Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary  Healthcare costs have risen massively in recent years and the trend does not seem to be reversed. According to the United States (US) National Health Expenditure, spending has tripled in the US since 1995 and amounted to 18% of GDP in 2018 [Body sensor network (BSN), alternatively wireless body area network (WBAN), technology could make a decisive contribution to relieving the burden on health systems and thus keeping them viable for society in the future ,3. A  including magnetic field sensors to monitor posture and motion, insole-integrated force sensing resistors to monitor ground reaction forces (GRFs), and surface-mounted electromyographic (sEMG) sensors to monitor muscular activity ,10,11,12IMUs, which include accelerometers and gyroscopes, were first proposed as a functional tool for gait analysis half a century ago  and has GRF sensing devices usually consist of different kinds of pressure sensors that are attached to the insole or the shoe of the subject. Pressure sensors are implemented by commercially available sensors, such as force sensing resistors (FSRs) ,28,29,30In this work, a modular BSN for long-term motion monitoring is designed and tested. It follows the basic ideas of the Integrated Posture and Activity Network by MedIT Aachen (IPANEMA). The first version of IPANEMA was developed in 2007 . In 2009A single network node of a BSN basically contains a CPU, a radio unit , an energy management system, and specific measurement circuits for the acquisition of physiological or biomechanical parameters. In the case of IPANEMA, each node is equipped with a mainboard, a sensor board, and a ub-bands . In the The basic idea of the IPANEMA BSN is to serve as a flexible scientific communication platform for different sensor modalities. Therefore, the mainboard contains expansion ports to connect to different types of sensor boards. The connectors contain the For communicating with the sensor boards, the SC provides different standard interfaces such as the Inter-Integrated Circuit (The IPANEMA BSN consists of a master node (MN) and several sensor nodes (SNs) that are organized in a star-shaped network. The proprietary internal network protocol is based on this topology. The MN initializes the network structure for a predefined maximum number of SNs connecting to the MN. The communication is controlled by a time division multiple access (TDMA) process that assigns dedicated time slots to each SN connected. A higher-level communication frame allows for periodical re-synchronization of the SNs and network error handling by the MN. For this purpose, the MN sends re-synchronization beacons each second and eventually reconnects lost SNs. In this case, a careful re-synchronization is not only necessary to comply with the dedicated time slots of the TDMA protocol, but additionally, ensure an accurate time-stamping of a single sampling point for any sensing modality. After each SN has registered and received its individual unique network ID, the MN broadcasts the start beacon initiating the TDMA protocol on all nodes .The core communication and sampling procedures take into account different timing modules to maintain reliably identical clocks. These include the MN core clock module, the SN core clock module, and the RF clock module of the respective SN. The MN clock provides the internal global network time After receiving a synchronization beacon, the corresponding timestamps, The internal 32-bit timer together with the timer resolution city cf. . Still, The inertial measurement node consists of the integrated inertial measurement unit (IMU) BNO055  and an absolute barometric pressure sensor BMP280 . The IMU contains an accelerometer, a gyroscope, a geomagnetic field sensor, and a Cortex M0+ microcontroller for on-chip sensor fusion. However, the magnetic field sensor was not used in our approach to reliable indoor orientation estimation. On the one hand, the usage of only accelerometer and gyroscope saves energy with respect to long-term monitoring applications. On the other hand, magnetic field sensors are often distorted by ferrous material or power lines within an indoor environment . The accThe measurement of ground reaction forces (GRF) was implemented by integrating four force sensing resistors (FSRs) into an insole. The four FSR402 sensors  with a diameter of the heel a. The FSEach FSR sensor is part of a voltage divider supplied by the reference voltage Ref=3.3V . The resWhile stationary gait analysis systems are traditionally used in movement laboratories, long-term monitoring systems require a maximized level of acceptance and compliance by the patient. Unobtrusive measurement of physiological or biomechanical parameters has become increasingly important in recent decades, as it provides insights into the everyday-life behavior of the patient. An unobtrusive sensor setup in a BSN application for gait analysis is characterized by wearable sensors that are located on the human body so that they do not affect natural motion and the patient ideally does not take note of the additional sensor equipment.Smart electronic devices, i.e., mobile phones and smart-watches, and their integrated sensor units offer a promising opportunity to unobtrusively collect motion data. Concerning the fundamental idea of an unobtrusive sensor setup for everyday-life monitoring, we chose a setup including three IMU sensors, attached to the thigh, the wrist, and the chest as well as one FSR insole. This setup is motivated by typical wearable devices and their preferable wearing location: A cell phone in the pocket (thigh) and a smart-watch at the wrist supplemented by a pendant sensor for fall detection (cf. Philips Lifeline AutoAlert or similar) and an ordinary insole. With this sensor setup, we ensure on the one hand a broad acceptance for wearing these sensors and on the other hand cover movements of the lower extremities, the trunk, the upper extremities, and the ground contact. The typical applications are twofold. On the one hand, it is possible to conduct long-term measurements with the MN directly attached to the human body, in our case at the second wrist. On the other hand, the BSN can be used to record data in a motion laboratory environment, where the MN is connected to a PC system to enable online tracking of the data streams. The two application scenarios are depicted in Detection of physical activity, more specifically the extraction of gait phases, during long-term measurements of everyday life activities is a key feature of the proposed sensor system. We identified the sensor node located at the thigh as the most reliable node for determining gait phases in specific. We implemented a simple algorithm to detect phases of physical activity using the signal vector magnitude (SVM) of the three-dimensional thigh acceleration data The standard deviation of the SVM, The most important characteristic points for wearable-based gait analysis are the heel strike (HS) and toe off (TO) events, respectively. These events indicate the two main phases in human gait, the stance phase, and the swing phase  and usuaFirstly, we calculate the local minimum of the summed-up FSR signal, which corresponds to the lowest pressure during one gait cycle. Since in higher walking speeds the plateau of the maximum foot pressure alters to a double peak due to the swing phase initiation of the opposite leg cf. b, the maSince the falling edge was found to be steeper as compared to the rising edge, the thresholds were identified asThe basic procedure of identifying the threshold values is depicted in quations  and (10)Three different test scenarios were chosen to validate the performance of the proposed sensor setup. Firstly, the functional capability of the system was investigated in a technical validation setup in terms of transmission performance and timings. Secondly, we recorded long-term motion data during everyday life activities to validate the system reliability in a real-life application scenario. Finally, we conducted a smaller clinical study with ten participants to validate the activity detection algorithm and confirm the ability to correctly extract gait parameters from the obtained sensor data, respectively.Technical laboratory setup: The validation of the technical functionality in terms of transmission and timing performance was performed in a technical laboratory. The static measurements were conducted inside a laboratory room with as low electromagnetic interference as possible. The spatial signal strength measurements were conducted outside the building to reduce reflection disturbances. The SN was mounted on a turnable robot arm (Mitsubishi Movemaster) to avoid human tissue interference during the measurements and guarantee accurate spatial recordings.Long-term measurements: We conducted several long-term measurements with healthy subjects equipped with the proposed system during their everyday office life, while normally continuing their business. The records were used to validate the system reliability in a real-life scenario. Comparable to the technical validation, we focused on the evaluation of the transmission performance of the system.Gait parameter study: Since gait analysis is in the main scope of this contribution and the main purpose of the proposed system, we validated the system with respect to the extraction of gait parameters. We conducted a clinical trial in the movement laboratory of the Department of Geriatrics of RWTH Aachen University Hospital to compare the outcome of the system to clinically relevant gait parameters. The study was approved by the ethical committee of the University Hospital at RWTH Aachen University (Ref. No. EK 024-20) and included ten healthy, young participants  and the Packet Error Rate (PER). Several measurements were conducted recording the RSSI and the number of transmitted packets (NoP) for each SN under different environmental conditions to evaluate the transmission performance of the IPANEMA v3.0 mainboard. The PER can directly be calculated from the NoP. The performance tests were conducted in a technical laboratory setup, as well as during long-term measurements of everyday-life activities and gait trials in a movement laboratory. The RSSI and the NoP are automatically captured by the MN for each packet arriving.The technical evaluation measurements include recordings of RSSI at an increasing distance with varying transmission power output in a laboratory environment, where the antennas of both sender (SN) and receiver (MN) have been aligned accordingly. As expected according to theoretical considerations following the free-space path loss, the RSSI decreases with increasing distance and decreasing transmission power output. Here, also the transmission output power, matching network loss, and antenna gain are included in the measurement results, which results in an overall offset of approximately obot arm . During istances b.Additionally, measurements with static and moving human tissue within the direct signal path were performed. An additional signal attenuation of approximately 5–15 dBm occurs with human tissue interference. For moving human tissue, the RSSI pattern shows inconsistent behavior with both increased and reduced signal attenuation compared to the non-moving measurements. For loss-less data transfer, a minimum of Finally, we investigated the long-term measurements with regard to the transmission performance of the proposed setup in an everyday-life scenario. Different healthy subjects were asked to wear the measurement setup , at the chest and as an insole (ankle), the master node at the right wrist) in their everyday office life. In addition to the actual sensor data, we additionally recorded the RSSI and the number of packages received by the master node to calculate the PER. The overall results are given in Several long-term measurements were conducted to evaluate the timing performance and long-term stability of the RF communication implementation. The devices under test were placed in a standard technical laboratory environment during the long-term recordings. The SNs were placed around the MN with a distance of about nces cf. .We recorded data of 20 devices under test during the long-term measurements of approximately Based on this approximation, we further analyzed the key characteristics of the oscillator deviations. The results are given in Equation . AdditioThe GRF can be obtained from the FSR sensor data and contains information about the gait stability, gait control strategy, or pathological gait patterns. For example, the position of the Center of Pressure (CoP) and its derivatives are key characteristics in terms of gait stability. It can be deduced from the 2D plantar GRF that is reconstructed by interpolating between the single sensor locations and recalculating the local foot pressure for the specific foot geometry. Further analysis of GRF-related gait quality indicators is not part of this contribution. Instead, we focus on a qualitative validation of the GRF signal morphology. The quality of the signal morphology has a high impact on the detection of HS and TO events using the proposed algorithm. Of course, the morphology individually differs due to foot size, footwear, and contact pressure. Therefore, the insole-based FSR signal is more sensitive to individual gait behavior as compared to the force plates.To validate the reproducibility of GRF by the FSR insole, we conducted a clinical trial study with 10 healthy, young participants, who volunteered in a treadmill walking trial. The subjects walked on the treadmill for 7 min per trial at increasing speeds from 1–4 We additionally calculated the coefficient of variation (CoV), which is defined as the standard deviation in relation to the mean value of a quantity, for the individual stride morphologies of both FSR and reference force plate data to give a measure of variability for the corresponding measurement principle. It should be noted that human gait, of course, inherits a certain amount of stride-to-stride variability. Therefore, the CoV in this case is not a measure of precision for the corresponding technical system. The CoV rather needs to be compared to the reference outcome. Secondly, the swing phase does not contribute to the CoV for the reference system since the effective output is zero when the foot does not have ground contact. As can be seen in The capability of the IMU sensor nodes have been intensively investigated before ,39 and iThe stride time is defined as the temporal distance between two successive ground contacts of the same foot. The exact stride time can, therefore, be extracted by any periodically recurring feature in the FSR signal, such as the HS or TO events. Since HS and TO are the two most dominant featured in the FSR signal, we calculated the stride time from both HS and TO and compared it to the reference treadmill data. The correlation and Bland–Altman plots for the HS-based and TO-based stride time extraction are given in The number of strides (NoS) is given by the sum of detected HS events. Concerning the conducted gait study trial, we counted the number of strides in each speed interval, which yields a second important gait parameter: the cadence (strides per minute). We calculated the detection accuracy to give a quantitative measure of the overall stride detectionThe percentage stance time (PST) indicates the temporal percentage of the stance time in relation to the entire gait cycle (stance time + swing time of one leg) and is calculated byN equals the number of observations.Both the mean stride time and the percentage stance time can be validated using the RMSEThe gait sequences were extracted from the overall recording time by applying the gait phase detection from Within this contribution, we introduced the recently redesigned version 3.0 of the IPANEMA BSN based on an Our results show that the concept of an only cf.  correspoAccording to A further indicator for validating a system for monitoring and analyzing gait in everyday life is the range of recorded sensor signals with respect to the configured measurement range given in evel cf. . In addiThe FSR insole is used to calculate important speed-related gait parameters, such as the total number of strides, the cadence, the stride time, and the percentage stance phase. GRF-based analysis is known to provide reliable results for these parameters and therefore often used in movement laboratories. The applicability to a wearable long-term monitoring system is evaluated in this contribution. The results of the validation measurements in the movement laboratory are given in or FSR1, a yields ates cf.  suggest The qualitative validation of the GRF measurements demonstrated that the proposed sensor setup satisfies the requirements of an unobtrusive gait analysis system. Although the signal morphology differs between the implemented insole and the reference force plate data cf. , it can The number of strides reflect the sum of the detected HS events. The cadence can be calculated from the number of strides per minute. purposes . Here, tThe mean stride time can be either calculated by the reciprocal cadence or the mean over the temporal distance between two HS events. In this case, we have calculated the stride-to-stride interval and averaged over the recording period of one minute. In contrast to the direct comparison of the stride times cf. a, the meAs can be seen in mh−1 cf. . This chThe cadence, the mean stride time and the percentage stance phase are of course important parameters to observe the progression of the locomotor system or superordinated movement disorders, which initially requires a reliable event detection. Nevertheless, the variability of different gait parameters, such as the stride time variability, contains valuable information about gait stability . The robIn this contribution, we presented the recently developed version 3.0 of the IPANEMA BSN based on an


Timely and secure evacuation of residents during flood disasters or other emergency events is an important issue in urban community flood risk management, especially in vulnerable communities. An agent-based modeling framework was proposed in order to indicate how the community properties , residents’ psychological attributes  and mutual aid mechanism affect the flood evacuation process. Results indicated that: (1) The community density negatively affected the flood evacuation efficiency. The greater the density of the community, the longer the evacuation time. (2) There was a negative correlation between the flood risk tolerance threshold of residents and evacuation efficiency. (3) The proportion of vulnerable resident agents had opposite effects on the evacuation efficiency of different types of communities, which was to negatively affect low-density communities and positively affect high-density communities. (4) Mutual aid mechanism can reduce evacuation time in low-density communities, and the effect was more pronounced with a higher proportion of vulnerable resident agents in the community. These findings can help managers to develop better emergency evacuation management for urban communities. Actually, casualties and economic losses can be reduced or prevented if people arrive shelters and transfer the assets before the flood disasters. Therefore, timely and securely evacuation of residents is of great importance during flood disaster events [Flood disasters are a major threat to human society and economic systems, and they can easily wipe out the wealth accumulated in the past . In recer events . Nowadayr events ,4,5. IndIn general, emergency evacuation is considered as a traditional routing problem and many improved classical algorithms have been proposed to solve this problem ,7. In faAlthough many studies have been conducted, most studies on emergency evacuation were restricted to single building ,24 or laConsidering the limitation of the existing studies and motivated by the two important issues mentioned above, this study presented an agent-based modeling framework to simulate the flood evacuation in vulnerable communities and this framework incorporated the resident psychological model and transportation network model. The aims of this study were to find out how the community properties , resident’s psychological attributes  ,27 and mThis evacuation process simulation mainly involved two components: agents and transportation network. Thus, the proposed agent-based model took both human components  and evacuation transportation network into consideration.The impacts of the community density on the evacuation processThe impacts of the average flood risk tolerance threshold on the evacuation processThe impacts of the percent of the vulnerable resident agents on the evacuation processThe impacts of the community mutual aid mechanism on the evacuation processThe agent-based model framework was structured in three steps: model initiation, model runs, and model outputs  referrereceived ,30.In this study, the calculation of flood risk perception was based on factors identified through the literature review. When the community was hit by a flood event, the resident agent rception ,31. HoweMany previous studies using a set of weighting factors to formulate agents’ flood risk perception driven by multiple information sources. In this study, the approach was adopted and two main information influence parameters When new information on flood risk was obtained, the agent So far, the process of how agents update their flood risk perception was modeled.At time step For the non-vulnerable resident agents who willing to aid, if the mutual aid mechanism was on and they had decided to evacuate, they would check whether there were vulnerable resident agents in their same community grid. If there was at least one vulnerable resident agent did not evacuate, the non-vulnerable resident agents would move to their nearest vulnerable resident agent’s location and form one-on-one evacuation team. Then, they would move to the nearest road edge first and go to the shelter together through taking the shortest route. If there was no vulnerable resident agent in their community grid, the non-vulnerable resident agents would go to the shelter directly. For the non-vulnerable resident agents who did not want to afford help, they would go to the shelter directly regardless of the mutual aid mechanism was on or not. When the resident agents arrived at the shelter, they would change their evacuation status to ‘arrived shelter successfully’.So far, the resident agents’ attributes and its behavior rules has been constructed. The simulation process end when the tick time agreed with the time set or all the resident agents arrived at the shelter. The staff agents represented the community residents committee staff in the real world. In each community grid, there were certain number of staff to responsible for the daily work in China. In the face of the flood risk, the community residents committee staff should inform and assist the residents to evacuate if the flood risk alert is red warning. In this study, the staff agents need to response to flood event and help the vulnerable resident agents to evacuate to the shelter. In this study, the attributes of the staff agents were the identification number, community grid number and evacuation status.The road network plays a key role in emergency evacuation and emergency management. A road network is a system of interconnecting lines and points  that represent a system of streets or roads for a given area, and it describes a structure which permits either vehicular movement or flow of people . GeneralAs for the movement rules, it stipulates the movement and interaction mechanisms of the vehicles and people in the road network. Among a variety of transportation network simulation methods, the Nagel–Schreckenberg model (N-S model) proposed by Nagel and Schreckenberg  is widelIn order to find more detail information during the evacuation process, in this study, the road congestion index (RCI) was used to reflect the degree of crowdedness in the transportation network. It can be estimated by calculating the proportion of agents whose speeds reduce more than a certain percent at each time step (as shown in Equation (7)).It is very difficult to consider all the impact factors in one evacuation simulation model. Therefore, some main assumptions were set to simplify the model and ensure the availability and credibility of results. They were: all residents in the community were required to carry out emergency evacuation, and all residents were in their homes when the flood risk warning was issued; the evacuees were assumed to have good knowledge about the transportation network and they will head to the nearest road node first and then look for the shortest route to the shelter.Besides, the community is a complex system due to it consists of residents, residents committee staff, roads, etc. When simulating the process of emergency evacuation, each agent interacted and influenced each other under certain rules. These interactions and influences happened in the process of flood risk perception update, evacuation decision, road congestion and other aspects. Actually, each agent makes different decisions during these interactions, and these behaviors were assumed to occur in a stochastic manner. Hence, the community evacuation system turns into highly randomness, nonlinear and noncontinuous. For such a system, obtaining the closed-form analytical solutions become very difficult.In similar studies, numerical simulation methods were usually used by researchers to solve such problems ,42,43. MMultiple indicators were used to measure agents’ evacuation behaviors at the system level . These indicators were: (1) road congestion index In this study, the agent-based model proposed above was applied in a synthetic community , community properties, and mutual aid mechanism affect the emergency evacuation process. Four different scenarios were designed based on five important parameters  on the evacuation process under different community density. In the fourth scenario, the potential influence of mutual aid mechanism on emergency evacuation was investigated which combined with other three influencing factors.The first scenario was mainly to investigate the impact of community density on the emergency evacuation process. To be specific, this scenario addressed two questions: (1) Does the community density affect the evacuation process? (2) To what extent does the community density affect the evacuation efficiency? The first question was to demonstrate that the community density can affect the evacuation process and the second question was to attempt to evaluate the importance and the impact of community density in evacuation process.In order to explore how community density affected the evacuation process, the changes in the percent of evacuated agents with time in communities with different densities were investigated  indicated the different response behavior to the same flood risk condition. As mentioned in As shown in The vulnerability of communities is an important issue that should be considered in the flood risk management of urban communities. As mentioned in In this section, the relationship between the proportion of the vulnerable resident agents and the total evacuation time was studied to investigate how the vulnerable residents affects the evacuation process in communities with different densities. Communities were divided into three types, namely, low-density, medium-density and high-density communities. There were less than 120 agents/grid in low-density communities and more than 120 agents/grid in high-density communities. As shown in Then, the road congestion index was used to explore why the proportion of vulnerable resident agents had different effects on the evacuation process in three types of communities. As shown in These results implied that in low-density communities, the total evacuation time was mainly affected by the movement speed of vulnerable residents, while in high-density communities, the road congestion caused by a large number of residents was the main factor. With regard to the medium-density communities, as the proportion of vulnerable resident agents increased, total evacuation time was first mainly affected by the movement speed of vulnerable resident agents and then affected by the road congestion. Therefore, in vulnerable communities with different densities, government decision-makers need to formulate different evacuation policies according to the evacuation characteristics of the communities.Mutual aid behavior between neighbors is a common way to respond to the flood disaster events. However, whether this kind of behavior can significantly reduce evacuation time and improve evacuation efficiency has not been widely concerned and is worth studying. In order to investigate how the mutual aid mechanism affects the evacuation process of different communities, in this study, the percent of non-vulnerable resident agents willing to aid was used to indicate the mutual aid mechanism between neighbors. Besides, the community density and the percent of vulnerable resident agents were considered together to represent different types of communities. Similar to the In the low-density community, as shown in With regard to the evacuation in the medium-density community, as shown in In the emergency evacuation of high-density communities, especially those with a high proportion of vulnerable residents, the mutual aid mechanism was ineffective and actually increased the total evacuation time  The community density was found to be negatively correlated with flood evacuation efficiency. Achieving a high level of evacuation ratio was much more difficult in high-density community than in low-density community due to the marginal time needed rapidly increasing. That was because evacuations from high-density communities can easily cause road congestion. (2) Lower flood risk tolerance threshold can help residents respond to flood disasters timely, but this effect was different in communities with different densities. In high-density communities, appropriately reducing the value of flood risk tolerance threshold of residents can obviously reduce the total evacuation time and improve evacuation efficiency, however, it was not effective in low-density communities. (3) The proportion of vulnerable resident agents had opposite effects in different types of communities. With the increase of the proportion of vulnerable resident agents, the total evacuation time increased significantly in low-density communities, decreased first and then increased in medium-density communities, and decreased in high-density communities. (4) Mutual aid mechanism can reduce evacuation time in low-density communities, and the effect was more pronounced with a higher proportion of vulnerable resident agents in the community. However, in high-density communities, the mutual aid mechanism would actually increase the evacuation time. Besides, the higher the proportion of vulnerable resident agents, the more the evacuation time would increase. In the medium-density community, the percent of non-vulnerable resident willing to aid had a negative relationship with total evacuation time, but it was not obvious.Such information can help urban community managers and decision-makers increase disaster evacuation efficiency by developing different evacuation strategies according to the specific characteristics of different communities. For example, managers must pay special attention to the impact of community density, especially evacuation in high-density communities. Appropriately lowering the residents’ flood risk tolerance threshold could prompt residents to make evacuation decisions earlier. The change of the proportion of vulnerable residents in low and medium-density communities can easily affect the evacuation efficiency. Community managers can adopt various means of transportation to improve the evacuation speed and encourage mutual aid behaviors between residents to reduce the evacuation time. While evacuation in the high-density community, the traffic congestion is the main challenge. Community managers should avoid road congestion caused by the mutual aid behaviors among a large number of people, and should arrange for evacuations in batches when road capacity is inadequate.But, the limitation of the study should also be taken into consideration. Firstly, in order to make the agent-based model uncomplicated, feasible and operable, not all the factors affecting the residents’ evacuation process were considered in this study. For example, the families or friends were assumed to evacuate at the same time in this study, but in reality, residents’ behaviors of seeking relatives and friends during the evacuation are common. These searching behaviors are more complex and can affect the simulation results, which will be considered in future studies. Secondly, the agent-based model was conducted in a synthetic community and the usability of the model was verified by comparing the simulation results with previous studies and empirical data. However, due to the difficulty in obtaining more detailed evacuation data, it is difficult to further verify the simulation results of the entire evacuation process. Thirdly, the more parameters in ABM model, the more likely random results will appear. In this study, the model was running 500 times and calculated the truncated mean value to avoid randomness. However, it would be more time-consuming and labor consuming to increase the number of runs in order to get more stable model results. Even so, some interesting results were found and can be used to optimize flood risk management in urban community.Future work will use the empirical data to measure the various behavioral parameters, modify and improve the mutual aid mechanism based on community interviews and survey results, use realistic communities and transportation networks as the research areas, consider the dynamic impact of flood events, and combine the spatial-temporal uncertainty of multi-stage flood warnings.


The FM parameter showed an inverse dependence relative to age, while the FM(R) parameter was inversely correlated to blood pressure. The FM(R) parameter reflects the strong effect of hypoxia on flowmotion, which is mainly due to increased myogenic activity in the vessels. The FMSF technique appears to be uniquely suited for the analysis of basal flowmotion and the hypoxia response, and may be used for the characterization of microcirculatory status.Oscillations in the microcirculation, known as flowmotion, are a well-recognized characteristic of cutaneous blood flow. Since flowmotion reflects the microcirculatory status of the vascular system, which is very often impaired in many diseases and disorders, a quantitative assessment of skin flowmotion could potentially be used to screen for early symptoms of such conditions. In this study, skin flowmotion was monitored using the Flow Mediated Skin Fluorescence (FMSF) technique. The flowmotion parameter was used for quantitative assessment of basal flowmotion both at rest (FM) and during reperfusion [FM(R)] following the post-occlusive reactive hyperemia (PORH). The study population was composed of healthy volunteers between the ages of 30 and 72 ( Oscillations in the microcirculation, known as flowmotion, are a well-recognized characteristic of cutaneous blood flow . The mecThere is substantial evidence that impaired flowmotion can be a symptom of various diseases and disorders, including diabetes, cancer, and cardiovascular diseases, as well as neurodegenerative and autoimmune diseases . HoweverOur new technique, known as Flow Mediated Skin Fluorescence (FMSF), is based on monitoring the intensity of NADH fluorescence emitted from skin tissue on the forearm . Since NThe FMSF technique has been used with the post-occlusive reactive hyperemia (PORH) test to evaluate vascular circulation and metabolic regulation in type 1 diabetes (DM1) patients . Based oIn this paper, we present an analysis of skin flowmotion monitored by the FMSF technique. As there is a very low noise in the recorded FMSF traces, flowmotion can be observed very distinctly and precisely. Two different periods of oscillations can be distinguished in the FMSF signal: basal oscillations at rest, and flowmotion during reperfusion stage. During the occlusion stage, when blood flow in the patient’s limb is blocked, no oscillations are observed. Two quantitative measures of oscillations are defined: FM – a parameter characterizing basal flowmotion at rest; FM(R) – a parameter representing flowmotion during the reperfusion phase. The FM(R) parameter reflects increased microvascular perfusion in response to hypoxia. This process plays a significant role in the physiological adaptation of Sherpa highlanders to hypobaric hypoxia, for example, and of sportsmen to long periods of exertion . Here, dAngioExpert (Angionica Ltd.) measures changes in the NADH fluorescence signal at 460 nm (excitation at 340 nm) from non-hairy skin on the forearm of a patient. It has been found that measurement of skin NADH fluorescence under resting conditions  can be successfully used for the assessment of spontaneous oscillations in skin microcirculation (flowmotion). Due to the low frequency of endothelial activity (0.008–0.021 Hz), the shortest time necessary to measure changes in fluorescence is around 2 min, and the optimal length of time is about 5 min. Although there is no time limit for FMSF measurements, the requirement for the patient to remain still during the examination makes 5 min optimal, to avoid artifacts and resulting errors in the estimation of the magnitude of flowmotion. Furthermore, it is advisable to discard the initial section of the measurement . Using this approach, any perturbation due to skin variations, such as skin pigmentation or differences in the fluorescence levels of patients, is minimized, as the relative change from the baseline is normalized . In mostIn this study, the FMSF signal was collected using the AngioExpert device at a sampling frequency of 25 Hz. An interval of 150 s was selected for analysis of oscillations during the reperfusion period. The second order polynomial was fitted to experimental points in all analyzed cases, using the least squares method . In other words, we calculate the mean squared error (MSE). This provides an unbiased estimate of deviation from the polynomial relationship. It is therefore possible to compare the oscillations at different time intervals. Defining the oscillation parameters in relation to MSE gives an objective, operator-independent characteristic for the tested patient. As MSE values are extremely low, the FM parameters are defined as the MSE values multiplied by a factor of 10The frequencies of the oscillations contained in the FMSF signal were analyzed using the Fast Fourier Transform (FFT) algorithm. Periodograms were derived from the FFT of the signal, with rectangular windowing and the Power Spectra Density (PSD) calculated as a mean squared amplitude. Although there are windowing functions that more effectively limit the frequency leakage problem associated with the FFT method, we have found that PSD values calculated by rectangular windowing are very well correlated with the FM values, which are also a function of the mean squared amplitude of oscillations . Fast FoThe analysis presented in this paper is based on a study described in previous articles , 2020 whPrior to the FMSF measurements, the healthy volunteers underwent an acclimatization at room temperature (for at least 15 min) and their blood pressure was measured. The temperature of the optical window surface in contact with the skin was continuously monitored during the measurement and was usually kept between 27 and 31 degrees. During a single measurement, the change in temperature was not greater than one degree. Two different protocols were used.The test group consisted of 40 healthy volunteers aged between 31 and 72 . Fluorescence was measured using the FMSF method on the forearm for 5 min, during the resting period only. Each measurement was repeated twice over 1–2 days to estimate the repeatability of the FM parameter and the effects of various oscillations .The test group consisted of 35 healthy volunteers aged between 30 and 50 . The participants were enrolled at different clinical centers. Fluorescence was measured using the FMSF method on the left forearm of each patient, over three different periods: a period of at least 3 min for fluorescence baseline measurements; a 3 min occlusion period caused by inflating a cuff placed on the left upper arm to 60 mm Hg above systolic blood pressure; a period of hyperemia/reperfusion lasting at least 3 min following cuff deflation, during which fluorescence returned to the baseline level. Each measurement was repeated within two consecutive days under the same experimental conditions.p < 0.05 was considered statistically significant.Quantitative data were presented as the mean and the standard deviation. The within-subject coefficient of variation (CV) was calculated as the relative standard deviation. Repeatability of measurements was expressed by the Pearson correlation coefficient , or by the Spearman correlation coefficient . A paired-sample Wilcoxon signed rank test was used to compare the FMSF parameters recorded for different groups of subjects. All reported probability values were two-tailed and a In this group, only basal flowmotion was analyzed. The demographic and clinical characteristics of the studied population are summarized in 6), FM is the mean fluctuation of the fluorescence from the baseline and thus an estimation of flowmotion oscillations. Low frequency changes are characterized by the presence of three components: endothelial, neurogenic, and myogenic. These are accompanied by weaker blood flow oscillations, caused by a heartbeat with a frequency of around 1 Hz, as shown clearly in the presented signal .The average within-subject coefficient of variation of FM found for the group was 20% and the repeatability of the measurements was good , but remained similar in our study for males and females. The FM for the whole studied group is shown Flowmotion (FM) is age dependent . Similar values were obtained for the FM(R) parameter (oscillations during the reperfusion stage): the average coefficient of variation was 41%, Spearman’s coefficient r = 0.440, p = 0.008.Because of the much shorter time of analysis, the average within-subject coefficient of variation in FM found for the group was 43%. As a consequence, the repeatability of the measurements was diminished, but can still be considered as a fair . The correlation between FM(R) and FM is shown in In Within the studied group, we selected several individuals for whom both the FM(R) and myo(R) parameters were in the lowest quartile, as shown in r = −0.271, p = 0.091).As shown in The FMSF technique is based on monitoring the intensity of NADH emitted from skin tissue on the forearm, the level of which depends on the supply of oxygen delivered by blood circulation. Since, due to physiological factors (predominantly skin pigmentation), direct comparison of absolute levels of fluorescence from the skin is rather useless for the evaluation of circulatory systems, the FMSF method is used in conjunction with the post-occlusive reactive hyperemia (PORH) test.The FMSF technique appears also to be uniquely suited to the analysis of skin flowmotion and its hypoxia response. Thus, it may be used to predict the microcirculatory status of a patient. The presence of oscillations is a necessary condition for proper functioning of the circulatory system. Although magnitude of flowmotion is very subtle, it can be observed very distinctly and precisely by the FMSF method, due to a very low noise in the recorded FMSF traces and the high sensitivity of the fluorescence-based technique. Two quantitative measures of oscillations can be evaluated from the signal analysis: FM – the flowmotion parameter characterizing basal flowmotion at rest; FM(R) – the flowmotion parameter representing oscillations during the phase of reperfusion.Monitoring skin flowmotion at rest (before the application of PORH provocation) enables evaluation of skin microcirculatory status. The FM parameter can be used to detect irregularities in flowmotion associated with disease processes, especially cardiovascular diseases (CVD). Therefore, flowmotion analysis based on the FMSF trace at rest could expand the scope of the application of FMSF. Oscillations which are seen directly, even without any additional data processing, may be supported by Fast Fourier Transform analysis, allowing evaluation of the various frequency components that contribute to total flowmotion. It seems likely that patients with low values for flowmotion parameters have abnormal microvascular function. The observation of high myogenic activity during rest periods (in a few cases to levels even higher than those induced by hypoxia) may indicate some dysfunction of the microcirculatory system.Even more advanced assessment of microcirculation can be obtained by measuring the flowmotion response to hypoxia. The extent of changes in the flowmotion caused by, for example, thermal or pharmacological effects, limited oxygen delivery, or restricted blood flow, can be used as a measure of the ability of the circulatory system to compensate for such effects. Vasomotion has been suggested as an important factor in reducing microvascular resistance and ensuring adequate blood flow in the presence of lower oxygen delivery .The observed correlation between the FM(R) parameter and blood pressure presented in A careful analysis of the FM(R) and myo(R) parameters identified three individuals  with a disturbed flowmotion response to hypoxia . PerhapsAnalysis of flowmotion during the reperfusion stage [FM(R)] makes it possible to identify individuals with disturbed activation of myogenic oscillations. Such analysis may help physically active healthy people to plan their exercise schedule, or with selecting athletes with strong responses to hypoxia, who may be especially suited to endurance sports . The flowmotion response to hypoxia evaluated by the FM(R) parameter may also assist in the prognosis of recovery in cases of diseases accompanied by hypoxia.The datasets generated for this study are available on request to the corresponding author.Written, informed consent was obtained from the participating individuals for the publication of any potentially identifiable images or data included in this manuscript.JK performed and analyzed the data and contributed to the manuscript preparation. TC prepared the computational program. LS contributed to the data analysis. AM performed the data and statistical analysis, interpreted the results, and prepared the manuscript. JG designed the study, performed the literature search, and revised the manuscript. All authors contributed to the article and approved the submitted version.JK, TC, and LS were employed by Angionica Ltd. JG and AM were inventors of the patents protecting the use of FMSF technology.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier. The incidence of brain tumors increases with age . This arGlioma is a tumor composed of a necrotic core, a margin of tumor activity, and edema tissue. Multiple MRI sequences can be used to image different tumor tissues , as showMRI-based segmentation of gliomas and their surrounding abnormal tissues facilitates the doctor to observe the external morphology of each tumor tissue of the patient's glioma and also facilitates the doctor's imaging-based analysis and further treatment. Therefore, the segmentation of glioma is considered to be a first step in the MRI analysis of glioma patients. Because gliomas have different degrees of deterioration and contain multiple tumor tissue regions and brain MRI is a multimodal and many-layer three-dimensional scan image, manual segmentation of glioma regions requires a lot of time and manpower. In addition, manual segmentation is often based on the brightness of the image observed by the human eye for area segmentation, which is easily affected by the quality of the image generation and the personal factors of the tagger. It is prone to erroneous segmentation and segmentation of redundant areas. Therefore, in clinical practice, a fully automatic segmentation method with good segmentation accuracy for gliomas is needed. However, the problems in the study of automatic glioma segmentation methods are summarized as follows: (1) glioma is often distinguished in the image by the change in pixel intensity between the lesion area and surrounding normal tissues. Due to the presence of a gray-scale offset field, the intensity gradient between adjacent tumor tissues will be smoothed, resulting in blurred tumor tissue boundaries. (2) The structure of gliomas differs in size, shape, and position, making segmentation algorithms difficult to model. And because the growth position of glioma is not fixed, it is often accompanied by a tumor mass effect. This will cause the surrounding normal brain tissue to be compressed and change its shape, thereby generating irregular background information and increasing the difficulty of segmentation.At present, computer-aided diagnosis technology based on machine learning has been widely used in medical image analysis in recent years –14. SincBrain tumor segmentation methods can be roughly divided into three categories: based on traditional image algorithms –20, baseA new depth classifier is proposed. The classifier is composed of a deep convolutional neural network and an integrated SVM algorithm. First, CNN was trained to learn the mapping from image space to tumor label space. The predicted labels in CNN together with the test images were input into an integrated SVM classifier. In order to make the results more accurate, we deepened the classification process and iterated these two steps again to form the framework of the next CNN-SVM in seriesThe traditional segmentation method is to use the training set to train a suitable classifier, and then test the set for verification. The method proposed in this study is completely different from the traditional method. The proposed model mainly includes three stages: one is preprocessing, feature extraction, and training CNN and SVM. The second is to test and generate the final segmentation results. The third is to deepen the order of our CNN-SVM cascade classifier through an iterative stepApply the proposed model to public datasets and self-made datasets for evaluation. Compared with the segmentation performance of CNN and SVM alone, the superiority of the proposed model can be reflected in various evaluation indexesThis paper mainly studies the segmentation method of glioma based on the deep learning method, aiming at automatically and accurately segmenting the glioma region from the brain MRI through the deep learning algorithm. For the task of glioma segmentation, this paper proposes a DCNN-F-SVM deep classifier. The main research contents of this article are as follows:In the currently proposed glioma segmentation method, the segmentation results of traditional image processing algorithms rely heavily on manual intervention, and a priori constraints are required to ensure the segmentation effect, resulting in poor robustness and low efficiency of the method. The glioma segmentation method based on machine learning needs to manually select the features of the image, so that the segmentation effect of this type of method depends on the artificial features, and the generalization ability of the segmentation algorithm itself is weak.The glioma segmentation method based on deep learning can automatically extract image features through the neural network model and segment the glioma region. Therefore, the shortcomings of strong prior constraints and manual intervention in the above method are overcame. The automation and robustness of the segmentation algorithm are improved, and good segmentation results can be achieved in large-scale complex glioma segmentation scenarios. CNNs are well-known practical models in the field of deep learning, and their innovative ideas stem from the processing of human brain nerves. The perceptron model proposed in 1980 is considered to be the original model of convolutional neural networks. The perceptron model is a classic model in the field of machine learning, but this model also has great shortcomings and cannot solve XOR problems well. On this basis, reference  proposedCNN is essentially a multilayer perceptron and a multilayer neural network, and there is an obvious sequence between these layers, which is composed of an input layer, a hidden layer, and an output layer. There can be multiple hidden layers, and each layer is composed of multiple two-dimensional planes. Each plane contains multiple neurons, and the hidden layer consists of a convolution layer, a downsampling layer, and a fully connected layer. The convolution layer and the downsampling layer appear alternately and can have multiple layers, and the fully connected layer can also have multiple layers. The network structure of the traditional convolutional neural network LeNet is shown in In the convolution layer, the feature maps output by the previous layer are convolved by the learned convolution kernel, and the corresponding partial derivatives are input into the activation function together to form an output feature maps. The downsampling layer is used for feature selection to select representative features. The fully connected layer is a neural network layer whose role is to map two-dimensional distributed features into feature vectors for better classification. The output layer is a simple classification layer, usually using logistic regression for classification. Here, we use the Softmax classifier for classification.f(x) = maxRelu function: f(x) = log(1 + ex)Softplus function: The activation function usually selects a nonlinear function to better fit the nonlinear model. Selecting the activation function needs to consider its monotonicity and derivability. Common activation functions are shown as follows:The CNN model structure is simpler and easier to expand than the neurocognitive machine. In the neurocognitive machine, the downsampling layer and the convolutional layer alternate to form the function of feature extraction and abstraction, while in the convolutional neural network, the convolutional layer and the downsampling layer alternate, and their functions are similar. The convolution operation simplifies feature extraction, the excitation function replaces multiple nonlinear functions of the neurocognitive machine, and the pooling operation is also simpler. The CNN algorithm flow is shown in The BraTS Challenge held in 2012 provided a brain MRI dataset with both low-grade gliomas and high-grade gliomas. The dataset provides MRI of multiple patients and provides a multiregion glioma segmentation ground truth for each patient. Among them, ground truth is the result of fusion of 20 segmentation algorithms and then manually labeled by multiple human experts. Every BraTS competition will provide a public dataset of gliomas. However, the glioma dataset provided since BraTS17 has been significantly different from the dataset provided before 2016. The dataset used between BraTS14 and BraTS16 contains images of gliomas before and after surgery, which leads to confusing glioma segmentation criteria in the dataset and does not have the conditions to be true segmentation criteria. Therefore, the datasets between BraTS14 and BraTS16 are no longer used in the games after BraTS17. The BraTS18 dataset is based on the BraTS17 dataset with the addition of the TCIA glioma dataset. The TCIA glioma dataset includes 262 high-grade glioma patient images and 199 low-grade glioma patient images. This dataset contains the MRI and ground truth of 543 glioma patients and is currently the most standard glioma segmentation dataset. The details of the datasets in the BraTS competition datasets over the years are shown in As shown in The common evaluation methods for evaluating the performance of each model in the field of image segmentation are shown in In addition to the above evaluation indicators, there are indicators such as Hausdorff Li and positive predictive value. The most commonly used are DSC and sensitivity.This study proposes a brain tumor segmentation model based on convolutional neural network fusion SVM. The proposed model segmentation of brain tumor images can be divided into two parts: one is preprocessing, feature extraction, and training CNN and SVM; the other is testing and generating the final segmentation results. It can be divided into 3 stages. In the first stage, CNN and integrated SVM are trained to obtain the mapping from the gray image domain to the tumor label domain. In the second stage, the labeled output of CNN and the test image are input into the integrated SVM classifier. In the third stage, an iterative step is used to connect the CNN and the integrated SVM classifier, which increases the number of layers. In order to select the optimal feature, an intermediate processing step is added to the model, as shown in Grayscale, mean, and median are used to represent each pixel. These features are used to train CNN to obtain a nonlinear mapping between input features and labels. In the testing stage, an aggregated SVM classifier is independently trained using the aggregated CNN label map and the same features as before.An iterative classification process is applied to the preprocessed input image. First, CNN classifies the pixels in the key area, thus generating a kind of presegmentation, which will be sent to the integrated SVM classifier. Then, a Region Of Interest (ROI) on presegmentation will be generated. In addition to presegmentation, classification based on integrated SVM will be performed on this ROI. After that, the integrated SVM explores the neighborhood of the CNN output. Use CNN to classify the marked ROI again. Repeat the above steps to further refine the segmentation results.σ=0.1, and C = 1000. The public dataset used is the BraTS18 dataset. The self-made dataset is the clinical MRI images of 26 patients. The evaluation index used in the experiment is DSC, sensitivity, and specificity. The description of the experimental software and hardware environment is shown in The experimental dataset used in this study includes the public dataset and the self-made dataset. The comparison models are SVM, CNN, and DCNN-F-SVM. In the setting of experimental parameters, set the window size to 5, After the model training is completed, the test set can be predicted by the model to obtain the glioma segmentation result obtained by the model segmentation. In the test set divided by three-fold cross-validation, the evaluation index pair of each model on the BraTS18 dataset is shown in In this section, clinical MRI images of 26 patients were collected, and brain tumors were trained and segmented using three models, and the experimental results were given. Tables Among the index values shown in Among the index values shown in The diagnosis of brain diseases requires accurate diagnosis without deviation. Any misdiagnosis will cause irreparable losses. The incidence of brain tumors in brain diseases has been high, and the number of patients has increased year by year. This has also increased the workload of medical personnel in this field to a certain extent. An accurate and efficient method of brain tumor image segmentation needs to be urgently proposed, which has solved the increasing demand. Based on this background, this paper proposes a depth classifier to improve the segmentation accuracy and achieve automatic segmentation without manual intervention. The classifier is mainly composed of DCNN and integrated SVM connected in series. The implementation of the model is divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from the image space to the tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. The simulation implementation verified the superiority and effectiveness of the proposed model. However, the proposed model still has shortcomings such as long calculation time. How to optimize the algorithm and shorten the running time will be the next research content.


An intrathoracic bleeding from the thyrocervical branch is not common in blunt trauma, but an interventional radiologist should be aware of the risks in order to prevent complications.A 30-year-old male presented with a right pneumo-haemothorax due to active bleeding revealed at contrast-enhanced CT, as a consequence of a fall occurred in the previous week. The patient was treated with endovascular embolisation in an angiographic room with coils placement, since the right thyrocervical artery was found to be supplying the pneumo-haemothorax. A radiculo-medullary branch rose from the thyrocervical trunk, impeding the proximal embolization with microparticles and needing selective isolation of the bleeding artery with the catheter to avoid spinal cord injuries. The treatment had a successful result and the following CT control showed signs of recovering, without any complication.Our paper presents a rare contingency, warning the operator to bear in mind the presence of arteries feeding the spinal cord. This crucial detail precludes the use of microparticles embolisation to prevent neurologic sequelae, whereas the use of endovascular coils for embolization should be mandatory. Moreover, this case reminds that the post-traumatic bleeding deriving from a cervical trauma may also occur later. The thyrocervical trunk is an ascending branch coming off the superior wall of the subclavian artery, laterally and in parallel to the vertebral artery. It divides into four branches: the inferior thyroid artery, the ascending cervical artery, the suprascapular artery and the transverse cervical artery. A noticeable anatomic variability of these vessels is known and therefore should be expected from the interventional radiologist. An important aspect to hold in consideration is that the anterior spinal artery, which runs longitudinally in the anterior median groove of the spinal cord, receives radiculo-medullary arteries arising from the neck arteries. Even the thyrocervical trunk emits feeding collaterals to the anterior spinal artery, usually from the ascending cervical branch; furthermore it may be linked to the brainstem through collaterals to the vertebral arteries. These anastomoses imply the risk of neurological sequelae after the embolisation of the thyrocervical trunk. Pérez-García et al.2019) describes in a wide case series this potential complication in the presence of spinal collateral of thyrocervical artery in endovascular embolisation procedures performed as preoperative treatment for the resection of tumours or as therapy for recurrent haemoptysis, aneurysmal bone cyst and post-traumatic active-bleeding using both microspheres and coils 9 describ.To the best of our knowledge, several cases of thyrocervical bleeding have been reported after invasive procedures, traumatic accidents, infective and vascular diseases with an unexpected association with the neurofibromatosis-1; all cases were treated successfully with endovascular coils, suggesting the value of this interventional approach –7. As coThe embolisation can be wide or local, according to the vascular territory involved; moreover it can be permanent or temporary according to the embolic agent . TemporaOur patient came to the Emergency Department complaining of a rapid onset of back pain and dyspnea, following a fall on his back the week before during a circus performance. Upon arrival, the patient was tachycardic (118 bpm), euthermic and eupnoeic with normal blood pressure. Nothing in particular emerged from the familiar, habits and historical anamnesis. At the physical examination, the breath sound at the e lung base was absent. After fainting, the patient presented asthenia and stupor.Chest X-ray, abdominal ultrasound and laboratory exams were performed. They showed signs of pneumo-haemothorax and lung collapse of the right hemithorax Fig. . The criFinally, the patient was transferred to the angiographic room for the treatment of the disrupted vessel. The procedure consisted in a right transfemoral artery access. Digital Subtractive Angiography was performed to assess the thoracic aorta and the right posterior intercostal arteries, resulting negative for active bleeding. The subclavian artery and its branches were evaluated, showing a consistent and faint contrast media leaking in the vascular field of the thyrocervical trunk Fig. . After cThe patient was hospitalised and the lower haemoglobin levels were stationary . A CT scanning was performed after 3 days from the endovascular treatment and it did not show any sign of contrast media blushing.Currently, juggling is spreading as a common practice, often considered as a new training activity, involving a high risk of trauma and unexpected complications. As the juggler does not show how hard his work is, thanks to a conscious control of the danger, a good interventional radiologist needs to foresee and avoid the risks, by having confidence in managing them.The endovascular treatment of the thyrocervical trunk branches may have difficulties due to the course of the vessels and the eventual presence of spinal cord supplying artery contraindicates the use of microparticles. CT angiography in our patient missed describing the presence of the spinal collateral branch and cannot be considered reliable in revealing anomalous connections with other vessels: a further study of the vascular anatomy during angiography is mandatory.In our experience a selective approach is mandatory in order to preserve the integrity of the axial nervous system. Therefore, during every interventional procedure, the operator should be prepared to choose the best technique to control the bleeding and prevent the predictable complications aiming for the best outcome for the patient.To the best of our knowledge, this paper is the first describing the procedure of embolisation for a bleeding of the thyrocervical trunk after a blunt trauma inserting the distal extremity of the coil along the bleeding vessel, like a wire, to permit to the proximal closure of the vessel itself and preventing from the risk of spinal infarct in case of microparticles use.


Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle. Rolling element bearings are one of the most widely used mechanical components in various industrial machines, such as gearboxes, railway axles, and turbines. Their health states tend to degrade due to repeating rotations under harsh working conditions. Fault diagnosis of rolling element bearings is essential to prevent machine breakdown and ensure production efficiency. Therefore, research related to bearing degradation prognostics and fault diagnosis has recently attracted much attention ,2,3.The narrowband-based amplitude demodulation of a vibration signal enables the extraction of more detailed information about the fault signature. The procedure of this method uses a band-pass filter to retain one of the resonant frequency bands. Hilbert transform is then employed to demodulate the signal preprocessed by the band-pass filter and to construct a squared envelope on the filtered signal . At lastHowever, vibration signals of bearings contain impulsive noise in some industrial situations, which bring challenges to further applications of this technology. Moreover, the Kurtogram algorithm was found to be sensitive to impulsive noise and this can sometimes lead to incorrect interpretations .A number of improved methods have been proposed in order to analyze and solve this problem, such as the smoothness index-guided approach , the spaMotivated by ideas from the field of thermodynamics, a new concept named Infogram was proposed , which iCyclostationarity is a property that characterizes stochastic processes whose statistical properties periodically vary with respect to some generic variable . This geAs a second-order cyclostationary component extraction indicator, SC analysis has become popular in the field of machine diagnostics. SC analysis  can simuRecently, a new cyclostationary analysis technology named cyclic correntropy (CCE) analysis has emerged to suppress impulsive noise ,22,23. CThe rest of this paper is outlined as follows. In A cyclostationary analysis method based on the correntropy function is introduced into the fault diagnosis of the rolling element bearing under an impulsive noise environment.The kernel size of the correntropy function is investigated to find out its influence on the rolling element bearing fault diagnosis.The diagnosis performance of the proposed method is compared with the Fast Kurtogram method using train axle bearing data to verify its suitability.The intended contributions of this study can be summarized as follows:Correntropy function can be regarded a similarity measure based on kernel function. The definition of correntropy function is defined as follows :Let ondition . The Gauondition . The kerications .By applying an extension of the Taylor series to the correntropy function, Equation (1) can be rewritten as follows :(3)VRxSx, the CCE function can be rewritten as :(11)Vxα=The CCE function has proved to be very useful in signal processing, although its applications are limited to communication engineering. It is often more effective and natural to transform the structure of a cyclostationary signal in the frequency domain. Note that the CCE is a function with variables The Gaussian variance, also known as kernel size, is a free parameter which must be chosen according to certain applications. A well-tuned kernel size can minimize the effect of the noise interruption. Therefore, the diagnosis performance of cyclic correntropy is largely determined by the kernel size. However, in previous studies related to cyclic correntropy, the selection method of kernel size was not addressed in detail ,22,23 anThe correntropy function is an extended work of the information theoretic learning (ITL) theory ,32. ThanAmong those kernel size estimation methods ,35,36, SThe influence of the kernel size on the bearing fault diagnosis result is discussed more in greater depth in Based on the introduction of above two sections, the estimation of CCES is shown as follows:Step 1. Denote the input signal Step 2. Calculate the kernel size Step 3. Calculate the average of the correntropy function for each block Step 4. Calculate the following formulation for each block of size Step 5. Calculate the mean value Step 6. Calculate the discrete Fourier transform for each Step 7. To clearly observe the fault frequency, project the CCES into the cyclic frequency domain and obtain the cyclic domain profile.The following example aims to illustrate the failure of the Fast Kurtogram in the presence of impulsive noise. Firstly, the simulated bearing fault signal is modeled as a single-degree-of-freedom system according to Reference :(18)y(k)Impulsive noise has a similar behavior to the bearing fault signal. According to the impulsive noise simulation method proposed by Antoni , the noiThe Fast Kurtogram method  is appliThen, the simulated signal is further analyzed with the CCES proposed in To further testify the performance of the proposed method, comparison experiments are conducted on real bearing data. The first example applies the dataset from the Western Reserve University (WRU) bearing data center . This daFirstly, the Fast Kurtogram is obtained for the optimal band selection, as shown in Then, with In this case study, industrial railway axle bearing fault data are used for further comparison experiments. One unique component of the industrial railway axle bearing signal is the impulsive background noise. During the train operation on the rail, impulsive force will be produced when the train passes through curves or small gaps between rail joints. This impulsive force will be conducted to the bearing casing through the train wheel and creates an impulsive noise environment. This phenomenon raises new challenges to the bearing fault diagnosis. Focusing on this issue, railway bearing data under impulsive noise interference are acquired to validate the superiority of the proposed method.Our experimental platform for collecting railway axle bearing fault data is shown in The input data are firstly analyzed with the Fast Kurtogram method and Then, the CCES is applied to analyze the input data of the inner race fault and the corresponding cyclic domain profile is displayed in cal load ,42. FurtThe previous procedure is applied to analyze the axle outer race fault signal and the relevant results are plotted in The application of the CCES method with An interesting point is that the CCES can obtain the cyclic frequency of the fault signature while suppressing the periodic noise, which also is achieved in the simulation signal analysis. Generally speaking, cyclostationary modeling aims to determine all those cyclostationary components in the signal. According to the CCES procedure proposed in The Gaussian kernel size is a free parameter selected according to certain applications. The variation of kernel size expands its application range, while improper kernel size may become its potential weakness. Previous studies on the kernel size selection of CCE were mainly based on multiple trials, which does not lead to the optimal parameter for the Gaussian kernel function. In this section, fault diagnosis performance of different kernel size is analyzed to provide an effective mechanism for kernel size selection.Input data are derived from the above two case studies for comparison, including the WRU data of the inner race fault numbered 169 and the axle bearing data of the outer race fault. The optimal kernel size of each experiment is based on Silverman’ rule. In addition, another random kernel size is used for comparisons.The random kernel size is set to 1 and the optimal kernel size is 0.015 according to Silverman’s rule. The analysis results of the WRU data numbered 169 are displayed in Similarly, the random kernel size is set to 0.1 and the optimal kernel size is 3.021 according to Silverman’s rule. The analysis results of the axle bearing signal of the outer race fault are displayed in The above two analysis results have shown that the kernel size plays an important role in the fault diagnosis through the CCES method, especially when signal components are complex and affected by impulsive noise. The kernel optimization method based on Silverman’s rule is highly efficient and accurate during the fault diagnosis. Thus, CCES can be regarded as a self-adaptation method which may also be applied other objects in mechanical fields.In this paper, we have investigated an impulsive noise suppression method for bearing fault diagnosis based on the CCE function. Silverman’s rule was used to obtain an optimal kernel size for CCE input. This adjustable parameter provides an effective mechanism to eliminate the negative effect of impulsive noise. Then, a simulated signal and two real cases were studied to validate the performance of the proposed method. The experiment results show that the proposed method has a good diagnosis performance, especially in the presence of impulsive noise. Furthermore, a powerful frequency band selection method named Fast Kurtogram was applied to analyze the same data for comparison. It was found that the fault diagnosis method based on CCE function outperforms the Fast Kurtogram method.Further research should mainly focus on the combination of CCE and mode identification methods. Typically, frequency domain features of CCES can be extracted to reflect different fault characteristics. Moreover, considering the computation time of cyclic methodology, a simplified and fast computation method should be developed in subsequent research works. Note that this new method can generate cyclostationary signatures for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other machinery, such as gears and blades.