We investigate the two-component quantum walk in one-dimensional lattice. We show that the inter-component interaction strength together with the hopping imbalance between the components exhibit distinct features in the quantum walk for different initial states. When the walkers are initially on the same site, both the slow and fast particles perform independent particle quantum walks when the interaction between them is weak. However, stronger inter-particle interactions result in quantum walks by the repulsively bound pair formed between the two particles. For different initial states when the walkers are on different sites initially, the quantum walk performed by the slow particle is almost independent of that of the fast particle, which exhibits reflected and transmitted components across the particle with large hopping strength for weak interactions. Beyond a critical value of the interaction strength, the wave function of the fast particle ceases to penetrate through the slow particle signalling a spatial phase separation. However, when the two particles are initially at the two opposite edges of the lattice, then the interaction facilitates the complete reflection of both of them from each other. We analyze the above mentioned features by examining various physical quantities such as the on-site density evolution, two-particle correlation functions and transmission coefficients. The dynamical evolution of isolated quantum many-body systems has been a topic of great interest in recent years in the context of non-equilibrium physics. The long time evolution of quantum states provides useful insights about the route to equilibration which is fundamentally very important to study physics related to localization and thermalization. While the time evolution of a many-body state provides actual dynamical behaviour of a system, quantum walk (QW) on the other hand, is an extremely versatile approach to address the dynamical behaviour of interacting systems at the few particle levels.1. Due to their relevance in fundamental physics by advancing our understanding of the quantum dynamics of different systems and possible applications in quantum technologies, QWs have attracted enormous attention in recent years9 The rapid experimental progress in the last decades have led to the observation of QWs in different systems such as trapped ions, neutral atoms, photons in photonic lattices and waveguides, biological systems etc20 at the single particle level. This has further facilitated to study the dynamical properties in systems with disorder, frustration and topological features29.The phenomenon QW is the quantum analog of classical random walk which deals with the stochastic evolution of quantum walker(s) on a graph42. The combined effect of the inter-particle interaction and indistinguishability results in interesting features in different systems such as quantum gases in optical lattice37, correlated photon pairs44, trapped ions11, and superconducting qubits46. One such revelation is the spatial bunching of bosons in QWs due to the interaction between the two particles initially located at the same site and the Hanbury-Brown and Twiss (HBT) type interference when the two non-interacting bosons are located at two nearest neighbor sites43. In contrast, the presence of strong interactions between two nearest neighbour bosons leads to spatial anti-bunching due to fermionization43. These developments have paved the paths for quantum simulations involving a few particles, and this provides a platform to have a bottom-up approach to understand the physics of many-body systems. Owing to their remarkable efficacy of probing many-body physics, QWs have been widely used to study different physical phenomena using both theoretical and experimental approaches50.Considerable efforts have been made to investigate the role of interactions in the case of QWs for more than one indistinguishable particle in various physical contexts57. The complexities of such binary mixtures yield significant insights into the interacting spin model, atom-molecule interactions, quantum entanglement, topological phase transitions etc69. Interestingly, the two component systems with hopping imbalance have shown to reveal exciting new physics in various context73. Moreover, the dynamics of these constrained systems under proper conditions may reveal a different scenario in terms of transport properties and relaxation which has been investigated in a recent experiment74. While the many-body simulations of such dynamical systems are challenging, the dynamics in the context of QW in such systems may reveal completely different physics which have gained attention in recent years77.On the other hand, the physics of two component systems hosts a completely different scenario compared to the single particle systems. The combined role of inter and intra-component interaction, correlation and statistics play a crucial role in revealing novel physics which have been explored in great detail in the context of the Hubbard models. However, the experimental realization of such systems was made possible in systems of ultracold atoms in optical lattices. Considerable progress has been made in creating and manipulating binary atomic mixtures in optical lattices. Although the experiments using atomic mixtures are extremely complex compared to the single species systems, recent progress on the experimental front has made it possible to access Bose–Bose, Fermi–Fermi and Bose-Fermi mixtures in the absence and presence of optical lattices78 is formed as a function of inter-particle interaction - a phenomenon similar to the case of two identical particles43. However, when the two particles start from two nearest neighbor sites, then the wavefunction of the fast component transmits through the slow component in the absence of interaction. With increase in interaction, the fast component completely gets reflected from the slower one before forming a weak doublon in the limit of weak interaction. However, when the walkers are few sites apart, the behaviour is similar to the previous case except that the doublon formation is not so prominent. Interestingly, when the two particles are initially located far from each other, then both the particles feel the effect of the interaction and reflect from each other.Exploiting the experimental advances in the creation and manipulation of two-component