The ability to sense external force is an important technique for force feedback, haptics and safe interaction control in minimally-invasive surgical robots (MISRs). Moreover, this ability plays a significant role in the restricting refined surgical operations. The wrist joints of surgical robot end-effectors are usually actuated by several long-distance wire cables. Its two forceps are each actuated by two cables. The scope of force sensing includes multidimensional external force and one-dimensional clamping force. This paper focuses on one-dimensional clamping force sensing method that do not require any internal force sensor integrated in the end-effector’s forceps. A new clamping force estimation method is proposed based on a joint torque disturbance observer (JTDO) for a cable-driven surgical robot end-effector. The JTDO essentially considers the variations in cable tension between the actual cable tension and the estimated cable tension using a Particle Swarm Optimization Back Propagation Neural Network (PSO-BPNN) under free motion. Furthermore, a clamping force estimator is proposed based on the forceps’ JTDO and their mechanical relations. According to comparative analyses in experimental studies, the detection resolutions of collision force and clamping force were 0.11 N. The experimental results verify the feasibility and effectiveness of the proposed clamping force sensing method. In recent years, more and more researchers, companies and hospitals have paid attention to the development, commercial aspects, and application of minimally-invasive surgical robot (MISR) techniques. The advantages of robot-assisted minimally-invasive surgery (MIS) include positioning accuracy, easier realization of MIS, an improved success rate, a reduction in pain, and a reduction in recovery time ,2. Many The wrist joints of a surgical robot end-effector usually have multiple degrees of freedom to ensure good flexibility and dexterity . Cable-dTwo methods for sensing external forces have been studied by MISR system researchers. One is the direct sensing method, which integrates force sensors into the surgical instrument’s tip. Some surgical tools have been designed for an MISR system with the ability to sense force ,16,17. LThe other method for sensing external forces is the indirect sensing method, which uses system information including current, torque, tension, displacement, pressure, and a visual image of the laparoscope. The design of an external force disturbance observer or a similar estimation method has attracted a great deal of attention. Zhao and Nelson developed a cable-driven decoupled surgical robot end-effector and proposed a method for estimating the three-axis force using a motor current . BecauseTo summarize, the property that these above proposed indirect sensing methods have in common is the combination or segregation of the disturbance observers and neural networks or learning methods. The accuracy, rapidity and robustness of the clamping force estimation are still the main challenges for the cable-driven surgical robot end-effector without the forceps’ internal force sensors. The motivation and contribution of this study are trying to find a new way of the clamping force estimation, which is considering the cable-driven system dynamics, the cable tension measurements and estimation, and the joint torque disturbance observer of the forceps in real time, such that aims to achieve a good comprehensive performance with low-cost and easy realization.This paper focuses on one-dimensional clamping force sensing method that do not require any internal force sensors to be integrated into the wrist joints of a surgical robot end-effector. The main contribution of this study is the development of a novel method for estimating the clamping force of a forceps based on a joint torque disturbance observer, which essentially considers the variations in cable tension between the actual cable tension and the real-time estimated cable tension using a Particle Swarm Optimization Back Propagation Neural Network (PSO-BPNN) under free motion of the end-effector. A clamping force estimator is proposed based on the JTDO and the mechanical relations in the forceps. The main advantages of this method are the combination of cable-driven system dynamics and joint torque disturbance observer using PSO-BPNN, for improving the comprehensive performance. We verify the estimation method through a series of experiments with an equivalent experimental system.A surgical robot end-effector is a key device in a surgical robotic system. In order to study external force and clamping force sensing methods, we designed and built an equivalent experimental prototype for the 3-DoF cable-driven surgical robot end-effector. As shown in ®, South Boston, MA, USA) are installed on the two sides of the triangular block, which is the grasping object for forceps A and B. In terms of overall function and robotic mechanisms, the equivalent experimental system  to (3), the following relationship can be obtained:The driving torques Suppose that Therefore, the set of cable tension disturbances from the tension sensor measurement modules can be defined as The simplified dynamic model of forceps A and B can be given as: Equation (9) can be revised as Equation (10) when there is no external force or clamping force under free motion:Considering that linear stepping motors were chosen to be the drive units for the wrist joints, the acceleration and deceleration modes were set to the ‘S’ type in the motion control card, their acceleration and deceleration time were set to be 0.02 s, and the constant velocity was set to be variable. Because the inertias of the wrist joints are very small, their inertial forces only have marginal effects on the wrist joints’ system dynamics, so the inertia forces of the wrist joints were ignored in this paper. If the joints’ driving torques can be estimated under free motion, then Equations (9) and (10) can be simplified as Equations (11) and (12), respectively:Combining Equations (11) and (12), the joint torque disturbance Under the circumstances of free motion and no clamping force, the joint frictions and cable tension losses of forceps A and B satisfy the condition approximated below:Therefore, the joint torque disturbance Furthermore, the estimated external force Finally, the estimated clamping force of forceps A and B can be calculated by Equation (17):According to the process for modeling the forceps’ clamping force, a strategy for estimating the clamping force of the forceps is given as shown in Firstly, the desired opening angle or grasping angle Secondly, angle Thirdly, the motors pull or push the cable loops with the desired displacement while the force sensors measure the driving and returning cable tensions Fourthly, the motor displacement Finally, the external force The purpose of designing the yaw joint torque disturbance observer shown in Equation (15) is to estimate or predict the variation in the motor’s driving cable tension as compared with the same motor’s displacement and velocity under free motion. This means that the forceps’ external force or clamping force affects the motor’s driving cable tension with respect to the condition of free motion or no clamping force. The core requirement for this joint torque disturbance observer is to build a high-accuracy model for predicting the cable tension under free motion with the same motor displacement and velocity. The parameters estimations with evolutionary algorithms using tweezers show its superiority in a coupled nonlinear dynamics . In thisThe artificial neural networks can approximate any function to an arbitrary degree of accuracy due to its high learning capability and parallel computing nature . The BPNi-th particle, respectively; i-th particle searches for; In this paper, the PSO-BPNN is employed to fit an artificial neural network model for estimating or predicting the motor driving cable tension under free motion. PSO-BPNN  is a com(a)Determine the topological structure of the BP neural network and set the number of neurons in each layer of the BP neural network. The particle population is initialized, and the velocity and position of each particle are randomly set. The main operating parameters of particle swarm optimization are shown in (b)Calculate the fitness value Fit (i) of each particle;(c)Compare the fitness value Fit (i) of each particle with the individual extreme value. If Fit (i) > pbest (i), replace pbest (i) with Fit (i).(d)Compare the fitness value Fit (i) of each particle with the global extreme value gbest (i). If Fit (i) > gbest (i), replace gbest (i) with Fit (i).(e)Update the position and velocity of each particle according to Equation (18);(f)If the condition is satisfied , exit; otherwise, return to the second step (b);(g)The global extreme value gbest (i) from the PSO algorithm is used as the weight and threshold for the BP neural network and to train the neural network with training samples;(h)The generalization ability of the PSO-BPNN can be tested by simulation with the test samples.The PSO algorithm was used to train the BP neural network, and the weight and threshold of each neuron were taken to be a particle’s iterative optimization of the solution space. The specific steps for PSO of the BP neural network algorithm are introduced in ,46. The To sum up, the PSO-BPNN plays an important role in the online estimation of motor cable tensions under free motion. The estimated values are input into the joint torque disturbance observer as contrasting values. The clamping force estimator can be established on this basis.As shown in In order to validate and evaluate the comprehensive performance of the proposed method for estimating clamping force based on a joint torque disturbance observer using PSO-BPNN, a series of experiments were carried out. In this section, we provide the results from the cable tension estimation model based on the PSO-BPNN under free motion, and the experimental results of clamping force estimation considering collision detection and a clamping action.A series of experiments were carried out to explore the potential relations between the motor’s displacement and velocity and the measured cable tension value under free motion. The displacement corresponding to one pulse of the linear stepping motor is 0.00127 mm. The acceleration and deceleration modes are selected as “S” type in the motion control card. The acceleration and deceleration time are set to 0.02s, and the constant motion velocity is set to be variable. Five kinds of motors’ velocities  with increasing ladder are planned for the joint motion with the forceps’ opening angle ranging from zero to maximum angle. The maximum opening angle of the forceps A and B is 130 degrees. This means that forceps A and B are actuated with joint angle range  degrees on the opposite direction, respectively. The input data were the displacement and velocity of the two linear stepping motors for pulling the driving cables of forceps A and B. The output data were the cable tension values that were measured through four tension sensors. The training data comprised 90% of the randomized input data and their related output data. The remaining 10% of the randomized input and output data were used as the testing data for examining the generalization ability and prediction accuracy of the PSO-BPNN. The number of training data was 4892, and the number of testing data was 544.The training and testing outcome indicators of the PSO-BPNN model are shown as As shown in Over the whole region of the first group of experiments as shown in Over the whole region of the second group of experiments as shown in When training a PSO-BPNN model, the estimation accuracy of the trained neural network can be limited by multiple effects, including the model parameter settings and the training data size. Because the five different velocities of the yaw joint motion were quantitative, the estimation accuracy of the trained PSO-BPNN model was only affected by the training data size. So, the estimation accuracy of the trained PSO-BPNN model could be increased by collecting more training data with a higher number of velocity bands. However, our trained PSO-BPNN model showed sufficiently good comprehensive performance to be employed in the estimation of the cable tension of forceps A and B.To sum up, the collision detection threshold was found to reach 0.11 N, and the clamping force estimation resolution was found to be the same as the collision detection threshold. The estimation accuracy for the static clamping force was greater than 86%. In this study, the proposed method for clamping force estimation was shown to perform well. The two groups of incremental clamping force estimation experiments demonstrated the more complete comprehensive performance of the proposed method, with average errors in the interval of  N. Meanwhile, the experiments showed that this method can provide effectively detection range in the interval of  N. Moreover, the length of forceps direct influences the arms of external force, this means that the effectively detection range can be extended by reducing the overall length of the forceps within reasonable limits. Overall, the experimental results mean that the proposed method for clamping force estimation has the potential to be used in the cable-driven end-effector of a surgical robotic system for MIS. In this study, we proposed a method for clamping force estimation based on a joint torque disturbance observer using PSO-BPNN for a cable-driven surgical robot end-effector. This estimation method considers both the cable-driven end-effector’s system dynamics and the estimated cable tension using the PSO-BPNN under free motion. Moreover, the clamping force can be estimated by only using known information about the motor’s displacement and velocity, and the measured cable tension value, without the need for internal force sensors to be integrated into the wrist joints of the surgical robot end-effector. The PSO-BPNN-based joint torque disturbance observer performed well in the disturbance estimation. The experimental results showed that the proposed method for clamping force estimation has good comprehensive performance. Our future work will focus on solving the decoupling precision problem in multi-DoF wrist joints, and the engineering and application of this method.


