Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods. Text summarization is an information compression technique that converts a collection of text or documents into a short summary that covers the important information in the original text with little redundant information and high readability ,2. NowadAt present, the text summary model based on deep learning has achieved remarkable results in the task of summary generation. Most of the existing models use an encoder-decoder framework, where the encoder encodes the input into intermediate states and the decoder processes the intermediate states and then outputs them. Among them, Sequence-to-Sequence (Seq2Seq) models ,8,9 usinTo adequately exclude irrelevant information, an attention gate is added to the gate control unit GRU.In the iterative process of the existing Gated Graph Neural Network (GGNN), irrelevant information will also be accumulated and amplified, resulting in redundant information in the decoding phase which cannot be eliminated, making the text abstract distorted. Therefore, an Attention Gate (GA) is added to the gating unit GRU to form a Gate Attention Graph Neural Network (GA-GNN) model.Use of parallelism in the coding phase to mitigate inadequate coding and high time complexity.If a single coder is used in the sentence encoding stage, the local information and the global information cannot be concerned at the same time, which easily leads to insufficient semantic information encoding. Tandem encoding can solve the problem of global local semantic encoding, but the time complexity is high. In this paper, parallel sentence coding mode is used to encode both local and global information of the text, which enriches vector information and shortens training time.A joint loss function optimization model is proposed.Decoding based on graph-extracted features not only ignores the connections between sentence levels, but also diminishes the accuracy of generated summaries due to the lack of multiple sample guidance. In this paper, the loss function is optimized by weights based on contrast learning, graphic feature extraction and confidence calculation of important sentences, and all the key information is effectively incorporated into the decoder.Most of the above models ignore the influence of local fine irrelevant information on the accuracy of summary generation. To address the problem, this paper proposes a gated attention graph neural network model framework. The main innovative elements include the following:The codec framework is an architecture method for machine translation and natural language processing, which mainly includes two parts: encoder and decoder. The encoder accepts the input sequence and encodes the entire sequence information, eventually mapping the input to a hidden state containing intermediate semantics. The decoder converts the original sequence to the target sequence using the intermediate semantic representation and output of the decoder. The codec framework can be Recurrent Neural Network (RNN) and its variant networks , Convolutional Neural Network (CNN), Attention mechanism and Transformer, etc.With the increasing difficulty of natural language processing tasks, single-layer encoders are gradually unable to meet the requirements, so layered encoders are proposed, which have now become the mainstream framework for text summarization tasks. The encoding model is mainly divided into two parts: sentence encoding and document encoding. Sentence encoding is to input the word vector representations corresponding to all words contained in each sentence of the original document into the network to generate the corresponding hidden-level sentence vector representations. Document encoding is the use of neural networks to generate document-level sentence vectors from the hidden layer sentence vector representations.The traditional neural network model mainly focuses on the sequence-related information and cannot express the non-sequence-related information in a friendly way. Graph is a kind of non-sequential structure, but its edges and nodes can effectively represent the dependency relationship between each other, so Graph Neural Network (GNN) was born. Because of its good learning ability and interpretation ability, it has become a widespread graph analysis model.The vertices in the graph represent the words and the edges represent the relationships between the vertices. The purpose is to obtain the feature representation of each node in the graph by learning the neighboring nodes. Early graph neural networks update node-like by iteratively exchanging information of neighboring nodes, but the problem of cyclic layer interdependence tends to occur during the iterative process, so variants of graph neural networks such as Graph Convolution Networks (GCN), Graph Attention Networks (GAN) and Gated Graph Neural Networks (GGN) have been derived.Gated Graph Neural Network (GGNN) is a GRU-based spatial domain model that solves the long-term text dependency problem through a gating mechanism. Each sentence in the document generates a graph structure, the words in the sentence are considered as graph nodes, and the edges between the nodes represent relational features. The main purpose of the GGNN model is to update the node information through the gating mechanism, so that the updated node can contain its neighboring node information. The GGNN model takes the neighbor information of the node as the input and its own state information as the hidden state, controls the generation of new information by updating the gate, resets the forgetting of the gate control information, calculates the newly generated information by using the tanh function, and finally controls the balance state between the forgetting degree of the hidden state at the last moment and the input degree of the newly generated information by updating the gate.Contrastive learning, which was first applied to CVs, has shown great potential in natural language processing in recent years. The core of contrast learning is to learn the difference between samples by constructing positive and negative samples. The specific idea is to compare a given sample with its semantically similar samples (positive samples) and semantically dissimilar samples (negative samples) by constructing a contrast loss function, so that the semantically similar samples are represented more closely in spatial distance and the semantically dissimilar samples are further in spatial distance, thus achieving a similar clustering effect.In the text summary generation task, the source text contains a large amount of redundant information, which is not only time-consuming but also of poor summary generation quality if it cannot be removed during the training process. This paper proposes a neural network model GA-GNN based on the gated attention graph, as shown in The layered encoder consists of sentence encoding and document encoding. Bi LSTM and CNN parallel networks are used for sentence encoding, and only Bi LSTM networks are used for document encoding. Bi-LSTM can preserve future and past information through a double-layer LSTM, capable of learning a more adequate and accurate global semantic representation of words. CNN is an excellent network for learning local semantic information, which can represent local semantic information more accurately than other networks.n, i-th word, and word embedding is expressed as k denotes the convolution kernel size, and Given a sentence To further sense the correlation between adjacent sentences, the sentence encoding vector h is input into the document encoder Bi-LSTM, and the hidden layer states learned before and after are stitched together as the document encoding vector In order to make full use of the graph structure information, eliminate irrelevant information, and allow each node to better fuse the neighboring information, a graph neural network incorporating the Gate recurrent Attention Unit (GAU) is introduced to make the nodes perform information fusion as comprehensively as possible and obtain a more advanced feature representation.t, the gated attention unit takes as input the result of the interaction between the word node and its adjacency matrix V| represents the number of nodes, and v in the adjacency matrix; t − 1, and b denotes the bias.Firstly, the text graph G =  is established according to the relationship between words, where V represents the set of nodes. Each independent word is regarded as a node, E represents the set of all edges, and the edges between nodes correspond to the relationship between words. The structured information of the graph G is stored in the adjacency matrix σ stands for sigmoid function, W and U stand for weight parameter matrix, and b stands for bias parameter.Then, feature extraction is performed by gating the attention unit GAU, which is a key component of the GA-GNN model and is formed by adding an attention gate based on GRU, whose structure is shown in To prevent the accumulation and amplification of small local irrelevant information and to ensure the accuracy of feature extraction, an attention gate GA is added to explicitly calculate the degree of influence between any nodes during each update iteration to eliminate the local information in the input Finally, the output N| is the number of samples, K is the number of negative samples. When all the remaining data in the original sample are taken as negative samples, the computational complexity is high, so a part of the data is selected as negative samples by sampling. τ is the temperature parameter, set to 0.1, The key of contrastive learning is to construct a contrast loss function. First, the positive and negative samples are obtained based on the data transformation; then, the feature space is compared by calculating the similarity of the two samples, and finally the positive samples are approximated and the negative samples are pushed away.softmax  is the activation function, W is the parameter matrix, * is the matrix product, and i-th sentence in the document. Calculated according to the following formula:There is a large amount of redundant information in the source text that is not related to the central idea of the text, and filtering this irrelevant information before generating the abstract can effectively improve the accuracy of the abstract. In this model, the document level coding vector is used as the input of the important sentence confidence calculation module, and the sentences are extracted based on the calculation results.iP.Finally, the important sentences are extracted based on the magnitude of the calculation result M denotes the total number of training sentences, i-th sentence is irrelevant and is not used as a summary sentence, and when i-th sentence should be used as a summary sentence.The loss function , and MR Dataset , respectively).In this experiment, word embedding size and hidden layer state vector dimension were both set to 521 during model training. Adam method was used to optimize the model, the learning rate was set to 0.001, and the batch size was set to 100.Evaluation indexes are used to measure the quality of the model. The evaluation indexes of the text summary are divided into manual evaluation and automatic evaluation. The manual evaluation will be affected by the subjective tendency of the evaluator. In the experiment, three indexes of Rouge-1, Rouge-2 and Rouge-L in ROUGE, a standard automatic text summary evaluation method based on recall rate proposed by Lin, were used as the evaluation criteria of the model.One-tuple evaluation indicators:L is the longest common subsequence, X and Y denote the standard summary and generated summary, Binary evaluation indicators:In order to verify the impact of each module proposed in this paper on the accuracy of summary generation, relevant ablation experiments are conducted and the optimal model is selected by comparing the performance results. The CNN/Daily Mail dataset is selected for the following ablation experiments.From Seq2Seq + Joint Attention (2018): Hou Liwei et al.  proposedDAPT (2022): Li et al.  proposedAGGNN (2022): Deng et al.  proposedGRETEL (2022): Qianqian Xie et al.  introducCNN/Daily Mail DatasetMR DatasetIn order to verify the validity of the models, the following four baseline models are selected as the subjects of comparison experiments.From the above experimental data in In this paper, a framework model based on gated attention graph neural network is proposed for generating more concise and fluent text summaries. This model uses a parallel mechanism to encode sentences to make full use of local and global semantic information in the text. After that, sentence vectors are converted into graph structure information for feature extraction. However, due to the accumulation and amplification of local redundant information in the extraction process, it will be impossible to eliminate them in the coding stage, affecting the accuracy and simplicity of summary generation. Therefore, gated attention units are introduced into the model, which are used in combination with graph neural networks to eliminate redundant information and improve model performance. In order to effectively incorporate key information into the decoding process, this paper proceeds to optimize the model loss function in three parts: contrast learning, important sentence confidence calculation and graph neural network to generate more reasonable and accurate text summaries. The effectiveness of the model framework proposed in this paper is demonstrated by conducting experiments on two standard general-purpose datasets. In future work, we will continue to explore how to optimize the model framework and establish the relationship between graph nodes and edges to obtain better results.


