In recent years, there has been an increasing focus on the need to integrate formal knowledge with clinical experience in the pre-clinical years since the initial years of medical education play an important role in shaping the attitudes of medical students towards medicine and support the development of clinical reasoning. In this study, we describe approaches that involve real patients and patient-simulation-based methodologies to teach gastroenterology to second year medical students. Our goals were to (i) demonstrate bio-psychosocial aspects of clinical practice, (ii) demonstrate commonality of gastrointestinal ailments, and (iii) help understand complex gastroenterology concepts. We used two main approaches including brief, pre-prepared questions and answers discussing with the patients in various sessions throughout the course and a two-hour session that included patient participation, patient simulation modalities with high fidelity mannequins, a lightening round of interactive cases, and a Patient Oriented Problem Solving (POPS) session. The approaches improved the effectiveness of the delivery of the content-heavy, fast-paced GI course and provided opportunities for the students to think about gastroenterology from both basic and clinical points of view. The approaches involved peer teaching, which supports knowledge acquisition and comprehension. Very positive feedback and overall engagement of students suggested that these approaches were well-received. The initial years of medical education play an important role in shaping the attitudes of medical students towards medicine and training them for their future role as a physician ,2. The CThe Cooper School of Rowan University (CMSRU) received full LCME  accreditation in 2016 and graduated its third cohort of students in 2018. The pre-clinical curriculum integrates basic and clinical sciences and includes a mix of lectures, laboratories, and active learning activities. Our pre-clinical courses are comprised of approximately six hours with each Active Learning Group sessions (ALG), lectures, and afternoon lab sessions. Some courses also include team-based learning (TBL), jigsaws, or other interactive sessions. Attendance for the ALGs and some afternoon sessions is mandatory. Each ALG group consists of eight students and two faculty facilitators (one basic science and one clinician) and meets for two hours, three times per week. Students are given opportunities involving patient interaction such as the courses, Week on Wards (WOW), and an Ambulatory Clerkship at the Cooper Rowan Clinic. The Gastroenterology (GI) course is taught in the fall semester of the second year of medical school. It is a four-week course that integrates the biochemistry, pathophysiology, anatomy, histology, and embryology along with the signs and symptoms, diagnostic methods, and treatment modalities of GI, hepatic, and biliopancreatic diseases and nutrition.The sheer volume of information that had to be presented to the students during the GI course made it a very fast-paced and somewhat daunting course. In response, we incorporated several methodologies in the course, which allow the students the opportunity to pause and reflect on the content being taught within the context of the big picture. Previously, we described the reception and efficacy of an interactive activity on nutritional pathology that was introduced in this course. The activity included discussion about various nutrition pathologies based on real-life cases, which made the exercise very clinically relevant to the students. The activity was very well received and helped enhance competency in nutrition for the students . In thisWe used two main approaches: inclusion of (i) brief, pre-prepared question-and-answer sessions with patients in various sessions throughout the course and a two-hour session that included various elements such as patient participation, patient simulation modalities with high fidelity mannequins for demonstration of upper endoscopy and colonoscopy, a lightening round of interactive cases with questions and answers, and a new activity that is reminiscent of the Patient Oriented Problem Solving (POPS) methodology ,17,18,19Helicobacter pylori, gallstones, celiac disease, liver cirrhosis, ulcerative colitis, and irritable bowel syndrome. Most of the patients who participated were people who the students saw every day such as faculty and staff. The course directors met with the patients to learn about their disease, the symptoms they experienced, the treatments they were undergoing, the biopsychosocial aspects including the challenges they faced, and how these diseases influenced them and their families. We then prepared the key features from each of these discussions in the form of questions and answers and shared these with the respective participating patients for accuracy. We also included a note for the GI concept that we wanted to emphasize for each disease. This pre-preparation allowed the patient participants to be brief yet effective with respect to time and delivery in the specific class sessions. The patients participated in various sessions throughout the course. Two patients participated as content experts in ALGs with one of the course directors. Two patients participated during lectures and in the interactive session on diarrhea described below. The entire class met the patients who participated in the ALGs or in the diarrhea interactive session since attendance for these sessions is mandatory. Attendance at lectures is not mandatory. Since the lectures are recorded and the recordings are accessible to the entire class, those students who did not attend the lecture in person were able to watch the questions and answers sessions with the patients. We selected six patients with each suffering from a different gastrointestinal disorder to participate in the course activities. The disorders were infection with Diarrhea is caused by a variety of gastroenterology disorders including both with infectious and non-infectious etiology. Our main goals were clinical aspects and physiological manifestations of different GI diseases that cause diarrhea and to demonstrate to the students how endoscopy and colonoscopy can be used for the diagnosis of some of these diseases.Escherichia coli, Giardia lamblia, Vibrio cholerae, Clostridium difficile) during the group activity, which was based on the Patient Oriented Problem Solving (POPS) methodology [Demonstration of endoscopy and colonoscopy using simulation modalities (25 min)Questions and answers session with real patient suffering from IBS (20 min)Lightening round of four, non-infectious GI diseases with diarrhea (15 min)POPS session with four infectious diarrhea cases and post-quiz (60 min)The session was carried out in a large lecture hall with two faculty facilitators who are the course directors of the GI course. It was two-hours in duration and was divided in two major parts. The first part focused on non-infectious diarrhea and included a virtual upper endoscopy and colonoscopy demonstration, question-and-answer time with real patients, and a lightening round of upper endoscopy/colonoscopy-based interactive diarrhea cases. The gastroenterologist, serving as the GI co-course director, used simulation modalities such as standardized patients and high fidelity mannequins that realistically replicate the clinical environment to demonstrate how upper endoscopy and colonoscopy is carried out. The demonstration was also shown on the computer screens using screen-in screen projection. Students were able to see the gastroenterologist carrying out these procedures and, at the same time, were able to see what he observed in the simulated patient on the computer screen. The lecture hall in which this activity was carried out has computer screens on all the walls, which allows the students optimal viewing regardless of their location in the classroom. The gastroenterologist provided live commentary as the scope was travelling down the simulated patient’s GI tract. He also asked the students questions about different regions of the GI tract when the procedure progressed. This was followed by a question-and-answer session with real patient who suffered from irritable bowel syndrome (IBS). The last component of the demonstration was a lightening round in which four case stems were displayed individually with their endoscopy/colonoscopy presentations and discussions followed about what the underlying diseases were. The diseases represented non-infectious GI diseases with diarrhea as one of the main symptoms including IBS, Crohns, Celiac disease, and VIPoma. The second part of the session included a group activity and a post-quiz. The students learned about four infectious diseases causing diarrhea  that corresponded to one of the four infectious diarrhea cases. They were asked to sit in their group when they arrived for the session.Escherichia coli), purple (Giardia lamblia), yellow (Vibrio cholerae), or purple (Clostridium difficile). Having colored sheets was very useful for fast distribution during the session. During this part of the session, the students were given one colored page containing the information about their stem with the clinical presentation and patient history as well as laboratory data for one of the four diseases. The underlying cause, physiology and treatment options for each disease was presented in question-and-answer form to facilitate thought-provoking discussions among students. Important concepts were underlined or bolded. Highlights of each case are given in Each student within the group was assigned to learn about one of the four infectious diarrhea cases. Each case was assigned a color such as green  collects students’ overall evaluation of the course as well as the evaluation of each of the course sessions  as a standard practice. These evaluations are collected anonymously following the school policies and are distributed to the course directors and participating respective faculty in an aggregate manner. We also carried out an additional evaluation of the approaches described here via a paper survey. The standard, electronic evaluation of the diarrhea session carried out by the CMSRU OME is designated here as Evaluation A. The additional paper survey collected after the completion of the diarrhea session is designated as Evaluation B. The comments pulled from the two questions asked in the standard, electronic overall course evaluation as described below are designated as Evaluation C.The students are asked to evaluate each session in a course. Since the students have to complete a large number of surveys, the school has adapted a policy that reduces the number of surveys each student has to complete in a course. According to this policy, about half of the class is asked to evaluate each interactive session. Therefore, 37 students completed the survey for the diarrhea POPS session. We have observed that the outcomes of these surveys are representative of the perception of the entire class. The standard evaluation form that is used for any interactive session  includes eleven Likert scale questions: (i) the objectives of the session were clear, (ii) the session was well organized, (iii) the session was relevant to my education, (iv) the content helped me meet session objectives, (v) the session content was related to course objectives, (vi) the session stimulated me to want to learn more about the subject, (vii) the faculty maintained my interest, (viii) the faculty demonstrated appropriate knowledge, (ix) the faculty explained the material clearly, (x) the faculty used questions and student participation effectively, and (xi) the faculty demonstrated professionalism. Students are also asked to provide qualitative comments on the session.As mentioned above, the evaluation form A is a common form used for any interactive session in any given course by the CMSRU OME. To maintain consistency for evaluation across various courses in the curriculum, the evaluation forms are not modified for individual courses/sessions. We, therefore, decided to include an additional evaluation in the form of a paper survey to assess the reception of the inclusion of patients throughout the GI course along with the inclusion of the patient simulation in the diarrhea session. The authors have used additional paper surveys to assess efficacy of individual interactive sessions before ,16. The As mentioned above, in addition to the evaluation of each of the course sessions, the CMSRU OME collects students’ overall evaluation of the course as a routine practice. The entire class is asked to complete the overall evaluation for the course. Two of the several questions included in this form are: (i) Were there any sessions that were particularly outstanding? and (ii) What was the most vivid/thought-provoking, useful, or otherwise memorable information they learned in the course? We used comments received for these two questions as evaluation C for this study.In order to do well in the post-quiz, each student in a group needed to be a responsible member of his/her group and effectively present the respective material to his/her teammates. The students also needed to carefully read and synthesize the information and understand the underlying concepts. This was especially imperative since this was the first time the diarrhea cases were presented to them. The efficacy of the session as evidenced by post-quiz scores demonstrated that the session was highly effective as only one group got one question wrong. The concepts were further reviewed and consolidated in a one-hour lecture the following week.A, B, or C. The thematic analysis of the comments suggests that the main aspects liked by the students were team work, peer learning, helpful for understanding, and enjoyable, engaging involvement of patients including humanizing medicine, retention of concepts, and patient simulation. There were several additional positive comments on the inclusion of the patients, patient simulation, and also about this POPS in the various other evaluations collected for the GI course. It is also interesting to note that there were no negative comments about these approaches in any of the evaluations collected, which suggests their very positive reception in general.The students’ reception of these approaches was very positive. All the comments received for the open-ended questions in evaluations A, B, and C are given collectively in Results of the eleven Likert scale questions in the Evaluation A are presented in The results of the responses received for the three Likert scale questions in evaluation B are presented in There were a total of 20 groups with 80 students in groups of four who took the post-quiz. 19 groups answered all of the eight questions in the post-quiz correctly while one group got one question wrong. This was remarkable since the students were exposed to new material for the first time during this session. The concepts discussed in the POPS session were briefly reviewed and consolidated in lectures following the session. The students did very well on the questions based on these topics in the final course exam. Early clinical exposure (ECE) integrates the knowledge of basic and clinical sciences and the psychosocial aspects of the medical practice . This apHigh-fidelity patient simulation has been increasingly used as a teaching modality for health professional students. Life-size manikins that mimic real patients are used to simulate normal and disease conditions. These have been mainly used for residents or medical students in their clinical years. However, some studies indicate that patient simulators have been successfully used for first-year medical students coupled with problem-based learning to reinforce curricular concepts while bringing the cases to life ,29,30,31We had given careful consideration in selecting patients for participation in the GI course and the aspects we wanted to emphasize for each disease. Important points include prior preparation of question-and-answer-based, patient-doctor scripts allowing the information to be delivered in a very succinct and time-efficient manner. Most of these patients in our learning activity were people the students encountered at the school every day. This emphasized the point about how common gastroenterology diseases are. Additionally, since the students knew these ‘patients,’ they were very comfortable asking them questions. The patients were chosen to emphasize specific aspects of GI disease. For example, we were able to discuss the genetics of and cross-relationship between different autoimmune disorders using the celiac disease patient while the gallstone patient was able to demonstrate how the gallstone disease can switch between acute and chronic phases, which is an aspect that the students usually find difficult to grasp. Patient participation encounters were brief given the time-constraints of the course, which were dispersed in different sessions throughout the course and aligned with the materials taught during respective weeks and ensured that all the students were exposed to the patient interviews. Each patient interview included the challenges they face living with a GI disease, which humanized the experience for the students.Important considerations for the two-hour diarrhea session include the patient simulation that was designed to demonstrate the healthy state versus the disease state of the GI system using scoping methodologies. Students were asked questions during this demonstration about which area of the GI tract they were observing along with comments on what they expect to see in that organ for a particular disease. This created further engagement in the demonstration and encouraged them to think about and build on the GI concepts they had learned in the course. The interactive lightening round cases and questions further consolidated the diagnostic role of endoscopy and colonoscopy in various GI disorders. The POPS cases were designed to promote team work and peer teaching to learn new materials in a time-efficient manner, which is consistent with the notion that peer teaching enhances comprehension since it depends on sharing both cognitive and social congruence ,33,34. TAs evidenced by the very positive student reception of these approaches and their detailed comments, our goals for these approaches were achieved. Students stated that inclusion of patients in the course allowed them to put a ‘face’ on the disease and gave a practical connection that helped them to visualize it. They experienced patients in real time with respect to their needs and challenges, which provided them with better insight into the pathophysiology of the case at hand. These approaches gave them an opportunity to ask questions, which consolidated their learning and enhanced their connection to the patients. Since the interactions were designed in the form of questions and answers with the gastroenterologist course director of the GI course, the students had the experience of how a physician might interact with a patient and create a doctor-patient bond rather than solely looking at the patient as a case to treat. This may contribute to them becoming more invested in their future patients’ welfare. They also stated that the interactive approaches helped make the concepts more memorable and easier to understand.One limitation of this study is that we examined a cohort of medical students in one medical school. However, benefits of early patient exposure have been reported in various healthcare disciplines such as nurse anesthesia students, dental students, and optometry students ,35,36. AWe designed and implemented several approaches that involved real patients and patient simulation in the M2 GI course to stimulate student interest in gastroenterology, help them understand complex GI concepts, and demonstrate the bio-psychosocial aspects of the clinical practice. The approaches improved the effectiveness of the delivery of the GI course in that these learning activities provided opportunities for the students to think about gastroenterology from both basic and clinical perspectives. The POPS activity was based on peer teaching that involves cognitive development as well as social collegiality and, therefore, plays an important role in enhancing knowledge acquisition and comprehension. The excellent overall class performance in the post-quiz demonstrated that the students were able to learn and effectively apply new concepts in a short amount of time. Extremely positive feedback about the experience and the overall engagement of students suggested that these approaches were well received, which encourages us to implement them in other courses in our curriculum.


We examined variation in the dynamics of the brain states with the Mapper graphs, and to determine how this variation relates to measures such as response time and performance. The application of the Mapper method to MEG data detected a novel neuroimaging marker that explained the performance of the participants along with the ground truth of response time. In addition, TDA enabled us to distinguish two task-positive brain activations during 0-back and 2-back tasks, which is hard to detect with the other pipelines that require collapsing the data in the spatial and temporal domain. Further, the Mapper graphs of the individuals also revealed one large group in the middle of the stimulus detecting the high engagement in the brain with fine temporal resolution, which could contribute to increase spatiotemporal resolution by merging different imaging modalities. Hence, our work provides another evidence to the effectiveness of the TDA methods for extracting subtle dynamic properties of high temporal resolution MEG data without the temporal and spatial collapse.The increasing availability of high temporal resolution neuroimaging data has increased the efforts to understand the dynamics of neural functions. Until recently, there are few studies on generative models supporting classification and prediction of neural systems compared to the description of the architecture. However, the requirement of collapsing data spatially and temporally in the state-of-the art methods to analyze functional magnetic resonance imaging (fMRI), electroencephalogram (EEG) and magnetoencephalography (MEG) data cause loss of important information. In this study, we addressed this issue using a topological data analysis (TDA) method, called Mapper, which visualizes evolving patterns of brain activity as a mathematical graph. Accordingly, we analyzed preprocessed MEG data of 83 subjects from Human Connectome Project (HCP) collected during working memory  Understanding relations or interactions that link many elements of neurobiological systems is highly important area of research due to the recent development of imaging methods and the increasing availability of neural data. Several analytical methods to explore the time-evolving patterns of interaction between the regions of the brain are proposed to extract valuable insight from mesoscale neuroimaging data including functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), magnetoencephalography (MEG), and electroencephalography (EEG). These approaches include the tools from topology and graph theory in mathematics, algorithm development in computer science, statistical mechanics in physics, and systems engineering.Earlier techniques focusing on the inter-regional functional connectivity (FC) over the entire time scan time successfully investigate modular structure of brain regions in fMRI ,2,3 and TDA methods  are not new to the neuroimaging community. Some remarkable applications of persistent homology include the detection of intrinsic structure in correlations between neural spike trains , analysiIn this study, we resorted to the Mapper method to investigate the dynamic features of the MEG data during cognition. One advantage of the Mapper over the other unsupervised machine learning (ML) techniques such as independent component analysis (ICA)  and clusMapper can visualize the complex multivariate relationship of various variables simultaneously as a network diagram. The resulting topological map can be investigated to understand the relationship of each variable across the topological space to identify groups of clustered individuals that can be further probed for specific relationships among outcomes, validation and hypothesis testing. In , applican-back task. With MEG, the sequence of neural events can be readily tracked with millisecond resolution. Hence, it is ideal for investigating the cognitive tasks that involve rapid decision making as it can detect directly the fast fluctuations in neuronal coherence. It is therefore natural to expect temporally more subtle biomarkers when applying Mapper pipeline to a MEG dataset rather than a fMRI dataset of the same cognitive task.In , the autApplication of the Mapper method to MEG data would detect novel neuroimaging marker that explains the performance of the participants along with the ground truth of response time.TDA would distinguish two task-positive brain activations during 0-back and 2-back tasks, which are hard to detect with other pipelines due to the low temporal resolution of the imaging modality and the collapse of the data in the spatial and temporal domain.The Mapper graphs of the individuals would reveal one large group in the middle of the stimulus detecting the high engagement in the brain with fine temporal resolution. Hence, our pipeline with other imaging modalities  could increase the spatiotemporal resolution in the analysis of brain dynamics.Here, we suggest an alternative visualization of MEG dataset from Human Connectome Project (HCP) using the Mapper algorithm to get new insights from the brain patterns dynamics. The nature of Mapper, which represents similar data points closer than dissimilar ones in a mathematical graph, enabled us to visually trace the brain states during the 0-back and 2-back working memory tasks. To show efficacy of our method, we tested the following hypotheses:Working memory data are available for a subset of 89 subjects  with two sessions per task. Each task has a similar or identical experimental design to the corresponding task acquired during fMRI imaging. In working memory task, people have to remember the occurrence of a n-back previously shown item  dataset. It is publicly available from the Human Connectome Project (HCP) consortium . It was or faces . Data arWe used the parcellated results  17-network parcellation ) of the To these data, we also added subjects’ accuracies from 0-back and 2-back tests and median reaction time for these tests. If one of these pieces of information were missing for a subject, we excluded it from the dataset.The TDA-based Mapper method is considered as an unsupervised machine learning technique to analyze high-dimensional data. Unlike the supervised methods such as support vector machines (SVM) that compare predefined groups, it is used to discover previously unknown groups within a dataset. Mapper algorithm returns a compact representation in the shape of a graph which preserves the geometric properties of the underlying dataset; hence, it helps to point out complex multivariate features of the input data in an unbiased and data-driven manner. Moreover, Mapper can apply a set of dimension reduction techniques , singular value decomposition (SVD), and stochastic neighborhood estimation (SNE)) to the data without the necessity of prior categorization or extensive cleaning, which might result in the loss of salient information.m points in n-dimensional space. The first step of the Mapper is to assign values to the data points; these values, called filter values, can be the output of a density estimator, measure of centrality, PCA, SVD, stochastic neighborhood estimation (SNE), etc. In the second step, Mapper divides the filter space into the overlapping bins. The shape of the bins  depends on the filter values. For example, if the range of the filter values is the interval We now explain the Mapper pipeline that we used to produce the Mapper graph. One can refer to ,35,36 foWe note that the Mapper algorithm has several parameters: the number of intervals, the overlapping percentage, the distance, and the clustering algorithm. Increasing the number of intervals increases the complexity of the final visualization as the number nodes increase. As a result, each node contains only the highly similar data points. On the other hand, the change in the overlapping percentage effects the number of edges in the final graph. An increase in the percentage results in an increase in the number of edges. Once the network is extracted, the parameters can be tuned to focus the network similar to focusing a microscope on an image.Mapper graphs can be investigated by focusing its structures at different scales, which include the local scale analysis of nodes (or edges), the intermediate  structures such as core-periphery and modality  and the We applied the Mapper algorithm to the MEG working memory task data from HCP. Our goal was to trace the brain activation patterns during the 0-back and 2-back memory tests of each participant.We refer to the flowchart in As a first step in the flowchart, we obtained the source-level processed data from HCP, which has 91 time-points vs. 642 voxels. The preprocessing step returned the parcellated results of source-reconstructed Working Memory averaged event-related and time-frequency responses using the Yeo et al.’s (2011)  17-netwoTo proceed with the algorithm, we next chose a metric to measure the similarity between the vectors  in the time space. We used the Euclidean metric, which has proven to be successful in previous applications of neuroimage data ,28.baseline brain states recorded before the stimulus. We chose a reference brain state The second step in the flowchart is to set a reference state to be used in filter selection. The first 30 out of 91 time-points in our data were given as the As a next step, we assigned a filter value to each brain state as the Euclidean distance between that state and the ested in  to traceThe metric and the filter together lead to the construction of the mapper graph via the next three steps indicated in the flowchart. In the first step, the brain states are binned by dividing the filter values into overlapping intervals. The number of intervals as well as overlapping percentage  is rigorously discussed in the next subsection. Two brain-states fall in the same bin if their distances from edge see .To trace the brain patterns, we used an unconventional visualization of the Mapper graph. In t, called return-point, during the stimulus where the brain state re-visits the beginning of the stimulus  see .We next compared the return-points obtained from 0-back (mapper0B) and 2-back (mapper2B) graphs of each participant with the accuracy and response time data from HCP , it is possible to find online markers for the relationship between dynamic neural networks and task outcomes . FollowiSecondly, TDA enabled us to distinguish two task-positive brain activations during 0-back and 2-back tasks, which is hard to detect with the other pipelines that require collapsing the data in the spatial and temporal domain. We demonstrated that the return-point of 0-back tasks are significantly smaller than the return-points of 2-back task providing a novel online marker to differ 0-back and 2-back tasks. This subtle marker might not be possible to obtain with a fMRI dataset because of the low temporal resolution. Similarly, more conventional methods such as the sliding window might fail as there is loss of high temporal resolution while choosing time-course intervals during implementation of these methods. Our approach is robust to parameter perturbation unlike the other methods for which the choice of the parameters is crucial to detect the characteristics of the dynamic evolution of the brain states.Furthermore, the Mapper graphs of the individuals reveal one large group in the middle of the stimulus, detecting the high engagement in the brain with fine temporal resolution, which could contribute to increase spatiotemporal resolution with other imaging modalities. The resulting big clusters during the stimulus indicate the similarity in the whole brain configuration during a high engagement, while the size of the clusters gets smaller at the beginning and the end of the stimulus. This is in line with the earlier study using ICA based characterization of dynamic MEG networks during a working memory task. The transiently forming and dissolving networks in different time intervals detected and characterized in  are compIt is also notable that the mapper graphs are highly variable across subject. This variation across subjects has been noticed in earlier static MEG connectivity studies ,46. As nAs a future work, one can also investigate the MEG dataset that contains different tasks such as math-story, motor and working memory. Due to the high temporal resolution of MEG, such data are very high dimensional and require high computational power to apply Mapper algorithm, unless they are collapsed on the temporal scale. Alternatively, the algorithm can be run in parallel for every interval and the overlapping clusters can be connected in the next step. Another future direction is to analyze the Mapper clusters quantitatively using graph centrality scores. In that case, we expect that the main clusters  during stimulus will posses high centrality scores. We can also look for a better model than linear regression to describe accuracy in terms of return points and reaction time. Finally, other TDA methods such as persistent homology might give interesting insight on the dynamic features of brain imaging data. One possible approach for studying graph representations of time series of dynamical systems is suggested in .Our approach can be later used to explore mental disorders such as autism and schizophrenia where cognitive impairment is common and unrelated brain regions are expected involve during the cognition. The certain characteristics of the Mapper graphs may differ between control and patient groups; hence, it might help to find the underlying neural reason for the cognitive impairment.http://db.humanconnectome.org.The MEG data used in this study are publicly available from the Human Connectome Project (HCP) consortium: In summary, our work demonstrates that TDA methods can be successfully applied to extract new perception from the complex brain dynamics associated with high temporal MEG data during a cognitive task. Unlike the standard techniques, it achieves these results without the need of temporally and spatially collapsing the data. It is also robust to parameter perturbation while the parameter choice is critical in the widely used methods such as the sliding window. Finally, it provides a compact and easily readable visual presentation of the data preserving the geometric structure.


Sepsis represents 20% of admissions in non-cardiologic intensive care units (ICU) and has a high mortality rate. Even with the advances in antimicrobial and vasopressor therapy, mortality remains high. Thus, early diagnosis of sepsis is necessary to avoid septic shock, which is associated with a mortality rate of 40% or moreIn 2016, the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM) published new definitions, simplifying the nomenclature of sepsis, defining it as life-threatening organic dysfunction caused by an unregulated response to infection. Septic shock is when sepsis occurs with the presence of hypotension, requiring vasopressor therapy to maintain mean blood pressure (MBP) ≥ 65 mmHg associate with lactate ≥ 2 mmol/L, after adequate fluid resuscitationIn response to the lack of improvement in the mortality of patients with sepsis, the same societies created the campaign Surviving Sepsis, which provided guidelines for the treatment of the disease worldwide. These guidelines were reviewed in May 2018 and underwent some changes. The main alteration was the proposal of the hour-1 bundleThe most important change in this review was that the 3-hour and the 6-hour bundles were combined into a single hour-1 bundle, with the purpose of starting interventions as quickly as possible. This measure favors the care provided at the bedside of patients, with therapy that begins immediately, especially for those with hypotension, instead of waiting longer and subsequently having to deal with more complex resuscitation measures for longer periods.This new bundle includes:Measuring serum lactate level. Increases in this concentration suggest the possibility of tissue hypoxia and accelerated aerobic glycolysis driven by excessive beta-adrenergic stimulation, which may be associated with worse prognosis.Obtaining blood cultures prior to administration of antibiotics. The collection of blood cultures is an indispensable step in the management of sepsis. Cultures must be obtained before antibiotic administration, considering that the sterilization of cultures may occur.Administering broad-spectrum antibiotics. Broad-spectrum antibiotic therapy should be initiated with one or more intravenous antimicrobials to cover all possible pathogens. It must begin immediately after collecting the blood.Begin fluid resuscitation with 30 mL/kg crystalloid for hypotension or lactate greater than or equal to 4 mmol/L. Early fluid resuscitation is crucial for the stabilization of sepsis-induced tissue hypoperfusion or septic shockBeginning vasopressor therapy if the patient presents hypotension during or after fluid resuscitation, to maintain mean blood pressure greater than 65 mmHg. If the hypotension is not controlled after the initial fluid resuscitation, then vasopressors should be commenced within the first hour to achieve MBP ≥ 65 mmHg.That’s the main change in the bundles. Previously, vasopressor therapy was commenced only in the 6-hour bundle. However, urgent restoration of perfusion pressure is essential for the proper functioning of the vital organs and a key part of resuscitation that should not be postponed.The bundles have evolved; however, the essence of the campaign Surviving Sepsis remains the same, that is, identification and care must be immediate in order to improve the prognosis. Despite criticisms, the care bundles for patients with sepsis and septic shock are supported in the literature and an individual clinical analysis of the patient at the bedside is necessary. In this sense, the nursing team, that remains close to the patient for longer periods, can assist in the recognition and early diagnosis of the disease. Through the identification of the basic needs affected, this professional can contribute to the multi-disciplinary team, evaluating the patient and using appropriate therapies that can contribute to the best prognosis. Thus, it is necessary to train nurses who work in the ICU to acquire skills and to have a protocol-oriented approach in order to start effective and early therapy. The guidelines in the campaign Surviving Sepsis offer a solid structure for the management of the disease, empowering nurses to make a difference in patient care. In addition, this professional can provide important data that should be considered in the formulation of these care bundles.


Wear debris in lube oil was observed using a direct reflection online visual ferrograph (OLVF) to monitor the machine running condition and judge wear failure online. The existing research has mainly concentrated on extraction of wear debris concentration and size according to ferrograms under transmitted light. Reports on the segmentation algorithm of the wear debris ferrograms under reflected light are lacking. In this paper, a wear debris segmentation algorithm based on edge detection and contour classification is proposed. The optimal segmentation threshold is obtained by an adaptive canny algorithm, and the contour classification filling method is applied to overcome the problems of excessive brightness or darkness of some wear debris that is often neglected by traditional segmentation algorithms such as the Otsu and Kittler algorithms. During the operation of tribo-pairs, specific wear debris will be produced. The rate of debris generation, size distribution, shape and color are closely related to the wear state of tribo-pairs ,2,3. AnaAmong the many technologies, machine vision-based methods can obtain more comprehensive wear debris characteristics, such as morphology, size and color, etc., which are hot spots in current research. Iwai et al.  developeFerrography is another important debris analysis technique. Compared with the wear debris analysis methods mentioned above, the biggest difference in ferrography is that a high-gradient strong magnetic field is used to deposit and separate wear debris from lube oil, which is then imaged to obtain a high-quality ferrogram . FerrogrFor real-time wear monitoring, Mao and Xie developed an online visual ferrograph (OLVF) using a combination of high gradient magnetic field deposition and image analysis techniques . Wu et aIn order to improve image quality, Li et al.  improvedExisting wear debris extraction studies are mostly directed to transmission OLVF ferrograms, and few are directed to reflected light OLVF ferrograms. The difference between transmission and reflection ferrograms is huge. Unlike transmission ferrograms, reflection ferrogram information is more complicated, making it more difficult to extract debris features . Wear deIn this paper, a method for wear debris segmentation based on edge detection and contour classification is proposed. First, a background subtraction method is used to simply distinguish the wear debris in the image, and then the morphological black hat operation is used to enhance the portion of the wear debris region that is close to the background color, and the bilateral filtering method is used to denoise the debris image. Next, the adaptive canny algorithm is used for edge detection to obtain wear debris contour information. Then, the inner contour is used to identify the enclosed area as the wear debris or the background by using a histogram similarity measure method. Finally, the classified wear debris contours are respectively determined. Filling is performed to achieve OLVF wear debris segmentation under reflected light conditions.In this paper, the wear debris was segmented using edge detection and contour classification methods to obtain a segmentation image for the extraction of subsequent wear debris features. First, background subtraction was performed to obtain a preliminary wear debris image. Second, the preliminary image was superimposed on the wear debris image treated by the morphological black hat operation to enhance parts of the wear debris region similar to the background image. Third, the enhanced debris image is subjected to denoising using a bilateral filtering method, and then the enhanced debris image was treated by Otsu binarization and edge detection before being superimposed. In addition, the contour classification method was adopted to perform contour differentiation for the images superimposed in the previous step, followed by wear debris filling and background filling, resulting in final binarized wear debris images. The conceptual diagram of the method is shown in As shown in As seen from the typical wear debris ferrogram in As seen in Background subtraction was performed first. The background image was subtracted from the wear debris image to obtain a preliminary processed wear debris image. The result is shown in Therefore, the original wear debris image was processed using the morphological black hat operation that the closed operation image subtracts the original image. Its function is to highlight areas darker than the area around the contour of the original image, as shown in The enhanced wear debris image applied edge detection based on the self-adaptive canny operator. The main processes included applying a Gaussian function to the filter, calculating the gradient and amplitude, and constructing a gradient histogram. The first derivative of the two-dimensional Gaussian function was used to low-pass filter the wear debris. Let the two-dimensional Gaussian function be:k is a constant and The gradient vector is:For the enhanced wear debris image, the image gradients in the Based on the gradient magnitude obtained from the above formula, a wear debris image gradient histogram was constructed.T0;Select an initial estimate for threshold T0 in which case two sets of pixels are generated: Image G1 is constituted by all pixels with a gray value greater than or equal to T0, and image G2 is constituted by all pixels with a gray value smaller than T0;The self-adaptive canny algorithm steps are as follows. The image is segmented using a threshold μ1 and μ2 in the range of G1 and G2;Calculate the average gray values T = (μ1 + μ2)/2;Calculate new thresholds T0.Repeat steps 2 through 4 until the threshold changes in successive iterations are smaller than the pre-specified parameters To determine the optimal segmentation threshold for the high-low gradient region in the gradient histogram, the self-adaptive canny algorithm steps are as follows:The optimal segmentation threshold for the high-low gradient region in the gradient histogram was obtained, and image edge tracking is performed, thereby realizing edge detection. The results are shown in As seen from the segmented image in Statistically, the Bhattacharyya distance measures the similarity of two probability distributions. It can be used to calculate the relative closeness of the two samples being considered and can be applied in target detection and tracking.For probability distributions Equation (8) is the discrete distribution function. The continuous distribution function is defined as:The Bhattacharyya distance was used to calculate the similarity between the target and probabilistic models for the initial image. The higher the similarity, the better is the representation of the target in the image area. Thus, the contour area could be distinguished as wear debris or background. The contour area of the debris was filled to finally obtain the binary wear debris image. The effect diagram is shown in The topography of the grayscale ferrogram is illustrated in The binarized image shown in As for In To further verify the effectiveness of the algorithm, experiments were performed on sets of small and large debris ferrograms. The red dotted circles in the pictures indicate reflective regions of wear debris. For the small debris ferrograms, the algorithm can obtain better segmentation. Under reflected light, the OLVF particle ferrogram suffers from interference that negatively impacts wear debris segmentation. Some wear debris is darker than the background and some is brighter, which may lead to missing wear debris of similar color to the background when using the traditional segmentation method. In this study, a wear debris segmentation algorithm based on edge detection and contour classification was proposed. First, the morphological black hat operation was performed to highlight the wear debris of similar color to the background. The wear debris image was enhanced by bilateral filtering, and the image was subjected to preliminary segmentation by the adaptive canny operator. Then, the contour of the preliminarily segmented image was searched. For the inner contour, the histogram similarity measurement method was used to identify whether the area enclosed by the inner contour was wear debris or background. Finally, the contours of classified wear debris were filled to achieve OLVF wear debris segmentation under reflected light. Compared with the traditional segmentation method. The proposed method effectively recognizes both light-reflecting and light-absorbing debris regions, and a more accurate wear debris segmentation is obtained, which is helpful for improving morphological characteristics extraction and pattern recognition of wear debris.


In the absence of adequate and appropriate actions, hazards often result in disaster. Oil spills across any environment are very hazardous; thus, oil spill contingency planning is pertinent, supported by Environmental Sensitivity Index (ESI) mapping. However, a significant data gap exists across many low- and middle-income countries in aspect of environmental monitoring. This study developed a geographic information system (GIS)-based expert system (ES) for shoreline sensitivity to oiling. It focused on the biophysical attributes of the shoreline with Rivers State as a case study. Data on elevation, soil, relative wave exposure and satellite imageries were collated and used for the development of ES decision rules within GIS. Results show that about 70% of the shoreline are lined with swamp forest/mangroves/nympa palm, and 97% have silt and clay as dominant sediment type. From the ES, six ranks were identified; 61% of the shoreline has a rank of 9 and 19% has a rank of 3 for shoreline sensitivity. A total of 568 km out of the 728 km shoreline is highly sensitive (ranks 7–10). There is a clear indication that the study area is a complex mixture of sensitive environments to oil spill. GIS-based ES with classification rules for shoreline sensitivity represents a rapid and flexible framework for automatic ranking of shoreline sensitivity to oiling. It is expected that this approach would kick-start sensitivity index mapping which is comprehensive and openly available to support disaster risk management around the oil producing regions of the country. Hazards are usually present across any landscape. However, when adequate and appropriate actions are not taken, such hazards can result in disaster. Therefore, it is apparent that hazards are inevitable; it is the human response and impacts that often dictate whether such hazards become disasters. Oil spills across any environment are very hazardous in relation to their social, environmental and economic impacts. It is with this understanding that oil spill contingency planning is very important in the marine, estuarine, lacustrine and riverine areas of the country. This is especially of significance for those areas where oil exploration and exploitation activities are taking place – the vulnerable regions. After any spill, the situation can quickly deteriorate with huge levels of impact when appropriate and prompt actions are not taken. However, in order to define priorities, initiate actions and deploy resources for oil spill response, there is a clear and definite need for locations of sensitive habitats to be known.The Exxon Valdez shipwreck 1989 (Alaska) was a wake-up call, which led to the development of the International Convention on Oil Pollution Preparedness, Response and Cooperation in 1990 (OPRC 90) by the International Marine Organization (IMO). The convention came into force in May 1995 and Nigeria is a signatory to the convention. This is aimed at fostering cooperation among countries towards international efforts in emergency preparedness in response to accidents with oil spills. It also stipulated that all ships and offshore facilities in the sector must have emergency plans or similar arrangements coordinated with national systems for response and effective management of oil pollution incidents.Environmental Sensitivity Index (ESI) mapping is an important tool in oil spill planning. It provides a support for the development of response strategies for oil spills, identification of elements at risk and helping to define and prioritise areas for protection and remediation. ESI has three components: shoreline sensitivity ranking, biological and human resources distribution. Shoreline sensitivity is very important in defining ease of clean-up as well as potential for natural clean-up. With the importance of this tool, it is therefore pertinent that methods and techniques are developed to take into cognisance the challenges of data collection in the middle- and low-income countries. A significant data gap exists across many of these countries in the aspect of environmental monitoring (Gutierrez https://oilspillmonitor.ng/) give a clear indication that the occurrence of such disasters is still prominent across the oil producing regions of the country. It is very clear that even with the best laid plans, the management of disasters requires an iterative and continuous evolution of techniques, actions and measures. It is a clear understanding that the impacts of oil spills are often worse because of the lack of adequate data and information to guide actions and deployment of resources for preparedness and response.Oil spill could be as a result of an accidental release of oil from platforms, tankers, wells, rigs, and so forth into either the marine or terrestrial environments. Over the years there have been numerous examples of significant oil spill in Nigeria; the work of Nwilo and Badejo  and a loOil spill contingency planning and response are ultimately aimed at protecting human life, reducing environmental impact of the spill and supporting remediation actions . Such understanding or expert knowledge can be provided to the computer system to develop rankings, which can be used to designate the sensitivity index for the different segments of the shoreline. ES represents a branch of applied artificial intelligence whereby the vast body of knowledge for a specific task is transferred from humans to the computer Liao . ES deplRivers State  is locatGeologically, it is situated within the coastal plain belonging to the Niger Delta sedimentary formation  1 Arc second resolution data set from the U.S. Geological Survey. These were used to compute the slope of the region. The data were extracted in the Georeferenced Tagged Image File Format (GeoTIFF), the horizontal datum is the World Geodetic System 1984 (WGS84 – Geographic) while the vertical datum is the Earth Gravitational Model 1996 (EGM 96) ellipsoid and the vertical unit is meters USGS .Shore types were manually digitised and identified from a mosaic of recent Google Earth imageries . Visually identifiable segments of the shoreline up to 40 km inland were classified based on visually identifiable features of the shoreline. The work of Oyegun  was alsoDominant sediment types were derived from the soil data. The Harmonised Africa Soil Map , moderate (240.65 ≤ x > 124.10) and high (> 240.65). Slope, exposure and dominant sediment types were all initially in raster format while the shoreline types were stored in vector format. The shoreline type was converted to point data whereby each segment is subdivided at 1 m intervals. The point data file was subsequently used to extract values from the raster-based data sets .Wave Exposure as represented by the sum of fetch length was grouped into three classes : low, moThe second stage of the operation involved the definition of the knowledge-based rules. The rules were adapted and modified based on the Oil Producers Trade Section (OPTS) classification and the NOAA guidelines. The shoreline sensitivity index (SSI) is organised into 10 classes, with the highest values indicating the highest sensitivity to oil spill. Based on this, each shoreline unit was assigned SSI based on the sensitivity index with the closest match to the guidelines (OPTS and NOAA). A decision tree was used to define guiding rules for assigning SSI values, based on the combination of the attributes collated. The decision tree is represented in Computed fetch data were used in the confirmation of the wave exposure of the shoreline; thus, shoreline segments with low relative wave exposure were considered as sheltered and all others were considered as exposed (i.e. medium to high wave exposure).The data sets were stored and manipulated within ArcGIS, and the ranking illustrated in the query language using logical operators. Each segment (rule) of the decision tree was programmed to ascertain the conditions required for a positive identification and subsequent assignment of SSI value. Essentially, the knowledge-based rules were implemented by an ES using a conditional algorithm in association with GIS-stored spatial data. The ES was modelled within SPSS . The approach adopted in this study considered all the types of environment  based on the sum of fetch length. This provides a uniform classification frame for comparison of exposure across the shoreline. From the result, about 89% of the shoreline is exposed, with the indication that in the case of oil spills natural wave action could aid the clean-up process. However, the inclination, sediment and shoreline types would significantly impede such action. Essentially, the high wave exposure also has an implication for the spread of the oil spilled, subject to the viscosity of the oil, wind speed, water temperature, current as well as tidal stream UNEP .The distribution of the SSI based on the decision tree  shows thRank 1 accounts for just over 2% of the entire length of the shoreline, whereas Rank 3 was found across 19% of the entire shoreline. Rank 8 has the smallest coverage with just over 4 km of the shoreline having characteristics fitting this rank.The sensitivity ranking gave an indication that a significant proportion (78%) of the shoreline studied is highly sensitive to oiling ; thus, this makes it very important that vigilance and adequate monitoring should be taken seriously. This also raised serious concern about the resources required in case of oiling across these segments of the shoreline. A spatial query of the Oil Spill Monitor Database could reveal the number of incidences across these highly sensitive shorelines. This could essentially offer a clue to where highly vulnerable segments are located, as well as offer an insight into why it happens in these places and the potential solutions to limiting the number of incidences.The results gave a clear indication that the alluvial plains of Rivers State have a complex mixture of sensitive environments to oil spill. The GIS-based ES with classification rules for shoreline sensitivity provided a rapid and flexible framework for automatic ranking of shoreline sensitivity to oiling based on known standards. This SSI, when incorporated with data on biological and human resources, would provide a complete picture of the environmental sensitivity to oil spills across different environments in this vulnerable region.As the data set is stored within a GIS environment, there is a clear opportunity to update the GIS database, thus making it possible to access up-to-date information relevant for emergency operation and response. With the ES in place, new data obtained from remote sensing and fieldworks can be quickly added to the database, thereby allowing for rapid assessment of ongoing emergency situations as well as allowing for taking proactive measures to forestall potential hazardous events.The GIS-based ES for shoreline sensitivity presents an opportunity for prioritisation of clean-up operations, intervention measures, planning as well as supporting evidence-based decisions on clean-up and containment procedures applicable for affected environments. The study adapted the combination of the Nigerian and the NOAA standards for the ranking of shoreline sensitivity. It is expected that the application of this approach within a spatial database supported by an ES would be extended to cover all of the oil producing regions of the country.Results from this study gave an indication of the potential of the approach in supporting response teams and regulatory agencies. Essentially, this is an endeavour of supporting decision-making for clean-up activities, damage evaluation as well as supporting environmental advocacy in the oil producing region.


The online Remaining Useful Life (RUL) estimation of underground cables and their reliability analysis requires obtaining the cable failure time probability distribution. Monte Carlo (MC) simulations of complex thermal heating and electro-thermal degradation models can be employed for this analysis, but uncertainties need to be considered in the simulations, to produce accurate RUL expectation values and confidence margins for the results. The process requires performing large simulation sets, based on past temperature or load measurements and future load predictions. Field Programmable Gate Arrays (FPGAs) permit accelerating simulations for live analysis, but the thermal models involved are complex to be directly implemented in hardware logic. A new standalone FPGA architecture has been proposed for the fast and on-site degradation and reliability analysis of underground cables, based on MC simulation, and the effect of load uncertainties on the predicted cable End Of Life (EOL) has been analyzed from the results. Underground cables are subject to varying loads and ambient conditions that progressively degrade their insulation, reducing their RUL. To guarantee the correct behavior of the system, preventive maintenance procedures are performed on the cables before their insulation completely degrades. When the cables are part of a dependable electric power distribution system, reliability-centered maintenance strategies are adopted, and accordingly, it is required that maintenance procedures are performed before the reliability level is below a threshold with certain confidence margin ,2,3. HowAn accurate measurement of the degradation level of the cable frequently requires the cable to be disconnected and the system to be put off-line, or in reduced operation mode . HoweverUnless direct measurements of the current degradation level can be performed on the cable, the present cable degradation state must be estimated from past measurements and the uncertainties must be considered in this process. Then in the prognosis phase, future forecasted data is employed to predict the future evolution of the cable degradation, either based on physical models or existing data .In this context, the main objective of this article is performing the online estimation of the RUL of a cable, given a reliability requirement, past load measurements, forecast load patterns, and a grid model. An architecture is developed for the cable condition monitoring and prognosis of the cable that performs a quasi-real time execution of thousands to millions of simulations, to evaluate the cable failure Probability Density Function (PDF).Some of the existing condition monitoring methods employ the online detection and analysis of partial discharges that appear when the cable is relatively close to its EOL. Techniques to identify the condition from partial discharges include fuzzy logic , contourReflectometry can also be employed for non-intrusive monitoring of the current degradation state of the cable . HoweverSome authors have taken the approach to monitor features in the partial discharge signals and predict their accumulated degradation effect and the cable failure time . WaveletWhen Cross-Linked Polyethylene (XLPE) cables are properly insulated from humidity electro-thermal stress is considered the main degradation factor . ConsequDegradation models based on laboratory samples have been scaled statistically to full-sized cables  and the Additionally, the time-to-failure results obtained by the existing cable degradation models are deterministic, i.e., they estimate a single failure time value without uncertainty information. However, cable operation and degradation parameters are surrounded by different sources of uncertainty, such as the expected load, system usage, variability of the installation parameters, and possible measurement or modeling errors that affect the reliability analysis and the associated maintenance strategy. Load measurements are affected by the accuracy of sensors, and future load predictions are subject to prediction errors and deviations caused by the time-varying grid conditions. Therefore, for dependable cable systems, it is necessary to consider uncertainties and to estimate confidence margins for the RUL results.The reliability analysis requires performing a probabilistic analysis of the cable failure time, based on the physical models. However, the degradation models are non-linear, and the statistics can be complex to solve analytically. Some authors have approached the case of aerial cable degradation models by means non-sequential MC simulations . HoweverEmbedded systems based on Digital Signal Processor (DSP) have been employed for online monitoring purposes . HoweverIn this context, this article proposes the use of an FPGA-based embedded architecture for the accelerated reliability monitoring and prognosis of an underground cable. A novel architecture is presented for the FPGA simulation of the cable electro-thermal degradation model entirely in hardware logic. Methods for adapting the non-linear thermal and degradation models into a pipelined FPGA architecture have been developed. The new architecture can perform the on-site evaluation of the present and future reliability of the cable considering past load measurements and future load pattern predictions by integrating within a hardware SAN MC simulator that simulates the power distribution grid. The architecture performs the estimation of the cable failure time, including confidence margins, with reduced processing times in comparison to software-based simulations.The rest of this paper is organized as follows: E and an absolute temperature w is the activation energy of the insulator destruction process, D is the enlargement factor, R is the universal gas constant.The degradation process of a cable insulation within an electrical grid follows a time-varying state-space model that depends on the present and past applied electric field and temperatures. The Zhurkov-based electro-thermal degradation model has been adopted for this article . Mazzanti for a time duration of h  can be approximated by an IPM model as [i.The fraction of cable lifetime loss model as (2)Δγi≈hIn this context, the total lifetime loss can be estimated from the continuous estimation of the insulator temperature and electric field.p by the total power dissipated in each adjacent cable k, and The thermal heating of the cable depends on the power dissipated both within the cable under analysis and within the adjacent cables. The thermal model adopted in IEC60853-2  has beenlated as (3)θ(t)= surface .The self-heating process is modeled using an equivalent RC ladder circuit that can be approximated with a reduced (two loop) circuit. The thermal response can then be represented by a second order Laplace transfer function H(s) (4)L{θsθe(t)k on the cable p can be calculated byFinally, the thermal response to a mutual heating caused by each adjacent cable For varying loads, the principle of superposition is employed , and theThe hardware implementation permits accelerating the simulation of the sequential and repeating thermal process by through the parallelization of each subprocess. Two approaches can be taken when dividing each process: (a) blocks with reduced latency; (b) blocks that are pipelined for high throughput with generally larger latency.N samples require latency of the block in clock cycles.In the non-pipelined approach, the subprocesses of a thermal response with a mission time of N is large, benefit from pipeline implementations where The pipelined approach divides the process into multiple sub-blocks of reduced complexity and has generally a larger To produce the maximum possible throughput, in this implementation each arithmetic operator has either a total latency of 1 clock, or they are fully pipelined arithmetic blocks to achieve a maximum pipeline block latency of Floating-point implementations in hardware logic are large and cause increased latencies, whereas fixed-point blocks are reduced in size and simple to extend for larger precision values. Therefore, each arithmetic block employs fixed-point arithmetic.The required resolution in each stage in the pipeline varies with the accuracy requirement for the output in each stage. The range and resolution required for the input and for each of the operators was established according to range and precision of the output. Due to this varying resolution required in each arithmetic operation in the pipeline, for all the diagrams in the paper, arithmetic blocks need to adapt their input values and output values to the data sizes and resolutions of the corresponding variables. Therefore, the blocks in the diagrams include implicit bit-alignment blocks in their inputs and outputs that perform bit shifting and slicing, to align the variables.Currents: a 32-bit data size and 16 fraction bits were employed to represent both short circuit currents and the required uncertainties.Temperatures: a 24-bit data size and 16 fraction bits were employed to represent self-heating, mutual heating, and soil effect processes. However, the intermediate variables have varying sizes, due to the varying coefficient values in each cable model. For the shown use case, up to 73-bit multiplier registers were required.Degradation: a 32-bit data size with a 31-bit fraction value is employed, due to the exponential curves in the Zhurkov-based degradation model Equation  and parts of the pipeline need to be disabled . The rest of the pipeline that has valid queued data continues processing its output until the last value in the pipeline is processed and the new cable condition is calculated as shown in Therefore, the cable model pipeline needs to attend to grid state changes in the SAN model. The SAN model simulator evaluates the time at which a state change will be occurring in the grid and defines a The following subsections describe the hardware logic implementation of the models for each thermal and degradation process.n, respectively.For currents modeled as a staircase signal, an accurate thermal response of Equation  can be eThe Infinite Impulse Response (IIR) architecture described by Equation  requiresThe transfer function in Equation  correspoThe mutual and soil heating processes are calculated from Equations  and 8),,8), and n due to the mutual heating, n, and The discrete-time thermal response of the mutual heating process can be defined by the discrete convolutionM hours. After those M hours, the growing rate decreases geometrically, and after L hours it reaches to a near steady-state condition. However, assuming that L can cause unacceptable estimation errors for the desired accuracy, even with L in the order of thousands of hours.The presented architecture takes advantage of the characteristics of the mutual and soil heating processes. The step responses of both thermal processes, described by L samples depending on the cable structure, and an IIR filter for the response after that time instant.The proposed architecture divides the Stage 1 filter output For the first segment FPGA implementation, the synthesis of a direct filter architecture would not be feasible within the given latency constraints, due to its large filter size that makes routing complex. A transposed filter architecture shown in L hours of transition time, For the last Equation  can be dMerging the parts of Equation  into a 3Accordingly, the convolution in Equation  is approL required for a given accuracy of Equation  and confidence margins. Simulations end after obtaining the required confidence margins.At any given time The FPGA model described in The architecture was tested with the cable setup shown in ℓ will beThe cable current is measured by a hall effect sensor. The sensor is characterized by an offset calibration error The sensor bias and noise models were implemented by the uncertainty generator block shown in A prognostics prediction analysis of the cable RUL is performed at production instant At the production instant Additionally, the load model for the prognosis stage includes a periodical load factor Physical cable model in the SAN model is governed by the electro-thermal degradation model FPGA implementation described in Cable failed state. Notice that the behavior of the cable activity in the model is dependent on the grid state, indicated by dashed lines. These dependencies are reflected in the FPGA thermal model as connections from the load uncertainty model in The triggering time of activity Prognosis and Increased load states are marked, an increased load factor When the SAN model simulator reports that the An FPGA simulator for the SAN model in The FPGA design use, clock speed and synthesis timing results are displayed in The cable was modeled to be initially as good as new with an initial degradation of ned from . The degius Tool . SimulatThe FPGA architecture permits performing hundreds of thousands or millions of MC simulations, required to represent uncertainty statistics accurately. Uncertainties include measurement errors, deviations on the expected future load or grid conditions, and model errors. Performing all the simulations in software was not feasible due to the required simulation times.The diagnostics stage is executed until the prognostics phase, which is performed at The software simulation run without considering uncertainties predicted a cable failure at 416,388 h or a RUL of 63,388 h at the prognosis time.When considering uncertainties after The future load prediction error distribution depends on the prediction algorithm employed. To evaluate the sensitivity of the error, a parametric distribution was modeled The switching of the grid that causes random load increases is included as a SAN model shown in Normal load state. Before the state change to the increased load factor, the electro-thermal model is flushed, i.e., the pipeline continues processing until the last valid input is processed, while all inputs are flagged as invalid. At this point, the thermal model reports the current cable degradation state and the pipeline is paused until the To analyze the impact of load prediction uncertainties in the prognostics phase, a sensitivity analysis was performed by running the FPGA model with varying standard deviations Stochastic load increases cause further degradation. The impact of the switching varies depending on the season of the year, due to the varying cable temperature, and the temperature dependent derivative in Equation . When thThe described FPGA implementations perform in a fraction of the time required by a computer system to perform the same task and the accuracy of the thermal and degradation model implementations was found to be acceptable for its purpose. This permits the on-site evaluation of continuously evolving cable degradation including uncertainties, which was unfeasible in software, due to the long simulation times and large computer resources required.The processing time of the implemented thermal model pipeline is deterministic for a given mission time, unless the process is interrupted by the grid SAN simulator. Therefore, the presented architecture can determine the degradation level in real time. However, it has to be noted that the simulation time can be altered by the simulation of the SAN network, due to the varying number of state transitions, and the varying number of MC simulations required for a given confidence margin of the results. Nevertheless, the simulation time can be considered negligible compared to the cable lifetime, for practical purposes.For this implementation, a Zhurkov degradation model has been adopted but a similar approach can be followed for an Arrhenius model if required .During the prognosis stage the load uncertainty error was modeled as a Gaussian process. This error depends on the load forecasting algorithm employed and the probability distribution could be time-varying for long-term predictions. However, adapting the uncertainty generator to other load forecasting algorithms should be straightforward as far as a probability distribution function of the prediction algorithm can be obtained.The objective of the article was to demonstrate the use of a hardware-based thermal model for the accelerated estimation of the cable reliability. In the designed use case, load measurement, grid switching, and future load uncertainties have been considered. However, different time-varying and uncertain factors can be included in the uncertainty generator, including varying degradation model parameters, apart from the uncertainties modeled in this use case. To adapt the architecture for different grids it is possible to include other factors such as the yearly thermal behavior, humidity of the soil and variation of its resistivity can greatly affect the thermal behavior and the degradation of the cable . Uneven The article proposes a new hardware architecture that permits performing an on-site reliability analysis and prognosis of underground cables based on past load measurements and future load predictions.New pipelined architectures have been proposed for the simulation of the non-linear functions. The architecture permits considering uncertainties and results are obtained within a fraction of the time required by software simulations. The effects of load uncertainties and grid switching events have been analyzed experimentally by the simulation of the effects on a specific underground cable system. Therefore, the architecture is deemed suitable for use in live monitoring applications.


Although the amount of waste photovoltaic (PV) panels is expected to grow exponentially in the next decades, little research on the resource efficiency of their recycling has been conducted so far. The article analyses the performance of different processes for the recycling of crystalline silicon PV waste, in a life cycle perspective. The life cycle impacts of the recycling are compared, under different scenarios, to the environmental benefits of secondary raw materials recovered. Base-case recycling has a low efficiency and, in some cases, not even in line with legislative targets. Conversely, high-efficient recycling can meet these targets and allows to recover high quality materials  that are generally lost in base-case recycling. The benefits due to the recovery of these materials counterbalance the larger impacts of the high-efficiency recycling process. Considering the full life cycle of the panel, the energy produced by the panel grants the most significant environmental benefits. However, benefits due to high-efficient recycling are relevant for some impact categories, especially for the resource depletion indicator. The article also points out that thermal treatments are generally necessary to grant the high efficiency in the recycling. Nevertheless, these treatments have to be carefully assessed since they can be responsible for the emissions of air pollutants . The article also identifies and assesses potential modifications to the high-efficiency recycling process, including the delocalisation of some treatments for the optimisation of waste transport and the introduction of pyrolysis in the thermal processing of the waste. Finally, recommendations for product designers, recyclers and policymakers are discussed, in order to improve the resource efficiency of future PV panels. This loMoreover, the management of PV waste is complex and influenced by legal obligations and the attitudes of users. The European Community Directive on Waste Electrical and Electronic Equipment (WEEE) represents the first example worldwide of regulation on PV waste . This DiConsumer behaviour around the purchase and use of PV panels has been investigated by various authors . HoweverPV technologies largely rely on the availability of various materials, including silicon. The demand for silicon for the PV sector in the European Union (EU) is expected to rise from 33 kilotonnes (kt) in 2015 to 235 kt in 2030 . The higImproper collection and/or disposal of PV waste entails the loss of valuable resources and the dispersion of potentially hazardous substances contained in the panels. This article aims to assess the resource efficiency of recycling processes for c-Si PV waste by following a life cycle approach with multiple objectives. In particular, efficient recycling of waste allows the production of secondary raw materials (SRMs), meaning materials recycled from waste that can be injected back into the economy . In a li2Life Cycle Assessment (LCA) methodology and underlying Life Cycle Impact Assessment (LCIA) methods have been considered as a good basis for screening the resource efficiency of product systems , includiSome EoL aspects of PV panels have been discussed by Only in recent years have some studies focused on dedicated application of the LCA to the analysis of recycling processes for c-Si PV. For example, In a previous study , we inveAn initial review of life cycle inventory data for PV recycling has been provided by A first example of comprehensive assessment of material recovery and life cycle impacts for PV recycling has only recently been presented by 2 of HF, while Interestingly, studies by Detailed research on the thermal treatment of plastics in PV backsheet is presented by It is thus necessary to optimise the efficiency of waste recycling (and to minimise the related impacts) to claim the sustainability of PV technologies. However, LCA practitioners have only recently showed a growing interest in assessment of the EoL of PV technologies. Assessment of the resource efficiency of PV recycling remains largely unexplored, especially concerning the benefits of increasing recovery rates for different materials in PV waste.3(a)The life cycle inventory data  for the (b)The system boundaries have been extended to the production of SRMs, with a focus on the recovery of precious metals and CRMs , in orde(c)The FRELP recycling process has been compared to base-case recycling practices (‘baseline processes’) as used in European WEEE recycling plants . The art(d)The resource efficiency of FRELP has also been assessed in comparison with impacts and benefits related to the manufacturing and operation stages of the PV panel .(e)Potential modifications to the FRELP process have been identified and assessed, including the delocalisation of some treatments for the optimisation of waste transport , and the(f)Finally, recommendations for product designers, recyclers and policymakers are discussed, in order to improve the resource efficiency of future PV panels.From a life cycle perspective, resource efficiency is assessed by accounting for the impacts and benefits of c-Si PV waste recycling according to LCA methodology . The fun4The resource efficiency of the PV recycling processes is assessed through various scenarios. Initially, two scenarios are considered; these are then expanded from a life cycle perspective and compared to benefits and impacts related to PV manufacturing and operation stages. Finally, 4.1Two PV recycling scenarios are assessed: innovative high-efficiency recycling , complemented by information available in the literature . We obse4.1.3In the comparison with the FRELP process, the impacts of the baseline recycling are estimated assuming: waste is transported a distance of 100 km; dismantling is done manually and does not imply any energy consumption or other impacts; electricity consumption for shredding refers to an average shredder for WEEE; unsorted shredded fractions are sent to landfill (including 100 km transport). Life cycle inventory data for electricity (European mix), transport (low-capacity lorry) and landfill  refer to the 2 equivalent (CO2 eq) per tonne of treated waste (compared to 116 kg CO2 eq/tonne for the baseline process). On the other hand, FRELP allows a saving of 2 400 kg CO2 eq/tonne, compared to 2 025 kg CO2 eq/tonne for the baseline.The results showed that the impacts of the FRELP recycling process , Fig. 4 FRELP resulted in lower net benefits for eutrophication  and photochemical ozone formation impacts.The benefits of the baseline process are also high for several impact categories, as the recycling of the aluminium frames alone brings large environmental gains. The benefits of the two processes are indeed very similar for certain impact categories (e.g. climate change and cumulative energy demand – CED), but they differ substantially for some other categories . This major difference is mainly related to silver, which is recycled in FRELP but lost in the baseline.Concerning generation of SRMs, aluminium is the most significant in terms of environmental benefits for several impact categories. Recycled silver and silicon are also important for various impact categories. In particular, recycled silver has significant benefits for ADP and freshwater eutrophication. The benefits from recycled silicon range from 1% to 10% for the different impact categories .4.1.4As mentioned in The potential fluorine content in PV panels represented a bottleneck for the FRELP process. Indeed, incineration of fluorinated plastics can cause emissions to air of HF, a pollutant potentially responsible for acute toxicity effects . TherefoIn this section, we analyse a new scenario for the treatment of PV panels with three different backsheets, including two fluorinated plastics (TPT and KPK) and a fluorine-free plastic (PPE) . The treInformation on the release of HF from fluorinated plastics in the backsheet has been derived from interviews we conducted with a manufacturer of PV backsheet, who also sponsored the collection and analysis of experimental data . Fraunho2 emissions from the incineration of both fluorinated and fluorine-free waste have been considered assuming complete combustion of all the carbon content in the polymers. Incineration of the ethylene-vinyl-acetate in the backsheet can be responsible for the emission of volatile organic compounds (VOCs) (We assumed that these HF emissions could be reduced by 80% in incineration plans equipped with dedicated abatement systems . In asses (VOCs) . Howevers (VOCs) .In the absence of fluorinated plastics, the PV could be treated through alternative processes (e.g. pyrolysis). This alternative scenario is analysed in The incineration of PV waste could be also be responsible for the emission of additional pollutants, including arsenic, cadmium, chromium and lead . However4.2LCA studies on PV technologies often exclude the EoL stage . PreviouAlthough a detailed assessment of the whole life cycle of the PV panel is ‘out-of-scope’, this section presents a comparison of the impacts of the FRELP process (for c-Si PV panels with TPT backsheet) with the impacts due to manufacturing and potential benefits related to PV operation.The impact of the manufacturing is based on inventory data from the literature , while the impacts of manufacturing for the same category are about 6%, and benefits of recycling are about 1% (impacts of recycling are negligible).Results of this analysis could be used by LCA practitioners to improve the detail and comprehensiveness of their estimations concerning the EoL of PV panels.4.3The previous sections investigated an innovative recycling process, as originally developed by industrial operators. Based on this analysis, we tried to identify potential areas for improvement. These focus, in particular, on optimisation of the overall logistics of the process  and potential alternative thermal treatments for the waste. Two new scenarios are introduced and discussed in the following sections.4.3.1Analysis of the FRELP process proved that transport is one of the main aspects responsible for the impacts of PV recycling . LunardiIn the analysis in the previous section, transport contributes to the impacts between 10% (for freshwater eutrophication) to 80% (for ADP). The high contribution from transport is related to the heterogeneous distribution of PV plants in the geographical areas, combined with the need to reach the specialised plant for recycling. Transport is also further affecting the incineration phase . On the other hand, the incineration of the PV waste must be separated from the treatment of other waste, in order to allow collection of the bottom ashes.This incineration process entails some technical problems. Firstly, it is necessary to provide a certain ‘critical mass’ and continuity of waste as input, to sustain the incineration in the plant. This could represent a constraint to full development of the FRELP process, since the volume of PV panel currently reaching EoL is still very limited. Indeed, as discussed by Alternatively, fluorine-free panels could be treated by different plants. A new ‘pyrolysis scenario’ is therefore introduced. This supposes that fluorine-free PV waste (with PPE backsheet), after the pre-treatments , making it easier to reach the critical mass of waste input. The use of a pyrolysis plant would allow the PV waste to be treated in small batches, with easier collection of the bottom residual for recovery of metals, and with minor losses (compared to those occurring in a large incinerator). The pyrolysis could also occur in a plant located close to the pre-treatment, also allowing a reduction in the impacts caused by transport. The construction of a specialised pyrolysis plant for PV waste treatment would be easier (and less expensive) than a large incinerator with a plant for the abatement of acid gases. Finally, such a recycling process would be free from HF emissions, although this benefit is related to the composition of the waste and not to the pyrolysis itself.The pyrolysis scenarios proved to have generally lower impacts than the standard FRELP process , includiThe benefits of the two processes are very similar. Compared to the standard FRELP process, the net benefits (i.e. benefits net of impacts) of the pyrolysis scenario are around 3% higher for climate change, human toxicity (ReCiPe) and CED. Benefits are 25% lower for photochemical ozone creation impact. However, emissions from pyrolysis were roughly estimated and direct measurements would be required for more precise estimation of the impacts. Moreover, parameters for the pyrolysis process (e.g. temperature and residence time) should be optimised according to the input PV sandwich waste.It is argued by the developer that the pyrolysis scenario could be industrialised more easily, thus overcoming some of the barriers identified for the FRELP process. Obviously, the pyrolysis scenario is only theoretical and could be used only if recyclers were certain that no halogenated components were embodied in the PV panel. 5The previous sections analysed the resource efficiency of PV recycling processes, including some improvement scenarios. FRELP has been listed among the most innovative processes available so far . HoweverAnother limitation of the study relates to information on the composition of the PV panels. Sample testing of PV waste has been conducted by the industrial developers of FRELP . HoweverAdditional research is also required concerning the improvement scenarios, as discussed in ex post analyses of the resource efficiency of PV recycling should in future be carried out based on documented uses of SRMs (mainly glass and silicon) recycled from the panels. Further research should take into account multidimensional values that span from other scientific domains   . It shoulogical) . It is eScenario 4.3.2 was judged technically feasible by the industrial developer of FRELP and in fact this scenario was initially considered the preferred option, until the problems relating to fluorinated pollutant emissions were detected. However, further experimental tests should be carried out on the energy and material flows for pyrolysis, and on the suitability of the pyrolysis residues for the recovery of silver and silicon.Finally, the labelling of fluorine-free PV panels is a pre-requisite for the scenario 4.3.2. However, this measure could only be applied to panels put on the market in future. This means that the sorting of fluorine-free panels could be practised only with a long time horizon (i.e. when labelled panels reach their EoL). Further research is also needed to demonstrate that the use of fluorine-free plastics can maintain or improve performance of the panel during operation.6The article assessed the resource efficiency, and related environmental benefits and burdens, of PV waste recycling processes. An innovative high-technology process (FRELP) was compared with current recycling processes used in European WEEE recycling plants (baseline process). An initial finding is that such an innovative process certainly meets the recycling targets set by European WEEE legislation, whereas the baseline processes have questionable capacity to reach such targets. The advantages of FRELP, compared to current recycling processes, are even more evident with regard to the recovery of silver and silicon (a CRM).The differences between the environmental burdens and benefits of the two processes are worthy of comment. The FRELP process is characterised by higher impacts, but also higher benefits, for all the impact categories considered. Despite low recycling efficiency, the baseline process is still characterised by high environmental benefits, especially for climate change. This is mainly due to efficient recycling of aluminium in the baseline process. The benefits of the FRELP process are more evident when considering impact categories focusing on the use and recovery of raw materials (e.g. ADP).Special attention was paid to air emissions during recycling and, in particular, during thermal treatments. The incineration step is essential to allow the further recycling of silver and silicon but, on the other hand, the presence of fluorinated plastics can be responsible for the release of toxic HF emissions. It was observed that various impact assessment methods commonly used in LCA are not capturing this type of emission. This highlights the need for LCA practitioners to carefully check that main process emissions are adequately characterised in the LCIA phase.The article also confirms that the benefits of PV recycling are generally very low compared to the benefits of the use phase, with some exceptions (e.g. ADP).A substantial reduction in the impact of the FRELP recycling process could be achieved with optimised logistics, based on local pre-treatment of PV waste followed by further treatment in a centralised plant. Even the potential treatment of the PV sandwich by pyrolysis could entail several benefits. However, pyrolysis would be applicable only for fluorine-free waste. Future labelling of PV panels (for new products put on the market) could help to sort fluorine-free panels for their optimal recycling.The results also highlight some considerations for policymakers and PV manufacturers. Firstly, mass-based indicators for waste policy targets are relevant but do not encourage the efficient recovery of materials present in small traces . Dedicated targets  could increase the efficiency of PV waste recycling.It is also important to couple waste legislation with product design considerations. Here, future policy measures could facilitate better availability of data on the material composition of PV panels put on the market. These measures could be particularly relevant, for example, in terms of the content of silver (as the main economic driver for high-efficiency recycling processes) and halogenated plastics. It is also questionable whether PV panels with fluorinated plastics could be recycled at all through thermal treatments, as discussed in Finally, it is confirmed that the low quantity of PV waste collected so far is discouraging investments in industrial processes for PV recycling. However, this situation is not a justification for delaying research in this field, or the problem of managing PV waste is simply postponed to the near future. Claims about the sustainability of PV technologies cannot be fully supported until efficient and environmentally-friendly recycling processes for them have been developed and are deployable.