Improvements in energy resolution of modern positron emission tomography (PET) detectors have created opportunities to implement energy-based scatter correction algorithms. Here, we use the energy information of auxiliary windows to estimate the scatter component. Our method is directly implemented in an iterative reconstruction algorithm, generating a scatter-corrected image without the need for sinograms. The purpose was to implement a fast energy-based scatter correction method on list-mode PET data, when it was not possible to use an attenuation map as a practical approach for the scatter degradation. The proposed method was evaluated using Monte Carlo simulations of various digital phantoms. It accurately estimated the scatter fraction distribution, and improved the image contrast in the simulated studied cases. We conclude that the proposed scatter correction method could effectively correct the scattered events, including multiple scatters and those originated in sources outside the field of view. In positron emission tomography (PET), Compton scattering refers to a scenario in which one or both annihilation photons suffer a change in direction and energy when interacting with matter. It is important to compensate Compton scattering in order to enhance the contrast of the reconstructed images and to improve activity quantification.Among several existing approaches to solve the scatter problem in PET ,2,3, theSome approaches use time-of-flight (TOF) information , attenuaAnother approach to correct scatter degradation consists of Monte Carlo scatter simulation (MCSS) . Monte CNowadays, there is a general trend to study scatter corrections by applying machine learning techniques  that havEnergy-dependent scatter methods try to estimate the percentage of events that have suffered Compton interactions based on the recorded energy of those events. These energy approaches have been broadly implemented in single-photon emission computed tomography (SPECT). However, implementations in PET are limited due to the poor energy resolution of the detectors  and the Among many different energy window-based scatter correction approaches in SPECT, the simplest implementation is the dual energy window (DEW). In this method, γ-rays are acquired within the photopeak and a lower-energy scatter window . The proNowadays, new scintillator detector systems have been developed for PET scanners that increase energy resolution and detection efficiency. This is due to the use of more appropriate radiation detection materials with higher density and atomic number (Z), as well as replacing the traditional photomultiplier tube (PMT), for more modern silicon photomultiplier (SiPM). A full comparison of these gamma photon detector systems can be found in Matthew Lowdon et al. . MedicalThese recent improvements in the detector energy resolution of PET scanners, as well as the capacity of the current detectors to estimate the energy of two photons in coincidence ,22 with In this study, we investigate an energy window approach to correct the scatter component using energy information only. Scatter correction is one of the major challenges for accurate quantification in 3D PET, in particular, if the scatter is due to activity originating from outside the field of view . We emplOur energy scatter correction algorithm is similar to the ones previously proposed by Chen et al.  and by PThe principal aim of this study is to implement a fast, simple and practical energy-based scatter correction method which uses predefined weighted factors and to study its accuracy through a Monte Carlo simulation test.List-mode format files normally store the information related to the impacts of two photons corresponding to an event. In particular, the impact position, the detector identifier, the time stamp, and the amount of energy deposited in the detectors by these photons are the required inputs for this method. Our method consists of a preprocessing of the energy information stored in list-mode file, in order to reduce the scatter effect, similar to the work of B. Guerin and G. El Fakhri  but avoiOur approach exploits the fact that a high proportion of the scattered photons recorded in the energy spectrum are set below the photopeak region as compared with those recorded near the photopeak, as it shown in Thus, the data recorded in the energy windows below or above the photopeak window, or both, can be used to derive an estimation of the scatter contribution through scaling factors. They account for the different sensitivity of the scattered events in the total and auxiliary windows, and they are derived from simulations of several attenuation media before reconstruction . In the System simulations were performed using the Monte Carlo package GATE (Geant4 Application for Tomographic Emission) . In our nsL SiPM . The axinsL SiPM . To calcz et al. . The eneUsing GATE, it is possible to simulate the random process of emission and detection of gamma photons in a PET system. The following physical processes were included in the simulations: Compton scattering, Rayleigh scattering, electron ionization, and photoelectric interaction. We chose Two configurations of auxiliary energy window were selected after several simulation tests. ith line of response (LOR), and A low energy window (LEW) (350–430 keV) and a photopeak energy window (430–650 keV) were chosen. The scatter events on the photopeak region A low energy window (LEW) (350–430 keV), a photopeak energy window (430–550 keV) and an upper energy window (UEW) (550–650 keV) were selected. We assumed that most of the counts in the upper energy window had not suffered scatter, as ascertained from Four steps are involved in the proposed scatter correction algorithm , as follThe estimation of the scatter coefficients of Equations (1) and (2) were computed using the data of the GATE simulation of the scanner, depending on the window configuration. Considering a window w, the corresponding scatter coefficient is defined by the fraction:Simulated data are necessary for the computation of the model parameters and the contour conditions, i.e., the correspondence between the fraction of scatter events and those registered in the auxiliary windows. Due to the energetic resolution of our system, it may happen that some photons are collected in the wrong energy window. When the simulations are performed, it is important to model the energy resolution of our system, and therefore, in this way, the introduced coefficients can partially mitigate this effect.To incorporate scatter correction into the iterative reconstruction, the expected scatter value per LOR is needed . HoweverThe next step of the method is to classify the list-mode events according to their energy. A list-mode event (or count) is parameterized by the energies of the pair of measured photons and the positions on their respective detector blocks. Two photons with energies within the photopeak window determine a photopeak event, and to record an event in an auxiliary window, one of the detected photons must be in the energy range of that window. Adjacent LORs are grouped in bins according to a user-defined final image resolution, i.e., a given LOR bin (or bin) contains all the counts within two specific pixels of a faced pair of detector blocks. Considering that the total number of registered events depend on the sample activity and the selected LOR bin, the distribution of counts among the energy windows is often distorted and statistically non-significant. This problem is further accentuated in those FOV regions with fewer events. To overcome this misrepresentation, the count pattern distribution for a given bin also considers the events registered by its adjacent bins. For this purpose, a spatial convolution of the list-mode file using a multivariate kernel density estimate (KDE) is proposed ,36. Thusx and n is the number of counts and ith LOR spatial coordinate [The convolution is performed independently for the different energy windows and a standard Gaussian kernel is employed. Given two events with LOR spatial coordinate vectors  ordinate . A cut-oAfter this preprocessing, an accurate estimation of the counts of each energy window is obtained for each bin.The coefficients previously calculated from simulations, along with the number of events in each energy window, were used to determine the scatter counts in the photopeak region for each LOR bin. The addition of this value to the LEW event contributions determines the total number of scatter counts present in each bin. Considering that the fraction of scattered events remains constant for the LORs creating a bin, the scatter fraction per LOR ith LOR is a scatter event.This scatter value provides an estimation of the probability that an event detected along the jth voxel value of the image (at the kth iteration), jth voxel being detected along the ith LOR, the ith LOR. This The scatter correction estimation was incorporated during the iterative reconstruction using maximum likelihood expectation maximization (MLEM) :(8)njk+1rd model .The conventional approach is to subtract the scatter estimation data from the noisy measured projections before the reconstruction procedure. Newly developed methods use the scatter component as a constant additive term with the forward model in the reconstruction loop . This apTo evaluate the proposed scatter correction method, a series of Monte Carlo simulations were performed using the numerical simulation software GATE , which hScatter events in the phantom can be tracked and stored in a list-mode file output (ASCII or ROOT output ). By perWhen using MonteCarlo simulations, it is possible to identify the true distribution of scatter photons along the FOV; thus, the correct scatter fraction can be extracted from simulations. Although such an ideal estimation method is not possible in real acquisitions, it allows us to isolate the degradation effects due to scatter and to determine the maximum gain achievable at the reconstructed image level. From the simulations, the 2 with 10 MBq of activity was placed in the center for each phantom. Seven water-filled cylinder phantoms were simulated with 60 mm of height and a radius ranging from 40 to 160 mm, as shown in Then, we extracted the necessary information from the GATE files and generated our own list-mode files from the simulations. We used these files as the input for our reconstruction algorithm and made an estimation of the scatter fraction, first, using only the information of a low energy window and, second, using both low and upper energy windows. We also compared the performance of our proposed algorithm with the standard single scatter simulation method (SSS). We used a model-based single scatter simulation algorithm, which was implemented in an open-source software library called Software for Tomographic Image Reconstruction (STIR) . A simulWe used the same simulated setup as before, i.e., seven water-filled cylinder phantoms with 60 mm of height and radius ranging from 40 to 160 mm. After obtaining the corrected sinogram, we