quantum gases in optical lattices, in this work we study the physics characterizing the QW of a two component system in a one-dimensional lattice. To this end we consider a system of two interacting particles of different hopping strength or different mass and show that the combined effect of hopping imbalance and interaction exhibits interesting physics in the two particle QW. Moreover, we show that the choice of initial states also plays an important role in the QW in such hopping imbalanced systems. Before going to the details of our studies, we briefly highlight the important findings which emerge from our analysis. We show that when the two particles start the QW from the same site, a repulsively bound pairU is the inter-component interaction strength and i’th site corresponding to each component The Hamiltonian for the model which describes the system under consideration is given by;81 which is based on the dynamical evolution of an initial state under the influence of a time independent Hamiltonian as shown in Eq.  approach43 for identical particles. For our analysis we compute t. Apart from these two important observables, we also analyze other quantities of interest such as the half-length occupation, point of contact and transmission coefficients which we describe in the following section. In our numerical simulations, we consider a lattice of length U from zero to a large repulsive limit. Note that similar physics is expected for attractive interactions as well. By considering different values of In order to understand the physics of the system, we primarily compute two important physical quantities such as the expectation value of the on-site number operator as43. It has been shown in both theoretical and experimental analysis that when In this section, we start with the QW of mentclass2pt{minimU and analyze the spreading of the on-site particle density, which is depicted in Fig. 38. Further increase in interaction to a large value results in only a single profile corresponding to a slow spreading of the density, indicating that the two particles performs QW as a composite object. This feature in the QW can be attributed to the formation of doublons  between the two components which we define asTo further complement the dependence of doublon formation on SDI as a function of U for different SDI are finite for smaller U and eventually vanish in the regime of large U. While the vanishing of the SDI for large U is due to the doublon formation - a process similar to the balanced case . The QW for the initial state  imbalance, the transmission ceases for a weak (strong) interaction U. This is because for small T is always zero because the T is very slow as a function of U for all the cases considered. In order to obtain the value of critical interaction strength (d. Note that in our analysis, we consider U on T.The effect of hopping imbalance on the QW is further studied by calculating the transmission coefficient defined asmentclasspt{minimasee Fig. a and est84. Moreover, we have obtained that the change in the initial position of the particles leads to a qualitative change in the results. These findings provide insights into the dynamical behavior of a mixture of two component systems in periodic potential at the few particle levels. Due to the recent experimental progress in controlled creation and manipulation of multi-component atomic mixtures in an optical lattice and the single site addressing techniques, our prediction can, in principle, be simulated in quantum gas experiments. While the hopping imbalance can be indirectly obtained by considering a two component atomic mixture of different masses such as 56, it will be impossible to tune the hopping imbalance to explore the physics in broader parameter space. Therefore, an appropriate platform can be the mixture of two hyperfine states of a particular atomic species in a state dependent optical lattice where the hopping strengths of each internal state can be independently tuned85.We have studied the QW of a two-component system in the presence of interaction and hopping imbalance in a one-dimensional lattice. By considering different initial states depending on the positions of the particles (


Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which recovers sparse point clouds from image sequences. However, large-scale scenes cannot be reconstructed using a single compute node. Image matching and geometric filtering take up a lot of time in the traditional SFM problem. In this paper, we propose a novel divide-and-conquer framework to solve the distributed SFM problem. First, we use the global navigation satellite system (GNSS) information from images to calculate the GNSS neighborhood. The number of images matched is greatly reduced by matching each image to only valid GNSS neighbors. This way, a robust matching relationship can be obtained. Second, the calculated matching relationship is used as the initial camera graph, which is divided into multiple subgraphs by the clustering algorithm. The local SFM is executed on several computing nodes to register the local cameras. Finally, all of the local camera poses are integrated and optimized to complete the global camera registration. Experiments show that our system can accurately and efficiently solve the structure from motion problem in large-scale scenes. Structure from motion (SFM) has rapidly developed in the field of 3D reconstruction. Image feature extraction and matching have generally achieved considerable success in computer vision. Until now, the existing research used a single computing node to reconstruct a three-dimensional (3D) sparse point cloud for small scenes with thousands of images as input. However, with the increase in the number of datasets, the reconstruction of large scenes must adopt distributed computing to ensure the accuracy and efficiency of reconstruction.In the entire SFM step, image matching and epipolar constraint filtering are the most time consuming. To solve the problem of time consumption, Li et al.  used theInspired by the divide-and-conquer framework, we propose camera clustering based on normalized-cut segmentation in this study. First, we divide the camera graph. The efficiency and robustness are weighed to perform the expansion work. We then delete the effective edges when graph cutting is restored. The clustering of cameras guarantees execution of the divide-and-conquer