This report describes a multi-vector variant of IMAP flap which allows to reconstruct composite head and neck defects. It was named the ‘shark flap’ because of its shape: a main body (the regular IMAP) and a superior ‘fin’ based on a randomic vascular pathway. In the last three decades, surgical procedures for head and neck reconstruction aimed to preserve organ function and quality of life. Historically, first option is represented by local muscle flaps. Nevertheless, they do not often provide a sufficient quantity of skin islands, may result too thick for being able to achieve the physiological function of the previously debulked area and may cause substantial comorbidities for the patient . To avoiThe internal mammary artery perforator (IMAP) flap proved to be one of the most commonly used options in head and neck reconstruction for many years since it was first described by Yu in 2006 . This flTraditional indications for the IMAP flap include reconstruction of various defects of the head, neck, and chest. Usually, the superior limit of IMAP flap is the lower third of the face. This flAccording to Taylor et al. , vasculaApplying the well-known concept of perforasome , the AutA 53-year-old woman presented with recurrent squamous cell carcinoma located in the cheek area, extending in depth until mandibular periosteum, through the parotid gland and masseter. She required a radical resection which included a large skin area (14 × 16 cm) of the cheek, a total parotidectomy and the resection of the zygomatic arch and the coronoid process, down to the level of the temporomandibular joint (TMJ) capsule. Fronto-temporal and buccal branch of the right VII cranial nerve were sacrificed to ensure safe margins. A prophylactic supraomohyoid neck dissection was performed during the same surgery.Reconstruction first involved a joint capsuloplasty and a deep temporal fascia rotation flap for TMJ coverage . The cranial skin defect was covered by rotating a flap from the bottom part of the cheek. After rotating the flap, the donor site defect was contiguous with the neck incisions for the dissection.Restoring the skin surface represented a great challenge in this case because of the extent of the residual wound and the relative stiffness of the surrounding tissues .2, which is about 25 cm2 larger than average second IMAP perfusion area [In the Author’s opinion the most suitable solution appeared to be an IMAP flap. Traditionally it is a fasciocutaneous axial flap based on the internal mammary artery perforators which originate at first to fifth intercostal spaces. Preoperatively, perforator vessels were studied using Color-Doppler Ultrasound (CDUS) selecting the perforator with the largest caliber . Its useion area ,10.If the conventional IMAP flap had been performed the entire defect would have not been covered, leaving the medial neck area unrestored. To solve this issue, the Authors decided to incorporate within the IMAP a narrower flap with a triangular shape and a random-based vascularization pattern with the possibility to be transposed anteriorly to fill the residual wound between the inferior margin of the rotated cheek flap and the superior margin of the neck skin.A customized intraoperative skin paddle was drawn, and dissection was carried out in a lateral-to-medial fashion maintaining a supra-fascial plane. During dissection, great care was taken to stay at 2 cm from the lateral border of the sternum performing a gentle microvascular identification of vascular pedicle to avoid its injury ,7,11. ThAt the end of dissection time no venous congestion of the flap occurred, allowing to rotate it of 90 degrees to restore entirely the skin surface of the neck . TherefoAt 4-month postoperatively the patient underwent a follow-up MR without any radiological sign of persistence of disease . At 6-moThe presented case sheds light on relevant surgical aspects which are only marginally present in current literature. In this case we are able to find two interesting topics: the combination of a randomic area to axial IMAP flap and the simultaneous association with a local cheek flap. For head and neck reconstruction, the use of local flaps can avoid significant morbidity related to the harvesting site and decrease intraoperative complications related to the prolonged surgical time required for a microsurgical intervention, as well as the risk of failure. Among local flaps, the IMAP was proven to be a reliable, thin and pliable flap with a suitable arc of rotation and perfect color match for head and neck .Moreover, the IMAP flap leads to an acceptable aesthetic result and low donor site morbidity. Its constant and reliable vascularization allows to design small to medium sized flaps; on the other hand, its arrangement as an axial flap limits the design especially in its transverse diameter. Furthermore, it may require costal cartilage resection when pedicle is short; it can affect cosmetic outcome in women as retracting scars can lead to breast asymmetry and nipple distortion; distal flap necrosis can occur if the skin paddle is extended to the deltoid region; and hair growth in hirsute skin in men . To dateThese variants could extend IMAP indications, including larger and composite defects. Moreover, clockwise rotation it as a propeller flap allows to consider it for more distant defects.The Indocyanine Green (ICG) angiography at the harvesting time would add more information about the complete angiosome.The use of local flaps, mobilized through long-pedicled flaps, provides several advantages, including the aforementioned reduction in morbidity, a close-looking texture pattern and a faster harvesting phase. Looking at this IMAP variant on the aesthetic point of view there can be found two further advantages. Even if it necessarily leaves more scars, in this specific clinical case the Authors were able to perform a direct closure of the donor site, without using a skin graft. Moreover, the second lobe scar of the shark flap stayed in the anterior axillary pillar, being easily hidden by the axillary fold.This preliminary report presents new strategies to the reconstruction of complex head and neck defects which can be treated by using composite design flaps. The chance to be able to customize a secondary, random-based area, perpendicularly oriented to the main vascular axis of the IMAP, may be useful particularly in head and neck because of its spatial complexity. The Authors suggest that this type of design can be considered reliable, reproducible and scalable for many types of reconstruction of the head and neck region. In addition, the supplementary, random-based, skin paddle attached to the IMAP flap extends reconstructive possibilities for this technique even for defects of larger size and located more medially. However, a more specific vascular study of the anatomic angiosome performed on higher numbers will be necessary to support this idea and define the underlying anatomical principles. In addition, it has surely to be mentioned that this was not the only viable strategy to cover this defect. Other options may be: first the submental flap which would enable the reconstruction of the initial defect avoiding the utilization of a second flap; second, the supraclavicular flap and, finally, the anterior supraclavicular artery perforator (a-SAP) flap based on the anterior supraclavicular vessels.Nowadays thanks to perforasome concept a reconstructive surgeon is able to combine pedicled, random and free flaps to obtain the most appropriate reconstruction in terms of skin color, flap thickness and function. This case report is an example of how versatile can be this combination of different techniques. A greater number of cases and necessary vascular studies are required to build higher evidence on this interesting topic.