isolated and compared the estimated number of scattered events and calculated a scatter fraction. The results obtained can be seen in To test the proposed algorithm with sources out of the FOV, a series of simulations with a line uniform source of 25 mm radius × 160 mm height inside a water cylinder of 120 mm radius × 160 mm height were performed .In addition, to validate the algorithm for different numbers of registered events, we repeated the same simulation setup with different activity sources. Around 30% of the simulated scatter events come from decays outside the FOV, our proposed method accurately corrects these events due to energy discrimination, as can be seen in Finally, to study the spatial dependence of our scatter method, a Utah phantom  was alsoWe implemented the scatter correction method in the iterative algorithm (MLEM) as well as its accelerated variant with ordered subsets (OSEM) . The purIn every iteration, a sensitivity correction was applied. Virtual detector pixels, in the range 1.4–2 mm, were tested, combined with voxel sizes of 1–2 mm. The reconstructed images of the Utah phantom are shown in The proposed method uses energy information to locally estimate the right proportion of scatter events in PET data for all FOV regions. The employed energy-based window approach succeeds in suppressing up to 90% of the scatter component using optimized factors according to the selection of energy windows here presented. Two configurations are analyzed in this study: the double energy window, with a low energy window below the photopeak energy region, and the triple energy window, which also incorporates a high energy window above the photopeak. Our method exhibits good agreement with the Monte Carlo simulation data and improves the contrast of reconstructed images of a Utah phantom. Furthermore, a better agreement with simulation data is achieved once the information of the upper energy window is incorporated.One of the advantages of this energy-based method is the use of list-mode PET data, instead of sinogram information, and therefore it can operate at the LOR scale, i.e., the scatter fraction is evaluated for each LOR. In addition, it can be adjusted to a user-defined energy and spatial resolution by means of conveniently binning the data. To compensate those sources with low activities, small-sized attenuation media or FOV regions with low count-rate statistics, a convolution of the list-mode file is proposed. That is, the calculation of the scatter fraction of a given LOR not only considers the events registered in its corresponding detectors, but also those placed nearby, provided an appropriate weight function is applied. Consequently, the method can predict true and scatter events in all FOV regions, and its count-rate dependency is palliated. Despite correcting the cases studied with few events, we have still found discrepancies (Our proposed method also exhibits better agreement with simulated data than the tested version of the SSS algorithm. The SSS method underestimates the number of scatter events. This may be due to the fact that multiple scatters are not included in this model, and therefore more scatter points need to be incorporated in the simulation. The SSS model is also more time-consuming than our method, once the MC simulated is done, we need to identify the attenuation medium and assign scatter points. Furthermore, we must organize the collected data into projections, this step must be undone before the reconstruction process. In contrast, our energy window method can work directly with raw data, once the phantom is simulated, the only time-consuming section is the convolution part of the file. Although further investigation is needed, we have shown that our energy window approach may be a good alternative to sinogram-based scatter algorithms.We presented a basic approach to a complex, multi-dependent problem, with the advantages of being fast, easy to implement, and with minimal computation expense in the reconstruction procedure. Our method also takes into consideration the scatter arising from activity beyond the axial FOV, as can be seen in We have shown that the energy window technique described in this study may provide a practical, accurate, and a general-purpose choice for correcting scatter in 3D PET. Our method corrects the scatter degradation without requiring a prior attenuation map of the studied object. Finally, we conclude that the scatter estimation of this method is in agreement with Monte Carlo simulated data, and yields good results for symmetrical objects with an approximately homogeneous activity distribution, such as is the case for dedicated brain PET systems.


Developing on-site earthquake early warning systems has been a challenging problem because of time limitations and the amount of information that can be collected before the warning needs to be issued. A potential solution that could prevent severe disasters is to predict the potential strong motion using the initial P-wave signal and provide warnings before serious ground shaking starts. In practice, the accuracy of prediction is the most critical issue for earthquake early warning systems. Traditional methods use certain criteria, selected through intuition or experience, to make the prediction. However, the criteria thresholds are difficult to select and may significantly affect the prediction accuracy. This paper investigates methods based on artificial intelligence for predicting the greatest earthquake ground motion early, when the P-wave arrives at seismograph stations. A neural network model is built to make the predictions using a small window of the initial P-wave acceleration signal. The model is trained by seismic waves collected from 1991 to 2019 in Taiwan and is evaluated by events in 2020 and 2021. From these evaluations, the proposed scheme significantly outperforms the threshold-based method in terms of its accuracy and average leading time. Destructive earthquakes often lead to significant loss of life and property. Issuing warnings before the arrival of strong waves is a potential way to reduce this loss during disasters. Regional earthquake early warning (EEW) systems usually detect seismic waves near the epicenter and provide warnings to more remote regions ,2. HowevThe characteristics in the initial part of P-wave have been used for various prediction purposes. A number of studies have been dedicated to revealing the relationship between the magnitude of an earthquake and the characteristics of its initial P-wave ,7. Wu anIn this study, an intelligent strong motion prediction (ISMP) method is proposed to predict earthquake ground shaking for on-site earthquake early warning. The proposed scheme uses a convolutional neural network (CNN) in an attempt to obtain a relationship between the characteristics extracted from the initial P-wave and the strong motion. The input data used are the three-axis wave acceleration signals and do not require preprocessing. Compared with the traditional methods, this technique can more effectively capture the implied features from multi-axis data, which may not even be observed by experienced seismologists. ISMP is intended to predict whether the maximum peak ground acceleration (PGA) of the tested seismic waves exceeds a pre-defined threshold using the first few seconds of the P-wave signal. The models were trained and tested using seismic waves collected from the Taiwan Strong Motion Instrumentation Program (TSMIP) network from 1991 to 2019 and the Central Weather Bureau Seismic Network (CWBSN) from 2014 to 2019. Experiments were also conducted to evaluate the performance of the proposed scheme using different durations of the initial P-wave time window. Based on the results, the proposed scheme outperforms the traditional threshold-based method in terms of accuracy and average leading time. Sample cases from 2020 and 2021 were selected from the CWBSN to evaluate the performance of the proposed scheme and illustrate the superiority against the traditional threshold-based methods.The rest of the paper is organized as follows. Ground motion prediction is a crucial issue for on-site EEW systems. Traditional on-site EEW methods usually use earthquake parameters measured from the original signal to trigger the system. Wu et al.  proposedIn California, Böse et al.  proposede et al.  applied e et al.  also useof Hsieh  is curreIn recent years, artificial intelligence techniques have been applied to predict the ground motions at seismic stations ,14. BöseCNN has been applied to several seismic problems, such as earthquake detection , earthquThe proposed scheme intends to make a prediction for whether the PGA of an earthquake at a certain seismograph station will be greater than a pre-selected threshold t is the size of the window starting at the arrival of the P-wave. The input is a two-dimensional matrix X can be defined as jth sample of the acceleration from the axis i in the initial window of the P-wave. Note that the values of the samples are the acceleration with the unit of The input data are the three-axis acceleration sampling values in a small window of the initial P-wave. Suppose that For feature extraction, CNN is used to determine the relevant features for intensity prediction from the input data. The first step in the process uses one-dimensional convolution (Conv1D), which applies variant filters on the input data lization  and rectlization  are addeAfter the convolution layer process, feature maps are generated. Each feature map represents a different abstraction of the seismic waves. Some may not have physical meaning and may be difficult to be identified by human beings. However, the CNN technique can find regularities in many waveforms to extract useful features.n be the size of the vector F and h be the dimension of the fully connected layer. The feed-forward network aggregates the extracted features to obtain the global information as follows:In this component of the network, the learned feature maps from the previous layer are flatted to a one-dimensional vector ith class and The model outputs the probabilities of the two classes, i.e., PGA kth class for the jth waveform record as defined in Equation  model is designed to predict early ground motion. ISMP uses a convolutional neural network to effectively extract relevant features from the initial P-waves to predict whether the peak ground acceleration of subsequent waves surpasses 80 Gal. Earthquakes with many strong motion records were selected to train and test these models, and experiments were conducted to evaluate their performance. The ISMP scheme was compared to the threshold-based Pd scheme through the testing set and some sample cases. From the evaluations, the ISMP scheme demonstrates higher F1-scores from the testing set. In the sample cases, the ISMP scheme significantly outperforms the Pd scheme. In the future, deploying the proposed ISMP scheme in some areas that require rapid warnings and evaluating the performance of the scheme in real time will be investigated.


