Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain–computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic. Brain–computer interface (BCI) is a collaborative setup between the human brain and a computer device, which takes brain signals as input and tries to decode them into computer commands to direct external activities such as cursor control, wheelchair control, and silent-speech recognition ,2. The eThe elimination of EMG artifacts from an EEG is more challenging as compared to removing other types of artifacts from an EEG signal. Generally, the EMG artifacts occur in EEG signals due to various muscle movements near the head. Various muscle movements such as teeth clenching, swallowing, head movement, chewing, jaw movement, and tongue movement are also captured through electrodes and contaminate the EEG signals with higher amplitude, broad anatomical distribution, and wide frequency spectrum . SpecifiIn contrast, the spatial distribution of ECG and EOG are comparatively localized. EMG artifacts occur from the movement of spatially distributed and functionally independent muscle groups with distinct topographic and spectral signatures . The speNumerous techniques have been proposed in the literature to overcome these difficulties of EMG removal from EEG. In early research, approaches based on blind source separation (BSS) were explored for the automatic removal of EMG artifacts from multichannel EEG . The autHowever, the recent trend in mobile healthcare applications has led to the reduction in the number of EEG channels for health monitoring, and in some cases only a single channel is used . In suchIn recent years, several hybrid methods ,30,31,32Zhang et al.  proposedWhile numerous methods have been studied for the elimination of EMG, developing robust single or hybrid algorithms that can operate automatically is still very challenging. In addition, most of the techniques are suitable for multi-channel EEG (performance degrades for single-channel EEG), hence, developing an automatic EMG artifact removal technique for single-channel EEG is still challenging. However, the combination of multiple cascading algorithms is essentially an unexplored area. The significance of cascading algorithms is to suppress the artifacts in a single stage, which suppresses artifact sources and achieves a higher degree of robustness. In this manuscript, a novel automatic cascaded system is proposed in which wavelet transform is combined with the NLM filter for the elimination of EMG artifacts from the EEG signals.(1)The proposed algorithm employs the wavelet transform for its good time-frequency localization and optimized NLM filter for better removal of wide frequency spectrum EMG artifacts from the corrupted EEG signals.(2)The main challenge in wavelet-transform-based denoising is the selection of appropriate threshold values. Hence, instead of thresholding the wavelet coefficients, they are corrected through NLM estimation.(3)The issue of optimum parameter selection for the NLM is properly addressed with the help of a meta-heuristic algorithm.(4)Further, the proposed algorithm corrects only the EMG-corrupted portion of the EEG and keeps the clean portion untouched. Hence, the proposed system preserves the true information contained in the EEG signal.In the proposed work, an EMG-contaminated EEG signal is automatically identified and decomposed into wavelet coefficients through WPD. Wavelet transform is used to separate the EEG features into different scales so that significant features of the EEG signals are preserved and noises can be removed. The mother wavelet and with an appropriate decomposition level is selected through a proper procedure. It is to be noted that artifacts will be reflected in the wavelet coefficients. Hence, correcting the corrupted wavelet coefficients will result in denoising the EEG signal. In the proposed method, corrupted wavelet coefficients are corrected through an optimized NLM algorithm. Finally, all the corrected coefficients are used in the inverse operation to get back the original artifact-free EEG signal. In this approach, no threshold calculation method is employed; instead, wavelet coefficients are corrected through an NLM filter, which makes the system automatic and fit for implementation in online applications. In addition, a modified NLM algorithm is proposed, in which all the parameters of the algorithm are optimized in a proper way. The proposed approach preserves the information of interest in the EEG signal, which is reflected in higher average correlation coefficients (CC) and higher MI values. The main contributions of the proposed denoising algorithm are stated as:SVM is one of the most efficient classifiers and hence was used in this work. Different artifacts have different characteristics, for example, eyeblink artifacts contaminate the EEG signals with 5 to 10 times’ larger amplitude than the normal EEG signals with very low frequency , and EMG artifacts contaminate the EEG signals with higher amplitude and higher frequency components. Similarly, ECG artifacts contaminate the EEG signals with periodic discharges. Moreover, we do not know which EEG patterns are hidden in the artifact portions (there is no ground truth). Hence, before removal of artifacts, it is necessary to identify which artifacts contaminate the EEG signals, because different artifacts have different characteristics and affect the EEG signals distinctively. If the system does not have any prior knowledge about the type of artifacts, then the system will be capable of denoising the EEG signals but the true EEG pattern  will not be reconstructed properly. Therefore, first the EEG signals corrupted with EMG artifacts are identified with SVM as a classifier.The frequency of muscle artifacts overlaps with the frequency of interest of EEG signals. Hence, removing those particular frequencies by making the relevant wavelet coefficients zero will result in a significant loss of information. Therefore, at first EEG signals are decomposed into wavelet coefficients using WPD to represent the different frequency bands of EEG signals with respect to time. Note that designing an efficient NLM-based filter for a very wide frequency band is very difficult. Thus, the corrupted EEG signal is decomposed into different frequency bands at different levels in WPD so that efficient NLM filters can be designed for decomposed signals at different levels for the effective removal (correction) of EMG artifacts.The optimization of parameters, especially the bandwidth parameter, λ of NLM algorithm at different levels (set of λ), simultaneously is a challenging task, and meta-heuristic algorithms are the most efficient ones in solving this type of problem. Hence, one of the recently developed meta-heuristic algorithms, GWO, is used for the task.The novelty of the proposed hybrid method in which SVM, WPD and NLM are combined is described as below:The manuscript is organized as: The SVM is generally used as a classifier that competently categorizes the data using a supervised machine learning algorithm . It creaThe wavelet transform decomposes the signals into approximation (cA) and detail (cD) coefficients —cD consix(t), the WPD coefficients can be derived as shown in Equation (1) [For an EEG signal tion (1) :(1)d0,0tIn the wavelet-transform-based signal analysis, the selection of the appropriate mother wavelet and decomposition level is a challenging task. Several mother wavelets are available in the wavelet family. The performance of the wavelet transform is affected by individual wavelet functions. Because hit and trial approach is a time-consuming task, the appropriate mother wavelet is selected according to the reconstruction error in this paper. It is to be noted that when any transformation technique transforms a signal into another domain, and again reconstructs the original signal through inverse operation, the reconstruction error should be ideally zero. The wavelet function is selected that gives the minimum reconstruction error. The reconstruction error  from the wavelet coefficients . Non-norBecause most of the information in the wavelet-transformed signals are stored in the approximation coefficients, the optimum level of decomposition is selected at which the entropy of cA becomes less than that of cD.A swarm-based intelligent algorithm, GWO, was first proposed by Mirjalili et al. , which iThe goal of the NLM algorithm is to solve the issues with local smoothing filters by computing the smoothed value as a weighted average of other values in the signal based on the similarity of the neighborhoods . The weiu is the original signal and n is the noise, then v is expressed as in Equation (6):Let v be noisy observation, where, s is calculated by weighted sum of the values at the other point N(s), where, N(s) is the “search neighbourhood”, as described in Equations (7) and (8) [Then, the estimation  and (8) .(7) u˜swws,η=It is evident from Equation (9) that the NLM algorithm extracts the non-local information contained in the data. Therefore, the similarity between non-local patches is exploited in the NLM technique. It is to be noted that the weights depend upon the patch similarities instead of the gap between them . Hence, 2 is the variance of noise and equal to the 0.002 microvolts [In the NLM algorithm, estimation is achieved based on the selected similar patches. The main challenge associated with NLM-based EEG denoising is the selection of appropriate filter parameters. There are three key parameters: bandwidth parameter (λ), search neighbourhood half-width (M), and patch half-width (P) that need to be optimized for a better estimation. The value of P determines the number of patches. The size of M specifies the area of search, and hence, the larger size of M results in higher computational complexity. Generally, M is limited to reduce the complexity of the system. Out of the three parameters, selection of appropriate λ is more complicated. The level of the signal’s smoothness is determined by the λ. A very small value leads to inadequate averaging, while a very large number can induce signal blurring by causing dissimilar patches to look like identical patches . Generalcrovolts . HoweverTo validate and test the proposed method, following parameters are evaluated:The mutual information measures how much information is drawn from the original corrupted EEG signal to reconstructed clean EEG signal . MI is cThe average CC computes the degree of similarity between original clean EEG signal and reconstructed clean EEG signal of same duration . The aveThe SSIM calculates the similarity index between original clean EEG signal and reconstructed clean EEG signal of same duration . It is dThe SSIM and average CC are evaluated on simulated EEG data as the ground truth is available. In the case of recorded real EEG dataset, the ground truth is unavailable, and hence to determine how much information are preserved from corrupted EEG to reconstructed clean EEG during denoising process, MI is evaluated.Step 1:The corrupted EEG (C-EEG) is identified and separated from the non-corrupted EEG (NC-EEG) with the help of a pre-trained SVM.Step 2:i (the mother wavelet and the decomposition level are selected as described in The corrupted EEG is decomposed into wavelet coefficients through WPD using mother wavelet function up to level Step 3:The wavelet coefficients are then corrected (estimated) through the weighted average of the non-local patches using optimized NLM algorithm. The key parameter (λ) of the NLM algorithm is optimized using meta-heuristic optimization technique.Step 4:Finally, all the corrected wavelet coefficients are reconstructed using the inverse operation to obtain the clean EEG signal.In this report, a novel hybrid technique for automatic recognition of EMG artifacts and its subsequent elimination from EEG is proposed. The basic flowchart of the proposed algorithm is shown in The different steps of the proposed approach are elaborated in the subsequent section.Data were recorded from 40 subjects consisting of 26 male and 14 female students (mean age: 21.5 years) to measure the stress of individuals . EthicalThe subject was initially asked to relax for a period of 25 s during which relaxing music was played to ease the subject. The subject was instructed to stay calm after completion of every impulse type, after which the instructions for the Stroop color and word test (SCWT) were shown to the subject. The subject was exposed to SCWT for 25 s. The subject then relaxed for 5 s and then the instructions for the next