In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion speed but the fusion image texture is blurred; the latter has a better fusion effect but requires higher machine computing capabilities. Therefore, how to find a balanced algorithm in terms of image quality, speed and computing power is still the focus of all scholars.We built an end-to-end Hahn-PCNN-CNN. The network is composed of feature extraction module, feature fusion module and image reconstruction module. We selected 8000 multi-modal brain medical images downloaded from the Harvard Medical School website to train the feature extraction layer and image reconstruction layer to enhance the network’s ability to reconstruct brain medical images. In the feature fusion module, we use the moments of the feature map combined with the pulse-coupled neural network to reduce the information loss caused by convolution in the previous fusion module and save time.We choose eight sets of registered multi-modal brain medical images in four diease to verify our model. The anatomical structure images are from MRI and the functional metabolism images are SPECT and 18F-FDG. At the same time, we also selected eight representative fusion models as comparative experiments. In terms of objective quality evaluation, we select six evaluation metrics in five categories to evaluate our model.The fusion image obtained by our model can retain the effective information in source images to the greatest extent. In terms of image fusion evaluation metrics, our model is superior to other comparison algorithms. In terms of time computational efficiency, our model also performs well. In terms of robustness, our model is very stable and can be generalized to multi-modal image fusion of other organs. Deep learning technology is currently revolutionizing medical diagnostic services. Convolutional networks are fusing or surpassing human operators in multi-modal brain medical image fusion and are increasingly proposed as an aid to human medical decision-making. Multi-modal brain medical images use different sensors to image the head to show the anatomy and metabolism of the head –3. AmongWe will explain with a detailed example below.As shown in Fig. At this stage, the mainstream algorithms that prevail in multi-modal brain medical images are generally divided into two categories, namely filtering hierarchical fusion algorithms and deep learning algorithms. Among them, Dual-Tree Complex Wavelet Transform (DTCWT)  algorithAlthough the above eight algorithms have achieved good fusion results, they also have their own shortcomings. The traditional algorithm pyramid decomposition wavelet transform fusion algorithm has low time complexity, however, the overall brightness of the fusion image is dark and some areas have inexplicable shadows, which shows that their details are mishandled; the image fidelity and color saturation of the convolutional neural network fusion are very good, which shows its ability to extract features. Of course, the finer the feature extraction, the higher the time complexity. At the same time, the more feature extraction, the easier it is to cause artifacts in the fusion image. In order to better express the tissue metabolism, the remaining algorithm enhances the brightness of the fusion image without changing the color information, resulting in low image contrast and affecting the presentation of the image structure. Based on the problems of the above algorithms, we propose a fusion model. The model selects the convolutional neural network to construct the feature extraction module and the image reconstruction module. In the feature fusion module, Hahn moments are used to guide the potential block to activate Pulse-Coupled Neural Network (PCNN) to realize feature map fusion. Convolutional neural networks are currently the best technology in the field of feature extraction and image reconstruction. PCNN is a global fusion algorithm that can retain more detailed information, and its signal form and processing principles are more in line with the physiological basis of the human visual nervous system . In solving the blurring of image structure, Hahn moments can represent the shape of the image well. As for how to avoid fusion artifacts, we introduce the Hahn moment energy of the block.N is a given positive integer. The moment invariants of the image are good at describing the characteristics of the image. It is not only simple, but also immune to interference from light, noise and geometric distortion. Among these moments, the Hahn moment is widely used because it has a more general meaning. The Hahn moment is a kernel function of the orthogonal polynomial of Hahn. The Hahn polynomial of order N can be expressed asf is defined asN and M are substituted with N-1 and M-1 respectively to match the pixel points of an image.In order to reduce the oscillation in the calculation of higher moments and avoid the divergence of polynomial values, the polynomial is regularized. The regularized Hahn polynomial is defined asF channel is the pixel gray value of the external image, and the input neuron in the L channel only considers the output of the internal neuron affected by the eight neighborhoods. This operation greatly reduces the time complexity.In view of the fact that many parameters of the original PCNN require feedback iteration to adjust, which is both inconvenient and time complexity. In this article, we use a simplified PCNN neuron model . This moWhen the In terms of image processing, the convolutional layer can extract features, which are usually more meaningful and valuable than the features obtained by traditional feature extractors. In addition, the convolutional layer also acts as a weighted average to generate output images. We design a multi-modal brain medical image fusion framework based on CNN with Hahn moment, hereinafter referred to as Hahn-PCNN-CNN. Hahn-PCNN-CNN consists of feature extraction module, feature fusion module and image reconstruction module. As shown in Fig. In the feature extraction module, we intend to use three convolutional layers to extract image features from source images. Feature extraction is a key step in the transform domain image fusion algorithm. For CNN, the stochastic initialization convolution kernel training regression model has been faced with instability problem and it is difficult to train. The best approach is to take advantage of model migration in migration learning. Therefore, we plan to use the first convolutional layer of GoogLeNet, which is pre-trained on ImageNet, as the First Convolutional Layer (CONV1) of our network , where it contains 64 convolution kernels of Although CONV1 has a wide acceptance range, more graphics functions can be used. Therefore, not all image features must be displayed in the fusion. The key to multi-mode brain medical image fusion is the fusion of effective information, that is, while giving clear edges to metabolic information, as much as possible to ensure that the texture of the structural image is clear, so as to help doctors make a correct diagnosis. Therefore, the Second Convolutional Layer (CONV2) and the Third Convolutional Layer (CONV3) are added to filter the features obtained by CONV1 and finally obtain a feature map that can be fused.If the image is undersampled, part of the image information will be lost. Therefore, in the feature selection module, we do not want to lose the information of the image. Adjust the value of the corresponding stride and padding for the kernel of each convolution layer. Details of each convolutional layer of feature extraction module are shown in the Table We input the feature maps that are obtained from CONV3 into the image fusion module. Firstly, divide them into blocks and calculate the Hahn moment of each block. Then, inspired by the expansion coefficient factor, we defined the potential of the image patch. Finally, we use the potential of the image block as the external stimulus of PCNN to achieve the fusion of feature maps.PE) of Hahn moments values is computed byThe size of a brain medical image I is PE are determined, they are both input into the pulse coupled neural network. The feature map fusion starts according to the following formula:A represents the structure image and B represents the functional image. F represents the fused images. In image fusion, how to make full use of the biological features of PCNN and combine PCNN attributes and image characteristics to determine relevant parameters has always been a research hotspot in PCNN adaptive image fusion. PCNN has a strong adaptive link strength and will not change due to changes in the image. Humans have always liked to judge the clarity of the image based on the visually significant area. Whether it is a functional image or a structural image, they all have a prominent area to express information and an inconspicuous black background. And we tend to pay more attention to the former. After all, the visually significant areas are clear. Visual saliency detection through image complexity function is introduced into multi-modal brain medical image fusion. In view of its good performance, it can detect the saliency of medical images well. In this article, the visually significant areas detected by the Complexity-Weighted Saliency (CWS) detection  model arSince our feature extraction module is composed of three convolutional layers, we continue to use the convolutional layer to reconstruct it to get our final fused image after the feature map is fused. In the image reconstruction module, we choose two convolutional layers, namely CONV4 and CONV5. The convolution kernel of CONV4 is 3 × 3, stride and padding are both set to 1. The convolution kernel of CONV5 is 1 × 1, stride and padding are both set to 0. The kernel number of CONV4 is the same as CONV3. Since CONV5 reconstructs feature maps into a three-channel output. Thereby, the kernel number of CONV5 is 3. Details of each convolutional layer of the image reconstruction module are shown in the Table All researchers who use neural