In recent years, the learning efficacy of online to offline (O2O) teaching methods seems to outperform traditional teaching methods in the field of education. Students can use a small private online course (SPOC) teaching platform to preview class-related materials, learn basic knowledge, and enhance the practical experience of system development in offline courses. The research team applied an artificial intelligence (AI) precision education strategy to design a teaching experiment that evaluated whether this approach may lead to better learning outcomes. In addition to questionnaire surveys to ascertain students' attitudes toward and their satisfaction with learning, this study employed in-depth interviews to understand a potential influence on changes in teachers' curriculum design and teaching approaches when SPOCs was integrated into the traditional university classroom, as well as the impact of the AI precision education model. The results showed that the AI precision education model may facilitate students' learning experience and enhance student achievement. The rapid growth of massive open online courses (MOOCs) has resulted in novel teaching and learning methods. However, the following issues inevitably arise as a result of this global trend: (1) high registration rate and low completion rate; (2) a considerable gap in the interaction between students and teachers compared with traditional physical courses; (3) effective prevention of course certification and online evaluation still needs to be resolved; and (4) the cost of platform maintenance.In recent years, models have begun to migrate from MOOCs to small private online courses (SPOCs). In a review titled “Forget MOOCs,” Oremus  stated tWith time, the focus on differences in learners' traits continues to expand, from differences in explicit responses and learning outcomes to differences in mental organization and representation, cognitive styles, motivations, as well as an increased emphasis on differences in brain structure and learning-related issues. The perception of learners' differences in traits has gradually shifted from defects or specialties to unique and diversity. This change represents respect for each learner's differences and highlights the importance of education to assist learners in discovering and displaying individual strengths and increasing their chances of success  has evolved into the modern emphasis on the flexibility and diversity of students' learning styles. It is crucial to approach students' individual differences with a more positive attitude and pay attention to learning opportunities. This is the core meaning of adaptive teaching  assisted learning systems that intervene in a hybrid learning model can more effectively improve teachers' teaching and students' learning.This study aims to compare whether the students have significant differences in learning effectiveness when using the online teaching course of MOOCs in conjunction with an offline teaching experiment platform (offline course) to conduct teaching experiments and introduce the intervention model of AI precision education. In addition, the study aims to gain a deeper understanding of the effectiveness of developing emerging technologies for innovative teaching. This study intends to use quantitative and qualitative data and teach experimental courses on the ShareCourse platform. It discusses innovative teaching strategies to compare students' learning effectiveness in the information departments of two national universities in the east.The goal is to create high-quality MOOCs online in an offline learning environment to compare MOOC learners' online learning behavior and learning effectiveness using the same core course of information network to be jointly studied on the ShareCourse MOOCs platform. Following the course design and production, promoting the innovation and research of deep learning entails the following: first, exploring the online learning platform of MOOCs and integrating the challenges and difficulties of MOOC curriculum teaching; second, developing and redesigning MOOCs and integrating SPOCs; third, integrating the auxiliary teaching model of AI precision education; and fourth, promoting and applying the results of the learning effectiveness of MOOC teaching. Finally, we plan to integrate and analyze the teaching mode, potential, and effectiveness of MOOCs' multiple innovations based on research feedback and improvement.Section 2 provides a review of the literature. Section 3 introduces the research methodology. Section 4 presents the results. Section 5 concludes the study and presents a discussion.MOOC is an open digital learning method that enables a large number of learners to participate in the course via the web. The format differs from MIT's “Open Course Ware” (OCW), which began in 2002. The OCW provides a long-term summary of physical teaching. The course content is extensive, and the learner's attention span is short. In addition, there is a lack of interaction between learners. Scholars' opinions on MOOC quality can be separated into two categories: evaluations based on user benefits Ehlers,  and asseLearning analysis and evaluation are essential factors in the design of MOOCs. Learning analysis can assist in mastering the learning process by identifying learners' challenges and preferred learning methods. Evaluation enables learners to understand solutions from teaching videos or course content with explanations, learning effectiveness, and different evaluation methods , and private refers to the students enrolled in the course at the school where the course has started, or the number of students is limited according to the course's characteristics. The term “online students” refers to courses that are not open to everyone on a large scale, intending to enhance learning participation and the course completion rate Laaser, . The SPOWith the rapid advancements in AI, it is necessary to take the initiative to transform the “teacher-oriented” education model into a “student-oriented” intelligent education model, as well as to construct a parallel teaching technology system that is interactive between virtual and physical reality . Finally, a learning achievement test was conducted to assist teachers and students in understanding the learning effectiveness. Furthermore, it was determined whether a substantial difference in students' performance between the two schools exists to understand whether the AI-assisted learning system can truly improve learning effectiveness.The learners registered in courses on the ShareCourse learning platform were subjected to a field experiment in this study. The two schools collaborated on a ten-week MOOC online learning course and a blended learning course using MOOCs and SPOCs. At Dong Hwa University, AI precision education was introduced to explore precision education. Students' learning effectiveness was evaluated based on academic achievement tests and learning satisfaction . The former's measurement index is based on the learning goals planned by the curriculum, and relevant questions are included in online examinations to assess learners' learning achievements. The latter measure is based on the course learning experience questionnaire, with the following description: the questionnaire used SPSS statistical software to analyze the quantitative data. In addition to the quantitative research discussed above, the qualitative research method involving in-depth interviews was employed to understand better teachers' and teaching assistants' perspectives on this innovative teaching model. Additionally, innovative teaching methods and effectiveness evaluations of AI precision education have been introduced for offline courses.This course learning experience questionnaire was primarily designed to understand students' views on their learning experiences, as well as the impact of AI precision education intervention  on learning. This questionnaire is divided into eight sections: learning satisfaction (including the teacher's role and teacher-student interaction as perceived by students), readiness for online learning, self-study evaluation, learner/teacher positivity, learning activities, comprehensive issues , basic personal information, and other suggestions. The experimental group had one additional part: views on AI precision education intervention.The AI precision education-assisted learning system collects the MOOC platform's relevant learning history data records. The data set contains all of the learner's learning history information  and is used as a training data set to produce a set of precision-learning auxiliary modules. The AI precision education-assisted learning system typically uses four classification learning methods: decision tree, random forest, neural network, and support vector machine modules. Due to the sample size limitation, this study relied exclusively on the support vector machine (SVM) method to evaluate and classify learners' learning history data records and predict learners' online learning effectiveness. The training sample set for this study was the relevant learning data recorded during the mid-term exam, and the mid-term results were used to evaluate the student's learning effectiveness. The mid-term test comprised the relevant test questions listed separately according to the course's online learning units, which included basic concepts of learning, advanced tests, and extension tests. Following model training, the relevant model was used to predict whether students would achieve the learning effectiveness of each subsequent online learning.Subsequently, teachers provide special guidance for students in need to improve learning effectiveness and achieve the final results of AI-assisted precision education.In-depth interviews were conducted at this stage using semi-structured interviews. The study employed more open interview methods to gain insights into teachers' and teaching assistants' perspectives on the SPOC teaching mode and AI precision education intervention . The interview process was recorded after the interviewee's consent was obtained to record the content of the interviewee's response. The outline of the interviews is as follows:Use of MOOC platform and videos for teachers and teaching assistants.Curriculum planning and design by teachers and teaching assistants.Observation of changes in student learning .Attitudes toward AI precision education intervention .Future improvements.This study aims to determine the impact of different teaching modes of AI precision education on students' learning effectiveness. The remainder of this section is divided into four parts. The first part uses questionnaire survey results to understand students' views on the learning experience, including classroom learning satisfaction and learning effectiveness. The second part evaluates the learning effectiveness of each student using the AI precision education-assisted learning system. The third part compiles the results of the in-depth interviews, and the last part presents the results of the statistical verification of the students' performance of the two schools before and after the experiment.The average of students' responses to questions about learning satisfaction, learner/teacher positivity, learning activities, and comprehensive problems from both schools was higher than 4 , indicatt-test was performed to understand whether there is a significant difference in the average of students between the two schools. There were no significant effects on learning satisfaction , readiness for online learning , self-study evaluation , learner/teacher positivity , learning activities , or comprehensive issues . However, when the question was tested one by one, it was found that there were significant differences in item 2  and item 3  of learning satisfaction, as well as item 2  of learner/teacher positivity. This shows that the means of Dong Hwa University in these items are significantly higher than those of Taitung University. This demonstrates that the AI-assisted learning system's intervention affects students' perception of teacher-student interaction, teachers' appropriate