Electroencephalogram (EEG)-based brain-computer interface (BCI) systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. P300, the main event-related potential, is the primary component detected by EEG-based BCI systems. Existing algorithms for P300 classification in EEG data usually perform well when tested in a single participant, although they exhibit significant decreases in accuracy when tested in new participants. We attempted to address this lack of generalizability associated with existing classification methods using a novel convolutional neural network (CNN) model developed using logistic regression (LR).We proposed an LR-CNN model comprising two parts: a combined LR-based memory model and a CNN-based generalization model. The LR-based memory model can learn the individual features of participants and addresses the decrease in accuracy caused by individual differences when applied to new participants. The CNN-based generalization model can learn the common features among participants, thereby reducing overall classification bias and improving overall classification accuracy.We compared our method with existing, commonly used classification methods through three different sets of experiments. The experimental results indicated that our method could learn individual differences among participants. Compared with other commonly used classification methods, our method yielded a marked improvement (>90%) in classification among new participants.The accuracy of the proposed model in the face of new participants is better than that of existing, commonly used classification methods. Such improvements in cross-subject test accuracy will aid in the development of BCI systems. Electroencephalogram (EEG)-based brain-computer interface (BCI) systems use brain signals to transmit information. The primary feature of these systems is that self-elicited EEG signals can assist patients with loss of motor function in the limbs and those with language impairments in communicating with others by enabling them to control external devices. The BCI system has gradually moved from the laboratory to the market, which requires a stronger adaptability of the BCI system .The study of EEG classification problems is important for the application and development of the BCI system . In EEG,Machine learning and deep learning have been widely used in image recognition , medicalSince traditional preprocessing and feature extraction methods are highly complex and time-consuming and can lead to a loss of important information after extraction, automated feature extraction algorithms are considered important . Deep leStochastic gradient descent (SGD) is commonly utilized to optimize deep learning algorithms . In 2009Previous studies have shown that the amplitude and latency of the P300 component vary across individuals according to sex and age . TherefoTo address this sharp decrease in accuracy when existing algorithms are applied to new datasets, we proposed an LR-CNN model that combines logistic regression (LR) and a CNN. In this model, optimization is achieved via the FTRL algorithm, which addresses the sparsity problem, and the network is applied to P300 detection, which also addresses sparse data features and improves the robustness of the algorithm across participants. We conducted experiments using a private dataset and compared our results with those of previous studies to determine whether our proposed method can achieve better results in the detection of the P300 component between different individuals.This study was approved by the Ethics Committee of Changchun University of Science and Technology. All participants provided written informed consent after receiving a detailed explanation of the experimental procedure. Eight healthy, right-handed individuals with no mental disorders, normal or corrected-to-normal vision, and a stereopsis acuity better than 60″ participated in the study.Preparation for the EEG experiment included placing electrode caps on each participant, applying electrode paste to reduce the resistance to a reasonable range, and informing the participants of the experimental procedure and tasks.We prepared two-dimensional and three-dimensional P300 speller paradigms, which were presented to participants using an NVIDIA stereo display with a resolution of 1,920 × 1,080 and a refresh rate of 120 Hz. Following EEG preparations, participants sat directly in front of the stereo display with their eyes 90 cm from the screen. Each task involved four sessions in which a word containing five characters was presented. Each session contained five runs, and each run output one character. The two tasks included a total of eight sessions. To avoid learning effects, the eight sessions were presented in pseudo-random order for each participant. Because the task paradigm was a 6 × 7 matrix of characters, the target character flashed twice every 13 flashes , which was called a sequence. Each run included eight sequences  represents the regression coefficient, b represents the bias, x =  represents the matrix of independent variables, d1 represents the dense EEG data, d2 represents the sparse (one-hot encoding) feature data of the participant, and dφ represents the data after cross-product transformation, which was defined as follows:where cki is a Boolean value that serves to control the multiplication of specific features.In Eq. (2), The prediction function is a Sigmoid function as shown in Eq. (3).This function controls the prediction value between  and makes the classification for judging the P300 signal.The generalizing model was a CNN, as shown in The FTRL algorithm was used to optimize our mixed model. The FOBOS algorithm introduces L1 regularization based on gradient descent and sets a threshold for parameter truncation; however, the threshold slowly becomes smaller as the number of iterations increases and needs to be improved in sparsity.The FTRL algorithm combines the advantages of the SGD and FOBOS algorithms, inheriting the accuracy and good sparsity of weights of the updated SGD algorithm from the FOBOS algorithm . This leG1:t represents the sum of the gradients accumulated up to time t, σs represents the learning rate , and λ1||W||1 and where In the training phase, FTRL was used as the optimizer, while logistic loss was used to represent loss. The loss function of logistic regression was formulated as follows:m samples. In this case, xi) denotes the sigmoid function, and w is the parameter vector required to solve such that (Twxi + b) yields the probability prediction value in the sigmoid function. yi). Therefore, the model we proposed would exhibit good real-time performance and predictive robustness.Y is the binary class label, σ(⋅) is the sigmoid function, φ(x) are the cross-product transformations of the original features x, and b is the bias term. Further, in this equation, wConv are the weights applied on the final activations fla.where We conducted three experiments to verify the effectiveness of the proposed model. We performed 5-fold cross-validation and took the average as the result. In the first set of experiments, data from eight participants were collected every other day to train and test the model. The data of each participant were divided into a training set (80%) and a test set (20%). Each classifier was trained using the training set, following which the test was conducted using the test set. The test results are shown in In the second set of experiments, data from seven participants were collected every other day to train the model and tested on the remaining participant. The test results are shown in In the third set of experiments, data from four of the eight participants were randomly selected for training, and data from these four individuals were collected every other day to train the model, which was tested on data for the remaining four participants. To avoid randomness of the results, we carried out the process of randomly selecting four participants for training and the remaining four participants for testing a total of four times, as shown in We plotted the training loss and test accuracy, as shown in The above experiments demonstrated that the proposed model can better learn individual participant features when compared with traditional classification methods, with increasing generalization capability as the size of the training set  increases.In this study, the proposed LR-CNN model exhibited better accuracy in the test phase than existing methods that are commonly used for P300 classification.The accuracy rates of each method in the three sets of experiments are shown in the box plots below. Outliers were observed when LR and LDA were applied in the first set of experiments, but the overall classification accuracy rates were relatively high with very little fluctuation when the outliers were excluded. This indicated that these two classifiers may have strengthened some common feature types and that some individuals were not sensitive to such features, leading to the presence of outliers in which the accuracy rate was much lower than the average. In the second and third sets of experiments, as for the above-mentioned strong classifiers, the overall accuracy rates of these two classifiers decreased significantly and exhibited large fluctuations across participants. This can also be explained by the failure of the classifiers to learn the individual features of participants.As shown in Our LR-CNN classifier maintained a high level of overall accuracy in the three sets of experiments, with no significant decrease for new participants. In the first set of experiments, when compared with that for other methods, the accuracy of our model fluctuated more across participants with a relatively high average accuracy rate, which suggested that our model learned both common and individual features. Due to the different degrees of learning for individual features, the accuracy rate fluctuated greatly across participants when compared with that observed using other models. However, in the second and third sets of experiments, the overall degree of fluctuation and accuracy rate did not change significantly when compared with those for other models, further suggesting that our model learned both common and individual features. The outliers in the second and third sets of experiments arose from individual features of these participants that differed from those of participants in the training set. Thus, the training samples did not have relevant individual features for our model to learn. This also highlights the adaptability of our model: As the number of individual participants increases, the model can learn more individual features, thus continuously increasing its generalizability.First, our test dataset was small, meaning that our method has a limited ability to learn individual features. A small number of participants in the training set would result in some individual features not being included, reducing our classifier’s accuracy rate when testing these participants. Second, although our model is capable of learning more features as the number of participants increases, further studies with larger datasets are required to verify its value.Our proposed LR-CNN model exhibited better test accuracy than existing methods that are commonly used for classification. Such improvements in the accuracy of extracting features from EEG data will be useful for the development of BCI systems.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.YW contributed to the conception of the study, assisted with data analysis, and participated in constructive discussions. YS and DZ wrote code and performed the experiments. MS contributed significantly to data analysis and manuscript preparation. DZ performed the data analyses and wrote the manuscript. QL was responsible for the overall experimental design. ZZ and JW checked the manuscript and made valuable suggestions on the data processing process. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The purpose of this study is to analyse data from the marine pilots’ bio-sensor readings to determine how experience affects their biometrical response during the port approach. The experiences play a significant role in the participant’s decision-making process and correlate with the repetitions. Through the repetitions of the experimental task, the participants gain experience, which correlates with the biometrical response, e.g., heart rate, electrodermal activity, etc. After exposing the two experience-distinct groups of participants to the same simulated port-approaching task, their collected biometric data is analysed and discussed. The results show that biometrical readings of the less experienced participants typically vary compared to that of the experienced participants, who take the simulated task more seriously. The study also yields insight into the workload process, involving disturbing factors during the task. In this paper, the marine pilots’ biometry is measured during the port approach using a full-mission simulator. The aim is to analyse biosensor data to gain a deeper understanding of the pilots’ bio-response during the simulated task, which may include a reasoning human factor error, thus leading to a potentially dangerous situation .A high workload, as per the ship’s collision reports, is induced by various disturbing factors, which typically distract the officer’s attention and response time in both urgent and non-urgent actions. The disturbance causes a high workload and working memory saturation, resulting in human-erroneous actions and human-factor errors , which bIn 2021, an accident occurred in the Suez Canal. The Egyptian authorities cited objective reasons (strong wind gusts) and implied “technical or human errors” . Navigation in the port area is specific and typically accompanied by fatigue due to situational awareness of constraints such as channel width and depth, traffic in the port, weather, currents, etc., all of which add to cognitive load. The pilot, familiar with local sailing conditions, assists the ship’s captain with steering the ship safely to the berth. Since many studies rely on young and/or student participants, the question arises: are psychometric responses of inexperienced pilots (trainees) in real-time different from experienced ones? Studies show that stressful events affect the biometric responses of pilots. A study was conducted at Shanghai Maritime University on the effect of social cognition and risk tolerance on the safety behaviour of marine pilots. The results showed that awareness of the hazardous nature of the manoeuvre had a direct negative influence, while risk tolerance had an indirect negative influence on pilots’ safety behaviour . Risk peResearch into human factors in navigation and/or collision avoidance, including assessment of cognitive load during the task, is normally conducted in a full-mission simulator for objective reasons. The related work shows three typical methodological data collecting approaches: basic, direct, and indirect. The advantages and disadvantages of each approach will be explained in detail in the next section. After careful study of the available literature, we decided to choose the adaptive indirect methodological approach in order to reduce negative excitement, artefacts, and false readings due to wearable sensors.The basic approach includes measuring the overall participant response to the decision-making time during the collision scenario, which was, in this case, run at the Maritime Institute in Korea . A longeThe quantification of officers’ cognitive load and stress during the navigation task by observing their stress hormone cortisol and brainwave intensity provides us direct insight into participants’ efforts during the task. However, the disadvantage of the cortisol method is the time lag of the results. The peak level of the stress hormone is reached 40 min after the event. Thus, it is unlikely that the method can be effectively used on ships . The sopRecognizing and prioritizing the important navigational information in the officers’ working memory, situation awareness, and making appropriate decisions may indirectly affect the participants’ body response, which is manifested in excessive heart rate (HR), blood volume pulse (BVP), electrodermal activity (EDA), and pupil diameter. Related work found behavioural performances to be possible causes of such effects, which will be the scope of our future research ,18,19. BVP is a measure of heart rate based on the volume of blood flowing through tissues in each capture area. The photoplethysmography (PPG) sensor detects changes by illuminating the body surface with an infrared light-emitting diode. The red light is absorbed by the haemoglobin of red blood cells and reflected by other tissues. The amount of light that returns to the PPG photodetector is proportional to the relative volume of blood in the tissue. The magnitude of the BVP is derived from the raw BVP signal and indicates the current relative blood flow. The units of measurement are sensor-dependent and have no additional physiological meaning. HR is derived from the raw BVP signal by measuring the distance between the two consecutive beats, i.e., the peaks of the raw BVP waveform. EDA refers to the electrical changes measured at the skin surface, which are influenced by signals from the brain. The EDA signal consists of two main components. The first component is the overall tone value EDA, which refers to the slower changing components and background features of the signal. The most common measure of this component is the skin conductance level (SCL), and changes in SCL are thought to reflect general changes in autonomic stimulation. The second component is the phase component, which relates to the rapidly changing signal element—the skin conductance response (SCR). The setting of the SCR detection threshold depends on the experimental conditions . The menThe studies proposing the indirect approach also involve eye tracking as a biometrical sensor, observing the number of eye fixations on a given object during the task, and the duration of fixation during the experiment. More fixations and longer fixation times are assumed to indicate a higher cognitive load. Disadvantages are related mostly to problems with the data collection, such as the specific and constant lighting condition requirements and the fact that participants must sit relatively still ,23,24. TThe paper is organized into five parts. After the introduction , the senTo overcome the disadvantages of the previous approaches, we designed and implemented an adapted indirect approach. The key advantage of the adapted approach was to assess the marine pilots’ biometric response during the port approach simulation using non-invasive biometrical measures, such as the wristband multi-sensor. The main goal of this study was to analyse data from the pilots’ bio-sensor readings to determine how experience affects marine pilots’ biometrical response during the port approach. The primary observables, e.g., HR, EDA, and BVP, were post-processed and modelled. The advantage of using a non-disturbing biometric sensor was the decreasing noise in the recorded data, which made post-processing less complex and improved the accuracy of the results—especially useful as the data was collected outside laboratory-controlled conditions. Note that we do not model specific conditions from psych-physiological signals such as stress or cognitive load, but we compare psych-physiological signals directly. We used the Empatica E4 multi-sensor wristband , a non-iWe used three measures to assess biometrical data during the experiment. The first was a BVP sensor with a 64 Hz sampling rate. The strength of a blood vessel dilatation (vasodilation) is indicated by the signal amplitude, which is used as an indicator of the biometric response during task performance.The second measure was the HR value, which was computed and excerpted from BVP inter-beat interval. The sampling rate of the HR was 1 Hz.Finally, the wristband was additionally equipped with one pair of silver electrodes for monitoring EDA with sampling rate 4 Hz. In addition to BVP, HR, and EDA, the sensor also monitored the body temperature and wrist acceleration up to +/− 2 g with sampling rate of 32 Hz. The sensors were synchronized with a UTC time server.The bridge simulator also had environmental sensors that collected data on humidity, temperature, and room noise. The environmental readings were made before and after each simulation to ensure equal conditions for all participants .An ad-hoc questionnaire was used to collect participants’ personal information regarding gender, age, and prior navigation experience. The experiment was conducted in the Faculty of Maritime Studies and Transport, University of Ljubljana, Slovenia, where the Wärtsilä’s TechSim5000 simulator is installed, consisting of the modern and ergonomic full mission navigational main bridge together with four video projectors and two LCD monitors, providing a 270° of visual angle. Additionally, two simulated Voight-Schneider full-mission bridges (Tugs) assisted the large container ship during manoeuvring. The simulator environment was air-conditioned and ventilated for steady room temperature during the experiment. We found that important to avoid noise in the EDA and body temperature measurements, which are environmentally dependent. The participants differed by experience, which proportionally correlated with age . They weThe groups of participants, therefore, differed in both age and experience because we were not able to find young and experienced pilots or old and inexperienced pilots; the difference related to experience and age combined reflects real differences in terms of experience. Due to lockdown at the time of COVID-19, when the data were collected, the sample is small and unbalanced. We consider these shortcomings during stability analysis . Finally, the tugboat captains from Port of Koper volunteered to control and steer full mission tugboat simulators to increase the realism of the experiment. The tasks of the simulation were designed according to analyses of accident and traffic predictions in the northern Adriatic ,31. The The task of the participants was to take over the large container ship, bring her safely through the dredged channel into the port basin and, finally, to the designated berth. In practice, the marine pilots often face similar tasks when container ships approach the Port of Koper. The participants become acquainted with the simulated ship bridge and adapt to the environment during the initial learning phase. To increase the realism of the simulation, the task is as close to reality as possible. The beginning of the simulation (before any disturbing factor) is considered as control data for the analysis and comparison. Additionally, for observing the biometrical response over gained experiences, each session was used as control data for the next one. During the task, the participants encountered two types of disturbing factors. The expected disturbing factors were strong NE wind gusts during all phases of the task, pushing the large container ship off course, which would happen in Port of Koper regularly (one day per month on average). There were several unexpected disturbing factors, such as the tug accidentally cutting the towing rope, engine failure, etc. Staying on the desired course is the key to success due to the 14 m ships’ draft and the 15 m dredged channel depth, so there is not much room for error. As an example, The aim of the experiment was to monitor and record the pilot’s biometrical response throughout the task to gain insight into participants’ immediate reactions to the disturbing factors and provide an estimation of the participant’s response while performing the task. The special focus of the research was the biometrical response of two groups during the learning period (repetitions) with the aim of determining how experiences and learning periods affect body response. To provide the maximum verisimilitude possible, the pilot was giving orders to the ships’ helm and constantly communicated with both tugboat captains assisting the ship. The experienced senior instructor from the Maritime Administration represented the Port authorities, monitored the ships’ approach from the instructor console with an exclusive right to abort approach, and was giving orders to escape the manoeuvre for the sake of safety reasons, e.g., to prevent collision, grounding, pollution, or damage when weather conditions became too severe. The design ensured a high degree of realism. The task was finished when the ship was safely moored in the port, grounded, collided, or finished the escape manoeuvre. If all went well, the manoeuvring task typically took half an hour. After the task, debriefing followed, wherein the instructor and participant exchanged opinions and suggestions to absorb the experience for the next attempt. The aim was to determine the participants’ biometrical responses in the specific situation. The results are shown in the next section.Expectations of the quality of the results in this study are based on the assumption that professional participants are as engaged in the simulation as they are in real life. The biometric response of the pilots during the task was measured using the non-invasive wristband sensor. Synchronisation of the sensors was developed using a UTC time server, but the different sampling rates of the sensor recordings  required resampling to allow analyses at the same time axis . The scrThe processed data from each session consist of BVP, EDA, and HR signals, as shown in For observing the effects of the gained experience, the visualization of the participants’ HR and BVP was conducted. The hypotheses were that the typical response and the excitement would lower over the sessions due to gained experiences, which we confirmed. However, we noticed that individual biometrical parameters could vary . Thus, a holistic approach is needed.The increased response in session 7 b was cauThe last part of the Results section compares the average biometric responses between experienced pilots and trainees in the learning phase without unexpected disturbances as recorded during the first three sessions. In p-value was high, so the data showed no significant change in the trainees’ biometric response (p > 0.005). We noticed that the approach did not satisfactorily reveal significant differences, and the p values lower than risk level α indicate a significant association since such low values can result from pure chance. For that reason, we applied a different approach.Since the number of participants was low, we used nonparametric statistical tests Mann–Whitney U. A difference between eight experienced pilots and four trainees over the interval −300 s and 1200 s with respect to time of channel entrance is shown in Because our data were not normally distributed, we applied the Mann–Whitney U statistical test to detect significant differences using the Python library Scipy ver. 1.7.3. Post-hoc (achieved) statistical power was assessed using GPower ver. 3.1.9.4. The anchored time-point was set to the first stressful test event at the moment of channel entrance, which was designated as time zero cf. . All meap = 0.002. Next was the significantly different skewness of the EDA signal for pilots (−0.87) compared to that of trainees (−1.78), p = 0.02. The results indicate significant differences in body response between the two groups. The reasons behind these differences are addressed in the discussion section.The results of extracted features are presented in Because the sample size is relatively small, we estimated the effect size (ES) of each measured difference between signal feature difference among trainees and pilots and also its post hoc statistical power . We observe there are differences with relatively high effect size and low post-hoc power. This observation indicates that, in these cases, differences between trainees and pilots are likely there, but we did not detect them. Such differences are the mean of the EDA signal , the mean of HR , and the std of EDA .The aim of the research was to gain as much verisimilitude involved in a port approach simulation as possible with insight into the participants’ biometrical responses regarding the experience gained through repetitions and the learning phase. Using simulators is a typical way to train pilots. Learning procedures normally include several repetitions. Although these learning repetitions on the simulators are an essential part of the learning process, there is very little literature related to the processes behind these repetitions. The aim of our study was to address, partly, an invisible dimension of pilots’ performance, the biometrical response, which is likely to indicate mechanisms behind decision-making in the berthing procedures. For this reason, we believe the paper contributes to our knowledge of how to make training sessions for pilots as effective as possible. The gathered results yielded two important findings. First, the participants’ biometrical response to the disturbance factor indicating correlation to unfavourable situations is measurable. Second, the trainees showed a different pattern of biometrical response on a simulator to that of experienced pilots; whereas the response in experienced pilots decreased with repetitions, the response in the trainees was relatively low and stable throughout the sessions. As mentioned, the trainees’ biometric response in the first session was relatively low compared to the pilots’ response, suggesting perhaps that the trainees were not as excited or focused as the experienced pilots . BVP meaIf trainees have significantly different body responses to different events on the bridge, they may not be included in studies simulating experienced pilots. To study pilots’ and marine officers’ responses during the repeated learning sessions, only experienced participants should be drafted; to study training procedures for the novices, only inexperienced participants should be studied. The study shows that the biometrical response decreases with the repetitions in the experienced pilots, suggesting that simulation with a high degree of realism reflects the biometric response of the pilots during the berthing procedures. The multi-sensor wristband  proved aThe study also implies some challenges related to stress and cognitive load quantification, which would be interesting for future research. The first challenge refers to the pilots’ personal differences. Participants are not a homogeneous group in terms of intelligence, temperament, and stress-generated responses. Personal traits may function as a moderating factor between disturbance factor/stress events and pilots’ behavioural responses. Thus, to prove the assumption that personality is a moderator between stress events and pilot’s behaviour, the test of biometrical response of pilots under different stress-related conditions, controlling for their personality within the Big Five model of personality, must be performed . The second challenge refers to time-series analyses that could be run from biometric data collected during the simulations of berthing procedures. In these analyses, biometric responses to the tasks within each session, which require high cognitive loads or are associated with high emotional arousal, could be compared to the biometrical response during the tasks with lower cognitive load or lower emotional arousal to test the dynamic aspects of decision making on the bridge. The third question relates to the technical challenges of multi-sensor data collections. The current study posed a number of challenges; we designed the study to monitor the data in real-time using a Wi-Fi connection but encountered several technical problems such as a temporal loss of internet connection and consequent loss of data, issues with sensor equipment such as the phone, which ran an update during the session, and in some cases, the wristband was switched off accidentally, thus causing loss of data, etc.; the experimenter’s immediate intervention may resolve the problem in terms of data collection, but interrupts the pilot’s problem-solving behaviour, brings in noise and thus affects the content validity of the data. In addition to that, synchronisation of all sensors with different sampling rates requires resampling and normalisation algorithms developed for each combination of sensors. Finally, the data from the multiple sensors with their temporal dimension, provide us with additional challenges. The current study provides several solutions to these problems that can be used for future interdisciplinary studies in sensor technology, marine studies, and psychology. The results can be provided as risk indicators for various analyses of navigation accidents where human error is the cause of an accident , or for The study reports the biometrical response of experienced pilots and inexperienced trainees during the port approach. The measures composed of HR, BVP, and EDA were used to measure the participants’ responses to the recurrence task. The scope of the research was to use as realistic a port approach simulation as possible to determine as much as possible a realistic biometrical response. The participants used a full mission simulator to navigate a large container ship and bring her through a dredged channel into a port basin to a designated berth. The meaningful finding is that trainees’ biometric responses in the simulation are different from those of the experienced pilots, so their results in the studies are not necessarily representative of professionals. The additional goal of this study remains to build a machine-learning (ML) algorithm to recognize the state of participants’ behavioural patterns in predicting decision-making processes and human-related errors in the berthing procedures. The health situation related to COVID-19 was persistently working against us. Thus, this part of the experiment is to be continued.


In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) treatments are the assisted reproductive technologies (ARTs) with the highest success rate. In vitro fertilization and ICSI treatment have a high cost for couples including both the technology and the drugs used for ovulation induction. It is common to face prematurely ruptured follicles at the time of oocyte pick-up (OPU). The premature rupture of the follicles can happen due to many reasons such as idiopathic rupture of the follicles before the expected ovulation time, particularly in poor responder patients and inadvertent timing of ovulation trigger injections due to misunderstanding of the drug regimen. Also, it may be due to a delayed arrival to the ART clinic for OPU. There is a lack of data in the literature to salvage the IVF/ICSI cycles in such a situation after the use of high-cost injections for ovulation induction.As we know from our basic knowledge about menstrual cycle physiology, after the rupture of the follicle, oocytes in the follicular fluid may be absorbed by the fallopian tubes with a negative pressure formed by the peristaltic movements of the tubes, directly or after spillage into the Douglas pouch. Therefore, following the rupture of the follicle, oocytes may be present in the peritoneal cavity, especially in the Douglas pouch, in the fallopian tubes, or in the uterus.ndday of her menstrual cycle and dydrogesterone 10 mg  was added twice a day on the 5thday of stimulation and was continued until the day of ovulation trigger to prevent premature ovulation. After 10 days of stimulation, 8 follicles ranging from 15 mm to 21 mm were observed and ovulation was triggered with choriogonadotropin alfa 500 micrograms , and oocyte pick-up was scheduled 36 hours after the ovulation trigger. The patient misunderstood the time of choriogonadotropin alfa injection for ovulation trigger at 11:30 pm as at 11:30 am. When she arrived at our ART clinic, she was on the 45thhour after ovulation trigger. Her transvaginal ultrasound examination revealed that all the follicles were ruptured, and there was some free fluid in the Douglas pouch. After a discussion of the situation with the patient, informed consent was given by the patient. Under sedoanalgesia, a single lumen 17G OPU needle  was administered with the guidance of transvaginal ultrasound into the Douglas pouch, and the free fluid was aspirated. Two mature oocytes were found after the microscopic evaluation of the free fluid. Then, an intrauterine insemination catheter  was administered through the uterine cervix into the uterine cavity. The uterine cervix was clamped with a single tooth tenaculum forceps to prevent reflux. Then, 60 ml of Quinn Advantage Medium with HEPES  was given intracavitaryly and the patient was positioned in a reverse Trendelenburg position. Free fluid newly formed in the Douglas pouch was also aspirated and, after the microscopic evaluation, three more mature oocytes were collected. After the ICSI procedure of the five metaphase II oocytes, four embryos were obtained, one of which reached the blastocyst stage. Preimplantation genetic testing for aneuploidy (PGT-A) was done to one embryo and one grade 5AB euploid embryo was obtained.Here, we present a 36-year-old patient in an ICSI and preimplantation genetic testing for aneuploidy (PGT-A) cycle. The couple had a diagnosis of unexplained infertility after 3 years of subfertility and a diagnostic work-up. She was administered 300 IU follitropin alpha  daily starting on the 2thhour. We obtained a euploid blastocyst at the end of the treatment. It is not known if this procedure has any detrimental effect on the endometrium and on embryo implantation; if used in a fresh IVF/ICSI cycle, the embryo transfer may be postponed to the thawing cycle after freezing all embryos. However, as this treatment cycle was a progestin-primed ICSI/PGT-A cycle, all the embryos were going to be frozen, so we did not have any concern about the endometrium.Although aspiration of Douglas pouch has been suggested in such situations,We suggest that any rescue intervention for oocytes should be performed according to the estimated timing of the follicule rupture in an IVF/ICSI cycle and to the presentation of the patient at the clinic.


Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration.This study aimed to develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair.We used deidentified data on past organ donors, recipients, and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients. To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis , random survival forests, and several artificial neural networks . We evaluated the performance of each model in terms of its ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were used: the C-index, integrated Brier score, and integrated calibration index, along with calibration plots.On the basis of the C-index metrics, the neural network–based models  had better discriminative ability than the Cox model and random survival forest model . The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of technology readiness level 4.Our technological solution based on the RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair. Deceased organ donation is the most common type of kidney donation  and can In current clinical practice, several kidney allocation algorithms are used to match donor organs with potential recipients. In the United States, the Organ Procurement and Transplantation Network uses a list of potential recipients that are ranked according to objective medical criteria  . SeveralWhen deciding the suitability of a kidney graft for a recipient, it is important to estimate how long the donated organ will remain functional. To address this question, numerous studies have used machine learning (ML) models to predict kidney transplant outcomes, each differing in variable and outcome definitions.Some models were built using data from either living donor  or deceaIn 2010, Reinaldo et al  evaluateA recent study by Luck et al  proposedThese studies built and evaluated various ML models; however, their termination at the stage of proof of concept makes it difficult to use the results for assistance in clinical decision-making.Several tools have reached advanced technological readiness levels. Patzer et al  built a objective of this project is to develop an innovative solution of technology readiness level 4  that would use ML to support medical decisions about accepting kidney transplants for particular donor-recipient pairs, with specific attention to DCD donations.The This study describes all stages of development of the ML technological solution: data acquisition and preparation, training and evaluation of ML models, and deployment of the solution.BI Expertise obtained permission from SRTR (United States) to access its extensive historical data on organ transplants that were previously used in research ,19.Special measures were taken to maintain both the confidentiality and security of personal data. The BI Expertise team leveraged Microsoft Azure public cloud to ensure that all the data were secured and only the team could access it remotely. Data exfiltration risk was avoided by disabling all direct remote accesses. The environment was only visible to end users using a virtual machine inside Azure. This virtual machine was entirely isolated from the computers that were accessing it (no cut and paste).The predictive modeling environment was based on the Azure ML data science platform and all the data resided in Azure Synapse Analytics. Both platforms were fully integrated to optimize the data preparation process and feature engineering activities. Once the predictive model was built and validated, it was deployed to a specific virtual machine that also hosted the user interface, which was accessible through a browser using a computer, tablet, or mobile device.The proposed architecture was approved by the SRTR research ethics board (REB 2020-020H), and upon deployment, BI Expertise agreed to submit it to unannounced audits.DONOR_DECEASED, REC_HISTO, CAND_KIPA, TXF_KI, and TX_KI. The tables contained individual deidentified sociodemographic and medical characteristics of kidney donors and recipients as well as outcomes of kidney transplantation such as graft failure, recipient death, or loss to follow-up.This study was based on several data tables from SRTR, namely, We included first-time kidney recipients who underwent transplantation between January 1, 2000, and December 31, 2019. This choice of subset was motivated by important progress made in the field of kidney transplantation at the beginning of the year 2000, and the chosen data set included transplants after these changes were made. In addition, by selecting recipients from the same transplant era, we ensured that all recipients would have undergone similar methods of matching donor-recipient pairs  (estimated with the Kaplan-Meier estimator on censoring data).Where The integrated Brier score (IBS) is simply the average Brier score across all prediction time points.t*. Evaluating the model’s calibration aims to answer the question whether the patient can trust this prediction. If 100 patients with identical characteristics as this one were under observation, it would be possible to look at their actual survival times and verify if approximately 70 of them survived to t*. If there was a significant difference between the predicted and observed survival rates, it would mean that the model was not well calibrated [Calibration of a model refers to the goodness-of-fit of its survival predictions . For exalibrated .t* is to stratify all the patients into groups based on the predicted probability of failure by time t*. For example, one method is to stratify the patients into 10 groups, where the cutoff points are the deciles of the distribution of the predicted probabilities. For each group, the observed failure rate by time t* is computed using a Kaplan-Meier estimator fitted to the patients of the group. This observed failure rate is then compared with the average predicted probability of failure by time t* for all patients in the group. The resulting pairs of predicted and observed values can be visually examined side-by-side or on a plot. This process can be repeated for all time points [In reality, the data sets are composed of patients with different characteristics. One common method for evaluating a model’s calibration at a chosen time point e points .. For a visual evaluation of the calibration at a time t*, an estimate of the observed failure probability before t* for every predicted failure probability i(t*)F can be obtained using the regression splines, and the resulting pairs can be plotted. With a perfectly calibrated model, this would yield a diagonal curve.However, Harrell  argued tOne of the suggested metrics for numerically assessing the calibration is the integrated calibration index (ICI) , which iThe developed end-user application provides the relevant graft survival probabilities in 3 steps. First, users must enter the required information related to the donor and the transplant candidate . Second,JIRA , Bitbucket , Confluence , Azure (Microsoft) Cloud Platform (cloud), Azure Machine Learning (computations), Google Suite, Teams (team communication), Azure Secured Virtual Machine (data security), VS Code (Microsoft), Python (ML model design and coding), and Expo.io  were the software used for the project.The code and the trained model can be available upon request if permission from Health Canada and SRTR is obtained in each particular case, which is needed for ethical considerations.The initial data sets contained information on 210,688 first-time kidney transplant recipients from deceased donors and included 402 variables. The final data set obtained after data cleaning and selection of variables contained data on 180,141 transplants  and included 35 variables. Feature selection methods such as LassoCV, ElasticNetCV, and recursive feature elimination did not recommend changing the set of variables chosen based on manually set exclusion criteria. After one-hot encoding of the categorical variables, the total number of input covariates was 170 .Demographics of the patients are shown in The 3 neural network–based models were trained using the Adam optimizer with a learning rate of 0.001 and batch size of 128. The optimal number of hidden layers and the number of nodes in the layers were determined separately for each model by testing a range of possible values, starting with small networks and gradually increasing their size. In the 3 cases, increasing the number of hidden layers in the past 3 models resulted in overfitting and decreased discriminative performance. Batch normalization and dropout with a rate of 0.10 were used. In addition, L2 regularization with a factor of 0.001 was used for the RNN model.DeepSurv consists of 2 dense layers, with 32 and 16 neurons in layers 1 and 2, respectively. DeepHit consists of 3 dense layers with 64, 32, and 16 neurons, respectively. The long short-term memory layers of RNN contain the same number of neurons.The RSF consists of 100 trees, with a maximum depth of 25 nodes. At each node, 13 randomly selected covariates were considered to split (the square root of the number of covariates). The minimum number of samples required to split a node was 400, and the minimum number of samples in the leaf nodes was 200. Adding more trees did not increase the discriminative ability of the model, and reducing the minimum number of samples to split resulted in overfitting.Different loss functions (or objective functions) were tested when building the RNN model. It was found that using the ranking loss proposed in DeepHit  yielded Definition of the loss function:whereSurvival Analysis Models section.Using this loss function to train the neural network yields a model with good discrimination ability but produces poorly calibrated survival predictions. This is because the loss function was mainly designed to encourage the correct ordering of pairs. This issue motivated the postprocessing of the RNN outputs, which is presented in the In preliminary experiments, 3 approaches were tested to obtain survival predictions for DCD kidney transplants with survival analysis models. DeepHit was used as a benchmark for this purpose. The first method was to train the model using only the DCD transplant data, which yielded an average C-index of 0.604. The second method was to train the model using data from both NDD and DCD transplants, which yielded an average C-index of 0.631 on the DCD evaluation set. The third method was to use transfer learning, which consisted of training the model on the larger NDD transplant data set , then training the model a second time on the DCD transplant data set to gain knowledge specific to DCD grafts. This approach yielded an average C-index of 0.625 on the DCD-only evaluation set. Thus, the model trained only on DCD transplants yielded the poorest results, which may be explained by the lower volume of data available for this specific transplant cohort. The best performance was obtained with the model trained on a data set that included both NDD and DCD transplants. Therefore, further development of ML models was based on the combined data set.For the final evaluation of the models, a 5-fold cross-validation was used. It consists of randomly splitting the data set into 5 partitions of equal size and repeating the training and evaluation process 5 times, each time using one partition (20%) as the evaluation set and the remaining (80%) as the training set. For the probability of graft failure in the first year, all 5 tested models had similar calibration, as shown by the ICIs in In the case of DeepHit, it is interesting to see that although it had the best discriminative ability, the model failed to produce sufficiently accurate survival predictions, especially at later time points. For the 5- and 15-year time points, DeepHit had the worst ICI (0.0285 and 0.1356) of all models, and its calibration curve had the most significant deviation from the identity line.The survival predictions produced by the RNN were better calibrated than those produced by DeepHit and RSF. However, as seen on the calibration plots, they are not as well calibrated as those obtained using the Cox and DeepSurv models.This study focused on the development of an ML-based decision support solution to help kidney transplant practitioners and their patients make informed decisions when a deceased donor kidney becomes available. All stages of the development process are described: data acquisition and preparation, evaluation of existing survival analysis models, development and evaluation of a new survival analysis model, and deployment of the technological solution of TRL-4.When building survival analysis models in the context of kidney transplantation, there are several factors that characterize the models and ultimately influence the final quality of the prediction tool.One factor is the size of the data sets used to build these models. It varies widely between studies, ranging from 80  to 131,7The performance of a predictive model is also strongly dependent on incorporating prognostically significant variables into the models. The number of variables used for survival analysis in the literature ranges from 6 to several hundred ,21,41,42The choice of a survival analysis model is also critical. Multiple options have been described in the literature, such as the Cox regression model , decisioIn this study, 5 different models were explored: a regression-based Cox proportional hazards model; RSF; and 3 neural network models, namely, DeepSurv, DeepHit, and a proposed RNN. To the best of our knowledge, the latter was used on kidney transplantation data for the first time in this study. These models were evaluated on the task of predicting kidney graft survival throughout the first 15 years following transplantation. Three metrics were used to evaluate each model: the C-index, IBS, and ICI, along with calibration plots.The results for the C-index metric shown in The main drawback of the Cox proportional hazards model and DeepSurv is the assumption that the computed hazard ratio is time invariant. In contrast, DeepHit and RNN make no assumptions about the distribution of time-to-event data and can learn the time-varying effects of covariates, making them more flexible. This is important when evaluating survival over a wide time frame, as in our study, over 15 years. For example, a covariate could have a negative effect on survival in the first few years after transplantation but no impact in the later years.Previously published articles on the prediction of survival of kidney grafts from deceased donors often described different evaluation metrics, such as accuracy ,44, meanThe performance of the proposed DeepHit and RNN models evaluated with the C-index is comparable with the previously published iChooseKidney technological solution (0.6640 at 3 years after transplantation)  and sligFrom the results presented in We developed a client web application to predict organ survival probability for each potential kidney donor-recipient pair for a period between 1 and 15 years after the transplantation. We opted to use the proposed RNN model to deploy our prototype application. This model offers a compromise between the good discriminative ability and the calibration necessary for the purpose of our application. Indeed, one of the main uses of the decision support application is to simultaneously present graft survival probabilities to a kidney transplant candidate and to offer a point of comparison by presenting graft survival predictions that the patient could expect with other potential donors.now + average time before a new kidney is available. To achieve this purpose, it would be necessary to compute the survival for every possible additional wait time and the probability of that wait time occurring, along with the patient survival to that wait time. This could be an objective for future studies.It would be possible to use an alternative approach for computing the predictions at the time average donor predictions at the same time now as the predictions for the offered donor kidney is a matter of simplicity and an effective way for patients without statistical background to look at 2 options (accept or refuse the transplant) and understand the possible outcomes.The presented choice of approach to evaluate the The client application is at the prototype stage (TRL-4), aiming to demonstrate the capabilities of the ML predictive model. The following information about the candidate recipient is entered in the first step of the application: age, height, weight, ethnicity, sex, diagnosis, number of years on dialysis, presence of diabetes, and presence of angina. The details about potential donor that are entered in the next step are donor’s age, height, weight, ethnicity, donation type, creatinine level, history of diabetes, hypertension diagnosis, hepatitis C diagnosis, and smoking habit. These covariates are used as input for the trained RNN model. In the next step, the user selects the number of years for the prediction target. The output page displays the probability of survival of the transplant for the given donor-recipient pair and specified period as well as for the candidate recipient and average NDD and DCD donors for comparison. It is also possible to expand the result boxes to obtain a detailed view of the results for any specific transplant prediction.The current application is recipient-oriented and specific to kidney transplantation. Future research could expand this application to other transplanted organs and nonrecipient users. For example, if connected to a candidate database, the application can produce an ordered list of optimal donor-recipient matches when an organ becomes available. The Expo.io development environment for the client was chosen for its capability to support web, Android, and iOS environments, leaving many options open for the distribution and accessibility of the service. The client also connects to the model by using an application programming interface. Thus, although the initial prototype was entirely run in a local environment, the solution could easily be transferred to a cloud-based environment.better kidney becomes available should the patient decide to remain on the waiting list. The solution could also be upgraded to enable the recommendation of the best candidate recipient for each newly available kidney from the existing candidate waiting list based on the predicted graft survival.In the future, the application could also be extended to include additional predictive models to further inform patients. For example, when a kidney is offered to a patient, it would be instructive to predict the expected waiting time before a Our study has certain limitations, which are important to mention. A built-in selection bias exists in the SRTR data set. It is evident that deceased donor kidneys accepted for transplantation have superior characteristics than those that were never used for transplantation and therefore do not appear in the data. The data were imbalanced according to different age, sex, and racial groups. These selection biases may negatively affect the accuracy of predictions made for candidate recipients or donors who fall into underrepresented populations.Another limitation is the level of detail available in the data set. The registry-level data from the SRTR certainly does not encapsulate all the characteristics of the clinical and functional status of donor-recipient pairs. Consequently, there must be factors that influence graft survival that were not present in the data. We also did not consider HLA typing, an important variable when matching donors and recipients, because of the complexity of modeling HLA mismatches. We must also consider the population of the United States, on which the models were built. Multiple factors, such as age, race, and state of residency, may reflect the socioeconomic status of patients, which itself may affect access to health care. To use the models built in this study in other countries, for example, in Canada, one must consider that some factors may differently affect graft survival.We analyzed and tested 5 ML models to predict kidney graft survival for a period of up to 15 years after transplantation. This study focused on patients who received deceased donor kidney transplants in the United States between 2000 and 2019 and included both NDD and DCD transplants. The resulting RNN predictive model was integrated into a decision support application designed to help kidney transplant practitioners and their patients make informed decisions regarding transplant options.


Space-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The proposed method can avoid selecting the user parameter. the proposed method constructs a dictionary matrix that is composed of the space-time steering vector by using the prior knowledge of the range cell under test (CUT) distributed in clutter ridge. Then, the estimated sparse parameters and noise power can be used to calculate a relatively accurate clutter plus noise covariance matrix (CNCM). This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method. The conformal array radar has many advantages, including minimal payload weight, the potential larger effective aperture, raised field of scan without the demand for cumbersome mechanical couplings, and easy integration with various sensor functions. The conformal array design also avoids signal modulation caused by antenna rotation . An effiSpace-time adaptive processing (STAP) is a technique for detecting moving target in the presence of a spreading clutter background . The opt2 DC) [In order to alleviate the influence caused by range dependence, a lot of signal processing methods have been proposed. These methods are Doppler warping (DW) , angle-D2 DC) ,11, and 2 DC) ,13. Thes2 DC) .To resolve the drawback of inadequate IID training samples and maintain the STAP performance, many algorithms have been proposed over the last decades. The reduced-dimension (RD) methods use a dimension reducing transformation before the adaptive processing to reduce the number of the required training samples ,16,17. BRecently, inspired by the development of sparse recovery theory ,21, clutIn this paper, a new signal and clutter model which consider the influence of polarization and mutual coupling is presented. This model can be used to arbitrary array geometry and in the complex scenes. A novel KASTAP method based on modified SLIM named with KA-MSLIM-STAP is proposed here. This method overcomes the problems mentioned above. The proposed method is based on SLIM combined with Laplace distribution, which provides a more accurate expression of signals. We utilize the knowledge of the clutter ridge to obtain the ideal space-time steering vectors of clutter. These vectors will form the dictionary matrix. Then, the proposed method estimates the clutter power and noise power and calculates a relatively accurate CNCM, which can obtain good performance in the conformal array clutter suppression.(1)A new clutter model which considers the influence of polarization and mutual coupling is proposed. The following theoretical analysis and simulations are based on this model.(2)The proposed method uses the accurate information of array geometry and radar system parameters to construct the CNCM based on the new received data model.(3)The original SLIM method has a hyper-parameter that should be chosen by user. The KA-MSLIM-STAP method utilizes the Laplace distribution to avoid selecting the user parameter. It can provide an excellent performance by using the CUT data only.(4)The proposed method is an iterative algorithm. It finds a local minimum of the cost function, but it converges rapidly. This method has lower computational complexity compared with other SR-STAP methods.The main contributions of the paper are listed as follows:The remainder of this paper is as follows. Notations: We use boldface lower case for vectors , two successive Euler rotation matrices are usually adequate. For the generalization of the conformal array signal model, three Euler rotation matrices mentioned would be used here.Now, conformal array spatial steering vector can be expressed in the following form:Given the influence of mutual coupling, a mutual coupling matrix We assumed that array transmits a series of The conformal array signal space-time steering vector can be expressed asIn clutter simulation, we make this assumption that the radar coherent processing interval (CPI) is short enough so that we can think that the velocity, RCS and polarization characteristics of the target and the RCS, polarization characteristics of the clutter patch are constant in a CPI. In this paper, we assumed that the wave received by radar array is the completely polarized wave whose polarized state does not change with time. From the above discussion, we can obtain that the conformal array can receive the polarized information of the wave. The polarization angle is  in Ref. .The Jones vector which can be used to express the completely polarized wave is showed asWith the above description, the data of the clutter patch from azimuth Therefore, the received data of the The detection can be represented by following .(19)H0:Considering that clutter patches are mutually independent, clutter and noise are independent of each other. The CNCM The STAP processor can alleviate the clutter component and provide coherent gain on target by performing an inner product between the weight vector Because the statistics of interference environment are unknown, the CNCM is usually unknown a priori. It should be estimated by some algorithms in order to obtain an excellent performance of STAP.The range cell can be separated into We note that the space-time steering vectors which is obtained from the clutter ridge are related with the array geometry and the radar system parameters. Because the elevation angle Here, we choose When given the range ambiguity situation, the dictionary matrix The received data from In the following subsection, we remark that In sparse recovery methods, we use as few atoms from dictionary matrix itten as (28)y^l= problem .(29)y^lIt can be equivalent to the following optimization problem .(30)minIn this framework, to utilize the above sparsity structure, we adopt the hierarchical Bayesian model . We suppIn Ref. ,(32)f, (36), (37), and later discarding irrelevant constants, The minimization problem of (38) could be resolved by utilizing the cyclic minimization (CM) method . First, We set the Then, suppose we have obtained We can set the complex derivative to zero and solve it for Then, solving (44), we can get the solutionSimilarly, setting the The solution of (38) can be obtained via repeating above iteration (42), (45), and (46) until this convergence criterion is satisfiedWe initialize the iterative process by applying a match filter, so that By the above iterative process, we can obtain the relatively accurate estimate of The weight vector could be acquired by (22). The KA-MSLIM-STAP method is summarized in Algorithm 1.Algorithm 1: KA-MSLIM-STAP Method.Input:Output:Steering Vector Dictionary MatrixDDataSpace-Time Adaptive Weight VectorStep 1Initialize values of parameters as:Step 2Until convergenceRepeat the following for Step 3Calculate CNCMStep 4Compute KA-MSLIM-STAP weight Due to the cyclic minimization character, the cost function is monotonically non-increasing in each iteration. This will indicate that the proposed method is convergent. We note that the cost function is not convex. Global optimum is not guaranteed. However, as we test numerically, excellent estimation results can be obtained, and the proposed method converges quickly. In the following simulations, this method shows excellent performance after five iterations.The computational complexity of the methods is measured in terms of the number of multiplications. Based on the analysis above, the proposed method consists of the estimate of The proposed KA-MSLIM-STAP method could acquire better performance by using the snapshot of the testing range cell only. The proposed method also possesses advantages, including low computational complexity and no need to select user parameters compared with other SR-STAP methods.In this section, some simulation results will show the performance of different methods with conformal array. In The relevant radar parameters are given in The clutter data of the 300th range cell will be the input data for the spectrums.In Compared with the ADC, SMI, and SR-RBC methods, the excellent performance could be seen to emerged at the 300th and 500th range cells with the proposed method, when the number of IID training samples is inadequate. The proposed method only utilizes the testing range cell and knowledge of the clutter ridge to construct the dictionary matrix and estimate the CNCM, which can avoid the problem of inadequate IID training samples.This subsection has exhibited the performance analysis of various methods in ideal conditions. In following subsections, we are going to probe the factors that impair the performance of these methods.In Because of the presence of model errors, there is a mismatch between the steering vector in dictionary matrix and the real steering vector of the clutter. This will result in the inaccurate estimation of CNCM. The model errors consist of array amplitude and phase errors.Intrinsic clutter motion (ICM) is an issue that will exist in practice. Many factors, due to both the radar system design and the environment, may in practice cause small pulse-to-pulse fluctuations in the clutter return. The presence of ICM requires a wider clutter notch and more adaptive degrees of freedom for effective cancellation. According to Ref. , consideIn In the presence of ICM, the IF curves with different methods have been shown in To further prove the performance of different methods, we use the probability of detection (PD) curves here. PD is a function of input signal-to-noise ratio (SNR) with a constant probability of false alarm In In this paper, we have presented a novel KASTAP method based on modified sparse learning via iterative minimization named KA-MSLIM-STAP to suppress clutter for a conformal array radar. The new method reformulates the clutter model by introducing the influence of polarization and mutual coupling. Then, a new sparse learning via iterative minimization is derived with Laplace prior. The proposed method exploits the knowledge of array geometry and radar system parameters to construct the dictionary matrix and utilizes Laplace prior to avoid selecting user parameter. The effectiveness of the proposed method is verified through numerical simulations under ideal and non-ideal conditions (model errors and ICM). The results show the superiority of the proposed method in terms of clutter suppression and target detection. Moreover, we show that the proposed method has lower computational complexity compared with other SR-STAP.For simplicity, we ignore the influence of the mutual coupling. Hence, this could be the subject of future work.


Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities  might be more frequent than others . None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors’ data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers. Regular physical activity plays a vital role in improving the health of individuals, whether it is a child under 5 or an elderly above 65. Physical activity has well-documented health benefits and can extensively improve the health and well-being of individuals and reduce the risks from noncommunicable diseases. Both moderate- and vigorous-intensity physical activity improve health. Physical inactivity increases the risk of noncommunicable disease mortality and puts inactive people at a 20–30% higher risk of death in comparison to physically active people . PhysicaPhysical activity and the promotion of healthy living can significantly lower the risks of non-communicable diseases. It also serves as the best remedy for obesity . ObesityObesity is one of the prevailing problems responsible for several health issues and medical conditions. Weight loss surgery, also referred to as bariatric or metabolic surgery, is one of the possible solutions for extremely overweight people. While the surgery can result in significant weight losses, it is still not termed a cure for obesity. Obesity is not a matter of concern only for the younger and older adults as it has become very common in children as well ,6. ThereThe increasing stress on the healthcare systems and the need to promote healthy living urge new measures to promote physical activities. The initial step in encouraging the physical activity is the ability to be able to quantify the physical activity into individual components of tangible impact. As such, physical activity classification can serve as a foundation by recording and transforming physical activities of an individual to give accurate quantification of a daily routine, thus encouraging active and healthy living. This highlights a clear need to develop feasible solutions to monitor the activities of daily living (ADLs) as a measure to avoid/overcome obesity.Physical activities and exercise both serve as necessary measures for healthy living and maintaining healthy weights. Exercise is the subbranch of physical activity, and it is more structured, repetitive and planned with an intention to maintain or improve body fitness . The proThe physical activities are logged in several ways where questionnaires and direct observations are conventionally used. The logging of activities requires information on the type of activity performed, the duration for which it was performed and the intensity of the activity. An example could be walking, where the information about how much time is spent walking in a day/week, walking pace, etc. However, these are not as accurate and add additional time commitments from the observee and observer. Therefore, novel techniques are needed to use technology-driven solutions to log the type of activity performed, its duration and intensity.The recent developments in the miniaturization of inertial sensors equipped with state-of-the-art processing and communication capabilities lay the foundations for the smart health and activity monitoring using machine learning techniques ,9. WearaThese small battery-operated wearable IMUs not only offer ease of use but are also equipped with transceivers to accumulate the vitals and activity data of patients to fog/cloud. The data accumulated at cloud or fog devices can be further processed using machine learning techniques ,12 to idIn , the autIn , the autIn , the autIn , the autThe existing literature and research studies use diverse techniques for activity classification with a wide scope of applications ,19,20,21The paper compares several machine learning techniques to identify the best-suited activity classification techniques on a balanced dataset.The physical activity dataset is intentionally skewed to introduce class imbalance and to evaluate the abilities of six well-known machine learning classifiers.The proposed work compares the performance of the selected state-of-the-art machine learning algorithms with different training splits and various degrees of imbalance and identifies the best-suited machine learning techniques.The main contributions of the paper are:The rest of the paper is organized as follows: In this work, a data communications and activity classification framework is presented, as shown in The wireless communications framework intends to collect the data from different sensory elements mounted on the patient’s body. In addition, these data need to be relayed to the access points to be transferred over longer distances. Considering the applicability of the proposed framework in medical facilities, private indoor environments as well as public outdoor places, a multi-layer hybrid network is recommended. A modular approach must be followed to define a hybrid network to enable flexibility and scalability in network sizes. The two main operational blocks in the hybrid network framework consist of the body network and the Internet of Things (IoT) framework.The sensory data collected from the potential multisensory agents on the body are communicated to the body communications hub (BCH) using the body network. A graphical representation of the body network is presented in The study utilizes a publicly available dataset  to detecRaw 3D accelerometer and gyroscope sensory signals obtained from the smartphone underwent several preprocessing and feature extraction steps to derive features that are fed to a machine learning algorithm later on to profile and classify ADLs.The preprocessing steps involved are (i) low pass filtering using butter worth 3rd order filter at cutoff 20 Hz and (ii) median filtering. The acceleration signal is then divided into body acceleration and gravitational acceleration signals. To achieve this, a frequency of 0.3 Hz is used to separate the gravitational signals (<0.3 Hz) from the body acceleration signal (>0.3 Hz). Furthermore, jerk signals are derived from the acceleration signal and gyroscope signal by taking their derivates. The cadence is also derived from these signals. To analyze the frequency components, fast Fourier transform (FFT) is also computed to detect the trends and variations occurring in the frequency domain when different ADLs are performed. These derivations resulted in a total of 17 signals, including the original 3D gyroscope and 3D accelerometer signals. Further details about the feature extraction process can be found in .The originals signals (3D accelerometer and 3D gyroscope) and the aforementioned derived signals are further processed using the windowing method to extract more features. The window length is set to 2.56 sec (128 samples of data) with 50% overlap (64 samples). Several statistical, time and frequency domains features are obtained from these derived signals, and each time, window of 128 samples is as follows: mean, standard deviation, median, signal magnitude area, maximum value, minimum value, angle between two signals, frequency domain band energy, skewness, kurtosis, average frequency component, maximum frequency component, correlation coefficient between signals, autoregressive correlation coefficients, interquartile range, sum of squares (energy) and band energy [Several experiments have been conducted using a different split of the training and testing dataset to train the machine learning classifiers and to observe the performance of different machine learning classifiers in overbalanced and imbalanced datasets. Class imbalance is a critical issue in machine learning, and this often occurs when one or few classes are underrepresented (having fewer samples) than other classes. This often creates biases during the training stage of the machine learning algorithm, and the performance of underrepresented class or classes is highly affected by these imbalanced distributions.Therefore, in this study, we also investigated the impact of imbalanced classes on the performance of the different machine learning classifiers by conducting different experiments. In addition, we also investigated which machine learning algorithms are relatively less sensitive to class imbalance or performed better than others.The class distribution used in the first experiment or experiment 1 (E1) is presented in After designing experiment 1, six further experiments are conducted by inducing class imbalance in each class to observe the performance of the machine learning classifier in classifying different imbalanced ADLs. It is important to note that training samples of the underrepresented classes are different in each of these experiments (from E1 to E7), while the test samples remain the same as per the original distribution presented in We implemented several machine learning algorithms on the dataset generated from 7 experiments and observed the performance of the different machine learning in classifying the ADLs in a balanced class distribution scenario (E1) and imbalanced class distribution scenarios (from E2 to E7).The classifiers used in this study are support vector machine (SVM), Gradient boosting (GB) classifier, Extreme Gradient boosting (XGB) classifier, catboost (CB) classifier, AdaBoost classifier using decision tree (ADA-DT) and AdaBoost classifier using random forest (ADA-RF) ,31,32,33All the simulations are performed in Python using its associated libraries. The parameters used to train these classifiers are as follows. The XGB parameters are maximum depth = 50, minimum child weight = 2, number of estimators = 100 and learning rate = 0.16. The GB parameters are objective function = multiclass, maximum depth= 50, learning rate = 0.1 and number of estimators = 100. The CB parameters are learning rate = 0.15, depth = 10 and loss function = Multi Class. The SVM parameters are kernel = linear, class weight = balanced and complexity = 1. The ADA(DT) parameters are Tree = Decision Tree Classifier with maximum depth = 10 and number of estimators = 100. The ADA(RF) parameters are Tree = Random Forest Classifier with number of estimators = 100, maximum features = auto and number of estimators = 100. Macro averaged F-score is used as a performance metric to compute the performance of the different classifiers in classifying the ADLs of daily living. The expression to calculate the F-score is expressed in Equation (1).The performance achieved using various machine learning classifiers for seven experiments (E1–E7) are presented in It is fairly evident from In experiment 2 (E2), only the walking class is imbalanced during the training stage with a total of 100 samples, while number of samples of all other classes remained the same as per the original or balanced distribution (please see In experiment 3 (E3), walking and walking upstairs are imbalanced classes at the training stage with a total of 100 samples each. The results in In experiment 4 (E4), the underrepresented classes or imbalanced classes are walking, walking upstairs and walking downstairs. The best performance of 87.88% is achieved by the SVM classifier and the second best candidates are ADA (DT) and ADA (RF), with performance of above 83%, as shown in In experiment 4 (E4), walking, walking upstairs, walking downstairs and sitting are underrepresented and imbalanced as compared to other majority classes. The results shown in During experiment 6 (E6), all classes are underrepresented except the majority class represented class, which is lying. The performance analysis of the classifiers using the E6 train/test split is shown in In experiment 7 (E7), all the classes are balanced with equal samples; however, the samples are very low  classifier. The possible rationale behind the high performance of the SVM in all the experiments could be due to the fact that it uses an adaptive weighting approach at the training stage . This adThe analysis of the class imbalanced datasets also suggested that most of the machine learning classifiers investigated in this work are sensitive to class balance except SVM, which is less sensitive to class imbalance due to its inherited property of adaptive weighting at the training stage to compensate for class imbalance up to some extent. The direction that can be opted in future works is to investigate the methods that can deal with class imbalance by performing a variety of data-handling techniques. These methods include synthetic minority over-sampling technique (SMOTE) , adaptivNevertheless, it is worth mentioning that the findings of the study are very encouraging and suggest that the proposed methods can obtain very high performance of above 96% in classifying the activities of daily living . This provides the strength of the proposed physical activity classification system and its applicability in real life conditions. Promoting quality of life and tracking daily life activities are strongly correlated with obesity since active life patterns discourage sedentary behaviors and reduce the onset of several diseases , including obesity. Profiling such ADLs for a relatively longer duration  not only provides a detailed insight to individuals but also provides a detailed overview of the activity behaviors to the healthcare care practitioners, who can then tailor and customize the treatment to those suffering from obesity and other severe conditions.The proposed physical activity classification system is applicable to a variety of different application scenarios in daily life conditions. Since the dataset used in this study utilizes the in-built motion sensors (accelerometer and gyroscope) of smartphones, there is no need for a separate sensing unit or equipment to acquire the activity patterns and retrieve sensory data. This sensory dataset acquired through smartphone can benefit from the on-device processing unit to compute the task requiring low computational power. Further processing can benefit from the scenario presented in While the proposed research offers great to possibly deal with real life situations, there are certain limitations. One of such limitations is that it uses the dataset of only healthy individuals due to the unavailability of the sensory datasets collected from overweight individuals. Therefore, future works should focus on collecting and analyzing the dataset of only obese or overweight individuals to classify the activity patterns. It is important to mention that conducting longitudinal studies for overweight cohorts to record sensory data requires significant resources. This is one of the reasons why the publicly available dataset is used for the analysis and classification of physical activities in the present work. In future work, it would also be interesting to investigate how the deep-learning-based machine learning classifiers’  , long-shThe study developed a novel physical activity classification system and investigated the impact of class imbalance on the performance of machine learning classifiers. The findings concluded that the proposed system is capable of classifying daily life activities such as sitting, standing, walking, lying, walking upstairs and walking downstairs with very high accuracy (above 96%). In addition, a thorough analysis of the impact of class imbalance on the performance of classifiers’ is also investigated. A number of experiments are conducted with class imbalance. The findings also suggested that the weighted SVM with penalized approach offered the best classification performance, followed by the ADA(RF) in most of the experiments. Out of the six classifiers evaluated, the SVM, with an overall performance of above 80% in all the class imbalance experiments, depicts its ability to deal with real life situations with certain types of activities being underrepresented.