In the UK 55,000 people live with a major limb amputation. The prosthetic socket is problematic for users in relation to comfort and acceptance of the prosthesis; and is associated with the development of cysts and sores.We have developed a prototype low-cost system combining low-profile pressure sensitive sensors with an inertial measurement unit to assess loading distribution within prosthetic sockets. The objective of this study was to determine the ability of this prototype to assess in-socket loading profiles of a person with an amputation during walking, with a view to understanding socket design and fit.The device was evaluated on four transtibial participants of various age and activity levels. The pressure sensors were embedded in the subject's sockets and an inertial measurement unit was attached to the posterior side of the socket. Measurements were taken during level walking in a gait lab.The sensors were able to dynamically collect data, informing loading profiles within the socket which were in line with expected distributions for patellar-tendon-bearing and total-surface-bearing sockets. The patellar tendon bearing subject displayed loading predominately at the patellar tendon, tibial and lateral gastrocnemius regions. The total-surface bearing subjects indicated even load distribution throughout the socket except in one participant who presented with a large socket-foot misalignment.The sensors provided objective data showing the pressure distributions inside the prosthetic socket. The sensors were able to measure the pressure in the socket with sufficient accuracy to distinguish pressure regions that matched expected loading patterns. The information may be useful to aid fitting of complex residual limbs and for those with reduced sensation in their residual limb, alongside the subjective feedback from prosthesis users. Major limb amputation affects up to five-thousand people a year in England alone , 2 and mThe socket must enable load transmission and provision of good stability and control for locomotion , withoutThe soft tissue of the residual limb is subjected to high epidermal stresses, shear stresses, and abrasive motions resultant from socket load transfer during ambulation , 7. ThesProsthetists attempt to control interface and shear stress combinations through alteration of socket shape, liner material, alignment, and prosthesis componentry using the subjective feedback from prosthesis users , 10. HowIt is crucial to develop a better understanding of the biomechanical coupling between the prosthetic socket and the residual limb in order to improve socket fit . PreviouA smart-socket system has been developed as a low-cost, low-profile device for use as a feedback tool indicating within socket loading profiles to aid in the fitting phase of socket production and subsequent rehabilitation . The smaData is sampled from the sensors at a rate of 200 Hz and transmit it to a smartphone application. Prior work examined the properties of these sensors under bench testing conditions, yielding cyclic drift measurements of up to 0.00715V/cycle (0–3.3V supply), a static drift of up to 1.17%/min of the full-scale output (FSO) and a hysteresis effect of up to 7.25% FSO . ThermalPrior work demonstrated the device could provide relevant and important clinical information; however, improvements are required to obtain an output as a real metric . The pur15/LO/1633, IRAS project ID: 177122).A preliminary cohort study was conducted on a group of subjects with lower-limb amputations that were asked to walk across the gait laboratory at a self-selected speed. Ethical approval was secured for the trial  with a 10-bit resolution. Data was sampled at a rate of 200 Hz and transmitted via Bluetooth to a nearby computer system , producing an output that reflected a sensor's instantaneous output in relation to its maximum and minimum observed pressures throughout the trial. All measurements were normalised to be relative to the T-Stance data taken at the beginning of each participant study.xn is the normalised data point, x is the original reading, xmax is the maximum observed sensor reading and xmin is the minimum observed sensor reading.Where Loading profile maps were generated from the normalised sensor output. Each segment of the maps corresponds to a sensor, with the inner-most sections representing the distal-most sensors, and top and bottom positions on the maps aligning with anterior and posterior sensors, respectively. A pressure map was generated for the standing task from the mean value of each sensor during this task . Similarly, data was averaged across five instances of heel-strike and mid-swing during central walking steps and turning steps as observed in the motion capture data. In some instances, connections to sensors were damaged, preventing readings. Affected sensors have been marked in the maps by cross-hatching.We calculated a centre of pressure metric, referred to as the locus of maximum pressure (LMP), from the normalised outputs for each sensor (see Equation 2), reflecting the highest concentration of maximal sensor output. The pressure distributions and movement of the LMP were extracted for points of interest such as loading of the limb during stance-phase. Position of the locus of maximum pressure and the average load values were compared with visual interpretation of the motion tracking data.xLMP is the coordinate of the LMP in a single axis, x is the position of the centroid of the sensor representation from the centre of the socket representation  and p(x) is the pressure reading from the sensor.Where Pressure profiles were generated using normalised sensor data for each participant during standing, walking stance-phase and walking swing-phase. During the walking task, both central steps and turning steps were extracted in which the participant was at the centre of the walkway and at the edge of the walkway, respectively. These allowed observation of unperturbed walking and directional change. Pressure maps were generated using data averaged across the duration of the standing task and across five central gait cycles and five turning gait cycles, respectively.Pressure profiles were produced for each participant whilst standing still see . In the During walking stance-phase, similar patterns were observed for each subject in comparison to their standing loading profiles, however, the magnitude of loading was greater see . VariatiDuring swing-phase, loads across the socket were significantly reduced for each participant. Small loads were indicated in select regions for each individual. In the patellar-tendon bearing case (subject S001), loading remained at the lateral distal region of the socket, corresponding to the gastrocnemius. Subjects S002 and S003 displayed moderate loads at the distal medial and distal lateral regions, corresponding to the lower calf muscle region. Subject S004 indicated a greater level of contact between the socket and residuum during swing-phase, however, this remained evenly distributed across the socket. Swing-phase patterns remained largely consistent for each participant regardless of whether they were turning.A pseudo centre of pressure measurement, named the locus of maximum pressure (LMP), was determined for each participant. This value reflects the point within the socket at which the greatest concentration of sensors displayed their highest outputs. The movement of this value across the socket provides an indication of the transfer of load across the socket during dynamic walking motion.For subject S001, using a patellar-tendon bearing socket, observation of the LMP indicated a linear movement between the posterior and anterior aspects of the limb from swing-phase to stance-phase, respectively. The LMP in this instance had an offset towards the mid-lateral side of the socket. For subject S002, the LMP moved from posterior-lateral position to anterior-medial when loading. The pressure map indicated unloading of the limb in a clockwise manner. For S003, the LMP moved from the anterior position to medial position during loading. During unloading, elevated pressure occurred at the proximal posterior position, pulling the LMP posteriorly. S004 displayed greater motion in the medial and lateral directions during walking than other participants.The aim of this study was to determine whether the sensor network had the potential for clinical benefit through collection of pressure distribution data in the socket. Data collected from the smart-socket device for the participants indicated the system was able to provide a measure of contact distribution between the residual limb and the socket in addition to a relative measure of load. Given the capabilities of the smart socket confirmed in this study, the set up may be appropriate for use in the clinical environment to aid prosthetic socket fitting.The data collected by the system consistently reflected the gait characteristics recorded by motion tracking. The patellar-tendon bearing (PTB) socket was expected to produce more distinct loading profiles than total-surface bearing (TSB) sockets, with particular load concentration about the patellar tendon, the medial tibial flare and the lower portion of the tibial medial condyle , 14. LoaMeasurement of subject S001, using a PTB socket, displayed prominent loads about the patellar-tendon, tibial, and lateral gastrocnemius regions. These observations agreed with the expected load pattern  and suggThe remaining three subjects used TSB sockets in which the load distribution is expected to be even across the entirety of the socket . An evenMinor variations in the loading patterns were observed for subjects S003 and S004 when turning. For subject S003 greater load variations were visible on the mid-medial side of their socket, possibly suggestive of a looseness of fit. Elevated pressure at the proximal posterior region of the socket during unloading of the prosthesis may indicate pistoning, as other participants tended to experience a reduction of load at positions of the socket that were already loaded. Subject S004 experienced lower pressures across the proximal rim of the socket when turning left, suggestive of an angling of the residual limb. This observation was confirmed by a wide stance when turning in the motion capture data.It is difficult to compare the results to those of other studies due to their different focus. Whilst other studies do measure the pressure inside the prosthetic socket, the focus has been on maximum pressure readings at specific locations of interest, rather than an overall understanding of the pressure distribution. Commercial technologies do exist to measure pressure in the socket , 19, butThe results indicate that the sensor network can map contact distribution throughout the socket in real-time, providing indications of how loading is transferring across the residuum during motion. This may useful to clinicians when fitting sockets, supplying them with information such as the closeness of fit, change in fit over time and motion of the socket in relation to the residuum. By providing direct feedback on these factors, prosthetists will have more information from which to estimate the effects of their design choices on skin and soft tissue health. The next steps of this work are to refine the sensing hardware and software interface to refine the information and usability of the system.The smart socket has not yet been tested in the clinical environment with clinicians as, due to their strained time, it was deemed appropriate to provide evidence of their value before taking up clinical time. This study is an initial step to demonstrate the functionality and value of the smart socket, which can then be enhanced for testing in the clinical environment.The system was not able to produce accurate measures of absolute pressure, due to the limitations of the transducer. However, the objective measurement of the socket's relative pressure distribution will allow clinicians to “see” whether their socket design and prosthesis set up is as intended relative to the socket types. The LMP gives an indication of the residual limb movement within the socket, which may not always be reported or noticed, particularly if it is not vertical i.e., pistoning. The change in LMP may indicate the movement of internal structures, e.g., bone, that may cause damage over time which may not be noticeable if the residual limb does not move in its entirety.Before the smart sockets are incorporated into clinical practice, further engagement with clinicians and users is vital to determine how the tool can be used and if they are willing to accept them into routine practice. Involvement is also important to design the software which must be used to receive and display the information.The results from this study show the clinical benefit the smart sockets could have, with each participant's results indicating that the system is capable of reflecting socket loading distributions. This information may be of use to prosthetists when attempting to fit sockets as it could provide a clearer and less skill-dependent indication of the overall loading profile than traditional techniques such as clear check sockets and skin blanching . Improvevia appropriate visualisation methods.In conclusion, the sockets of four participants with transtibial amputations were instrumented with low-cost, low-profile sensors. Initial studies demonstrate the sensors can produce useful indications of residuum-socket contact and load distribution inside the socket. The information collected by the sensors shows their potential clinical utility in aiding prosthetists with socket fitting, by providing quantitative information to complement their skills and the subjective feedback from prosthesis users. Work is ongoing with prosthetists and physiotherapists to further develop the hardware and understand how they would like to use the information a.mcgregor@imperial.ac.uk.The datasets presented in this article are not readily available because Ethics restrictions mean the data cannot be shared. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by the Integrated Research Application System, Imperial College London. The patients/participants provided their written informed consent to participate in this study.MH led the writing of the manuscript. All authors contributed equally through critical review and editing of the manuscript. All authors contributed to the article and approved the submitted version.This work was conducted under the auspices of the Centre for Blast Injury Studies at Imperial College London. The authors would like to acknowledge the financial support of the Centre for Blast Injury Studies.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This paper introduces a technique to transform between geometric and barometric estimates of altitude and vice-versa. Leveraging forecast numerical weather models, the method is unbiased and has a vertical error with a standard deviation of around 30 m (100 ft), regardless of aircraft altitude, which makes it significantly more precise than established comparable conversion functions. This result may find application in various domains of civil aviation, including vertical RNP, systemized airspace, and automatic landing systems. With increasing air traffic, there is a great need to develop systems that enable robust, reliable, and predictable 3-D aircraft navigation, i.e., in the lateral as well as in the vertical domain. At present, reliable navigation services for aircraft can be provided in the lateral domain, i.e., in latitude and longitude. At present, there are clear rules for route/procedural separation in the lateral dimension but the uncertainty around the vertical dimension results in the reservation of large blocks of airspace per procedure. The relatively lower accuracy of vertical is insufficient to provide seamless 3D navigation to service a larger number of aircraft within a given service volume. This necessitates the development of robust and accurate systems that can provide seamless and reliable estimates of aircraft altitude for all phases of flight.In air-traffic management, two different methods of representing vertical information are widely used: barometric and geometric. Altitude measurements from a barometer also known as pressure altitude. Barometric altitude relies on converting the pressure reading taken at a particular location and converting it to the height above mean sea level using the reference pressure and temperature values provided by the ICAO Standard Atmosphere (ISA) . The conIn civil aviation, vertical navigation and localization are typically performed either in terms of a geometric path, or else as a barometric reading expressed, not in units of pressure, but in units of height. The conversion from barometric pressure to height is regulated by the ICAO Standard Atmosphere (ISA)  or basedControlling aircraft vertical dynamics by barometric measurements has certain advantages dating back to the times before GNSS was pervasive. In the upper airspace, barometric altitude allows for the strategic deconfliction of air traffic going in different directions. Roughly speaking, aircraft going in one direction stick to one isobaric surface, while aircraft going in the opposite direction must be a thousand feet above or below.The main disadvantage is that barometric measurements of altitude vary with the weather. The ISA does account for that fact but leaves residual biases that can be significant, relative to the requirements associated with the vertical guidance in landing the aircraft. Using the ISA as a measurement of altitude can lead to errors over several hundred feet. The current methodology as described in the DO-236C/ED-75D [By contrast, GNSS-based measurements of height are unbiased and can achieve accuracies in the order of a few meters. The advantage of GNSS-based altitude over barometric altitude is that they are not affected by variations in the atmosphere. With adequate processing, satellite navigation services offer sufficient performance to support all phases of flight, most notably departure, en-route, and approach. Note that satellite services can also support more stringent operations, such as precision approach and landing, a similar analysis of the methodology proposed in this paper is left as an item for future work.The method presented in this paper: BiG-C (Barometric to Geometric Converter), offers a way of converting barometric estimates of altitude to geometric altitude and vice-versa. The proposed algorithm improves the vertical repeatability of barometric altitude by accounting for the impact of off-nominal atmospheric effects such as weather fronts or ducting on barometric altitude measurement. By using numerical weather forecasts, these conversions have negligibly small biases and a residual uncertainty that is small enough to support navigation services in the vertical for most en-route, as well as departure and arrival applications.The proposed conversions are interesting, in civil aviation applications, for a variety of reasons. These include improved repeatability in the vertical position, reduced workload in the cockpit, and vertical guidance in case of a GNSS failure, to name a few. An unbiased conversion function between barometric and geometric altitudes, and vice versa, would find wide application in making air traffic management more efficient.DO-236C [An altimetry system based on barometric measurements must be able to provide real-time, robust, and repeatable altitude estimates, irrespective of any atmospheric variability. The underlying methodology of the BiG-C and the corresponding validation setup described in this paper leverages the forecast NWM to demonstrate a near bias-free conversion between the barometric and geometric altitudes. The evaluation of the BiG-C methodology is performed by validating its behavior with respect to the GNSS altitude collected as part of flight experiments conducted by the German Aerospace Center (DLR).Different terminologies are utilized to differentiate the altitude derived from barometric measurements based on the reference surface with respect to which the altitude is derived. The reference surface selected depends on the barometric pressure set on the altimeter sub-scale. Three references for barometric pressure are in common usage: QNH, QFE, and Flight levels.QNH: This refers to the height above the mean sea level (MSL), i.e., the height above the geoid or also referred to as the orthometric height (H). The QNH pressure values correspond to the local sea level pressure (with the ISA lapse rates) provided by the nearest ground stations.QFE: This refers to the height above the runway. Here, the local measurements in the airport are transmitted to the aircraft to calibrate the barometric altimeter. In the PANS-OPS Doc 8400 [Flight Levels: Flight levels are used to ensure safe vertical separation between the aircraft, despite local fluctuations in atmospheric pressure and temperature. It is used by all aircraft operating above the transition altitude to provide a common reference for vertical measurement. Though this setting is used to maintain the aircraft separation, it does not give the actual height of the aircraft above sea level or the ground. Thus, we use the transition altitude to indicate the shift from calibrating the barometer to QFH to using flight levels. While operating at or below the transition altitude, the aircraft altimeter shows the altitude above the local runway. Additionally, when above the transition altitude, the barometer is adjusted to the ISA pressure surface .Each aircraft operating in airspace where vertical performance is specified shall have total system error components in the vertical direction that are less than the specified performance limit 99.7% of the flying time ,3. To enIf the temperature at the sea level is different from 15 C then the rate of decrease in pressure will be different and thus will lead to erroneous altitude measurements. For example, as shown in The surface pressure varies significantly when compared to the ISA or the QNH measurement.The assumption of ICAO standard atmospheric (ISA) values results in significant deviations in the derived altitude between warmer or colder climates. The assumption of an ISA would be acceptable at high altitudes since all the aircraft make similar assumptions and are all making the same error in their vertical altitude. Since at these altitudes the important thing is to maintain the required minimum separation between the aircraft. However, during the approach phase of the aircraft, the assumption of the standard ISA atmosphere is not sufficiently accurate due to the following reasons:The current solution specified in DO-236C/ED-75D requires temperature compensation functions to correct for the difference in the temperature between the ISA and the atmosphere in which the aircraft is flying. An inherent problem with the present solution is that even with temperature corrections for the reference level, the present approach of utilizing the ISA does not account for altitude variations of pressure and temperature in real time. Thereby, neglecting any changes in the pressure and temperature profiles and the resulting impact on the barometric altitudes due to severe atmospheric conditions such as weather fronts, ducting, etc. In addition, the current approach of utilizing the temperature compensation functions is only applicable below the transition altitude and when the aircraft is in the vicinity of the airport. Thus, above the transition altitude, the barometric altitudes still exhibit significantly large altitude differences resulting from the deviation of the ISA from the true state of the atmosphere.In addition to the correction functions to compensate for atmospheric variability, the pilot has to manually set the reference level to QNE or QFE for the barometric altitude measurements depending on its altitude. Occasionally, a pilot may forget to reset the barometric pressure reference setting to standard (1013.25 hPa) when transitioning through the transition altitude. This can lead to the aircraft flying at a different altitude than that assigned. Aircraft cruise within flight routes at assigned altitudes. If an aircraft is not flying at its assigned altitude, it becomes a threat to other aircraft operating in the same flight route.Once the aircraft is above the transition altitude the barometric setting is changed to QNE to maintain the altitude measurements with reference to the MSL using the ISA reference values for pressure and temperature. Once the aircraft is below the transition altitude the pilot needs to manually change the barometric reference to QFE where the reference values are utilized as measured from the specific airfield. Non-uniform transition altitudes defined across the world , lead to a significant pilot workload to ensure an accurate reference plane is chosen along with the appropriate corrections being applied to compensate any deviation from the ISA.Altimeter measurements are relative to the reference surface, which could range from sea level to the local terrain (such as the mountain top). If airborne data are to be compared, it is necessary to introduce a reference surface to which all measurements are compared. Historically, the mean sea level (MSL) has been used as the zero of elevation, and this is closely approximated by an equipotential surface for the earth’s gravity field called the geoid . AlthougDefinitions of height and altitude vary depending on the vertical datum or reference level used. In dealing with airborne research data, it is common to encounter different technical terms relating to altitude and vertical direction. These include geometric altitude, GNSS altitude, pressure altitude, and geopotential height. Two altitude scales are widely used in Air Traffic Management (ATM): geometric altitude and geopotential altitude or height. Geometric altitude is the scale that is typically referenced to the WGS-84 ellipsoid or the local terrain. On the other hand, the geopotential height is referenced to the so-called geoid or the mean sea level (MSL) .Ellipsoidal Height: The normal distance of a point from the reference ellipsoid to a point above the earth’s surface is termed the ellipsoid height  to the height of the mass (z = h), given as [z. Now, while the geopotential  is useful for atmospheric dynamics studies , it is more convenient if it is expressed as a height above the geoid. Thus, the geopotential Φ=∫0hravity g4 at a latTransformation of geopotential to orthometric height is a non-linear process. As described in Reff) , given aFollowing this, the geopotential height can be transformed into orthometric (geometric) height as :4)Ho=R(Ho=R((4)Hk is the Somigliana’s constant [Now, for civil aviation, the atmospheric effects need to be accounted for up to the mesosphere , which is considerably small in comparison to the earth’s radius. Within the mesosphere, the altitude variability of the gravity over the ellipsoid is found to be negligibly small and the fractional difference in the geopotential height is primarily dominated by the variation in surface gravity ,5)Ho=RHo=R(5)HoFinally, the orthometric height . Atmospheric pressure varies in space and time; if an aircraft were to maintain a constant geometric altitude, the barometer would read different pressure values. Thus, to have a constant barometric altitude reading, the ISA model must be updated with local atmospheric conditions. The ISA is described with a sea level pressure of 1013.25 hPa and temperature of 15 C, and thus may not correspond to the aircraft’s actual altitude above the true mean sea level or above the ground level.To validate the proposed methodology the computational setup compares the aircraft altitude in two ways: from GNSS altitude derived from the improved barometric altitude (h) and from the GNSS altitude  forecast and re-analyses model level numerical weather model (NWM). This comprises the required meteorological data: surface geopotential and pressure, together with 3D temperature on 60 model levels, at a horizontal resolution of 0.125 × 0.125 degrees in latitude and longitude, and sampled every 6 h.h with a certain horizontal and vertical resolution. The finest or highest horizontal resolution is 0.125 × 0.125in latitude and longitude [The meteorological data from NWM are available in a 3D field of latitude ongitude . Since tT is temperature, q is specific humidity and In meteorology, the virtual temperature is often used instead of the static temperature . CompareMost often the location of the aircraft does not coincide with the coordinates where the NWM is available. Therefore, for a given aircraft coordinate (latitude and longitude), we horizontally interpolate the NWM data at all model levels, that spatially surround the aircraft location. This provides a 3D snapshot of the meteorological parameters at the exact aircraft latitude and longitude. In this work, we use bilinear interpolation , which is the usual operational altitude for civil aviation  km, whic,(14)T=Th) for a given pressure measurement  to obtain the barometric altitude of the aircraft above the mean sea level.Next, the geopotential altitude 15) 14,1,114,15. The results of this paper are backed by data collected using the German atmospheric research aircraft shown in  High AltHALO is equipped with a large set of instruments including the Basic HALO Measurement and Sensor System (BAHAMAS) which is used to measure and collect meteorological and aircraft state parameters including static pressure, temperature, and wind data, among several other atmospheric parameters, ,18. As sAccess to the basic aircraft position data.Measurement of the 3-D wind field and turbulence.Measurement of pressure, temperature, and humidity with different sensors up to the tropopause level.Radiation sensors.Surrounding conditions in the aircraft.Aircraft Parameter from the Air Data Computer.The data relevant to this study consist of a track of GNSS position fixes  along with a measurement of static pressure, temperature, and humidity from the BAHAMAS instrument. The unique ability of the HALO aircraft to collect measurements encompassing a wide range of aircraft altitudes ranging up to 15.5448 km  above the surface of the earth, makes the data collected using HALO aircraft a perfect candidate to test the validity of the methodology proposed in this paper over a variety of atmospheric conditions. The full details of the OMO mission are described in ,19.The test flights offer a chance to verify the impact of providing accurate reference atmospheric values for barometric altitude measurements in civil aviation. In Utilizing the ISA for the reference atmospheric values  results in vertical errors larger than the required Vertical Path Performance Limit (VPPL) limits; in fact, at greater altitudes, the ISA introduces biases that are greater than two standard deviations. The fact that residuals are relatively lower at lower altitudes in comparison to higher aircraft altitudes is a direct consequence of the ISA, which is designed for simplicity, rather than accuracy. From the barometric equation, we know that the altitude is derived by projecting the reference pressure and temperature values to the point of the static pressure measurement by the aircraft. Thus, any deviations in the reference values from the existing pressure and temperature profiles will lead to errors, which grow with increasing altitude.The residual error when using the reference pressure and temperature profiles from the forecast NWM is significantly smaller than those using the ISA. The proposed methodology enables estimating the barometric altitude of the aircraft with sufficient accuracy to comply with vertical RNP, as defined in DO-236C [The altitude residuals (Equation ) using tal error  obtainedThe residual error (Equation ) has twoEquation  and re-aEquation  NWM, resEquation  of the mThe unmodeled altitude error .The principle of operation proposed in this paper could be potentially applied to other domains, beyond aviation, for example in UAS  and UAM (urban air mobility).Aircraft vertical guidance is at present primarily dependent on GNSS, and in the event of loss of GNSS, it may lead to inefficient use of airspace and potentially lead to service degradation. Developing a robust altitude estimation approach using barometric measurements provides an alternate means to enable improved vertical guidance to ensure efficient use of airspace both in the presence or absence of GNSS.Established methods as described in the DO-236C [The methodology proposed in this paper provides a means to improve the vertical repeatability of the altitude estimates derived using barometric measurements, regardless of aircraft altitude. BiG-C achieves this by utilizing the forecast NWM data to derive near real-time estimates of reference pressure and temperature profiles considering any anomalous weather conditions such as weather fronts and ducting between the aircraft and the surface of the earth.BiG-C can achieve altitude estimates accurate to 30 m (100 ft), which is well within the VPPL limits defined in DO-236C/ED-75D, irrespective of the aircraft altitude measurements  analyzed from the flight experiment data. In addition, we validated the forecast NWM with respect to the more accurate re-analysis of NWM and showed that forecast NWM provides accurate reference atmospheric profiles enabling unbiased and robust transformation between barometric and geometric altitudes with the unmodeled error in the order of ±12 m (40 ft). Finally, we can completely overbound the residual errors arising from the difference between the altitude estimates using forecast and re-analysis data with a Gaussian distribution with a standard deviation of around ∼7 m (23 ft), which is significantly smaller compared to the residual error obtained utilizing the ISA.The proposed methodology of improving the altitude estimates using barometric measurements enables a new type of service by applying a more complex conversion than ISA, demonstrating that the method provides a viable solution for vertical RNP based on barometer altitude.


A large number of transfemoral amputees living in low-income countries could not access a much-needed prosthesis. Hence, affordable semi-active prosthetic knees have been designed in recent years. As the swing phase of the gait cycle is unstable as compared to the stance phase, these designs could not perfectly mimic this phase of a healthy human being. In contribution toward such a gap, this study proposes the modeling and design of a robust controller for magnetorheological (MR) damper-based semi-active prosthetic knee. A dynamic model representation for the swing phase of the single-axis knee is derived first. Subsequently, an MR damper valve model is developed. Then, a higher-order sliding mode controller is designed and evaluated for its stability and performance. The numerical simulation results show that the super twisting sliding mode controller improves the semi-active prosthetic knee’s tracking efficiency. The design exhibited the finest performance, providing a low normalized mean square error as compared to previous designs. The variable speed performance and robustness evaluation for this controller also showed its ability to continue providing excellent performance in the presence of disturbances. Transfemoral amputation is among the types of leg amputation which occur between the hip and knee joints. A recent study by the World Health Organization (WHO) states that 35–40 million amputees are living in developing countries, with knee amputation being the most common one. The most familiar causes of physical disabilities include traffic accidents, natural disasters, war, and the like. Those people with leg amputation will have reduced ability to move. Hence, a prosthetic knee is required to substitute the amputee limb to enhance the quality of their life by assisting them in performing activities such as standing and walking like wholesome people.Throughout the years of prosthetic knee development, three types of knees have been studied to date, namely, active (powered), semi-active (adjustable damping), and mechanically operated passive prosthetic knees. The active prosthetic knee has its power supply to operate effectively. Amputees having this prosthetic knee will be able to perform complex activities such as walking up stairways and standing up. However, the main drawback of this prosthetic knee is its consumption of a large amount of energy and a large actuator, which leads to a heavy weight and reduction of battery life .In contrast, mechanically passive knees do not need a power supply to function. They only provide a constant amount of damping suitable for only one speed of walking. Semi-active knees have several advantages over mechanically operated passive prosthetics, including improved knee stability, the capacity to change the walking pace, and the ability to react to changing environmental conditions .Semi-active knees are mostly known for their use of magnetorheological (MR) fluid (MRF)-based mechanisms, for instance, MR dampers. As mechanically passive prosthetic knees cannot change damping values, they expose amputees to health problems and injuries using MRF-based dampers on prosthetic limbs, which deliver an extensive dynamic range and will avoid these conditions from occurring. Since an MR damper has a lot of advantages such as a small volume, a simple structure, low power consumption, and large resistance, it is considered to be a good alternative for the semi-active control of prosthetic limbs .The prosthetic knees incorporating readily made MR dampers designed and manufactured by Lord Corporation have some drawbacks. Hence, the main purpose of the author  was to pIn another research, , a seconAnother promising work  proposedThe previous study by the author  considerTo solve the drawbacks which are found in most of the semi-active prosthetic knees, the authors  have preA modified dynamic model for the single-axis knee is developed by using Lagrangian mechanics in During a regular gait cycle, cyclic occurrences can be seen when walking on a level terrain. Toe-off and foot strike are the two different occurrences that can distinguish the periodic pattern. One gait cycle is classified into two main phases/periods: stance and swing .• The ankle is considered rigid and fixed on the shank, and the shank and thigh are assumed to be connected by pin joints.• The thigh is allowed to have vertical and horizontal movement in two dimensions.• The swing motion of the two-link rigid body chain is taken as a two-degree-of-freedom double pendulum.The dynamic model of the swing-phase leg model with the prosthetic knee, depicted in The resulting swing-phase representation of the knee by applying Lagrangian formulation will beFrom this relation, the inertia, Coriolis and centrifugal, and gravitational matrices are as follows:I1, I2 are the masses and moments of inertia of the thigh and shank, respectively. a1 and a2 are the distances from hip and knee joints to the center of masses of the high and shank, respectively. g is the gravitational acceleration. ld is the length of the damper.where e is the extension length of the damper from the knee joint, b is the distance where the base of the damper is attached on the shank from the knee joint, and From the damper resolution, the angle the damping force makes with the vertical axis is derived from the aforementioned figure to beMRFs are members of the family of fluids whose properties are governed by the strength of the magnetic field. When subjected to a magnetic field, MRFs can convert from a linear viscous liquid to semi-solids, with the yield strength being controlled quickly and constantly. When a magnetic field is supplied to the suspended particles, they become polarized and migrate, reducing the ensemble’s stored energy. As the employed magnetic field increases, the energy desired to yield those chain-like arrangements will increase, making the fluid rigid to flow .Modeling MRF involves dealing with the rheological behavior of these materials in pre-yield and post-yield regimes. Two models have been used popularly to represent MRF models.H, G represents the complex shear modulus, and sgn(.) is the signum function , respectively  with respect to the employed field intensity, ic field . In thisic field  is adoptFrom this optimal design of the MR damper valve, the curve-fitting relationship between the input current and yield stress can be derived to beClassified under the VSCS category, the sliding mode controller is by far one of the most robust controllers, with the characteristics of switching its control law that leads the system states to the anticipated sliding surface . ConventNow, the sliding variable can be defined using the following equation:To derive the aforementioned sliding variable to zero in a finite amount of time, the following continuous control law can be applied:The compensated sliding variable dynamics will betr. On the other hand, when e and u is the control input.The specified control u forces the sliding variable to nil in a finite amount of time, w) in However, due to the presence of a disturbance term, the sliding variable dynamics will fail to converge to zero using the disturbance-freederived control law of the form in In this study, the error dynamics will be described by the following equation:The simulation of the designed controllers proceeds in place. The schematic block diagram used in the simulation of the controller’s performance is provided in The physical parameters of an average human being with a mass of 56.7 kg are listed in 4.68 kmph walking speed are shown in Considering the dynamic system model, the results of trailing performance and tracking error of the controller at a 0.372 s. Now comparing with the simulation study result obtained from the previously designed model by The simulations were performed over the swing-phase period of the gait cycle, that is, for Now the controller’s simulation outputs of the maximum flexion angle, the period of the swing phase, the shank velocity at the end of the swing phase, and the normalized mean square error (NRMSE) can be contrasted with the former design, as seen in As is clearly shown in the table, Instabilities occur, mainly, when amputees walk on a rough terrain, and the road irregularities will expose them to vibration, causing the controller to face instabilities. Unless the controller is robust, this causes the controller to be less efficient in mimicking the normal gait cycle of a healthy human being.In this research, two major causes of disturbances had been applied to the system model, that is, on the vertical and horizontal hip accelerations of the amputee. When amputees face a sudden increase or decrease in their forward motion, the hip horizontal acceleration will vary considerably. Furthermore, similar variable vertical acceleration will occur as amputees face irregularities on their walkways. For this evaluation, the ideal hip horizontal and vertical accelerations are curve-fitted by applying the following equations obtained using a curve-fitting tool on MATLABTo represent the disturbances that occurred due to the aforementioned reasons, the amplitudes and frequencies of the aforementioned curve-fitted formulas of hip accelerations are scaled independently and simultaneously as plotted in The robustness evaluation of ST-SMC was performed considering a disturbance due to the rough walking terrain as shown in The simulation outputs in Furthermore, the various walking speed performances of the ST-SMC showed that it keeps tracking the desired knee angle trajectory at those various walking speeds with a minimized tracking error. In As is seen from the NRMSE calculation, the developed controller on the modified system model showed a better efficiency in tracking the desired knee angles at different speeds. On the other hand, the comparison of the controllers with previously designed prosthetic knees shows that the ST-SMC provided a better result while tracking the desired knee angle with a minimum error.The computer simulation findings of the controller applied on the modified mathematical models are presented. It is seen that at a walking speed of 4.68 kmph, the designed controller displayed an excellent performance as compared to the results found in the former mechanically designed prosthetic knees by Furthermore, the robustness of ST-SMC was evaluated considering a disturbance due to the rough walking terrain. The analysis was performed by applying the amplitude and frequency scaling on the curve-fitted experimental data of vertical and horizontal hip accelerations. The analysis outputs depicted that the ST-SMC was exceptionally robust compared to the previous design by This study presented the design and simulation of ST-SMC for a transfemoral prosthetic knee. The dynamic system models and the MR damper model were developed using the Lagrangian mechanics and Bingham Plastic model, respectively. The relation between the yield shear stress and input current of MRF is obtained from an optimized MR damper valve design. It was observed that super twisting SMC provided a superior tracking efficiency with a reduced NRMSE of 1.32 and chattering compared to the previously designed prosthetic knee. In addition, the final shank velocity of the prosthetic knee was reduced to 0.183 rad/sec and the maximum flexion angle near to the natural gait.Moreover, the measure of the robustness of the designed ST-SMC was performed by applying different walking speeds and several disturbances related to the rough terrain. This research had a significant contribution in designing a more stable system model controlled with ST-SMC showing negligible chattering and increased robustness. Future work includes evaluating the system with other controllers such as adaptive SMC, nonlinear PID, and integral SMC to minimize exhibited errors. Furthermore, experimental investigation and prototyping of the designed controller will be conducted.