impulse were displayed for 10 s. In the next impulse, the subject was shown mirror images and was asked to identify whether the images are symmetric or asymmetric and respond with a thumbs up or thumbs down gesture, depending on whether the images displayed represent symmetric mirror images or not. The mirror image symmetry task was carried out for 25 s, after which the subject again relaxed for 5 s and then the instructions for the next impulse were displayed for 10 s. Finally, the subject was instructed to solve arithmetic problems mentally and respond with a thumbs up or thumbs down gesture, depending on whether the answer displayed on the screen was a correct solution for the arithmetic problem or not. The arithmetic task was also repeated for 25 s. The completion of the arithmetic task marked the completion of a trial. Moreover, when the subject was responding, an operator also gave feedback as to whether the answers provided by the subject were incorrect or correct. The experimental setup is shown in In our experiment, students performed various arithmetic tasks and color tests to measure the stress of individuals. During the experiments it was obvious for a subject to move the head or swallow or move the tongue, which caused the EMG artifacts in the EEG signals. The presence of EMG artifacts in our recorded EEG data were visually inspected by a clinical expert. Generally, normal waveforms of the EEG ranges from 0.1 to 40 Hz. In our experiment, EEG signals were filtered between 0.1 to 40 Hz to remove the frequency components above 40 Hz.From the recorded data, clean and corrupted EEG segments were extracted by an expert by studying various parameters such as the variance, Shannon entropy. In addition, the expert visually inspected the EEG for segmenting. A total of 400 segments (duration: 10 s) were extracted to represent each of the two classes, namely C-EEG and NC-EEG. The SVM network was trained with the features extracted from both the dataset of same size of 10 s. The data were split in an 80:20 ratio for training and testing. Further, 10-fold cross-validation was used for evaluating the experimental performance. Three distinctive features, variance, Shannon entropy, and peak-to-peak, were calculated from all the signals and provided to train the SVM network. To demonstrate their effectiveness in classifying C-EEG from NC-EEG, an SVM network was trained with these features extracted from EEG data of both the datasets. The average values of these extracted features of NC-EEG, and C-EEG were evaluated respectively and are shown in After successful identification of the EMG-corrupted EEG signal, it was passed to the second stage of the proposed algorithm for EMG correction. The C-EEG was decomposed into wavelet coefficients through WPD. Wavelet transform was used to separate the EEG features into different scales so that significant features of the EEG signals were preserved and noises could be removed. To cover the shortage of fixed time-frequency decomposition in DWT, WPD was employed in the work, which not only decomposed the cA but also cD simultaneously. As a result, the WPD had the same frequency bandwidth in each resolution while DWT did not.The mother wavelet was carefully chosen by considering the lower reconstruction error as described in It is to be noted that the EMG artifacts present in the EEG signal will be reflected in the corresponding wavelet coefficients. Hence, correcting them will successfully remove the EMG artifacts from the EEG signal. Now the artifact free wavelet coefficients are estimated from the artifact wavelet coefficients using weighted average of the similar patches as described in Equation (7). The estimation is performed through the NLM algorithm. The selection of the optimum parameter of the NLM algorithm is described below.i number of λ were optimized for the 2i number of wavelet coefficients  through GWO. The selection of appropriate parameters of the NLM algorithm is explained in An artifact EEG signal was decomposed into WPD coefficients. All the artifacts will be reflected in WPD coefficients. Then, artifact-free coefficients were estimated through the NLM algorithm by tuning its parameters . Four corrupted EEG signals of duration 10 s were analysed by varying the parameters of the NLM algorithm. The SAR improvements achieved by varying several values of P and M are shown in The parameters of the NLM algorithm were carefully chosen so that the proposed algorithm gave the best performance for removing EMG artifacts from the EEG signals. In the proposed algorithm, several sizes of P and M were investigated and their performance in terms of SAR was compared in the abovementioned The bandwidth parameter, λ is the most important parameter in the NLM algorithm as it decides the degree of smoothness. In the proposed algorithm, λ was selected through meta-heuristic optimization technique as to avoid the over-smoothing problem as well as to select the correct similar patches. Two meta-heuristic optimizers, PSO, widely used by researchers due to its fast convergence, and GWO, due to its better execution, were employed for searching the optimal λ for performance comparison.x(t) be an EEG signal and n(t) be a muscle artifact; together they formed C-EEG as in Equation (13):Let Y using “fk6” as mother wavelet up to level i. Next, corrected WPD coefficients  denotes the NLM function. As suggested in [2 is the noise-variance and equal to the 0.002. However, EMG artifacts vary with muscle contraction; in this case, σ2 may vary. Considering this fact, λ was randomly initialized between 0.01 to 0.9 in the proposed algorithm. The fitness function used for PSO and GWO is stated in Equations (15) and (16):Now the WPD coefficient ested in , the banAfter estimating the parameters of the NLM algorithm, the corrupted wavelet coefficients were corrected through the modified NLM algorithm. In the proposed method, P = 4 and M = 50 was selected through a hit-and-trial approach, and the λ was selected through GWO. Next, all the corrected wavelet coefficients were passed to the final stage of the proposed method.After successfully estimating the clean wavelet coefficients, all the corrected coefficients were used in inverse WPD to reconstruct the original artifact-free EEG signal. The inverse WPD function is expressed as Equation (17). Finally, the artifact-free reconstructed clean EEG signal was provided by the proposed model as output.The proposed technique was first tested on simulated EMG artifact EEG data and then verified on multi-channel real EEG data. The testing was performed on a simulated signal as the original clean EEG signal (the ground truth) was known. As a result, it was possible to compare the original EEG signal and its correction after adding EMG artifacts with the original EEG. In this way, using a simulated EEG signal, the proposed method was tested.The EEG signal was contaminated with EMG artifacts, but the information suppressed by the artifacts was not known (there was no ground truth). Hence, to validate the proposed approach, it was evaluated on simulated C-EEG data for which the ground truth was available. The EMG-contaminated C-EEG signal was simulated in MATLAB using the method described in . At firsThe simulated artifactual EEG signal was passed through the pre-trained SVM as a classifier. The SVM automatically recognized C-EEGs through the extracted features. The performance in terms of accuracy, specificity, and sensitivity of the SVM classifier was compared with the Naïve Bias Classifier (NBC) and is shown in To check the superiority, the proposed model was compared with the recently reported EEG denoising techniques ,35,38 anAfter successfully verifying on single-channel simulated C−EEG, the proposed model was also tested on recorded EEG. The 32-channel raw EEG data of duration of 10 s were chosen from the dataset and filtered between 0.1–40 Hz through a bandpass filter. However, all the channels were affected by the EMG artifacts. The proposed model automatically recognized the EMG artifact EEG data and subsequently corrected them. The proposed model successfully eliminated all the EMG artifacts from the corrupted EEG while preserving the true EEG structure. For quantitative analysis, MI between the corrupted EEG signal and reconstructed clean EEG was measured and compared with the recently developed techniques as presented in In this paper, a fully automatic denoising method is proposed for removing EMG artifacts from the EEG signals. At first, the artifact EEG signals are identified through SVM as a classifier. After that, the corrupted EEG signals are decomposed into wavelet coefficients through WPD. Wavelet transform is used to separate the EEG features into different scales so that significant features of the EEG signals are preserved and artifacts can be removed. Generally, in wavelet denoising techniques, wavelet coefficients are thresholded to remove the artifacts. However, selection of an appropriate threshold makes the system unfit for automatic operation. In the proposed method, the corrupted wavelet coefficients are corrected through a modified NLM algorithm instead of thresholding them. The NLM algorithm has various parameters and needed adjustment for different types of EEG data. The NLM algorithm has been modified in this paper by optimizing the bandwidth parameter through GWO. In addition, a set of λ is optimized at different scales of the wavelet-transformed EEG signal, which enhances the performance of the proposed method for denoising the EEG signals. Finally, all the corrected wavelet coefficients are used in inverse operation to get back the original clean EEG signal. The proposed method is fully automatic and does not need any intervention of the user.The proposed method was first validated on simulated EEG signals (the ground truth is available) and then tested on recorded EEG data. It is observed from the experimental results that the proposed method is superior among all the denoising methods in terms of highest CC and SSIM. Hence, the proposed algorithm preserves more true structure of the clean EEG signal compared to other recently reported EEG denoising algorithms. For recorded EEG data, the original true clean EEG is unavailable (there is no ground truth); hence, MI is calculated to check how much information is mutual between corrupted EEG and denoised EEG. It is observed that the proposed approach achieved higher MI as compared to other recently developed techniques.One of the main advantages of the proposed hybrid method is that it is insensitive to any threshold value and there is no need to tune the parameters of the NLM algorithm, which makes the system fully automatic and it can be implemented in online applications. WPD efficiently deals with the non-stationary characteristics of EEG signals. Although the proposed method is capable of correcting EMG artifacts from the multi-channel EEG signals, correcting one by one increases the computational time.In this paper, a new automatic hybrid system for denoising muscle artifacts from EEG is introduced for the first time, in which WPD is combined with an optimized NLM algorithm. The WPD is adopted for its multi-resolution analysis while using only a single-channel EEG. Unlike other current state-of-art methods, the proposed system removes artifacts from its time-frequency response of the EEG signal through an optimized NLM algorithm instead of thresholding. The proposed system removes the muscle artifacts from the EEG signal, no matter how many artifacts contaminate the EEG. Further, several challenges using the NLM algorithm for EEG denoising are discussed and properly addressed in this work. One major challenge associated with the NLM algorithm is the selection of proper bandwidth parameter. It is also shown that the bandwidth parameter is different for different EEG. In this work, a meta-heuristically optimized NLM algorithm is presented, in which the bandwidth parameter is selected according to the nature of the EEG. The proposed system can operate automatically and does not require any intervention of the user while using it. The proposed system was first validated with a simulated C-EEG in which ground truth was available, which demonstrated better performance, and then tested with recorded real EEG data for its practical validation. In addition to a single channel, the proposed system is also able to remove the muscle artifacts from multi-channel EEG data. Future studies are likely to investigate the performance of the proposed method in removing other kinds of artifacts.


Haptics as a communication medium has been increasingly emphasized across various disciplines. Recent efforts have focused on developing various haptic stimulation technologies; however, most of them suffer from critical drawbacks stemming from their bulk, complexity, large power input, or high cost. Here, we describe a strategy to design portable and affordable refreshable haptic interfaces composed of an array of individually addressable and controllable liquid pouch motor-based haptic units embedded in either rigid or flexible substrates for different application contexts. The pouch motor filled with low boiling fluid, under a controlled manner, expands or contracts by Joule heating or cooling, enabling the haptic pin in contact to be protruded or retracted. Programming the actuation sequence of an array of haptic units enables the haptic interface to apply different stimuli to the skin to convey corresponding information. We finally demonstrate the applications to portable rigid braille displays and flexible epidermal VR devices. This study opens the avenue to the design of ubiquitous refreshable haptic interfaces that is portable, affordable, scalable, and uninjurious. Haptic perception is one of the most important sensory abilities inherent to humans . Humans Recent advances in haptic interfaces are being developed based on various actuation techniques. Amid, vibrotactile, and electrotactile are the most commonly used forms of haptic actuation, but these techniques are difficult to use in braille applications and cannot provide the user with the type of displacement and persistence of touch for a natural experience –20. On tHere to satisfy these requirements, we introduce a refreshable haptic interface consisting of an array of liquid pouch motor-based millimeter-scale haptic units embedded in either rigid or flexible substrates with applications to rigid braille displays and flexible epidermal VR devices Figures . The hapThe proposed haptic interface is designed by embedding multiple haptic units into a rigid or flexible substrate according to different application contexts. The central component of the haptic interface is the haptic unit mainly consisting of a liquid pouch motor, a haptic pin, and a compression spring, where their relative positions are determined by the frame . The pouThe key component of the haptic unit is the liquid pouch motor. Its cover layer is made of the metallic film  without air permeability to avoid fluid loss over time, but many other heat-sealable materials and composites could also be used. The embedded resistive circuit is formed by copper traces on a polyimide film in filamentary serpentine patterns to maximize heat dissipation. A thin layer of Teflon tape is pasted to the inner side of the cover layer. This tape enables the heating element to avoid direct contact with the cover and prevent the cover from being damaged by overheating. The boiling point of the filled fluid is chosen as 61°C, which is higher than the temperature of the human living environment but will not harm the human body. Ω, was embedded in the pouch to trigger the liquid to vapor phase change of the filled fluid by Joule heating. μL of fluid to ensure that the pouch can be fully inflated, and it can also ensure that the internal pressure after inflation will not damage the pouch  are arranged in a three-by-three array, with two adjacent heating elements separated by grooves having the same width of 1 mm but different depths of 0, 2, and 4 mm, respectively . ConsideForce characteristics of the same liquid pouch motor were also examined . The first experiment was conducted to measure the blocking force on a fixed surface, comparable to the scenario of the skin waiting for the display pins to be felt. d labeled in Despite promising qualities, the low actuation frequency within 0.1 Hz is one of the most critical drawbacks of liquid pouch motors that depend on phase transition for actuation , 36. A cThe deformation of the pouch motor was affected by both the temperature of the heating element (HE-1) embedded in the pouch  and the ambient temperature. To implement the mentioned control strategy, a base heating element (HE-2) and two thermistors (TH-1 and TH-2) are added to the haptic unit to maintain the pouch motor at any desired deformation . To elimd . For a specified stroke, when the temperature at the lower position is detected, a current (of 1 A) will flow through HE-1 to heat and inflate the pouch. When the temperature at the higher position is detected, the current will be linearly reduced to maintain the displacement in the desired time interval. Then, it can be cut off to make the inflated pouch contract through free cooling until it drops to the lower position, followed by the beginning of the next cycle. Figure d . Figures6) displayable patterns of the braille display. The distance between braille dots is determined as 7 mm, which enables the six dots to accommodate the size of an adult's fingertip. As a demonstration for braille application, a six-dot braille haptic display consisting of six haptic units is designed, and In contrast to the haptic units enclosed in rigid frames, we further explore the capabilities of the haptic units by embedding them in a flexible matrix to create an epidermal VR device that provides haptic stimulations in the form of various virtual touch. The epidermal VR device, consisting of nine independently controlled haptic units, is capable of producing different display patterns to mimic different tactile effects Figure . Our skiIn summary, we introduced the liquid pouch motor based on liquid-vapor phase transition to the design of a refreshable haptic interface with applications to braille displays and epidermal VR devices that are representatives of a broad spectrum of potential applications. The actuation force and frequency of the pouch motor can be customized by determining the size of the pouch and purposefully utilizing different deformation stages of the pouch motor. By selecting the rapid deformation stage in a cycle, the available actuation frequency of the pouch motor has been increased several times, or even increased by an order of magnitude, compared with previous studies, enabling the refresh frequency of the haptic interface is within an acceptable range. The small size liquid pouch motor can be used to design the millimeter-scale haptic unit, and arrays of independently controlled millimeter-scale haptic units further have the potential to design small-resolution pixelated haptic interfaces. Due to the modular design, the haptic interfaces can be easily scalable by integrating many haptic units into either rigid or flexible substrates to meet the needs of various applications. Take the flexible epidermal device as an example, in principle, the modular design enables the size of flexible haptic interfaces to be extended to length scales or large areas that match the human body. These haptic interfaces can be directly attached to the skins of different parts of the body, such as the chest, back, arms, and legs, to generate haptic stimulations in the form of various virtual touches. Moreover, the haptic interface is electronically actuated by low voltage; thus, it can be applied to portable devices without the need for cumbersome appendages. Besides, the easy availability of materials and ease of fabrication also enable low production cost at different scales. Lastly, the liquid pouch motor as one of the soft robotic actuators enables the haptic interface with inherent advantages of the soft robot, capable of providing soft and harmless tactile sense. Altogether, our study opens pathways for the creation of ubiquitous haptic interfaces that are compact, portable, affordable, scalable, and uninjurious.


Nowadays, variable preparation, forming and processing methods of fiber metal laminates are constantly developing to meet the requirements of different application fields, hence the characteristics and evolution of residual stresses under different manufacturing conditions deserve more attention. In this work, the evolution behaviors of curing residual stresses in GLARE under a hot-pressing condition were studied, and the residual stress reduction mechanism was also explained. Results suggested the FE prediction models of the entire cure process, verified by the fiber Bragg grating (FBG) sensors, were more precise than the traditional elastic model. Moreover, the stress evolution during the cure process mainly occurred in the cooling stage, in which the different coefficient of thermal expansion (CTE) of aluminum and GFRP played a major role. Meanwhile, curing shrinkage stress in the GFRP layer during the holding stage at curing temperature obviously influenced the final stress level. The residual stresses in GFRP layers differed by 9.6 MPa under a hot-pressing and autoclave condition, in which the convection heat transfer condition played a major role as it caused lower thermal stress in the holding stage and a smaller temperature gradient in the cooling stage. Considering this, a lower cooling rate could be a feasible way to obtain GLARE with lower residual stress under a hot-pressing condition. GLARE laminates, manufactured by alternating aluminum alloy sheets and S-glass fiber prepregs , possessFinite element analysis (FEA) is an ideal approach to recognize the composition and evolution behaviors of curing residual stresses during preparation and could also modify the preparation craft to control the stresses level ,19. For Moreover, the simulated results should be verified by reliable experimental method. However, previous measuring methods such as layer removal and X-ray diffraction (XRD)  mainly fIn this work, the evolution of curing residual stresses during the cure process of GLARE was simulated using sequential coupled FE models. Then, FBG sensors were embedded into the laminates to monitor the variation of the temperature and strain, and the simulation results were further verified. This way, the stress evolution behaviors in the cure process were analyzed in detail. Finally, the stress evolution behaviors under hot pressing and autoclave were compared, and a possible stress reduction method for hot-pressing craft was proposed. The analysis presented in this paper is a novel approach on GLARE laminates which can also be extended to any FML structures. In fact, the residual stress always exists and changes during the preparation, forming, machining, connection and service of FMLs. This study intended to accurately predict the formation process of residual stress in the preparation process through the establishment of the finite element model, which laid a foundation for the study of residual stress in the subsequent processing process of the laminate. Additionally, we have been trying to make it possible for FMLs to be applied in fields other than aviation, such as automobile and rail transit, by developing the low-cost preparation method. However, the residual stress caused by different preparation methods needed to be concerned.The GLARE laminates were composed of alternating three layers of aluminum alloy sheets  and four layers of S2-Glass/FM94 prepreg , and the stacking sequence was [Al/0°/0°/Al/0°/0°/Al]. The parameters of each ply are shown in The FE models were established in ABAQUS based on the GLARE laminate with a dimension of 300 mm × 300 mm, the basic settings of the laminate are presented in ρ, pC, xk, yk, zk denote the density, specific heat and anisotropy thermal conductivities of the composite, respectively. T is the temperature at time t and Q is the internal heat generation due to the resin curing reaction.The surface metal layers are in direct contact with the top and bottom plates of hot pressing, and thus the temperature on the metal surfaces is defined the same as the curing craft. Except for the effects of thermal properties on the temperature field in GLARE laminates, the heat release of the resin during the cure process should also be considered. Reaction heat of the resin generates with the increase in the curing degree, and the quantity of heat depends on the inherent properties of the resin. Hence, a thermo-chemical model is needed to characterize the evolution of temperature and cure degree throughout the laminate during the entire cure cycle. The process of heat transfer can be described by the Fourier’s heat conduction equation as:Two kinds of curing kinetic models are usually used to present the relationship between the resin chemical reaction, temperature and cure degree: phenomenological model and mechanistic model. Phenomenological model has been widely used since it focused on the overall reaction and ignored the specific reaction of active substances in cure process. The parameters of the kinetics model for the curing of epoxy FM-94 have already been reported by the other authors . Cure raR is the universal gas constant and T is the temperature in Kelvin. AE is the activation energy, k0 is a coefficient and m and n are two constants.In the above equation, t were together subscribed by user subroutine HETVAL and USDFLD. Initial cure degree was set as 0.0001 to meet the demand of the program, and the initial temperature of the entire laminate was 20 °C. In total, this model contained 4400 DC3D8 elements and 6174 nodes.In the above FE model, heat transfer procedures were selected to simulate the thermal-chemical behavior of GLARE laminates, and the thermal boundary condition was set by subroutine DISP, meanwhile the heat release of the resin and the curing degree at any time mE and shear modulus mG were regarded asFM 94 epoxy, as a typical thermosetting resin, underwent a transition from a liquid to solid state during the cure process. In this process, the elastic modulus thε induced by temperature change, shrinkage strain shε induced by the shrinkage of the matrix resin and eε induced by mechanical load. User subroutine UMAT defines the stress and Jacobi matrix eε and stress evolution of prepreg layers. In this process, glass fibers are assumed to be evenly distributed in the prepreg, and the interface between the fiber and matrix is ideal and the void in the resin is ignored. Thus, the micromechanics method can be used to predict the macroscopic properties of the composite based on the properties and structural parameters of glass fiber and epoxy.During the entire cure process, the strain of the prepreg layer is composed of thermal strain Previous FE models of residual stresses in FMLs mainly focused on the cooling process, hence the final results were only relevant to the temperature gradient and material properties of each ply. If the entire cure process is observed, the curing shrinkage strain should also be predicted based on the properties and distributions of GFRP prepregs.Similar to an isotropic material, the shrinkage strain of pure resin is equal in all three principal directions. Then, the shrinkage of the resin at an increment step r and f, respectively, represent resin and fiber, E is Young’s modulus, ν is Poisson’s ratio and fV is the volume fraction of fiber.Similarly, the shrinkage of unidirectional GFRP prepreg at an increment step can be shown as:thε could be obtained combining the temperature gradient. This process mainly accomplished by subroutine UEXPAN, in which the thermal expansion at the integrate point was calculated.The CTE of the GFRP layer could be calculated based on the properties of glass fiber and epoxy, then In the stress evolution model, static-general procedures were selected to simulate the mechanical behavior of GLARE laminates, and the UMAT calculates the mechanical strain except the thermal strain and shrinkage strain caused by the temperature and curing degree. The non-mechanical strain caused by the temperature and curing degree in each incremental step is calculated by the subroutine UEXPAN. Moreover, UEXPAN also transmits variables such as temperature, cure degree and non-mechanical strain to UMAT. In total, this model contained 4400 C3D8 elements and 6174 nodes.The GLARE laminates were fabricated with layers of aluminum alloy sheets  and S4-glass/epoxy prepregs (0.125 mm thickness per layer) by hand layup technique. Laminates were prepared by hot pressing, in which the temperature cycle was applied to the top and bottom surfaces of the laminate. All aluminum alloy sheets were treated before the lay-up process, including degreasing with acetone, alkali cleaning, acid cleaning and anodizing in a phosphoric acid electrolyte . After tThe FBG temperature and strain sensors (the basic parameters are listed in effn) of the fiber core and the Bragg period  were lower than the above results. The stress evolution FE models used earlier in this article were credible to display the composition of the final residual stresses, hence the residual stress reduction methods could be found by comparing the stress composition under hot-pressing and autoclave conditions.The FE model of the cure process in autoclaves was established to compare with the results of the hot-pressing method. According to related literature, major differences between these two methods in the preparation of GLARE included the loading mode and thermal boundary condition, and the specific settings in the FE models were shown in The stresses’ evolution during the cure process in hot pressing and autoclave were firstly simulated, then a variable-controlling approach was used to explore the most influential factor on the evolution of residual stress. Each stress evolution curve is shown in This phenomenon can be explained by the temperature evolution behavior at Point A, as shown in As shown in On the other hand, the temperature gradient in the thickness of the GLARE laminate during the curing process also affected the value of the final residual stresses. Although the convection heat transfer condition obviously influenced the final residual stress value, the change in the thermal boundary condition was difficult for hot pressing. Hence, a lower cooling rate was applied to the stress evolution FE model to investigate its effect on the final residual stress value. As shown in On the basis of these conclusions, we can obtain the GLARE laminate with lower residual stresses using simple hot-pressing equipment by adopting a control program of lower cooling rate and enough holding period at the curing temperature. However, if the quality of GLARE cured in a hot pressing condition is expected to be exactly the same as that in the autoclave, measures must be taken to restrict the overflow of the resin and stacks should be vacuumized to reduce internal voids. Furthermore, the simulation and experimental methods in this work are also suitable for all kinds of fiber metal laminate structures.(1)Sequential coupled FE models including the thermal-chemical model and stress evolution model were established to analyze the residual stresses of GLARE during the entire cure process, then the simulated results were successfully verified by embedded FBG sensors between GFRP layers. This way, the stress evolution behaviors in the cure process of GLARE were analyzed in detail.(2)The evolution of stress during the cure process mainly occurred in the cooling stage, in which the different CTE of aluminum and GFRP played major role. Meanwhile, curing shrinkage stress in the GFRP layer during the holding stage at curing temperature obviously influence the final stress level. Simulated residual stresses (−92.4 MPa) of GFRP were reliable compared to the monitored data (−81.8 MPa) from the FBG sensors. The prediction accuracy of residual stresses was improved by 5% compared to the conventional linear elastic prediction models (−97.1 MPa) which only covered the cooling process.(3)Between the hot-pressing and autoclave methods, the convection heat transfer condition was the key factor to obtain lower residual stresses as it caused lower thermal stress in the holding stage and a smaller temperature gradient in the cooling stage. However, additional loads on the sides of the laminate hardly affected the stress level in the cured laminate. Considering this, a lower cooling rate could be a feasible way to obtain GLARE with lower residual stress under a hot-pressing condition.


Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datasets do not have enough images and are unevenly distributed. The particle swarm optimization algorithm has been proved to be a good tool to deal with various extreme value problems, which requires little data and does not require pre-training. In this paper, the segmentation problem is converted to a set of extreme value problems. The scheme performs data preprocessing based on the features of the fundus map, reduces noise on the picture, and simplifies the search space for particles. The search space is divided into multiple sub-search spaces according to the number of subgroups, and the particles inside the subgroups search for the optimal solution in their respective sub-search spaces. The gradient values are used to calculate the fitness of particles and contours. The entire group is divided into some subgroups. Every particle flies in their exploration for the best solution. During the iteration, particles are not only influenced by local and global optimal solutions but also additionally attracted by particles between adjacent subgroups. By collaboration and information sharing, the particles are capable of obtaining accurate disc segmentation. This method has been tested with the Drishti-GS and RIM-ONE V3 dataset. Compared to several state-of-the-art methods, the proposed method substantially improves the optic disc segmentation results on the tested datasets, which demonstrates the superiority of the proposed work. According to statistics of the World Health Organization (WHO), glaucoma is the second leading cause of blindness in the world . It is eAn early diagnosis of glaucoma is of great importance. Glaucoma is irreversible after blindness, which leads to structural modifications as the disease progresses . FortunaIn actual medical diagnosis, ophthalmologists usual use cup to disk ratio (CDR ) as one In recent years, different approach of optic disc segmentation such as machine learning and clustering have been proposed so far. Thus, a deep learning architecture called M-Net is proposed by Fu, H. , which uThere are many drawbacks when applying deep learning to the segmentation of optic discs. One of the main reason for this is that the glaucoma datasets are too small to meet the training requirements of deep learning. The datasets originate from hospitals, and to protect the privacy of the patient, the patient’s permission must be obtained . FurtherTo overcome the drawback of the lack of glaucoma dataset, a new particle swarm optimization-based approach for optic disc segmentation SePSO is proposed in our work. The proposed algorithm does not require large amounts of data for preprocessing, which is a self-learning approach. In addition, the information of image color and optic disc shape is also taken into our consideration, which reduces the interference of relevant information on experimental results .Furthermore, the subsequent sections are organized as follows: This section described the Particle Swarm Optimization algorithm that we modified in this paper to segment the optic disc in retinal images. As described by Shi and Eberhart , PSO is The basic idea of PSO is to find the optimal solution through collaboration and information sharing between individuals in the animal populations. Usually, the PSO algorithm can be used to optimize continuous nonlinear functions and to solve some discrete problems.In this paper, the optic disc segmentation problem is converted into a series of the extreme value problem. Based on a large number of experiments, we found that it is not sufficient to solve the optic disc segmentation problem by original PSO. To solve this problem, we propose an improved algorithm based on Particle Swarm Optimization, called SePSO. The goal of SePSO is to find a set of best solutions for the particles. Moreover, the closed shape formed by these particles is the boundary of the predicted optic disc. The components of the SePSO algorithm we modified are described in subsequent sections.N subgroups in the original group, where the subgroups consist of the same number of particles ith subgroups. Each subgroup In this paper, the PSO approach has been used in segmenting OD in a fundus image. In order to achieve this goal, the concept of subgroups are drawn into PSO. The entire particle group is divided into a certain number of subgroups ith subgroup;The parameter The closed curve, which consists of all the Equation :(2)Edgejjth potential OD boundary;The parameter N is the number of subgroups;As the search progresses, most particles in each subgroup converge to the optimal solution and the Using the SePSO algorithm for OD segmentation means that each particle searches in the two-dimensional space for a set of Firstly, the ROI images prepared for OD segmentation are initially resized to polation  over an Secondly, according to prior knowledge and the circle nature of the optic disc, we set the entire exploration area as an annular area. Considering the center of the disc as the center Equation .(3)EoutIn addition, a polar transform  is applilar axes . The disEquation :(4)∀i∈Z∩Therefore, the Cartesian coordinates of pixel Equation .(5)∀i∈ZThe entire exploration area Equation .(6)EA={Similarly to the relationship between quations  and 9)  POP and N. The influence of the parameter N will be shown in subsequent experiments.The shape of the optic disc varies from circle to ellipse, and usually it is an irregular approximation of the ellipse. If too few points are used for fitting, it is difficult to express the true disc’s boundary. The more points are used for fitting, the closer the fitted shape is to the real boundary. However, too many points will slow down the calculation and increase time complexity. We need to find the proper number of points to balance speed and accuracy. In this paper, the number of points on the boundary In PSO, the particle number In this paper, the particle number j. Therefore, the initial location of each particle is set using the following rules:r, r is the distance between For all particles r of particles The radius Each particle is assigned a randomized location at the beginning. Considering of the circle nature of OD, the distances from particles on the predicted boundary When particles fly in the subgroup’s exploration area ion area . The difWe have the following equation:P;P.gradient  of pointjth particle in every subgroup jth particle in subgroup In addition, the potential contours of OD, which is described as Equation :(11)valuIn the traditional PSO, each particle changes the location toward the particle’s best location Equation : (12)Vi,jth particle in subgroup However, after a series of experiments see , the fitThe modified velocity is calculated as Equation .(13)Vi,As the system iterates, the individual agents, which are based on the interaction of the subgroup’s public search and the particle’s search, are drawn toward a global optimum. The position vector of each particle is updated as Equation .(14)Xi,PP and GP are also updated as iteration proceeds. The updated rules are described by Equations  and 16)16).(15)SePSO evaluates the quality of particle positions by using the radial gradient value as the fitness value. On fundus diagrams, the location with high radial gradient values include the optic disc boundary, the optic cup boundary, and the vascular region. In order to segment the clear boundary of the optic disc and reduce the interference of blood vessels and optic cup located in the central area of the optic disc, the exploration area of the entire population is confined to a relatively narrow circular area. This exploration area can not only meet the exploration needs of particles but also avoid particles falling into local optimization as much as possible. In addition, the application of active shape models transforms image segmentation tasks into a set of extreme value problems that are easy to solve by PSO. To implement this mechanism, we divide the entire particle swarm into multiple subgroups pop, and at the same time, we divide the exploration space into multiple subspaces accordingly; each subgroup solves an extreme value problem, each particle on the subgroup corresponds to a feature point on the shape model, and all subgroups form a multi-group solution of the shape model. The quality of the shape model In the search for an optimal solution, each particle is represented by a triplet  and normal. The most recent one, which is used for our experimentation, consists of 159 images , which iThe performance of the proposed method for segmenting OD when compared with the ground truth is evaluated using many evaluation metrics that can be defined according to four parameters: True Negative Equation :(17)TN=1False Negative Equation .(18)FN=True positive Equation .(19)TP=False Positive Equation .(20)FP=In this paper, the evaluation metrics such as Dice similarly, overlapping error, and accuracy are used to identify the level of accuracy of OD segmentation, which can be defined as Equations –23)..23).Accuracy: It is the measure of correctness or preciseness with respect to some standard [standard . It is cDice similarity: It is a metric that is used to calculate the similarity between two samples that may be images or any other data. It is calculated as provided in Equation (Equation .(22)DicOverlapping error: Overlapping error E is the one of the important indicators of image segmentation. In this paper, it is calculated using the detected boundary and the ground truth by Equation (Equation .(23)E=1N, which affect the In order to obtain the best results with proposed method, there are some important parameters that should be adjusted. These parameters are as follows: the number of subgroups E produced by the proposed approach for optic disc segmentation are observed to be 84.88%, 0.73%, 99.27%, 15.12%, 92.28%, 90.35%, and 17.49% in the Drishti-GS dataset.Based on a large number of experiments on dataset, the proposed method produces the best solutions with the following parameter settings:  100 see . The valWe have varied each parameter individually to observe its influence. Some of these parameters mainly affect the quality of the solutions, while some have more impact on the computation time needed to obtain the solutions.The number of subgroups N: This parameter affects the number of points that make up the boundary. The higher the value N, the closer the fitted shape is to the true boundary. However, too many subgroups increase the calculation time’s complexity. There are some experiments that show the impact of this parameter on accuracy and calculation time. E,  These parameters affect the velocity of particles. E, The inertia weight ω: To obtain a better accuracy of OD segmentation with proposed method, an inertia weight strategy is applied to our experiments. Inertia weight proposed . In thisA recent comparison of optic disc segmentation methods was presented in  using RiAs we can see, the proposed SePSO algorithm performs better in the Drishit-GS dataset than the best method (region growing) in the performance parameter In this paper, we propose an improved method based on particle swarm optimization, called SePSO, for optic disc segmentation in retinal fundus images. In the disc segmentation task, the deformation of the segmented contour is completed by changing the position of the particles, and the optimal contour is solved by iterative update. In addition, the constraint equations for particle position and velocity are optimized. The particles are additionally attracted by adjacent subgroups in addition to the attraction of local optimal solutions and global optimal solutions during flight. Based on Drishti-GS, we test the influence of different parameter of the proposed method on the segmentation effect.Particles in subgroups can learn optimal solution information between adjacent populations in the process of optimization, which greatly enhances the anti-interference ability of particles. The proposed method has been tested using Rim-ONE and Drishti-GS datasets and compared to other state-of-the-art methods, such as methods based on superpixels, contours, thresholds, and region growth method. Experimental results show that the algorithm performs better on both datasets than the other four algorithms, which confirms its effectiveness and superiority.In future studies, we aim to conduct the proposed SePSO approach for solving optic cup segmentation. The simultaneous division of OC and OD has great clinical medical value, and the segmentation of the OC will become the next research content of our work.


The understanding of multiphase flow phenomena occurring in porous media at the pore scale is fundamental in a significant number of fields, from life science to geo and environmental engineering. However, because of the optical opacity and the geometrical complexity of natural porous media, detailed visual characterization is not possible or is limited and requires powerful and expensive imaging techniques. As a consequence, the understanding of micro-scale behavior is based on the interpretation of macro-scale parameters and indirect measurements. Microfluidic devices are transparent and synthetic tools that reproduce the porous network on a 2D plane, enabling the direct visualization of the fluid dynamics. Moreover, microfluidic patterns  can be specifically designed according to research interests by tuning their geometrical features and surface properties. In this work we design, fabricate and test two different micromodels for the visualization and analysis of the gas-brine fluid flow, occurring during gas injection and withdrawal in underground storage systems. In particular, we compare two different designs: a regular grid and a real rock-like pattern reconstructed from a thin section of a sample of Hostun rock. We characterize the two media in terms of porosity, tortuosity and pore size distribution using the A* algorithm and CFD simulation. We fabricate PDMS-glass devices via soft lithography, and we perform preliminary air-water displacement tests at different capillary numbers to observe the impact of the design on the fluid dynamics. This preliminary work serves as a validation of design and fabrication procedures and opens the way to further investigations. In this framework, underground gas storage facilities are recognized as one of the best large-scale storage options since geological formations such as depleted hydrocarbon reservoirs and deep aquifers offer large storage capacity and safety . Undergrunderway . Eventuaunderway ,6,7,8. TTo gain a better understanding and achieve improved modeling ability of the fluid-dynamics aspects related to the conversion of depleted gas reservoirs to hydrogen storage, it is fundamental to investigate the pore-scale phenomena that occur in the reservoir. Rock properties such as grain size, pore size distribution, porosity and absolute permeability, and rock-fluid interaction properties such as wettability and capillary pressure affect pore-scale phenomena and thus both storage capacity and injectivity. Furthermore, viscosity, density, and interfacial tension of the fluids that coexist in the pore space play a fundamental role in flow dynamics.Transparent microfluidic devices have found application in the oil and gas industry because they can be designed to reproduce porous media networks, allowing for the direct visualization of the fluid dynamics occurring at the pore scale. This characteristic of microfluidics solves one of the main drawbacks of traditional core-flooding experiments, which require powerful imaging techniques to provide insights into pore-scale dynamics . MoreoveIn this work, we have designed, characterized, and fabricated two different microfluidic devices, and performed preliminary multiphase flow tests using air and water. The first designed geometry is a regular pattern, while the second geometry is extracted from a thin section of a Hostun rock sample. The two designed patterns differ in terms of tortuosity, heterogeneity, and regularity of the porous volume. The path-finding algorithm A*  and CFD In order to investigate the physical phenomena that occur in microfluidic devices, it is necessary to introduce a few parameters that play an important role in multiphase flow in porous media. In microfluidic experiments, we observe multiphase flow behavior directly in the pore space, at a microscopic scale of investigation, which is characterized by a set of parameters that, in turn, have an influence on macroscopic parameters such as porosity and permeability. Details on the theory of pore-scale phenomena and pore-scale modeling can be found in ,12. MultThe capillary number  follows ,13:(1)CaThe higher the viscosity ratio and the capillary number, the more stable the fluid displacement. When the capillary number decreases, capillary forces start to prevail over the viscous forces, pore size distribution controls the invasion and capillary fingering may occur. When the mobility ratio decreases, viscous fingering occurs, and the front cannot be stabilized . Typicalilibrium :(3)Pc=Pnequation :(4)Pc=Pnified as :(5)Pc=2σhases as :(6)Pc following the Young–Laplace equation. For slow displacement, this also determines the threshold capillary pressure at which one phase is able to displace the other. The threshold capillary pressure depends on the contact angles of the fluids, and at the macro-scale, it determines the way each phase flows and how much is displaced . In geneThe micromodels used in this work are designed to observe the multiphase flow and pore-scale phenomena. In this Section, we describe the procedure used for extracting characteristic parameters such as porosity, tortuosity, and average and smallest pore size of the micromodels. The values obtained for the considered geometries are all summarized in As shown in In our devices, the total dimensions are 70 mm × 35 mm, and the distance between the inlet and the outlet port is 50 mm .Inlet areas have been designed to obtain a flat fluid front at the entrance of the patterned core. The branched layouts shown in For the development of the micromodel patterns, two different solutions are proposed: a regular grid and a real-rock image mosaic. These two patterns offer an incremental geometrical complexity and different degrees of control over the geometrical properties. Dimensions are kept inside the constraints of fabrication feasibility, meaning that no proposed feature is smaller than a few microns.D50 = 300 μm. First, preliminary image processing was applied to convert grayscale images to binary images. The microfluidic layout was created by removing the dead ends. In HO49Y, tortuosity is equal to 1.34. The borders of the images were then slightly modified to make it possible to replicate the images as a mosaic without interrupting the fluid flow. This is a common procedure, as reported in the literature [Two different micromodels were designed and tested: a regular grid, named GRID01, and a mosaic of a real rock image, named HO49Y. The regular grid is a regular pattern because all throats and all pores have the same dimensions . The griterature . The mosBoth micromodels have been fully characterized by extracting data with dedicated procedures. In particular, the values of tortuosity were calculated via CFD simulation, and the static values of average pore size and smallest pore size using a procedure based on the A* algorithm ,19,20. AGlass–chrome masks for each pattern are prepared via laser writing. The chrome–glass mask blanks are purchased by MB Whitaker & Associates. A LaserWriter system by MICROTECH is used to transfer the desired pattern onto the blanks and create the final mask. The masks undergo a process of resist development, chrome etching and piranha cleaning to be ready to use. Optical inspection is performed to guarantee that the final mask has no dimensional mismatch with respect to the designed geometry.Su-8 2050 is used as a photoresist for the fabrication of the master molds. The Su-8 2050 belongs to a series of negative photoresists. The resist is spin-coated on a silicon wafer and soft-baked. The sample is then loaded inside a Mask Aligner and exposed to the UV-light beam. A post-exposure bake follows, and the resist is then developed with appropriate solvents to remove the undesired material and keep the microfluidic pattern mold only. A final hard bake is added to ensure that SU-8 properties do not change in actual use (Su-8 2000 Processing guidelines). All baking steps are necessary to optimally cure the photoresist and process the structures. A level hotplate with good thermal control and uniformity is required. Two steps at 65 °C and 95 °C are performed during soft bake and post-exposure bake. The hard bake reaches 150 °C.The sample undergoes wet silanization after the hard bake. This treatment makes the silicon surface more hydrophobic, which means that PDMS will have less affinity to it and will be easier to detach. Toluene and Trichloromethylsilane (10.1 ratio) are used for the silanization bath.PDMS Sylgard 184 is used for the microfluidic replica. A mixture of the prepolymer and the curing agent in a 10:1 ratio is poured on the master mold after degassing and let cure.The replica is then peeled off the mold. The inlet and the outlet holes are drilled with a puncher, to achieve a hole diameter of 1 mm.To seal the device, both the PDMS replica and the glass slides are exposed to an oxygen plasma surface treatment before being permanently bonded together by the application of a gentle pressure. Oxygen plasma treatment guarantees irreversible bonding and good sealing resistance even after multiple cycles of experiments .The fabrication of our PDMS microdevices is based on soft lithography . A firstIn this Section, we discuss the observations and results of the experiments carried out on microfluidic devices. In particular, the possibility to observe the whole area of the devices allowed us to monitor the device filling and visualize the flow patterns, which are both influenced by the capillary number and viscosity ratio. At higher magnifications, we could also capture local pore-scale phenomena.Capillary number and viscosity ratio are expected to play a substantial role in the displacement pattern and displacement efficiency during the multiphase fluid flow in porous media. Under the influence of these two dimensionless numbers, the fluid flow pattern of the injected fluid can be categorized as either viscous fingering, capillary fingering or stable displacement. In our experiments, the viscosity ratio is equal to -2 for drainage and 2 for imbibition.Usually, in order to identify the flow regime occurring in the micromodels, the logCa-logM phase diagram proposed by Lenormand et al.  is used.First of all, we run drainage tests. In this case, the system is initially fully saturated with water and then air is injected at different flow rates. We could observe a very uniform fluid front at the inlet of the devices, which validates the design of the inlet channels. After steady state conditions are reached, i.e., there is no variation of flow patterns in time, the test is stopped, and the final configuration and saturation are used as the initial configuration for imbibition experiments. In In drainage processes, the non-wetting phase fills the largest pores while the wetting phase remains in the smaller pore throats because of the highest capillary pressure . This caDifferently, GRID01 does not present heterogeneity in the structure. The fluid front proceeds along the direction aligned with the fluid injection, and leaves trapped water in the perpendicular channels . The dirTo serve our purposes of preliminary testing the imbibition and drainage processes in micromodels representative of reservoir rock formations, the combination of air and water provided a reliable observation of gas-brine fluid dynamics at the pore scale. Further investigation will be carried out using an exact combination of fluids because there are phenomena, such as dissolution, that may extensively vary according to the substances. In particular, the analysis of static and dynamic contact angles and contact angle hysteresis will be taken into account and discussed according to the fluids involved. Moreover, in order to reproduce the correct fluid behavior at reservoir conditions, reservoir pressure and temperature will be considered. Reservoir thermodynamic conditions change with depth and, when taken into account, can improve the representativeness of microfluidic experiments. Reasonable reservoir conditions can be estimated by calculating the pressure according to the hydrostatic gradient and the temperature according to the geothermal gradient. For example, at a reservoir depth of 3000 m, the static pressure is about 300 bar and the temperature is about 100 °C.In the work presented in this paper, we designed and fabricated two microfluidic devices with the purpose of investigating pore-scale phenomena that occur in reservoir rocks during gas injection and withdrawal during underground gas storage operations. Multiphase flow experiments were performed on the two microfluidic devices and different pore-scale and trapping phenomena were observed. We could observe that the microfluidic device hosting the regular pattern would induce fewer pore-scale phenomena compared to the Hostun pattern, as expected. This is induced by the regularity of the structure and the very low tortuosity. On the other hand, the regular pattern allows to better isolate the effect of specific factors such as channels-flow alignment and pore-throat ratios. It would be interesting to modify the angles of the channels to increase tortuosity and increase the heterogeneity of the structure. The Hostun pattern allowed us to visualize more complex pore-scale phenomena, such as Heines jumps, and appears to be a promising platform for further analysis. The designed inlet channels demonstrated to serve the purpose of simulating homogeneous fluid front at the entrance of the porous pattern. New types of patterns will be investigated in future works.


Leishmania parasites plays central roles throughout the life cycle. Discoveries over the past decade have begun to elucidate flagellar functions at the molecular level in both the insect vector stage promastigotes and intra-macrophage amastigotes. This focused review will highlight recent advances that contribute to understanding flagellar function in the various biological contexts encountered by Leishmania parasites.Like other kinetoplastid protozoa, the flagellum in  Leishmania and Trypanosoma, employ the flagellum for a wide variety of biological functions [Leishmania spp. with particular emphasis on molecular characterization of this organelle, although relevant longstanding observations and some material from Trypanosoma brucei will be introduced where appropriate. The intention is to provide a short summary of recent advances at the cellular and molecular level and to suggest where new frontiers may emerge.The flagellum is an organelle that has long been associated with motility in many eukaryotes, but more recently, it has been recognized to have both sensory  and secrunctions ,4, incluPFR1 and PFR2 genes encoding the most populous of the PFR proteins [One striking feature of the parasite life cycle is the extensive structural changes to the flagellum . Most noproteins , to playproteins , and indFor a significantly more detailed structural study of the promastigote and amastigote flagellum and flagellar pocket, readers are referred to the comprehensive serial section electron tomography study by Gull and colleagues . This woLeishmania flagellum is the comprehensive proteomic analysis carried out by Gluenz and colleagues [L. mexicana promastigotes employing a calcium shock followed by mechanical shearing and density gradient isolation of highly enriched flagellar (separated at the point of exit from the cell body) and deflagellated cell body fraction. These fractions were further resolved into detergent-soluble and -insoluble components, and the fractions were subjected to quantitative label-free proteomic analysis to identify proteins enriched in each fraction. This analysis identified hundreds of probable flagellar proteins, both cytoskeletal and some integral membrane polypeptides, and many previously known flagellar components were among these lists, validating the results. An interactive website  has been developed by these authors that reports proteins selectively enriched in both the detergent-soluble and non-soluble fractions of the flagellum versus the cell body, and readers can access this site for a comprehensive identification of flagellar proteins.A recent major advance in dissecting the lleagues . These aLutzomyia longipalpis sand flies with both mixed populations of bar-coded null mutants and discrete infections with specific mutants, the results demonstrated that paralyzed mutants are strongly impaired in development within the insect, with most parasites excreted during defecation and the few remaining ones restricted to the midgut. Uncoordinated swimmers were also largely lost by day 6 of infection, with the remaining parasites also restricted to the fly midgut and unable to migrate forward to the mouthparts and stomodeal valve. This detailed analysis strongly suggests that the directional motility of promastigotes is essential for development within the insect vector and likely required for successful transmission from the fly to the vertebrate host.The authors proceeded to knock out about 100 genes encoding flagellar proteins employing the high-efficiency CRISPR/Cas9-mediated technology they had pioneered previously  that medMore recently, Beneke et al.  also useL. mexicana. Although most results are likely to apply widely across diverse Leishmania species, there are biological and genetic differences between species, and some significant distinctions in the function of flagellar proteins may emerge as these organellar components are explored more broadly.Overall, the above corpus of work illustrates the power of (i) identifying the complex cohorts of proteins within an organelle and (ii) applying high throughput genetic methods, such as gene knockout libraries, to elucidate the structure and function of an organelle and of individual protein components. The work summarized here and in several other sections of this review was conducted in T. brucei, where it represents an adhesion between one face of the flagellum and the cell body membrane that extends almost the entire length of the flagellum [The flagellum attachment zone (FAZ) is a prominent morphological feature of lagellum . This stLeishmania parasites exhibit a flagellum that is free from the cell body  existed in L. mexicana, but the structure was restricted to a discrete adhesion between the base of the flagellar membrane and a site toward the anterior of the flagellar pocket membrane. Thus, the FAZ is radically different between the two parasites, raising the issues of what functions it serves in Leishmania and how it is constructed so differently despite sharing many orthologous proteins.In contrast, ell body  and thusarasites . The tagL. mexicana FAZ may be involved in determining the proper propagation of cell structure and relative organelle localization during cell division, as it is in T. brucei [Serial section electron tomography was applied to the FP region of both promastigotes and amastigotes to develop a three-dimensional model of this region, including the FAZ . The mod. brucei .faz5 null mutants were able to initially infect sandflies and populate the endoperitrophic space within the midgut on days 1–2, but by days 6–8 post-infection, few sand flies were still infected, and among those that were, the parasites did not migrate forward to the stomodeal valve but remained in the endoperitrophic space and anterior midgut. Hence, the development of the parasite within the sand fly is severely compromised and presumably would prevent transmission from the fly to an animal. Infections of mice with the Δfaz5 null mutant led to very small footpad lesions, compared to wild-type or add-back lines, and to greatly reduced numbers of parasites within the footpad tissue or draining lymph nodes, establishing that virulence is strongly impaired in the absence of FAZ5. The reasons for poor survival of Δfaz5 amastigotes in animals are not certain, but the authors suggested it could have to do with the virtual disappearance of the tightly apposed FP neck region in null mutant amastigotes. This morphological alteration may leave amastigotes in the animal vulnerable to import harmful material from the host into the FP, resulting in the impaired long-term viability of amastigotes. Overall, these studies confirm the critical role of the FAZ in both promastigotes and amastigotes in vivo and underscore the essentiality of this structure throughout the parasite life cycle.Understanding of FAZ function was further probed by the deletion of the gene encoding FAZ5, a polytopic protein integrated into the FP membrane component of the FAZ . The delfaz2 null mutant [Subsequent analysis of a Δl mutant  uncovereFAZ7B gene impaired the growth of both promastigotes and amastigotes in vitro and caused altered cell division, flagellar length, and FP structure and function, and an intact conserved kinesin domain was required for wild type phenotypes to be preserved. In particular, the arrangement of components of the FP collar, a ring-like structure surrounding the FP membrane on its cytosolic side, was altered in the knockout. The FAZ7B null mutant was also strongly impaired for replication and migration in the sand fly and for virulence in mice, again confirming the importance of the FAZ in vivo in both insect and vertebrate hosts.Another component of the FAZ, FAZ7B, was also found to play an important role . This isLeishmania flagellum has focused on the cytoskeletal components, with impressive advances in recent years. Less is understood about the molecular components of the FM, both about how they are targeted to this organelle and what functions they play during the parasite life cycle. Nonetheless, it is clear that the flagellar membrane plays a variety of vital roles for the biology of the parasite [Leishmania research.Most work on the parasite  and likeL. mexicana. Whereas the GT2 and GT3 isoforms trafficked to the cell body plasma membrane and were excluded from the FM, the GT1 isoform was localized largely in the FP and FM [GT1 gene experienced a catastrophic drop in viability as promastigotes grew to high density and depleted glucose from the medium, suggesting that flagellar GT1 may serve a role as a glucose sensor required for successful transition of parasites from logarithmic to stationary phase [kharon null mutants fail to traffic GT1 to the FM and accumulate this protein in the FP membrane. A KHARON ortholog is also expressed in T. brucei, where it is required for FM targeting of a calcium channel [T. brucei KHARON on the flagellar cytoskeleton , using deconvolution microscopy and marker proteins with known positions, established that KHARON is located at the interface between the basal body (BB) and the transition zone (TZ). The TZ is a complex structure just distal to the BB [One striking example of differential transport between the flagellar and cell body membranes emerged from studies in our laboratory on three glucose transporter isoforms of P and FM . Null mury phase . Deletiory phase , and subry phase . Althoug channel . Recentlo the BB  that medo the BB  at the do the BB ,24,25, mo the BB , so KHARkharon null mutants [Parallel studies on other FM proteins have established that different FM trafficking machinery must exist that is distinct from KHARON. Thus, localization of FLA1BP to the FM component of the FAZ or of a membrane bound cAMP-phosphodiesterase  and an a mutants . SimilarL. major [L. donovani, LdAAP3, was shown to localize to both the FM and glycosomes [A variety of other FM proteins have also been identified. The flagellar aquaporin, AQP1, was discovered in L. major  and showL. major ,30. The L. major . These rL. major , but thiycosomes . Its expycosomes .T. cruzi FCaBP are sufficient for dual lipid anchor addition, the first 24 amino acids are required for FM targeting, thus indicating that lipid anchor addition is not the sole determinant of flagellar targeting. Other integral FM proteins include various adenylate cyclases in both Leishmania promastigotes [T. brucei bloodstream [SMP-1 is a small palmitoylated/myristoylated protein that attaches to the luminal face of the FM via its lipid anchor , and duastigotes  and T. bodstream  and procodstream . A numbeodstream  and for odstream . In partLeishmania flagellum, an organelle with a wide range of functions during the parasite life cycle. Particularly promising is the application of high-throughput approaches, such as quantitative proteomics and focused gene knockout libraries, to identity and elucidate the functions of an array of flagellar proteins. Unexpected discoveries, such as the existence and functional importance of the FAZ, have also infused novelty into this discipline. Many vistas remain to be explored. One of the most interesting and challenging frontiers involves the roles of the short amastigote flagellum, particularly its potential activities in sensing the environment inside the macrophage and delivering parasite effectors to the host. Another is to explore specific sensory functions of Leishmania flagella. A recent report [The past decade has witnessed pronounced advances in our understanding of the t report  demonstr


DAS and geophones are the two most popular sensors for borehole seismic acquisition. As such, it is important to get a good understanding of how these two types of sensors compare to each other. The natural measurand for the techniques is different; millivolts are approximately proportional to particle velocities for geophones vs. changes in the phase of light linked to the changes in strain on the sensing fibre. This paper focuses on the experimental comparison of absolute values of these measurands derived from a VSP survey acquired in Curtin GeoLab training well. We describe the acquisition setup for the walk-away VSP acquired with DAS and geophones, allowing the direct comparison and the workflow, which we can use to represent the data in strain rate. Albeit this is unlikely to be universal, we find that the absolute values are similar for this experimental setup. The rapid uptake of distributed acoustic sensing (DAS) for borehole seismic acquisition prompts fundamental research to identify how its performance compares to the performance of conventional borehole geophone sensors. These two types of sensors, DAS and geophones, are fundamentally different. The geophones are electro-mechanical sensors that sense the motion of the earth in the form of particle velocity. In contrast, DAS observes a change in elongation of an optical fibre that acts as the sensor. The geophone consists of a permanent magnet mounted on a spring with a conductor (copper wire or coil) around it. When the earth moves, the magnet and coil also move. This movement generates a voltage across the coil’s winding, which is proportional to the rate at which the coil cuts the magnetic flux, that is, to the velocity of the earth’s motion . In the For borehole seismic measurements, a fibre-optic sensing cable is deployed in the well. Borehole geophones and DAS have different factors that affect the measured signal. For example, the geophone measurements are affected by the geophone type and its performance, the probe’s housing, the quality of the probe’s coupling to the formation or the casing, etc. ,4. The DInitial DAS tests for borehole seismic application used preinstalled cables behind the casing or on tubing inside the casing and demonstrated that seismic energy could be detected on FO cable at a depth over 4 km ,10,11. DMoreover, various DAS interrogators can have very different designs. The differences in the design of DAS recording units and selection of acquisition parameters significantly affect the data quality ,13,17,18These tasks are best accomplished in a controlled environment at designated test sites that should allow for the repetition of the experiment using different sensors in stable conditions. In addition, such test sites should enable the comparison of different seismic receivers for both borehole and surface arrays using different types of seismic sources. There are several test facilities for carbon capture and storage (CCS) around the globe, where DAS technology is used, including CaMI , Otway IIn this paper, we compare DAS and geophone data acquired during the walkway vertical seismic profile (VSP) experiment performed at the Curtin GeoLab in 2020. First, we convert the particle velocity data from the geophone dataset to the strain rate. Then we use the first-break amplitudes and the entire wavefield to compare the absolute values of the strain rate of both types of sensors. We demonstrate that the DAS response is similar to the geophone response converted to the strain rate averaged over the gauge length of the DAS system. Furthermore, we show that the amplitudes of the strain rate measured with DAS have similar absolute values to strain-rate amplitudes obtained using converted geophone measurements—the losses of seismic energy due to the geophone coupling and housing are similar to the losses in the fibre-optic cable construction and installation.The Curtin GeoLab Research Facility was established at Curtin University, Western Australia, with aims to conduct applied geophysical research, equipment testing and training of students and industry personnel. The key components of the GeoLab are a 900 m vertical well drilled on Curtin campus and state-of-the-art seismic equipment, which includes surface and borehole three-component geophones, impulse and vibroseis seismic sources and a variety of fibre-optic cables (including a cable permanently deployed in the borehole). The Geolab Well (NGLd), previously referenced as the NGL training well , is locaThe GeoLab facility was previously used for a wide range of geophysical experiments: some were dedicated to comparing different borehole seismic receivers , and othThis study uses data acquired during the walk-away VSP experiment carried out at the GeoLab in June 2020. The survey area with all source locations is shown in Direct comparison of geophone and DAS performance requires converting the data to the same physical property. In practice, DAS response is often converted to the particle velocity for comparison with geophone data ,14. BakkWe apply the following processing flow to get an absolute strain rate from the geophone dataset  and the first-break amplitudes (correlated) for DAS and converted geophone. The gauge length is one of the most important DAS parameters as it affects both the resolution and signal-to-noise ratio of the acoustic signal . The IU To compare the performance of the DAS system and geophones, we use data from two source locations at the near offset  and the far offset .To compare the absolute values of the strain rate obtained using both types of sensors we analysed the uncorrelated wavefield. First, we applied Ormsby bandpass filter 5–8–150–200 Hz to denoise the data and adjusted the length of uncorrelated datasets to 27 s. Then we computed the average amplitude spectrum of the entire seismogram. The frequency spectrum of DAS and converted geophone data are similar for the near-offset shot. However, we observe more discrepancy in the spectrum with increased offset. The low-frequency signal of the DAS data has a lower amplitude compared to the converted geophone data, and towards the high frequency, the entire spectrum is biased b. DiscreThen we correlate the data with the pilot sweep and compare the first-break amplitudes for near and far offsets  to vertical strain rate brings the phase of the direct wave closer to the phase of DAS data . The polApart from the higher noise level on geophone data observed for far offset, DAS and converted geophone records look similar. However, near-offset data with higher frequencies present in the record show a significant discrepancy in the phase of the direct arrivals (arrows in For the same source effort (single shot), DAS and converted geophone amplitudes are similar for near offset. However, for far offset, the high-frequency part of the DAS spectrum (>100 Hz) looks constant even beyond the frequency of the sweep (150 Hz), which might indicate that this part of the spectrum is mostly noise . This agSimilar strain-rate response between DAS and converted geophone datasets suggests using dense geophone data to simulate DAS measurements. Simulation of DAS response allows analysis of strain-rate wavefields and further understanding of DAS data . For example, the gauge length is one of the most important parameters for DAS survey  as it afFor purposes of comparing the same physical property collected with a different type of sensor, the conversion approach by differentiation of particle velocity over the gauge length could be better than the integration of strain proposed by Bona, et al.  because The first-break strain-rate amplitudes are calculated from the vibroseis data correlated with a pilot sweep. This process affects the phase of a signal. The vibrator output is defined in terms of its baseplate motion . When correlation with a pilot sweep is performed on motion data, the resulting correlogram is assumed to be zero-phase . TherefoWe brought geophone and DAS measurements to the same property and calibrated them to the absolute strain rate. We compared the amplitude spectrum of the entire absolute strain-rate wavefield (uncorrelated) and the first-break amplitudes (correlated) for DAS and converted geophone. In addition to amplitude spectrum analysis, we simulated the DAS response with two additional gauge lengths.We observed that DAS and geophone have similar absolute values of strain-rate amplitudes, despite different factors affecting the performance. The losses of seismic energy due to the geophone coupling and housing are similar to the losses in fibre-optic cable construction and installation. Albeit similarity in absolute response is surprisingly good, we do not expect this finding will be universal, different cable design, coupling, etc., can produce situations where geophone and DAS response can be different. A denser geophone dataset can be used to simulate DAS response.GeoLab provided an excellent opportunity for such kind of research. Different equipment can be tested and assessed versus the reference dataset. Data is publicly available.