One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy. Several Location-Based Services (LBS) for personal navigation and Intelligent Transport Systems (ITS) are subject to the availability of robust positioning, navigation and timing measurements. The Global Navigation Satellite Systems (GNSS) provides a positioning solution that is free of drift as opposed to dead-reckoning systems such as the Inertial Navigation System (INS) ,2. HowevInitial offerings include  aerial lNovel airspace structuring concepts are emerging in response to the drive to introduce UAM and UAS operations in urban environments. These include stacked layers , dynamicThis paper has two key objectives that are prerequisites for the implementation of a safe UAM framework. First, the model-based prediction of GNSS performance for operations in urban environments; Second, the development of a guidance-based augmentation strategy that allows the assessment of planned UAS trajectories in terms of navigation performance metrics. The scope of this paper is restricted to unintentional fault and error modes and an augmentation strategy that supports their mitigation. Beyond this scope, other important research initiatives that are necessary for fully realizing UAM operations include jamming and spoofing detection, cyber-security and Assisted-GNSS, which are not included in this work. This paper draws on work from ,9,10, anA large body of knowledge exists on GNSS performance for aviation. A comprehensive performance analysis of GNSS performance for manned and unmanned aircraft including augmentation techniques was performed in . Fault mHorizontal Alert Limit (HAL): Radius of a circle in the local horizontal plane  with its centre being at the true position, that describes the region that is required to contain the computed horizontal position with the required probability for a given navigation mode.Vertical Alert Limit (VAL): Half the length of a vertical segment, with its centre being at the true position, that describes the region that is required to contain the computed vertical position with the required probability for a given navigation mode.Time To Alert (TTA): The maximum allowable time measured from the onset of a positioning failure to the annunciation of the alert to the autopilot/mission-planner.Integrity risk: The probability of computing a position that is out of defined bounds without warning the autopilot/mission-planner within the TTA.Navigation system performance has traditionally been quantified and evaluated through four metrics under the Performance Based Navigation (PBN) framework: accuracy, integrity, continuity and availability. An accuracy requirement places an upper limit on the maximum allowable difference between the estimated position and the true position. Accuracy requirements are specified as the maximum allowable 95% percentile of the position error. Integrity relates to the level of trust that can placed in the output of the navigation system, and is typically specified as a maximum allowable probability of the system providing hazardous and misleading information to the user. Integrity includes the ability of a system to provide timely and valid alerts to the user. Integrity requirements are described by the following metrics: Continuity relates to the availability of the navigation system for a given phase of flight, and is typically specified as an allowable false alarm rate. Availability specifications are typically expressed as a minimum percentage of flight time for which the accuracy, integrity and continuity requirements must be met.Limiting values for each of these metrics are rigorously defined by ICAO for manned aircraft for different phases of flight. However, these standards cannot be directly applied to UAS since separation minima for unmanned aircraft do not currently exist. Aviation standards are in the process of evolving to accommodate UAS operations, and as such, performance requirements are not yet formally specified for this use-case. Even the standard approach RNP 0.3 (1823 ft/556 m) and minimum RNP 0.1 (607 ft/185 m) are too large for UAM and UAS platforms considering their intended applications. In a recent NASA study , a quantThe third augmentation layer, Aircraft-Based Augmentation Systems (ABAS) implements monitoring functionality wholly within the user receiver itself, and is independent of the other two layers. ABAS is typically dependent on the availability of redundant sensor measurements to perform consistency checks and detect faults, although a model-based approach can also be used to detect or to predict loss of integrity ,9. ABAS The augmentation module outputs the expected navigation performance over the requested trajectory, and over the local area. In order to compute the expected navigation performance, the GNSS satellite orbits are projected over the intended mission duration. Urban elevation databases are also required to model signal interactions with buildings. The expected navigation performance is output to the trajectory generator in the form of discrete datapoints that vary over the operational area and over the duration of the mission. The datapoints convey expected accuracy and the number of visible satellites at a given location and time which relate to accuracy, integrity, continuity and availability requirements. The expected navigation performance is then included as an objective to be maximized in the trajectory generation module. The output trajectory is then the result of a trade-off between conventional objectives such as minimum fuel/ minimum time and navigation performance. The performance requirements for the given airspace sector are included as an operational constraint. This includes accuracy specifications, maximum allowable missed detection and false alarm rates, and percentage availability. The output of the trajectory generation module can either be an optimal trajectory that meets the assigned constraints, or a rejection of the trajectory altogether owing to an assessed non-compliance.This section presents error models for GNSS, and in particular, analyses errors dominant in the urban environment viz. Non-Line of Sight (NLOS) and multipath errors. Secondly, the analysis is used to generate a guidance strategy that maximizes GNSS performance.pseudorange, which is essentially the difference in time between when a signal is transmitted from the satellite, and the time at which it is received at the receiver antenna, multiplied by the speed of light. The reception of at least four such pseudoranges allows the receiver position to be determined, typically through a least-squares solution. The pseudorange essentially comprises the true range between the satellite and receiver, along with a number of errors and biases that contaminate the measurement and cause the estimated position to deviate from the true position. The model of the pseudorange observable from a single satellite is ;Relative polarization: Reflection of the Right-Hand Circularly Polarized (RHCP) L1 wave from a perfect conductor results in a polarization reversal to Left-Hand Circularly Polarization (LHCP). RHCP receiver antennas can therefore partially mitigate multipath. However, since the urban environment has an unpredictable distribution of conductors and dielectrics, reflected signals will typically be a mix of right- and left-handed polarization, resulting in partial attenuation of the reflected signal.If the NLOS ray is not significantly attenuated by interactions with objects in the local environment (reflection/diffraction), the receiver will be unable to reject the subsequently contaminated pseudorange from the navigation solution. NLOS conditions, therefore, can result in hazardously misleading information being provided to the vehicle guidance and control loops (when operating autonomously). The following signal reflection parameters directly impact pseudorange error:The relative phase of the reflected signal impacts the fading characteristic of the overall received signal, but does not directly impact code-ranging error, and is therefore, not the primary focus of this paper. NLOS/multipath modelling approaches include numerical methods such as the traditional Method of Moments and parabolic methods, asymptotic methods such as ray-tracing, and hybrid stochastic-deterministic methods . Driven The first-order specular ground reflection model is illustrated in The delay in this case is the distance  Fleisch  for dielThe following parameters . This is illustrated in The largest delay will be from a low elevation satellite (relative to the UAV body frame {B}) and can be derived through the same trigonometric relationships used to model ground reflection delay. Knowledge of the maximum dimension of a given UAV allows an approximation of the range of possible echo delays from the body of the aircraft. A reference platform is adopted as shown in The height of the antenna above the mounting bed and the half-span (center to motor distance) is known , it can be seen from In addition to geometrical effects, pseudorange error also has a dependency on the receiver architecture and the specific type of discriminator function used to extract a pseudorange measurement from a satellite. The primary focus of this paper is the effect of multipath on the code-phase. Therefore, the receiver model implementation is limited to the Delay Lock Loop (DLL) aspects viz the employed discriminator function. In order to estimate the pseudorange error, the estimated power delay profile must be fed through the In-phase (I) and Quadrature-phase (Q) arms in order to form a discriminator function as demonstrated in . In ordeStandard correlator spacings of one chip (d=1 chip) have the largest potential pseudorange error. However, most modern receivers employ narrow correlators, where the spacing is 0.1 chip. Equation 16 assumes infinite bandwidth of the autocorrelation function. Modeling finite bandwidth would be more accurate in terms of the estimated pseudorange error. However, peak error is reduced when accounting for bandwidth in the model and therefore the infinite bandwidth assumption yields more conservative estimates . The recStreet width: 30m Number of buildings: 8Building widths: 50mBuilding spacing: 5mBuilding height: (An urban canyon test domain of (L) 100m × (W) 50m is defined to model the described electromagnetic interactions with building facades. The following parameters are tunable in the implemented urban canyon model and are set to: A grid of receiver points is defined over the model domain with a horizontal resolution of 1m and vertical resolution of 5m up to a ceiling of 10m above the maximum building height. Satellite elevation is varied in increments of 10 ° from a masking angle of 10° to 90°. The previously described steps to check for NLOS/multipath conditions are applied for each angular increment.It can be observed that the UERE captures both the errors allocated to the space and control segments viz the User Range Error (URE), and the errors allocated to the user equipment viz the User Equipment Error (UEE). The UERE at any given epoch is dependent on the time lapsed or the Age of Data (AOD) since the last upload of the satellite clock and ephemeris corrections from the control segment to the space segment. UERE, therefore, is not temporally static and grows in a predictable manner from the navigation message upload to the maximum AOD under normal operating conditions (Approximately 24 hours). GNSS UERE budgets therefore tend to vary widely depending on assumptions made regarding receiver design and the operating conditions. Apart from the UERE, the receiver position uncertainty also depends on the relative geometry of the satellites and the user receiver. This is characterized by the Dilution of Precision (DOP) factors, which are presented here without the full proof. The reader is directed to  for a depression :(27)Cx¯=The parameters Equation 27 essentially represents positioning uncertainty as a function of the pseudorange errors discussed so far, the number of visible satellites and the geometry of the visible satellites.The predicted GNSS error over the test domain is used to define a cost-map that is applied to the guidance problem. Several options are applicable for this use-case including the well-known A* algorithm . This isThe cost of traversing a cell A UAS operation in an urban environment is chosen as a case study. The models presented in a priori known binary occupancy grid map is assumed for the navigation area i.e., the environment is represented in discrete voxel form with labels corresponding to either occupied (Boolean-1) or free-space (Boolean-0). This provides a straightforward means of spatially mapping GNSS parameters over the urban airspace. An urban canyon was simulated since most dense urban environments are characterized by this type of scenario. The error models presented in RMS accuracy was computed over the test domain for the intended flight profile. Two scenarios were studied: (1) A single-frequency receiver scenario wherein ionospheric error contributed significantly to the overall UERE owing to the relative inaccuracy of the employed ionospheric model; (2) A dual-frequency receiver scenario wherein ionospheric error is practically eliminated. In this scenario, multipath contributions are the limiting source of error. In each scenario, a conventional A* algorithm is run to generate a baseline trajectory, and is compared against a navigation cost-augmented A* algorithm. The comparison is on the basis of two parameters: Average accuracy over the trajectory, and percentage availability. A representative urban environment is simulated along with a satellite constellation. An In particular, the NLOS/multipath error was focused upon owing to its high variability with satellite elevation and azimuth, and receiver altitude. In all the simulated operations, aircraft path-planning was performed using the A* algorithm, which is widely employed in aerial robotics applications and is guaranteed to converge to the least-cost or shortest path given start-end points and obstacle locations. As expected, accuracy is degraded in the low-altitude shadowed regions of the test domain owing to poor geometry and larger multipath contributions. Minimum performance requirements have not been standardised for UAM navigation systems at the time of writing. Therefore, a limiting threshold value approximately equal to one-third of an average urban street width (10 m) is set to compute the system availability, which lies between the standard and precision PBN boundaries presented in GNSS performance degradation in urban environments is a critical challenge to overcome to support safe urban air mobility. A focused study on GNSS performance for UAS navigation in urban areas was proposed and numerically assessed. The analysis supported the development of a framework which allows prediction of performance and a corresponding trajectory generation strategy. The key GNSS error sources and fault modes affecting navigation performance in urban environments were presented, along with metrics for predicting navigation performance over short UAS missions. In particular, NLOS/multipath characteristics in an urban canyon scenario were modelled, including the impact of building facade, ground and airframe reflections. A guidance strategy accounting for the predicted GNSS performance in urban airspace was developed. The presented methodology allows the assessment of trade-offs between distance and navigation performance in dense urban environments. User-requested routes can be assessed by the UTM operator against the required navigation performance for a given airspace sector and either accepted/rejected/amended. The methodology utilizes 3D models of the navigation environment, GNSS almanacs and models characterizing receiver characteristics to predict positioning error over a specified mission timeline and mission area. The error prediction model accounts for both satellite elevation dependencies and signal reflection from urban structures in generating the UERE. A primary deliverable of the proposed methodology is the improved ability to predict positioning performance over the intended operational area accounting for all significant ranging error sources. The study confirmed the overall feasibility of the approach and sets the foundation for targeted experimental verification in future work. Another area of future work is the investigation of an optimization strategy to balance navigation performance and trajectory length.


Ex situ assays depend on bindings that occur outside of the normal cellular environment and include yeast two hybrids, pull-downs, and nucleic acid-programmable protein arrays (NAPPA). In situ assays depend on bindings that occur inside of host cells and include affinity purification (AP) and proximity dependent labeling (PDL). Either ex or in situ methods can be reliably used for generating protein–protein interactions networks but it is important to understand and recognize the limitations of the chosen methods when developing an interactomic network. In summary, proteomic methods can be extremely useful for interactomics but it is important to recognize the nature of the method when designing and analyzing an experiment.Viral and pathogen protein complexity is often limited by their relatively small genomes, thus critical functions are often accomplished by complexes of host and pathogen proteins. This requirement makes the study of host–pathogen interactions critical for the understanding of pathogenicity and virology. This review article discusses proteomic methods that offer an opportunity to experimentally identify and analyze the binding partners of a target protein and presents the representative studies performed with these methods. These methods divide into two classes:  The interactions between viral and host proteins are responsible for all aspects of the viral life cycle; from infection of the host cell, to replication of the viral genome, and assembly of new viral particles. The analysis of these protein–protein interactions (PPI) is an emerging field in biology and can elucidate the critical pathways involved in immunity, cellular signaling, replication, and cellular division. In addition, viral–host interactions are a potentially potent target for antiviral treatments, such as the HIV-entry inhibitor maraviroc that inhibits viral entry by binding to the cellular receptor (CCR5) and preventing its binding to the viral glycoprotein (gp120) .Recent advances in molecular biology, mass spectrometry, and bioinformatics have increased the throughput of analysis while simultaneously decreased the false-positive rate of interactomic assay results, with an increased focus on separating the true-positives from the false-positives identified during mass spectrometry. The overall goal of this review is to analyze the most common methods of interactome identification with their benefits and drawbacks. In the end, the readers should be able to design an assay to acquire an interactomic dataset and to screen the dataset for the high confidence results along with the system implications of those results.ex situ binding assays  and in situ binding assays [affinity purification-mass spectrometry (AP-MS) and Proximity-Dependent Labeling (PDL)]. In general, these assays use a protein of interest (POI) as a bait for a pool of possible prey proteins. None of these assays are perfect for every protein nor are they exclusive. In fact, a common method of confirming high confidence results is to repeat the experiment using one of the other methods  ] and genex situ and in situ. Ex situ experiments are those where the interactions occur not in a host cell but instead occur either in a non-host cell or solution, while in situ experiments are those where the interactions occur inside a host cell. Generally, ex situ experiments can be adapted to screen large numbers of bait and prey combinations simultaneously, but the identified interactions can include artifacts due to the forced colocalizations or modified folding of the proteins being analyzed  required for analysis. In contrast, interactions identified in these procedures may be completely artificial and unrelated to the protein’s functional role  while a second protein is constructed as a chimera fused to the activation domain of a transcription factor (prey). Both proteins are then expressed in yeast cells and, if binding occurs, an active transcription factor forms and a reporter gene is expressed  and the second protein is bound to the C-terminal portion (Cub) linked to a reporter protein. When the two proteins bind, the Nub and Cub regions join and form a full ubiquitin moiety that binds to ubiquitin-specific proteases that cleaves the reporter protein from the complex producing a positive result  , Hepatitex situ binding assay. Pull-downs involve the use of purified proteins as the bait and can use either purified proteins or cellular lysate as the prey. One of the most common types of pull-down experiment are GST pull-downs. In a GST pull-down, the bait protein is expressed as a chimera fused to a GST domain in a suitable culture, usually Escherichia coli but can also be Saccharomyces cerevisiae or insect cells. The GST-fusion protein is then purified by the binding of the GST to a glutathione-labeled matrix, often agarose beads. The purified proteins can then be eluted from the matrix by exposure to free glutathione then mixed with the prey proteins prior to binding to a fresh matrix or the matrix-bound bait can be mixed with the prey proteins. The matrix is washed to remove any non-specific interactors and the prey proteins eluted for analysis . A chip-based assay, NAPPA uses in vitro expression of proteins encoded by nucleic acids fused to the chip to generate a protein chip studded with a library of proteins. These bait proteins, which are linked to the chips by covalently bound antibodies, are then exposed to purified prey protein labeled with a fluorescent marker. After washing, a spot of fluorescence will be detectable where the prey protein has bound to the immobilized bait . A Gram-negative pathogenic bacterium, L. pneumophila injects nearly 300 effector proteins into host cells and infections can cause legionnaire’s disease. Interestingly, both proteins showed strong affinity to multiple Rab GTPases, a class of proteins which are known to affect membrane trafficking . A common benefit of AP-MS experiments is their relative ease of use. Often the most difficult portion of the assay is deciding which type of affinity to use for precipitation. In addition, depending on the type of analysis used following the precipitation, the sizes of complexes can be analyzed following the initial precipitation. A shared drawback is that AP-MS experiments often precipitate not only the proteins bound to the POI, but also those bound to the tubes, matrix, pipets, etc. To address this, a control sample is used where the assay is repeated without adding the antibody required to precipitate the POI and compared to the samples containing the antibody. Additionally, the lysis of the samples prior to AP-MS requires the addition of detergents to open the cell membranes and solubilize the protein complexes. Depending on the binding strengths of the proteins, even mild detergents may dissociate binding partners from the POI. In this review we will focus on three of the most common versions of AP-MS: native, GFP-tagged, and tandem.Native AP-MS is dependent upon binding the POI with an antibody. The antibody can be precipitated by exposure to a Protein A-, Protein G-, or Protein A/G-bound to a matrix. Proteins A and G are antibody-binding proteins isolated from bacteria while Protein A/G is a chimeric version of both proteins. All three can be readily purchased covalently linked to agarose or magnetic beads. Prior to purchase, the researchers should confirm which type of bead is best for their antibody; some antibodies bind best to one over the others. After the binding of the antibody-bound proteins to the beads, the non-specific and unbound proteins are removed by washing, then the precipitated proteins are eluted and analyzed by MS .The benefits of this assay include the POI can be precipitated from viral-infected host cells including patient samples. The co-precipitated proteins will include proteins that bind either directly or indirectly to the POI and when combined with LC-MS/MS analysis nearly any host protein can be identified. In addition, the MS analysis can also indicate the presence of many post-translation modifications, including phosphorylation.The drawbacks of this assay include the requirement of strong binding for a protein to co-precipitate with the POI – weak binding or interacting proteins will be lost – and the potentially extensive optimization required to precipitate the POI along with its binding partners. Native AP-MS requires an antibody toward the POI. These may be available from vendors but often require inhouse generation, which takes time and increases cost . AnotherWhile native AP-MS can be difficult and requires screening of antibodies for their efficiency, it has been used to map the interactomes of several viral proteins. Using a mouse monoclonal antibody, researchers identified over 50 host and viral proteins that bind to the herpes simplex 1 virus (HSV-1) protein ICP8, a DNA-binding protein required for viral genome replication . In KapoBecause antibodies toward a POI are often unavailable and would require time and money to generate, a common solution is to generate a protein chimera fused with a tag that contains and well characterized epitope. These tags can be range from short epitopes like FLAG or hemagglutinin (HA) to large molecules like GST. One of the most useful tags is green fluorescent protein (GFP). As a well-studied protein, GFP has several commercially available antibodies that can precipitate it during AP-MS experiments. The overall precipitation procedure is identical to that of native AP-MS; the cells are lysed, an antibody added to the mix to create antibody-bound protein complexes, and those complexes being precipitated by the affinity of the antibodies to Protein A-, G-, or A/G-labeled beads. The main difference between native and GFP-tagged AP-MS is the requirement of expressing a GFP-tagged version of the POI in your target cell. This can be accomplished by transient transfection, stable expression , or genome editing  .The benefits of this assay include the large number of commercially available anti-GFP antibodies, including some that come covalently linked to beads so the use of antibody binding beads can be avoided. The common use of this tag also means that the amount of optimization can be reduced since the buffer conditions to compatible with the binding of GFP to its antibodies is readily available. An added bonus of a GFP-fusion is that the GFP signal can be used to confirm expression of the POI prior to lysis and to map the localization of the POI, which can be used to filter out potential false positives. In addition, the GFP molecule can be expressed unfused to the POI to act as a control sample to identify proteins that bind to the beads, tubes, GFP, or antibodies and not the POI.The drawbacks of this assay include that cloning the POI into a GFP-tagged vector could lead to changes in the fold and function of the POI, which can affect its binding partners. Also, the GFP tag can affect the localization of the POI in cells, which can lead to its binding to new partners. Additionally, the most common way to express the GFP-tagged POI is transient transfection, which can lead to unnatural expression levels of the POI and limits the availability of host cells because not all host cells are compatible to transfection. Lastly, while the GFP-tag is easy to add to a POI in a plasmid using modern cloning techniques, this tag will not be present on the POI in natural samples such as those gathered from patients. This means that GFP tagged AP-MS cannot be used on wild type virus.With the recent improvements in molecular cloning, GFP tagged AP-MS has become one of the most common AP-MS techniques. GFP AP-MS was used to identify the binding partners of the human respiratory virus (HRSV) RNA polymerase complex and found that replication of the viral genome requires the binding of heat shock protein 90 (HSP90) to the polymerase by stabilizing the viral L-protein . In HSV-In most AP-MS experiments a single affinity mechanism is used to link the POI-containing complexes to the matrix. An alternate procedure is two use two affinity mechanisms sequentially to precipitate the POI-containing complexes. Termed tandem AP-MS (TAP-MS), this process begins by cloning a tag containing two binding regions separated by an endoproteinase site  motif). This tag allows for a stage one precipitation using the distal tag followed by cleavage of the POI-containing complexes from the first matrix by adding TEV protease. The released proteins are then precipitated using the proximal tag and the POI-containing complexes eluted from the second matrix .The benefits of this assay include the increased selection of using a two-step precipitation which can limit the amount of false positive results by limiting the accumulation of matrix binding proteins. This benefit can be enhanced by using tags that involve differing mechanisms, such as the binding of antibody to antibody-binding beads followed by the binding of streptavidin to biotin. Similar to a standard tagged AP-MS, TAP-MS requires less optimization since the bindings of the tags to the matrix is well established prior to use. Additionally, the extension of the tag from the POI reduces the chances of the tag being blocked during the precipitation by a binding protein or fold.The drawbacks of this assay include the need for twice the material for two-step purification process than for one-step process. The purification process could also require advanced equipment such as a high-performance liquid chromatograph (HPLC) or specialized spin columns. The addition of the tag to the POI could also affect the fold of the protein leading to artificial localization and binding partners. In addition, because the tag must be cloned onto the POI and the resulting chimera expressed in host cells, the procedure cannot be done on patient samples or wild type virus and the expression levels of the POI can be vastly different from that found in nature.Epstein-Barr Virus-encoded nuclear antigen 5 (EBNA5) is a protein that acts with the EBV protein EBNA2 to activate the expression of the EBV oncogenic protein latent membrane protein 1 (LMP1) and can affect the processing of mRNA. Using TAP-MS, researchers found that EBNA5 binds to several proteins involved in pre-mRNA processing, protein folding, protein degradation, and transcription . In a brA recent addition to the interactomics toolkit is PDL, a process where an enzyme chemically labels a protein(s) in close proximity to the POI. For a PDL assay to be a viable experiment the labeling must not be restricted by any other factor than distance from the POI. The labeling must also be able to survive the precipitation process until the non-specific proteins have been separated from the coprecipitated fraction. In addition, it would be beneficial if the mechanism of the PDL had little or no background labeling to enhance the difference between the non-specifically labeled proteins and the interacting proteins. The first system to fulfill these requirements is the biotinylation based BioID system.Biotinylation is the process of covalently binding a biotin molecule to a lysine by a biotin ligase. While biotin can be linked to lysines by a ligase, it can also be bound by avidin, streptavidin, and NeutrAvidin. This allows a biotinylated protein to be precipitated from a solution by mixing the protein solution with avidin-bound beads or a similar matrix. In addition, the binding of biotin to avidin is one of the strongest non-covalent bonds ever studied, which allows for the use of extremely stringent wash strategies. To use biotinylation in interactomics, mutants of naturally occurring biotin ligases have been generated to produce a promiscuous biotin ligase (termed BioID) that would biotinylate proteins in close proximity of the BioID-fused protein. The biotinylated proteins are then precipitated by binding to an avidin matrix .There are many benefits of this assay. The biotin ligase will label any proteins in close proximity to the POI inside the cell including both strong binding proteins and weak interacting proteins. In addition, the binding affinity of biotin to avidin is one of the strongest in nature and thus allows for extremely stringent washing of the matrix to remove non-specific binding proteins. While the original BioID had a slow reaction rate and required hours of labeling time to generate enough biotinylated proteins for a proper analysis, newly developed versions of the ligase termed TurboID and MiniID offer enhanced biotinylation .It is worth mentioning that several other proximity labeling methods have been successfully developed and used in other types of studies, so they may prove useful for identifying virus–host interactions in the future. These include the ascorbate peroxidase assays (APEX and APEX2) ; and theThe drawbacks of this assay include the possible effects of the cloning of the chimeric fusion with a biotin ligase on the activity and localization of the POI. When designing the chimera, it is also important to consider the positioning of the biotin ligase. This is due to the nature of the labeling – the biotin ligase has a range of 10–15 nm and cannot label across plasma membranes. If the prey protein binds the bait distant from the fused biotin ligase, then the assay can produce a false-negative result. In the case of transmembrane proteins, fusing the ligase to the portion of the protein on one side of the membrane can provide a completely different interactome from the other. In addition, the 10–15 nm labeling range means that not only those proteins that bind to or interact with the POI may be labeled but also those proteins in close proximity to the POI at the time of labeling, leading to false positive results.Using BioID, researchers were able to generate interactomes for all ten polypeptides encoded by the Zika virus (ZIKV). In total, 1224 human proteins were identified as interacting with Zika viral proteins. An analysis of the proteins identified many pathways previously reported as required for viral infection, including translation, protein processing, vesicle trafficking, and lipid metabolism, along with several novel interactors . In EBV,ex situ or in situ binding assays can generate lists of possible binding proteins, it is important to be able to filter the false-positives from the true-positives. While yeast two-hybrid and NAPPA screens are relatively straight forward to analyze, with a simple comparison to a control bait as a useful comparison to identify preys that specifically interact with your POI  and to compare the quality of the identification by the LC-MS/MS , intensity of signal, etc.). After this sorting, the high confidence results were compiled into a searchable database of known non-specific interactors called the contaminant repository for affinity purification (CRAPome) .H. sapiens, S. cerevisiae, and E. coli results based upon the previous results of labs from across the globe. In addition, the software can use a user provided control sample to generate a fold change score (FC) of the biological replicates for each protein in the data set. The program then compares the average FC (FC-A) to the geometric mean FC (FC-B) to generate a graph of with the FC-A score in the Y-axis and the FC-B score on the X-axis. The end result of this analysis is that the weakest candidates are closest to the origin and the strongest are the furthest away.A free to use tool, CRAPome acts as an initial filter of AP-MS and BioID data that can remove the common non-specific interactors found in Once you have removed the common false-positives, it is important to sort the list based upon a set of criteria designed to target the potential high-confidence true-positives. To rank the possible candidates, the results from the LC-MS/MS runs can provide invaluable information beyond the identification of the proteins in the sample. To make better use of this additional data generated by the LC-MS/MS, researchers have generated several software packages that compare the results from controls to samples and then generate a score for each candidate protein. Three of the most commonly used programs are the CompPASS, SAINT, and MiST.To move beyond a simple presence v absence binary evaluation the first piece of data to consider is the number of PSMs or total spectral counts (TSC) for each protein. By generating a table of the identified proteins with the number of PSMs for each, the candidate proteins can be ranked by comparing the TSC of each protein. To accomplish this, researchers developed the CompPASS, a free to use tool that uses a standard (Z) score and a newly developed Normalized Weight D (NWD) score. The Z score is a traditional statistical measurement of deviation in the number of spectral counts per protein in the experimental samples over the whole dataset. The NWD score is an improvement on the Z score because it includes reproducibility and accounts for differences between amount of each unique protein . A benefWhile CRAPome uses a comparison between a control sample and an experimental set and CompPASS uses a comparison of all the proteins found in the experimental set, a combination of both could be a potentially more effective process. The combination of both factors is the basis of the significance analysis of interactome (SAINT). During a SAINT analysis, the TSC of each protein in the experimental and control datasets, along with the TSC of the bait protein found in each dataset, are analyzed using Bayesian statistics to generate a plot of the spectral counts versus the estimated distribution. The relationship between two points is then analyzed to probability of interaction score ranging from 0 to 1. Because the SAINT score is based upon a comparison of the experimental and control datasets, a SAINT score can also be calculated using peptide ion intensity along with other continuous variables . BenefitMass spectrometry interaction statistics (MiST) is a scoring system for interactome results that was designed to target viral–host interactions. The basis of the MiST score is to compare the abundance of the protein in the elution, the reproducibility of the result from run to run, and the specificity of the recovery of the prey based on the bait. While SAINT uses a two-axis comparison to plot the data as the basis of its analysis, MiST uses a three-axis comparison to produce a single MiST score that ranges from 0 to 1 . As a beOnce an interactome is established the next question is what processes or pathways are affected by the binding of the proteins. With the large amounts of PPI identified in the literature, many proteins have known binding partners in at least one or more species. By comparing the genomes or proteins in the database between species it is also possible to generate a network of possible connections for even unstudied proteins. One of the most expansive and potently useful is the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Another useful tool that builds on the STRING suite, while enhancing the coverage of interactions to include not only host PPI but also host-viral and viral-viral PPI, is Viruses.STRING. While both of these programs are very useful tools for analyzing PPI, it is important to remember that databases are only as good as the data entered into them. If the data entered is incomplete or incorrect then the interactions listed will also be flawed. This requires the researchers to always consider the confirmation of novel interactions or loss of a previously reported interaction.p-value, which indicates the confidence that STRING can link your protein and its identified binding partners to an indicated pathway. When used on a pathway known to be involved with your POI, the p-value can indicate the quality of your precipitation. In addition, STRING reports an enrichment analysis of the dataset based on gene orthologies, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and domains ], the co-expression score [uses microarray data along with known interaction pathways (KEGG)], and the experiments score (uses previously reported interactions imported from up to six PPI databases)  change, mutate, or evolve at a slow rate, viral proteins can rapidly mutate and as such orthologous proteins from related viruses may not retain the same interactomes even when comparing between strains of a single viral species (While STRING has been extremely useful in analyzing PPI in a host species, the database does not account for the interspecies interactions found in viral-infected hosts. While innumerable studies have been conducted to map the interactomes of viral proteins, there have been very few suitable databases that compile this data and allow for its analysis. Recently, a new modified version of STRING named Viruses.STRING attempted to address this deficiency. Building on the methodology used to develop the STRING database, Viruses.STRING was built by text-mining the available scientific literature along with incorporation of experimental data from several common public databases prior to scoring based on the KEGG database. Once the pathways were established in one host-viral set, the interactions were then compared to orthologous in related species of both hosts and viruses. The end result is a database containing over 170 thousand PPI from 239 viruses that infect 319 host species. Similar to STRING, Virus.STRING allows the researches to map their proteins into GO-term pathways and provides a FDR  species .ex situ, or those methods where the binding occurs outside of their natural localization, and in situ, or those methods where the binding occurs in their natural localization. Once the results of the assay are compiled, the filtering of the true-positives from the false-positives followed by the ranking of the true-positive results to generate a high confidence protein target list is critical to efficiently study the role of the viral protein in the cell. These high confidence protein targets can then be further analyzed with pathway analysis tools, to generate a systems map of the viral life cycle with important nodes clearly defined. By targeting the critical nodes for the virus, we can develop new avenues of antiviral therapy and patient care.Viral-host PPI are responsible for every aspect of infection and thus the root cause of all viral diseases. To analyze the binding of proteins researchers have developed several methods of interactomic analysis. These methods can be divided into two distinct classes: JG and AN-L conceived the study and reviewed and edited the manuscript. JG drafted the manuscript. AN-L supervised the study.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.


As the characteristic scale of products and production processes decreases, the plasticity phenomena observed start to deviate from those evidenced at the macroscale. The current research aims at investigating this gap using a lower-order gradient enhanced approach both using phenomenological continuum level as well as crystal plasticity models. In the phenomenological approach, a physically based hardening model relates the flow stress to the density of dislocations where it is assumed that the sources of immobile dislocations are both statistically stored (SSDs) as well as geometrically necessary dislocations (GNDs). In the crystal plasticity model, the evolution of the critical resolved shear stress is also defined based on the total number of dislocations. The GNDs are similarly incorporated in the hardening based on projecting the plastic strain gradients through the Burgers tensor on slip systems. A rate-independent formulation is considered that eliminates any artificial inhomogeneous hardening behavior due to numerical stabilization. The behavior of both models is compared in simulations focusing on the effect of structurally imposed gradients versus the inherent gradients arising in crystal plasticity simulations. In metals, the primary mechanism of inelastic deformation is plastic slip due to dislocation movement along glide planes. Due to the inhomogeneous nature of the microstructure, however, this movement is hindered by obstacles and, hence, prevails resistance to slip. According to  the magnDuring plastic slip, this number increases due to generation mechanisms such as Frank-Read sources and decreases due to dislocation annihilation where usually the net effect remains non-negative . These type of dislocations are commonly referred to as statistically stored dislocations (SSDs) due to the statistically random nature of their generation. This randomness results in a vanishing closed-circuit evaluation of the Burgers vector .Another source of dislocations is geometrical necessity, as proposed by . When laApart from their sources, however, both types of dislocations, locally, are in essence the same and, hence, contribute to the hardening of the material similarly in terms of creating dislocation pile-ups.If the thermodynamics of the total deformation is considered, two types of state variables, namely the plastic slip and its gradient, contribute to the dissipation of free energy of the system. In other words, both terms can be considered as thermodynamic fluxes which must have associated thermodynamic forces. The force associated with slip is the Cauchy stress itself in the macroscopic approach and the critical resolved shear stress in the microscopic definition. For the gradient of slip, the associated thermodynamic force is the microscopic stress ,5,6. TheIn the literature, based on their consideration of the microscopic stresses in the definition of the (micro)equilibrium, there are mainly two approaches regarding strain gradient plasticity. According to the terminology defined in , the modIn the higher-order approach, in addition to microscopic stresses, additional boundary conditions related to the added degrees of freedom must be defined with respect to the weak-form of the boundary value problem. At the boundaries, which can also be the grain boundaries for the crystal plasticity framework, this gives more flexibility in defining realistic phenomena such as permeability of dislocations across the grain boundaries. On the other hand, inaccurate description of the physics at the boundaries of a structure may result in peculiar and even counterintuitive behavior .In this work, we adopt a lower-order approach represented by two distinct plasticity models, namely macroscopic plasticity and crystal plasticity. In previous studies the effects of GNDs have been included in macroscopic plasticity models by considering an underlying phenomenological hardening function and relating the gradient of the strain either to the equivalent strain term or an additional stress multiplier through the use of a length-scale parameter ,9,10. OnIn previous research on gradient enhanced crystal plasticity models mostly rate dependent theories have been introduced ,19,20,21The computation of the gradients of slip and plastic strain is done using an explicit formulation after each converged time step in an implicit, non-linear FE simulation. The result is a weak coupling of the slip gradient and the equilibrium solution. Accordingly, within each increment a small error is introduced that is corrected in the following step.A comparison of gradient enhanced theories with phenomenological and micromechanical plasticity models is carried out considering their formulations as well as their associated responses in disc torsion simulations.As opposed to the commonly used rate-regularized formulations in the context, rate-independent plasticity models are considered. This choice is crucial especially for problems where strain rate gradients prevail for which viscous regularization might cause an uneven and non-physical treatment of material points in the whole problem domain.The used discrete gradient method does not require a regular lattice. Moreover, the explicit nature of the utilized implementation does not impose a restriction on the selected finite element technology. Thus, the developed gradient framework proves to be robust and mesh independent.The noteworthy features of the current work are:d is a scalar. The following notations are used: Consistently assuming In the following, a short summary of the underlying macroscopic and crystal plasticity constitutive models are given. The theory behind gradient enhancement that is included in the lower-order approach solely based on the additional hardening mechanism due to the generation of dislocations is given next in terms of the hardening effect and the relation of GND density and the strain gradients.We utilize a small strain theory in which the plastic and elastic strain rates additively make up the total strain rate that the material point encountersThe elastic stress follows Hooke’s lawThe plastic flow is taken as associative and, therefore, the normality rule appliesOur point of departure is the additive decomposition of the displacement gradient Here, the elastic distortion, which describes the stretch and rotation of the underlying lattice, is given by Taking the symmetric part of above relation, one recovers the rate form of Equation , that isDenoting Following, the flow criteria per slip system is introduced asSince we limit the scope to small displacements we consider irrotational crystal plasticity which implies that the rotation of the slip system vectors, The core component of strain gradient theories is the additional slip resistance due to the increase in the total number of dislocations by the creation of GNDs. The dependence of slip resistance on the number of dislocations are generally described using Taylor’s shear flow model (13)τ=cμb the Burgers vector length and c is the constant Taylor factor. No distinction is made in this equation with respect to the generation mechanism of dislocations and SSDs as well as GNDs contribute to the slip resistance identically. Hence, the total number can be decomposed additively into the SSD and GND contributions.Here, By keeping the same definition for slip resistance in both macroscopic models and in crystal plasticity a more direct comparison between the gradient enhancement on hardening can be obtained. Hence, a hardening function based on Equation  is chosec, in this context, is a material parameter that incorporates the Taylor factor as well as the relation between the shear stress and the equivalent stress. Furthermore, Bergström describes the evolution of SSDs with plastic strain asU is a function representing storage of dislocations (immobilization) and U can be approximated as Here, stresses . The conIn defining a similar hardening function for crystal plasticity, it is essential to take into account the interaction of different slip systems with each other through self and latent hardening relations. Latent hardening is described by a matrix given as ribed in . FinallyNext, we propose that the SSD density evolution is based on a phenomenological constitutive law as given in Here Equation . G can b a plane ,31. In tary kind  viz.18) is apIn order to relate nvariant (23)χ=χ:Although not directly comparable with Equation , this isHere, Here, ing from  it can bEquation  is exactOn the other hand, it is clear from these expressions that in this form they are not applicable to the flow theory approach where history of deformation is taken into account. Therefore, instead of the total deformations, Equation  is writtUMAT in the Abaqus software (version 6.12).Both algorithms as presented here are implemented as user subroutines The equations for macroscopic plasticity are solved using a Generalized Return Mapping Algorithm . Within Based on the Kuhn-Tucker conditionsThe nonlinear equation set defined in Equation  can be sIn the rate-independent crystal plasticity implementation, the stress is updated following the conventional approach of return-mapping algorithms. The Kuhn-Tucker conditions are defined per slip system The incremental plastic multiplier on each slip system is obtained by a suitable solution scheme of the residual function (32)R(α)In the implementation of rate-independent crystal plasticity, two issues arise: non-uniqueness and active set determination. The algorithms presented in ,29,37 prNon-uniqueness occurs when at least two slip systems have exactly the same resolved shear stress and their mutual influence on their hardening is the same, such as in isotopic hardening. However, using singular value decomposition or the more efficient perturbation methods, the correct slip amounts are recovered. The active set In this work, computation of strain gradients is realized explicitly making use of a discrete gradient computation method proposed by Liszka and Orkisz , see alsUSDFLD subroutine found in Abaqus software is used for implementing this algorithm.Equation  is basedEquation  for eachOther studies can be found where the gradients are computed using the FE interpolation functions within an element . An advaIn the case of polycrystal simulations, the gradient computation is limited within each domain of elements belonging to individual material definitions. This implies that the jump of the plastic strain across the grain boundaries is not treated as a source of GNDs. In the literature, studies can be found describing grain boundary effects on GNDs such as ,43,44.In the weakly coupled scheme that is presented above, the gradient effects do not directly influence the current equilibrium solution increment. On the other hand, the introduced GNDs (as a result of the increase in the gradients of plastic deformation) affect the hardening response of the material. This additional hardening term only influences the next increment in the equilibrium solver, making the current algorithm a staggered one.Abaqus with emphasis on the utilized user-defined subroutines reads:The general equilibrium solution scheme in the commercial finite element software Algorithm 1. Equilibrium solution scheme including gradient computation and user defined material in the commercial FE software package Abaqus.incrementn USDFLD: assign SDV UMAT: global iterations SDV USDFLD: compute increment USDFLD: assign SDV UMAT: global iterations SDV USDFLD: compute ⋮⋮The geometry and boundary conditions considered in this test are depicted in on tests  suggest The shear component that is the main deformation mechanism is εψz see  and it vAs the disc diameter decreases, the grain size of the material remains constant if not explicitly altered. Due to the nature of the wire-drawing process, through which the disc is assumed to be cut, it is expected that the grains in the disc are elongated in the primary axis of the wire. Therefore, the simulation models shown in The structure is meshed using quadratic tetrahedral elements with 4 integration points. The same mesh sizes are used for crystal plasticity and macroscopic plasticity, which is chosen to yield 72 elements per full-size grain.Since the aim of the simulation is to observe and compare the gradient enhanced hardening behavior of the two models, the underlying plastic hardening response should be made as similar as possible. On the other hand, the crystal plasticity model has an inherent dependence on the strain gradient which arises due to the existence of grain boundaries even in the absence of an imposed structural gradient. Hence, the macroscopic model is fitted to the crystal plasticity results in the case of a test with no structural strain gradient, which is chosen to be a plane-strain tension test. The grain size is set identical to that in the torsion simulations.fcc crystal structure is chosen and the complete set of slip systems are utilized in the computation. The material data, which is based on an Aluminum alloy, used for both cases are summarized in The simulations are carried out in 3D in order to achieve a realistic fit - due to the orientation of the slip systems out-of-plane components of strain gradients contribute to the hardening of the material. An The two models, macroscopic and crystal plasticity, have a good agreement at the starting stages of hardening, see The resulting normalized torque (One reason for this is as expected the increased strain gradient due to the boundary conditions. On the other hand, at these diameters only a few grains, hence crystallographic orientations, are available for generating slip, see The normalized torque and twist angle curves for simulations of the disc using gradient enhanced macroscopic plasticity are shown in A representative case (radius = It should be noted that since the main deformation mechanism in the torsion simulation is shear, care needs to be taken with respect to the effects of rigid rotation. In the simulations above, dominantly, small strains can be assumed. The phenomena that is observed start to occur already at the small strains and therefore cannot be attributed to either rigid or crystal rotations.Gradient enhanced plasticity theories stem from the micromechanical observations that when the gradient of strain in crystalline materials reaches certain levels, the number of GNDs that need to be generated in order to accommodate the incompatibility of deformation reaches similar orders of magnitudes as the SSDs. The source of the gradient can be structural, due to strain concentrations or imposed gradients, or microstructural due to inhomogeneities such as obstacles, second phases and more importantly grain boundaries. In order to assess the two approaches that exist in the literature an explicit gradient computation algorithm is added to a commercial FE program which can utilize the underlying arbitrary integration point distribution for the computation. It is shown that it works successfully in both cases for macroscopic as well as crystal plasticity models.Two approaches are implemented which stem from the same underlying theory. Due to its nature the macroscopic model only captures structural gradients. The same type of hardening is used for both models for valid comparison based on dislocation densities.GNDs naturally appear at the grain boundaries and, therefore, contribute to the overall hardening behavior of the material regardless of the existence of a structural gradient. The macroscopic hardening model is consequently fitted to a simulation that includes this effect.Both models are able to predict hardening due to this structural gradient, but the hardening effect seems small. On the other hand, below a certain size another size dependent mechanism starts to dominate the hardening behavior. Due to the limited number of slip systems existing in the disc, the plasticity is much more constrained. Therefore, depending on the crystallographic orientations, a harder response is observed. This is tested with different generations of orientations and it is shown that, statistically, the change in hardening is much more significant compared to the effect of the structural gradient.


Underwater Acoustic Sensor Networks (UASNs) have become one of the promising technologies for exploring underwater natural resources and collecting scientific data from the aquatic environment. As obstacles hinder the communications among sensor nodes in UASNs, designing an effective bypass routing protocol to avoid obstacles is an urgent need. Moreover, the sensor nodes are typically powered by batteries, which are difficult to replace, restricting the network lifetime of UASNs. In this paper, an Energy-efficient and Obstacle-Avoiding Routing protocol (EOAR) is proposed not only to address the issue of marine animals acting as obstacles that interfere with communications, but also to balance the network energy according to the residual energy. In the EOAR protocol, when the current node perceives the existence of marine animals, the interference area of the animal-nodes is first calculated using the underwater acoustic channel model, and then the candidate forwarding relay set of the current node is obtained according to the constraint conditions. The optimal candidate forwarding relay is determined by a fuzzy logic-based forwarding relay selection scheme based on considering the three parameters of the candidate forwarding relay, which includes the propagation delay, the included angle between two neighbor nodes, and the residual energy. Furthermore, in order to solve the problem of energy waste caused by packet collision, we use a priority-based forwarding method to schedule the packet transmission from the candidate forwarding relay to the destination node. The proposed EOAR protocol is simulated on the Aqua-sim platform and the simulation results show that proposed protocol can increase the packet delivery ratio by 28.4% and 11.8% and can reduce the energy consumption by 53.4% and 32.7% and, respectively, comparing with the hop-by-hop vector-based forwarding routing protocol (HHVBF) and void handling using geo-opportunistic routing protocol (VHGOR). Underwater Acoustic Sensor Networks (UASNs) have proven their potential in a variety of aquatic applications, such as marine environmental monitoring, resource exploration, marine animal studies and assisted navigation ,2. However, due to the complex underwater environment, realizing effective communication among sensor nodes in the UASN is much more difficult than those in traditional terrestrial wireless sensor networks (TWSNs) . The reaIn order to overcome the above limitations and make aquatic applications viable, efficient communication protocols, especially the routing protocols, are essential for successful communications. Geographic routing can work together with opportunistic routing (geo-opportunistic routing)  to improBesides, the lifetime of UASNs is restricted by the limited energy due to the difficulty to replace the batteries of the underwater sensor nodes. How to balance the energy of the sensor nodes in order to extend the network lifetime has become a hot issue in UASN research . In 8],,8], a loIn recent years, to handle the void area problem, many routing protocols ,10,11 ha(1)In a network where marine animals and sensor nodes coexist, the underwater acoustic channel model is used to determine the interference area of the animal-nodes and then form the candidate forwarding relay set, which can reduce the possibility of those sensor nodes that are interfered by marine animals becoming the candidate forwarding relay set.(2)A new routing protocol call EOAR is proposed in this paper. In EOAR, the forwarding order of the nodes belonging to the candidate forwarding relay set is sorted using a fuzzy logic-based forwarding relay selection scheme. By considering more metrics, including propagation delay, the included angle between two neighbor nodes and the residual energy, the proposed EOAR protocol improves packet delivery ratio and extends network lifetime compared to routing protocols previously applied to fuzzy logic .To solve the issues above in UASNs, in this paper, we design an energy-efficient and obstacle-avoiding routing protocol (EOAR), which mainly contains three steps. Firstly, when a current node perceives the presence of marine animals during the underwater communications, the interference area of the animal-nodes and the candidate forwarding relay set are first calculated using the underwater acoustic channel model . Then, tThe simulation results show that our protocol can avoid animals’ interference more effectively, increases the packet delivery ratio by 28.4–11.8%, and reduces energy consumption by 53.4–32.7% compared with the classic hop-by-hop vector-based forwarding routing protocol (HHVBF) and void handling using geo-opportunistic routing protocol (VHGOR).The rest of this paper is organized as follows: in In UASNs, the oceanographic data collection is often completed by communication among nodes. An efficient communication is inseparable from the support of routing protocols. In recent years, the design of acoustic network routing protocol has gradually become a hot topic in underwater acoustic network research ,15, and The Vector-Based Forwarding (VBF) protocol  is a claIn , the autIn , a powerBesides, recharging the battery for a sensor node deployed under water for communication is a very laborious task , so the Moreover, sensor nodes are usually sparsely deployed in UASNs, and movement of the sensor nodes caused by water currents is inevitable, which cause the void problem. In order to reduce the probability of encountering void holes in the sparse networks, the protocols proposed in ,23 can rHowever, these routing protocols basically do not consider the problem of obstacle avoidance. In particular, marine animals which use the acoustic signals to communicate interfere with communication among sensor nodes. In this paper, we propose an energy-efficient and obstacle-avoiding routing (EOAR) protocol that mainly focuses on the network scenarios of marine animals as obstacles. We use a fuzzy logic system to calculate the forwarding probability of each candidate forwarding relay independently by considering the propagation delay, residual energy and included angle of each relay, and we use a priority-based forwarding method to reduce packet collisions.In this section, we present the network model and underwater acoustic channel model used in the EOAR protocol.As shown in Ma) to study. And the center of the area where the marine animals are located is the geographical location of the marine animal Ma. The marine animal Ma is always existed in the network and randomly moves around the network. The EOAR protocol derives the location of the marine animal Ma based on the probability that the marine animal will appear in a certain area. Moreover, the marine animal Ma does not move frequently in a certain area, and the range of movement is not large in a short time. Therefore, we can assume that the location of the marine animal Ma is kept in a certain area within a short period of time. It is assumed that the movement of sensor nodes caused by water currents is slow, and the sensor node is relatively stationary for a certain period of time. Sensor nodes in the network are defined as network nodes, and each sensor node can perceive, receive, and forward packets. In addition, it is also assumed that all the sensor nodes have homogenous transmission range and energy consumption. A sensor node is in the sleep state if it is not working.To facilitate the analysis of research objects, the entire network structure is divided into 1000 m × 1000 m × 1000 m sub-volumes. Each sub-volume contains one common sensor node. Sensor nodes strategy  method tA0 is a normalization constant and d is the distance between the current node and neighbor nodes of it. The geometry of propagation is described using the spreading factor k. In the proposed protocol, k is given as 1.5, and f is the signal frequency with unit kHz. The absorption coefficient α (The Thorp propagation model  is used nt α (f) , in dB/kd represents the distance from the current node to the neighbor node and k is the spreading factor which equals to 1.5. sP is the transmit power of a sensor node. N , which ifined in ,28 as foTo improve packet delivery ratio and reduce energy consumption, the EOAR protocol uses geo-opportunistic routing. It assumes that sensor nodes know the geographical location of themselves and their neighbor nodes. Besides, EOAR uses three constraints which contained signal-to-noise ratio (SNR), the distance between the marine animal and the neighbor nodes of the current node, and the included angle between the current node and its neighbor nodes, to determine the candidate forwarding relay set, and then uses the fuzzy logic-based forwarding relay selection scheme to calculate the forwarding probability of the nodes in the candidate forwarding relay set. Then, EOAR uses a priority-based forwarding method to set different waiting time for the candidate forwarding relays based on their forwarding probability. The overall procedure of the proposed EOAR protocol is detailed in In the EOAR protocol, when sensor node has a packet to send, the current node perceives whether there is interference from any marine animals around it and broadcasts the packet to its neighbor nodes. When the current node perceives the presence of the marine animal, the current node broadcasts the packet that contains the interference information and the time-stamp to its neighbor nodes. The interference information is the geographical location information of the marine animal. When the current node does not perceive the presence of any marine animal, the current node broadcasts the packet with only the time-stamp to its neighbor nodes. If one neighbor node receives the packet, it extracts the interference information in the packet and combines its own geographical location to calculate whether it satisfies the constraint. If one neighbor node can satisfy all the constraints, then it labels itself as a candidate forwarding relay of the current node, otherwise this neighbor node will discard the packet. The collection of all the candidate forwarding relays of the current node is called candidate forwarding relay set of the current node. Note that the candidate forwarding relay set is calculated by the neighbor nodes of the current node, not the current node itself. Since the packet of EOAR contains time-stamp information and location information, this article uses a joint time synchronization and localization design for mobile underwater sensor networks (JSL) method  for timeR1 and R2 represent the communication radius and interference radius of the marine animal, respectively. R3 and R4 represent the communication radius and interference radius of the sensor nodes, respectively.When the current node perceives the presence of a marine animal, the first step to determine the candidate forwarding relay set is to calculate the interference area of the animal-nodes. The communication radius and the interference radius of the sensor nodes and the marine animal can obtain according to the above Equations (1) and (2). As shown in R5. In order to prevent interference caused by the animal, it is necessary to ensure that there is no overlap between the interference area of marine animal and the communication area of sensor nodes, and also no overlap between the interference area of the sensor node and the communication area of marine animal. Therefore, the radius of animal-nodes interference area R5 = max {R2 + R3, R1 + R4}. Consider the location of the marine animal as the center of the sphere, R5 as the radius, and then the determined spherical area is the interference area of animal-nodes. After receiving the packet with the interference information, each neighbor node of the current node determines whether it is in the interference area of animal-nodes by calculation, and the neighbor node found itself in the interference area will be in the sleep mode to further save network energy consumption.Define the interference radius between the marine animal and sensor nodes as The first constrain is R5 + 1000 as the new radius R6) may include the communication range of the neighbor nodes. The second constrain is that if the current node perceive interference from any marine animal, the distance The third constrain is the included angle between the current node and its neighbor nodes. For example, when the current node After confirming the interference area of animal-nodes, EOAR specifies three constraints to determine the candidate forwarding relays from the neighbor nodes of the current node.Algorithm 1 Candidate Forwarding Relay Set DeterminationOutput: N(i) = {j is the number of the candidate forwarding relaysInitialization: N(i) = ∅;1: M = nN; // Number of neighbor nodes 2: forj = 1: M do3: Calculate SNR, 4: ifthen5: N(i) = N(i) + {6: end if7: end forInput: the positions of the The neighbor nodes of the current node that satisfy the above three constraints will be recorded in the candidate forwarding relay set. Algorithm 1 details a method of determining a candidate forwarding relay set in the situation that the current node perceives marine animal. Algorithm will be repeated in each transmission hop until the data packet finally reaches the destination node.If the current node cannot perceive the presence of the marine animal, then there is no interference from marine animal, thus the constraints are reduced to two, including the signal-to-noise ratio (SNR) and the included angle between the current node and its neighbor nodes. Similarly, the neighbor nodes of the current node that satisfy the above two constraints will be recorded in the candidate forwarding relay set. In EOAR, the fuzzy logic system  is used if for the fuzzy logic inference system established for each node in the candidate forwarding relay set can be expressed as:The input-output mapping function Re  vsouThe specific description of the three input variables is as follows:The input variables of fuzzy logic system and their linguistic levels are listed in The membership function of the triangle and the trapezoid is used in the fuzzifier to simplify the calculation. For an instance, The output of the inference engine, the probability, is classified into five types: excellent, good, medium, bad, and weak. The membership functions of the probability can be seen in p , it is observed that the first term reflects the relationship between the waiting time and In this section, we evaluate the performance of the proposed EOAR protocol. The experiment verifies the packet delivery ratio, end-to-end delay, energy consumption and average network lifetime. We compare EOAR with HHVBF and VHGOR protocols.3 area. These sensor nodes are identical in every feature, such as the initial energy, communication power, transmission range and so on. Among these sensor nodes, we only set up one source–destination node pair to communicate with each other. Marine animal is also randomly active in this area. The source node generates packets follows the Poisson distribution. The transmission bit rate is 9 kb/s. The location information of marine animal accounts for 12 bytes, including latitude and longitude and depth information, the time-stamp consists of two unsigned 4-bytes integers representing seconds and nanoseconds, and the length of the packet which contains the location information of the marine animal and the time-stamp is 200 bytes. The transmission radius is 1500 m. The bottlenose dolphin is chosen as the marine animal, which occupies a frequency band of 0.52 kHz to 33 kHz [In our simulation, sensor nodes are randomly deployed in a 9000 m × 9000 m × 7000 m three-dimensional underwater space. There is at most one sensor node per 1 kmo 33 kHz , and theo 33 kHz . The expPacket delivery ratio (PDR) is one of the fundamental performance measures of every routing protocol. The formula for calculating PDR is given as:PDR in the network refers to the ratio between the packets received by the destination node and the packets sent by the source node. We simulate the relationship of packet delivery ratio and the number of nodes of three routing protocols.In this simulation, the simulation duration is 500 s and the number of nodes varies between 50 and 500 with a step of 50. As shown in The end-to-end delay is expressed as the total time that a packet is sent from the source node to the destination node. The end-to-end delay primarily includes several aspects of propagation delay, transmission delay, waiting time and calculation time. The number of nodes is the same as the simulation above varies from 50 to 500 with a step of 50. The simulation duration is 500 s.As shown in jN to transmit packet, and jN to receive packet, and jN for idle overhear mode of sensor nodes. M is the total number of the sensor nodes.Energy consumption is also a very important indicator of network performance evaluation. The energy consumption formula in EOAR is:The energy consumption of underwater acoustic communication mainly includes the energy required to collect data, send data packets, receive data packets and the energy for calculation and idle state. In EOAR, only the nodes in candidate forwarding relay set are responsible for forwarding the packets to reduce the unnecessary node participation, thus EOAR can reduce the energy consumption. Besides, in order to prevent the same packet from being repeatedly forwarded by different nodes, EOAR sets a waiting time for each candidate forwarding relay. To further confirm that our proposed routing protocol has efficient performance, we compare its communication energy consumption with HHVBF and VHGOR. 9 J. The power consumption for transmitting state is 10W and the power consumption for the idle state is 30 mW. It can be seen from 7 J, and it is higher than the other two protocols. This is because the current node needs to adopt the acoustic perceive method to calculate the interference area of animal-nodes in the EOAR protocol when it encounters the interference of the marine animals. However, after a period of communication, the total energy consumption of the EOAR protocol is lower than the other two protocols because EOAR is able to avoid packet collisions and reduce the number of nodes participating in forwarding. In the simulation time of 500 s, the total energy consumption of EOAR is lower than the total energy consumption of the other two protocols. Compared with HHVBF and VHGOR protocols, the energy consumption of EOAR is reduced by 53.4% and 32.7%, respectively. This proves that the EOAR protocol has obvious advantages in reducing communication energy consumption in long time operation.In this simulation, the number of sensor nodes is 300. The initial energy of each node is 10kth simulation run and the time at the start of the kth simulation run, respectively. In this simulation, the results are averaged from a total of 50 runs.It is very difficult to replace the batteries for the sensor nodes in UASNs, so extending the network lifetime is important. In this paper, the network life cycle is defined as the duration from the network startup to the first sensor node exhausting its energy. The formula for the network lifetime is expressed as:The average network lifetime was measured for the EOAR protocol and compared to the HHVBF and VHGOR protocols, as shown in In UASNs, avoiding obstacle interference and high energy consumption is a hot research topic for designing effective routing protocols. In this paper, we have designed an energy-efficient and obstacle-avoiding routing (EOAR) protocol. When current node wants to send a packet to the destination node, the current node first perceives the interference information of the marine animal, and then determines the interference area of the animal-nodes using the underwater acoustic channel model. The neighbor nodes of current node, which meet the three constraints label themselves as the candidate forwarding relays of the current node. According to the fuzzy logic-based forwarding relay selection scheme, which considers propagation delay, included angle and residual energy, each candidate forwarding relay calculates its forwarding probability. In addition, EOAR uses a priority-based forwarding method to prevent packet collisions in order to reduce energy consumption. Experiments show that compared with the HHVBF and VHGOR protocols, the proposed EOAR protocol in this paper has greatly improved the performance of data packet delivery ratio, end-to-end delay, energy consumption and network lifetime. In future, the channel capacity, communication reliability and other performance indicators can be integrated into the candidate forwarding relays selection of the EOAR protocol for different research targets.