To achieve high durability and excellent mechanical performances of cementitious materials, research on fiber-reinforced cementitious composites (FRCC) containing various fibers has been actively conducted. On the other hand, in robotics and other fields, research on artificial muscles using Twisted and Coiled Polymer Actuator (TCPA), which have similar functions to human muscle fibers, has attracted much attention. In this study, use of this TCPA as a reinforcing fiber in high performance FRCC (HPFRCC) was proposed. The employed TCPA has a structure of coiled nylon fibers with wrapping stainless-steel fibers. The effect of the TCPA and its shrinkage motion on the crack dispersion properties of HPFRCC was investigated. The experimental results showed that the strain-hardening with multiple cracks in HPFRCC continued up to more than 7% of the ultimate strain when the TCPA was electrically stimulated to shrinkage motion. This information indicates that the TCPA has high potential to further improve HPFRCC performance. Concrete is the most widely used construction material in the world. Concrete has many excellent properties, e.g., high compressive strength, durability, economic efficiency, and easy availability of local raw materials. However, several drawbacks also exist. Concrete shows low tensile strength and low strain capacity, making it a brittle material. Due to these poor mechanical properties, cracks cause durability and safety problems from a long-term perspective. In recent years, there have been approaches to inhibit crack propagation at the nanoscale using nanomaterials ,2. This The strain-hardening property of HPFRCC is due to a mechanism in which the bridging force of the dispersed fibers in the mortar matrix causes crack refinement and multiple cracks . For exaArtificial muscle fibers are actuators that expand and contract under some external control and perform work. The major classifications based on the driving source include electrochemically driven polymers ,19,20,21In cementitious materials, several studies using artificial muscles-like materials have been conducted . Shape mBased on this background, this study combines HPFRCC and TCPA as a new approach in controlling the cracking of cementitious materials. This study aims to control the crack width and mechanical performance of HPFRCC by applying external electrical heat to TCPA. The method involves embedding the externally controlled TCPAs in the mortar matrix. By shrinking the embedded TCPA, the compressive stress at the cracked surface can be controlled. The experimental investigation is conducted to determine the effect of the crack width control and ductility of HPFRCC.In this study, three types of experiment were conducted. The first is the evaluation of the TCPA’s performance; the relationship between the amount of shrinkage and temperature was evaluated because the shrinkage behavior of TCPA changes with temperature. The second is the uniaxial tensile test of HPFRCC with embedded TCPA to evaluate the relationship between shrinkage motion of TCPA and crack dispersion. The third is experiments to evaluate the effect of TCPA on the fiber bridging law at a single cracked surface, which is one of the most important material properties of HPFRCC.Materials used for this study are early-strength Portland cement, silica fume, high-range water-reducing admixture, cellulosic thickener, and fine silica sand. Polyvinyl alcohol (PVA) fiber was used together. As shown in Specimens shown in Uniaxial tensile test was performed to evaluate the stress–strain relationship of HPFRCC with embedded TCPA. Specimens with three different cross-sectional shapes were employed. The details of the test specimens are shown in In Test B, specimens with different cross-sectional geometries were prepared to evaluate the change in the stress–strain relationship and the multiple crack behavior as a function of the temperature (voltage) applied to the TCPA. The cross-section of the specimens were 20 mm square and four TCPAs embedded see b2,c2. ThIn Test C, to evaluate the effect of TCPA on the fiber bridging law at a single crack face, a notch was introduced into the center of the specimen as shown in TCPA is a material that shrinks when heated and returns to its original state when cooled. To obtain the basic performance of the TCPA used in this study, shrinkage behavior of the employed single TCPA was measured during its temperature change. The relationship between the surface temperature and the shrinkage strain of the single TCPA is shown in The uniaxial tensile test was conducted on the specimens with TCPA embedded in HPFRCC to clarify the effect of TCPA on the ductility performance of HPFRCC. The experiments were conducted at two levels of surface temperature: 22 °C (room temperature) and 90 °C, to control the shrinkage strain of TCPA. A voltage of 15 V was applied to the stainless-steel fibers placed on the TCPA surface to heat. The amount of PVA fiber mixed into the HPFRCC was set at three levels: 1.50, 1.75, and 2.00 vol.%. Six specimens were used for the test under each condition, as shown with different colors in These results indicate that the shrinkage strain behavior of TCPA has a positive effect on the tensile properties of HPFRCC at the fiber incorporation ratio where multiple cracks and strain hardening behavior, which are the characteristics of HPFRCC, are observed.Compared to the tensile force in the cross section, the compressive prestress induced by TCPA is negligible. On the other hand, the compressive force due to the shrinkage strain behavior of TCPA can suppress the localization with crack widening. This is thought to have caused stable multiple cracks one after another, and the strain hardening region continued for a long time.From the experimental results up on the previous section, it was confirmed that TCPA shrink behavior by heating significantly increases the ultimate strain of HPFRCC. The number of microcracks was also shown to increase. In order to clarify the factors that led to this change in mechanical behavior, experiments were conducted to evaluate the effect of TCPA on the fiber bridging law at a single cracked surface, which is one of the most important material properties of HPFRCC.These results suggest that the shrinkage behavior of TCPA on heating allows for stable stress-bearing up to large crack opening displacements. This is due to the additional bridging stress is available at the crack surface. As indicated in the previous section, this additional bridging force helps to ensure that multiple cracks continuously generated. In other words, the localization of cracks is reduced by heating the TCPA to generate shrinkage stress. It is clear that the shrinkage behavior of TCPA has an effect on the multiple cracking behavior of HPFRCC and on the strain hardening behavior.Here, we proposed the application of TCPA, also known as artificial muscle, to HPFRCC. The findings obtained within the scope of this experiment are as follows.TCPA was fabricated from nylon fiber and its performance was evaluated. A clear correlation was observed between the shrinkage strain of TCPA and the surface temperature. Within the range of about 100 °C or lower, which is acceptable for cementitious materials, a larger surface temperature of TCPA resulted in a larger shrinkage strain;Specimens with TCPA applied to HPFRCC showing multiple cracks and strain hardening behavior were prepared and uniaxial tensile tests were conducted. As a result, it was clarified that the shrinkage strain generated in TCPA was effective in improving the toughness of HPFRCC;By increasing the surface temperature of the embedded TCPA and increasing the shrinkage strain, the toughness of the HPFRCC was improved;Prestressing in the compressive direction due to the shrinkage strain of TCPA can suppress the crack localization and crack width expansion. This was accompanied by a significant increase in the number of cracks.


Before the declaration of the COVID-19 pandemic in March 2020, primary care in most countries relied on face-to-face consultations, with relatively limited use of telemedicine. Lockdowns and social distancing measures during the early stages of the pandemic led to rapid, widely spread telemedicine adoption in healthcare settings. The rapid uptake that occurred following the onset of these pandemic-induced measures in countries such as the UK, Canada and New Zealand prompts questions around the drivers, extent and sustainability of this transformation in clinical practice at the global level, as the research in this area is still emerging. The purpose of this scoping review is to explore the global evidence surrounding the rapid adoption of telemedicine in primary care settings during the first 2 years of the COVID-19 pandemic through three lenses: patient experience, health inequalities and patient-clinician trust, with the purpose of identifying elements contributing to the sustainability of this innovation.A draft protocol was tested through an initial search on Ovid Medline, Web of Science and Google Scholar with additional searches on the Cochrane Database. This informed the final selection of terms which will be used to search Ovid, Web of Science, Google Scholar, PROSPERO, Cochrane Library and others, filtering for studies from the pandemic declaration onwards. Additional grey literature reports will be sourced through simplified searches on Google in widely spoken languages. Duplicates will be removed by screening titles. Abstracts and grey literature text extracts will be screened based on pre-set eligibility criteria by two researchers. Abstracts (and extracts in the case of grey literature) will be mapped against the domains of the Non-adoption, Abandonment, and challenges to Scale-up, Spread and Sustainability (NASSS) framework by two researchers. Data will be presented in table format.This review will map the current literature to identify current gaps in evidence related to the adoption of telemedicine after the declaration of the pandemic in March 2020. The use of simplified searches in the several spoken languages in the world is aimed at capturing more immediate non-academic reflections and experiences on this major service change at a global level.https://osf.io/4z5ut/The study has been registered on Open Science Framework and can be accessed through the following URL:  Pre-pandemic evidence synthesis (published/submitted in late 2019) .In England, for example, data for older patients shows that the rate of remote consultations more than doubled between February and May 2020 , followiIn the Netherlands, the shift from mainly face to face to virtual consultations happened within a week, as part of the pandemic response plans .In the USA, the implementation of the ‘Expansion of telehealth with 1135 waiver’  signalleFollowing the WHO’s announcement on 11 March 2020 declaring COVID-19 a pandemic , the radA preliminary search for existing systematic/scoping reviews on the topics of interest was conducted in PubMed (4 results) and PROSPERO .The review will focus on the clinical practice of telemedicine (the interaction between patient and clinician). As such it draws on the World Health Organization’s definition of telemedicine .The deliTelemedicine will be defined, in the negative, as those modalities of clinician/patient interaction that do not require physical presence of the individuals in the same premises (either a primary care clinic or the patient’s home). In the positive, these are modalities where the clinician/patient interaction is held or mediated in a ‘virtual’  environment, through the use of a particular telecommunication technology. This definition is closely aligned with that provided by Sood et al.  followinTelephone visits: conversation happens over the telephone.Video visits: conducted through a (secure) video platformE-consultations: asynchronous discussions between clinician and patient, which are initiated by the patient through a patient portal which allows to specify their complaint. In some cases, the portals include a triaging protocol and allow to capture relevant details from the patient’s history.These ‘virtual’ environments have been further described in Olayiwola et al., .TelephonIn some settings, the terms ‘econsultations’ or ‘econsults’ can also apply to clinician/clinician discussions; but for the purposes of this review, these will be excluded.…essential health care based on practical, scientifically sound and socially acceptable methods and technology made universally accessible to individuals and families in the community through their full participation and at a cost that the community and country can afford to maintain at every stage of their development in the spirit of self-reliance and self-determination. It forms an integral part both of the country’s health system, of which it is the central function and main focus, and of the overall social and economic development of the community. It is the first level of contact of individuals, the family and community with the national health system bringing health care as close as possible to where people live and work, and constitutes the first element of a continuing health care process.Traditionally, primary care covers a multidisciplinary team of healthcare professionals dealing with areas related to communicable, non-communicable disease, prevention and management . A standThe above definition also specifies this is a community-based . Hospital or secondary healthcare services, as well as educational activities, will be excluded from the review. Notwithstanding the multidisciplinary nature of primary care, searches will focus on the consultations of doctors and nurses as medical professionals.The above definition of primary care also helps specify the population of interest, as healthcare services which are ‘universally accessible to individuals and families in the community’ . No popuThe Non-adoption, Abandonment, and challenges to Scale-up, Spread and Sustainability (NASSS) framework  identifiThis framework (co-developed by one of the co-authors of this review), has been selected because (i) it is evidence-based, (ii) focused on healthcare (as opposed to other more generic models of technology adoption), (iii) explores what happens ‘after’ adoption—and in our case has a specific domain focused on sustainability and (iv) because the multiple domains facilitate the mapping exercise of the findings, without the need to resort to further thematic analysis/grounded theory. The mapping will be undertaken by highlighting the various themes identified in the literature and comparing them to the relevant NASSS dimensions. For example, in the case of the US regulatory provision allowing for financial recognition of telemedicine appointments would be labelled as both a simplifying ‘wider system’ change but also as enabling change in the value proposition offered by the technology.The scoping review will be conducted in accordance with (i) the Joanna Briggs Institute (JBI) methodology , the latWhat is the evidence available surrounding the rapid adoption of telemedicine in primary care settings worldwide during the first 2 years of the COVID-19 pandemic?I.Understand the drivers behind the rapid adoption of telemedicine since the official declaration of COVID-19 as a pandemic in March 2020 as to identify elements that can support its sustainability beyond the pandemic;II.Determine impacts of telemedicine adoption across countries across three specific domains: patient experience; health inequalities; patient-clinician trust;III.Map the findings in the context of the NASSS framework  to identSpecific objectives are to:The review will focus on primary care services offered to the general population. Studies focusing on specific population groups or those suffering from particular conditions within a particular country or geographical area will be included.While the key concept under consideration is the adoption of ‘telemedicine’, as defined above, we will narrow our inclusion criteria on the sustainability of the interventions, patient experience, health inequalities and patient-clinician trust.The context is primary care services provided during the COVID-19 pandemic in any setting or country during the first 2 years of the pandemic.Table This scoping review will consider quantitative, qualitative and mixed methods study designs for inclusion. In addition, systematic reviews, protocols, other text and opinion papers will be considered for inclusion in the proposed scoping review.The search strategy has been developed to ensure transparency and reproducibility and the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR)  checklisGiven the rapid developments associated with the implementation of telemedicine in primary care settings during the first 2 years of the pandemic, this scoping review considers emerging thinking by mapping ‘grey’, non-academic literature. Preference will be given to ‘first-tier’ grey literature, comprising of government policy and reports, as well as think-tank reports and white papers (mostly found in PDF format). This documentation is highly retrievable and credible , 34. A sSearching for grey literature, the use of other languages other than English and the focus in other countries is aimed at increasing representativeness , to capture non-academic voices and experiences . We willAbstracts and excerpts for searches in other languages will focus on PDF documents published between March 2020 and the date of the search to focus on the emerging evidence surrounding the early stages of the coronavirus pandemic period only. These will be translated using Machine Translation if no abstract in English is available . In caseFinal searches will be undertaken between February and March 2022, including academic databases such as EMBASE, LIVIVO, SCOPUS, Ovid Medline, Web of Science, Scielo and Google Scholar (first 30 results by relevance), as well as PROSPERO results. No language restrictions will be included in the academic searches.An initial search on Ovid Medline, Web of Science and Google Scholar was undertaken in December 2020. Two researchers with different academic backgrounds  independently screened all abstracts to (i) refine search terms , (ii) teThe abstracts and excerpts across all languages of the final search results will be uploaded to the automated tool for prioritisation and final screening by two researchers. Reasons for exclusion of evidence that does not meet the inclusion criteria will be recorded and reported in the scoping review.As a final part of the search strategy, the lead researcher will undertake forward and backward reference searches to identify any other potential studies that might have been missed in the search process.Data will be extracted by two researchers. Data extraction from the full-text selected academic documents using an Excel/NVIVO table template as a data extraction tool . The template will include headings such as author, title, year, abstract, type of document, population, concept, context, methods, key findings relevant to review questions and the country of study. These elements will be mapped in tabular form against the NASSS framework  domains Any modifications to the protocol will be reported in full in the scoping review.In the first section, the results of the search will be presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) flow diagram In the second section, the elements of the PCC inclusion criteria will be used to provide a narrative summary of the results and describe how they relate to the review objective and question. The authors will also include a mapping in tabular form against the NASSS framework  of the eData will be analysed by two researchers. In agreement with the latest JBI methodological guidance  29], no, no29], This session briefly outlines the strengths and limitations of the research design.The JBI methodology , 29 for It is recognised that this is an emerging field in the literature and as such, there is limited academic output at this stage that has been through peer review. Further review might be beneficial in a few years to capture new published academic research in this area. There are limitations in the reproducibility of google searches given that search results are regularly updated depending on the popularity of individual results over time. To mitigate known limitations in comprehensibility and usability of Machine Translation of abstracts and excerpts , 40, the


With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways. The operating mileage of China’s high-speed railway has exceeded 42,000 km , and it With long-term operation and wheel–rail interactions, fatigue cracks will happen, which rapidly expand and lead to accidents without treatment ,5. Wirel(1)A multi-agent framework based on a WSN is adopted for collaborative structural health monitoring of the track with three layers: a distributed sensor data acquisition layer, sensor data processing layer, and application layer.(2)WSN nodes with SMFS are successfully assembled onto a 100-m-long track for structural health monitoring and crack detection of the track. Based on the sensor data from the SA and feature data from SPA, neural network DFAs are applied to fuse the feature data for the crack detection of rail surfaces.The use of a multi-agent system (MAS) framework for the structural health monitoring of rail structures is effective to complete the monitoring tasks cooperatively via the cooperation of the sensor agents. The strain, temperature, and piezoelectric sensors are embedded into the WSN node to collect the strain and temperature data from the tracks during the wheel or rail rolling process. A piezoelectric Lamb guided wave is applied to actively detect the structural health data from the track. The experiments on the rail transmit base in East China Jiaotong University have validated that the framework based on a WSN with soft sensors is effective for crack detection of track surfaces. The main contributions of this paper are the following:The rest of the paper is arranged as follows. An online monitoring framework based on a WSN with SMFS is built in For complicated environments and large-scale rail structures, new sensing devices and data processing methods are required for the structural health monitoring of the rail structure. WSN nodes with different sensors are arranged onto the rail to collect the multi-source signals of the rail structure, as shown in In large-scale rail structures, WSNs with SMFS are developed for the structural health monitoring of the rail structure. Due to the high-speed wheel–rail rolling, it would be more complicated to perform damage detection on the tracks. The cooperation mechanism for different WSN nodes is studied to ensure the consistency and coherence of the cooperation. Intelligent algorithms are proposed to optimize the information process for rail healthy data monitoring.A WSN with SMFS is applied here for the structural health monitoring of a large-scale rail structure using distributed sensing data and an intelligent algorithm. An MAS based on the WSN is adopted for the distributed monitoring of the infrastructure , and a s(1)Data sensing layerThe data sensing layer is composed of various sensors laminated onto the surface of the rail structure to collect strain, vibration, pressure, and temperature data for structural health monitoring applications. The sensing data can be exchanged between different sensor agents to improve the data processing efficiency for the defect detection of long-distance rail structures.(2)Data transmission and processing layerIn the data transmission and processing layer, different signal processing methods  are adopted for the feature extraction of the rail structure. An information processing and transmission module should be designed to process the multi-source sensing data from the data sensing layer. The SPA is designed for feature extraction from the sensing data, and the feature data are transmitted to the next layer for the damage detection of the rail structure.(3)Damage identification layerIn the application layer used for damage identification, the neural network, genetic algorithm, and support vector machine are adopted to evaluate the feature data from the data transmission and processing layer, which are used for damage recognition under various operation conditions.Sensor agents (SAs) are used to collect the structural health data from the different parts of the rail cooperatively. According to the condition–action mapping rules, they cooperates with other agents in collecting the structural health data. The sensor processing agent (SPA) processes the structural rail health information from the sensor agent, and different data processing methods , wavelet analysis agent, Hilbert transform) are integrated into the SPA to extract the feature data. The internal structure of the SPA is shown in The data fusion agent (DFA) is designed to fuse the feature data from different SPAs for damage recognition. The internal structural principle of the DFA is shown in The online monitoring of the rail structure is decomposed into subtasks by different agents  in different layers in the proposed framework . A multiThe large-scale rail structure is divided into several monitoring areas 1, 2, …, N, and WSN nodes with different sensors are arranged in the designated locations. The SA collects the structure information in the subareas, while the SPA extracts the feature data. Data communication and cooperation are implemented for the structural health monitoring of rails between the SPAs. The DFA is applied to fuse the feature data using different data fusion algorithms for damage identification. The cooperative data processing strategy between the sensor network nodes is adopted for the structural health monitoring of the rail structure, which is used for the cooperative damage identification of the rail structure.The piezo.SA generates the Lamb guided wave signal, which spreads in the rail structure, and the echo signal is collected by the piezoelectric sensor, as shown in A strain sensor agent is integrated into the WSN nodes to collect the strain data from the rail structure under high-speed wheel–rail rolling. The strain sensor is designed with a flexible metal strain gauge to detect the external strain data. Temperature sensors with temperature-sensitive resistance are integrated into the WSN nodes to collect the temperature data for the rail structure in dynamic environments. In the time domain, frequency domain and time–frequency domain, several physical parameters are selected to extract the feature data from the echo signal, such as the amplitude, root mean square (RMS), correlation coefficient, and spectrum loss. The amplitude, energy, and correlation coefficient are selected to evaluate the degree of rail damage. The amplitude is the peak value of the echo signal. The other feature data are illustrated as:(1) (2) The correlation coefficient (3) The spectrum loss is an important index used to depict the performance of the echo signal in the frequency domain, which is represented as:(4) The central spectrum loss is represented as:The feature data ) of the rail structure from the strain and temperature sensors are extracted for the structural health monitoring of the rail structure. The feature data are illustrated as:(5) (6) e, ρ, SL, CSL, RMSs, RMStDI = DI (RMS). The DI value gives important evidence for a comprehensive evaluation of the track damage.Multi-sensing data from the rail structure are obtained from the SA in dynamic environments for wheel–rail interactions. The WSN with SMFS is designed for strain, temperature, and echo signal data collection. SPAs are applied to extract the feature data (Equations (1)–(6)) for the strain, temperature, and echo signal. DFAs are applied to fuse the features from the SPAs for damage recognition. The WSN nodes with SMFS were arranged on the rail structure (~100 m distance) at the Rail Transit Base of East China Jiaotong University, as shown in A cooperative sensing and signal processing strategy is studied here for the structural health monitoring of a rail structure based on a WSN embedded with SMFS. The strain, temperature signal, and acoustic surface wave information are collected by the different sensor agents embedded into WSN, which is applied for damage recognition for the rail structure. The sensing data are transmitted to the SPA layer in the built framework based on the MAS for further data processing. The strain and temperature sensing data are collected by the strain and temperature sensor agents, as shown in For the temperature and strain sensing data from the sensor agents, the feature signals (RMS) are extracted to evaluate the healthy information of the track by SPA. The guided Lamb wave is generated by the piezoelectric PZT actuator and the echo signal is received by the PZT sensor unit, as shown in WSNs embedded with SMFS have been applied for the structural health monitoring of long tracks, and their wireless communications ability is illustrated by their RSSI values, as shown in DI = DI . It is shown that the DI values for the 25 blocks are around 0.03 to 0.18, for which labels 6 and 20 of the block are larger than the others. The experiments show that label 6 of the track block is damaged and the surface of the track has a deep dent, as shown in the optical image in WSNs with SMFS were assembled onto the 100-m-long track, and the 100-m-long track was divided into 25 blocks (4 m is one block), whereby one WSN node is placed onto each block. (1)A multi-agent framework was built for the structural health monitoring of a rail structure based on a WSN with three layers: the sensor data acquisition layer, sensor data processing layer, and application layer. The distributed data sensing method was used to collect the sensing data from the different blocks in the long-distance rail structure by the SAs, and feature data at different levels were extracted by the SPA for online monitoring of the rail structure.(2)The experiments verified that the WSN with SMFS was successfully assembled onto the 100-m-long track for crack detection of the rail surface. Based on the sensing data from the SA and feature data from the SPA, the DFAs were designed to calculate the damage index (DI) of the track with the multi-sensing data through an intelligent fusion algorithm for damage recognition.A multi-agent framework based on WSNs with soft multi-functional sensors (SMFS) was built for the collaborative data sensing and damage detection of a rail structure, which included three layers: the sensor data acquisition layer, sensor data processing layer, and application layer. WSN nodes with strain, temperature, and piezoelectric sensing units were developed for the continuous structural health monitoring and damage detection of the rail structure. The main findings of our work are concluded as follows:(1)WSN nodes with more functionality should be developed for the structural health monitoring of tracks. A flexible hybrid electronic technology could be adopted for WSN nodes to improve the integration ability of sensing devices with good electrical and communication performances;(2)Field tests for rail monitoring could be implemented using WSNs with soft sensors. This would be useful to improve the electrical performance of WSNs with soft sensors during long-term structural health monitoring in complicated environments due to the high-speed wheel–rail rolling interactions.WSNs with SMFS have been applied for the continuous structural health monitoring of tracks for damage detection. However, the performance of WSNs with SMFS could be improved in several aspects: