Nature-inspired optimization algorithms are a recent topic of research, and they are based on using some nature-inspired behaviors to solve optimization problems. Currently, a large number of approaches have been developed in this area, such as particle swarm optimization, bat algorithm, ant colony optimization, bee colony, dolphin algorithm, wolf search, flower pollination algorithm, and cat swarm. However, how to design efficient nature-inspired algorithms and how to use these algorithms for real-world application problems in control and robotics are still important issues. In particular, the design of neuro-fuzzy models, like type 2 fuzzy neural networks, type 1 fuzzy neural models, and intuitionistic fuzzy neural networks, has some current interest. In addition, new emerging neural models have been recently proposed. In all these models, a common problem is how to obtain an optimal structure, which can be handled by nature-inspired optimization algorithms. This special issue aims to bring researchers to report their latest research work on development of new nature-inspired algorithms or innovative applications of existing algorithms in the design of neural models for real-world applications in control and robotics, with an ultimate goal of exploring future research directions. In this special issue, we have five papers selected after a careful reviewing process. The five papers are representative of the current state of the art in this area.G. López-Vázquez et al. present a grammatical evolution- (GE-) based methodology to automatically design third-generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal performs the SNN design by exploring the search space of three-layered feedforward topologies with configured synaptic connections (weights and delays) so that no explicit training is carried out. Besides, the designed SNNs have partial connections between input and hidden layers which may contribute to avoid redundancies and reduce the dimensionality of input feature vectors. The proposal was tested on several well-known benchmark datasets from the UCI repository and statistically compared against a similar design methodology for second-generation ANNs and an adapted version of that methodology for SNNs; also, the results of the two methodologies and the proposed one were improved by changing the fitness function in the design process. The proposed methodology shows competitive and consistent results, and the statistical tests support the conclusion that the designs produced by the proposal perform better than those produced by other methodologies.On the other hand, R. Soto et al. presented a binary cat swarm optimization for solving the manufacturing cell design problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through cat swarm optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the autonomous search algorithm was developed with dynamic mixture ratios. The experimental results for both normal binary cat swarm optimization (BCSO) and autonomous search BCSO reach all global optimums, both for a set of 90 instances with known optima and for a set of 35 new instances with 13 known optima.2 polymerized into a gel state under ultraviolet (UV) light irradiation. The soft actuator structure consists of a layer of ionic liquid polymer gel sandwiched between two layers of activated carbon capped with gold foil. The volume of the cationic BMIM+ in the ionic liquid BMIMBF4 is much larger than that of the anionic BF4−. When voltages are applied to both sides of the actuator, the anions and cations move toward the anode and cathode of the electrode, respectively, under the electric field. The volume of the ILG cathode side therefore expands and the volume of the ILG anode side shrinks, hence bending the entire actuator toward the anode side. The Ogden model was selected as the hyperelastic constitutive model to study the mechanical properties of the ILG by nonlinear analysis. As the ILG is an ideal material for the preparation of a supercapacitor, the equivalent circuit of the ILG can be modeled by the supercapacitor theory to identify the transfer function of the soft actuator. The central pattern generator (CPG) control is widely used in the area of biology, and CPGs based on bioinspired control methods have attracted great attention from researchers worldwide. After the continuum soft actuator is discretized, the CPG-based bioinspired method can be used to control the soft robot drivers. According to the simulation analysis results, the soft actuator can be smooth enough to reach the specified location.C. Zhang et al. proposed the ionic liquid gel (ILG), a new type of soft actuator material, which is a mixture of 1-butyl-3-methylimidazolium tetrafluoroborate (BMIMBF4), hydroxyethyl methacrylate (HEMA), diethoxyacetophenone (DEAP), and ZrOQ. Zhu et al. proposed a novel motion planning method for autonomous ground mobile robot to address dynamic surroundings, nonlinear program, and robust optimization problems. A planner based on recurrent fuzzy neural network (RFNN) is designed to program trajectory and motion of mobile robot to reach target. And, obstacle avoidance is achieved. In RFNN, inference capability of fuzzy logic and learning capability of neural network are combined to improve nonlinear programming performance. Recurrent frame with self-feedback loops in RFNN enhances stability and robustness of the structure. Extended Kalman filter (EKF) is designed to train weights of RFNN considering kinematic constraint of autonomous mobile robot as well as target and obstacle constraints. EKF's characteristics of fast convergence and little limit in training data make it suitable to train the weights in real time. Convergence of the training process is also analyzed in this paper. Optimization technique and update strategy are designed to improve robust optimization of the system in dynamic surroundings. Simulation experiment and hardware experiment are implemented to prove effectiveness of the proposed method. Hardware experiment is carried out on a tracked mobile robot. Omnidirectional vision is used to locate the robot in the surroundings. Forecast improvement of the proposed method is then discussed at the end.Finally, C. Sepúlveda et al. proposed a population pharmacokinetic (PopPK) model allowing the researchers to predict and analyze the drug behavior in a population of individuals and to quantify the different sources of variability among these individuals. In the development of PopPK models, the most frequently used method is the nonlinear mixed effect model (NLME). However, once the PopPK model has been developed, it is necessary to determine if the selected model is the best one of the developed models during the population pharmacokinetic study, and this sometimes becomes a multiple criteria decision making (MCDM) problem; frequently, researchers use statistical evaluation criteria to choose the final PopPK model. The use of the evaluation criteria mentioned above entails big problems since the selection of the best model becomes susceptible to the human error mainly by misinterpretation of the results. To solve the previous problems, we introduce the development of a software robot that can automate the task of selecting the best PopPK model considering the knowledge of human expertise. The software robot is a fuzzy expert system that provides a method to systematically perform evaluations on a set of candidate PopPK models of commonly used statistical criteria. The presented results strengthen our hypothesis that the software robot can be successfully used to evaluate PopPK models, ensuring the selection of the best PopPK model.


Value assessment of vaccination programs against serogroup B invasive meningococcal disease (IMD) is on the agenda of public health authorities. Current evidence on the burden due to IMD is unfit for pinning down the nature and magnitude of the full social and economic costs of IMD for two reasons. First, the concepts and components that need to be studied are not agreed, and second, measures of the concepts that have been studied are weak and inconsistent. Thus, the economic evaluation of the available serogroup B meningococcal (MenB) vaccines is difficult. The aims of this DELPHI study are to: (1) agree on the concepts and components determining the burden of MenB diseases that need to be studied; and (2) seek consensus on appropriate methods and study designs to measure quality of life (QoL) associated with MenB induced long-term sequelae in future studies.We designed a DELPHI questionnaire based on the findings of a recent systematic review on the QoL associated with IMD-induced long-term sequelae, and iteratively interviewed a panel of international experts, including physicians, health economists, and patient representatives. Experts were provided with a controlled feedback based on the results of the previous round.Experts reached consensus on all questions after two DELPHI rounds. Major gaps in the literature relate (i) to the classification of sequelae, which allows differentiation of severity levels, (ii) to the choice of QoL measures, and (iii) to appropriate data sources to examine long-term changes and deficits in patients’ QoL.Better conceptualisation of the structure of IMD-specific sequelae and of how their diverse forms of severity might impact the QoL of survivors of IMD as well as their family network and care-providers is needed to generate relevant, reliable and generalisable data on QoL in the future. The results of this DELPHI panel provide useful guidance on how to choose the study design, target population and appropriate QoL measures for future research and hence, help promote the appropriateness and consistency in study methodology and sample characteristics.The online version of this article (10.1186/s12955-019-1159-0) contains supplementary material, which is available to authorized users. In the short term, IMD causes meningitis and/or sepsis. In the long term, IMD survivors can suffer from sequelae of various types and severity levels.- Current available evidence does not fully capture the burden of long-term sequelae in IMD survivors and their societal consequences, mainly due to gaps and weaknesses in methods of assessment.- A DELPHI questionnaire was designed and submitted to a board of international experts, including medical doctors, health economists and patient representatives.- The experts reached consensus on optimal and recommended sequelae classification, study sample characteristics, study design, and quality of life measurement instruments and indicators.- The experts’ recommendations should help harmonise methodologies to assess the quality of life of IMD survivors (as well as their close family members and carers) with long-term sequelae in future studies so that more consistent data can be meaningfully compared, combined, and utilised.- High-quality and relevant data are a prerequisite to support evidence-based public health decision making.Neisseria meningitidis and predominantly affects infants and young children [Invasive meningococcal disease (IMD) potentially leads after an acute phase to serious long-term sequelae and complications and possibly premature death, thus imposing a high burden onto the patients and their carers –3. The dchildren , 5. Mainchildren . Althougchildren –11.The availability of vaccines and the implementation of universal mass vaccination (UMV) programs for children decreased the incidence of serogroup C in Europe , 11. DecAlthough the incidence of MenB-related IMD is relatively low, the disease has high social and economic costs , 18. HowOlbrich et al. (2018) argue that the quality of life (QoL) loss caused by IMD cannot precisely be quantified and assessed since published studies are highly heterogeneous regarding the study design and QoL measurement. This is due to a lack of QoL measurements which differentiate between survivors of IMD with and without sequelae, or QoL measurements specific to types and severity levels of sequelae associated with IMD . GaspariMoreover, the measurement of QoL in infants and children is methodologically more challenging than in adults, as shown by Herdman et al. 2016) for IMD cases 16 for IM. The impagree on the concepts and components determining the burden of MenB diseases that need to be studied; andseek consensus on appropriate methods and study designs to measure the QoL associated with MenB-induced long-term sequelae in future studies.The aims of this DELPHI study were to:We used a DELPHI panel approach to find consensus on data gaps and the most appropriate study design to measure the QoL associated with IMD sequelae. The purpose of a normative DELPHI approach is to identify and answer questions of what should be and what is desirable in the future, rather than to assess the current, highly imperfect literature . The conThe iterative process inherent in the DELPHI technique enables the evolution of the experts’ opinion on the specified research topic, where, due to the controlled feedback, experts re-consider and re-assess their perspective by including broader views and additional information provided by the other participating experts , 25. FurIn accordance with the expert definitions provided by Keeney et al. (2001), we invited specialists with experience in measuring QoL, especially that associated with meningococcal long-term sequelae . The expn given items numerically, where 1 represents the most important and n the least important option. Hence, increasing rank numbers correspond to less preferred answers [The first DELPHI round was informed by a systematic literature review. The initial questionnaire provided to experts covered the relevant gaps identified in the recent review by Olbrich et al.  and comp answers . Two quaAlong with the initial questionnaire, experts received in March 2017 instructions, a glossary, and a detailed introduction providing a summary of the results from the systematic literature review to ensure proper understanding of the questionnaire. The experts answered the first round questionnaire individually (via e-mail) and anonymously not knowing of the other experts, which Dalkey et al. (1962) recommend as a strategy to minimise group-based interactions between experts .The second round of the DELPHI panel was held in a presence meeting in March 2017 enabling the provision of a summary of the first-round responses with a subsequent discussion giving each expert the opportunity to voice or revise his/her previous answers. Subsequently, an adapted questionnaire with eight questions was given to the experts. For the second round, the two qualitative questions 4 and 11 were dropped for parsimony, since potential answers were collected within the first round or recorded and debated during the discussion. We further dropped question 6, which was unanimously answered during the first round and, hence, a consensus on that specific question was already reached. This second questionnaire also provided feedback from the first round. For the choice tasks the answer distribution was given. Regarding the ranking tasks, experts were provided with the group ranking derived from the rank sum of each item per question.All closed questions had to be either ranked or selected by the experts. Descriptive analyses of the results of the ranking and choice tasks were undertaken. We aggregated the individual ranks into a group ranking by calculating the rank sums; since lower numbers represent a better rank, the items with the lowest rank sum will be considered as the preferred options. The minimum and maximum rank, the mean rank, and the mode of each item were analysed (not reported). Additionally, we evaluated the results from the choice tasks with the help of histograms inspecting the frequency distribution of the items for each question.Convergence was measured using Kendall’s W, a non-parametric test assessing the level of agreement in an expert group. The test statistic ranges from 0 to 1, where 0 means no agreement and 1 represents full agreement among the experts . The conW > = 0.7, moderate for W = 0.5 and weak for W < 0.3 [A consensus was defined differently for rank and choice questions. In terms of the choice tasks, a consensus was reached when 2/3 of the experts agreed on addressing an issue in a certain way and no veto was raised . A veto  W < 0.3 .We present key results of the two DELPHI rounds conducted in this study from 5 questions. The first-round questionnaire can be accessed in the Additional file We achieved an expert consensus on all ranking and choice tasks. From the first to the second round of ranking tasks we observed Kendall’s W to be monotonically increasing Tables , 3 and 4○ a detailed classification of sequelae,○ allowing to distinguish the severity of sequelae,○ reflecting the impact of multiple simultaneous sequelaea conceptual model of sequelae, withputting in place the processes for measuring sequelae.Table Experts also stated that the sought-after concept of sequelae must be meaningfully measurable and therefore requires a sufficiently large number of patients for each category.The results of this ranking task showed good ability to distinguish among the options Tables  and 3. AW < 0.3) on using any of the presented methods to measure the QoL for the two IMD patient age groups  and no such history (controls). Thus, after the first round, a tendency towards pragmatic and less costly study designs was observed. In the meeting prior to the second round, this question was discussed more in depth and in the light of the preceding question 9. In the second round, eight experts voted for a cross-sectional design, while 6 experts preferred a case-control study and 2 experts chose the cohort study design, hence with 50% of the respondents voting for a cross-sectional design no 2/3 majority for a specific observational study design was reached.The experts reasoned that in a cohort study the evolution of sequelae could be assessed; however, it would be time-consuming and expensive. By comparison, a case-control study as defined above addresses the rarity and long latency of IMD and its corresponding sequelae, allows comparison against healthy controls and covers a variability of patients. Nevertheless, case-control studies are prone to bias. Finally, a cross-sectional study can be supplemented with retrospective data, would also allow to include a control group, is easy to conduct, inexpensive and therefore most practical. No concerns about direction of causality were raised.IMD is an uncommon, but severe disease. MenC UMV programs in Europe helped decrease the MenC related disease burden. While MenB is still responsible for the majority of IMD cases in Europe, respective UMV programmes are currently implemented less frequently in European countries. Studies evaluating the cost-effectiveness of uncommon but severe diseases such as MenB are challenging and draw on incomplete evidence on the actual disease burden for this particular serogroup , 19.Our results reinforce the need for a more detailed examination of sequelae attributable to IMD and the direct long-term consequences for patients’ QoL as well as the indirect impact onto their parents, siblings or spouse. Experts argued that a detailed classification of sequelae should distinguish both the severity of sequelae and the impact of having multiple simultaneous sequelae. The experts’ perception of an unclear structure of sequelae accords with the findings of a recent literature review on this topic, which reports considerable heterogeneity in prevalence rates and inconsistent sampling strategies . The expOn the other hand, the sought-after classification of sequelae should also be pragmatic, since highly stratified patient samples cannot be achieved in future prospective studies due to the small number of patients worldwide as well as the rarity of some sequelae. For example, in 2015 only 1682 prevalent serogroup B IMD cases were reported in countries of the European Union .Generally, the expert consensus on the remaining questions of this DELPHI study suggests pragmatic and goal-driven methods for future studies to overcome the current lack of knowledge, which is impeding rational decisions on the allocation of resources to vaccine development and delivery. The experts acknowledged budget and practical constraints in their answers, and therefore preferred a single cross-sectional study. Further, it was argued that a strategic sampling approach, targeting patients who contracted the disease at different times, as well as the inclusion of a control group might help to examine the evolution of sequelae and to assess QoL losses. However, with respect to the study design we did not achieve a 2/3 majority for a specific design; nonetheless, a tendency towards a retrospective data study with a control group design was observed.The preference for existing generic and disease-specific QoL measures also reflects pragmatism. However, it should be acknowledged that disease-specific QoL measures are problematic, since they usually do not allow comparisons across different disease areas. They either need to be preference-based or mapped onto a generic preference-based measure in order to be comparable across disease areas or to produce utilities for use in cost-utility or modelling studies, which was desired by the experts. Further, the choice of an appropriate QoL measure is constrained by decision-making bodies in several countries, such as the National Institute for Health and Care Excellence (NICE) in the United Kingdom .Moreover, if the aim is to measure QoL across different age groups, as it is necessary for IMD cases, the assessment of health states along the transition to adulthood is complicated. For this purpose, several established and child-specific QoL measures were discussed. To date, standard paediatric QoL measures, such as the EQ-5D-Y  or the CSince IMD mainly affects children and adolescents, negative spillover effects on family members and carers must be considered in a comprehensive disease burden study. Our results suggest that, as a practical matter, these primarily need to be considered for the family network and carers, i.e. the parents or siblings of patients ≤18 years. The expert panel agreed that the relevance of the closest relatives and carer, i.e. the parents or the spouse, spans all the included age groups and severity levels. Our conclusion accords with findings from Al-Janabi et al. 2016), who found the most impactful health spillovers in close family members, but with a declining rate with increasing social distance to the patient [016, who Our DELPHI study of an expert panel with an international composition and multi-disciplinary experience yielded robust results showing a high level of consensus after the second round, reaching a moderate to strong level of agreement on ranking tasks and a majority of 2/3 in choice tasks, except for one question. The structure of our study combined the advantages of anonymity in the first round with the conveniences of a group discussion prior to the second round, where all experts were given the chance to elaborate on their opinion. Given the often not observable or quantifiable differences in the disease outcome between serogroups found in studies, the guidance from this study may help to improve the understanding of the meningococcal disease burden in general, but may also do so by emphasizing MenB to detect potential differences in the disease outcome compared to other serogroups. Several limitations of this study should be acknowledged. Despite providing equal opportunities during the discussion, we cannot rule out that some arguments were prioritised over others. It is inherent to the DELPHI technique that the results will depend on the choice and composition of the expert panel and on how the questions were framed. In this sense, we note that the position of patient representatives was slightly underrepresented numbers-wise. To achieve appropriate framing of the questions, the questionnaire underwent quality control checks within the study group. Finally, the individual expert views and the degree of consensus among them could have changed between the time the DELPHI meeting took place and the time the study was written and reviewed. A long-term follow-up survey might be undertaken in some future study.A better conceptualisation of the structure of IMD-specific sequelae and of how their diverse forms of severity might impact the QoL of survivors of IMD as well as their closest relatives and care-providers is needed to generate reliable and generalisable data on QoL in the future. The sought-after conceptualisation builds the basis to overcome the general lack of reliable and relevant measures of the health burden attributable to serogroup B IMD. However, the results of this DELPHI panel provide useful guidance on how to choose the study design, target population and appropriate QoL measures for future research. This will help promote the consistency in study methodology and sample characteristics.Additional file 1:The first-round DELPHI questionnaire. (DOCX 225 kb)


Suture anchor placement for subscapularis repair is challenging. Determining the exact location and optimum angle relative to the subscapularis tendon direction is difficult because of the mismatch between a distorted arthroscopic view and the actual anatomy of the footprint. This study aimed to compare the reliability and reproducibility of the navigation-assisted anchoring technique with conventional arthroscopic anchor fixation.Arthroscopic shoulder models were tested by five surgeons. The conventional and navigation-assisted methods of suture anchoring in the subscapularis footprint on the humeral head were tested by each surgeon seven times. Angular results and anchor locations were measured and compared using the Wilcoxon signed rank test. Interobserver intraclass correlation coefficients (ICCs) were analyzed among the surgeons.p < 0.05), respectively, and the translational errors were 15 and 3 mm (p < 0.05), respectively. All participants showed a narrow range of anchor fixation angular and translational errors from the original target. Among the surgeons, the interobserver reliabilities of angular errors for ICCs of the navigation-assisted and conventional methods were 0.897 and 0.586, respectively, and the interobserver ICC reliabilities for translational error were 0.938 and 0.619, respectively.The mean angular errors of the targeted anchor fixation guide without and with navigation were 17° and 2° (The navigation system may help surgeons be more aware of the surrounding anatomy and location, providing better guidance for anchor orientation, including footprint location and anchor angle. Thus, only a limited part of the footprint can actually be exposed.The subscapularis muscle is the largest and most powerful rotator cuff muscle, which is important in shoulder joint movement and mechanics . The chaMoreover, recognizing the orientation of suture anchor insertion is difficult. First, to secure satisfactory anchor placement, Burkhart et al. recommended that the anchors be advanced via the anterior portal, with the surgeon’s hand at the patient’s chin for proper anchor orientation . Second,Suture anchor insertion at the optimum angle is essential to secure the rotator cuff at the footprint , resultiRegardless of the uncertainty of anchor insertion angle, this study focused solely on measuring the actual anchor angle and location, and its accurate guidance. This study aimed to (1) assess the accuracy and reliability of the designated angle and location of suture anchor insertion into the humeral head for subscapularis repair, comparing the navigation and conventional techniques in the experimental laboratory setting and (2) assess whether the navigation-assisted surgical system allowed the surgeons to customize their preferred target angle and location. We hypothesized that use of the surgical navigation system would result in more accurate and reliable suture anchor placement than the use of the conventional method.The study used arthroscopic models of the shoulder joint  which has been used in several studies –12. The pt and orientation Rt of the anchor were determined by a senior surgeon and used as a reference for comparison . The orientation symbol R was represented as a 3 × 3 matrix, which was built from the XYZ Euler angle. Position p was a three-component vector composed of the x, y, and z coordinates of the anchor. The difference between the measurement [Ri| pi] and target poses [Rt| pt] of the anchor was calculated. The angle θ between the target and measured poses could be calculated as follows:The arthroscopic shoulder model were tested by five surgeons. The participating surgeons were novices with limited experience on shoulder arthroscopic surgery. Before surgery, all participants were shown the designated anchor position and orientation on three-dimensional models of the shoulder . The posterior portal was used to view subscapularis repair, thus establishing a standard gleno-humeral view. The footprint on the lesser tuberosity was determined. The tap for the pilot hole of the suture anchor  was inserted through the anterior portal Figs. , 2. All son Fig. . When and between the two positions was calculated as a second norm, which represents a scalar distance.The difference θ and distance  d were measured.The same procedures were performed seven times, with the assistance of navigation for anchor insertion. Each time fixation was finished, the angle (angular error) At the end of the experiment, the surgeons rated the utility of the navigation-assisted arthroscope system in aiding suture anchor insertion using a Likert scale questionnaire system .The median values of the angular and locational differences of insertion from the designated angle and location were calculated and compared using Wilcoxon signed rank tests. The significance level and intra- and interobserver reliabilities analyzed using repeated measure analysis of variance were used to calculate intraclass correlation coefficients (ICCs) and their 95% confidence intervals –15. ICC p < 0.05), respectively  and slight-to-almost perfect (0.010–0.898) values using the navigation system and conventional method, respectively Table . ICC of Assessment of mean errors in each trial showed that the navigation-assisted method resulted in 91 and 83% fewer angular and translational errors, respectively Fig. . MoreoveMost surgeons consider the orientation of suture anchor insertion for the repair of the subscapularis muscle as identical to that of the supraspinatus muscle. However, the footprints of both rotator cuff muscles are in different spatial orientations. The images provided by the arthroscope are intrinsically distorted, and the surgeons operate without any other visual feedback. Because surgeons are solely dependent on external anatomical appearance and arthroscope images, they must attempt optimal suture anchor insertion with limited information. Our findings suggest that the navigation system may help provide multiplanar visualization of the footprint, thereby helping surgeons perform these surgeries.Under ideal conditions, the 45° angle of anchor fixation can be determined from the thickness of the tendon and distance from the suture site to the anchor. Theoretically, this can result in better pull-out strength and less stress on the suture. Recent improvements to the anchor, including a change in thread, can change the axis of fixation by 90° Fig. . TherefoIn basic operating settings for subscapularis repair, the use of the navigation system improved the ability of surgeons to orient the angle of suture anchor placement. Real-time feedback from the navigation system with multiplanar situational awareness of the tool on the humerus rendered the overall procedure arthroscopically more accurate. The interobserver reliability of ICCs showed reduced angular errors, indicating that angular orientation is more difficult to achieve than location. Although viewing through the posterior portal allows the anchor angle to be easily measured in the coronal plane, sagittal movement of the anchor is difficult to discern because of the use of a single arthroscope, which cannot measure depth. Rotating the instrument in the intraarticular space can better determine the angle when the instrument is moved along a plane perpendicular to the arthroscope. In contrast, determining the angle of the instrument is difficult when the instrument is moved along a plane parallel to the arthroscope. The angle and location of the anchor and instrument can vary and be difficult to measure when viewed from the posterior or lateral portal. In contrast, viewing from the top of the cone allows the location to be easily selected, with the angle determined from the cone height and distance from the anchor tip to the center of the radius Fig. . Thus, pArthroscopic subscapularis repair is a well-established technique , 19. TheThe present study showed that the navigated approach enhanced reliable results for all glenoid positions. Although optimizing anchor placement should theoretically improve the biomechanical behavior of the repaired area, these clinical data were not generated from the present study. This study aimed to assess whether intraoperative multiplanar visualization could reduce the number of errors from optimal angles. Furthermore, by determining the anchor location and angle, this method can be useful for randomized control studies or follow-up examinations related to anchor location and angle. The inclusion of quantitative information regarding the accuracy and duration of the procedure can enable this method to be utilized to evaluate a surgeon’s skills in arthroscopic shoulder surgery or to determine training objectives , 21.The use of the navigation system altered the understanding of the surgeon while inserting the anchor into the subscapularis and supraspinatus footprint site, by making the surgeon more aware of the instrument location, along with their angular trajectory and penetration location of the anchor. The navigation system also provides information regarding the surrounding cortical thickness if the surgeon follows the trajectory depicted in the navigation experiment. Determining the correct orientation of the desired angle for subscapularis repair is difficult with the conventional method. As the number of trials increase, achieving the target angle and location remain difficult. The targeted angle and location may vary widely even when a plastic model is used. The use of the navigation system can better achieve a direction close to the predesigned angle and location, particularly for determining the reference guide for orientation of the tool while making the pilot hole or inserting the anchor.We are aware that the navigation system is developed to assist in practical surgical application involving real patients. In practical surgical application, there may be two possible ways to secure the reference marker of the tracker. One is to attaching the marker on the patient’s skin with an elastic strap, which has been widely used for brain applications in commercial systems. Another option is to have a small stab skin incision, which allows K-wire fixation to the bone. Later, reference markers can be attached to the end of the K-wire. A similar method has been widely used in robotic arm-assisted surgery in orthopedic field .This study is limited by our use of a plastic model in the operative setting. No subscapularis tendon is attached to the lesser tuberosity of the plastic models, such that the direction of the subscapularis tendon could not be determined. We used plastic models , which are is used for arthroscopic training in a dry laboratory , 11 to mNavigation may help surgeons become more aware of the surrounding anatomy, resulting in better decisions regarding anchor angle and location from preoperative guides. The navigation system may better determine the spatial orientation of the footprint for the subscapularis. Navigation assistance can result in more accurate and reliable anchor fixation.


Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patient’s virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy. Epilepsy is characterized by perturbed dynamics that originate in a local network before spreading to other brain regions. We studied patient-specific brain network models of epilepsy patients, comprising 88 nodes equipped with region specific neural mass models capable of demonstrating epileptiform discharges. Applying stability analysis led to a seizure control strategy that is significantly less invasive than the traditional surgery, which typically resects the epileptogenic regions. The invasiveness of the procedure correlates with graph theoretical importance of the nodes. The novel method subsequently removes the most unstable links, a procedure possible by advent of novel surgery techniques. Our approach is entirely based on structural data, allowing creation of a brain model based on purely non-invasive data prior to any surgery. Propagation of activity through a network is a non-stationary spatiotemporal process and is the most fundamental representation of information processing in the brain , 2. In tIn the diseased and aging brain, the spatiotemporal organization of information processing is disrupted , 8. LocaThe influence of network topology and the anatomical organization of the epileptogenic process are particularly important in the context of seizure control and epilepsy surgery . BancaudPractically, however, there are fundamental obstacles to the application of network control in the spatiotemporal organization and propagation of healthy and pathological activity. Antiepileptic drugs are most common and act globally by mostly targeting ion channels and inhibitory neurotransmission and synaptic receptors across the entire brain network. Yet approximately 30% of epilepsy patients suffer from drug-resistant epilepsy and continue to experience seizures despite appropriate antiepileptic drug treatment . For patSuccessful outcomes of either treatment depend critically on the ability to accurately identify the epileptogenic zone, which is a long, complex, and costly process. In the case of intracranial electrocorticographic (ECoG) recordings the skull has to be opened through a craniotomy, thus allowing the surgical implantation of electrodes into the patient’s brain. Afterward the patient undergoes prolonged and costly monitoring, with the risk of contracting infection and neurological comorbidities, while the clinical team waits for several seizure events to accrue sufficient EEG data. Manual inspection of the signals finally allows specialized epileptologists and technicians to localize the epileptogenic zone. However surgical outcomes are highly variable irrespectively of the presurgical evaluation via invasive recordings: between 30%-70% of patients continue having seizures 6 months after treatment, which may be due to mislocalization of the epileptogenic zone. To overcome this problem, scientists have done an intensive search for an accurate data analytics tool to reduce time, risks and costs of invasive monitoring. To address this issue in models and patient brains, we here construct patient-specific connectome-based large-scale brain network models and identify the least invasive strategies to stop seizure propagation. Personalized connectomes are composed by the structural links derived from diffusion weighted tensor imaging (DTI) and tractography, and have been shown to predict individual functional connectivity better than generic approaches. Empirical studies are impossible to be performed systematically in the human and are limited to sparse sampling  only. In silico modeling allows to complete the analysis with high-density sampling  and identify the best network modulation for network control. We adapt a disconnection approach, aiming to identify the minimal ablation for a particular patient to stop the propagation of activity through the personalized network, let this be propagation due to stimulation or spontaneous seizures. Neuroelectric discharges are captured by gold standard data of epileptic patients recorded via intracranial electrodes. Brain areal dynamics is derived from the mean activity of neural populations via neural mass models able to capture the details of the autonomous slow evolution of interictal and ictal phases. Personalized brain network models are then derived for 15 patients. We systematically investigate all possible realizable disconnections of the epileptogenic zone and demonstrate that for all patients a partial targeted disconnection, accounting for the resection of one or a few pathways, is sufficient to stop seizure propagation in the brain, systematically reducing invasiveness and outperforming generic  procedures. In particular partial targeted disconnection is designed by combining the analysis of patient specific structure with model dynamics and performing a Linear Stability Analysis of the system. The preferred pathways along which the  perturbations spread, turn out to be fundamental to identify the propagation and recruitment process of a seizure, thus suggesting the virtual ablation to perform in the personalized connectomes.s/mm2) were acquired on a Siemens Magnetom Verio 3T MR-scanner. From the gathered data clinicians conclude potential epileptogenic zones (EZ). Further elaboration on the EZ are done in the second phase, which is invasive and consists of placement of stereotactic EEG (SEEG) electrodes in or close to the suspected regions. These electrodes are equipped with 10 to 15 contacts that are 1.5 mm apart. Each contact is 2 mm of length and 0.8 mm in diameter. The SEEG was recorded by a 128 channel DeltamedTM system using a 256 Hz sampling rate. The SEEG recordings were band-pass filtered between 0.16 and 97 Hz by a hardware filter. All the chosen patients showed seizures in the SEEG starting in one or several localized areas (EZ), before recruiting distant regions (PZ). The position of the electrodes was pinned down by performing a computerized tomography (CT) scan or a MRI after implanting the electrodes.We selected 15 drug-resistant patients  with different types of partial epilepsy accounting for different EZ localizations. All patients underwent a presurgical evaluation see . For eacThe data processing to import structural and diffusion MRI data in The Virtual Brain has been done using SCRIPTS. This processing pipeline makes use of various tools such as FreeSurfer , FSL 3131, MRtri6 fibers and it was corrected using Spherical-Deconvolution Informed Filtering of Tractograms  the steady state solution of In particular, if we denote with If the eigenvalues of the Jacobian matrix all have real parts less than zero, then the steady state is stable. In terms of seizure propagation, this means that the seizure should not be able to propagate. On the other hand, if at least one of the eigenvalues of the Jacobian matrix has real part greater than zero, then the steady state is unstable. In this second case we expect a seizure to propagate along the network.Moreover the eigenvectors of the Jacobian matrix give the axes along which the behaviors indicated by the eigenvalues are centered. This means that, in case of negative eigenvalue, the associated eigenvector points in the direction along which the fixed point attracts. In case of positive eigenvalue, the eigenvector points the direction along which the fixed point repels. Therefore the computation of the eigenvectors allow us to identify the pathways along which the seizure propagates. In particular if we identify the largest components of the maximal eigenvector, i.e. the largest elements of the eigenvector related to the maximal (positive) eigenvalue, we can straightforwardly suggest the most unstable directions able to convey the propagation along the entire network. In other words the elements corresponding to the largest components of the maximal eigenvector will be the most easily recruited during the propagation. On this basis we estimated the Propagation Zone (PZ) by identifying the elements with the biggest absolute value of the maximal eigenvector: these elements represent the dominating sub-networks involved in the transition towards the seizure state. These results can be compared with the PZ which is empirically obtained by neurosurgeons or during the data analysis of the time series of the cortical and implanted EEG electrodes , resultiKij but also the dynamical variables of the system: the recruitment process is a dynamical one and cannot be predicted by the connectivity alone. Therefore a combination of structural and dynamical information as it is used in the Linear Stability Analysis, is fundamental to predict the PZ and to suggest minimally invasive disconnections.Finally it is worth noticing that in the Jacobian matrix J, appear not only the structural connectivity matrix x0: x0 < xc for the epileptic zone nodes, x0 > xc for the healthy nodes. There is no need to define a third level of excitability for the nodes in the propagation zone to describe the recruitment mechanism, since PZ emerges self-consistently from the dynamics. Furthermore, the Jacobian matrix is an explicit function of the fixed points of the model, meaning that the eigenvectors and eigenvalues obtained from the Linear Stability Analysis will also be influenced.The hypothesis of EZ, estimated by medical doctors, is implemented in the model The SEEG samples neuroelectric activity from various sources, which are more than the number of contacts on the electrods. This undersampling would pose an enormous problem if we were inverting the SEEG signals to source signals, which is an ill-posed problem. Instead we compute the physical forward solution and then perform validation in signal space (rather than source space). Therefore, even though contacts are limited in number and in placement positions, our capacity of model identification is not affected by undersampling.clustering coefficient, which gives the fraction of triangles around a node and it is equivalent to the fraction of node’s neighbors that are neighbors of each other as well. This is an important measure of segregation, since the presence of a dense neighborhood around a node is fundamental to the generation of clusters and cliques that are likely to share specialized information.Topological properties of a network can be examined by using different graph measures that are provided by the general framework of the graph theory. These graph metrics can be classified in measures that are covering three main aspects of the topology: segregation, integration and centrality. The segregation accounts for the specialized processes that occur inside a restricted group of brain regions, usually densely connected. Among the possible measures of segregation, we have considered the efficiency. It is defined as the average inverse shortest path length in the network, and is inversely related to the characteristic path length.The measures of integration refer to the capacity of the network to rapidly combine specialized information from not nearby, distributed regions. Integration measures are based on the concept of communication paths and path lengths, which estimate the unique sequence of nodes and links that are able to carry the transmission flow of information between pairs of brain regions. In particular the path length measures the sum of the edge weights in a weighted graph (as the one that we have considered) and the shortest path length suggest a stronger potential for integration between different brain regions. At the global level it is moreover possible to define a “characteristic path length” of the network, which is calculated as the average inverse shortest path length between the all pairs of nodes in the network. Among the possible measures of integration, as the most meaningful for our structural connectivity matrices we have chosen the node degree, which gives the number of links connected to the node; for this measure connection weights are ignored in calculations. The node strength is the sum of weights of links connected to the node, while node betweenness centrality is the fraction of all shortest paths in the network that contain a given node. Nodes with high values of betweenness centrality participate in a large number of shortest paths. Finally, closeness centrality is the inverse of the average shortest path length from one node to all other nodes in the network.Centrality refers to the importance of network nodes and edges for the network functioning. The most intuitive index of centrality is the t2), or by activating multiple areas at the same time (t3 − t5). The distinction of EZ and PZ based on the electrographic signatures is non-trivial, in particular, when the spreading continues after the epileptogenic zone has seized its intense activity (t6). In case of asymptomatic seizures, the activity is often limited to the epileptogenic zone and no propagation takes place.During an epileptic seizure the brain shows an abrupt onset of fast oscillatory activity, which typically propagates to other regions and recruits these as oscillatory subnetworks. The seizure originates in the Epileptogenic Zone (EZ) and propagates to the Propagation Zone (PZ) see . The recactivity , time t6o(1). From a computational point of view the virtual ablation of a link corresponds, in all the mentioned cases, to the removal of the corresponding row and column in the structural connectivity matrix of the investigated epileptogenic brain.Once the EZ is schematized as one (or more) nodes inside the brain network, then the most common epilepsy surgery actually performed, which is ascribed to the resection of the epileptogenic zone, corresponds to the disconnection of all the pathways outgoing the focal nodes in addition to the removal of the node itself. a priori a good indicator to understand the role played by the topology in the spreading of the seizure all along the brain. Actually, the mechanism underlying the seizure propagation is more subtle and the topological features of the structural connectivity matrices are not sufficient themselves to fully explain the recruitment of other areas occurring in the propagation. This means that if we try to stop the seizure propagation cutting the strongest links outgoing the macroarea EZ+PZ, the number of fundamental links that we identify according to this procedure are more than the ones actually responsible for the mechanism. When we apply the same criterion to the subgraph outgoing the EZ only (case iv), thus disconnecting the strongest links, we are able indeed to design an effective targeted disconnection. Due to the distribution of the weights of the structural connectivity matrices, connections with biggest weights turn out to be easy to recruit. The least invasive method identifying the minimal number of pathways, along which the seizure propagates, is the Linear Stability Analysis (case v), that univocally identifies, via the calculation of the stability of the system in presence of perturbations (represented by the seizures), the most unstable directions along which the recruitment and the seizure spreading take place. These most unstable directions, that are represented by the links connecting different populations, are often the strongest links outgoing the epileptogenic zone. In particular this is always true when the epileptogenic zone is composed by a single area, i.e. a single node.The random choice of the links to be cut among the subgraph connecting the EZ with the rest of the network (case ii), represents the mathematical procedure that better approximate the results of standard clinical resections, where all the subgraph is removed, since a big amount of connections needs to be resected in order to stop the seizure propagation and to prevent the emergence of epileptic symptomatic seizures. On the other hand, if we restrict our attention to the connections outgoing the region composed of both EZ and PZ (case iii), the strength of the links in this macro-area might be However, it is worth noticing that purely structural information is not sufficient to predict the propagation and eventual stopping of the seizure, and the availment of a mathematical model is required to predict correctly the least invasive intervention. To demonstrate this, we compute for the structural connectivity matrix of a specific patient (CJ), various network measures, including efficiency, strength, betweeness, centrality and clustering see , which sFrom the computational point of view, the dynamics of each node of the network parcellation is given by a neural mass model able to reproduce the temporal seizure dynamics and to switch autonomously between interictal and ictal states . Nodes aA different visualization of the seizures onsets, taking place in the network at different times, is shown in The dynamics presented in In The former analysis has been done on the basis of the medical doctors’ prediction for the EZ. However it is worth doing a systematic analysis of the network dynamics, by supposing that the EZ is placed in every possible node of the connectome. In particular, by taking into account the structural connectivity matrix of patient CJ and by systematically placing the EZ in different nodes, we investigate the dynamics of the full network for all possible epileptogenic nodes. For every shuffled position of the EZ, the Linear Stability Analysis is calculated, henceforth the eigenvalues and eigenvectors of the system are obtained for all these configurations. Even though the results depend on the position of the EZ, it is possible to demonstrate that, in general, by performing a virtual resection of a small number of pathways, up to a maximum of 15, the seizure propagation can be stopped for any initial EZ considered see . This prThe lesioning procedure for a given structural connectivity network directly affects the size of the PZ. As expected, it shrinks when increasing the number of removed links. This is a natural consequence of the fact that the number of possible unstable directions decreases for every resection, and the number of regions that are likely to be recruited vanishes. More specifically, the PZ generally shrinks to 0-2 areas, after 5-6 performed disconnections see . In mosta posteriori on the dependence of the partial targeted disconnection on the different graph connectivity measures, highlights the significance of these quantities. Namely, starting from the results reported in A topological analysis −5). Hence, it is more difficult to individuate a small number of nodes that actively participate to the dynamics and that are overwhelmingly involved in the seizure propagation. On the contrary, many more nodes are now involved in the propagation, and many more directions are viable for the spreading of the pathological activity. A comparative analysis of the number of links that must be cut for stopping the seizure propagation, obtained by following different procedures, is reported in The importance of separately considering the structural connectivity matrices of single patients, for better designing the partial targeted disconnection, can be illustrated comparing the previous results with the ones obtained by performing the Linear Stability Analysis procedure on an average connectome. The latter is constructed by averaging the 15 structural connectivity matrices of the considered patients . It turns out that the analysis on the average connectome is much less predictive and that the individual variability has clear effects upon the outcome of the prediction results, and therefore, of possible therapies and treatment approaches. In particular, the resulting average connectome has different topological characteristics with respect to the matrices of the single patients: the degree of each node is larger and in general, it is more dense and homogeneous. If we select as EZ, the area suggested by the clinical prediction  and we calculate the maximum eigenvector, it turns out to be no longer localized, like the one shown in While it is sufficient to cut only 6 links detected via the Linear Stability Analysis procedure on single patient’s connectome, it is necessary to remove up to 66 links before the seizure stops propagating on the averaged connectome, even thought the same procedure have been used. The latter number is comparable with the results obtained by random removal of the links belonging to the subgraph of the EZ in the patient’s connectome, and hence it is no better than a “blind” resection.Connectivity is fundamental to the transmission of information through a network, let it be pathological or physiological. With regard to the mechanisms underlying propagation in networks, several mechanisms have been hypothesized. A basic form of propagation is energy dissipation , which dIn all these cases of propagation, the frame, in which the activity evolves, is defined by the energy dissipation properties of the network and can be controlled and rerouted through network rewiring, of which the disconnection type is the simplest to realize, even though in clinical practice it has not found many applications so far. The few existing applications exclusively rely on a complete disconnection such as corpus callosotomy or hemispherotomy, since in absence of understanding the network effects upon propagation, more subtle and minimally invasive interventions are impossible. As a paradigmatic type of pathological propagation, we considered seizure spread. Our in-silico approach allowed an exhaustive search, which would have been impossible in vivo. The model predicts the activity propagation of a seizure originating in a certain epileptogenic zone and to identify the most unstable pathways that support and allow the propagation. In other words, activity propagation cannot be disjointed by the network topology outside the epileptogenic zone and by the topological features linked to the propagation zone that both contribute to the propagation. We have demonstrated that the energy dissipation of seizure propagation may be controlled by performing a Linear Stability Analysis of the system, thus allowing identification of the properties and the response of the system undergoing a perturbation (such as a seizure emission). In this framework, the most unstable directions that carry on the spreading may be identified and, by studying the effect of lesions on the dynamics of the simulated brain processes, it is possible to visualize when the seizure stops propagating, and the recruitment of the PZ does not occur anymore. A similar stability analysis has been performed in  on a funOther groups have already studied the effects of removing brain regions and connections on the dynamics of simulated network processes , but in Our approach generalizes to oscillatory activity propagation so far, that it does not necessitate the presence of the hallmark of epilepsy, that is an onset and offset of high-frequency oscillatory activity. It thus can be applied in the future to understand the directionality of state changes and manipulate the activity in the network. On the other hand, concerning the applicability to the seizure propagation problem, it may be determinant to design a new surgical approach, providing support and improving the resection surgical procedure. A less invasive and more effective surgical approach would make the difference in short and long-term outcomes and in improving the quality of life of the patients. In particular reducing the involvement of propagation networks is a major factor to reduce the impact of seizures, particularly the loss of consciousness , which iS1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S1 FigPanel a: Localization of the maximum Lyapunov vector, correspondent to the maximum Lyapunov exponent. Panel b: Maximum eigenvector as a function of the node index. The dashed lines indicate the node that are first recruited in the propagation zone and whose links with the epileptogenic zone represent the directions along which the seizure spreads. For both panels the structural connectivity matrix of patient CJ has been used.(EPS)Click here for additional data file.S2 FigCJ. The clinical estimated EZ is highlighted in yellow, while the estimated PZ  is highlighted in magenta.Left panel: Efficiency value of each population. Right panel: Strength value of each population. Both properties are calculated on the structural connectivity matrix of the patient (EPS)Click here for additional data file.S3 FigCJ. The clinical estimated EZ is highlighted in yellow, while the estimated PZ  is highlighted in magenta.Left panel: Clustering value of each population. Right panel: Degree value of each population. Both properties are calculated on the structural connectivity matrix of the patient (EPS)Click here for additional data file.S4 FigCJ. The clinical estimated EZ is highlighted in yellow, while the estimated PZ  is highlighted in magenta.Left panel: Betweeness value of each population. Right panel: Centrality value of each population. Both properties are calculated on the structural connectivity matrix of the patient (EPS)Click here for additional data file.S5 FigLeft panel: Seizure events as a function of time. EZ (red dots): rh-Precentral Gyrus that corresponds to node 77. Disconnections PrG- PoG, PrG-CMFG, PrG-PoP, PrG-SFG, PrG-Th, PrG-Pu, PrG-PaC, PrG-SMG must be performed in order to stop the seizure. All the disconnections are performed among populations belonging to the right hemisphere. Dashed blue line corresponds to the time at which lesions occur. Right panel: Number of lesions necessary to stop seizure propagation as a function of the epileptogenic region.(EPS)Click here for additional data file.S6 FigLeft panel: Seizure events as a function of time. EZ (red dots): lh-Posterior Cingulate Gyrus, lh-Precuneus Cortex corresponding respectively to nodes 33 and 35. Partial targeted disconnection procedure to be performed in order to stop seizure propagation: PCunC-SPC, PCunC-PCG, PCunC-ICC, PCunC-Cun, PCunC-rPCunC, PCunC-Pac, and the links between PCG and nodes SPC, ICC, Cun, rPCunC, Pac, SFG, CACC. The targeted disconnection is performed among populations in the left hemisphere. Dashed blue line corresponds to the time at which lesions occur. Right panel: Number of lesions necessary to stop seizure propagation as a function of the epileptogenic region.(EPS)Click here for additional data file.S7 FigLeft panel: Seizure events as a function of time. EZ (red dots): rh-Lateral Orbito Frontal Cortex, rh-Temporal pole corresponding respectively to nodes 65 and 86. Partial targeted disconnection procedure to be performed in order to stop seizure propagation: LOFC-RMFG. The targeted disconnection is performed among populations in the right hemisphere. Dashed blue line corresponds to the time at which lesions occur. Right panel: Number of lesions necessary to stop seizure propagation as a function of the epileptogenic region.(EPS)Click here for additional data file.S8 FigLeft panel: Seizure events as a function of time. EZ (red dots): rh-Hippocampus, rh-Amygdala corresponding respectively to nodes 51 and 52. Partial targeted disconnection procedure to be performed in order to stop seizure propagation: Hi-EntC, Hi-PHiG, Hi-Pal, Hi-Th, Hi-FuG, Hi-Amg, Amg-TmP. The targeted disconnection is performed among populations in the right hemisphere. Dashed blue line corresponds to the time at which lesions occur. Right panel: Number of lesions necessary to stop seizure propagation as a function of the epileptogenic region.(EPS)Click here for additional data file.S9 FigLeft panel: Seizure events as a function of time. EZ regions (red dots): Posterior Cingulate Gyrus (node 33), Caudal Middle Frontal Gyrus (node 14), Superior Frontal Gyrus (node 38). All the areas belonging to the EZ are located in the left hemisphere. Links between regions SFG-RMFG, SFG-(rh-SFG) and PCG-CMFG are cut in order to stop the seizure. Dashed blue line corresponds to the time at which lesions occur. Right panel: Number of lesions necessary to stop seizure propagation as a function of the epileptogenic region.(EPS)Click here for additional data file.S10 FigLeft panel: Seizure events as a function of time. EZ regions (red dots): Amygdala (node 9), Hippocampus (node 8), Entorhinal cortex (node 16), Fusiform gyrus (node 17), Temporal Pole (node 43), rh-Entorhinal cortex (node 59). All the areas belonging to the EZ are located in the left hemisphere. Links between all the regions in the EZ and the regions in the PZ must be cut in order to stop the seizure. Dashed blue line corresponds to the time at which lesions occur. Right panel: Number of lesions necessary to stop seizure propagation as a function of the epileptogenic region.(EPS)Click here for additional data file.S1 TablePartial seizures were recorded with stereotactic EEG (SEEG) electrodes in 15 drug-resistant epileptic patients undergoing presurgical evaluation. The clinical characteristics of each patient are given here: N, normal; L, left; R, right; Th, thermocoagulation; Gk, Gamma knife; Sr, surgical resection; NO, not operated; PVH, periventricular nodular heterotopia; FCD, focal cortical dysplasia; SPC, superior parietal cortex; F, Frontal; NA, not available.(EPS)Click here for additional data file.


Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the entire product manufacturing process to improve product quality and production efficiency. Data-driven product development is considered as one of the critical application scenarios of industrial IoT, which is used to acquire the satisfied and robust design solution according to customer demands. Performance analysis is an effective tool to identify whether the key performance have reached the requirements in data-driven product development. The existing performance analysis approaches mainly focus on the metamodel construction, however, the uncertainty and complexity in product development process are rarely considered. In response, this paper investigates a robust performance analysis approach in industrial IoT environment to help product developers forecast the performance parameters accurately. The service-oriented layered architecture of industrial IoT for product development is first described. Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. Furthermore, the predicted performance analysis method based on confidence interval estimation is developed to deal with the uncertainty to improve the robustness of the forecasting results. Finally, a case study is given to show the feasibility and effectiveness of the proposed approach. With the rapid integrated development of information technologies such as artificial intelligence, big data and Internet of Things in the manufacturing industries, industrial IoT is considered a crucial manufacturing infrastructure to efficiently change how the products are customized, manufactured and delivered ,2. CurreIn the applications of industrial IoT such as health management  and qualIn general, the product development process usually involves the following fundamental activities: problem and requirements formulation, exploring alternative solutions, evaluation and documentation of the results . By percOn the other hand, with the increasing product complexity, manufacturers are paying more and more attention to ensure whether the desired performance is achieved within given design constraints when conducting product development ,23. It iAlthough the above studies have made great contributions to performance analysis and product development, there is still lack of a systematical performance analysis approach in the early product development for industrial IoT. Firstly, how to explore the gathered data from implementing industrial IoT to support product development is rarely discussed. The huge product lifecycle data can provide tremendous value for product developers to exploit the implicit user preference. Especially, previous researches mainly focus on the detailed design process, and these have very limited means to support computational and dependable performance analysis process for conceptual design phase. Secondly, in order to deal with the complexity of performance analysis, extracting key design variables is a considerable issue for model simplification in data-driven product development. Thirdly, the uncertainty is a crucial factor to have a big influence on the accuracy of the forecast results, which is mainly from information loss and external disturbances. The approximation model is established to facilitate the performance analysis, and the information loss is inevitable to acquire the optimal model. Furthermore, due to the external disturbances of the collecting device, the uncertainty of the data samples is also considered. Therefore, in order to validate the design solution precisely, the uncertain factors such as data, model and result should be taken into account comprehensively. If some important performance parameters cannot be determined precisely, it will lead to extend product development time and increase costs.To overcome the limitations of prior approaches, this paper develops a service-oriented layered architecture of industrial IoT for data-driven product development. Through the collected data from industrial IoT, a robust predicted performance analysis approach is proposed. The proposed approach is mainly divided into the following stages: the dimension reduction based on MI and outlier detection, the performance prediction based on LSSVR, and the performance analysis based on confidence interval. An empirical example is also shown to illustrate the applicability of the proposed approach. The main contribution of this paper is to develop a systematic and robust performance analysis framework to assist developers forecast product performance parameters in industrial IoT environment.This paper is organized as follows: The detailed service-oriented layered architecture of industrial IoT for product development is presented in With the popularization and application of sensor network and wireless communication, industrial IoT provides intelligent information processing and smart control abilities to help manufacturing factories organize and manage lots of production systems effectively and efficiently. Analyzing the multidimensional dynamic data collected from different kinds of plant equipment, manufacturers can enable to conduct health monitoring, performance prediction, and remote diagnosis in real time to make better decisions for the optimization of production processes .(1) Perception layer: this layer links the physical devices of factories though sensors, RFID readers and other data collection terminals ,34 and t(2) Management layer: this layer is viewed as the brain of industrial IoT, which is introduced to manage a variety of heterogeneous data collected from perception layer. The management platform can assist manufacturers to make best-fit decisions by making use of these abundance data and extracting the helpful information; (3) Service layer: this layer is the entry of industrial IoT for manufactures, which is used to provide various high-quality services such as remote monitoring, performance analysis and fault diagnosis. The integrated service applications can be published in the form of micro-service, and then the manufactures can employ them on-demand as pay-as-you-go mode.In general, the excellent performance depends on the reasonable defined ranges of the corresponding design parameters under the specific conditions. There are lots of industrial applications that are required to analyze the relationship between the performance and design parameters based on the collected data. Taking the product development of a turbo-expander for example, thermal conversion efficiency is very important performance index for developers to design and optimize in the product development. As a rule of thumb, there are about nine key design parameters such as inlet temperature, inlet pressure, and velocity coefficient that should be considered, which are closely related to the performance measurement. If the inlet pressure and inlet temperature are set out of scope, the thermal conversion efficiency will be observably reduced. Due to the lack of an accurate mathematical description of the specific performance in the product development, developers tend to use approximation models to analyze and predict the performance for judging which solution is the best choice. In the product development process of industrial IoT, the performance analysis process of mechanical products mainly depends on the collected data from actual operation, simulation analysis and prior information. As a result, there are three important issues that should be considered in detail for the robust performance analysis.Firstly, the establishment of the approximation model relies on the input variables which are defined and represented by developers. In order to reduce the computational complexity, it is necessary to select the performance-related variables as the inputs for the performance analysis. Secondly, it is necessary to take into account the efficient approaches for the construction of the approximation model that can render a view of the entire design space. The last but not the least, due to the ubiquity of uncertain information, it is always found that there is a large error between the estimated value and real value, so the performance analysis approach should consider the uncertainty of complex system and provide product developers dependable results to anticipate design defect accurately.Based on the above description, this paper puts forward a robust performance analysis approach to assist developers forecast and evaluate the key performance parameters in the early product development, as shown in In order to enhance the effectiveness of performance analysis process, this section focuses on the robustness processing of the data samples, which mainly includes the following two steps: key-variable selection for dimension reduction and outlier detection for the improvement of model precision.Due to lots of the coupling design variables in data-driven product development process, it is always difficult for designers to create a reliable predictor to analyze product performance efficiently and accurately. Furthermore, the model constructed by all design variables will generate the curse of dimensionality and the computation time is also increased exponentially. So key-variable selection plays an important role in performance analysis which can identify and retain the important input variables whereas remove less important ones in the problems of interest so that the complexity of the design problems is reduced . Many prk-nearest neighbor distances which is first proposed by Kraskov [MI is used to measure the dependent degree of two given design variables, which can be perceived as evaluating the information shared by the two given design variables. If the MI between two given variables is large , it represents two variables are closely (not closely) related. To guarantee the effect and accuracy of computation, the MI is estimated based on  Kraskov .k-th neighbor, Considering the design space Immense amounts of product data have been gathered from product lifecycles, which can provide a rich source of knowledge for product development, manufacturing and maintenance. Due to various factors such as equipment fault, human error and operating condition change, the collected product data is inevitably corrupted. The outlier which does not comply with the general behavior of the data model is viewed as careless error and should be rejected ,39. Apprk, k-distance neighborhood of Given a data set k-nearest neighbors, and it can identify the point being an outlier, as shown in Local outlier factor means the average of the ratio of the reachability density of In the early product development, the performance prediction process can assist designers quickly understand whether the key performance features satisfy the design requirements, and this will reduce the probability of poor design results and improve design efficiency. Due to the lack of exact mathematical models, developers always feel powerless to obtain the qualitative performance estimation in specific working conditions. In response, this section introduces LSSVR-based approximation model to construct the response function between design variables and performance outputs for performance analysis.The appropriate data sample is a vital factor to acquire available performance model for product development, which can use the fewer sample points to reflect the global design problem. Latin hypercube sampling (LHS) is an effective and powerful sampling approach for performing computer experiments , which pDue to the time-consuming and computation-prohibitive performance analysis process, building a metamodel is considered as a promising approach to describe the implicit approximate relationship between design variables and product performance. Support vector regression (SVR) is one of the most popular meta-models with easy generation and low standard deviation , which cThe least squares version of SVR, named LSSVR, has gained lots of attention because of its low complexity and low computational cost. LSSVR only requires to solve a linear equation as a surrogate of quadratic programming so that it can reduce the training complexity obviously. Given a set of standardized training data  follows :(6)minΦThough applying Lagrange multipliers and taking the Karush-Kuhn-Tucker conditions for optimality, the least squares-based estimation function is formulated as:To reduce the computational cost, this paper uses the radial basis function (RBF) kernel as the kernel function of meta-model, and the RBF kernel can be presented as:To date, many parameter optimization methods such as cross validation, theoretical analysis and heuristic algorithm have been developed by experts and scholars. This paper introduces particle swarm optimization (PSO) algorithm to obtain the optimal solution for the meta-model construction of performance prediction. PSO is a population-based stochastic optimization technique proposed by Kennedy et al., inspired by the social behavior of bird foraging . This apThe predicted performance results are always uncertain due to the various uncertain factors such as model uncertainty, information loss and external disturbances. It is difficult to describe the accurate and comprehensive performance using real number representation. Product developers will adjust design variables considering the worst case scenario in the absence of confidence intervals, and this will increase the complexity and unreliability of product development. So this section extends the predicted performance using confidence interval estimation to improve the robustness of performance analysis.i-th true value of the performance response, i-th predicted performance. Sample randomly N times from the data samples, repeat L times, and then obtain a training sample set To improve the effectiveness of predicted results, interval estimation is a popular tool to cope with the influence about model uncertainty and data uncertainty . Bootstrx) confidence intervals can take the form:t distribution. To summarize, Then the approximate Due to the huge mass and bearing size of heavy-duty hydraulic presses, they always use a composite frame style to conduct product development. Under the condition of mutation overloading, the bends of the composite frame structure will appear high local stress area to result in the unrepairable fatigue crack. In response, the prestressed composite structure is considered as the popular way in product development of the bearing component. Developers define the overall performance to describe the situation that the adjoining planes of the composite structure don’t appear fatigue crack in the case of loading and unloading. The overall performance has a major impact on product quality in the engineering design of hydraulic press. The slotted coefficient and gap length of the adjoining planes are always used to evaluate the overall performance at present. In order to validate the effectiveness of the proposed paper, this paper selects the slotted coefficient as the evaluation indicator. Traditional overall performance analysis of the hydraulic press mainly depends on the simulation and verification of the physical prototype, and it will greatly increase the development costs. In the industrial IoT environment, the product lifecycle data which is from product development, manufacturing and service process can be collected by networks, and developers are convenient to acquire this valuable data to produce high-quality products. In general, the design variables mentioned in 2) are introduced as the accuracy metrics to verify the feasibility of the approximation model. According to verify the test sample, the testing performance  of the approximation model is . The lower value of MSE, and the larger value of R square, the more accurate the metamodel.The performance prediction process applies the 137 samples as the training sample, and the rest samples as the test sample. The program of LSSVR algorithm is programmed in Matlab 2010b environment and run on a desktop computer equipped with a dual 2.63 GHz Intel i5 processor and 8 GB RAM. In order to obtain the optimal prediction model, the kernel parameter 1 = 2.7693, S2 = 1.6971, S3 = 3.7346, and S4 = 2.1549. In order to analysis the confidence interval of the predicted results, bootstrap is used to resampling 1000 times to acquire the bootstrap sequence of the slotted coefficient. According to the working conditions of the factory, there are four design solutions of the press should be considered as alternatives for users. Applying the established metamodel to predict the overall performance, and the slotted coefficient can be obtained as STo investigate further the superiority of the proposed approach in performance analysis in the early product development, a fair comparison is made among the standard SVR with MI, Kriging model with MI and ANN with MI. The results are represented in With the high-speed development of information and communication technology, industrial IoT is becoming a popular intelligent manufacturing mode to manage and optimize the entire product manufacturing process in real time. Manufacturers can implement operation management, real-time monitoring and performance prediction to ensure the stable operation of the production platform. To improve the product quality in the early product development, a robust performance analysis approach in industrial IoT environment is proposed. The detailed service-oriented layered architecture of industrial IoT for data-driven product development is developed to describe the relationship between industrial IoT and product development. In the face of the high dimensional design space, the dimension reduction approach based on MI and outlier detection is put forward. And then a performance prediction approach based on LSSVR is applied to explore the implicit performance meta-model. Furthermore, a confidence interval-based predicted performance analysis considering the uncertainty is adopted to improve the robustness of the forecasting results. In the future, we will plan to explore the more effective intelligent algorithms considering the big data environment to improve the practicality of the proposed approach.


Simulating the additive manufacturing process of Ti-6Al-4V is very complex due to the microstructural changes and allotropic transformation occurring during its thermomechanical processing. The  Powder Bed Fusion (PBF) is the technique of building thin layer over layer by melting the fine metal powder. Directed energy deposition (DED), on the other hand, is usually used for building features on large existing parts as well as for repairing damaged ones. PBF typically adds layers that are thinner than DED and can therefore create high-resolution structures, whereas DED produces components at a higher built rate. The primary challenge of DED is that the higher energy input from the heat source may lead to substantial distortion and higher residual stresses.DED additive manufacturing (AM) can be considered as computer numerically controlled (CNC) multipass welding with progressive weldments made on a substrate to create free-form structures. The added metals can be in either powder or wire form and the heat source a laser or electron beam. The deposition path is generated from computer-aided design (CAD) geometry and is preprogrammed in a CNC machine, which makes the process very flexible and suitable for low volume production, eliminating the need for tooling and dies.This also enables the production of complicated geometries that are traditionally difficult to produce with conventional manufacturing processes. Additively manufactured parts of Ti-6Al-4V are traditionally found in human implants  and aeroA few researchers have performed AM simulations or similar processes for Ti alloys using thermomechanical–microstructural (TMM) coupled material models. In Baykasoglu et al. , a thermIn this work, a material model combining metallurgical and flow-stress models described by Babu et al.  is used.An incompressible von Mises model was used here with the assumption of isotropic plasticity. Flow stress was split into two parts ,13,14,15. Conrad  proposedm is the Taylor factor that translates the resolved shear stress in various slip systems to effective stress, b is the magnitude of Burgers vector, The long-range term from Equation  is derivp and q define the shape of the obstacle barrier for dislocation motion. Further, k is the Boltzmann constant, T is the temperature in kelvin and (The strain-rate-dependent part of the yield stress from Equation  can be dThe evolution of Equation  is modelg) and dislocation subcell or subgrain diameter (s) asThe average distance moved by dislocations before they are annihilated or immobilized is called mean free path  by Holt .(7)s=KcVacancy motion is relevant to the recovery of dislocations. Restoration of the lattice commonly occurs at elevated temperatures and is therefore a thermally activated restructuring process. Creation of vacancy requires energy and increases entropy. With increasing temperature and deformation, vacancy concentration also increases. High stacking fault materials usually exhibit constant flow stress because of the balance between hardening and recovery. The current model assumes that the mechanisms of restoration are dislocation glide, dislocation climb and globularization.The model for recovery by glide can be written on the basis of the formulation by Bergström  as(9)ρiMilitzer et al.  proposedBabu and Lindgren  proposedSemiatin  modelledM is a material parameter. The rate of dynamic globularization was computed on the basis of a model by Thomas and Semiatin 45], respectively .The yield strength of the phase mixture can be written according to the linear rule of mixtures aseference , this caIn this article, a DED process described in Reference  was simuThe dimensions of the substrate (152.4 × 38.1 × 12.7 mm) and AM component are given in Three beads were added per layer with a total width of 6.7 mm and a height of 38.1 mm; 42 discrete layers and their respective beads are also shown in In the current work, the scope of the model was to predict microstructure evolution, the overall distortion of the component and residual stresses. This requires a solution where thermomechanical–metallurgical models are coupled by using a staggered approach. Modelling of weld-pool phenomena requires high-resolution discretization and at least one other physics domain, namely, fluid flow. This requires an impractical amount of resources for solving the problem and can be avoided considering the scope of the current work. The heat input can be modelled by using volume heat flux in a geometric region representing the weld pool, and is calibrated using measured temperatures. Goldak’s  double eThe inactive-element approach was used here, where all elements representing the added metal were deactivated before the start of the simulation, and only activated after meeting certain criteria. In each increment, the set of elements that overlapped and the geometric region represented by the current weld-pool position were thermally activated, whereas mechanical activation occurred only when the thermally active elements cooled below the solidus temperature. Before thermal activation, the elements may have had to be moved to accommodate for the distortion of the substrate and the already activated elements during the process. The moving elements maintained connectivity with the activated elements, and their volume matched the added material during that time step .In DED, much of the heat input in the initially deposited layers is absorbed by the substrate. To balance the heat input, losses by free and forced convection, and conduction to fixtures as well as radiation were included in the model. A lumped convective coefficient of 18 W/mIn situ measurement of temperature and distortion was done during the AM process. Three thermocouples were attached to the bottom of the substrate at the positions shown in the left part of The component distortion is measured at the free end by using a laser displacement sensor. In The yield strength of the material is very much dependent on state variables of dislocation density and excess vacancy concentration. The density of these state variables changes by many orders of magnitude during heating and deformation. Deformation increases dislocation density and results in hardening, whereas an increase in vacancy concentration (due to heating and deformation) assists in the remobilization of dislocations, thereby material recovery. The advanced material model described here combining the metallurgical and flow-stress models has proven to be suitable for AM simulation. Diffusional and instantaneous transformations are included in the metallurgical model. This model was formulated in a way that it could be implemented in any standard kind of finite-element software. Temperature measurements and results from the simulations demonstrated a good overall fit. This model predicted the final distortion of the component with good accuracy except for the case with 40 s dwell time. This trend was also evident in the residual-stress measurements. The comparison of distortion before the onset of cooling showed a larger difference between model and measurements. This might be because the stress-relaxation behavior was less accurately predicted by the model. The computed phase fraction in One of the challenges involved in the AM process is residual deformation and stresses due to the thermal dilatation of the substrate and added structure. The final properties of the AM structure are strongly influenced by the microstructure, which is dependent on the thermomechanical-processing history of the component. For the industry to fully adopt additive manufacturing and be able to qualify titanium parts for critical applications, such as in aerospace, a complete understanding of the microstructure properties and mechanical behavior is necessary. This paper showed the implementation and application of a coupled microstructural–thermal–mechanical model to an AM process. A physically based constitutive model was explicitly coupled to the microstructural model. The phase composition predicted by the microstructural model therefore affected the mechanical properties, namely, flow strength, in a direct way. Validation of the thermal and mechanical model was performed by comparing the simulation results with the available measurements in the literature. The comparison had good agreement between the results from the model and the measurements.