and constructive responses to students, and teachers' active participation in the course.An independent samples ow as the number of times a video is viewed, ot as the viewing time, and op as the number of exercises, and assigns a weight to each parameter. Therefore, the following are the overall characteristic parameters:Regarding the AI precision education-assisted learning system, the study used MOOCs to teach students basic theoretical knowledge and for offline implementation of low-power long-distance wireless networks. Thus, determining whether a student's learning effect is good and whether additional guidance is needed to achieve a certain degree of learning effectiveness is critical. The study used SVM as the model to make judgments to identify the students' learning situation and consider ways to efficiently reduce errors when dealing with low data volume. To complete the training of the SVM model, it is necessary first to determine which features need to be imported into the SVM. Because MOOCs can fully record students' learning processes, including the number of times a video is watched, viewing time, practice time, and online test results, the indicators above are incorporated as characteristic values. Online test results were utilized to determine the learning outcomes. The number of times a video was viewed, viewing time, and exercises were integrated and set as learning parameters. The study defines Next, this study used basic ability test scores as an additional feature to assist the SVM in determining students' learning effectiveness. After successfully categorizing students' learning effectiveness, this approach provides personalized tutoring for students in need, enabling them to develop the confidence necessary to improve their learning effectiveness. Finally, this study evaluates the effectiveness of the AI precision education-assisted learning system using the learning achievement test as an indicator. The results of the model trained using SVM are shown in The most important aspects of using the MOOC platform and videos are the course progress, course videos, after-school exercises, classroom tests, and course discussion areas. In addition, the on-campus learning platforms, namely Dong Hwa's e-Learning and Taitung University's eClass, are used simultaneously. The course planning and design part is mainly O2O. Online videos allow students to grasp the concepts clearly before moving on to the implementation, and the connection is seamless. In addition, because the course is required to produce thematic results at the conclusion of the semester, it is based on network programming and the concept of Long Range (LoRa). Related theories and practical content are planned according to the student's topic. For example, concepts employed in related topics such as automatic watering, temperature sensing, mobile phone recording, and transmission will be discussed, similar to the ideas of micro-credits and micro-courses.Regarding the observation of student learning changes at Dong Hwa University, the learning materials on the platform are vital for students with non-professional backgrounds (Information Management Department) at the beginning of the course. They watched the video on the platform more seriously. Furthermore, due to their familiarity with the basic concepts of networking and the ability to develop programs, students with a professional background in information technology (Information Engineering) believed that they already understood all the conceptual explanation videos; hence, they watched less videos in comparison. The advantage of MOOCs is that they can assist in learning. Students who are motivated to learn can access a wealth of knowledge on the platform. At Taitung University, students watched the teaching videos on the platform before class, which resulted in increased discussion in the class, and they could understand the content explained by the teacher more quickly. On the whole, compared to physical courses, this learning platform collects and analyzes the learning materials of MOOC learners to ascertain their learning conditions, difficulties, and learning modes and adjusts the teaching content and methods accordingly. As a result, students' learning motivation and participation, as well as the learning effect, have significantly improved.Both teachers and teaching assistants agreed with this approach regarding the attitude of AI precision education intervention. Special counseling can be provided for learners with poor learning conditions based on the students' learning history, through the AI system grouping, to identify problems early and further improve learning effectiveness, successfully achieving AI precision education-assisted learning, and teaching students according to their aptitude.t(61) = 0.868, p = 0.389]. However, there was a significant difference in the learning achievement tests of the two schools' students after the experiment . This demonstrates how the AI-assisted learning system can improve students' learning effectiveness.Independent samples t-tests were used to determine whether there was a significant difference in the scores of the students from the two schools on the basic ability test of information knowledge before the experiment. In Online teaching videos enable students to understand the basic concepts of the course before taking the physical course, and the connection can be smoother in the physical course. At the beginning of the course, the learning materials on the platform were vital for students with a non-professional background (Information Management Department). They watched the teaching videos on the platform more seriously. In fact, the advantage of MOOCs is that they are flexible, allowing students to learn at their own pace without being restricted by time and space, which assists learning, as Fellman et al.  also indAlthough the test results of the two schools' course learning experience questionnaire surveys were not significant, the achievement test scores of the two schools' students following the experiment revealed a significant difference. There was no difference in the initial basic ability test scores prior to the experiment. However, there was a significant difference in the achievement test scores after the experiment, indicating that the AI precision education-assisted learning system can effectively assist students in learning. As Dekker et al.  concludeThe research limitation was the smaller number of samples. This is because the sample size was restricted to students enrolled in the courses. Increased sample size can improve the accuracy of the model trained by AI and enhance the interpretation of the results. Future studies should consider large-scale research and see whether any other variables influence students' learning experiences. Additional AI training models, such as decision trees, random forests, and neural networks, can also supplement the research. In addition, experimenting with the same courses taught by the same teacher in different classes or the same course offered in different semesters may result in less interruption during the experiment process.The original contributions presented in the study are included in the article/Y-SL proposed the whole research theory, conducted literature research, and designed course experiment flow of AI precision education. Y-HL collected experimental data and analyzed the data. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The prediction of membrane failure in full-scale water purification plants is an important but difficult task. Although previous studies employed accelerated laboratory-scale tests of membrane failure, it is not possible to reproduce the complex operational conditions of full-scale plants. Therefore, we aimed to develop prediction models of membrane failure using actual membrane failure data. Because membrane filtration systems are repairable systems, nonhomogeneous Poisson process (NHPP) models, i.e., power law and log-linear models, were employed; the model parameters were estimated using the membrane failure data from a full-scale plant operated for 13 years. Both models were able to predict cumulative failures for forthcoming years; nonetheless, the power law model showed higher stability and narrower confidence intervals than the log-linear model. By integrating two membrane replacement criteria, namely deterioration of filtrate water quality and reduction of membrane permeability, it was possible to predict the time to replace all the membranes on a water purification plant. Finally, the NHPP models coupled with a nonparametric bootstrap method provided a method to select membrane modules for earlier replacement than others. Although the criteria for membrane replacement may vary among membrane filtration plants, the NHPP models presented in this study could be applied to any other plant with membrane failure data.  Membrane filtration systems have been widely applied to water purification, including household-level systems and wastewater reuse ,2. AmongTo ensure treated water safety in membrane filtration plants, integrity testing of membrane modules is conducted on- or offline ,7. ThereOnce the integrity loss is detected in a membrane module, a failed hollow fiber is repaired by plugging with a stainless-steel pin or epoxy adhesive ,13. To aTo estimate the time and number of membrane failures, lifetime prediction methods based on accelerated laboratory tests are commonly applied ,19,20. GThe failure data of actual systems are used to predict the lifetimes of systems in other engineering fields, such as the occurrence rate of failures or the interval time between failures ,26. As aIn membrane filtration systems, once a membrane failure is found, the failed membrane fiber is plugged to reinstate the membrane module to the operational state similar to one without membrane failure . Thus, aThe rate of membrane degradation may also be influenced by the variation in membrane diameter and strength caused in manufacturing processes . This inTherefore, the application of these models to the prediction of membrane failure in a membrane filtration plant is limited, although there are a couple of studies on the lifetime modeling of the membrane by combining the accelerated test and a Weibull distribution model  or combiIn this study, we aimed to construct a novel method to predict membrane failures using NHPP models. In this study, two NHPP models with different intensity functions, namely a power law model and a log-linear model, were employed to predict membrane failures in a water purification plant and in each membrane module. To reach a decision on membrane module replacement, a novel strategy based on two criteria, namely the membrane failure rate and the performance reduction due to both membrane fouling and fiber failure, was developed. The individual property variation of modules was incorporated into the performance reduction criterion by combining NHPP models and a bootstrapping method. The membrane failure data were obtained from a small-scale membrane filtration plant in Japan . The plaThe operational age of the membranes was 12.7 years at the time the operational data were obtained. Pressure-based integrity testing was implemented once or twice a year when the offline chemical cleaning was conducted. Once a membrane failure was detected, the damaged hollow fiber membranes were repaired by plugging with a stainless-steel pin. During the course of membrane filtration operation, no membrane module was replaced.The number of failed hollow fiber membranes in each module was obtained when direct integrity testing was conducted in association with the offline chemical cleaning. Thus, the exact date of membrane failure of each module could not be identified, but recorded as failure events between two dates of integrity tests. Then, the yearly membrane failure was calculated by dividing the number of failed membranes by the duration of the two integrity tests in terms of years.Hollow fiber membranes are subjected to physical and chemical stress  throughout their operation, and the rate of membrane failure may increase due to the progress of membrane degradation. Therefore, membrane failure models need to assume the instantaneous failure rate as a function of time, ν(t) . In NHPPThe expected number of failures by the time in failures in interval If the exact failure times are unknown and only the number of failures within a time interval are recorded, e.g., the number of membrane failures rameters . The numThe last equation in Equation (2) is the likelihood function.In this study, two NHPP models, i.e., the power law model and the log-linear model, were investigated.Power law modelIn the power law model, the expected number of cumulative failures is expressed as:The cumulative failure rate The instantaneous failure rate at time For k-h failure event.For estimation of parameters Maximum likelihood estimators (MLEs) of  ‘bbmle’ . The Dua ‘bbmle’ ) was useLog-linear modelIn the log-linear model, the intensity function The likelihood function for the log-linear model is described as:Parameters The confidence interval  based on Liu :(18)LRV=The flow rate through an intact hollow fiber membrane −3), A bypass flow rate was calculated as follows: the equation for a bypass flow rate was determined by the flow regime in a hollow fiber, which can be identified by Reynold’s number When equation :(22)qb=0The maximum number of failed fibers per year to meet LRV 2 is calculated to be 22 fibers per module or 330 fibers per plant from Equation (18). When a membrane failure is found by the integrity test, the failed fiber is plugged and not used for the rest of operation; thus, the number of intact hollow fiber membranes and membrane surface area for filtration decrease. Furthermore, the permeability of the membrane reduces due to the chemically irreversible membrane fouling as the filtration operation proceeds. Reductions in the effective surface area and the permeability require a higher transmembrane pressure, which may necessitate replacement of the membrane module.The reduced water production by module at the operation time The failure rate, i.e., the number of membrane failure per year, is shown in Although there was a decrease in the failure rate in the 11th year from the 10th year, it increased again in the 12th year a. This kThe power law model and the log-linear model of NHPP were applied to fit the membrane failure data.Power law modelThe membrane failure rate curves for the whole plant estimated by the power law model using the failure data are shown in The predicted failure rates using the data up to the 10th year (10 yr) of operation exceeded the permissible failure rate (330 failures per year) after the 20th year of operation. The failure rates predicted from the data up to the 11th (11 yr), 12th (12 yr), and 13th (13 yr) years exceeded 330 after the 18th year of operation. Thus, the power law model predicts that the plant could be operated up to the 17th year without violating the criterion (LRV ≥ 2). As shown in this case, the power law model can predict the years of operation without violating the criterion with a small margin of error when it is applied for the membrane failure rate analysis of the whole plant.Log-linear modelThe membrane failure rate curves for the whole plant estimated by the log-linear model are plotted in Power law modelThe predicted cumulative failure curves using data up to the 9th to 13th years of operation are shown in The dotted lines in Log-linear modelThe cumulative failure curves predicted by the log-linear model are shown by the blue lines in The first year and the following trends in membrane failure varied significantly among the modules . ModulesThe actual cumulative fiber failure trends also varied significantly by module , leadingThe 95% confidence intervals for the log-linear model are always wider than those for the power law model, suggesting the larger variability in the failure predicted by the log-linear model than the power law model, which is in agreement with the AIC values shown in The model parameters of both NHPP models for each module were significantly varied, as shown in the previous section . In someThe bootstrapped distribution of median of each model parameter  as well as the model parameter for each module are shown in l trends , Table 473 for λ . AlthougThe distributions of log-liner model parameters, e values , which sectively . This diThe cumulative failure curves predicted using the bootstrapped parameters  for both the power law and log-linear models and their 95% confidence intervals are shown in We proposed two criteria for membrane module replacement: the membrane failure rate and the reduction in membrane performance. Although these two criteria used to be reported independently, to the best of our knowledge, we are the first to show how to combine these two criteria.A criterion of 330 membrane fiber failures per year for a whole plant, which corresponds to LRV 2, was proposed for module replacement a. If theThe reduction in filtration performance is estimated from two factors: the observed flux reduction due to membrane fouling and the reduction in membrane surface area caused by the plugging of failed membrane fibers b. The prThe cumulative failure curve predicted from the data up to the 13th year by the power law model crosses the performance level curve of 60% in the 14th year of operation. This indicates that all modules should be replaced in the 14th year if the performance level below 60% of the initial level is not permissible. If the permissible performance level is set to 50%, the predicted curve crosses the performance level curve of 50% in the 18th year, which indicates a longer service life of the membrane modules. However, the predicted failure rate curve exceeds the permissible level in the 17th year, as explained in the previous section. Thus, in this case, the failure rate criterion should be preferred.In the case of log-linear model curves from the data up to the 13th year of operation, a similar decision would be made as with the power law model. However, careful attention should be paid to the prediction from the log-linear model curves using a smaller number of data up to the 9th, 10th, or 11th years, as they might lead to the overestimation of cumulative failures. The difference between the cumulative failure curves predicted by the power law model and those by the log-linear model are quite large up to the 11th year of operation, and the predicted cumulative failure from the log-linear model is significantly greater than that predicted by the power law model . As mentIn a large-scale membrane filtration plant, it may be necessary to replace some of the membrane modules earlier than others because all modules cannot be replaced at one time . Thus, iThis paper proposed a new strategy for determining when and which module should be replaced. In this strategy, the year of membrane module replacement is determined by comparing the actual failure data and the predicted cumulative failure curves with their confidence interval by the bootstrap method. As described in (1)Estimate the NHPP model parameters for each module from the actual failure data, and draw the predicted cumulative failure curve from the estimated parameters of the NHPP models.(2)Obtain the bootstrapped median and its 95% confidence interval of model parameter for each module, and draw the bootstrapped cumulative failure curve with a confidence interval.(3)Compare the predicted cumulative failure curve and the bootstrapped cumulative failure curve, and select the modules to be replaced when the predicted curve is above the upper boundary of the confidence interval of the bootstrapped curve.The proposed procedure c is as fIf the trend of the actual cumulative failure and/or the predicted failure curve of a certain membrane module is above the upper boundary of the confidence interval of the bootstrapped cumulative failure curve, the module is assumed to have a significantly greater number of failed fibers than others. Modules F, G, L, and M could be categorized as such modules because the failure curves predicted by the power law model or by the log-linear model are above the bootstrapped cumulative failure curves and the upper boundary of the confidence interval . Among tThe membrane failure data used in this study are specific to the membrane modules used in the membrane filtration plant of this study. Thus, the membrane failure rates and trends might be different in other plants with membrane modules different from those in this study. However, the proposed approach could be applied to the failure prediction of different membrane filtration plants using different membrane modules, as it requires only the membrane failure data. Accordingly, the proposed approach could be also applied to the failure prediction of other membrane filtration plants of wastewater treatment, water reuse, or gas/oil treatment systems in which the membrane replacement is determined in a similar manner with a water purification system ,45. Because of ageing, the probability of membrane failure changes with membrane filtration plant operation over time. Therefore, we proposed two nonhomogeneous Poisson process (NHPP) models, namely, the power law model and the log-linear model, to predict the number of membrane failures in an actual membrane filtration plant. The methods to apply these NHPP models to the membrane failure data obtained from a full-scale plant were delineated. The power law model showed lower AIC values than the log-linear model in predicting the membrane failures of the plant. In addition, the log-linear model showed overestimating tendency and wider 95% confidence ranges, especially with short operational periods to acquire failure data, suggesting its lower prediction accuracy compared to the power law model. Although the failure trends predicted by both models converged within a small range when the operational period of failure data acquisition extended, the power low model was found to be the preferred model for predicting the membrane failure trend of the whole plant. To estimate the year of membrane replacement in a water purification plant, two criteria, i.e., the membrane failure rate and the membrane performance reduction, were proposed, and their use was verified using the actual membrane failure data. The membrane failure rate was set based on the microbial safety of more than a 2-log reduction. The reduction levels of membrane permeability were set to between 50% and 70% of the initial flux. Combining these two criteria, it is possible to integrate membrane replacement strategies based on filtered water quality and the filtration performance. Using the membrane failure data obtained in this study, it was demonstrated that membrane service life before replacement is 14 to 17 years depending on the selection of the performance level. The trends in the failure rates per module, as well as the cumulative failure numbers, varied significantly due to the variations in the properties of the modules, probably resulting from material variations during membrane production. Thus, it might be better to replace those membrane modules with higher failure rates than others in order to save the cost of membrane replacement. Thus, a bootstrap method was employed to consider the property variation in the membrane modules in the NHPP models for failure prediction, which successfully simulated the overall cumulative failure trend of a module using bootstrapped median parameters and their 95% confidence intervals. It is suggested to replace a membrane module if the predicted failure trend is higher than the upper confidence boundary of bootstrapped failure curve. In the case of the membrane modules examined in this study, 4 out of 15 modules were selected to be replaced earlier than others.Until now, the determination of module replacement has been dependent on the experience and knowledge of seasoned waterworks staff. However, the strategy for membrane module replacement proposed in this study provides a systematic framework for membrane replacement based only on the failure data observed in each membrane filtration plant, which provides support to the plant operators and waterworks staff.


The analysis of human physiological systems from the point of view of complex systems theory remains a very ambitious task. The complexity of the problem often encourages the use of innovative mathematical methods analyzing the processes that take place in space and time. The main goal of this paper is to visualize the cardiovascular system during auricular vagus nerve stimulation (aVNS) using the matrix differences to evaluate the dynamic signal interfaces by cointegrating the initial signal data into the matrices during each case. Algebraic relationships between RR/JT and JT/QRS cardiac intervals are used not only to track the cardiovascular changes during aVNS but also to characterize individual features of the person during the transit through the therapy. This paper presents the computational techniques that can visualize the complex dynamical processes taking place in the cardiovascular system using the electrical aVNS therapy. Four healthy volunteers participated in two verum and two placebo experiments. We discovered that the body’s reaction to the stimulation was very different in each of the cases, but the presented techniques opened new possibilities for a novel interpretation of the dynamics of the cardiovascular system. The vagus nerve (VN) is the longest cranial nerve in the human body and it is involved in the regulation of multiple systems . HistoriThe external ear is the only place on the body where VN sends its only peripheral branch. In fact, the auricular branch of VN surfaces as the afferent auricular VN (aVN) and, thus, forms a cutaneous receptive field in the pinna of the ear. This field is susceptible to external stimuli in terms of peripheral nerve stimulation. In particular, the aVN allows for an easy external access via electrical stimulation in terms of aVNS, which then connects, directly and favorably, the applied stimuli to the brainstem, as shown in The human functional state changes as a result of vagus nerve stimulation. In terms of neuromodulation, it recruits sensory aVN fibers and so simulates/projects sensory information to the brainstem, forming the auriculo-vagal afferent pathway . Both thThe parasympathetic system is activated in several ways, one of which is arterial baroreflex activation. The principle of negative feedback governs the regulation of vascular tone, as it does many other systems. Arterial baroreceptors in the aortic arch transmit blood pressure to the brainstem via the afferent VN, and so provide this as feedback for controlling arterial blood pressure in the near term (baroreflex). The arterial blood pressure (ABP) and vascular tonus often fluctuate in a wave-like pattern due to these causes. Hypertension stimulates baroreceptors in the aortic arch, which send signals to the brainstem through the afferent VN. In reaction to hypertension, the brain, particularly the NTS, restricts sympathetic outflow to the heart and periphery instinctively, whilst stroke volume and total peripheral resistance decrease, respectively. The parasympathetic outflow is automatically enhanced by efferent VN fibers, decreasing the heartbeat through the efferent VN link to the sinoatrial node. As a result, blood pressure falls and returns to normal. Because of these brain-mediated effects, auricular VNS can be used as a systemic therapy, affecting the entire human body and its complex subsystems .However, the individual tuning of stimulation parameters remains an open problem in the practical implementation of aVNS. Several studies have shown that the outcome of therapeutic effects of aVNS is observed in long-term treatment ,11. The Numerical algorithms for the identification of algebraic relationships between cardiac intervals have already been designed and implemented in several previous studies ,14,15. TChanges in the patterns of interconnection (connectivity) and patterns of variation over time (variability) offer essential information about the state of the overall system since the spatial and temporal organization of a complex system defines its own character . MeasuriSystemic monitoring methods are required for the registration, validation, and optimization of systemic effects induced by the auricular VNS. Methods of interest that reflect the non-linear complex systems of the human body are coupled mutually with feedback loops . Here, tA single ECG feature, such as the RR interval, which is a systemic parameter, indicates the functional status of the entire organism’s regulatory system and is used as a measure of neurocardiac function that reflects heart–brain interactions and ANS dynamics ,21. For In particular, the JT interval as an ECG feature is linked to the metabolic rate in myocardium; namely, when heart activity is the strongest, the JT interval is the shortest and vice versa. The JT interval describes the duration of ventricular repolarization. The JT range determines different electrophysiological phenomena and is divided into the JTa interval (from the point J to the peak of T wave) and the Te interval (from the T wave peak to the end of T). JT intervals are linked to variations in cardiac metabolism . The JT Another ECG parameter, QRS duration, is related to interventricular synchronization features, and could be connected to the heart intrinsic regulatory system. During sympathetic activation, during load in a healthy heart, we can see shortening of this parameter. This prolongs some heart pathologies–ischemic heart disease. The QRS complex is a part of the regulatory system of the heart, which reflects a spreading of the depolarization in the ventricle and synchronization of the depolarization spreading between ventricles. The wider QRS complex shows a slower conduction in the heart ventricle . The durFor example, RR–JT interconnection shows how tied the regulatory system is connected to the metabolism of the heart; this relationship reveals the evolution of complex dynamical processes in the self-organization of the heart system during the load and the recovery processes . AdditioRR–QRS interconnection reflects the interconnection between the organism regulatory system and the heart regulatory system by the reaction of the duration of the QRS complex to the RR interval shortening. It must be kept in mind that certain feedback responses (based on the type of stress induced) may include possible changes in vascular tone, with or without influencing HR, and certain elements of the individual cardiopulmonary and arterial reflexes may involve counteracting or inhibiting influences with regards to other elements . For exaFor the evaluation of the human functional state during vagus nerve stimulation, we used the model of integral health evaluation. This model is shown together with the short-term neural baroreflex feedback loop response model in The presented research met all standards for the ethics of experimentation. The permit to perform biomedical investigation was granted by Kaunas Regional Ethics Committee for Biomedical Investigations, No. BE-2-51, 23 December 2015. Our study consists of 4 single cases. Each of the volunteers signed a consent form to take part in this research. They were assessed four times, two verum and two placebo experiments. . In thisTwelve lead ECG were assessed and analyzed using the Kaunas–Load system . Twenty-Percutaneous auricular VNS (pVNS) was performed with three needle electrodes located in vagally innervated regions of the auricle, in/around the cymba conchae . StimulaLet us consider that vectors ented in . This alStep #1. Six elements duced in , where δences is (1)Lδ,k=Step #2. Every matrix  of Lδ,k ,14. Step #3. Finally, internal and external smoothing is applied for the produced scalar sequence of maximum eigenvalues. If the radius of the internal smoothing is denoted by lated in  for the Case reports suggest that the right vagus can be used in circumstances where approaching the left vagus is inadvisable. Since the right vagus innervates the sinoatrial node, stimulating on the right is best conducted with ECG monitoring . It is wIn the first case, we can observe that when the body relaxes, the curve goes to the upper right corner . Both JTThe second subject shows more complicated changes: during the second stimulation, the tension in both fractal levels (RR/JT and JT/QRS) is also reduced . The sysThe third case  has a clThe fourth subject showed the classical behavior in the same way as the first stimulation, however, for some reason the second stimulation did not show much effect (second red curve) . This coComplex systems have properties that cannot fully be comprehended by understanding the parts of the system . The proWhen it is possible to objectively measure the body’s reactions, the parameters such as at the time of stimulation can be optimized. Even though the human body’s reaction to any stimulus could be very different and unique, with our visualization technique we could successfully track the whole process of the electrical auricular vagus nerve stimulation in two different fractal levels of the human organism. The proposed methodology for monitoring subtle variations of algebraic relationships between cardiac intervals during aVNS is able to track individual reactions of the patient in real time. The presented techniques provide a unique insight into the complex dynamical processes taking place in a patient’s cardiovascular system during the stimulation. The ability to track these dynamical processes can build the ground for the optimization strategy, which could lead to personalized aVNS treatment. The development of such a personalized approach to aVNS treatment is a definite objective of future research.The main limitation of this paper was the size of the cohort. As mentioned previously, the main objective of this study is to present the methodology for tracking physiological changes during aVNS in real time. Cardiac intervals play the major role in the presented technique. In other words, the proposed technique does not measure the direct impact of the nerve stimulation to targeted systems under the planned treatment. Instead, the proposed technique visualizes the real-time subtle reactions of the cardiovascular system. However, the cardiovascular system is affected not only by aVNS during the stimulation. The patient is asked to stay calm in a supine position during the whole experiment. The fatigue, drowsiness, excitement, and the momentary pain caused by the insertion of the needle electrodes are all factors that have an integrated impact on the cardiovascular system. Therefore, the observed dynamic relationships represent the integrated response to all external and internal stimuli. In other words, the presented technique cannot produce reliable results if the patient is not in a calm state. Further studies, including extensive clinical trials are required before validated optimization algorithms can be designed for personalized aVNS treatment.The process of using only the ECG signal, which does not require any additional measurements, is non-invasive, revealing information in a short-term measurement.Unfortunately, we did not have a large cohort to investigate. The psychological state of the subject fluctuated because the experiment required a supine position for 90 min.