networks for image fusion will face the problem of information loss. Therefore, how to reduce information loss is particularly important. The loss function that determines the amount of information loss has become the focus of research. In the case of ground-truth image, Mean Square Error (MSE) is generally used as a loss function and the objective function is to reduce the distortion of images. However, multi-modal brain medical image fusion has two characteristics that make it unable to use MSE as a loss function. One is that there is no reference image for brain medical images and the other is that brain medical images are multi-modal. Therefore, the fusion image should retain the texture, edge and metabolic activity information in the source image as much as possible, not simply inherit all the information in the source image.I represents the input image and O represents the output image. F is the pixel value at point .In the article, we plan to use cross_entropy loss function, Multiscale-Structural Similarity (MS-SSIM) loss function and Total Variation (TV) loss function. Cross_entropy loss function can reduce the loss in the pixel model pixel by pixel; MS-SSIM loss function can reduce the loss of brightness, contrast and structural information in the reconstruction; TV loss function can make our images smoother while eliminating noise in the source image. The total loss function formula is as follows.As shown in Fig. We use the brain medical images provided by the Harvard Medical School website as the data set. The data set of this website consists of normal brain images, stroke images, brain tumor images and brain images of other diseases. In order to enhance the robustness of our model, we selected 8000 images from the normal and abnormal images on the website as the training images of our model. All images have a resolution of Qualitative methods are to combine the knowledge of medical nuclear imaging to qualitatively analyze our fusion images. The quality of brain image fusion is judged by the texture, edge and color of the image; Quantitative methods are to use several representative image fusion evaluation metrics to evaluate the quality of brain image fusion. Evaluation metrics are generally divided into five categories. There are evaluation metrics based on information theory, mainly including information entropy, mutual information, edge mutual information and normalized mutual information; there are evaluation metrics based on structural similarity, mainly including SSIM , MS_SSIMent}QABF ; there aent}QABF . In thisent}QABF , SSIM, SWe selected multi-modal medical images of four diseases from the Harvard Medical School website to verify the performance of our model. The four diseases are Glioma, Huntington’s disease, Metastatic bronchogenic carcinoma and Alzheimer’s disease. For structural images, we generally choose MR-T2 images that are good at expressing lesion information; For functional images, we choose SPECT-Tl images, SPECT-Tc images and FDG-PET images. Our model is implemented in the PyTorch framework and trained and tested on a platform with Intel Core i7-8700k CPU and NVIDIA RTX 2080 GPU. In this experiment, we plan to adopt eight representative image fusion algorithms that are mentioned in Background.The source images in Figs. The source images in Figs. The source images in Figs. The source images in Figs. Inspired by Image Fusion Framework based on Convolutional Neural Network (IFCNN), we proposed our model. In multi-modal brain medical image fusion, IFCNN can not fuse images containing metabolic information of organs and tissues and only performs image fusion on CT and MRI. Therefore, the functional image with color information can not be directly fused in IFCNN, however, our model can do it. In the process of model training, we added color images to the training set so that our model can fully process this type of image. In order to prove that our model is more suitable for multi-modal brain medical image fusion than IFCNN, we designed a set of experiments to convert functional images with colors to three representative color spaces that are RGB, HSV and YUV. Then we compare their fusion results with ours.In Fig. In this part, we focus on the time complexity of our model. Time complexity is an important metric that measures the efficiency of a model in this field. Generally, time complexity is directly proportional to the fusion effect. That is, the more time it takes, the better the result of the fusion. However, this is not the result we want. Our goal is to obtain a better fusion result in a short time through our fusion algorithm. In the article, we conducted a total of eight sets of multi-modal brain medical image fusion experiments.We calculate the average time loss of eight sets of images under different algorithms and put it as the average time loss of the corresponding algorithm. Figure Figure We used a non-parametric test method to verify the performance of our model. Among the many non-parametric testing methods, Friedman’s test can make full use of all the information in the relevant samples. Others such as Kruskal-Wallis test and the popular median method are also correct to judge whether multiple related samples come from a population with significant differences in size, but this approach is like using a non-parametric method to test two independent samples. The information about the differences between different individuals in the sample is ignored, thereby reducing the power of the test. Therefore, We used the non-parametric Friedman’s test and the post-hoc Nemenyi test to analyze how the analyzed methods differ from each other. Tukey’s distribution is introduced to calculate the Critical Difference (CD). If the difference between the algorithm levels is greater than the CD, the difference is considered significant . We use In Fig. Brain medical images are often different from other types of images. Since the imaging comes from multiple sensors, each sensor has a different focus. CT images focus on skill tissues; MRI images have better imaging effect on brain tissues; PET images help doctors observe the metabolism state of tissues. Therefore, it is very challenging to extend the traditional fusion method of two-mode medical images to three or more images while avoiding the overlap of information and the blurring of key textures. Such research is of great value. Most fusion methods are designed for image fusion of two modes. If you want to achieve tri-modal image fusion, you can only manually select two images for fusion and then merge the fusion result with the third image to get the final result. This is not the best choice because the fusion algorithm assumes that the image prior may not appear in the intermediate fusion results. The previous neural network methods all fuse two images from the training set and continuous fusion will result in multiple false images.Our fusion model just overcomes shortcomings of the above fusion method. There will be no continuous fusion of multiple false images, nor will it affect the effect of later fusion image because of different selection order in the early stage. Please note that the addition of modalities will not affect the processing time of our neural network because feature maps are extracted in parallel. Figure Current research shows that the image obtained by our model retains the texture information of the anatomical image and the color information of the functional image to the greatest extent, while maintaining the contrast of the image during fusion. On the six representative evaluation metrics, the performance of our model is significantly better than the other eight algorithms. In addition, the diversity of images enhances the robustness of our algorithm. Since our algorithm is a lightweight and high-quality algorithm, it has broad application prospects in intelligent medicine. If it is extended to other image data sets, the performance of the model can be improved.


Binary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluation. This work aims to define the discrimination ability of CATA participants by calculating the consensus values of 44 binary similarity measures. The proposed methodology consists of three steps: (i) calculating the binary similarity values of the assessors, sample pair-wise; (ii) clustering participants into good and poor discriminators based on their binary similarity values; (iii) performing correspondence analysis on the CATA data of the two clusters. Results of three case studies are presented, highlighting that a simple clustering based on the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination . In food sensory analysis, check-all-that-apply (CATA) experiments show an increasing tendency among practitioners. Its high popularity is due to the simplicity, effectiveness, and fast execution, as well as the many different applications . ApplicaDuring a CATA experiment, a list of sensory attributes is presented to the assessors, from which they choose only those they identify in the given sample . The finCATA data analysis is a widely researched area; however, one of the most known methods is graphical correspondence analysis (CA). CA might be considered as a generalized principal component analysis (PCA) for ordinal data . Its popIt is also important to evaluate the panel participating in a CATA experiment. Reproducibility was investigated first among the performance metrics and within-assessor reproducibility index was introduced . Recent Further analysis of the agreement among the participants has also been discussed. The authors of CLUSCATA and CATATIS introduced an overall index of agreement among participants based on the cosine between two matrices associated with two respondents in a Euclidean space .From the above-mentioned developments in CATA data analysis, it can be seen that there is a lack of methods aiming to describe the discrimination ability of consumer panels. There are multiple methods which are used to assess the discrimination ability of trained panels conducting descriptive sensory methods  to three case studies of CATA evaluation scenarios. The python code used for the presented calculations, along with an example input is published open source in the same GitHub repository to allow for easy reproducibility of our results and the application of the presented approach to other CATA datasets.This is based on the application of binary similarity metrics (as published in our recent work and available at Acheta domesticus) powder, oat, and buckwheat flour. Cricket powder was shipped from Thailand , while oat and buckwheat flour were purchased from Hungarian producers . Other ingredients of the doughs were lactose free butter and sour cream, baking powder and salt. Samples contained 0% (Ctrl), 5% (CP5), 10% (CP10), and 15% (CP15) cricket powder. Sixty-seven consumers evaluated the prepared four samples using 38 CATA attributes . Apple pomace was made from Idared apples, at the Department of Food Preservation at Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology. Other ingredients of the doughs were margarine, icing sugar, and vanilla sugar. Samples contained 0% (AP0), 2.5% (AP2.5), 5% (AP5), and 10% (AP10) ground apple pomace. Sixty consumers evaluated the prepared four samples using 34 CATA attributes  in a oneStrawberry varieties were analyzed by 117 consumers in a supermarket in Montevideo, Uruguay. Consumers rated 16 CATA attributes  and overThe CATA experiments provide a list of sensory attributes, in which the presence of an attribute in a sample is denoted by “1” by the assessors. In this way, a long vector with the binary coded absence or presence of the attributes in the sample is given for each sample (row). The samples can be compared based on their binary vectors using a 2 × 2 table, which is presented in , Simple matching coefficient (SM), Driver-Kroeber or cosine coefficient (DK).The mentioned five parameters are the basis of the different equations of the binary similarity metrics. These are summarized in the work of Todeschini et al. , as wellSimilarity and distance metrics can be transformed into each other in the following way: https://github.com/davidbajusz/fpkit, accessed on 18 May 2021.Similarity metrics often, but not always, fall in the range between 0 and 1. For those metrics, which are in a different range, Todeschini et al. have proposed a simple scaling method to transform them to the  range: package , which iDue to the large selection of binary similarity metrics, the average values of the different metrics can be used for the comparison of the assessors, as the simplest consensus option. The reason for this choice is that we cannot arbitrarily select the best similarity metric a priori. By contrast, the merit of consensus metrics/methodologies have been extensively demonstrated ,17,18. BThe data matrix obtained after running and averaging the 44 similarity measures on the binary CATA data table consists of columns corresponding to the assessors, and rows corresponding to the pairs of products. The values in the matrix express the average similarity metrics computed for the given assessor and the given pair of samples. These pair-wise differences were subjected to a (column-wise) agglomerative hierarchical cluster analysis (AHC) using Ward’s method and the Euclidean distance to create two clusters, one representing assessors with good discrimination ability  and the other containing assessors with poor discrimination ability . In order to visualize the differences between the two groups, correspondence analysis (CA) was computed for the total panel and for the created two clusters (good/poor discrimination ability). AHC was run on the matrix of similarity measures while CA was run on the binary data matrix obtained from the sensory tests. Optimal cluster numbers were determined using the Silhouette index . AHC andThe 44 similarity metrics were calculated for each dataset in the following way: the original binary dataset contained the attributes in the columns and the samples in the rows. The assessors were also assigned to the rows. After the calculation of the 44 metrics, the averaged values for each sample combination can be found in the cells of the matrix, where the assessors were in the columns and the compared sample combinations in the rows. The workflow can be followed in The average similarity values for each assessor are plotted in a heatmap visualization, colored dark green to red, from the lowest similarity to the highest similarity. To allow for easier discrimination among the assessors, averages were calculated from the similarity values column-wise (assessor average), as well as for the whole data matrix (grand average). Those assessors, whose assessor averages are below the grand average (defined here as a consensus limit) can be considered as the better ones, i.e., having higher-than-average discrimination ability. These individuals were marked with brackets in Based on the average similarity measures of the assessors, 37 participants were marked as good discriminators , which is similar to those cluster numbers found in the previous case study. Compared to the previous dataset, the average similarity measure values are lower, meaning that the participants in the strawberry group discriminated the samples better compared to the group evaluating the cricket powder enriched biscuit samples. Considering this observation, we propose that the similarity measures could be generalized to compute a global measure describing the discriminator power of the test. However, it should be noted that this is a combined result, we cannot judge the participants  independently of the similarities of the products.Comparison of overall liking values between the two consumer groups determined by agglomerative hierarchical clustering of the similarity values is presented by The strawberry dataset shows also that members of C1 differentiated the products on a higher level, their mean OAL across products ranged between 4.358 (Yurí) and 5.753 (L20.1) and significant differences were found. Interestingly, while the OAL values reported by the members of C2 span across a slightly greater range (5.0–6.6), due to the wide distribution  of the values, the differences between the products are statistically not significant.The apple pomace-enriched biscuits dataset showed a different result. In this case, both clusters showed significant differences among samples with the same pattern, meaning that AP0 proved to be the most liked and AP10 as the least liked one. It has to be mentioned that these products were highly different because the added amount of apple pomace changed the texture, color, and taste to a great extent, which is why the consumers differentiated the products very well. Panelist agreement was calculated by the CATATIS method introduced recently . CATATISThe authors would emphasize the joint application of the homogeneity index (as the measure of panelist agreement) and the introduced methodology (as the measure of discrimination ability) in order to identify assessors with good or weak performance. An example of this joint application is presented by https://github.com/davidbajusz/fpkit, accessed on 18 May 2021. The presented results of the three case studies show that a simple clustering of the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination .The proposed methodology aims to fill a gap in check-all-that-apply (CATA) tests by providing a method to assess the discrimination ability of the panelists participating in CATA evaluations. The method fits into the scope of newly introduced CATA data analysis methods. The proposed analysis is easy to run using our open-source python package, available at It must be mentioned that good discrimination does not mean good repeatability and/or panel agreement. In order to describe the performance of a panel/panelist correctly, all three metrics should be calculated. In the present paper, the joint application of discrimination ability and repeatability has been introduced, however by conducting replicate sessions (which is more common with trained panels), repeatability can also be calculated using 2 × 2 tables .The distance metric and linkage rule were defined as the most popular ones, namely Euclidean distance and Ward’s method. Further analysis could assess the effect of the combination of distance metrics and linkage rules on the separation of good and poor discriminators, ideally on a greater number of datasets.In particular, since the sensory similarity of the products is difficult to assess prior to the tests, the method should be tested with a wide range of products, from highly similar to highly different. Higher average similarity values  might be achieved due to the high similarity of the samples and not only because of the panelist’s poor performance.Our proposed method gives a new tool to the hands of consumer sensory panel leaders for the assessment of the discrimination ability of consumers participating in CATA tests. As the used scripts are free to download and use, these calculations can be built into any data analysis routines easily. The obtained information improves the reliability of CATA tests, as well as gives another piece of important information regarding the performance of consumer panels. Application of binary similarity measures with the earlier proposed homogeneity index gives a complete evaluation of the consumer panels.A further future direction would be the application of the proposed method on trained panel data, where the panel agreement and discrimination ability are more important than they are during a consumer test. Additionally, comparison of binary similarity metrics to the existing methods used to assess the discrimination ability would further validate the proposed approach in trained panel tests. A further advantage of the proposed approach would be to use as a screening method for the identification of well-performing consumers who could be potentially transferred into trained panels after completing the required trainings.


