The definition of rectal cancer based on the sigmoid take-off (STO) was incorporated into the Dutch guideline in 2019, and became mandatory in the national audit from December 2020. This study aimed to evaluate the use of the STO in clinical practice and the added value of online training, stratified for the period before  and after  incorporation into the national audit.Participants, including radiologists, surgeons, surgical and radiological residents, interns, PhD students, and physician assistants, were asked to complete an online training program, consisting of questionnaires, 20 MRI cases, and a training document. Outcomes were agreement with the expert reference, inter-rater variability, and accuracy before and after the training.p = 0.027). Its use in multidisciplinary meetings was not significantly higher . Agreement with the expert reference was similar for both groups before  and after the training . Training resulted in significant improvement for both groups in classifying tumors located around the STO .Group A consisted of 86 participants and group B consisted of 114 participants. Familiarity with the STO was higher in group B (76% vs 88%, The results of this study show that after the inclusion of the STO in the mandatory Dutch national audit, the STO was consequently used in only 67% of the represented hospitals. Online training has the potential to improve implementation and unambiguous assessment.The online version contains supplementary material available at 10.1007/s10151-023-02803-4. The sigmoid take-off (STO) landmark was chosen as the preferred landmark to define the rectum during a two-round Delphi consensus survey in 2019 with international colorectal experts . The STOThis guideline update was disseminated in 2019 through a newsletter of the Dutch Association of Coloproctology with only one accompanying MRI example. To evaluate the implementation and to provide additional information, an online STO training program with a survey was created in 2020 . This trIn December 2020, the new definition was also officially included in the annual mandatory colorectal cancer audit in the Netherlands. From this moment onwards, Dutch hospitals were obligated to use the STO-based definition of rectal cancer for registration of all surgical resections into the audit. Because of the observed added value of the STO training, the training was offered to surgeons, radiologist, residents, and interns across the Netherlands to further contribute to the implementation of the assessment of this radiological landmark.This study evaluated whether and to what extent the participants who engaged in this second training round  were using the STO consequently during multidisciplinary team (MDT) meetings. Furthermore, this study analyzed the performance and effect of training to apply the STO compared to the first published cohort of participants who participated in the training between 2019 and 2020, to evaluate whether the new cohort, who had more time to use it in the daily practice beforehand, had a higher baseline agreement and whether training is still beneficial. In contrast to the data published in 2020 by Hazen et al., which used a three-category score , the data were dichotomized as “above” (sigmoid cancer) and “on”/“under” , to be able to evaluate the classification as used for clinical decision making.Surgeons, radiologists, surgical and radiology residents, surgical interns, PhD students, and physician assistants were invited to participate. Group A consisted of 86 participants, who were collaborators of the Snapshot rectal cancer 2016 study, and who completed the STO training after the introduction of the STO in the Dutch guidelines, but before its official adoption as an obligatory landmark in the Dutch national auditing program (December 2020). This group is provided as a historic baseline in this study. Group B consisted of 114 different participants that were recruited via an invitation to participate in the training that was disseminated through a newsletter of the Dutch Association of Coloproctology and through the research network of the Snapshot rectal cancer study. The Dutch Association of Coloproctology has approximately 250 members and consists of colorectal surgeons, surgical residents, and PhD students. The number of specialized abdominal radiologists and radiological residents in training is thought to be similar. Group B completed the training after the official adoption of the STO as an obligatory landmark in the Dutch national auditing program (December 2020).All 200 participants completed the full training. There were an additional 12 participants who completed the survey, but not the training, and these participants were excluded. There was no overlap in group A and B. To evaluate changes in the use and assessment of the STO-based rectal cancer definition after official adoption in the national auditing program, results of group A  and grohttps://www.pluvo.co). The content and image quality of the PowerPoint format and the e-learning remained equal. The questionnaires contained questions concerning knowledge of the STO. Surgeons and radiologists specifically were also asked about the use of STO during the MDT meetings. The e-learning platform recorded the completion of the training, while the participants who received the PowerPoint format had to confirm that they completed the training document to ensure that the entire training was completed.The training consisted of a baseline questionnaire and 20 MRI cases, a training document explaining the rationale of the STO with five examples on how to classify the tumor, and a post-training assessment with a repeated questionnaire and the same 20 MRI cases in a different order. A single sagittal and axial MR image was provided per case in which the lower border of the tumor and the STO were visible. The participants had to classify the tumor as “below”, “on”, or “above” the STO. A more detailed explanation of the training is provided in a previous publication . The traA consensus meeting was organized with the steering committee, which consisted of experienced specialists from the surgical and radiology department, who also participated in the Delphi consensus rounds, in which the MRI images of the 20 cases and the examples in the training were discussed. In this meeting consensus was reached regarding the classification of the tumors and this was used as the expert reference. A mix of distal rectal tumors, mid rectal tumors, and tumors straddling the STO were chosen. Nine cases were clearly located in the rectum (evident) and 11 cases tumors were located around or above the STO (non-evident). All cases were initially  diagnosed as rectal tumors, but three out of 20 cases were reclassified in consensus as sigmoid tumors according to the STO definition.Primary end points derived from the questionnaires were the percentage of participants being familiar with the new definition for rectal cancer and the percentage of hospitals using the STO as the standard definition for each case during MDT meetings. Primary end points from the case-based assessments were the baseline agreement with an expert reference, improvement in agreement after training, and the inter-observer variability. The hypothesis was that group B was more familiar with the new definition and that the baseline agreement would be higher, owing to more exposure in clinical practice.The STO identifies the point where the rectum with its mesorectum is no longer fixated to the sacrum and where the mesocolon becomes mobile. This point can be identified on MRI or CT. On the sagittal view this is the point where the sigmoid colon moves away horizontally from the sacrum and on the axial plane the sigmoid colon starts to move ventrally. When the lower border of the tumor is located on or under the STO, the tumor is classified as a rectal tumor.n) and percentages and continuous variables with means with standard deviations (SD) or median with interquartile range (IQR) for non-normally distribution. The chi-squared test and t test were used to compare variables between group A and group B. The generalized estimating equation was used to compare the scores before and after the training within the groups. Furthermore, the sensitivity, specificity, positive predictive value (PPV), and the negative predictive value (NPV) were calculated. A p value of less than 0.05 was considered statistically significant. The three locations “under”, “on”, and “above” the STO were dichotomized in “above” (sigmoid cancer) and “on”/“under”  for all analyses, to be able to evaluate the classification as used in daily clinical practice.Analyses were performed with IBM SPSS Statistics, version 26.00 (IBM Corp). Categorical variables were described with numbers  before the start of the training. For the certified specialists among the group  there was no difference between the two groups . Although STO was more often consequently used during MDT meetings in group B (50% vs 67%) before the start of the training, this was not significantly different (p = 0.237). Some hospitals were represented by more than one specialist. When data were analyzed per hospital that was represented by one or more specialists, 13 of the 28 hospitals (46%) did consequently use the STO during their MDT meetings in group A. In group B, 21 of the 38 hospitals (55%) did consequently use the STO. After the training, only one participant (from group B) indicated that they would still not use the STO as the reference for diagnosing rectal cancer. All other participants were already using the STO or planning to use it as the standard definition of rectal cancer to determine the appropriate treatment after the training.Participants in group B were more familiar with the STO landmark  and also the scores after the training were comparable . When data were stratified by type of participant, again no significant differences were found between group A and B. The SDs of the scores were smaller for both groups after the training, showing a reduction in the variation. For group A the SD changed from 10 to 5 and for group B from 11 to 5. Non-significant improvement after the training was found for group A , but significant improvement was observed in group B . When agreement was determined for the non-evident cases only , effects became more apparent and the scores improved from 69% to 79% in group A and from 67% to 79% in group B after the training (p < 0.001 for both groups).The agreement with the expert reference per case can be found in Table The sensitivity to diagnose rectal cancer (positive outcome) before the training was 85% in group A and 82% in group B. This improved to 96% and 96%, respectively. The specificity to diagnose sigmoid cancer (negative outcome) before the training was 55% in group A and 60% in group B. This decreased to 38% and 40%, respectively. The PPV was 92% before the training and 90% after the training for group A as well as group B. The NPV improved in both groups: from 39% to 63% in group A, and from 37% to 63% in group B.The results of this study show that the familiarity with the STO-based definition of rectal cancer in the Netherlands before the start of the training has improved from 76% since its initial introduction into the Dutch national guidelines in 2019 to 88% after the official introduction in the mandatory Dutch colorectal cancer audit in 2020. However, despite this higher familiarity , the classification of the tumors according to the STO landmark on MRI, compared to an expert reference, did not show further improvement compared to the previous “baseline” study published following the Dutch guideline updates. Additional targeted training in the assessment of the STO still improved the classification of particularly tumors located around the STO from 67% to 79% with less variation between the observers. Therefore, these results highlight the importance of accompanying training when implementing new guidelines, as unstructured introduction into clinical practice appears to be less effective.Although the familiarity with the STO improved to 88% in group B , the reported consistent use of the STO during MDT meetings was only 67%. This is an important finding considering that STO has been adopted as a mandatory landmark in the Dutch clinical guidelines and as an integral part of the Dutch clinical auditing system, this seems to be a disappointingly low number. On a positive note, all participating medical specialists, except one, indicated that they were planning to use the new definition during the MDT meetings after the training. This shows that extra exposure in the form of a training program can encourage specialists to change local practice. Another way to improve assessment and reporting of essential characteristics is by using templates for MRI rectal cancer staging reports. Templates for MRI rectal cancer reports have been increasingly used over the past years and resulted in an increase in the reporting of key elements for clinical decision-making . By inclThere is often a gap between evidence generated by research and its subsequent implementation into daily clinical practice , 13. A SDespite increased familiarity with the STO following the Dutch national guideline updates, agreement with an expert reference (before dedicated training) did not improve over time and after the mandatory adoption of the STO into the Dutch clinical auditing system. When the STO-based definition of rectal cancer was included in the updated national guideline, the surgeons were notified through a newsletter with only one MRI picture as an example, which was derived from the original Delphi consensus publication . This inIn the current study, still 13% of the cases were classified incorrectly after the training and this was 21% for cases which were located around the STO. This resulted mostly in an increase in false positives  and consequently a decrease in specificity. A recent study which evaluated the reproducibility of the STO by radiologists and surgeons showed at least 80% agreement in only 58–63% of the tumors near the rectosigmoid junction . In thisIn the current study, improvement in agreement after the training was especially seen for the non-evident cases, which were located around the STO. A second training session specifically focusing on the difficulties and pitfalls can contribute to further improving the assessment of the STO and to further minimize the variability in classifying the tumors according to the STO. When the STO is a proven workable landmark with acceptable variability, other guidelines will be encouraged to use this definition, for which it is essential to provide accompanying training for optimal implementation.The new definition is expected to have consequences for the treatment of the redefined sigmoid tumors. These tumors will not qualify for neoadjuvant radiotherapy anymore, except cT4b tumors. Furthermore, in case of pathological lymph nodes there would be an indication for adjuvant systemic chemotherapy according to the Dutch guideline. However, the Dutch guideline deviates from other guidelines on this subject, because in the American Society of Colon and Rectal Surgeons (ASCRS) guideline rectal tumors also qualify for adjuvant chemotherapy. The proportion of redefined sigmoid tumors, the consequences for the treatment, and the oncological outcomes of these tumors will be evaluated in another study by the Dutch Snapshot Research Group.This study has several limitations. Firstly, the participants were provided with two or three images of the MRI, which makes it more difficult to determine the lower border of the tumor and the exact location of the STO compared to investigation of the full MRI. However, only cases were selected in which the STO and the tumor were visible on the two-dimensional images. Secondly, the voluntary character of the training may have caused selection bias regarding participation by invited physicians. Clinicians who are experienced with the STO might be underrepresented, because they do not see an added value of the training, or over-represented, as clinicians who generally do not participate in training programs will also not be reached by voluntary training initiatives and remain inexperienced. Lastly, the gold standard was the expert reference, and considering the previously reported agreement of 72% under expert radiologists , the golAfter the official introduction of the STO-based definition of rectal cancer the familiarity with this new definition has increased. However, increased familiarity together with an extra year of experience in the assessment of this landmark in clinical practice did not improve the baseline assessment. Online training still appeared to be beneficial, especially in decreasing variability between the participants, and the results of this study therefore highlight the importance of an accompanying training program in the implementation of a guideline or a new clinical development.Supplementary file1 (DOCX 142 KB)Supplementary file2 (PDF 6866 KB)Below is the link to the electronic supplementary material.


Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health‐based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre‐processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated. Decision support model using uterine contractions and for the first time ever, using maternal and foetus heart rate signals. The WorThe underlying causes and physiological manifestations of preterm births remain the subjects of continuous research, where known causes of premature delivery include early induction of the pregnant mother for safety reasons, rupture of the membrane, as well as spontaneous contractions . To a deThere also exist financial implications of a preterm birth, the magnitude of which vary depending on the severity of the preterm delivery, while statistics from England and Wales suggest costs of care of a preterm individual all the way towards adulthood are in the range of £62 000–£95 000 , 4, 5. ACurrent means towards detecting preterm pregnancies include an estimation of the length of the cervix, which has been seen to be linked towards the duration of pregnancy, and where ultrasound measurement is used as the auxiliary measurement tool for this method; but this approach has been seen to be inaccurate due to its qualitative nature . The useIn terms of uterine contractions, there has been a surge of research work using machine learning models towards the development of pattern recognition algorithms that can proactively aid in the diagnosis of preterm predictions , 15, 16.In work undertaken towards tackling the shortcoming in published literature, Nsugbe et al. adopted principles of hierarchical cybernetics towards the theoretical assembly of a cybernetic system which hosts a prediction machine , 19. ThiRecent work by López‐Justo et al.  involvedPreterm prediction of Hispanic women in active labour using the Raw acquired EHG, Foetus Beat‐to‐Beat Signal, and Maternal Beat‐to‐Beat Signal;Preterm prediction of Hispanic women in active labour using EHG, Foetus Beat‐to‐Beat Signal, and Maternal Beat‐to‐Beat Signal alongside the Linear Series Decomposition Learner;Comparison of the Machine Learning model prediction performance across the following supervised learning machine learning algorithms: decision tree, linear discriminant analysis, logistic regression, support vector machines (i.e. linear SVM), quadratic SVM, cubic SVM, fine Gaussian SVM, and k‐nearest neighbour;Pilot exploration and contrast on the use of self‐learning unsupervised algorithms observing the self‐sorting of pregnancy states purely from physiological data.This work aims to use a range of machine learning to quantify the extent to which these signal sources can be used to predict preterm deliveries for a group of Hispanic women in active labour. This work also presents a novel insight into preterm prediction exercises with data collected from a large group of Hispanic women based on published research. Thus, explicitly speaking, the contributions of this research paper are as follows:22.1The data used as part of this study is from the published work of López‐Justo et al. , where pData was collected from term patients and preterm patients where the heartbeat signals from both the foetuses and mother were recorded using the Monica AN24 system, designed by Monica healthcare . This isThe characteristics of both patient cohorts and foetuses can be seen in Table 2.22.2.1The resulting contraction signals from the uterine wall during pregnancy—especially during the third trimester—have been seen to be linked towards the overall state of the pregnancy, where the dynamic behaviour of the waveform is dependent on the phase and cycle of the gestation, in addition to several other physiological factors , 25. On 2.2.2The measure of the heart rate is referred to as the heart rate variability, which quantifies the amount of time elapsed between successive heartbeats, and can be a window towards a physiologically driven means of diagnosing not only heart‐based problems but also psychological issues such as mental illness and psychiatric disorders . The norThe human heart has an intrinsic baseline which varies depending on the activity being carried out at the time, where a relaxed state is reflected with a slower heartbeat, whereas a faster heartbeat frequently reflects a state of stress and/or danger . There a2.2.3The heartbeat of a foetus can be heard around six weeks into the gestation during a sonomyography scan. The heartbeat can be anticipated to be around 110 beats per minute, increasing to around 150–170 beats per minute in and around 10 weeks of gestation .The foetal heart and circulatory system develop throughout the three trimesters as part of the maturation process. During the first trimester, blood vessels form inside the embryo which eventually grow and develop into the heart and circulatory system . This inDuring the second trimester, at approximately 17 weeks gestation, the heartbeat of the foetus shifts from a spontaneous to a regulated rhythm due to the ongoing developmental activity the brain . As partAn illustration of cardiotocography simultaneously recording the foetus heart and uterine contractions can be seen in Figure The heart of the foetus has been acknowledged to work differently within the uterus relative to when the baby has been born. As the foetus does not breathe oxygen directly in the womb, the lungs are closed prior to birth. The circulatory system relies on the oxygenated blood carried in the vein of the umbilical cord for the steady supply of nutrients . The vei2.2.4As part of the female physiological system adaptations to carrying a foetus, the cardiovascular system goes through a degree of adaptation whilst maintaining a functional baseline based on haemodynamic demands , 31, 32.A reduction in peripheral vascular resistance, as well as the emergence of low resistance circuits to the placenta, leads to a decrease in the resting arterial blood pressure up until the end of the second trimester of pregnancy , 31, 32.The resting cardiac output—which is a product of stroke volume and heart rate—increases at around the fifth week of gestation and continues to steadily rise, where it peaks in the third trimester at around 30–50 % above that of a non‐pregnant patient , 32. TheA good level of fitness during pregnancy may help to ease the transition to a higher cardiac baseline and thus it is encouraged for physical activity to be maintained antenatally as reduced fitness could lead to a higher level of cardiovascular strain during the delivery process, and as a result could present an avenue for cardiac‐based disease , 31, 32.2.3The uterine contraction signals were obtained from the MonicaSDK software, which is the supporting post‐processing software associated with the physiological instrumentation. From this, data from a single optimal channel was exported to produce an envelope—and therein a down‐sampled version of the uterine contraction signal—with a 2‐second epoch averaging scheme. The final exported files from the software varied in length due to the duration of the data collected from the various women, thus the files were down‐selected to ensure that 4 seconds worth of uterine contraction data was available for the files which were used for the analysis. Using this selection criteria, a total of 47 patients’ data was used for the final signal processing exercise, with 27 of them being term and 20 being preterm. The SMOTE synthetic sample generation algorithm was used for class balancing purposes .For the heart rate variability , the filtered beat‐to‐beat signal was exported from the MonicsSDK software where, like the uterine contraction, an optimal channel was exported, but this time with the readings in milliseconds. From the exported signals, select patient data files which had a minimum of 3 ms in the case of the MHR and 4 ms for the FHR were used, therein leading to 46 patients’ data used for the maternal heart beat (22 preterm and 26 term), and 45 patients (17 preterm and 28 term) for the foetus heartbeat.For both the uterine contraction signals and the various heartbeat signals, a windowing scheme of 10 disjointed windows was used, which divided each candidate signal into 10 equal windowed slices.2.4Signal decomposition methods are signal processing methods which contribute towards a systematic separation of parts and components of a signal in order to minimise redundancy and boost overall signal quality . DiffereThe original inception case study for the LSDL was honed towards source separation for a powder mixture using structural borne high frequency acoustic emission signals, in order to infer and estimate the particle size distribution of a sample heterogenous mixture, where the decomposition prowess and prediction performance was seen to surpass that of the wavelet decomposition method when dealing with non‐linear and stochastic signals , 40, 41.ED is the Euclidean distance given coordinates p and q, w is the wth feature in a feature vector Nw, whilerw is a specific feature within a feature vector, μ is the mean of the features, and σm is the mean of the standard deviations of the various features being considered.The embedded cost function employed as part of the evaluation of the decomposed series to assess the information quality and discriminatory power of a decomposed section of a signal was the normalised variant of the Euclidean distance which—given two sets of samples from different classes—estimates the separation between the two samples in Euclidean space . The matFurther insight and description of the LSDL can be seen in Nsugbe and Sanusi  and Nsug2.5For the search of the optimal threshold regions for the various signals listed in Tables 2.6The list of features extracted from all the physiological signals represents a concatenation of a diverse range of features which have been used in prior study for the modelling and identification of highly variable, non‐linear and stochastic physiological signals . The ensThe list of features is as follows: mean absolute value (MAV), waveform length (WL), zero‐crossing (ZC), slope sign change (SSC), root mean square (RMS), fourth order autoregression (AR), sample entropy (SampEN), cepstrum (Ceps), maximum fractal length (MFL), median frequency (MedFrq), peak frequency (PeakFrq), number of peaks (NP), simple squared integral (SSI), and variance (VAR). For all features which require a threshold, 1 μv was utilised, as adopted from prior studies; while in the case of entropy, 0 and 0.2 values were chosen for m and r, respectively , 47.2.7As part of this study, supervised and unsupervised learning methods were applied as part of the pattern recognition and classification exercises. The supervised learning methods and models work with an iterative learning scheme upon being supplied with class labels; while on the other hand, the unsupervised learning is capable of automated partitioning of data clusters with respect to a performance index, and represents a greater degree of intelligence and learning, which runs off less human intervention in contrast to the supervised variant .2.7.1Decision Tree (DT): is a class of grey‐box models which are underpinned by a Boolean‐like logic towards the partitioning of data into various classes using a tree and hierarchical flow fashion . Their mThese classification models refer to grey‐box models whose classification method is based around the use of a Boolean logic‐like approach towards the sorting of data into different classes in a tree‐like hierarchical fashion . The whiLinear Discriminant Analysis (LDA): is a statistically driven machine learning method which utilises a lower dimensional projection of the data towards the placement of class boundaries, and makes the assumption that the data is normally distributed while being Gaussian. This version of the method utilises a linear boundary towards the separation of data classes and is recognised for being computationally efficient due to its model architecture .Logistic Regression (LR): is a parametric classification model which is based upon a statistically driven framework, where the output of the classification process ranges from 0–1, with data classes determined as a function of a given threshold . Due to Support Vector Machine (SVM): this classification model is based around the higher dimensional projection of data into a feature space where class boundaries are instilled in an iterative fashion, and subsequently followed by a downscale and downward projection of the data while preserving the structure of the class boundaries implemented from the higher dimension in a feat which is regarded as a ‘kernel trick’ . Due to K‐Nearest Neighbour: represents a type of non‐parametric classification model which hinges upon a majority vote with respect to the nearest neighbours, before classes are assigned to samples . In thisAll classification models were designed using the MATLAB Classification Learner application, which—given model options—automatically tunes the hyperparameters for the optimal values as part of the training process. The models were validated at the end using the K‐fold cross validation approach, with K chosen as 10 to obtain the final performance metric for the classification model.2.7.2K‐Means: involves the iterative sorting of data into various classes based on the Euclidean distance metric. The approach is based on the expectation‐maximization (EM) algorithm where the E step includes the cluster assignment process using an objective function, while the M step is the model update phase with respect to the cluster centroid .Gaussian Mixture Models (GMM): is an unsupervised learning method which is based off probabilistic reasoning as a means towards cluster and data partitioning . The modFor both models, the number of clusters was selected to be two, on the basis of the knowledge that the exercise involved a binary prediction between term and preterm pregnancies.33.1Four key statistical metrics were utilised towards the characterisation of the performance of the various designed models, as adopted from previous work, as follows: accuracy (Acc), sensitivity (Sens), specificity (Spec) and area under the curve (AUC) .3.1.1The results for the EHG signals can be seen in Table 3.1.2The utilisation of the heart rate signal for the inference of the state of a pregnancy has not been studied deeply in the machine learning literature, while the physiological literature suggests that during the onset of labour, the maternal heart rate adapts from its baseline in order to accommodate the imminency of labour . HoweverThe raw data, first of all, show some encouraging results in the preterm predictions from the maternal heart rate, with the best prediction in that case coming from the KNN model. Similar to the prior scenario with the EHG signals, the LSDL provides a notable increase in the prediction accuracy provided by a model upon its application, as can be seen in Table 3.1.3Table As part of further work, research would involve the investigation of what specific heart rate conditions could be inferred from the obtained maternal and foetal heart rate physiological signals as part of a broader decision support platform that is capable of providing insight on both term/preterm as well as selected conditions and diseases of the heart.A principal component analysis (PCA) visualisation of the data sample and class separation for all the three physiological signals considered in this study, contrasting the raw signal with the LSDL decomposed signal, can be seen in Figure From the PCA plot, a first comparison of the Raw plot with the LSDL decomposed shows a greater cluster separability in the LSDL plot relative to the Raw, due to the signal decomposition act of the LSDL. For the LSDL FHR, a nicely decomposed and linear projection is achieved for both clusters with a notable visual separation between the two data classes, implying that the decomposed signals have low noise and a good degree of separability. In the final case of the LSDL MHR, the projections also appear to be projected nicely with minimal noise in the data post LSDL decomposition, although unlike the FHR, there appears to be a slight overlap between the two clusters.Contrasting the three physiological signals, the heart‐based signals appear to be less noisy once decomposed by the LSDL, as can be seen and as discussed, which may present a reliable alternative towards uterine contraction‐based monitoring for preterm pregnancies. As the uterus is a complex biological system—as well as an anatomical tissue within the body—its electrophysiological signals are prone to crosstalk and biological‐based interferences, which are reflected in the acquired EHG signal. However, the dynamics of the heart are different. It is an organ which, although it is a complex system, appears to produce signals with less interferences for the prediction of preterm pregnancies. This could potentially be due to the fact that all patients were in active labour and thus the physiology was actively adapting to the labour process. Further work should involve the acquisition of the heartbeat signals earlier in the third trimester, allowing for the conducting of prediction exercises in order to further evaluate and assess the feasibility of using heartbeat signals as a reliable means towards the prediction of a preterm pregnancy.3.2Although the results from the prior section showed largely positive results—particularly after pre‐processing with the LSDL—it is worth noting that these results were obtained using the supervised learning models which rely on the prerequisite of labelled data from expert knowledge. In a clinical setting, this accounts for further expenditure of resources; and from the viewpoint of the intelligence complexity hierarchy, it represents a form of “weak intelligence” due to the dependency of external input labels. The use of unsupervised learning represents a superior form of artificial intelligence which allows for self‐sorting and partitioning with reduced human supervision and input, albeit with the challenging task of efficient partitioning of data with complex geometries and dispersions. Tables 3.2.1The results of the EHG unsupervised learning exercise can be seen in Table That being said, working with the law of ergodicity and the overall sample partitioning accuracy, a supervised learning algorithm robust to noisy labels and outlier detections could be utilised towards building supervised prediction models based on the labels provided by the K‐Means model as part of a multi‐stage model building stage.3.2.2For the case of the maternal heart rate, the preterm cluster appears to be the best sorted cluster for both clustering algorithms, with a relatively lower accuracy when compared with the uterine contraction signals, therein highlighting the compatibility of the EHG signals with the applied clustering algorithm. For the case of the GMM, the preterm cluster is partitioned with a high accuracy, as can be seen in Table In the case of the K‐Means method, the same trend is shown with the higher accuracy belonging to the partitioning obtained for the preterm cluster. Although the clustering of the term cluster provided a relatively higher accuracy in comparison with the results from the GMM—hence the slightly improved score—the accuracy figures are considerably lower than that of the supervised learning exercise from the prior section, therein suggesting that the unsupervised learning method may not suffice for the maternal heart rate‐based separation of the preterm and term pregnancies.3.2.3The foetal heart‐rate clustering exercise to predict preterm produced a slightly improved set of results in the case of the GMM, where the trend continued, as seen in Table 4The preterm epidemic is one which has been seen to cause a large number of deaths in infants, as reported by the WHO, and which brings not only health implications with significant financial impacts to the economy. There continues to be active research for effective and optimal means towards the reliable prediction of a premature birth.The use of uterine contraction signals alongside machine models is a method that has seen a substantial amount of research, yielded positive results, and forms a part of what has been investigated as part of this paper. The use of EHG, FHR and MHR physiological signals were investigated to observe the extent to which they can be used to predict the delivery of a premature foetus in a group of Hispanic patients, who were in active labour. As part of the prediction exercises, the pattern recognition models used involved the utilisation of supervised learning models which are reliant upon the supply of labels; and also unsupervised learning which represents an advanced form of intelligence that is capable of self‐learning from data.In terms of the results for the array of supervised learning models tested it was seen that the LSDL provided a considerable improvement in the prediction accuracy. In the case of the EHG, the LDA and LR provided the best prediction metrics. For the MHR, the LR and QSVM produced the best prediction metrics, while in the case of the FHR, the LDA and LR produced the best metrics. Thus, either one of these physiological signals can be utilised towards the prediction of preterm deliveries with women who are in active labour. However, the heart rate signals were seen to use a much smaller time window and therein less data, and their models can also be expanded towards the monitoring of cardiac‐based diseases from the same signal source. For the unsupervised learning, the K‐Means results showed that the EHG data could be partitioned and sorted as part of a self‐learning setup, whereas for the case of the MHR and FHR a relatively low clustering result was achieved, therein showing its ineffectiveness in self partitioning of information from these signals.Further work in this area would involve continuous research in the use of unsupervised methods towards feature extraction, along with the further tuning of the parameters of the unsupervised learning models used in this work for potential improved model performance. Along with an application of a wider range of unsupervised learning techniques where possible, that is, Spectral Clustering, and further validation exercises of the designed supervised learning models. . In addiThe authors declare no conflict of interest.None.


This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations, five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation, as well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container used to generate the data are published as part of this work. Specifications Table1•Despite its high complexity, the present multiphysics problem can be both interpreted and comprehended from the perspective of fluid dynamics. This allows the results proposed by innovative design optimization algorithms to be properly examined and comprehended.•The dataset holds significance for researchers engaged in the optimization of topologies, shapes, or the design of components.•The dataset offers the potential to develop innovative algorithms for design optimization, particularly those based on deep learning methods that typically require a substantial volume of data. Currently, publicly available datasets of such magnitude for this purpose are limited.•To measure the performance of different algorithms, this dataset serves as a reliable benchmark in the field of design optimization.•Due to the number of partial differential equations, there are large numbers of target variables, which are particularly interesting for concepts from transfer learning. For instance, it becomes feasible to potentially decrease the number of partial differential equations that necessitate explicit solution.•Each sample of the dataset is represented in three different ways , enabling multi-modal learning.2The dataset may be of interest to researchers combining the fields of deep learning and design optimization using numerical simulations. In that area, this dataset can serve as a benchmark, as it is very versatile since each sample is represented as three different data types. Research in multimodal features, transfer learning, active learning and learning from image and/or graph data using supervised, unsupervised and or semi-supervised learning methods are feasible. The dataset was originally used as a case study to demonstrate the versatile applicability of a developed framework for anomaly and novelty detection 3The data contains the results of fluid flow and heat transfer simulations to optimize the design of U-bends. The structure of the introduced dataset is shown in Parameters folder the representation of the parameter values are stored. A file params.txt is contained in the Input representing a vector with 28 design parameters, partly between Output includes the results.txt file with a vector of two target values which are on the one hand the pressure loss between inlet and outlet in shape.png which represents the fluid as well as the solid area. One image each for the solution variables of the differential equations is included in the output. These solution variables are the velocity vector U, the pressure p and the temperature T. The files are named accordingly . All images are provided in five different resolutions. For the sake of brevity, not all are depicted in Cells folder. They are saved in the format of PyTorch solid.pt and fluid.pt are provided for the input. These contain information about the coordinates of the respective cell centers of the numerical mesh. The output contains a file for each solution variable of the differential equations representing the solutions for the cell centers . In addition, there is a dataset.json file in the main path of the dataset, which contains the data structure of all folders and files of the dataset. A file dataset_red.json is stored in which the structural information about all successfully simulated samples is stored. Choosing one of the two json files may be advantageous depending on the specific tasks at hand. Similarly, the choice of the data type can vary depending on the intended investigations. In addition, the use of multiple data representation is viable, thus allowing multi-modal learning techniques.The dataset includes 10,000 designs that are independent and identically distributed (i.i.d.), ensuring that deep learning models are trained and evaluated under appropriate assumptions, promoting reliable and robust performance on unseen data and facilitating proper statistical analysis. Each design has a dedicated folder in the main path and contains an input and an output folder. In these folders are additional directories that contain the data of the different design data formats. These three data formats are parameter values, which define the design, as well as images of the design and the numerical mesh. In the main.py is the primary file, which starts the generation of the data. With the file export_batch_file.py a new process and with it the generation of one sample is triggered. The file export_blockmesh_file.py is the interface between Python and OpenFOAM. The read_folder.py is a utility function with which the *.json files are generated. The file Allrun_template.sh is used to pass commands to the podman containers.The functionality of the source code that has generated the data is described in detail in the Experimental Design section. The file 4Parameterization of the design space: The parameterized model developed in this work is based on a benchmark test case from the von Karman Institute for Fluid Dynamics. It was originally introduced by Verstraete et al. In this section, the parameterization of the design space, the material properties, the boundary conditions and two different objective functions are first presented, and finally the experimental setup is explained.It has a circular U-bend with an outer radius of Material properties and boundary conditions: In the following the basic material properties and used boundary conditions are presented. These are kept constant for each sample in the dataset. The U-bend depicted in noSlip condition applies to the flow on all walls. This means that the flow velocity must be zero. A fixed value is specified at the inlet. In normal direction, the gradient of the velocity must be zero at the outlet due to the free flow. The fluid enters with a temperature of 300K. The pressure walls and at the limit wall the fixedFluxPressure boundary condition is used. At the outlet, the pressure heat, a specific heat flux externalWallHeatFluxTemperature of Boundary conditions for each area are specified separately and described according to the names commonly used in OpenFOAM turbulentTemperatureCoupledBaffleMixed, these two boundary conditions are realized and must be specified for both regions. Therefore, two boundaries instead of one boundary needs to be defined. For the fluid region, the limit is defined as fluid_to_solid. It is the other way around for the solid region and is defined as solid_to_fluid.At the interface between fluid and solid the temperature must be the same, therefore the heat flow supplied to the fluid must correspond to the heat conducted from the solid. With incompressible assumption. A completely turbulent flow is indicated by the Reynolds number of 40000. It was decided to employ the two-equation A Mach-number of 0.05 allows to use an inletOutlet is usually the same as zeroGradient. Nevertheless, it changes to a fixedValue when the velocity vector targets into the domain next to the boundary. This fixed value is the value at the inlet.The boundary condition inlet, a one-dimensional channel flow is precomputed. Furthermore, turbulence values In order to ensure a fully developed velocity profile at the Using the initial geometry a comprehensive mesh study was performed. The most reliable results were obtained by using a mesh with 60 cells along the cross section in the fluid region, 15 in the solid region, and 780 cells along the flow course. The initial solution result was matched and validated with the help of measurements from Coletti et al. Objective functions: Two objective functions are introduced to evaluate the different designs and to measure their performance. An obvious optimization problem in fluid mechanics is the minimization of pressure loss. The pressure lossThe second objective function is used to quantify the cooling performance, which is done with the help of the temperature at the heating surface. A lower heating surface temperature with a defined heat flow results in a bigger heat transfer. Objective functionExperimental design: The workflow of the computer experiment is shown in Design-Sampler generates a vector of 28 independent and i.i.d. random variables. In this step they are further processed into a format that can be read by OpenFOAM to create the mesh. Subsequently, the ➁ Job-Scheduler is used to parallelize a predefined number of jobs. The Job-Scheduler creates a job for each sample, that is fully automated and able to schedule jobs across multiple nodes in the computing cluster. Once simulations are completed, the results are gathered by the ➂ Data-Collector, so that new jobs are scheduled until the number of desired samples has been generated.The ➀ Design-Sampler is received by the Image 1 Mesh-Generator to generate the computational mesh. Now, the quality of the mesh is checked in Image 2 Mesh Control. Since the solution quality of numerical simulations is very sensitive to the quality of the mesh a sample with a mesh that does not fulfill defined quality criteria is discarded. In addition, the parameterization allows the inner and outer layers to intersect, which is physically impossible and thereby prohibited. The parameters used to evaluate the mesh quality criterion are the maximal skewness, the maximal aspect ratio and the maximal non-orthogonality of single cells in the mesh.Inside the three large and parallel dark grey boxes the process of a simulation is shown in chtMultiRegionSimpleFoam solver is used as Image 3 Evaluator. It is a steady-state solver for buoyant, turbulent fluid flow and solid heat conduction with conjugate heat transfer between solid and fluid regions. The Image 4 Residual Control monitors the quality of the solution, using the residuals of the individual numerical solution variables. Residuals are monitored for two reasons. Firstly, to stop the simulation as soon as a certain solution quality has been achieved and secondly, to filter samples that do not meet the minimum requirements for the solution quality after the simulation has been completed. The simulation of a single sample using an AMD EPYC 7002 processor (boost clock rate up to 3.35 GHz) lasts between 15 and 240 min. Samples that do not have sufficient mesh quality or sufficient solution quality are penalized with artificially high objective values. Whether these samples should be considered for further investigations or excluded depends on the research question to be investigated and therefore they are basically still part of the dataset. The Image 5 Post-Processor calculates the defined objective values from the numerical solution and passes them to the Image 6 Data Export. There the solutions are prepared and exported according to Data-Collector. As a last step, the ➄ Data-Processor deletes unnecessary log files. A *.json file is compiled to represent the existing folder and data structure of the dataset. This *.json file allows the data to be used quickly and efficiently for deep learning tasks.The This study does not involve experiments on humans or animals.Jens Decke: Conceptualization, Methodology, Software, Investigation, Data curation, Writing – original draft, Visualization. Olaf Wünsch: Writing – review & editing, Supervision, Resources. Bernhard Sick: Resources, Project administration, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Acoustic properties of buried graphitized layers in diamond formed by ion implantation followed by annealing were studied using the picosecond ultrasonic technique with spatial resolution. Two methods of elastic pulse generation were used: heating an aluminum film deposited on a diamond sample by femtosecond laser pulses and direct illumination of the graphitized layers by these pulses. We applied a multilayered model of the acousto-optical response to fit experimental results and estimate the distribution of the acoustical parameters  of the structures under study in depth. It was found that unique sets of spectral lines are present in the Fourier spectra of measured responses in regions with different internal structures. Mapping of the Fourier spectra made it possible to visualize regions with different internal structures. The combined use of depth profiling and mapping can serve as a tool for hypersound tomography. Choosing the type of implanted ion, its energy, and radiation dose enables one to modify physical properties such as the refractive index, density, photoelastic, and mechanical properties in given thin near-surface regions in the bulk diamond Annealing at a high temperature after implantation provides additional possibilities for structure formation. Regions with a radiation defects concentration below the graphitization threshold are restored to diamond, and above the threshold are restructured to a graphitized phase. In this case, the diamond/graphitized phase interface can be very sharp with a few nanometers root-mean-square roughness. Graphitized layers conduct electricity and effectively absorb optical radiation The acoustic properties of graphitized layers, such as sound speed and viscoelastic damping, have not been sufficiently studied. The most suitable nondestructive method for studying acoustic properties is picosecond ultrasonics, which makes it possible to register acoustic vibrations of nanostructures with high lateral (∼1 µm) resolution. For example, in Tomography is used to obtain a three-dimensional distribution of a parameter of interest over a data set of smaller dimensions. Tomography methods were used in Hypersound tomography can be realized by methods providing information about the sample elastic properties in depth  with various doses into a diamond matrix and subsequent annealing at high temperatures. We used two methods: the first was probing by elastic pulses generated by femtosecond optical excitation of an aluminum film deposited on the sample, and the second was based on the elastic pulses generation by the graphitized layers themselves upon their optical excitation. We used the maximum likelihood method to profile the acoustic properties by building the one-dimensional layered structure model and fit its response to laser excitation. The acoustic and optical parameters of the layers were chosen by the least squares method to achieve the best agreement with the experiment.In this work, we studied the propagation of hypersound elastic waves (picosecond elastic pulses) in the structures with buried graphitized layers obtained by implanting carbon ions  are restructured into diamond, and regions with that above the threshold value are restructured into a graphitized phase during subsequent high-temperature (∼1500ºС) annealing in vacuum. As a result, graphitized diamond layers with ∼100–300 nm thickness were formed at 200–400 nm depth, see For the preparation of two samples, referred to below as DR230 and DR232, polished natural ∼400 µm thick diamond plates (type Iab) with the (001) orientation were selected. The diamond surface roughness according to AFM was less than 4 nm. CWe used the data on diamond swelling in the implantation area and the "piston" model 2.2iφ of the sample caused by the hypersound pulses propagation. An electro-optical modulator was installed in the excitation channel to realize synchronous detection. Thus, the direct results of the measurements were the time-resolved dependences: Re(ΔR/R)= Δ|R|/|R| – amplitude of the response and Im(ΔR/R)= Δφ – phase of the response. The sizes of the focal spots of the pumping and probing beams were ∼2 µm (FWHM). The samples were placed on a 3D table for response mapping with 1 µm resolution. For a more detailed optical scheme of the experimental setup see We used a two-color pump-probe technique to study the acoustic properties of the samples . The pul33.1The first series of experiments consisted in measuring the time-resolved phase changes of the reflection coefficient on regions with different implantation doses and on undamaged diamond using an aluminum hypersound generator. The response was caused by a displacement of the aluminum film surface and a change in the complex refractive index as a result of both the arrival of elastic pulses and heating of the film. Therefore, the responses a have twAl/4dAl∼ 31 GHz). This fact suggests that the contact between the aluminum and the diamond is not rigid.The spectra obtained on graphitized regions also contain low-frequency and high-frequency peaks, but they have a more complex structure. These peaks are due to the elastic pulse propagation over the entire structure and its reflection peculiarities at the layer boundaries.15 cm−2 implantation dose draws special attention. This shape is due to the fact that the spectral components of the pulses reflected from aluminum/diamond interface and from the diamond cap and graphitized layer boundary come approximately in antiphase to the aluminum surface at 21 GHz frequency. In general, it can be seen that the response spectra measured in areas with different implantation doses differ significantly from each other, and therefore can be used to reveal the structural features of the samples.The doublet shape of the peak in the region with 12∙1015 cm−2 and 12·1015 cm−2 implantation dose on the DR232 sample. The recorded responses also consist of slow and fast oscillating components. The sharp peak at the initial moment is caused by hot photo-generated carriers in the graphitized layer. The fast oscillating component is shown in the main part of The next series of experiments was devoted to measuring the graphitized layers responses to pulsed optical excitation. In this case, light absorption occurs directly in the graphitized layer. The generated strain pulse in the diamond cap is exponential with a spectral width of 100 GHz, while in the graphitized layer it is bipolar due to the thermoelastic effect. The right insets in 1 − τ4, shown in the left inset in The complex and seemingly chaotic type of responses is due to the fact that the optical response is recorded from the entire structure, in which a set of several elastic pulses propagates. The vertical lines mark the arrival times of deformation pulses τ15 cm−2 dose, peaks at 51.5 GHz and 85 GHz are present both in the amplitude spectrum and in the phase spectrum, while oscillations with frequencies of 28 GHz and 107.6 GHz appear only in the amplitude spectrum. For 12·1015 cm−2 dose, peaks at 45.5 GHz and 102 GHz are appears in the phase spectrum, while peak at 21 GHz appear only in the amplitude spectrum. A similar situation is observed for other implantation doses.The 104.6, 107.6 GHz doublet in the amplitude spectrum attracts attention. The 104.6 GHz line corresponds to Brillouin scattering in the diamond bulk, while the 107.6 GHz peak, as will be shown below, corresponds to the eigen oscillations of the structure.3.215 cm−2. The dose was accumulated in a series of sequential implantation processes through the masks, which led to their slight displacement. Due to this fact, a zone with a stepwise dose was formed, as schematically shown in 2 in size. During scanning, the response was recorded at each point, its slow thermal component was subtracted, and the Fourier transform of the fast component was performed. As shown, the response spectrum depends significantly on the implantation dose. This allowed us to visualize the internal structure of the sample from the spatial distribution of the selected spectral components. 15 cm−2. The vertical line at 20 GHz corresponds to the mapping frequency. The coincidence of the spectra enables us to determine the dose of implantation at a given point. Thus, in 4Profiling the acoustic and structural sample parameters in depth requires constructing a model for elastic and optical pulse propagation in an inhomogeneous medium. The corresponding theory was developed in The elastic pulse propagation and its interaction with light are considered in The optical response was calculated similarly to 19 N/m3 (characteristic frequency is f0 =η·(Z1 +Z2)/(2πZ1Z2)= 220 GHz), and viscoelastic damping length is 4.5·1016/ω2 [m]).First, when processing the experimental data presented in 16 cm−2. To improve agreement with experiment at high implantation doses additional layers were introduced. The partitioning was as follows. Let us assume that estimates of the thickness and sound speed in the layer are obtained as the result of the fitting. This layer was divided into two equal parts, where the sound speeds were assumed to be the same. The experimental response was re-fitted, and the sublayers thicknesses and the sound speed in them could vary independently. Further, the partitioning procedure can be applied to the sublayers until the response approximation error became weakly dependent on the number of layers. We also investigated the dependence of the thickness and velocity estimates on the given layer density. It emerged that the product of velocity and density, i.e. the wave resistance, as well as the ratio of the layer thickness to the velocity, vary by no more than 3% when the density changes by ± 30%. This result is not surprising, since the acoustic properties of the layer depend on two parameters, the elastic wave propagation time through it (i.e. the ratio of thickness to sound speed) and the wave impedance 15 cm−2 into five, and with a dose of 12·1015 cm−2 into eight sublayers (∼30–40 nm average thicknesses). The introduction of a small sound damping associated with the phonon viscosity allowed us to reach an even better agreement between the calculation and experiment. According to our estimates, the viscoelastic damping length in the graphitized layer is 1.6·1018/ω2 [m], which corresponds to a 100 GHz phonon lifetime of ∼400 ps. The calculated responses and spectra are shown in 11 – longitudinal elastic modulus – was calculated. The calculation results are shown in + ion implantation with 350 keV energy and the corresponding doses.Next, we introduced a layer into the model, its depth and thickness corresponded to the data in The horizontal dashed line marks the depths at which the defect concentration exceeds the graphitization threshold and, accordingly, graphitization occurs during annealing. Since the graphitized diamond density is 1.6 times less than that of the original one, this region expands in the direction of lower resistance  and swelling occurs. Accordingly, the position/location of the graphitized region leading edge and the position/location of the maximum radiation damage region are shifted to lower values, and the position/location of the sample surface after implantation and annealing corresponds to negative depth values.15 cm−2 and 12∙1015 cm−2. It can be seen that the back side of the graphitized layer corresponds to the radiation damage calculations, and the front side is shifted towards shallower depths. The position of the maximum radiation damage area is also shifted towards shallower depths, and it is in this area that we observe the minimum wave resistance.The broken lines in 15 cm−2) did not exceed 6 nm. For thinner layers (lower radiation doses), it was less. Such small error values are associated with multiple passages of elastic pulses through the structure. The thickness of the structure is ∼500 nm, the transit time of the elastic pulse is ∼50 ps. At the same time, the duration of the response is up to 500 ps, which means tenfold passage of an elastic pulse through the structure. Therefore, when modeling, even small deviations in the layer thicknesses lead to a significant deviation of the calculated response from the experimental one at long times.In order to estimate the depth resolution, we estimated the errors in the joint determination of the layer depth and the layer thickness. To do this, we calculated the error covariance matrix. It turned out that under the experimental conditions, the root-mean-square (RMS) error of the layer depth ranged from 0.6 to 1.1 nm. The maximum root-mean-square error in determining the layer thickness for the thickest layer , we needed to know the density of graphitized layers. In the most general case, when there is no priori information about the sample, within the framework of the described technique, the sample can be modeled as a set of a finite number of layers and an elastic half-space behind them. If the structure is opaque, then the acoustic parameters of the layers are wave impedance and sound propagation time. It is these parameters that can be obtained as a result of fitting. If the sample is semi-transparent, then the additional parameters will be the complex refractive index, the thickness, and the photoelastic parameters of the layers.The simulation of optical excitation and propagation of elastic pulses in diamond with a graphitized layer was also carried out. The layer parameters corresponded to those determined above in experiments with an aluminum generator see . To achiThe response spectra are shown in The optical response of a layered structure can be approximately represented as the sum of the 15 cm−2 (Simulation makes it possible to elucidate the nature of spectral lines. For example, in the region with a dose of 4·1015 cm−2 b, the 2815 cm−2, there is no 61 GHz line, and the 21 GHz and 45.5 GHz lines are present only in the phase channel.Similarly, in the region with a dose of 12·10The given examples illustrate the different "optical activity" of the vibrational modes of the structures under study. A similar effect was observed previously on GaAs membranes 515 cm−2 to 12∙1015 cm−2, the wave resistance of the layers decreases by 20%.The acoustic properties of graphitized layers buried in diamond by ion implantation were studied using the picosecond ultrasonic technique. The wave resistance of such layers is approximately three times less than that of diamond. It has been established that with an increase in the implantation dose in the range from 4∙1015 cm−2, the layers cannot be considered acoustically uniform in thickness, as the wave impedance changes by more than 20% within the layer, reaching a minimum approximately in the middle, in the region with maximum radiation damage.In addition, a comparison of experimental data with simulation results showed that, starting from a dose of 10∙1018/ω2 [m], which corresponds to the phonon lifetime at a frequency of 100 GHz, approximately 400 ps.The graphitized layer's longitudinal modulus of elasticity was determined depending on the dose of implantation. Our estimates show that the length of the viscoelastic damping in the graphite layer is 1.6∙10The generation of coherent phonons by graphitized layers under femtosecond laser irradiation has been studied. Phonons are generated with frequencies ranging from approximately 20 to 100 GHz, depending on the layer depth and thickness. It has been found that different vibration modes of diamond structures with graphitized layers have different "optical activity," which means that individual modes may not contribute to the optical response.The studies performed have shown that even in cases when the length of elastic pulses is greater than the characteristic dimensions of structural inhomogeneities, it is possible to determine their parameters with high accuracy. By combining acoustic depth profiling and scanning along the structure's surface, one can realize hypersound tomography.The large difference between the wave resistance of graphitized layers and diamond makes them promising in terms of potential applications in acoustoelectronic devices and microwave MEMS based on diamond. Our studies show that graphitized layers can also be used as an efficient generator of elastic pulses embedded into diamond with durations of up to a few picoseconds.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


To overcome the COVID 19 pandemic, the medical community was called on to use intellectual and industrial resources on a scale never seen before.Over the last 3 years, the human race has faced the greatest healthcare challenge of the 21From the first cases, it was clear that the effects of COVID19 on the paediatric population would not be limited to acute respiratory conditions, but that young people’s mental health would be severely effected due to lockdowns. Endocrinological consequences, whether directly caused by the virus, as shown in some studies on DM1  soon begGnocchi et al., a review of the impact of the COVID-19 pandemic on paediatric endocrine disorders, which highlights well documented phenomena such as increases in obesity as well as less researched consequences, for example the impact on vitamin D and Calcium metabolism.This special edition focuses on the endocrinological consequences of the COVID19 pandemic in children and adolescents. A good place to start is the work of Barberi et al. present interesting data on the role of electronic devices in triggering PP during the pandemic period in Italy. Choi and Park present important data on the increase in prevalence of PP from 2016 to 2021 in the Korean population, emphasizing the prevalence in boys which increased 2-fold during the pandemic period. Chen et al. suggest that ghrelin levels play a role in the regulation of puberty based on their analysis of the increase of PP cases in Shanghai during the first year of the COVID-19 pandemic compared with the five previous years.A widely noted phenomenon was the increase in cases of PP. Alfayez et al. conducted a systematic review and metanalysis showing that diabetic ketoacidosis (DKA) risk, especially for the severe form, increased significantly during the pandemic. Abdou et al. showed that during COVID waves 1 and 2, there was an increase in the incidence of newly diagnosed cases compared with the pre-COVID period, and that patients presented with more severe DKA with a significantly higher incidence of hypokalaemia. Zubkiewicz-Kucharska et al. analysed the influence of lockdown in T1D management, finding increases in weight gain and daily insulin doses during the period.McCowan et al. showed that overall rates of thyroid dysfunction have not altered since the beginning of the COVID-19 pandemic, however the number of patients with transient thyroid dysfunction, not requiring ongoing treatment has increased. Finally, Corica et al. describe the difficulties that the pandemic brought for the management of obesity in children and adolescents.More studies will be needed to explain these phenomena, focusing not only on clinical presentation but also on the role of epigenetics. The covid pandemics is over, however the challenge of interpreting its effects on the paediatric population has just started for paediatric endocrinologists and healthcare professionals.GG: Writing – original draft. SS: Writing – review & editing.


The results validate key performance indices such as path following, body weights, path weights, fault tolerance and conservative motion. The MOVE solver can run in real-time on a standard computer at frequencies greater than 1 kHz.Master-Slave control is a common mode of operation for surgical robots as it ensures that surgeons are always in control and responsible for the procedure. Most teleoperated surgical systems use low degree-of-freedom (DOF) instruments, thus facilitating direct mapping of manipulator position to the instrument pose and tip location (tip-to-tip mapping). However, with the introduction of continuum and snake-like robots with much higher DOF supported by their inherent redundant architecture for navigating through curved anatomical pathways, there is a need for developing effective kinematic methods that can actuate all the joints in a controlled fashion. This paper introduces the concept of navigation with Minimal Occupation VolumE (MOVE), a teleoperation method that extends the concept of follow-the-leader navigation. It defines the path taken by the head while using all the available space surrounding the robot constrained by individual joint limits. The method was developed for the  Minimally Invasive Surgery (MIS) is a well-accepted surgical technique that typically uses long, rigid instruments and an endoscope to perform surgical procedures through small incisions . AlthougRecent advances in surgical robotics are aimed at overcoming these difficulties with teleoperated master-slave systems combined with articulated instruments. Most current surgical robots consist of a master console operated by the surgeon and a slave robot for relaying and executing the motion commands. Features such as motion scaling and tremor removal can be incorporated. The teleoperation method typically maps the motion of the master manipulator to the instrument tip (tip-to-tip mapping) without explicitly controlling the resulting motion of the other joints. Although this approach is sufficient for rigid instruments with a direct line-of-sight access, this is problematic for more complex flexible systems such as hyper-redundant continuum robots.Endoscopic surgery, either via intraluminal or transluminal routes, represents an advanced MIS technique that uses natural orifices rather than body incisions to access the target lesion . This isiSnake  , motion i2Snake . This method builds up on top of our previous work on teleoperation of highly redundant snake-like robots which highlighted the need for intuitive algorithms that would simplify the control from the user standpoint . The basi2Snake simulator. i2Snake robot. This is followed by a discussion in This paper is structured as follow: i.e., joints that can only move on a single plane, to follow an unknown-in-advance 3D path in serpentine-like locomotion by allowing the use of available surrounding space. The method relies on differential inverse kinematics with the capability to perform full-body shape control. The approach consists of four key phases: {navigation, path creation, virtual robot fitting, inverse kinematic}.The MOVE navigation combines teleoperation and navigation of redundant snake-like robots. The motivation is to allow snake-like robots with non-holonomic joints, These can be summarized as follows. The head of the robot is controlled by the surgeon in a similar fashion as in traditional endoscopy. The operator can control the roll, pitch, yaw and forward/backward motion of the robot. The forward motion is determined by the head orientation as depicted in i2Snake robot as an example to demonstrate the functionalities of the algorithm. As MOVE uses the Denavit-Hartenberg (DH) convention, this framework would also work on other types of articulated robots.To illustrate how the proposed method works in practice, we used the i2Snake was previously presented in (33). The i2Snake is a tendon-driven articulated robot composed of 12 rolling-joints arranged orthogonally. The 12 joints are grouped into 3 sections named proximal, middle and distal and having 4 DOFs each, which are then mechanically coupled into 2 DOFs. The base of the i2Snake further provides 1 DOF roll motion. The i2Snake is then attached to a robotic arm providing the insertion motion required to navigate inside the patient. All together, the i2Snake presented in (33) has 8 controllable DOFs. However, due to the type of joint used (rolling-joint) and its particular rolling motion, the i2Snake DH table requires 26 joint variables with intrinsic mechanical coupling. The reader can refer to  around the point a will induce a translation of vhead along itself:t, and t + 1. This process is represented geometrically in During endoscopic surgery, the tip of the endoscope is usually equipped with an imaging system such as a camera or an optic fibre bundle. In traditional endoscopy, 4 DOFs are available to the endoscopist: the roll, the pitch, the yaw, and the insertion, all controlled manually from the back of the endoscope and using the video. Therefore, the same 4 DOFs are also used on the proposed robotic approach. The head of the robot, where the camera is installed, is determined by two 3D points, one at the base d is calculated. If d is equal or greater than the sampling resolution s, a new point is added to the path at index m. This process is depicted in The head, more specifically the base of the robot head Algorithm 1Path CreationOnly points that correspond to a change in insertion are saved as the operator might explore the surroundings by changing the head orientation before moving forward toward the site of interest.A virtual robot is used to identify target points for the real robot along the path. This virtual robot is a modified version of the real robot being controlled. It has the same amount of links and same link length. The only difference is that each revolute joint is modelled as a universal joint with 3 DOFs. This feature ensures that the virtual robot’s link ends can be fitted exactly on the desired path as in typical FTL navigation.n DOFs, with each link’s end being the start of the next one, and is repeated until the base is reached. The head link extremities are already known, so the next step is to fit the remaining link’s end onto the path. Two cases can arise while doing so.The head link of the virtual robot is known from the previous navigation step. The rest of the link’s position is determined using a backward fitting (from head to base) onto the path. In this section, the first link will refer to the head and the following links will be starting from the head, going towards the base of the robot. The fitting consists of mapping each extremity of each link onto the path. This is done in an iterative process for the d between the link’s end path [j] at index j is longer than the link length l. In this case, it is required to interpolate between the link’s end and the path point, and to find the intersection coordinates x between a sphere of radius r = l and the line formed by the previous path point path [j − 1] and current path point path [j] using the following equations  is the geometric Jacobian matrix of the robot defined in the base frame, n is the robot’s DOFs, ωe the angular velocity with J defined as follow  × n) stacks the Jacobians of each link as if it was an end effector and is defined as follow.In order to follow a desired path in a serpentine motion, one must consider all the joints of the kinematic chain. To this end, we introduce herewith the concept of a full-body Jacobian matrix terature . In the Jacobian . EntriesAlgorithm 3full-body Jacobian ComputationJF computed using a singular value decomposition, and vf is the full-body velocity vector. Eq. α a scalar that can be used to scale the convergence at each iteration, and ef ((3n + 3) × 1) is the full-body error between the current robot position and the desired path target points, defined as follow:eP (3 n × 1) the position error and eO (3 × 1) the orientation error of the snake’s head. eP is calculated as follow:pd (3 n × 1) the vector of desired target points, and pe (3 n × 1) the vector of the current state of each link. eO is computed using the angle and axis approach  ((3 n + 3) × (3 n + 3)) the diagonal weighting matrix calculated using the error vector. There is a vast variety of weighting scenario that can be used to calculate W (ef) depending on the final application, the sensing method used and whether the signal is discrete or continuous.However, the concept of MOVE consists of using all the available surrounding space which Eq. W (ef):wi a scalar allowing to turn weighting on (wi = 1) or off (wi = 0), a and b two scalars than can be used to set the desired threshold distance. An example of used values is plotted in φ the secondary objective function allowing to reduce the tendon stress by keeping the joints as close as possible to their centre position, and is defined as follow . Considering that a tendon failure can be detected by either monitoring the motor current, or by measuring the tendon tension with sensors, the proposed MOVE framework has the intrinsic capability to compensate for the loss of one or more DOFs. In (33), we introduced a method to handle joint-limits by modifying the corresponding Jacobian columns. This approach can be further extended to also handle mechanical failures and ensure a fail-safe mode allowing the operator to finish the procedure and safely extract the robot. The full-body Jacobian JF((3n + 3) × n) presented earlier can be represented as an aggregate of column vectors  and can be found here: http://takskal.free.fr/EndoRob/. The code depends only on the standard c++ library as well as Eigen for the linear algebra . c) Shows the same faulty i2Snake with the fault tolerance feature activated.The results of fault tolerance are shown in i2Snake as close as possible to the desired path which would not be possible with a simple approach such as stopping the actuation of the faulty tendon as shown in i2Snake  would act as a spring keeping the joints almost straight as in traditional endoscopy.It can be seen that the algorithm is able to find a solution to fit the faulty e.g., optical sensing , and reaches (381 × 126) with n = 126 in the most complex robot architecture tested.The performance of the solver was evaluated to assess its real-time capabilities. To evaluate the real-time performance, a trajectory was pre-recorded using the simulator. This trajectory recorded in the master ‘space’ is then used to generate a trajectory in the “slave” space that can be fed to the solver (solver trajectories are robot dependent as they depend on the amount of joints). The “slave” trajectory is then fed to the solver at varying frequencies (from 1Hz to 2 kHz) which means that the higher the frequency, the less time the solver has to converge to a solution. In the case of the extended i2Snake-like robots architecture with increasing complexity with ‘n’ being the amount of joint variables before mechanical coupling. The plots show the averaged RMS error between the link position and the path. The initial starting error is not zero as the i2Snake is not able to exactly follow the path. For the extended i2Snake, the error does not significantly change up to 700 Hz and can run at more than 1 kHz with sub-mm accuracy. The error of more complex robots is lower at low frequency as they have more joints and hence the average error per joint is decreased.i2Snake robot presented in (13). The setup consists of an iiwa 7 robotic arm  holding and providing the insertion motion to the i2Snake robot as shown in i2Snake would be inserted from the top of the head as depicted in The MOVE framework was implemented and tested on the real • ‘I’ uses the Kuka robot to do the insertion motion.• ‘O’ uses the Kuka robot to do the retraction motion.i2Snake up.• ‘Up arrow’ moves the head of the i2Snake down.• ‘Down arrow’ moves the head of the i2Snake left.• ‘Left arrow’ moves the head of the i2Snake right.• ‘Right arrow’ moves the head of the • ‘7’ moves the head of the snake counter-clockwise.• ‘9’ moves the head of the snake clockwise.For the control, a laptop computer with an intel i7-8750H CPU and 16 Gb of RAM was used to run the MOVE framework and to teleoperate the navigation using keyboard input. The control is done as described in i2Snake performs the MOVE smoothly as validated in i2Snake is inserted, the rest of the body follows the path taken by the head. No further characterizations were performed on the joint motion accuracy of the physical robot. The i2Snake’s joint motion characterization has been previously presented in (34) and further characterization is beyond the scope of this paper. The current i2Snake hardware is currently being further developed to improve the overall positioning accuracy.The purpose of this hardware implementation is to validate on a real robot the MOVE concept. During clinical applications, the control would be done with a different master interface such as a joystick or a custom made master manipulator. Two types of motion were tested: vertical and horizontal insertion as shown in There are various parameters that can be tuned in the MOVE framework. The error tolerance function and parameters can be tuned to either match the size of the lumen or to define a soft limit, hence allowing to use the available space. This function was implemented to ensure a smooth transition between the forbidden and the allowed regions (rather than a ‘bang-bang’ type of control), but other types of functions could also be used. While in the allowed region, the other parameters such as tendon-stress reduction will induce joint motion, but once in the forbidden region, the solver will always aim to converge to the minimal error and hence override these factors.Regarding the path sampling resolution, it is defined in the workspace of the robot. It determines the level of precision of the path taken by the robot. Hence, it is task dependent rather than robot dependent. If the resolution is 1 mm, additional control points will only be added if the robot motion is greater than 1 mm. For navigation through the oesophagus, a sub-mm resolution is sufficient clinically. In the case of lung, endovascular, or brain application, this resolution should be smaller.i2Snake cannot always satisfy the desired path is an intrinsic design limitation which cannot be compensated by a simplistic FTL navigation. The i2Snake cannot intrinsically perform FTL motion since its joints can only move in one direction. The concept of MOVE is to allow the use of the surrounding space in a controlled way. In The fact that the The use of sensors could further improve the safety by avoiding excessive pressure on the lumen. In practice, the surgeon must be aware of the limitations of the system  but it should be handled by the robot and not the operator. The use of sensors could alert if an excessive pressure is detected. If the lumen is closing on each side, which is likely to happen during endoscopic application, the ideal approach would be to balance the pressure on each side to ensure a safe navigation. In all cases the system should have a maximum pressure allowed to avoid the tearing of the lumen.During endoscopy, excessive forces against sharp bends such as in the colon can result in patient injury. In the context of MOVE, this risk is significantly reduced, as the robotic endoscope will actively follow the path without relying on anatomical structures as a guide. However, it is inevitable that the patient moves during the procedure, which could result in unexpected anatomical change and excessive contact force between the robot and the anatomy. This can be avoided if the robot is equipped with force or contact sensors. The sensor information can be used to alter the virtual robot fitting, so the new fitting point is away from the head’s path and in a contact free area.This process could consist of translating the head path segment where a contact was detected, and to translate this segment opposite to the contact and normal to the contact direction as illustrated in In conclusion, this paper presents the MOVE framework for full-body control of snake-like surgical robots. The proposed framework is not system specific as it uses the standardized DH convention, so it can be applied on many different types of articulated snake-like robots.The key contributions of the paper include a novel FTL-like navigation concept relying on the notion of extended full-body Jacobian matrix where the available surrounding space can be used, and finally a set of navigation algorithms to follow a desired path while ensuring reduced tendon stress and fault tolerance.i2Snake robot as shown in the attached video.All the introduced new features were validated in simulation and the proposed solver’s performance was benchmarked for real-time performance. The results show that all the features of MOVE: path following, body weights, path weights, fault tolerance and conservative motion behave as expected. The solver can run in real-time on a standard computer at frequencies greater than 1 kHz. The MOVE framework was then successfully implemented on the real i2Snake robot hardware and instruments to improve the overall precision and reliability of the system for clinical applications.Future work will focus on the sensing part of the MOVE framework to provide the system with real-time information on the available surrounding space and contact forces with the environment. Further work will also be dedicated to the