In the target-controlled infusion (TCI) of propofol and remifentanil intravenous anesthesia, accurate prediction of the depth of anesthesia (DOA) is very challenging. Patients with different physiological characteristics have inconsistent pharmacodynamic responses during different stages of anesthesia. For example, in TCI, older adults transition smoothly from the induction period to the maintenance period, while younger adults are more prone to anesthetic awareness, resulting in different DOA data distributions among patients. To address these problems, a deep learning framework that incorporates domain adaptation and knowledge distillation and uses propofol and remifentanil doses at historical moments to continuously predict the bispectral index (BIS) is proposed in this paper. Specifically, a modified adaptive recurrent neural network (AdaRNN) is adopted to address data distribution differences among patients. Moreover, a knowledge distillation pipeline is developed to train the prediction network by enabling it to learn intermediate feature representations of the teacher network. The experimental results show that our method exhibits better performance than existing approaches during all anesthetic phases in the TCI of propofol and remifentanil intravenous anesthesia. In particular, our method outperforms some state-of-the-art methods in terms of root mean square error and mean absolute error by 1 and 0.8, respectively, in the internal dataset as well as in the publicly available dataset. In recent works, clinical pharmacology research has focused on identifying best practices for ensuring patient safety by maximizing anticipated drug effects while reducing drug-induced side effects. This tradeoff is particularly important when developing anesthetic delivery patterns . SpecifiIn clinical practice, total intravenous anesthesia (TIVA) is primarily used for drug delivery. To control the depth of anesthesia (DOA), target-controlled infusion (TCI) of propofol for TIVA is one of the most widely used techniques in anesthesia and sedation. To accurately administer medication, the DOA must be continuously measured to assess the patient’s level of anesthesia and reduce drug-induced side effects. In anesthesia operations, the bispectral index (BIS) is the preferred metric for clinicians to monitor the DOA of patients. The BIS is a noninvasive system that calculates electroencephalography values ranging from 0 (no brain activity) to 100 (fully awake patients) as dimensionless numbers ,3.To achieve precise drug administration, various models that relate the rate of drug infusion to the DOA in humans have been proposed in recent decades. The traditional approach to target-controlled infusion is based on a pharmacokinetic and pharmacodynamic model (PK-PD) that uses propofol doses to predict propofol effect-site concentration (Ce) as an alternative to the DOA ,5. AlthoIn recent works, Lee et al. proposed a deep learning model for investigating the influence of the infusion histories of propofol and remifentanil and patient physiological characteristics on the DOA . In addiTo address the above two limitations, this paper proposes a new deep learning framework for DOA prediction according to drug infusion history. The key idea of the proposed approach is to use domain adaptation to address the issue that the data distribution varies across patients. Furthermore, knowledge distillation is adopted to learn the intermediate feature map of the teacher model by using the prediction model, which is obtained by the historical moment BIS as the extra feature. Specifically, an adaptive recurrent neural network (AdaRNN)  is appliA new deep learning framework for DOA prediction according to drug infusion history is proposed to overcome the shortcomings of the existing DOA prediction methods.A modified AdaRNN algorithm is developed in our framework for DOA prediction to address the issue of distribution shifts in the physiological characteristics of different patients.A feature-based knowledge distillation framework is proposed for time series prediction, which allows the prediction model to obtain more useful information. This framework enables the intermediate features of the model to accurately represent the DOA, thereby ensuring reliable and stable output results. To the best of our knowledge, this is the first time that knowledge distillation has been implemented to predict the DOA.Extensive experimental results show that our method has better performance than the existing DOA prediction methods on a publicly available dataset during all periods, including the induction, maintenance, and recovery periods.The main contributions of this work can be summarized as follows:The remainder of this paper is organized as follows. In The traditional TIVA method is the PK-PD model ,5, whichWhen the distributions of the training, validation, and test sets are markedly different, domain adaptation (DA) or domain generalization (DG) is usually used to reduce these distribution differences. Yu et al.  proposedDeep neural networks are adept at learning multilayer feature representations with increasing levels of abstraction. Bengio et al.  proposedDifferent from the previous DOA prediction technique, our proposed method uses domain adaptation and knowledge distillation techniques to further improve the DOA prediction accuracy. Specifically, for domain adaptation, a modified AdaRNN is utilized to enhance the generalizability of the proposed model. For the knowledge distillation, the teacher network aims to provide intermediate feature representations of the BIS, while the student network aims to predict the BIS more precisely by transforming the intermediate features in the teacher’s intermediate feature domain. AdaRNN is mainly adopted to solve the temporal covariate shift (TCS) problem. AdaRNN can characterize the temporal distributions and capture the long-term time dependencies in time series. In general, AdaRNN includes temporal distribution characterization (TDC) and temporal distribution matching (TDM). In order to take advantage of AdaRNN for our task, the TDM approach in AdaRNN is modified to efficiently adapt the proposed network. i-th time series segment.According to the maximum entropy (ME) principle , it is rK. If the model learns from the worst-case scenario, it has better generalizability to the unseen test data. This assumption has been verified in theoretical analyses of the time series models [Algorithm 1: Temporal distribution characterizationThe goal of the optimization problem (1) is to maximize the difference in the distribution in each period by determining s models ,30, shows models  with a sM has better generalizability on the test data than other methods that only rely on the local or statistical information.Once the TDC approach determines the time periods to be learned, the temporal distribution matching approach is required, which learns the public knowledge shared across each time period by matching the distributions of different time periods. Thus, the learned model The TDM prediction loss In contrast, the minimization of Formula (2) can learn only the predictive knowledge for each period and cannot reduce distribution differences between each period to exploit common knowledge. A primitive approach is to use some distribution measure of distance as a regularization term for each period pair research ,33 in whresearch  applied Since each hidden state only contains the part of the distribution information of an input sequence, Du et al.  adopted t, and V denotes the V hidden states of the RNN.Given a period pair By integrating (2) and (3), the final temporal distribution matching loss can be formulated asg denotes an activation and normalization function and ⊙ stands for the elementwise product.Du et al.  used a bSince response-based knowledge is mainly used in classification tasks and our task is a regression task, we use a knowledge distillation framework based on feature representations to enable the student network to learn better intermediate feature representations from the more informative teacher network. Previous work on feature-based knowledge distillation ,26,27 haFirstly, we introduce the historical moment BIS data  in the teacher network to learn the DOA representation. The student network uses the propofol and remifentanil infusion histories as the inputs. Secondly, to propagate the knowledge from the teacher network to the student network more effectively, the hidden states of the output of the student RNN are used to mimic the hidden states of the output of the teacher RNN in order to improve the prediction performance of the student network. Moreover, the feature map of the bottleneck of the student network is adopted in our framework to mimic the feature map of the bottleneck of the teacher network. In this situation, the similarity between the predictions of the student and teacher networks will be increased.z, which denotes the input data to the teacher network and includes historical BIS data, and x, which includes the propofol and remifentanil infusion histories. Since the teacher network introduces the historical moment BIS data, the output hidden states of the teacher RNN contain more richer temporal information about the DOA, thus allowing the model to predict the BIS change trends more accurately. Therefore, the output hidden states of the student RNN is utilized in our proposed framework to imitate the output hidden states of the teacher RNN. In contrast to the TDM regularization term, we imitate the feature representations of the output hidden states of the teacher RNN to learn the temporal relationships of the teacher RNN for improving the prediction performance of the student network. Therefore, the feature imitation loss is introduced. Specifically, we use a gated recurrent unit (GRU) as the basic unit of the RNN module. Assume that z and x, For the RNN output feature, only the effects of the propofol and remifentanil on the BIS are considered. However, according to the experimental results in , the druTo obtain more effective feature representations for the teacher network, combining (2) and (3), the teacher loss can be formulated as follows:The loss of the prediction network has three components: the predicted loss, the TDM regulation loss, and the distillation loss  (accessed on 1 January 2022). database, which is collected and registered by the Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul Metropolitan Medical College, Seoul, Republic of Korea [https://vitaldb.net (accessed on 1 January 2022). In addition, the VitalDB database is a comprehensive dataset that includes the intraoperative biosignals and clinical information of 6388 surgical patients. It not only contains the demographic data , but also contains more than 60 procedure-related clinical indicators of basic equipment used in operating rooms, such as patient monitors, anesthesia machines, and BIS monitors, as well as target-controlled infusion pumps, cardiac output monitors, and local oximeters. The BIS data include the BIS values and the signal quality indices collected by BIS VISTA at 1 s intervals. The propofol and remifentanil data include the cumulative infusion volumes, Ce values, and plasma concentration (Cp) values of the two drugs collected by target-controlled infusion pumps at 1 s intervals.The dataset used in the experiments is the VitalDB  that is not available to the student network. Our network includes an RNN module, a bottleneck module, and an output module. We use a GRU as the basic unit in the RNN module because of its high performance in practical applications. The bottleneck module focuses on learning the anesthetic effects of propofol and remifentanil on patients with different physiological characteristics. In our work, the bottleneck module consists of two fully connected networks. After the bottleneck network, our proposed model learns the combined dynamic (propofol and remifentanil drug dosage information) and static  information. Finally, the predicted BIS values with a fully connected network are obtained.https://github.com/chanwendy/Domain-adaptation-DOA-prediction (accessed on 25 June 2023).In the experiments, PyTorch 1.7.1 is used to implement our proposed model with a 24 GB NVIDIA TITAN RTX GPU and an Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50 GHz with 32 GB RAM and 128 GB SSD, and our teacher and student networks are trained simultaneously. The network is trained by an Adam optimizer with an initial learning rate of 0.005. The learning rate is decayed every 10 epochs by a factor of 0.1, and the weight clipping is utilized to prevent gradient explosion. We use a batch size of 256 in the training phase and a batch size of 128 in the validation and testing phases. The balance parameter The experimental results are expressed as the regression errors. To evaluate the regression performance of our proposed framework, the predicted values are assessed according to the following commonly used evaluation metrics ,43: the In the experiment, we conduct a comprehensive analysis of mean comparisons across all periods, including the induction period, the maintenance period, and the recovery period. The compared methods are, respectively, the baseline method , FEDformTo further validate the generalization ability of our model and the applicability of our model under different measurements of DOA type, we perform the training and testing on our in-house dataset. This dataset uses the Narcotrend index (NI) as the measurement for the depth of anesthesia. Compared with the BIS, the NI has a larger response gap for patients of different age groups and different disease states, creating a larger domain shift. The main difference between the VitalDB dataset and our in-house dataset is shown in In the experiment, we randomly select 24 cases as the training set, and others as the test cases. The experiment results are shown in t test and F-test, with these compared methods in both public and in-house datasets, and the experimental results are shown in p-values for the pair-t test and F-test are less than 0.05 in both datasets, which indicates that our proposed model has better performance than other compared methods from the viewpoint of statistical test. There is only one case where the p-values for the Crossformer in the F-test are greater than 0.05. The main reason may be that the Crossformer takes into account the information interaction between multiple variables and achieves relatively good stability. However, from the results in In addition, we perform some statistical tests, such as pair-−5 s per prediction of the BIS value. In addition, we compute model complexity between our model and the compared methods; the results are shown in −5 per second, which can fully meet the clinical requirements. Therefore, our proposed model can be suited for real-time monitoring applications.In a real clinical environment, the closed-loop target-controlled infusion system is divided into two main parts, which are the prediction of DOA and the control of drug dosage according to the DOA, respectively. Our work mainly addresses the first part of predicting the DOA based on drug efficacy, and the part of controlling the dose based on the DOA is our future work. The prediction of DOA is commonly performed at a frequency of one prediction per second, aiming to estimate the BIS value. To simulate this inference process and evaluate the real-time capabilities of our model, we set the batch size to one in the experiments. Remarkably, our findings reveal that our model achieves a remarkable inference time of only 1 × 10To verify the effectiveness of the neural-network-based importance evaluation method, a comparative experiment with our TDM method and the boosting-based TDM method is derived to evaluate the respective performance. The experimental results of the ablation study are illustrated in According to Accurate anesthesia depth prediction is crucial in clinical surgeries. In this paper, a new deep learning framework is proposed that combines the domain adaptation technique and the knowledge distillation technique to improve the generalizability and prediction accuracy of anesthesia depth prediction models. Specifically, different from previous works, the proposed framework first considers the problem of distribution shifts among patients with different physiological characteristics data and trains the model from cases with the largest distribution differences in order to enhance the generalizability of the proposed model. Moreover, to improve the accuracy of anesthesia depth prediction, the BIS values of the historical moments are introduced in the teacher network of the proposed method to extract the intermediate features, representing the anesthesia depth accurately. After that, the prediction network is utilized to mimic the feature representations of the teacher network. Experimental results have shown the effectiveness of our proposed framework on a publicly available dataset compared with the traditional response model and the baseline model.In future studies, more information on historical drug doses or patient physiological indicators related to clinical anesthesia, such as plasma concentration, effect-site concentration, target concentration, and infusion rate, can be added to our model to further improve the prediction accuracy. Furthermore, an attention mechanism can be incorporated to allow the model to focus on anesthesia depth features that are effective for predicting anesthesia depth. Finally, due to the imbalance in the quantity of data . Thus, addressing data imbalance in our task also can improve the performance.


Embedding fiber optic sensors (FOSs) within parts for strain measurement is attracting widespread interest due to its great potential in the field of structural health monitoring (SHM). This work proposes a novel method of embedding FOSs using capillaries within solid structures and investigates fiber positions and orientation uncertainties within capillaries of different sizes and their influences on strain measurement accuracies. To investigate how the fiber positions and orientation variations influence strain measurement accuracy, both analytical and numerical models are utilized to predict strain distributions along embedded fibers at different positions and with different orientations within the specimen. To verify the predictions, a group of specimens made of Aluminum 6082 was prepared, and the specimens in each group had capillaries of 2 mm, 4 mm, and 6 mm diameters, respectively. Fibers were embedded within each specimen using the capillaries. Four-point bending static tests were conducted for each specimen with embedded FOSs, performing in situ strain measurement. Subsequently, the specimens were partitioned into several pieces, and the cross sections were observed to know the real positions of the embedded fiber. Finally, the strain predictions at the real locations of the fiber were compared with the measured strain from the embedded FOSs. The predicted strain distributions as a function of the fiber positions alone and as a function of both the fiber positions and orientations were compared to assess the influence of fiber orientation change. The results from a combination of analytical, numerical, and experimental techniques suggest that the fiber position from the capillary center is the main factor that can influence strain measurement accuracies of embedded FOSs, and potential fiber misalignments within the capillary had a negligible influence. The fiber position-induced measured error increases from 10.5% to 18.5% as the capillary diameter increases from 2 mm to 6 mm. A 2 mm capillary diameter is able to lead to the lowest measurement error in this study and maintains ease of embedding. In addition, it is found that the measured strain always lies within a strain window defined by the strain distribution along capillary boundaries when there are no cracks. This can be further studied for crack detection. Structural health monitoring (SHM) is a process aimed at providing accurate and timely information concerning structural health condition and performance. An SHM system can perform real-time monitoring and makes non-destructive testing an integral part of structures, which can reduce maintenance costs and increase structural life safety in aerospace and civil engineering . SensorsWith the development of novel manufacturing technologies, new fiber-embedding techniques are emerging. Additive manufacturing (AM) featuring layer-wise deposition of layers can be used in fiber embedding. Strantza  fabricatHowever, some challenges can still be expected from this fiber-embedding technique. When a fiber is embedded into a specimen using capillaries, because the diameter of a capillary is normally larger than that of a fiber for ease of embedding, the fiber may not be placed at an exact position. Ideally, the fiber is placed along the capillary center; however, it could be anywhere within the capillary in reality as shown in The main objective of this research paper is to investigate the influence of possible variations in the position and orientation of a distributed sensing fiber placed within a predefined capillary on strain sensing accuracy. Geometries of used specimens are simplified to a beam structure, and four-point bending tests are performed, as shown in the An analytical model based on the Euler–Bernoulli beam theory was developed to predict strain distribution along the embedded fiber, which is assumed to be along the capillary center shown as the ideal position of fiber in b, h, L, and d denote the specimen width, height, length, and diameter of the capillary, respectively. The spacing a of the load introduction points results in a constant internal bending moment, along the central part of the beam denoted by w. The dimensions of the specimen are given parametrically, as the analytical model for this study was set up in parametric form.In order to study the potential influence of sensor position variations on measurement accuracy, a reference structural geometry was needed. As the distributed sensing fibers only measure normal strains, a simple beam under four-point bending loading is a logical choice, as it creates a region along the beam where normal strains should not vary in the length direction. This would permit the observed scatter in strain measurements along the length of a FOS from later experiments to be more easily correlated to variation in its position and orientation. The reference geometry used in this study is illustrated in w in M is the bending moment and is given by F is the force exercised on each of the loading, a is the distance between the upper and lower loading pins as shown in y is the distance from the neutral axis, and I is the moment of inertia around the neutral axis. As this section of the specimen was under pure bending loading, there are no other stress components, allowing the normal strain E:In order to investigate the influence of the fiber position and orientation on the expected strain, a simple analytical model was made. The model focused on the central portion of the specimen (denoted by width m theory :(1)σx=MyThe Euler–Bernoulli bending theory in the form given in Equation  is only In order to apply Equation  to a fiby direction For the four-point bending specimen, the normal stress in the quations  and 4)  y directUsing Hooke’s law and the equations above, the normal strain in the y and arbitrary orientation Using this Equation , the norR within the capillary can be seen in y in this figure represents the distance from the neutral axis of the beam to the nominal position of the FOSs.Because the capillary size is much larger than that of the fiber, the embedded fiber can be at any location within the capillary, and any orientations can also be possible as long as it is within the fiber bending limit (bending limit of the fiber used in this study is 6 mm ). In addy-position , th, th12), The specimens tested in this study have a slenderness ratio which violates the assumption in the Euler–Bernoulli bending theory used in the analytical prediction model. Therefore, a numerical model was established using ABAQUS to investigate how much this violation may influence strain prediction accuracy.The established model includes the specimens made of Aluminum 6082-T651 alloy and adhesive resins within its capillary. The material model is linearly elastic, and the material properties can be seen in In order to model the bonding of the adhesive resin within the capillary of the specimen, a single part was divided into two pieces, each of which was assigned a separate section and material mentioned above. The two pieces represented the aluminum specimen with a capillary and the adhesive resin separately. The concentrated force was exercised on the upper pin supports. For boundary conditions, the lower two supports were constrained for all degrees of freedom. The upper pin supports were under a displacement/rotation constraint to limit their lateral movement along the X and Z directions. In addition, the movement of the central plan of the specimen along the X direction was confined. The boundary conditions can be seen in In order to investigate fiber position inaccuracy within the specimen capillaries, FOSs were embedded within specimens containing capillaries, and the specimens were subjected to four-point bending loading. Strain measurements from the fiber optic sensors were captured during loading, and post-mortem sectioning of the specimens was performed in order to accurately characterize the location of the FOSs within the capillary at various points along the beam length. Details of the manufacturing, testing, and post-mortem sectioning of the specimens are described in the remainder of this section.Aluminum 6082-T651 specimens were fabricated using traditional subtractive machining processes. The use of subtractive manufacturing processes over additive manufacturing processes was chosen to avoid potential variations in the capillary and overall specimen geometry that are known to occur in metal additive manufacturing processes. Specimens with different sizes of capillary are provided in After specimen fabrication, the following procedures were conducted to embed a fiber within the capillary of each specimen. First, a commercially available optical fiber  was placed within the specimen capillary. The optical fiber has a cladding diameter of 125 In order to evaluate the strain measurement accuracy of the embedded FOSs, all specimens were subjected to static loading in a four-point bending setup, which can be seen in The fiber-embedding process used in this study could not ensure an accurate fiber location within the capillary. As a result, after the testing of the specimens was completed, they were sectioned at various positions along the length of the specimen in order to assess the precise location of the optical fiber within the capillary. Sections of the specimens were observed with a laser microscope, which contained built-in software that could be used to precisely measure the location of the optical fiber within the capillary.A cross section of a 2 mm diameter capillary with straight fibers in the Aluminum 6082 specimen was observed using the Keyence Laser Scanning Confocal Microscope , which is presented in Comparison of the analytical, numerical, and test results shows that violation of the analytical model assumption does not have an influence on the prediction accuracy of the analytical model. Since it is much easier to plot the axial strain along the fiber length when the fiber is wavy with the analytical model compared with the numerical model, the analytical model is necessary for investigating the influence of fiber orientations on the measured strain distributions. In addition, the results of the numerical model are also used in establishing the analytical model as shown in the subsequent section.y and fiber orientation y has a much larger influence than the fiber orientation. The fiber orientation’s influence is not considered when using the measured strain for crack detection.When an optical fiber is embedded within a specimen using capillaries, fiber positions with respect to the specimen centroid and fiber orientations may vary along the fiber. It is interesting to know whether both factors influence strain measurement, and how much their influences are. Analytical strain distribution along the fiber can be predicted using Equation . The rigEquation  is from When assuming that the nominal strain distribution is the strain along the capillary center, variations of the fiber positions within the capillary lead to deviations of the measured strain from the nominal strain. The deviation can be quantified by relative measurement errors (RME), which can be defined by the following equation:When an optical fiber is embedded within a specimen using capillaries, both the fiber positions and orientations may vary along the fiber. An analytical model was established to investigate the influence of fiber positions and variation change on strain distribution. The model was subsequently verified by the four-point bending static test. Based on the results of the analytical model and static four-point bending test, the following conclusions can be made.1.When an optical fiber is embedded within a specimen using capillaries, both the fiber positions and orientations may vary along the fiber. Variations of the fiber orientations have a negligible influence on the strain measurement compared with fiber position variations. Therefore, fiber position uncertainty is the main factor influencing strain measurement accuracy.2.Even the positional uncertainties of the embedded fiber within capillaries can cause strain measurement uncertainties; the measured strain distributions lie within the strain windows defined by the strain distribution along the capillary boundary when there are no cracks in the specimen. It is possible to perform crack detection using the strain window.Because the measured strain distribution always lies within the strain window when there are no cracks in the specimen, it is possible to compare the measured strain distribution against the strain window. Any exceedance of the measured strain beyond the strain window indicates the existence of an anomaly, such as a crack. It is possible to perform crack detection based on this principle, which will be studied in a follow-up study.https://github.com/Yuzhe17/Measurement-accuracy-of-embedded-fiber-optic-sensors.git, (accessed on 17 January 2023)]. The code for building the analytical model can be seen in the GitHub repository [


Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy. Color imaging systems have been widely used in industrial manufacturing, medicine, geology exploration, art and other fields. Color characterization ,2,3 is aAccording to international standards, color characterization can be generally divided into two categories: spectral response-based and target color-based characterization methods ,12,13. Tlectance . In 2013lectance . Essentilectance . The prolectance .The accuracy of color space conversion is constrained by the regression model, the training samples and the feature extraction. The effectiveness of feature extraction is the key to improving the accuracy. We propose a new method for colorimetric characterization of color imaging systems based on KPLS. The method establishes the mapping relationship between the three-channel response kernel function of the color imaging system and the CIE1931 tristimulus values. Through canonical correlation analysis, the direction vector with the strongest correlation between input and output is determined. Based on different direction vectors, a multivariate nonlinear regression model is established. The method considers the relevance between the input vector and the output vector; this method can promote predictive ability when a feature vector is selected. It can effectively solve the multicollinearity problem caused by kernel function expansion, and eliminate irrelevant input variables. It makes the model more concise and stable. It is proved that this method is an effective colorimetric characterization method by using two nested cross-validation.The basic idea of the colorimetric characterization method for a color imaging system is to establish the mapping relationship between the device-dependent space RGB and the device-independent space XYZ, so as to realize the transformation model from RGB to XYZ. In order to obtain the colorimetric characterization mapping from RGB to XYZ, it is usually necessary to use a standard color chart as a measurement sample, and measure its three-channel response values and tristimulus values at the same time, so as to obtain a series of calibration data pairs of XYZ and RGB. In this study, the KPLS method is used for modeling research. Firstly, the RGB dataset is expanded via the kernel function, then the dataset is divided. Next, the PLS regression model is trained. Finally, using color difference as evaluation indicators, the trained model is used to predict on the test set and evaluate the performance of the algorithm. The colorimetric characterization method based on KPLS proposed in this paper is shown in A series of data pairs of Since the dimension of To enhance the accuracy of the conversion model between the source color space (RGB) and the target color space (XYZ), we use a kernel function to expand the feature vector for regression. Taking into account the correlation among the expanded feature vectors, we apply the PLS method. PLS regression is related to principal component regression. However, instead of finding a hyperplane of maximum variance between the response and independent variables, it finds a linear regression model by projecting both the predicted and observed variables to a new space. First, the data are normalized.relation . The firUsing the Lagrange multiplier method to solve, we obtain To meet the accuracy requirement, we continued to search for the second pair of direction vectors with the highest correlation. The residual matrices The color space conversion algorithm based on KPLS is shown in We chose three different color difference formulas according to different application domains to evaluate our model. CIEDE2000 is a widely used color difference formula that considers the needs of different fields . CIELAB To validate the proposed model, we first constructed a dataset using the RGB and XYZ values of the color samples. To verify the colorimetric characterization accuracy of the model proposed in this paper, we further divided the dataset into training and testing sets. We trained different colorimetric characterization models based on the training set, and evaluated these models based on the testing set. Finally, we compared the colorimetric characterization effects of different models. The experimental scheme is shown in The measured sample dataset is shown in The input vector is the kernel function expansion of RGB. Therefore, there is multi-collinearity among input feature variables. The Variance Inflation Factor (VIF) represents the magnification of model parameter estimation variances between non-collinearity and multi-collinearity. It can be calculated with Equation (17)The correlation between input and output vectors directly affects the accuracy of the regression model; therefore, we analyze the RGB kernel function expansion and its correlation with X, Y and Z. The absolute value of the correlation coefficient reflects the degree of correlation. When the absolute values of the correlation coefficient are greater than 0.6, that means high correlation between the two vectors. The Pearson correlation coefficients between each term and X, Y and Z are shown in In this paper, we perform kernel expansion on the input features of the KPLS model using the polynomial kernel function, the Gaussian kernel function and the root polynomial kernel function. We optimize and evaluate their hyperparameters. The hyperparameters include the order of the polynomial, To assess the performance of the KPLS model in colorimetric characterization, we use three different color difference formulas—CIEDE2000, CIELAB and CIELUV—to calculate the average color difference on the test set in the outer loop. We collect the test set data from each fold of the cross-validation. We obtain a regression analysis of 96 pairs of actual and predicted data. Polynomial regression model ,22 and nWe observed a large color difference point in the colorimetric characterization results of the KPLS model and the weighted nonlinear polynomial regression model. This point is the 60th pair of data, corresponding to the 6th fold of the ten-fold data. The sample distribution of the training and test sets for this group of data are shown in This paper studies a colorimetric characterization method for color imaging systems based on KPLS. The method uses device-dependent RGB space as input features and expands the feature vectors with kernel functions. According to feature analysis and canonical correlation analysis, a KPLS colorimetric characterization model for color imaging systems is established. The hyperparameters of the model are determined via nested cross-validation and grid search, and the color space conversion model is constructed. The experimental verification is carried out by using a Canon 1000D commercial digital camera to shoot the SG color card, and CIELAB, CIELUV, CIEDE2000 color differences are used for evaluation. The results show that the color difference of the KPLS colorimetric characterization model based on root polynomial function expansion is better than that of the weighted nonlinear regression model, the neural network model and the root polynomial regression model. The colorimetric characterization method for color imaging systems based on KPLS is an effective method with good prediction accuracy and nonlinear fitting ability, which can support the cross-media color management of color imaging systems well.


We present a case study involving wind energy permitting, weighing the effects of potential mortality caused by a hypothetical wind energy facility on the projected abundance of four imperiled bat species in the Midwestern United States.Bats provide ecologically and agriculturally important ecosystem services but are currently experiencing population declines caused by multiple environmental stressors, including mortality from white-nose syndrome and wind energy development. Analyses of the current and future health and viability of these species may support conservation management decision making. Demographic modeling provides a quantitative tool for decision makers and conservation managers to make more informed decisions, but widespread adoption of these tools can be limited because of the complexity of the mathematical, statistical, and computational components involved in implementing these models. In this work, we provide an exposition of the BatTool R package, detailing the primary components of the matrix projection model, a publicly accessible graphical user interface ( Pseudogymnoascus destructans, has contributed most to the decimation of many populations of cave-hibernating bats , except fecundities , probability of growth (percent of simulations with greater than the starting abundance at time step 0), probability of extinction (percent of simulations with ending abundance less than the quasi-extinction threshold), and the median time to extinction.s-year average annual growth rate between abundance estimates N(t) and t between scenarios i and j asi and j asWe define the https://code.usgs.gov/umesc/wthogmartin/BatTool with support for installation via R and the BatTool application is hosted publicly at https://rconnect.usgs.gov/battool [The BatTool application gives the user the ability to control all model inputs we have discussed, some of which are shown in the left panel of Fig. /battool , 25.Fig.https://code.usgs.gov/umesc/wthogmartin/BatTool.git”) and friends. The package can then be loaded with library(BatTool).The BatTool R package , 25 can The Shiny application can be launched using the function runBatTool. Within the application, pressing either Run Scenario button will initiate the demographic model to project the starting population abundance into the future using an ensemble of simulations, with the results summarized into a plot and table, shown in the right panel of Fig. There are a handful of primary functions in the R package for command line use. The run_demographic_model function projects an ensemble of simulations given abundance, growth rate, stressors and stochasticity model inputs, corresponding to pressing the Run Scenario buttons in the Shiny application. The project_population function produces one simulation consisting of an abundance time series, and is used within run_demographic_model. The summary_stats function summarizes the output into summary statistics over time while pop_plot creates a plot using the simulations from run_demographic_model (the table and plot in the app are slight variations of the output from these functions).After launching the app, the user can find a description of the model, definitions of all model inputs and outputs, instructions for saving and loading data, and more in the Information tab in the right panel.Once the Run Scenario button has been clicked, the Scenario 1/2 results tabs will populate with a table of summary statistics and a plot of the projected median abundance with uncertainty bounds. The model inputs and outputs for one sample simulation are also summarized and plotted (dashed black line); in addition, the projection matrix and its eigendecomposition for the simulation are displayed. The Scenario comparison results tab contains a summary of the difference between scenario 1 and scenario 2 simulations.All model inputs and outputs can be saved as CSV files contained in a ZIP file after the model has been run. The saved CSVs containing the direct mortality and white-nose syndrome vital rate reductions can then be uploaded into the app for future use. The projection plot can additionally be saved as a PNG within the app.Finally, the Population table & map tab gives the user the option to try the BatToolPro version of the Shiny application. Clicking the checkbox loads a sample population table where each row corresponds to a population and contains model inputs, including a unique pop_name identifier, a range of abundance and growth rate values, the year of arrival of WNS, and optionally latitude and longitude values for the leaflet map displayed in the app .The starting point for using BatToolPro is to click a row in the population table which selects a population to be modeled and updates the model inputs with the corresponding values in the table. The sample population table consists of fictitious populations that makes it easy for the first-time user to utilize the application. The R package includes this sample CSV  that can be used as a template for the user to format their data for upload within the app.The application is initialized with a set of default starting parameters arbitrarily chosen to serve as a template, so that the user can immediately press either Run Scenario button to run and produce results from the demographic model. The choice of the distribution of the starting growth rate is the most critical, determining the sets of vital rates that are possible for simulations.This tool is flexible enough to model the population dynamics of any North American bat. The user can upload their estimates on the species of their interest. But, because the tool infers vital rates from growth rates there is inherently parametric uncertainty. It is up to the user to employ knowledge of the system to anchor growth rates and therefore vital rates to reasonable values.Growth rates and vital rates vary among populations and can be difficult to estimate due to data limitations. Estimates are often made by aggregating populations into regional spatial scales, pooling data and thus hopefully making estimates more robust. However, even when assessing the growth rate across the entire species range there is uncertainty in estimates, and estimates change over time and as more data are collected. For these reasons, we do not include estimates of growth rates of real populations or metapopulation aggregates in the application, but inquiries about obtaining data on North American bats can be made to the North American Bat Monitoring Program.Version >2.0.0 of the BatTool R package incorporates a number of changes to the command line tools and graphical user interface described in . Model fMyotis lucifugus, M. septentrionalis, M. sodalis, and Perimyotis subflavus).In this case study we demonstrate a typical workflow using the BatTool which can serve as a user template. Our goal is to investigate the potential impacts of a hypothetical wind farm in the Midwestern United States on several regional bat populations, specifically four bat species of special conservation concern whose ranges overlap with the energy generation site  under Section 10(a)(1)(B) of the ESA in consultation with USFWS . The appMyotis spp. and Perimyotis subflavus, respectively, estimated from previous studies as well as banding data [There are no known winter hibernacula in the immediate vicinity of the hypothetical wind farm shown in Fig. ing data –48.Fig. Myotis spp.) and 908 kilometers (Perimyotis subflavus) of the hypothetical wind energy generation site [The North American Bat Monitoring Program (NABat) has collated contributions of winter colony counts from many partners . A countion site . While nWNS caused severely declining growth rates in many populations of all four of the species we considered since its presumed arrival in North America in winter 2007 , thus, uDirect mortality of bats in the form of collision with wind turbines is the primary stressor in the scenarios we investigated here. We compared three wind mortality scenarios to assess the potential impacts of wind development, a) the expected mortality from the project, b) the authorized (worst-case) mortality from the project, as well as c) a baseline scenario where no direct mortality is applied. The expected and authorized mortality data were provided by USFWS and were calculated using existing carcass recovery data from a real site. Mortality estimates are given in Table Myotis lucifugus indicated populations often exhibit annual variation on the order of 4% of mean value in vital rates due to environmental stochasticity [Previous work analyzing variation in long-term time series of Using these inputs, we utilized the BatTool to project the four regional populations 20 years into the future, equivalent to several generations of bats, from 2021 to 2041, using 600 simulations for each population and scenario. Columns five through eight of Table The results for all four species are similar, showing stationary or slightly declining trajectories with average annual losses between 0–6% . That we see a positive effect on the population indicates mortality from wind energy generation is negligible in this case, and therefore the positive effect must simply be a result of stochastic variation applied in the simulations. That is, a positive difference outcome indicates the magnitude of stochasticity in the inputs obscures any effects on mortality the wind energy facility may impose. Note the Perimyotis subflavus population is stationary while the Myotis sodalis population is declining over the projection period, yet the impacts of the single wind farm appear negligible for both.For the regional Myotis lucifugus there is a -2– -10.2% median difference between the ending population abundance compared to the baseline scenario, and a -0.1– -0.6% median difference in ending growth rate. The impacts of wind mortality are even more severe for the Myotis septentrionalis population, which showed a -4.4– -28.1% median difference between the ending population abundance compared to the baseline scenario, and a -0.7– -2.2% median difference ending growth rate. An example graphical depiction of the difference in projected population trajectories between baseline and mortality scenarios for the Myotis lucifugus regional population from 2021 to 2041 are shown in Fig. Conversely, for Myotis lucifugus and Expected and Authorized Myotis septentrionalis scenarios intervals would not overlap zero if input uncertainty were sufficiently reduced.Note that all uncertainty intervals about estimates of Myotis lucifugus and Perimyotis subflavus uncertaintry intervals to increase by about 100% for all scenarios, to increase by about 50% for all Myotis septentrionalis scenarios, and to cause a negligible increase for all Myotis sodalis scenarios  up to 50% in the Myotis lucifugus and M. septentrionalis scenarios, according to the Finally, we assessed the sensitivity of the case study results to the size of the spatial catchment area, where we used five (instead of four) times the average migration radii to define the population of interest. Clearly, changing the population of interest is bound to change the results. Table These results are based on mortality from a single wind facility and do not account for the impacts from cumulative mortality incurred from dozens of wind facilities occurring across the range of the populations. In light of this, these results give insight into whether mitigation of the additional stressor of wind energy mortality can have an effect on the future viability of these species. These outcomes may suggest that use of feathering, reduced operations during low wind speeds, and cut-in speeds could help extend the future viability for at least some of the threatened and endangered species investigated here by lessening the number of bats annually killed by wind energy generation , 52. ThiIn this work, we introduced the updated BatTool and utilized it in a case study to quantitatively estimate potential impacts of hypothetical wind energy development. Conservation managers have been using similar analyses for over a decade, and this work is intended to provide guidance on the BatTool demographic model, graphical user interface, and an example of their application to a real scenario. Our case study can serve as a template for future analyses of stressors on bat populations. While several methods used in the case study are not specifically part of the BatTool application and demographic model , these methods can serve as a template for conducting similar population viability analyses. Many modifications are possible and can be tailored to the data available for the application at hand.Given the present and expected future growth of wind energy development, it is clear mortality associated with wind energy can have measurable impacts at regional scales. More generally, this type of quantitative analysis may inform permitting and listing decisions. The summarized results from these simulations are not immutable forecasts of the population trajectory, however, but rather a glimpse into what would happen under the hypotheses and assumptions comprising each scenario. Projections incorporate a substantial amount of uncertainty, and we use the tool under the assumptions that the stage-structured demographic matrix projection model can capture the population dynamics of these species, the true abundance and growth rates of the population lie in the ranges specified, stressors are quantified and estimated reasonably, and aggregation using an abundance-weighted geometric mean is a reasonable way to assess trend of regional population. Our model also assumes a closed population, but we address this commonly violated assumption by modeling a large regional population.Critically, the model and results are only as good as the data informing the population parameters. Most importantly, because they determine population vital rates, estimates of population-level trends must be representative and accurate, but can be sensitive to data quality. Thus, while the demographic model rests on a solid theoretical foundation, there is a danger that mis-specification of the user-supplied inputs may mis-characterize the impact of anthropogenic and environmental stressors, either by design, error, or by simply not knowing how precisely to specify the model. Safeguards against improper use of this tool arise from rigorous inspection by users and reviewers of input data and the resulting inferences.In our case study, we modeled a regional population exhibiting heterogeneity in vital rates over space. Rather than individually projecting site-specific abundances within the region using estimated site-level growth rates , we usePublicly available software such as the BatTool allows producers and users of science to work collaboratively to improve decision-making . The Bat


In this study, structural transformations induced by an electric field in the chiral nematic under tangential-conical boundary conditions have been considered. The composition influence of the orienting polymer films on the director tilt angles, the formation of orientational structures in the LC layer, as well as the electro-optical response and relaxation processes have been studied. It has been shown that the poly(tert-butyl methacrylate) concentration change in the orienting polymer mixture allows for smoothly controlling the director tilt angle without fixing its azimuthal orientation rigidly. Liquid crystals (LC) are materials of great interest, owing to their anisotropy of physical properties and specific orientational molecular ordering ,2. The un  at the interface. There are four types of boundary conditions [The boundary conditions, under which the director is in the orienting film plane and has a zero pretilt angle, are identified as the tangential The boundary conditions, under which the director is perpendicular to the orienting film plane, are identified as homeotropic The tilted boundary conditions are characterized by the fixed azimuthal director orientation and the tilt angle The boundary conditions, under which the tilt angle The orientational LC structure is determined by some factors, including the boundary conditions, which are set by thin orienting films for LC layers. The boundary conditions specify the orientation of the director nditions :The bounThe director orientation under conical boundary conditions is characterized by the fixed polar angle at the interface and a possibility of azimuthal gliding creating the cone of revolution . The seaectively . The perθC=47.7∘ .p  liquid crystals (CLC) have a helicoidal director structure . Owing tnchoring . Such a p, and the polarization ellipticity was controlled by a low electric field influencing the effective anisotropy of the refractive index For example, the formation of the tangential-conical boundary conditions due to the wetting effect allows one to create the periodical structure whose orientation is determined by the cholesteric layer thickness in the mesophase ,23. In t used in  to realiThe above-described examples have demonstrated two main advantages of conical anchoring characterizing a reaction of cholesteric structure under tangential-conical boundary conditions. Thus, the value of the polar tilt angle crylates . In the As was mentioned above, the PiBMA polymer assigns a conical anchoring for the nematic LN-396 with the director tilt angle r defect ,21.The sample is placed on the microscope stage so that it is illuminated from the side of the substrate with tangential anchoring;R of the substrate with tangential anchoring is oriented perpendicularly to the polarizer direction P;The rubbing direction A is rotated into a position corresponding to the intensity minimum of passed light for the considered sample area;The analyzer A is parallel to the azimuthal director orientation at the substrate with conical anchoring [In this case, according to the waveguide Mauguin regime, the orientation of analyzer nchoring .To determine the azimuthal director orientation on the substrate with conical anchoring, the rotating analyzer method can be applied for the defect-free structure and the structure containing linear defects, or the periodic structure :The sampA orientation.Studying the director distribution near the linear defects (domain borders) by the rotating analyzer method, it is necessary to focus on them . Then, a series of photos should be taken at different orientations of the analyzer in the range from 0 to p of cholesteric. This change in the azimuthal director orientation can be considered as the reason for the decrease in the effective value of the The dielectric anisotropy of the used LC is positive, and the director tends to align along the field direction under a voltage (it is perpendicular to substrate plane). At a relatively low voltage ); however, the domain structure appears at the PiBMA concentration of more than 30%. Below, the peculiarities of the electrically induced response of the structures formed under the tangential-conical anchoring specified by PiBMA: PMMA (where PMMA is poly(methyl methacrylate)) and PiBMA:PtBMA were considered in detail.ers move b,c, resud and The initial domain structure does not restore after switching off the voltage at the considered  d/p=0.6 . MoreoveThe structure under study relaxes into the azimuthally uniform state for approximately 300 min g. It shoIn addition, the above described scenario of structure transformation is observed in the cells with the orienting PiBMA:PMMA:LN-396 films at a concentration of PMMA The samples based on the PiBMA:PtBMA polymer mixtures demonstrate another transformation type.  voltage b,c, and  voltage d. The hoSuch a transformation type is observed for all considered samples based on the polymer mixtures PiBMA:PtBMA. The structure relaxes to the homogeneous state between one and several tens of a second in the samples with PiBMA The inhomogeneous director configuration in the samples based on PMMA relaxes for a long time after switching off the field. The response and relaxation processes occur sufficiently fast in the sample based on PtBMA; thus, the transient storage of the azimuthal director orientation is not observed. Thus, it is revealed in the response of the defect-free structure to an electric field.  U=1.4 V a–d. At t U=1.4 V e–h. Thert scheme d,h.The PtBMA polymer stimulates the larger turn of the azimuthal orientation of polarization in comparison with PMMA, owing to the better director gliding at the conical anchoring interface. Thus, this material is more promising for its application.As the portion of the PMMA or PtBMA polymer assigning the conical anchoring increases, the director tilt angle decreases; then, the degenerate state of the azimuthal director orientation remains . As a re U≤0.6 V b. The st U≤0.6 V c charactmplitude d–f and tude (5∘) , which lThe complicated director reorientation under the voltage at the tilt angle In this paper, the electrically induced response and relaxation processes of the chiral nematic at tangential-conical boundary conditions have been studied. An influence of the orienting film composition on the director tilt angle The PtBMA-based cells under study, with the smoothly operated director tilt angle due to the conical boundary conditions, can improve the parameters of present devices as well as develop novel ones. For instance, an application of the conical boundary conditions with different tilt angles provides a continuous tuning of the parameters of diffraction LC gratings  or the eThe observed memory-like structures in the samples based on PMMA films can be considered as the metastable states, the lifetime of which is determined by the composition of the orienting film and the value of the applied voltage. At that, the metastable structures appearing after a domain shrinking are nonrenewable, unlike the undulations. The application of two- or many-component orienting films allows for fine-tuning the balance between the switching parameters  of undulations in a metastable state and their lifetime. These memory-like structures are of interest in order to develop the electro-optical devices with reduced operating voltages, switchable diffraction gratings, etc.The sandwich-like CLC cells consisting of two glass substrates with the ITO (indium tin oxide) coating were studied. The orienting polymer film was deposited over the ITO layer. Polyvinyl alcohol (PVA)  was used as the orienting polymer with tangential anchoring. The conical boundary conditions were set by the mixture of poly(isobutyl methacrylate) (PiBMA)  and poly(methyl methacrylate) (PMMA) , or PiBMA and poly(tertbutyl methacrylate) (PtBMA) . Nematic LC LN-396 , at a concentration of 20% of the polymer weight, was added to the polymethacrylates to provide a homogeneity of the LC structure and to eliminate an azimuthal inhomogeneity of the director orientation in CLC cells caused by a flow effect . The melThe orienting films were deposited by the spin coating, and thereafter they were dried for 1 h at 60 The director tilt angles set with polymethacrylate mixtures were determined by the light transmittance dependence of nematic LC cells on the incidence angle ,30. SuchThe orientational CLC structures and their transformations under an electric field were investigated by the polarizing optical microscopy (POM) with the Axio Imager.A1m  microscope. The AC voltage of varied amplitudes at 1 kHz was applied perpendicular to the LC layer.


This paper presents the first results on the characterisation of the damage behaviour of recycled carbon fibre (rCF) rovings manufactured into unidirectionally (UD) reinforced plates. In the first step, the mechanical properties of several material combinations were determined by mechanical tests . This proves the usability of the material for load-bearing structures. For example, a tensile modulus of up to 80 GPa and a tensile strength of 800 MPa were measured. Subsequently, the fracture surface was analysed by scanning electron microscopy (SEM) to characterise the fibre–matrix adhesion and to obtain first indications of possible failure mechanisms. Despite the high mechanical properties, poor fibre–matrix adhesion was found for all matrix systems. In situ X-ray microscopy tests were then performed on smaller specimens under predefined load levels as transverse tensile and bending tests. The results provide further predictions of the failure behaviour and can be compared to the previous test results. The three-dimensional scan reconstruction results were used to visualise the failure behaviour of the staple fibres in order to detect fibre pull-out and fibre or inter-fibre failure and to draw initial conclusions about the damage behaviour in comparison to conventional fibre composites. In particular, a benign failure behaviour in the transverse tensile test was demonstrated with this procedure. In addition, first concepts and tests for the integration of AE analysis into the in situ setup of the X-ray microscope are presented. In comparison, only 6.91 kg is released in a pyrolysis process to obtain recycled carbon fibres (2.70 kg in solvolysis). By recycling carbon-fibre-reinforced plastics, the fibres can be obtained in a more environmentally friendly way than by producing new fibres and the material cycle can be closed. The use of recycled fibres can therefore reduce the production of new fibres to the extent that they can be replaced [At a time when energy efficiency and sustainability are of key importance, there is no alternative to fibre-reinforced plastic composites. In particular, carbon-fibre-reinforced plastics (CFRPs) play an important role in the automotive, aerospace, civil engineering and wind energy sectors ,4,5,6,7.replaced ,11,12,13The recycling of CFRP is challenging both in terms of isolating the fibres from the matrix and in terms of reusing the reclaimed fibres in a technical or structural application with load-bearing requirements. Various pyrolysis and solvolysis processes offer the possibility of separating the fibres from the matrix. When considering recycling methods for carbon-fibre-reinforced plastics, the following materials can be recycled ,17,18,19-Pre-impregnated fibres .-Cured fibre composites .Depending on the material present, a suitable process must be selected and the process parameters must be adjusted to recycle the fibre composite and obtain recycled carbon fibres. Composites with thermoplastic or cured thermoset matrices, as well as pre-impregnated fibres, can be freed from the matrix material by means of pyrolysis, electrodynamic fragmentation or solvolysis, among others. In all cases, the technical requirements of the process lead to the shredding and thus shortening of the fibres ,20.Subsequent continuous fibre reinforcement, as with virgin fibre components, is not possible due to the shortened fibres. The shortened rCF fibres have to be converted into other types of semi-finished products, such as roving into a continuous staple fibre yarn . One proKnowledge of the mechanical and damage behaviour is important for the design of components. The characterisation of mechanical properties by means of standardised tests  is well established . More coThe motivation for the work presented in this paper is, on the one hand, the characterisation of the mechanical properties of the rCF staple fibre yarn with different matrix systems and the associated assessment of the usability in load-bearing structures. Furthermore, the influence of the sizing on the mechanical performance will be investigated. To what extent does the presence or absence of sizing affect the properties and how does it affect the damage behaviour? For this purpose, extensive characterisation of both the dry and embedded fibre was carried out before and after the mechanical tests using Fourier transform infrared spectroscopy (FTIR), micrographs and scanning electron microscopy (SEM). Finally, in situ X-ray microscopy experiments were carried out to evaluate the failure behaviour and the associated influence of the sizing.The questions that arise here are what kind of failure event will occur? Is it more likely to be fibre-to-fibre or inter-fibre breakage? Is the resulting failure behaviour comparable to that of a UD composite or is it more similar to the behaviour of short fibre-reinforced composites? Does fibre slippage occur under load? Does fibre pull-out occur and is there sufficient fibre–matrix adhesion? Does fibre alignment occur under load? More generally, we want to find out what damage mechanisms can be detected by in situ X-ray microscopy.The staple fibre yarn used from Wagenfelder Spinnereien GmbH   consistsThe test setup and the determined force–elongation curves are shown in Due to the inhomogeneity of the rovings, the resulting tensile strength (in N) is widely scattered between 10 and 18 N. Therefore, the lower limit of 10 N was used as the pre-tensioning force for the subsequent panel production to avoid possible cracking or slipping of the rovings.Further analysis of the rovings was carried out using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) on the dry rCF fibres to characterise any residual sizing, matrix or foreign matter that may reduce the fibre–matrix adhesion. For FTIR, an amount of 5 g of fibres was placed in acetone for 24 h to remove contaminants from the rCF rovings. After evaporation of the acetone, the remaining material was analysed. The results of this test and the corresponding SEM images are shown in A film can be clearly seen on the fibre when looking at the SEM image . Three different resin systems were used. The aim was to characterise the influence of the sizing of the rCF fibres (possible influence by the pyrolysis process) on the mechanical properties and the failure mechanism. The resin systems used with the corresponding manufacturing process in the winding process and their sample declaration are shown in The first step is to examine the microstructure of the plates produced by micro-sectioning. The impregnation quality, possible pore content and fibre volume content are to be determined approximately. The impregnation of the EP-rCF, PA6-rCF and the Bio-rCF specimens is of good quality cf. . There aIn order to obtain an idea of the failure behaviour of the unidirectional rCF plates, static mechanical tests were carried out. In addition to the characteristic values obtained, the fracture surfaces were analysed and compared using SEM images. This allows fibre fracture, matrix failure or fibre pull-out to be distinguished. In addition, the mechanical properties can be used to draw initial conclusions about the fibre–matrix bonding and the compatibility of the sizing with the resin system.-Tensile tests according to EN ISO 527-5:1997 ;-Compression tests according to EN ISO 14126:1999 ;-Bending tests according to EN 2562:1997 .The tests on the samples of all resin systems were:All measurements were tracked with an optical measurement system to determine strain more accurately.For the time being, the focus is on the tensile and flexural properties of the material, as a unidirectional structure is being tested. The compressive properties were also tested for completeness, but will not be discussed in detail later as no in situ compression tests have yet been carried out.In order to detect and characterise the damage and failure behaviour in addition to the mechanical parameters, in situ X-ray microscopy tests were carried out on the Bio-rCF and the EP-rCF. For this purpose, the test setup, the test procedure and, above all, a suitable specimen geometry have to be determined. No standard can be used for this series of tests.The test set-up for the in situ tests is shown in The first step was to design a specimen geometry that would not exceed the maximum load to failure, but would also achieve the required resolution while still containing sufficient material structure (at least one complete roving). Different geometries were milled from a wound rCF plate and tested cf. . The samAt first, the tensile properties of the in situ samples were measured ex situ with the in situ test device to determine the load levels for the actual measurements. This is important for finding relevant points, where the actual CT scan can be started to detect the failure mechanism. It is also important to find the relaxation time of the specimen at certain load levels in order to reduce specimen movement during the CT scan . The resFrom these tests, the stress levels were selected and the relaxation range and time at these levels were determined:The same procedure was followed for the bending specimens . The speThe results of the quasi-static mechanical tests, the associated microscopic examination of the specimens and fracture surfaces and the in situ X-ray microscopy tests are presented below.As mentioned in For all samples, tensile tests were carried out. Due to the unavailability of the matrix material, compression and flexure tests could only be carried out on the EP-rCF and PA6-rCF samples. However, the tensile properties are the most relevant and informative for the initial assessments and the following in situ tests. The influence of the matrix system on the properties can nevertheless be determined and assessed through the mechanical tests and fracture surface analysis.Looking at For the Bio-rCF and the EP-rCF samples, large fibre channels can be seen, which are due to pure fibre pull-out. In addition, no matrix attachment is visible on the exposed fibres. Gaps between the fibre and matrix are also visible on the visible embedded fibres. This could be due to delamination by the applied stresses. However, it is also possible that the matrix adhesion was not well established during the manufacturing process. In contrast, the fibre–matrix adhesion in the PA6-rCF is slightly better. Fibre channels are also visible, but more fibres are embedded and fibre–matrix adhesion is visible on the exposed fibres. The fibre–matrix adhesion is not ideal, but looks better than with the other system, which is in contrast to the FTIR results.In the following, the flexural properties of the EP-rCF and PA6-rCF specimens are presented and their characteristic values are compared. The stress–strain diagrams  show an When looking at the SEM images  of the fIn accordance with the standard, the specimens fail by wedge-shaped splitting or by continuous thickness shear. The mechanical properties  of the PIn the following, the results of the in situ X-ray microscopy tests are presented. First, the transverse tensile tests of the bio-resin and the epoxy resin system are shown. Subsequently, a bending test transverse to the fibre orientation of the bio-resin system is explained as an example. The respective load levels are shown in Chapter 2 and will not be repeated here.Beginning with the results of the testing of the Bio-rCF, the following behaviour, which can be seen in In the first load step (100 N), a small crack (30 μm) appears at the edge of the specimen. This crack spreads through the matrix with a higher load (crack length of 124 μm at 170 N). It can be seen that the crack propagates between the roving structures. In addition, bigger resin pockets were not affected by crack initiation or propagation. At the third load level (220 N), a nearly horizontal crack through the whole specimen at the smallest diameter of the sample geometry can be observed. A reason for crack propagation from load level one to three is that the crack propagation is independent of the material structure (parts of higher/lower fibre content) and the tailored shape of the specimen promotes a defined failure event at the middle of the sample. The sample geometry is therefore suitable for receiving a defined failure event at the centre plane and can be reliably detected in the region of interest in the X-ray microscope. At the fourth load level, a crack opening of 0.1 mm was performed to reconstruct the fibre pull-out while post-failure. Here, it can clearly be seen that isolated fibres cross the crack and connect the two separated parts. A fibre pull-out because of free fibre ends in the crack zone cannot clearly be detected because of the small elongation of the sample (overlapping length of the fibres up to 50 mm). This will always lead to a compromise: by increasing the field of view to detect a larger pull-out length, the resolution will downgrade and single fibres cannot be detected. Only an estimation can be made with the in situ tests and it has to be proved in standard tensile tests.The same behaviour can be detected in the EP-rCF sample. In this test series, eight load steps were performed. This resulted from the difference in applied load on the sample in the in situ measurement and the pre-tests. Relaxation processes during the CT measurement  apparatus into the Deben CT 5000 in situ module, new clamps must first be developed that allow the sensors to be guided through the closed construction into the interior. The clamps were designed in such a way that a new sample geometry can be used . The setFirst AE tests (no in situ tests) on the rCF test specimens showed that the general setup works. The sensor positioning is sufficient for obtaining a large scan field of the sample. In addition, the sensor position delivers conclusive signals. The results of one Bio-rCF sample with the new test setup and sample geometry are shown in The typical stress–strain curve for the rCF material (blue line) is shown. The orange dots represent the recorded AE events, which provide clues to events in the material. Here, it can be seen that, up to an elongation of 0.13%, no conspicuous signals were recorded. From there on, not only does the number of recorded events increase (increase in point density), but there is also a clear increase in the amplitude of the signals, especially from 0.15% strain. This increase correlates with the onset of damage and ultimately the failure of the specimen at 0.17% strain in the tensile test. This in turn means that the acoustic signals have detected initial damage in the material.In order to obtain more precise conclusions and interpretations about the AE signals of the rCF samples, more measurement series are planned for the future. These should show statistically representative statements about possible damage events that are characteristic for the rCF material. When these data are available, first, coupled in situ measurements can be carried out. By describing the AE signals for the rCF material more precisely, it is then possible to determine the load levels more precisely in the in situ tests and to record the crack initiation and crack growth on the basis of the signals determined during the tests. These results can then be taken as a basis for automating the test procedure to reduce time and also to reduce time-dependent effects on the material during the test series .The mechanical behaviour of the unidirectional rCF material from rCF rovings shows some remarkable properties. The tensile modulus is close to those of composites with virgin fibres , despiteFailure in bending tests shows a combination of fibre and inter-fibre fractures. Mainly compression (upper side of the sample) and tensile failure (lower side of the sample) with subsequent fibre pull-out occur. Delaminations are not visible. The compression failure generally corresponds to those of a classic continuous fibre-reinforced composite. The variations that occur within but also in comparison between the matrix systems can be related to the inhomogeneity of the rovings, which leads to a different distribution and orientation of the fibres in the component. However, the slightly different processing methods can also influence this.The in situ tests shown above give a first insight into the static failure behaviour of rCF staple fibre yarn depending on the fibre orientation. Comparing this behaviour with conventional unidirectional CFRP components, some differences can already be seen. In CFRP components, mainly matrix failure occurs in transverse tension, and the fibres remain unaffected. This means that the applied load is taken up by the matrix and that crack formation and propagation take place exclusively within the matrix and the fibre–matrix interface. In the rCF specimens, crack initiation and propagation also occur within the matrix. However, due to the inhomogeneity of the staple fibres , a larger number of fibres are aligned in the direction of the load and thus also absorb some load. This leads to higher transverse tensile strengths, but also to a different post-failure behaviour of the material.The failure that occurs can be described as good-natured, as individual fibres or rovings bridge the fracture and thus there is no complete structural failure a,b. The The effect of good-natured failure was also demonstrated in the in situ bending tests c. In addSeveral series of tests were carried out to determine the failure behaviour of rCF staple fibre yarn. After the characterisation of the rCF staple fibre yarn and the sizing, panels with three different matrix systems were produced in a winding process and then extensively characterised. Mechanical tests  were carried out to determine the general material properties and to obtain first impressions of the failure behaviour from the fracture surface using SEM. In situ X-ray microscopy tests were then carried out to characterise the failure behaviour in detail. More precisely, transverse tensile tests and bending tests were performed.The results described above on the mechanical behaviour of rCF staple fibre yarn un-der quasi-static loading provide a first approach to describing the failure behaviour of this material. The mechanical properties, particularly the high Young’s modulus, indicate the potential of the material for use in load-bearing structures. In addition, the benign failure behaviour transverse to the fibre orientation, which has been determined so far, offers advantages in load-bearing structures for avoiding abrupt failure and thus minimising the risk or reducing the safety factor in the design of the components.Despite the positive aspects, the main drawbacks are the lack of information on the origin and history of the rCF fibres used in the yarn and, consequently, the poor fibre–matrix adhesion due to missing or inappropriate fibre sizing. This would require the incorporation of production steps, both in the manufacturing process and in the further processing of the rCF staple fibre yarn, which would lead to improved adhesion. This could further improve the mechanical properties. However, it is questionable whether this would lead to a loss of the “good-natured” failure behaviour and whether the fibre pull-out effect would be replaced by a higher probability of fibre breakage.On the experimental side, the combination of AE and XCT can increase the information density and accuracy on the failure behaviour. The combination can also significantly reduce the measurement time for a series of tests, as the CT scans are started when a failure event occurs in the material, eliminating the need for preliminary tests or a careful approach to the fracture strength of the specimen being carried out. As described above, initial tests are already underway, including automation of the test procedure.The specimens used in Chapter 4 in in situ tensile tests result in (a) a defined gauge length in which the specimens fail, (b) the specimen geometry is also suitable for testing in the 0° direction (no failure in the restraint) and (c) the specimen can be used both in situ and ex situ, thus providing a greater comparability of results and facilitating preliminary testing.The approach of combining AE and in situ CT does not yet provide meaningful results. However, it has been shown that the approach and the sample geometry used are suitable. Further investigations with the rCF material may provide a deeper insight into the failure behaviour and whether and to what extent pure matrix failure with fibre pull-out or additional fibre fractures occurs.


Robotic surgery has gained popularity for the reconstruction of pelvic floor defects. Nonetheless, there is no evidence that robot-assisted reconstructive surgery is either appropriate or superior to standard laparoscopy for the performance of pelvic floor reconstructive procedures or that it is sustainable. The aim of this project was to address the proper role of robotic pelvic floor reconstructive procedures using expert opinion.We set up an international, multidisciplinary group of 26 experts to participate in a Delphi process on robotics as applied to pelvic floor reconstructive surgery. The group comprised urogynecologists, urologists, and colorectal surgeons with long-term experience in the performance of pelvic floor reconstructive procedures and with the use of the robot, who were identified primarily based on peer-reviewed publications. Two rounds of the Delphi process were conducted. The first included 63 statements pertaining to surgeons’ characteristics, general questions, indications, surgical technique, and future-oriented questions. A second round including 20 statements was used to reassess those statements where borderline agreement was obtained during the first round. The final step consisted of a face-to-face meeting with all participants to present and discuss the results of the analysis.The 26 experts agreed that robotics is a suitable indication for pelvic floor reconstructive surgery because of the significant technical advantages that it confers relative to standard laparoscopy. Experts considered these advantages particularly important for the execution of complex reconstructive procedures, although the benefits can be found also during less challenging cases. The experts considered the robot safe and effective for pelvic floor reconstruction and generally thought that the additional costs are offset by the increased surgical efficacy.Robotics is a suitable choice for pelvic reconstruction, but this Delphi initiative calls for more research to objectively assess the specific settings where robotic surgery would provide the most benefit. Pelvic floor reconstructive surgery (PFRS) is an emerging field of application for robotic platforms, with expert users experiencing advantages from the improved vision and dexterity, which they assert are particularly useful during abdominal procedures for prolapse. Clarity of vision in narrow anatomical areas and facilitated dissection and stitching in the deep pelvis are often indicated as key reasons why surgeons choose a robotic approach to perform abdominal pelvic organ prolapse (POP) reconstructive procedures. Robotic assistance has been used for all the described abdominal techniques, including sacral suspension, lateral suspension, rectal suspension, and para-vaginal repair, as well as anti-incontinence procedures –6.Evidence corroborating the perception that robotics makes PFRS easier or more effective is so far missing, including safety, reproducibility, and efficacy in the hands of surgeons with different levels of experience. Nonetheless, studies have suggested that robotic surgery reduces the conversion rate to laparotomy and shortens the learning curve for complex procedures, such as colposacropexy –9. ExperImproving the efficacy of PFRS would have a relevant impact because this surgery is performed in high numbers and repeat procedures due to relapses increase expenditures . In thisThe current study uses a Delphi methodology to report the experience of expert users on the performance of the Da Vinci® robotic platform  for PFRS. Our results represent the opinions of an international group of high-volume surgeons from various specialties on the role of robot-assisted PFRS.The study is based on the Delphi process, a method of structured communication applied to reach a consensus on a specific topic with input from a panel of experts. The Delphi technique uses a multistage, self-completed questionnaire with individual feedback , 15.Between November 2020 and September 2021, 37 high-volume robotic surgeons with extensive experience in pelvic floor reconstruction from all over the World were invited to join this project. Urogynecologists, urologists, and colorectal surgeons with an interest in proctology were included. Candidates were identified primarily based on peer-reviewed publications on robotic PFRS procedures or because of involvement in robotic special interest groups from international scientific societies dedicated to pelvic floor disorders. Participant selection was performed by the organizers of the initiative .Upon invitation of the 37 candidates, 4 declined participation or did not answer and 7 accepted but did not complete either the first or the second round of questions and were therefore excluded. Nonresponders were evenly distributed in the different geographic areas and surgical specialties. Twenty-six experts completed all Delphi rounds and participated in the joint discussion during the final teleconference, where the results were presented. The number of participants is within the range that is considered appropriate for Delphi studies .A scientific committee was formed at the beginning of the process and included the 2 organizers plus 5 surgeons who were representative of the various specialties, geographic distributions, and both sexes . The professional characteristics of the experts involved in the Delphi process are described in Table The Delphi process was initiated by identifying the research statements. This was done by the scientific committee through a series of teleconferences. A total of 64 questions and statements were formulated for the first round, divided into 5 categories: 8 questions related to surgeons’ characteristics and 56 general statements, indications, surgical technique, and future-oriented statements. The questionnaire was sent to the participants using Google modules. The participants were asked to vote using a binary Likert scale of 1 through 9 to express their degree of agreement with the statement (with 1 indicating strong disagreement and 9 strong agreement). The participants were also asked to provide a comment explaining their choice.Upon completion of the first round, the scientific committee analyzed the responses. As predefined in the study setup, those statements for which 70% or more of the group declared disagreement (score of 1–3) or agreement (score of 7–9) were excluded from the second round. Similarly, statements for which the group showed no concordance (scatter distribution of scores) were considered areas of no consensus and were excluded. Those statements for which 60% to 70% of the group either agreed or disagreed were reanalyzed by the scientific committee by looking at the explanatory comments. The committee rephrased these statements for the second round.The second questionnaire was composed of 20 statements, and the same procedure was followed for analysis. The Delphi statements and the level of agreement of round 1 and round 2 are listed in Table The scientific committee then adjudicated the areas of consensus and of lack of agreement and performed subanalyses looking at how the opinions of the group varied based on the specialty of the surgeons, the surgeons’ experience with the use of the robot, surgeons’ annual case load, and on their habit of using also standard laparoscopy.The third and concluding step of the Delphi process consisted of a face-to-face virtual meeting with all the participants, where the data were reviewed, and several comments were discussed and agreement with the analysis of the committee was verified.χ2), Fisher’s exact test, and χ2 test for trend. Significance was set at 0.05. All analyses were performed with SPSS technology v.27 .Categorical data are described as absolute frequency. Stratified analyses of responses to questions of the 2 Delphi rounds, as a dichotomized agreement, were performed with chi-square test .The experts also agreed that the learning curve of PFRS procedures is shorter for robotic surgery than for laparoscopy and that it is easier to transfer surgical competencies to other surgeons using the robot. To this extent, the experts subscribed to the idea that having a dedicated center for robotic surgery with expert staff would facilitate use of the robot for non-oncologic applications such as PFRS and that training sessions for inexperienced surgeon assistants are important to reach proficiency.The experts generally agreed that the robot facilitates quicker discharge of patients. In addition, there was a generalized opinion that collecting patient-reported outcome measures and/or patient-reported experience measures will be a useful tool to show the multidimensional  benefits of this technology.Overall, the experts strongly concurred that they would recommend the use of robot-assisted surgery for PFRS to other colleagues.The second set of practice questions tested the opinions of the experts on the correct indications for the use of the robotic platform for PFRS procedures.The panel agreed that robotic surgery is preferable to standard laparoscopy for obese patients and for patients with previous abdominal surgeries. Cases of advanced or multicompartmental prolapse, relapsed/recurrent prolapse, or previous reconstructive surgery with meshes were also considered indications for preferring use of the robot over standard laparoscopy. Although the experts concurred that the use of robotics is preferable in more complex cases, they did not believe that robotics should be limited to selected indications.The third set of practice items addressed the technicalities of using the robot to perform PFRS.The experts agreed that the robotic platform provides specific technical advantages over standard laparoscopy for performing all the key steps of abdominal PFRS. Specifically, experts perceived an advantage for dissection of the sacral promontory, the vesicovaginal space, the rectovaginal space, and the Retzius space. In contrast, the experts did not feel that the use of robotics decreases the risk of nerve injury at the level of the promontory compared with standard laparoscopy.Experts considered suturing in all the districts that are relevant for PFRS procedures to be significantly easier using the robot, including the anterior and posterior vaginal wall, the Retzius space, and the sacral promontory. Suturing on the anterior rectal wall was considered both easier and safer with the robotic technique compared with laparoscopy. Further, mesh placement was thought to be more precise with the use of robotic instruments compared with standard laparoscopy.Related to safety, the experts did not feel that major vascular damage at the level of the sacral promontory would be any more difficult to manage with robotics than with standard laparoscopy.On a more technical note, the group agreed that use of the fourth robotic arm is not necessary to perform PFRS in simple cases and that standard robotic monopolar and bipolar instruments are sufficient. The experts expressed interest in the development of a single-port robotic platform for PFRS.When the experts were asked about the future role of robotics in the management of pelvic floor defects, they agreed that they would recommend activation of a robotic program for the management of these disorders. The group also deemed it important from the patient’s perspective that a hospital managing pelvic floor diseases is equipped with a robotic platform.From a health economics point of view, considering both the costs and benefits, the experts agreed that robotic surgery should be considered a suitable technology for patients requiring abdominal correction of POP. The experts also thought that the expanding use of the robot for PFRS may offer socioeconomic advantages .When asked to select possible refinements of the robotic platform that could improve the performance of PFRS, most experts indicated the development of tactile feedback, artificial intelligence or deep learning dedicated to PFRS, and tools for preoperative anatomical planning and surgical navigation.The results were reanalyzed to explore whether surgeons of different specialties, experience, caseloads, and patterns of surgical practice may differ in opinion on selected questions. Items for which statistically significant differences were found are reported.Gynecologists strongly believed that pelvic floor reconstruction is a suitable indication for robotics only for selected patients, whereas colorectal surgeons and urologists did not Table .Table 4SWhen the opinions of surgeons were analyzed by years of experience (< 5 vs ≥ 5 years), only the more-experienced surgeons agreed that after a day of robotic surgery they felt less tired than after a day of conventional surgery Table .The opinions of experts were also compared by case-load of robotic PFRS procedures  .Another interesting difference was that the more-experienced surgeons were more likely to emphasize the ergonomic advantages of the robotic platform and were convinced that it decreased the duration of PFRS procedures, possibly indicating that continued use leads to better exploitation of the benefits of the robot. This may suggest that centralization of robotic procedures in the hands of high-volume surgeons could have advantages.Overall, the general feeling of the group of gynecologists, urologists, and colorectal surgeons’ expert in the use of the robotic platform for PFRS was that robotics is appropriate for this type of surgery given the significant technical advantages and the overall cost–benefit balance. This Delphi initiative calls for more research to objectively assess the specific settings where robotic surgery might be more wisely chosen over standard laparoscopy, with the overarching objective of improving patient care and sustainability.This study has some limitations. The study population is a self-selected group of expert pelvic floor surgeons who may have more positive feelings and confidence with robot-assisted surgery than those who did not participate. Also, while the sample size of the total group is within the recommended range for a Delphi analysis, groups in the subanalyses are smaller, which is an obvious limitation.