Neural oscillations are repetitive patterns of neural activity in the central nervous systems. Oscillations of the neurons in different frequency bands are evident in electroencephalograms and local field potential measurements. These oscillations are understood to be one of the key mechanisms for carrying out normal functioning of the brain. Abnormality in any of these frequency bands of oscillations can lead to impairments in different cognitive and memory functions leading to different pathological conditions of the nervous system. However, the exact role of these neural oscillations in establishing various brain functions is still under investigation. Closed loop deep brain stimulation paradigms with neural oscillations as biomarkers could be used as a mechanism to understand the function of these oscillations. For making use of the neural oscillations as biomarkers to manipulate the frequency band of the oscillation, phase of the oscillation, and stimulation signal are of importance. This paper reviews recent trends in deep brain stimulation systems and their non-invasive counterparts, in the use of phase specific stimulation to manipulate individual neural oscillations. In particular, the paper reviews the methods adopted in different brain stimulation systems and devices for stimulating at a definite phase to further optimize closed loop brain stimulation strategies. Brain stimulation paradigms including electrical deep brain stimulation (DBS), optogenetics brain stimulation (OBS), transcranial electrical stimulation (tES), and transcranial magnetic stimulation (TMS) and repetitive transcranial magnetic stimulation (rTMS) are being increasingly employed as a therapeutic and diagnostic tools for neurological disease conditions such as movement disorders and psychiatric illnesses ,2,3,4,5.Over the recent years, there have been many advancements in invasive brain stimulation paradigms with size-reduced, tether-less and low power devices. Most of the existing brain stimulation devices in clinical therapeutical use are ‘open-loop’, where the stimulation settings are fixed and subsequent adjustments to the device settings are done manually during hospital visits . HoweverFor the development of closed-loop or adaptive brain stimulation strategies, the feedback signal is generated from the subject by reading different biomarkers  that refThe repetitive patterns in the neural activity are observed in LFP as oscillations. These neural oscillations or rhythms are produced by multiple neurons communicating with each other and for allowing synchronized action during normal brain operation. Power or amplitude of these neural oscillations are related to different cognitive functions , and altGenerally, in an ON-OFF feedback control strategy, stimulation pulses are delivered when the amplitude/power of the oscillation under consideration deviates from a certain threshold value. Dual-threshold strategies can also be found in literature where the stimulation voltage is either increased or decreased based on the upper and lower range of oscillatory band power . In eithIn the following sections, neural oscillations, the importance of neural oscillations as biomarkers for neurological diseases, and significance of phase selective close-loop brain stimulation strategies are discussed. Some of the current research work involving the strategies being used by studies for implementing stimulation in correct phase are explained. These works support the expectation that phase-specific closed loop brain stimulation will demonstrate higher efficacy in modulating neural pathways.Neural oscillations are the repetitive electrical activity generated spontaneously or in response to stimuli by neurons . OscillaConsidering one of the most common neurodegenerative motor impairment diseases, PD, the role of modulations in beta band oscillations in basal ganglia- cortical circuits has been widely studied ,45,46,47Dystonia ,58 is yeTalking about the debilitating neuropsychiatric disease, SZ, there is still more to be understood about the origin and progression of the disease and hence a definite biomarker for early detection and diagnosis of the disease is still under study ,65. AlonConsidering the literature on the common psychiatric illness, major depressive disorder , abnormal oscillatory patterns in different frequency bands have been widely reported. Among these, the role of alpha band oscillations in MDD can be seen as a consistent finding ,69,70. TWith the modulation in neural oscillation being an emerging biomarker for different neurological diseases, a closed loop brain stimulation system with adaptive stimulation according to the online reading of the biomarker is a very good tool to establish the relation between these neural oscillations and different neurological conditions. This will further enable science to decode these disease features for developing medicines and other treatment methodologies. The concept is to read the biomarker continuously and change the stimulation accordingly. By applying the stimulation, the aim is to target the particular neural oscillation frequencies, and this is where phase specific stimulation comes into picture.On the device engineering side, the essential difference between a closed and an open loop control system is in the feedback mechanism. In a closed loop control system, a feedback control is present, which can efficiently change the stimulation parameters depending on the actual and desired output. Using extra circuitry, the biomarkers are quantitatively analyzed to provide precise stimulation at accurate times. This helps address one of the major issues associated with neural disorders, i.e., that the symptoms do no stay constant, and the condition of the patient could vary throughout a period. With the help of an effective closed loop control in place, it can be ensured that the person is given optimal stimulation by continuously monitoring the biomarkers and modulating the stimulation parameters accordingly. Moreover, closed loop brain stimulation, as mentioned before, helps in the diagnosis as well as study of neural disorders. This is important as the scientific community is still investigating the cause of origin and progression of multiple commonly occurring neurological diseases. In addition to this, for untethered devices, extra stimulation uses up battery life unnecessarily.With neural oscillations as biomarkers, closed loop brain stimulation would be benefitted from phase specificity.As pointed out in the previous sections, manipulating the neural oscillations in LFP could prove to be a useful treatment approach for different neural conditions. However, controlling the power of an individual frequency band of oscillations in the LFP is not an easily achievable task . AnotherAn approach to enhance the specificity of brain stimulation and modulate the oscillatory power in specific bands of neural oscillations is to time-lock the stimulation to the phase of the present oscillation. Adopting the concept of constructive and destructive interference , stimulaReviewing the literature on brain stimulation systems employing phase-specific stimulation, we came across very few developed systems, including both invasive and non-invasive systems. These systems are further described in the following.Mansouri et al.  discusseSiegle et al.  used a pIn another related work on phase-specific deep brain stimulation for movement disorder, Cagnan et al. , consideZarubin et al.  used a cIn another important work in this area, an analog feedback circuit is used to give phase-specific stimulation. In reference , a closeWhile reviewing these systems, it is clear that these systems lack a dedicated circuit for the phase detection and phase specific stimulation. Most of the reviewed systems work in a bench-top setting and the software for phase detection is implemented on a separate PC. This could be a hindrance to the miniaturization and further tether-less operation of brain stimulation systems and there is scope of improvement which could be the development of application specific integrated circuits specifically for the phase detection and precise phase-specific stimulation.The essential components of a closed loop brain stimulation system with phase specific stimulation for LFP-based biomarkers are the following: a neural sensor to sense the LFP signals from the subject, a neural stimulator for producing the stimulation patterns in the required specifications at the right time, and a software unit to process the signals received by the sensor and generate a control signal for the stimulator. A conceptual block diagram of such a device is presented in Electrophysiological signals in the brain are sensed using electrodes invasively implanted into the patient’s brain. A sensor circuit, comprising amplifiers and filters, conditions the sensed signal for further analysis. Initially, the micro amplitude range signals from the electrode are amplified. This is followed by a band pass filter to filter out unwanted frequency signals. The cut off frequency of the band pass filter is adjusted to meet the frequency range of the neural oscillation bands—delta (0.5–3.5 Hz), theta (3.5–7 Hz), alpha (8–13 Hz), beta (18–25 Hz), gamma (30–100 Hz), and high frequency oscillations, HFO (100–200 Hz). This is followed by a controller which extracts the features of the conditioned signals after digitizing them. Features such as amplitude, phase, and power of the received signals are extracted and based on the extracted features, signals are classified into classes.Following this, a control signal is generated for closing the feedback loop. A control algorithm identifies deviations, if any, of the actual stimulation output from the required level of stimulation. Accordingly, the algorithm adjusts the stimulation parameters to minimize the difference in actual and desired outputs with the help of the stimulation control circuit. Thus, the algorithms and software module for real time classification of the LFP signals into pathological and non-pathological also have a very important role to play in the realization of an efficient closed-loop brain stimulation system. The actual clinical implementation of closed-loop DBS for rapid behavioral changes relies on accurate and quick detection of these disease states using efficient algorithms. Algorithms ranging from very simple classification algorithms to sophisticated machine learning and artificial intelligence-based ones can be found in literature.While adding the attribute of phase specificity in the stimulation to the device, the controller needs to have the extra feature of calculating the phase of the ongoing signal oscillations, and the delays in the network to produce the control signal for the stimulator to send the correct amplitude pulse at the right time and phase to the stimulation electrode placed on the brain surface. Thus, the most significant requirement in the realization of such a phase-specific deep brain stimulation is the implementation of an algorithm for real time determination of the phase of the uninterruptedly recorded local field potential information. A key difficulty in such a system is accurately estimating the oscillatory phase in real time. An efficient algorithm to control the stimulation pulse and trigger the stimulation at the right time considering the phase of the ongoing stimulation as well as the delays along the network needs to be realized. In the existing digital signal processing systems, by the time a signal is digitized and its phase is computed through a Fourier transform based algorithm, the target phase has long gone. A quicker but complex method involves outputting phase-locked stimulations using analog-circuitry which is still in the early stages of development. Additionally, such an analog circuit can be implemented into an application specific integrated circuit chip.Another important factor to consider for potentially optimizing brain stimulation is the functional connectivity of the brain. It is a well-understood fact that brain regions don’t operate in isolation. Our brain is a network, which consists of spatially distributed, but connected sections that work together in realizing different function ,84. HencPhase selective stimulation is an added attribute to the deep brain stimulation paradigm for increasing its efficacy in altering the neural oscillations in specific frequency bands. As more and more studies are being carried out to find the link between the variations in the energy of different frequency bands of neural oscillations and neurological disorders, a hybrid strategy combining closed-loop DBS strategies and phase selective stimulation could be an effective approach. Selective modulation of neural synchrony using phase-locked stimulation offers the potential to enhance the efficacy of therapy and minimize the side effects of the stimulation. While reviewing the literature on this approach, the authors could not find much work done using phase selective stimulation in invasive DBS. This paper reviewed the methodologies adopted in different brain stimulation systems and devices for stimulating brain at a definite phase. There is also an attempt to put forward a conceptual model incorporating the concepts of phase selective stimulation and deep brain stimulation for standalone DBS devices.


Biorobotic fishes have a huge impact on the development of underwater devices due to both fast swimming speed and great maneuverability. In this paper, an enhanced CPG model is investigated for locomotion control of an elongated undulating fin robot inspired by black knife fish. The proposed CPG network includes sixteen coupled Hopf oscillators for gait generation to mimic fishlike swimming. Furthermore, an enhanced particle swarm optimization (PSO), called differential particle swarm optimization (D-PSO), is introduced to find a set of optimal parameters of the modified CPG network. The proposed D-PSO-based CPG network is not only able to increase the thrust force in order to make the faster swimming speed but also avoid the local maxima for the enhanced propulsive performance of the undulating fin robot. Additionally, a comparison of D-PSO with the traditional PSO and genetic algorithm (GA) has been performed in tuning the parametric values of the CPG model to prove the superiority of the introduced method. The D-PSO-based optimization technique has been tested on the actual undulating fin robot with sixteen fin-rays. The obtained results show that the average propulsive force of the untested material is risen 5.92%, as compared to the straight CPG model. Robotic technologies are attracting significant attention from researchers, especially in the fields of outer space and ocean exploration that are difficult for humans to access. For example, in the area of ocean science, autonomous underwater vehicle (AUV) has been strongly developed by using different propulsion mechanisms such as jets, axial propellers, and body or fin –3. BecauIn motion control, the rhythmic movements of biorobotic fishes are produced by central pattern generator (CPG) networks –8. BioloTo simulate the CPG behavior, many researchers have established some of the different mathematical models to seek the parametric values of CPG to obtain the desired oscillation profiles. These researchers have attempted to adopt the CPG model to generate the rhythmic movements of a species of elongated undulating fin robot, which comprises multiadjacent fin-rays interconnected to a flexible membrane. The authors in reference  proposedA brief of our main contributions can be expressed as follows: At first, a CPG using the chain topology of sixteen coupled Hopf oscillators is offered to generate fishlike rhythmic movements. Following this, the improved D-PSO is exploited to optimize the amplitude values of the CPG network to increase the average propulsive force of the undulating fin robot to make a faster movement. Finally, the obtained result of CPG parameter synthesis using the different optimization methods, including D-PSO, PSO, and GA, is implemented to prove the superiority of the proposed D-PSO algorithm.The rest of this study is categorized into four parts. In θi is the sway angle of the ith fin-ray at the time t; θimax is the maximum sway angle for each fin-ray; f is the oscillatory frequency; ϕi is the phase difference angle between two adjacent fin-rays called the phase lag angle.The elongated undulating fin robot is composed of sixteen links and a flexible membrane mounted on the straight axis base of underwater vehicles. Each link includes an RC servo motor and an oblique fin ray that can rotate around a joint, as illustrated in t∗ corresponding to the change of the amplitude envelope  is a nonlinear function; Yi is a perturbation vector.As mentioned above, the structure of the elongated undulating fin robot includes a set of joints in which a CPG unit regulates each joint. In other words, each CPG output corresponding to a state variable It is noted that the amplitude values of an oscillator are affected by the perturbation signal, and hence each oscillator can be coordinated with another through a perturbation to recognize the change of amplitude and maintain the stable frequency and phase. There are various proposed coupling structures, including ring, chain, star, and full topologies. In the paper, however, the chain structure with adjacent neighbor connections is chosen to obtain the oscillatory coordination of fin-rays. It means a chain of CPGs will use serially connected joints for the gait generation of the elongated undulating robot. Furthermore, the proposed CPG structure uses bidirectional couplings that can be influenced by both anterior and posterior adjacent fin-rays, as shown in It is clear from Ai to obtain the different propulsive forces. In this research, a novel variant of PSO is introduced to optimize the oscillatory amplitudes of the modified CPG model for the purpose of improving the swimming ability of the undulating fin robot.From the above equation, we can see that the modified CPG network can generate the rhythmic movement of the undulating fin robot by adjusting the amplitude values Pbest), and the best fitness value (Gbest). The theory of PSO can be consulted in detail in references  degree based on the actual mechanical structure. According to a set of parameters, as given in It can be seen from w) is in the range of The weight coefficient (c1 and c2) are set as 2The constant factors (c3) is selected as 0.04The scaling factor (The updating procedure is employed in 100 iterationsTo reach the best performance of CPG units, a new D-PSO algorithm is governed for the first time to obtain the maximum average propulsive force. However, the D-PSO only achieves the best optimum results once the appropriate parameters are selected in order to increase the converge speed as well as avoid trapping the local optimal point. After ten times repeated running, we obtained the parameters of D-PSO corresponding to the population size of ten particles as follows:To prove the feasibility of the developed D-PSO technique in finding the extremum of functions, some of basic math functions given have been initially tested, as shown in It is clear from 1–A16 is given in It can be observed from From In the term of motion control scheme, the CPG based on coupled Hopf oscillators not only generates the desired rhythm for the undulating fin robot but also removes the effect of radial interference on the oscillatory amplitude.The proposed D-PSO algorithm is the capacity to successfully solving the basic math functions and effectively improving the average thrust of the untested undulating fin fish obtained from the output signals of modified CPG.The novel D-PSO algorithm gives the fastest propulsive force as compared to PSO and GA. Moreover, the superiority of the proposed method is presented in the ability to avoid the local maxima in order to increase the accuracy of the optimization process.In this paper, the locomotion control and optimization task have been successfully performed on the elongated undulating fin robot inspired by the black knife fish. A bidirectionally coupled sixteen Hopf oscillator-based CPG network is investigated to create rhythmic movement as a perfect paradigm for robotic locomotion. In addition, a novel variant of PSO, namely D-PSO, has been developed to obtain a set of optimum amplitudes of the modified CPG network for the purpose of maximizing the average propulsive force. In brief, the new contributions in our study can be drawn as follows: