of new methods such as MammoWave are one of the new efforts to improve breast imaging. It is very difficult to identify an option that can support methods with high diagnostic accuracy, However, experiments with results that are negative or not positive, as well as those that end in failure, are part of the development and allow research to continue .


The codon usage and dinucleotide frequencies of an organism depend mostly on its nucleotide composition, but also on the evolutionary forces that have acted on its genome including mutation, genetic drift, and selection. Different selection pressures may contribute to shape the genome composition and codon usage of viruses:- Codon usage and dinucleotide biases may result from the need to maintain RNA secondary structures involved in splicing and gene expression . There is generally a fair codon adaptation in humans that is not so evident in rodents. Similarly, Roy et al. compare the codon usage pattern of MERS-CoV, SARS-CoV, and SARS-CoV-2 and found that in this latter case there is a lower degree of adaptation to the human host, suggesting that it could contribute to its lower virulence. In Jordán-Paiz et al., the role of codon usage and CpG dinucleotide content in the HIV-host interaction is reviewed. Codon deoptimization of HIV genes decreases its replication capacity. In HIV-1 the codon-driven attenuation is related with the RNA secondary structure, which regulates splicing and viral gene expression, rather than with the control of the speed of translation through tRNAs availability. In fact, late in infection HIV-1 alters the cellular tRNA pool enriching it in rare human tRNAs to match the codon usage of the late genes. On the other hand, increases of the CpG content reduce the HIV-1 replication. One explanation is related with the binding of CpG-rich mRNAs with the innate response factor ZAP; another reason could be the changes in RNA splicing and RNA secondary structure induced by the higher CpG content.Pereira-Gómez et al. elegantly review genome recoding strategies for viral attenuation from an evolutionary perspective. These strategies include alteration of dinucleotide frequencies, deoptimization of codons and codon pairs, and decrease of viral mutational robustness. A very nice example of codon deoptimization is the development of live attenuated strains of foot and mouth disease virus (FMDV) described in Diaz-San Segundo et al. The strategy was based on the deoptimization of the non-structural proteins coding regions, which are more conserved than the capsid coding region, to provide a backbone for the development of multiple-serotype live attenuated vaccines. Giménez-Roig et al. review the approaches employed for the development of adenoviral-based vaccine, and they discuss a provocative novel strategy using codon optimization of the transgene, pursuing a good balance between transgene expression and virus attenuation through the competition for tRNAs. Finally, Chavarria-Miró et al. describe the growth characteristics of a hepatitis A virus strain isolated through genomic selection, which contains epistatic mutations located at the Internal Ribosome Entry Site (IRES) and in the capsid coding region, resulting in a fast growing virus. While IRES mutations lead to a more active initiation of translation, the right combination of deoptimized and optimized codons in the capsid coding region results in a correct capsid folding despite a faster translation. Overall, this strain shows a higher virus production in a shorter time compared to the standard virus strain presently used in vaccine production from which it derived.In the second part of the Topic, The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.RP wrote the Editorial. CB and GM contributed to the final version of the Editorial. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This work is concerned with the semi-mechanistic prediction of residence time metrics using historical data from mono-component twin screw wet granulation processes. From the data, several key parameters such as powder throughput rate, shafts rotation speed, liquid binder feed ratio, number of kneading elements in the shafts and the stagger angle between the kneading elements were identified and physical factors were developed to translate those varying parameters into expressions affecting the key intermediate phenomena in the equipment, holdup, flow and mixing. The developed relations were then tested across datasets to evaluate the performance of the model, applying a k-fold optimization technique. The semi-mechanistic predictions were evaluated both qualitatively through the main effects plots and quantitatively through the parity plots and correlations between the tuning constants across datasets. The root mean square error (RMSE) was used as a metric to compare the degree of goodness of fit for different datasets using the developed semi-mechanistic relations. In summary this paper presents a new approach at estimating both the residence time metrics in twin screw wet granulation, mean residence time (MRT) and variance through semi-mechanistic relations, the validity of which have been tested for different datasets. Wet granulation is a size enlargement process wherein a liquid solvent is added to a primary powder bed that consists of one or more components. The solvent is usually water or a polymer solution ,2,3,4,5.In recent years, there has been an accelerating shift towards continuous manufacturing, partly driven by the growing need for industries to reduce the time-to-market for products, while maintaining strict quality requirements. This has resulted in extensive investigation of continuous wet granulation processes, and its associated processing equipment. Wet granulation is carried out continuously in twin screw extruders (TSE) by employing powder feeders in order to maintain steady inlet flow into the system, and the liquid (with or without a polymer binder dissolved in it) is pumped at the desired flow rates into the mixers in a controlled flow ratio with respect to the primary powders. The liquid is discharged through nozzles into the granulator in order to achieve uniform and proper wetting of the powder particles. A typical continuous wet granulation setup and a computer-aided-design (CAD) render of a TSE, are illustrated in An improved process understanding and characterization of the continuous granulation unit operation necessitates mechanistic analysis of the physical processes occurring within the unit operation. The typical critical quality attributes of a wet granulation process are the exit particle size distributions, densities and the distribution of the formulation material components within the granules of different sizes. Population balance models have been extensively developed and studied in order to completely and accurately characterize number and mass distributions of various important particle traits such as size, liquid content and/or porosity ,10. HoweExperimental studies are typically performed to develop and validate an RTD model, which can be further used for process model development and optimization. The RTD of continuous wet granulation systems is measured via the use of a tracer material, which tracks the movement of bulk material within the system. The tracer is added at the entrance of the granulator and then blended and distributed inside according to the conveying and mixing profile of the system. The granules exiting the system are analyzed using different techniques to quantify the RTD. The tracer content in the exiting granules can either be measured from offline samples collected or in-line measurement techniques. The concentrations of the tracer in these granules are measured using ultraviolet-visible (UV-Vis)  or near The development of an accurate and predictive model of the RTD of a system would provide a thorough understanding of the process dynamics, and the model can be used to optimize the design space of the process. Shirazian et al.  presenteRTD models have been extensively developed for continuous reactors in traditional chemical engineering such as continuous stirred tank reactors (CSTRs) and plug flow reactors (PFRs) dealing with mostly incompressible liquid systems and fully-developed flow profiles. These models were fitted to non- reacting systems using the experimental data of different industries such as food, pharmaceutical manufacturing and even in environmental engineering ,18,19. OIn addition to studying the MRT and RTD profiles, researchers have also attempted to understand axial mixing in TSG. Axial mixing in continuous equipment is characterized by the variance of the RTD profiles. The mixing in radial direction is neglected due to the narrow geometries of extruders characterized by their length-to-diameter (L/D) ratios . The degIn general, processes involving wet powder flow behavior require close examination. Therefore, it is the overall aim of the researchers working in this field to develop mechanistic models accounting for the chosen vessel’s geometry , its operating parameters (screw speeds and rotation direction of screws), process parameters  and the material properties  of the constituent materials.This work proposes to further understand the physical processes governing the RTD in a TSG by developing a method to predict the central moments of RTDs, namely the MRT and variance using semi-mechanistic relations that can be extended to systems of different scales, screw configurations and process parameter ranges. The predicted values of the central moments of RTD have been compared qualitatively with the main averaged effects of individual parameters at different settings for each subset considered. In addition, the fidelity of the predictions were further validated by comparing them with the corresponding experimental points and evaluating statistical measures of goodness of fits. The historically produced twin screw wet granulation experimental data that were made available in literature by various researchers in the field were leveraged to train the semi-mechanistic models and evaluate the validity of the relations developed. It is to be noted that, in this work, the focus has been kept on having as mechanistic a model as possible for predicting the metrics across datasets and hence, the relations and tuning parameters were chosen to reflect the physics of the system.The rest of the paper is organized as described in this paragraph. FR), processing screw speed (RPM), liquid-to-solid percentage (LS), screw configuration described by number of kneading elements (NK) and stagger angle (SA) between them. The table also lists the available number of points sourced from each study.The RTD experimental data were collected from the published available literature ,21,26. TIn addition to the above mentioned categories, the database also contained details on the equipment parameters such extruder shaft outer diameter, and effective length scales of operation. Lastly, the dataset also considered material properties affecting the flow in a wet granulation system, namely the bulk and true densities of the powder processing material and the density of the binder liquid fed into the system. The collation and categorization of along these parameters aided us in comparing published residence time metrics data from various experimental sources using different materials on different equipment. This mode of organization is the first step in building a more generalizable prediction model as opposed to being confined within the bounds of a chosen DOE, equipment and formulation material.For this study, three new physically relevant reduced order expressions have been introduced namely, Flow factor, Holdup factor and Mixing factor. Furthermore, these reduced order expressions have been employed to formulate equations for the prediction of MRT and variance.Flow factor: Flow factor defines the convective/ bulk flow of the powders in the axial direction. It has been found that as the feed rate is increased the residence time of powders in a TSG decreases, which has been attributed to the axial velocity component provided by the feed rate ,28,29. IHoldup factor: In previous studies either dimensionless or physically significant reduced order parameters were developed in order to describe the barrel fill level in TSG systems . In thisAvailable volume in screw sections: While it is known that the sector volume containing the stagger angle is responsible for the fill in kneading elements, the relations for conveying section are unknown as they are continual helical screws. Therefore, in this study the conveying section of a particular length and angular turn has been approximated as a kneading section of the same length where the number of kneading elements is large, the thickness of each element in the section and the stagger angle between the kneading elements are infinitesimally small. The finite element approximation schematic has been shown below in Mixing factor: Mixing in flow systems can be divided into global mixing and local mixing . The varMean residence time (MRT): Mean residence time (MRT) is defined as the average amount of time powders or a particle spends in the system of interest. In this study, this metric has been obtained from the more traditional definition in chemical engineering by dividing the material holdup by the flow rate through the system.Variance: Most of the studies involving RTD and describing variance in a TSG have either directly correlated variance to the mixing efficiency or have described mixing via the change in Peclet Number assuming the closed-closed boundary condition approximation for TSG . HoweverR is the outer radius of the kneading element, The expression for the net volumetric material feed-rate Equation : (1)FRvoEquation : 2)AvaiEquation :3)AvailAvaiEquatAvailAvaiThe expression for the volumetric material displacement rate by a conveying screw of 1 lead length in the equipment Equation : (5)DispBy using the expressions developed in Equations  and 4),,4), the Equation : (6)Holdquations  and 5),,Holdup hEquation : (7)FlowEquation : (8)MixiDividing Equations  and 7),,7), and Equation : 9)MRT=MRT=b1, tlated in : (10)−b2Similarly, dividing Equations  and 8)  8) and mEquation : 11)Pe=Pe=b5, thlated in : (12)b3+From the open-open boundary condition of the TSG, the normalized variance Equation : (13)VarIn order to ensure a good prediction of the model, validation of the model needs to be conducted. However, especially when we have smaller datasets, a simple validation  might not be sufficient. Hence, in such cases cross-validation techniques need to be employed for higher degree of confidence in model predictions. For this reason, we have employed a k-fold cross-validation model. To further increase the confidence in our predictions, we initially split the experimental dataset into 2 subsets—one consisting of approximately 70–80% of the data points (Training set) and the second one comprising of the remaining points (Test set), respectively. Prior to splitting, the corresponding dataset rows were jumbled and the test set runs were randomly selected using the ’rng’ function in MATLAB with a specified seed number number to ensure that the procedure is reproducible . The lark datasets and every time the model is run k times such that model is validated on each of the k datasets. k-fold cross-validation was used to further enhance the parameter optimization process by introducing the parameter output of one iteration as an input to the next iteration.K-fold cross-validation: In a k-fold cross-validation the parent dataset is arbitrarily divided into Three different metrics were used to determine the end point criteria of the model.iComparison between main effects plot of the experiments and that of the modeliiSatisfactory parity plots, realistic narrow upper and lower bounds were set, and the model’s performance was determined based on how many predicted points fell between these limits; in addition the root mean square of errors (iii)Wherever possible, the values of the tuning parameters in the model when trained on different datasets were compared, and physical interpretation was made for their rangesThe expression for the root mean square errors is given as follows in Equation (14)RMSEFR), number of kneading elements (# KEs) in the configuration, stagger angle between the kneading elements (SA) and the rotational speed of the shafts of the extruder (RPM) have been shown on the experimental MRT and on the model predicted MRT in The individual main effects of the varied parameters, powder feed rate , it can be seen that the model (solid curve) predicted a much steeper increase and wider range of MRT values compared to the experimental runs . The average model predictions were closer for powder feed rate values of 25 kg/h at higher MRT values. A similar observation is made from the second sub-figure  whereby, the average model prediction was lower compared to the experimental values at two kneading elements configurations yielding low MRT values. On the other hand, from the fourth sub-figure , the average MRTs predicted were lower at lower screw speeds corresponding to larger MRT settings. Lastly, it can be seen from the third sub-figure  that MRTs were under-predicted at settings corresponding to lower experimental MRT , and were over-predicting for the intermediate setting of 60Upon closer inspection of the main effects plots for the FR, # KEs, SA and RPM), there is an addiotional factor LS along which the effects have been in plotted too as indicated by the pink line and points. From the figures, it is seen that the MRT of the TSG showed an increasing trend with respect to the increasing powder feed rate range from 10 kg/h to 25 kg/h. On the other hand, the MRT decreased with respect to increasing screw speed from The main effects of the varied parameters have been shown on the experimental MRT and on the model predicted MRT in The main effects of the parameters have been shown on the experimental and the model predicted variance in The main effects of the parameters have been shown on the experimental variance the model predicted MRT in The parity plot for the experimental values of the MRT (X) vs. the predicted MRT values (Y) for the Kumar et al. 2015 dataset has been given below in From LS% ages were varied from 6 to FR have been contrary for the Kumar et al. 2015 and Kumar et al. 2016 experimental datasets respectively as seen in The parity plots between experimental values and model predictions obtained by combining the two datasets, Kumar et al. 2015 and Kumar et al. 2016 are seen below in From l et al. .The values of the fitting constants that were obtained by training the model on different datasets have been shown below in quations  and 11)b2, b3, bEquation .From On the other hand, an additional effect is seen in the estimated values of the constants From the main effects plots in Therefore, in summary, this research paper presents (i) a historical data-driven approach to estimating the RTD central moment metrics of a TSG system; (ii) a semi-mechanistic development of the predictive model relations (iii) validation of the said relations on different datasets; (iv) and lastly physical explanation for the values of the tuning parameters and empirical correlation for one of them with the liquid-to-solid percentage, one of the varying process parameters across datasets.


The recent advances in lung ultrasound for the diagnosis of cardiogenic pulmonary edema are outstanding; however, the mechanism of vertical artifacts known as B-lines used for the diagnosis has not yet been fully elucidated. The theory of “acoustic trap” is useful when considering the generation of vertical artifacts. Basic research in several studies supports the theory. Published studies with pilot experiments indicate that clarification of the relationship between the length and intensity of vertical artifacts and physical or acoustic composition of sources may be useful for differentiating cardiogenic pulmonary edema from lung diseases. There is no international consensus with regard to the optimal settings of ultrasound machines even though their contribution to the configuration of vertical artifacts is evident. In the clinical setting, the configuration is detrimentally affected by the use of spatial compound imaging, the placement of the focal point at a deep level, and the use of multiple focus. Simple educational materials using a glass microscope slide also show the non-negligible impact of the ultrasound machine settings on the morphology of vertical artifacts. In lung ultrasound, the presence and severity of pulmonary edema are evaluated with vertical artifacts known as B-lines. In an international consensus statement published in 2012, B-lines were defined as discrete, laser-like vertical hyperechoic artifacts that arise from the pleural line and extend to the bottom of the screen without fading . A systeRecent advances in lung ultrasound for the diagnosis of cardiogenic pulmonary edema have been outstanding, as mentioned above. However, the mechanisms underlying the vertical artifacts in cardiogenic pulmonary edema have not been fully elucidated. Furthermore, there is no international consensus with regard to the optimal settings of ultrasound machines even though their contribution to the configuration of vertical artifacts is evident ,8,9. TheIn cardiogenic pulmonary edema, a rapid increase in hydrostatic pressure in the pulmonary capillaries leads to increased fluid transfer into the interstitium and alveolar spaces. High capillary pressures can also cause lung injury and barrier disruption which increases permeability and fluid transfer .Chest CT is not a standard imaging modality to diagnose cardiogenic pulmonary edema; however, it is very useful for grasping the distribution of edema in the lung tissue. The findings of interstitial pulmonary edema are ground-glass opacities and interlobular septal and bronchovascular thickening. Alveolar edema appears as airspace consolidation, in addition to the above findings ,12.Lichtenstein et al. compared ultrasound images with CT images and indicated that B-lines originate from the thickening of the sub-pleural interlobular septa and ground-glass opacities . Many reSoldati et al. introduced the interesting theory of “acoustic trap” in the generation of vertical artifacts, including B-lines . An acouWe developed simple experimental models that generate vertical artifacts ,8. In onIn another study, we used an ultrasound gel spot to imitate the source of the vertical artifact and a block of bacon as a chest wall phantom. As the size of the point of contact between the gel spot on the sheet and the phantom decreased when the sheet was peeled, a vertical artifact was generated and/or extended deeper. Next, objects of different shapes made using gel balls were used to observe the generation of vertical artifacts and compare the echo intensity. For a given shape, the intensity was markedly higher in one model with the point of contact than in the other model with the plane of contact. With the same point or plane of contact, the echo intensity was higher in the taller model. The results obtained from the simple experimental models support the acoustic trap theory . The lenIn the latter study, we used materials that have similar acoustic properties to water as the sources of vertical artifacts . The modThe optimal machine settings to visualize B-lines are not completely clear, even in the current situation . Many puSpatial compound imaging is now available on most ultrasound machines. The main purposes are to improve contrast resolution and to reduce acoustic shadowing. This method acquires three or more multiple images by multiple transmission with different angles, and creates averaging images by overlaying them incoherently  Figure . When a Many ultrasound machines are now equipped with spatial compound imaging for several applications . If one of the presets for these applications is accidentally selected for the evaluation of B-lines, they may be erroneously counted. To avoid misinterpretation, spatial compound imaging should be set to “off” ,19,21, oThe focal point can also affect the quantification of B-lines. As the single focal point is shifted from the level of the pleural line to deeper levels, the dispersion of B-lines becomes wider during multiple reflections  Figure . As the In daily practice, confluent B-lines are often observed in cardiogenic pulmonary edema. The confluent B-lines are also called white lung pattern, especially when they cover the intercostal space . Howeverp < 0.001). As mentioned above, the visualization and length of the artifacts depend on the frequency; thus, the term “vertical artifacts” has been preferred to “B-lines” in recent studies [Recent in vitro and in vivo studies have revealed the effect of the frequency on vertical artifacts. Demi et al.  and Ment studies ,30.B-lines are detectable using sector, curvilinear, or linear transducers with low to high central frequencies ,6. In seMany published studies did not mention the presets or used a variety of presets, including cardiac, abdominal, and lung presets set by the manufacturers ,31,32; tUnderstanding the influence of the above-mentioned machine settings and the selection of transducers on the configuration of vertical artifacts is crucial for planning clinical research, sharing the appropriate use, and providing education in relation to lung ultrasound. However, the necessity has not been fully shared among users. Furthermore, it is not easy to observe the influence of these factors on ‘moving’ vertical artifacts in real patients with or without dyspnea and tachypnea.A more simplified experimental model is useful for the educational purpose. A motionless stable vertical artifact is easily generated by the model using a glass microscopic plate, which is easily obtainable in each medical facility. We herein demonstrate simple educational materials that help users understand the influence of machine settings on the configuration of vertical artifacts . A diagn6 Pa s/m) is placed perpendicularly on the footprints with a thin layer of ultrasound gel. The thickness of the slide is made parallel to the scan direction  into one scan line image. As a result, the synthesized image has higher spatial resolution due to the multiple focal points. However, this method causes a “patchy” step-wise configuration in the vertical artifact a,b.As shown with the series of ultrasound images, the configuration of the vertical artifacts is detrimentally affected by the use of spatial compound imaging, the placement of the focal point to at a deep level, and the use of multiple focus. These simple educational materials tell us the non-negligible impact of the ultrasound machine settings on the morphology of vertical artifacts in lung ultrasound.The acoustic trap theory is useful when considering the generation of vertical artifacts in lung ultrasound. Several studies employing basic research support the theory. Published studies with pilot experiments indicate that clarification of the relationship between the length and intensity of the vertical artifacts and the physical or acoustic composition of the sources have potential for differentiating cardiogenic pulmonary edema from lung disease.In the clinical setting, the configuration of the vertical artifacts is detrimentally affected by the use of spatial compound imaging, the placement of the focal point at a deep level, and the use of multiple focus. Simple educational materials using a glass microscope slide also demonstrate the non-negligible impact of the ultrasound machine settings on the morphology of vertical artifacts. An international consensus needs to be reached on the optimal settings of ultrasound machines, which affect the diagnostic accuracy.