Fiber-reinforced rubber composites with integrated shape memory alloy (SMA) actuator wires present a promising approach for the creation of soft and highly elastic structures with adaptive functionalities for usage in aerospace, robotic, or biomedical applications. In this work, the flat-knitting technology is used to develop glass-fiber-reinforced fabrics with tailored properties designed for active bending deformations. During the knitting process, the SMA wires are integrated into the textile and positioned with respect to their actuation task. Then, the fabrics are infiltrated with liquid silicone, thus creating actively deformable composites. For dimensioning such structures, a comprehensive understanding of the interactions of all components is required. Therefore, a simulation model is developed that captures the properties of the rubber matrix, fiber reinforcement, and the SMA actuators and that is capable of simulating the active bending deformations of the specimens. After model calibration with experimental four-point-bending data, the SMA-driven bending deformation is simulated. The model is validated with activation experiments of the actively deformable specimens. The simulation results show good agreement with the experimental tests, thus enabling further investigations into the deformation mechanisms of actively deformable fiber-reinforced rubbers.  Adaptive structures have been an increasingly popular field of research in recent years, with the numbers of publications and patents rising significantly since the year 2000 . An adapRecently, shape memory alloys (SMAs) have emerged as promising candidates for creating adaptive structures. Their ability to react to thermal stimuli with reversible shape changes allows users to reduce the complexity, size, and cost of mechanical systems by replacing conventional actuators, such as electrical motors, hydraulics, or pneumatics . They ofFor the development of practical applications, a comprehensive understanding of the new IFRC material behavior and the interactions of the separate components is required. To date, no feasible approaches for dimensioning and investigating the deformation behavior of IFRC exist. Therefore, a simulation model capable of recreating and predicting the active deformation behavior of real specimens is required. In this work, multilayer flat knitting is used to develop biaxially reinforced multilayered knitted fabrics with integrated SMA wires. The fabrics are manufactured into FRC and IFRC specimens with vacuum-assisted resin infusion (VARI) technology and tested regarding their deformation behavior. A novel mesoscopic model within the framework of the finite element method (FEM) for predicting the structural IFRC behavior is developed and validated with the results from the experiments, providing a basis for future developments of more complex IFRC structures and applications.s and Af (heating).The start and finish temperatures of austenite forming As and Mf (cooling).The start and finish temperatures of martensite forming MSMAs are capable of regaining their initial shape after an applied deformation through heating to a value above the material-specific transformation temperature. This property is based on their ability to perform a diffusionless solid-phase transformation between two distinctive microscopic phases—the martensite (M) phase, which is stable below the transformation temperature range, and the austenite (A) phase, which is stable above the transformation temperature range . The swiSeveral effects result from these thermomechanical properties—most prominently the one-way shape memory effect (OWSME), which will be the focus of this work. For more details on other SMA-specific effects (two-way shape memory effect and superelasticity) and the complex material behavior of SMAs, refer to, for example, ,9,22.f, the microstructure transforms from martensite to austenite. Since austenite possesses a much higher stiffness and lacks the property of detwinning, a contraction of the wire now occurs toward the undeformed state 1, which, however, is not fully reached due to the external load. Instead, state 3, which is afflicted with residual deformation, is adopted, corresponding with the stress–strain behavior of the austenitic SMA. The extent of this contraction, i.e., the difference between states 2 and 3, is referred to as the actuator contraction Δl. Upon cooling to a value below the transformation temperature Mf, the microstructure of the wire transforms back into martensite. If no external force was applied here, the wire would remain in the geometric state 3 or, in the case of a slightly trained material, deform to a small extent toward state 4. However, due to the force still applied, which now acts as a resetting force, the wire is stretched again and assumes state 4. Thus, by applying temperature and using a setup that provides a resetting force, it is possible to repeatedly switch back and forth between states 3 and 4. The usable actuator contraction of a typical nickel–titanium (NiTi) SMA usually is in a range of 4 to 8% based on the initial length. For applications requiring long-term stability, thermal stabilizing methods are sometimes used that reduce the usable contraction to 4–5%. Due to their large actuation forces and multitude of intrinsic effects, SMAs are widely used in different fields of technology, such as aerospace [The basic principle of the OWSME is displayed in erospace ,24, medierospace ,26, autoerospace , and soferospace ,28. Thermoset fiber-reinforced polymers (FRPs) possess high stiffness and strength. The reinforcement fibers carrying tensile loads along their longitudinal axis and the resin matrix provide stability between the fibers and take loads perpendicular to the fibers, as in pressure or shear load cases . In contUsing the capabilities of textile technology, the mechanical properties of the FRCs can be tailored in such a way that they match the properties of the SMA actuators and thus enable large and reversible deformations. The multilayer knitting technology ,18,30 enThe SMA-driven IFRC mechanism of this work is based on the bending beam, as depicted in L is assumed to stay constant throughout the deformation process. The length il of the inner arc corresponds to the length of the SMA wire under a given contraction L. For a given thickness h and length L, the resulting geometry is calculated withThe two-dimensional shape of a deformed beam with an integrated SMA is assumed to match a circular arc, as depicted in R denotes the radius of the arc. This geometrical consideration assumes the maximum possible deformation for a given SMA contraction neglecting mechanical interactions of the structural stiffness that reduce the effective SMA contraction. Two basic design principles can be derived:h).Thinner structures enable larger deformations .Longer structures enable larger deformations , Italy). The material was obtained in a pre-strained state, able to initiate the OWSME by thermal activation and thus providing the required condition for application as structurally integrated actuators. As the rubber matrix material, the two-component liquid silicone polydimethylsiloxane (Sylgard 184™) was used. As fiber reinforcement, glass fiber (GF) rovings with 410 tex and twisted GF twin-yarns with 2 × 136 tex were used.® flat knitting machine  process was used for the manufacturing of the composite specimens. Cuttings of the GF knit (120 × 80 mm²) were placed onto a metal plate and covered with a multilayer buildup as listed in The silicone was degassed before the infusion process. This step is required to avoid air bubble formation. After the infusion, the specimen was cured for 48 h at room temperature until it was unwrapped b. To achTo achieve a comprehensive understanding of the material properties as well as obtain relevant mechanical and thermomechanical properties of the IFRC components and the composite itself, characterization experiments of all components were conducted. Due to the SMA wires possessing the most complex material behavior, the characterization experiments were the most comprehensive. Here, the tensile properties below and above the transformation temperature range had to be obtained, as well as the temperature-dependent shape change behavior, including the start and end temperatures of the transformation process. For reinforcement fibers and silicone rubber, the tensile properties were the most relevant and were obtained from tensile tests. Since the primary deformation mode of the composite during SMA-driven activation is bending and the SMA wires are acting against the structural bending stiffness, the composite bending behavior was the most significant mechanical property for calibrating the simulation model. Therefore, four-point-bending tests were conducted on composite specimens without SMA wires, to be used for comparison with the simulation model in Differential scanning calorimetry (DSC) .s).Isothermal tensile test at room temperature (below Af).Isothermal tensile test above 130 °C  test.The basic properties of the material were obtained by the following experiments:s, Mf, As, and Af are determined. The DSC measurement was conducted with a DSC Q2000 by TA Instruments . The SMA material was heated twice from 0 to 180 °C. In this way, the pre-strain within the material was removed during the first cycle.In DSC measurements, a crucible with sample material and an empty reference crucible are heated or cooled uniformly in a defined manner. From the comparison of the heat flows of both crucibles, statements are made about the transformation processes in the sample material. For the SMA material, the four transformation temperatures MThermomechanical experiments were conducted on a Zwick & Roell Zmart.Pro tensile testing machine  with a 500 N load cell and a connected temperature chamber with active nitrogen cooling, capable of performing mechanical testing under controlled temperature with a heat rate of 5 K/min . SpecimeIsothermal tensile tests were conducted at room temperature in order to capture martensite properties. Both as-delivered material with pre-strain as well as unstrained material were tested. The latter was obtained by heating pre-strained material up once to 130 °C under load-free condition. For measuring the austenite properties, unstrained SMA wires were clamped at 130 °C in order to avoid unwanted SMA contraction before the actual testing procedure. The tensile test was then conducted at 130 °C.l0 = 200 mm, with the load cell set to a low, constant holding force of 0.1 N. As the temperature chamber heated up, the wire began to contract, exerting a force on the clamps. The machine automatically changed the position of the mobile clamp in order to restore the initial constant holding force. The clamp position data were then used to measure the deformation of the wire. The SMA wire contraction To estimate the maximum usable contraction of the SMA wires upon activation, UPFR tests  were car®. Tensile tests of single yarns were conducted based on ISO 3341 , enablin2-Laser. Before molding, the silicone requires thorough degassing in order to avoid defects caused by air bubbles. Since the stress–strain curve of rubbers is usually nonlinear, an elastic modulus cannot be determined accurately. The elastic modulus obtained from the beginning of the tensile test  based on DIN EN ISO 14125 [Since the primary deformation mode of the IFRC used in this work is bending, the flexural modulus is the most relevant property for validating the mechanical behavior of the simulation model with regard to the manufactured specimens. Therefore, the bending behavior of the FRC was investigated by using a four-point-bending test on a Zwick & RoellThe test setup for deformation measurements of the IFRC specimen is set such that the specimen deforms parallel to the ground, minimizing the influence of gravity . The IFR® as a silicone block with a cylindrical channel for the SMA and elliptical cavities for the reinforcement fibers. The SMA wire is modeled as a cylinder positioned within the channel without structural connection to the silicone. The fibers are modeled as cylinders with an elliptical cross section and placed inside the matching cavities. The fiber dimension parameters were obtained from a microscopy image of the real cross section and are listed in The FRC plate is modeled in ANSYSAs preliminary simulations have shown, the influence of the warp yarns on the bending behavior in the weft direction is small, while the modeling and computational effort for adding the warp yarns is high. Therefore, only the weft yarns are modeled, which reduces the computation time by a factor of ~100.A 20.75 mm wide section of the total structure with only one SMA wire is modeled. It is assumed that the actuation force of the SMA spreads evenly over the width of the specimen. The number of reinforcement fibers is reduced accordingly, reducing the total amount of elements and therefore the computing effort.The loop-shaped fixation of the SMA wire on the free end is neglected. Due to the necessity to pre-strain the wires during the simulation see , a bent The plate length was reduced by 15 mm from the real length, replicating the fixation area of the activation test setup cf. . Since tThe mesh fineness was determined by performing a convergence analysis with varying numbers elements. The chosen number of elements is a compromise of accuracy and computational time and can be found in D is the material elastic stiffness tensor; ε is the total strain; T) is a function of the temperature with T is the temperature; T0 is the temperature below which no austenite is present in a stress-free state; h is a material parameter related to material hardening during the transformation; and m describes the asymmetry between tension and compression behavior. The model reproduces the primary features of shape memory materials in a 3D stress state and is defined by nine parameters  is required. This stress value is used for the pre-strain loading of the simulation model in l obtained from the data is found to be 4.4l is regarded as a maximum value. Furthermore, a partial setback upon deactivation can be observed. During cooling, the SMA wire extends again by a significant amount. As was confirmed by the manufacturer, the heat treatment during manufacturing resulted in a partial two-way effect. This effect has a significant influence on the setback behavior of the IFRC structures, enabling larger achievable setback angles  leads to a local curling of the free end of the plate, with barely any global bending deformation. The actuation force of the SMA is concentrated on the small area at the free end, where the SMA is fixed by its loop shape. The stiffness in the weft direction is not high enough to distribute the load evenly over the whole plate, curling the area locally instead.The bent structure shows creases along its length, diverting from the ideal arc shape. The creases are assumed to be caused by local defects of the interface between fiber and matrix.Both Var1 and Var2 were equipped with two SMA loops, resulting in four parallel SMA wire strands a. The spThe specimen with one loop showed the aforementioned local curling of the free end, which resulted in deformations close to no bending. Furthermore, a specimen with six loops showed a large deformation, up to 180°, which could not be captured with the laser triangulation setup. The resulting deformation angles after activation and deactivation are displayed in For both specimens, the residual angle after deactivation increases with each cycle. This effect is typical for SMA-driven composites and was observed in previous works, e.g., by Ashir et al. ,45. AddiThe specimen Var1 shows significantly larger deformation angles. This confirms the assumption that structures with lower stiffness allow for larger deformations induced by the SMA.Due to the overall irregular deformation shape of the specimens, their uneven surface, and the local curling at the free ends, evaluating the deformation angle from the measurement results was challenging. Therefore, the estimated deformation angles can only serve as a rough estimation of the real deformation behavior that was achieved. However, several effects can be observed:®, where the software iteratively determines the parameters for a good agreement with the provided experimental data. Since no compression data were entered, the bulk modulus parameters d1–d3 are determined as zero. No details are given in the ANSYS® instructional appendix on how the model processes these zero values, which would lead to division by zero in Equation (7), but it can be assumed that the bulk terms of Equation (7) are neglected.The material model parameters of the hyperelastic material model for the silicone, the SMA material model, and the anisotropic material model for fiber reinforcement are displayed in The same material model was used for both GF materials. The stiffness parameters of the GF material were calibrated iteratively by comparing the results of the four-point-bending test with the simulation. The starting value obtained from the tensile test data of the fiber material resulted in higher bending stiffness than in the experiment by a factor of ~10. It is thus assumed that for thin FRC specimens and due to the low rubber matrix stiffness, an effect similar to that in pure textiles occurs: the high tensile stiffness is no longer coupled to the bending stiffness in the specimen, resulting in an overestimation of the bending stiffness.The results of the four-point-bending simulation conducted with the calibrated parameters for the fibers listed above are compared with the experimental results, as displayed in The IFRC simulation model shows the expected arc-shaped bending deformation, as displayed in The deactivation experiments and simulations show a disparity, which is assumed to be related to the partial two-way shape memory effect (TWSME, cf. The simulation model is capable of capturing the bending behavior of SMA-driven IFRC structures with overall good agreement with the measurements conducted in this work. The model enables the user to estimate the influence of many different parameters on the deformation behavior, such as the used materials; the amount and positioning of the reinforcement fibers; the overall geometry; and additional external factors, such as the weight of the structure or contact interactions with solid bodies. Thus, the model can be used for the development and dimensioning of future IFRC structures and applications.Several aspects make model development a challenging task. A significant matter is the fixation of the SMA at the free end of the beam. As the activation tests have shown, the stiffness of the IFRC structure at the free end of the plate is too small for transmitting the SMA force onto the whole structure when using only one SMA loop, resulting in curling of the plate edges. To apply the necessary pre-straining into the SMA wire, the model was simplified with a straight SMA wire. With a more advanced model, this drawback could be replaced by the SMA material model. Such an approach was developed recently .For the bending structures investigated in this work, the 90° fibers had a low influence on the deformation behavior. However, for future investigations of, e.g., more complex structures and deformation patterns, the necessity of modeling the 90° fibers should again be verified.An undesirable effect is the local creasing of the bent structure, which leads to uneven bending arcs. The creasing is assumed to be caused by defects of the interface between rubber matrix and glass fiber. The high viscosity of the silicone impedes the full impregnation of the fiber bundles during the VARI process. Therefore, this aspect should be improved in the future.Future investigations will use the established model for investigating the influences of both geometrical and material parameters on the deformation behavior, as well as the development of more complex geometries and mechanisms. For this, further developments of the model, such as using advanced SMA material models or the application of macroscopic formulations for the IFRC behavior, will help to increase the model’s accuracy and capabilities.


Microwave breast imaging has seen increasing use in clinical investigations in the past decade with over eight systems having being trialled with patients. The majority of systems use radar-based algorithms to reconstruct the image shown to the clinician which requires an estimate of the dielectric properties of the breast to synthetically focus signals to reconstruct the image. Both simulated and experimental studies have shown that, even in simplified scenarios, misestimation of the dielectric properties can impair both the image quality and tumour detection. Many methods have been proposed to address the issue of the estimation of dielectric properties, but few have been tested with patient images. In this work, a leading approach for dielectric properties estimation based on the computation of many candidate images for microwave breast imaging is analysed with patient images for the first time. Using five clinical case studies of both healthy breasts and breasts with abnormalities, the advantages and disadvantages of computational patient-specific microwave breast image reconstruction are highlighted. In recent years, more than eight microwave breast imaging systems have been used with patients for some ongoing trials including almost 1000 patients ,4,5,6,7.In addition to patient trials, both analytical and experimental investigations of the fundamentals of microwave breast imaging have also continued, examining factors such as antenna layout, artefact removal, imaging algorithm selection, prior information integration, multi-modality imaging, contrast agents and phantom development ,26,27,28Inefficient coupling of energy into the breast ;Imaging domain changes during acquisition ,32,33;Intrapatient variation due to the menstrual cycle, hormonal changes or weight differences ;Interpatient variation in breast size, shape and composition ,18,21.Many of the challenges faced when imaging patients identified from the clinical studies to date can be categorised in four broad areas:These four challenges can have a large impact on image quality: if microwave energy is inefficiently coupled into the breast , the tumour response in the scattered signals may be very small or even below the noise floor. Practical solutions employed by the operational systems to overcome these challenges are discussed in this section, including the aspects of these challenges yet to be resolved.The patient interface design can help address challenge 1. The design of biocompatible coupling media which are stable over time, have appropriate dielectric properties and are cheap and easy to replace between patients ,35 improThe acquisition hardware can also help address challenge 1 . For exaThe imaging domain is also subject to change during the scan : the breast can move due to patient breathing or discomfort, or blood flow or temperature changes may occur in the living breast tissue ,32. ShorStudies using TSAR have looked at the repeatability of the scans , highlighting the differences that can occur between patient scans and proposing metrics which can be used to quantify these differences independently . StudiesAs microwave imaging is used with more diverse study populations, challenge 4 is becoming increasingly important . PatientRadar-based images are typically compared to images from other modalities such as mammography, ultrasound or magnetic resonance imaging which are acquired from different orientations;Images from other modalities exploit different properties of human tissues (such as X-ray attenuation using mammography), which makes the comparison of breast structures between images from different modalities difficult. For example, microcalcifications are very prominent in mammograms but may not be visible at all in radar-based images;The breast often contains multiple regions of interest which would be expected to be visible in the microwave breast image and it is not clear what the optimal radar-based image should look like in these cases;The true dielectric properties of the breast are not known quantitatively, only qualitative assessments of breast density from mammography are known, which measure the proportions and distribution of glandular tissues but not the dielectric properties.A number of complexities exist when interpreting the results of clinical studies using microwave breast imaging:In this work, the performance of a computational patient-specific imaging algorithm is analysed in five clinical case studies from the TSAR system developed by the University of Calgary . These cThe remainder of this paper is structured as follows: the patient population, clinical procedure and imaging algorithm are described in In this section, the patient population, clinical procedure, imaging algorithm and analysis techniques are outlined. All patient images were reconstructed using scattered data acquired from the TSAR system developed at the University of Calgary and were originally published in . The opeAll patients were recruited from the Breast Health Clinic, Foothills Medical Centre, Calgary, AB, Canada and provided written informed consent to participate in the study . Patients with a breast size of B or C cup, with suspicious areas in the breast not located in the axilla, were considered for inclusion in the study. Only patients who were eligible for magnetic resonance imaging  were included.Prior to radar-based imaging with TSAR, the ipsilateral breast was scanned using magnetic resonance imaging no more than four days before the TSAR scan, except for one patient for which it was twelve days earlier. The magnetic resonance image was acquired using a Five clinical case studies are analysed in detail in this work, and the relevant clinical information for each case is summarised in For each patient scan, the patient lay prone on the examination table with the breast pendant through an opening. The breast was immersed in canola oil which has relative permittivity of 2.5 and conductivity of ribed in , was useScattered data were acquired between 50 The coupling medium with a known relative permittivity of The breast interior with an assumed reconstruction permittivity The neighbourhood-based skin subtraction artefact removal algorithm was employed to isolate reflections from the breast interior . TSAR inIn this work, the assumed reconstruction permittivity of the breast interior was swept in the range rameters . This peεr′=9 in , and by D of the image.For each reconstruction permittivity value  toolbox . Using lThis metric and similar approximations to the three-dimensional gradient have been used in both autofocus and imaging applications to select images based on sharpness, and the choice of metric and theoretical understanding of the metric are further explained in . In the D) as:D axis where the magnitude of the image is less than half the maximum magnitude in the positive and negative directions, respectively:The FWHM of the image, defined for each dimension , defined in this work as:Similarly, the signal-to-mean ratio (SMR), defined in this work as:Additionally, each image is examined quantitatively using:The cost function values, s, as in .Secondly, the fitness for many patients shows multiple local maxima. In some cases, such as Patient 1, the local maximum at ous work  and alsoous work , where aFinally, the images analysed in the original study were reconstructed at The right breast of 53-year-old Patient 1 was scanned. Three regions of interest in the breast were identified from mammography, the magnetic resonance image and the original study , which aThe approximate locations of Two images are highly rewarded by the cost function in Considering the image at the global maximum at  blue in , a largeThe image at the local maximum at Comparing the three images, the maximum amplitude of the image reconstructed at The two images rewarded by the cost function at al study , in Tablal study , showed Case 2 involved a 64-year-old woman with a fibroadenolipoma in the lower inner quadrant of her left breast. The mammography report notes the breast tissue is extremely dense (BI-RADS category D). Fibroadenolipomas  are typically benign masses containing an admixture of ducts, lobules, fibrous stroma and adipose tissues in varying proportions. The contrast between the fibroadenolipoma and the surrounding tissue is uncertain, particularly in a breast noted as heterogeneously dense.The global maximum of the cost function in Extremely dense breasts such as that of Patient 2 would be expected to have higher dielectric properties. However, the cost function rewards images reconstructed at lower dielectric properties very highly. Although it is difficult to draw any definite conclusions as the dielectric properties of the breast are not certain, it is likely the images rewarded by the cost function are reconstructed below the average dielectric properties of this particular breast and the images contain mostly spurious noise and clutter.Uncertain contrast between the fibroadenoma and the glandular and fibrous tissues in the rest of the breast;Difficulty in isolating reflections from the benign lesion from the reflections from the other glandular and fibrous structures in the breast;Acquisition challenges due to high attenuation in the dense breast tissues.However, across the entire reconstruction permittivity range, no one image is characterised by one single response. As may be expected from a breast noted as extremely dense, all images contain many responses of similar magnitude. The poor image quality of this clinical case study may also be explained by a number of other factors, such as:Due to these factors, it is difficult to predict what a “correct” radar-based image should look like for this clinical case study.The left breast of the 35-year-old Patient 3 was scanned. The mammogram indicated extensive microcalcifications around 3 o’clock in the lateral aspect and the magnetic resonance report showed enhancements from the 2 o’clock to 6 o’clock radian. Additionally, the magnetic resonance report showed a focal mass near the nipple. A region of invasive ductal carcinoma in the upper outer quadrant of the breast was reported after post-mastectomy pathology. The invasive ductal carcinoma was measured as A prominent global maximum is present in the cost function at The image in the original study, reconstructed at Although the breast contained extensive disease in this case study, no image clearly shows a response which could definitively be said to correspond to the invasive ductal carcinoma. The breast was noted as scattered heterogeneous according to the mammogram, meaning the average dielectric properties of the breast would be expected to be low. However, the image reconstructed at Patient 4 was 44 years old when her left breast was scanned with TSAR. Ultrasound and magnetic resonance imaging of the left breast showed an The global maximum of the cost function is located at Firstly, considering the global maximum at Secondly, looking at the image at the local maximum at Finally, comparing the images rewarded by the cost function to the image reconstructed at Patient 5 was 32 years old when her left breast was scanned. The patient had no history of breast disease and the mammography report indicated the left breast was heterogeneous (BI-RADS C) with some glandular tissue both on the inner and outer sides of the breast. An initial magnetic resonance image suggested an unidentified lesion at 4 o’clock which was not apparent from mammography, a follow-up ultrasound or on a second magnetic resonance image.The image at In this work, computational patient-specific microwave breast imaging is tested in five clinical case studies. The clinical case studies were obtained from the first-generation TSAR operational system developed at the University of Calgary and the microwave images were analysed using knowledge of the clinical history of the patient including recent mammograms, magnetic resonance images and pathology reports, where available. No definitive conclusions or trends can be drawn from only five clinical case studies, and in three of the five cases, a lot of uncertainty exists as to the dielectric composition of the breast and the location and extent of lesions in the patient breast. Despite these limitations, some interesting results were observed from the clinical case studies.Firstly, in one case study (Case 1: the only case with known disease in a known location), reconstruction permittivity estimation rewards an image where a response corresponding to this tumour is identified. When compared to the fixed-value estimate used in the original study, the tumour response has a higher SMR, SCR and amplitude. This is consistent with the conclusions of the previous work in the literature, and may suggest that patient-specific beamforming could improve the sensitivity of the microwave breast imaging modality.Secondly, it may be possible to reconstruct two images with the characteristics of correctly reconstructed images (tumour) within the relative permittivity range as seen in Patient 1. In this case, this may correspond to another benign lesion within the breast. It may be necessary to reconstruct multiple images with various reconstruction permittivity estimates to obtain a complete picture of the entire breast. At the very least, this clinical case study suggests that further testing in breasts with multiple regions of interest need to be investigated further.Thirdly, these images are consistent with the hypothesis that obtaining high specificity with radar-based imaging may be difficult, but that patient-specific beamforming does not increase the false positives. For example, images of Patients 1 and 5 are visually very similar, even though one breast contains a tumour and the other does not, and it is likely that a radiologist review would identify suspicious regions in both images. However, it is interesting to note that the difference in maximum image magnitude between the diseased and healthy breast is Finally, these cases studies are useful for identifying potential limitations of patient-specific beamforming. In many of these clinical cases, images reconstructed with very low reconstruction permittivity estimates are highly rewarded, including for a breast classed as extremely dense from mammography (contrary to expectations). It is difficult to understand why exactly this may be as the dielectric properties of those breasts are not well known, but this surprising result does suggest that if a very broad reconstruction permittivity search space is used, images with spurious responses may be reconstructed.


Levitating nanoparticles trapped in optical potentials at low pressure open the experimental investigation of nonlinear ballistic phenomena. With engineered non-linear potentials and fast optical detection, the observation of autonomous transient mechanical effects, such as instantaneous speed and acceleration stimulated purely by initial position uncertainty, are now achievable. By using parameters of current low pressure experiments, we simulate and analyse such uncertainty-induced particle ballistics in a cubic optical potential demonstrating their evolution, faster than their standard deviations, justifying the feasibility of the experimental verification. We predict, the maxima of instantaneous speed and acceleration distributions shift alongside the potential force, while the maximum of position distribution moves opposite to it. We report that cryogenic cooling is not necessary in order to observe the transient effects, while a low uncertainty in initial particle speed is required, via cooling or post-selection, to not mask the effects. These results stimulate the discussion for both attractive stochastic thermodynamics, and extension of recently explored quantum regime. In the transient ballistic regime, the surrounding environment does not modify the statistics of the instantaneous velocity11. Moreover, the initial uncertainty of the levitating particle can be controlled, by postselection12, feedback cooling16 and ultimately, by coherent scattering to the mechanical ground states19. All these key ingredients encourage broader investigation of the fundamental aspects of statistical mechanics21 and accelerate development of applications in mechanical sensing24 and thermodynamical engines26. Recently, the highly unstable motion of a levitating particle in the cubic potential has been analysed28 and experimentally verified30 in the overdamped regime. It was demonstrated that the mean particle position, induced by the initial position uncertainty, increases faster than that uncertainty. Atypically, the position distribution maximum shifts in the opposite direction to the mean. These investigations have already been stimulating experimentally verifiable thermodynamical consequences27.Stochastic levitating optomechanics in vacuum is a dynamically expanding experimental platform with a unique potential to test and exploit strong nonlinear motional effects without any friction, and bring them close to the quantum domain. This uniqueness arises from the possibility to combine manipulation of the optical trapping potential by a spatial light modulator, inducing new unexplored nonlinearities, and fast optical measurement to verify rapid transient effects using modern optical detectors. At low pressure, it allows direct observation of stochastic underdamped mechanical phenomena33. We observe that the only requirement for a reliable experimental observation is a reduction of initial velocity uncertainty. Importantly, we indicate that both the velocity and acceleration distributions’ maxima, stimulated by initial position uncertainty, shifts normally in the same direction as the velocity and acceleration mean. It is a crucial step to further accumulate such uncertainty-induced phenomena, and later use them in the aforementioned applications.In the low pressure limit the particle is deep in the underdamped regime so that the instantaneous particle speed and acceleration become new transient quantities to be first explored and later exploited for applications. In this paper we simulate and analyse nonlinear ballistic effects for instantaneous velocity and acceleration induced by the initial position uncertainty, and predict the experimental regime where such phenomena are visible using parameters for current setups in laboratories30, and aim to achieve them close to the zero-damping deterministic limit. First, we describe the properties of the stochastic motion of an underdamped Brownian particle in the unstable cubic potential. Second, we explore the high pressure limit in comparison to the over-damped approximation30, and low pressure limit with comparison to the zero-damping deterministic approximation. The dynamics of the damped Brownian particle in the cubic potential T is the absolute temperature, 11, therefore we use the averaged velocity averaged acceleration dt to allow the computation of the averaged quantities. The details of the time scales, and convergence are presented in the Methods. Considering that To understand the low-pressure nonlinear effects, we must distinguish them from the already measured high-pressure overdamped limitmentclass2pt{minimmentclass2pt{minim28,28. The direct integration of Eqs.  reach the statistics of d in Eq. , and  with a constant signal-to-noise ratio. Fig.30 for over-damped dynamics. A new feature arises for the-most-likely speed, where both in high and low friction regime, the maximum of average and instantaneous velocity distribution shifts alongside the potential force as seen by the red arrow in Fig. To complete the analysis, the dynamics of a levitated particle in cubic potential needs to be characterised also by means of the most-likely position, speed and acceleration. The maxima of the respective distribution are not only a directly measurable characteristic of unstable motion, but they also open a new local use of Maxwell’s deamon in stochastic thermodynamicsThe shift of the most-likely acceleration is alongside the potential force, as shown in Fig.27, where the fast and unstable dynamics of x drags all the trajectories quickly to the divergence.The minimal requirement to observe a shift of the maximum in the acceleration distribution, is a non-zero uncertainty in the initial velocity state, which mediates from the sharp tail on the right of the distribution, making its maximum hard to identify. Moreover we report that the most-likely acceleration, when the dynamics starts at different initial position 12, together with the parameters of cubic optical potential30 directly motivating our predictions to be experimentally tested to witness these new nonlinear mechanical phenomena. However, the analysis of the presented regime is also applicable to other underdamped experiments33. The first point worth experimentally verifying, shown in Figs. 30  where the uncertainty-induced effect with increasing We have demonstrated the uncertainty-induced instantaneous speed and acceleration of a levitated particle in the highly unstable cubic potential. Moreover, all the simulations have been performed using parameters of current underdamped experimentsAll these experimental tests will verify new underdamped transient effects of a particle living at the edge of instability, paving the way to explore highly nonlinear stochastic phenomena. Future targets comprise of quantum mechanical analysis of deeply underdamped and highly cooled particles in the unstable cubic potential, initially close to the mechanical ground state.q being the dimensionless position, l the position re-scaler, 30, and then focus attention on the positive part of the potential, of length P belonging to it, with coordinate . With this point one can build a straight line passing through the origin and the chosen point, and notice that it forms a triangle containing the piece of potential up to point P chosen. The angle between the origin and the hypotenuse is then given by the stiffness k of the potential. Subsequently, the position re-scaler can be calculated as A denotes the area of the triangle previously built, and the factor 2 comes from the fact that we extend the calculation to the whole domain of the cubic potential. The re-scaler l calculated, now has a clear dependence on the stiffness of the potential T, and it varies between To numerically simulate the Langevin dynamics described in Eq.  using rem has been absorbed by Following the re-scaling procedure explained before, the Langevin equation can be transformed intoSNR  show no deviation from SNR undergo an initial decrease (for velocity and acceleration), they all converge to Throughout the paper we have always assumed to have a particle initially positioned at the inflection point s), Eqs. –16) sti sti\docuich Eqs. –(16) no ich Eqs. . While t


This Special Issue is focused on the use of modern food processing technologies to retain the highest possible content of health-promoting compounds in raw foods. Additionally, these technologies enable the amount of salt and preservatives added to foods to be reduced. These additives are known to be prejudicial to human health, and are associated with a variety of health problems. This collection includes four reviews and six original research articles ,7,8,9,10Power ultrasound is another technology that can be used alone or to improve the efficiency of traditional food processes. Apples were sonicated before drying, which sped up the drying process and improved the product quality . One oth2 preservative, added to wine at different stages of wine production. The review showed minimal impact of pulsed electric fields (PEF), HPP and ultrasound on the quality of the wine [Another review is focused on the potential application of non-thermal technologies to stabilize wines, as an alternative to the SOthe wine . A compathe wine .Sulforaphane exhibits a whole range of unique biological properties, and is present at 10 to 100 times higher levels in broccoli sprouts than in most mature plants. An interesting study demonstrated how to retain the sulforaphane content of broccoli sprouts during various processing steps  . AnotherAn optimization study of the extrusion process of whole black-grained wheat flour, to improve the physical properties and maximize the nutritional components, is published in this Special Issue . A revie


To gain a deeper understanding of consumers' brain responses during a real-time in-store exploration could help retailers to get much closer to costumers' experience. To our knowledge, this is the first time the specific role of touch has been investigated by means of a neuroscientific approach during consumer in-store experience within the field of sensory marketing. This study explores the presence of distinct cortical brain oscillations in consumers' brain while navigating a store that provides a high level of sensory arousal and being allowed or not to touch products. A 16-channel wireless electroencephalogram (EEG) was applied to 23 healthy participants , with interest in cosmetics but naive about the store explored. Subjects were assigned to two experimental conditions based on the chance of touching or not touching the products. Cortical oscillations were explored by means of power spectral analysis of the following frequency bands: delta, theta, alpha, and beta. Results highlighted the presence of delta, theta, and beta bands within the frontal brain regions during both sensory conditions. The absence of touch was experienced as a lack of perception that needs cognitive control, as reflected by Delta and Theta band left activation, whereas a right increase of Beta band for touch condition was associated with sustained awareness on the sensory experience. Overall, EEG cortical oscillations' functional meaning could help highlight the neurophysiological implicit responses to tactile conditions and the importance of touch integration in consumers' experience. Aware that the consumer's behavior is the complex result of a multifaceted interaction between the organism and its environment  of touching products  to measure the involvement of senses, specifically touch, elicited by sensory cues during customers' store exploration. The use of an EEG wireless technology implies the capability to record brain waves at very small-time intervals, in the order of milliseconds, while consumers are exploring space. This is extremely valuable, considering the speed at which we acquire information through our senses and the speed of our thoughts (subseconds). Besides a good temporal resolution, within the neuromarketing field, the advantages of using EEG have been previously highlighted also by other scholars . In addition, customers are also given the chance to easily touch and try all the products exposed, thus providing a higher level of sensory arousal. With the purpose of exploring a single sense experience and the relative impossibility to use the others, selective sensory deprivation supports  were applied on the person during a free shopping experience. The field of cosmetic was functional for exploring sensory integration/deprivation for two reasons: (i) the intrinsic features of cosmetic items are known to appeal to all five senses; (ii) if, during the analysis of a cosmetic product, consumers' senses are positively activated, this will possibly result in a positive appraisal of the perceived quality of the product and lead to an approach behavior due to the product's emotional connection  observed during different conditions of sensory stimulation inside the store and their functional meaning were considered in order to understand the relevance of the presence and the absence of tactile stimulation in consumer experience. Within the neuroscientific Dual Systems model . Inclusion criteria were as follows: (1) interest in cosmetics, (2) not being a frequent customer of the store used as the experimental setting, and (3) normal or corrected-to-normal hearing and vision. Exclusion criteria were the presence of sensory and cognitive deficits, a history of psychiatric or neurological diseases, and ongoing concurrent therapies based on psychoactive drugs that can alter central nervous system functioning. No compensation was provided for their participation in the study. One participant was excluded from the statistical analysis due to the high presence of movement artifacts.The study has been designed following the principles of the Declaration of Helsinki. Procedures and methods were approved by the Ethics Committee of the Department of Psychology, Catholic University of the Sacred Heart of Milan, Italy. Subjects gave written informed consent for their participation in the study.Participants have been introduced in a neutral point of the store (warehouse), where non-invasive EEG sensors were placed, and then they were guided inside the store. Sensory deprivation supports were applied before starting the store exploration. Subjects were equally divided into two conditions based on the senses that subjects could use: (1) Touch (participants can use touch); (2) Non-Touch . The sight was kept as a constant to let participants explore the ecological setting (that is the store) freely. They were assigned to only one condition in order to avoid order and habituation effects. After the placement of the sensory deprivation supports, subjects were informed that they had time from a minimum of 5 min to a maximum of 15 min to explore the store freely, according to their experimental condition. During the whole experiment, EEG cortical activity was recorded.via an EEG wireless System (Live-Amp) and processed via Analyzer2 software . The montage included 15 active electrodes  indices.A set of mixed repeated measures ANOVAs with independent within-factors Region Of Interest (ROI)  and Laterality (2: Left and Right) and as between factors the Condition related to sense of touch isolation or deprivation (2: Touch vs. Non-Touch) was applied on dependent EEG measures. This mixed repeated measures ANOVA was performed for each frequency band  in order to highlight the differences between two conditions: the isolation of a sense  and the deprivation of the same . F = 5.49, p = 0.012, η2 = 0.33]. Post hoc pairwise comparisons revealed increased Delta power in left frontal area (F3) for the Non-Touch condition compared to the Touch condition   = 7.77, p = 0.004, η2 = 0.41]. Post hoc pairwise comparisons revealed increased Theta power in the left frontal area (F3) for the Non-Touch condition compared to the Touch condition   = 3.82, p = 0.038, η2 = 0.25]. Post hoc pairwise comparisons revealed increased Beta power in the right frontal area (F4) for Touch condition compared to Non-Touch condition [F = 5.11, p = 0.045, η2 = 0.31)  .The aim of the present study was to explore customers' cortical response (brain oscillations) related to the presence and the absence of tactile experience during a free in-store navigation. Indeed, to isolate different conditions of sensory fruition helped us to deepen the knowledge on the role of the sense of touch in consumers' experience.The performed frequency bands analysis contributed to underline the following main results connected to the role of frontal brain areas when cognitively processing the in-store exploration (in this case corresponding to the fruition of cosmetic products) with and without tactile sensory insights. Firstly, a higher presence of Delta and Theta band activity mainly lateralized on left frontal areas for the Non-Touch condition was found. In this condition, participants could explore the store freely using the senses of sight, hearing, and smell, yet with the instruction not to touch products. Secondly, an augmented Beta band power for the Touch condition was detected in the right anterior regions.According to previous literature, this evidence can be discussed mainly focusing on the cognitive aspects related to the functional meaning of the detected frequency bands. Regarding our first results, the high presence of low-frequency bands in frontal anatomical structures could be considered mainly as a marker of cognitive operations involved during the situation in which a tactile exploration of products was not allowed. Indeed, starting from the cognitive features related to the manifestation of Theta band, previous studies identified that complex bimodal sensory stimulation increase the frontal processing in this band range  has ancestral roots and covers a central role within the sensory system, both phylogenetically and ontogenetically. Previous evolutionary studies show that even infant macaque monkeys prefer to physically approach a surrogate soft cloth mother (closer to their haptic representation of mother) than a wire mother Harlow, . SpecifiIn the field of neuromarketing, the importance of touch, especially for clothing and fabric retail, has previously been recognized, and retailers use different marketing strategies aware and guided by the importance of touch (Citrin et al., However, so far, no previous studies investigated the specific role of touch by means of EEG power spectral analysis within the field of sensory marketing, specifically when exploring cosmetic products. For this reason, future research will need to deepen our insights and investigate if they can be broadly transferred into the wide context of sensory marketing, in which we believe that touch can cover a discriminating role for consumers' experience exploring cosmetic products.Moreover, further studies will be necessary to investigate the lateralization effect we found in relation to the different frequency bands and the possible specific role of Delta and Theta oscillations in left frontal structures as a possible marker of processing the emotional valence of consumers' experience. Indeed, frontal and prefrontal cortex lateralization has been previously related to cognitive control over emotional stimuli and emotional behavior in basic research and in studies on cross-modal integration of emotional cues (Balconi et al., So far, to our knowledge, there is only one basic research on the valence of tactile stimulation showing an increase in right temporoparietal and frontal electrodes in the beta range for pleasant products compared to unpleasant ones (Singh et al., To summarize, we examined customers' cortical oscillations recorded with the aid of an EEG wireless device during a free in-store navigation. The partial isolation of different sensory fruitions helped us to discuss the results in the light of the presence or deprivation of the ability to touch cosmetic products, thus exploring the role of this sense in consumers' experience.The performed analysis allowed to determine some main results connected to the presence of specific frequency bands in frontal brain areas when exploring a store with and without the chance to acquire tactile sensory insights. Firstly, a higher presence of Delta and Theta band activity on frontal areas for the non-touch condition was found and interpreted as a need for cognitive control perhaps caused by an incomplete perceptual understanding deriving from the absence of the sense of touch. Secondly, an increase of Beta power for the touch condition was detected in the brain anterior regions, suggesting a cognitive state of sustained attention and enhanced network activity of higher-order somatosensory areas encoding perhaps the sensory aspects of the stimuli. The salience of touch was finally discussed at the light of its evolutionary importance and as a key sense able to provide consumers a complete and coherent perception of their experience.via EEG technology. To our knowledge, only one study focused on the evaluation of pleasure–displeasure induced by the application of a cosmetic product in terms of cerebral activation exploiting near-infrared spectroscopy (Tanida et al., Despite our study providing novel results exploitable in the sensory marketing field, it also presented some limitations to take into account by future studies. Since our sample size was limited and not balanced for gender variable, it is possible that some gender differences be considered in terms of experiencing the cosmetic store exploration; in one previous study on cosmetics and brain activation, only women were considered (Tanida et al., Overall, the potential of using neuroscientific tools in sensory marketing is still not so widespread, and for this reason, we suggest that future studies could consider the use of EEG wireless device to explore the wider role of touch in consumers' experience in various ecological contexts.The datasets generated for this study are available on request to the corresponding author.The study involving human participants was reviewed and approved by Department of Psychology, Catholic University of the Sacred Heart of Milan, Italy. The participants provided their written informed consent to participate in this study.MB, IV, RS, and LA contributed to the conception and design of the study. MB and LA wrote the first draft and each section of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.