framework and preserves most of the connectivity between cameras. For merging blocks and reconstruction results of the subgraph, we select a reference camera and use it to calculate the scale ratio of the two blocks. The advantage of our system is that it only needs to meet two coincidence point pairs between the blocks—it does not require each block to have an overlapping relationship. The graph segmentation method can also solve the problem of global SFM sensitivity to outliers. The registration of global camera poses is completed through merger and optimization of the blocks.We propose a method for calculating the GNSS neighborhood, which greatly reduces the time of image matching and ensures the robustness of the matching relationship.We propose a distributed camera registration algorithm to ensure a strong correlation within the camera blocks and robustly merge and optimize all of the blocks to obtain an accurate global camera pose.Our algorithm is deployed in a distributed system that can ensure the efficiency and accuracy of large-scale 3D reconstruction work.In summary, this study makes the following contributions to existing literature:The traditional SFM is divided into two methods: incremental SFM and global SFM. Incremental SFM ,9,10,11 Compared with the incremental SFM, global SFM ,25,26,27Global SFM is particularly dependent on the accuracy of image matching and is sensitive to outliers. Otherwise, its reconstruction integrity is not as good as that of incremental SFM. To counter this problem, hybrid SFM (HSFM) was introduced. HSFM uses the respective advantages of incremental SFM and global SFM to solve large-scale 3D reconstruction problems. It calculates the global camera rotation at once and incrementally retrieves the camera projection centers ,43,44,45The application of deep learning in geometric matching pipelines mostly focuses on local feature detection and descriptor learning ,50. MismSome studies attempted to solve large-scale SFM through multi-core computing nodes ,21,61,62With the rapid increase in the number of relative motion calculations ,36,37,38To solve the problem of large-scale motion averaging in a distributed way, we propose a divide-and-conquer framework to complete global camera pose registration in a distributed manner. We also introduce GNSS information as a filter criterion for image matching, as it can reduce the time complexity of the image matching part of the traditional SFM method to linear time complexity.Image matching ,29 is a h represents height, which is the height from the surface of the ellipsoid. As GNSS coordinates uses the WGS84 ellipsoid as a reference surface, we need a Cartesian 3D model to correctly calculate the distance between the two cameras in space. We need to convert the WGS84 coordinate system to the Earth-centered Earth-fixed (ECEF) coordinate system [a and b represent the length of Earth ellipsoid’s semi-major axis and semi-minor axis in the geodetic system, respectively; f represents the flattening factor of the earth; and e represents the eccentricity of the Earth.N is defined as WGS84 to ECEF coordinate system conversion: The GNSS system uses position satellites to locate and navigate in real time. Almost all cameras used in UAVs have the possibility of storing the drone’s GNSS information inside the exchangeable image file (EXIF) of the image. GNSS coordinates are based on the World Geodetic System 1984 (WGS84). We mark the GNSS coordinates in the WGS84 coordinate system as e system . The conN images, we calculated the spatial Euclidean distance between Spatial threshold calculation: Given the input D is calculated such that each image is matched with other images in less than the distance The farthest distance m hash functions. We then used scale-invariant feature transform (SIFT) for feature points detection and mapped the image feature points to a hash table, which contains H should be similar. Finally, we selected multiple hash function sets k for each image to improve the time efficiency of image matching. In our experiment, we added the matching relationship to the pre-matched map only when the k of two images is both less than 100. The image matching and epipolar geometric constraint filtering are performed according to the pre-matching map.Pre-matched view graph establishment: After obtaining each image and all GNSS neighbors that meet the distance threshold, we generated a pre-matched camera graph structure based on the GNSS neighborhood. First, we selected the set of hash functions according to the locality sensitive hash (LSH) algorithm  and randCascade hashing image matching: We used the LSH algorithm to implement a cascade hashing image matching method. This method includes three steps: rough query, mapping Hamming space, and hash sorting. First, we recorded the feature points in all hash tables that fall into the same hash bucket and used these hash buckets as the scope of the query. We then mapped each feature point to a higher dimensional Hamming space (128 dimension in our experiment) and only calculated the descriptor similarity between the feature points for which the Hamming distance is less than the threshold. Finally, the problem was transformed into a V represents the cameras and E is the matching relationship between the cameras. The vertex Epipolar constraint: We first obtained all matching relations that were accurate and satisfied the epipolar geometry. We then established the camera graph With the increasing number of input images for the SFM, two problems need to be solved. The first is that the program exceeds the memory limit of a single computing node. Second, the parallelized computer cluster resources are difficult to use ,21,61,62enforceability constraint on a single computing node. The expansion makes all adjacent subgraphs overlap enough to meet the consistency constraint. This ensures that the corresponding poses are merged in motion averaging. At the same time, accurate global camera poses and complete reconstruction results can be obtained.The segmentation of the camera graph should be divided into two steps, namely division and expansion. Division enables each sub-problem to solve the We use a normalized-cut algorithm to divide the camera graph. The matching relationship among the images was used as the weight. The camera graph was divided into multiple subgraphs. As there needs to be a coincidence point between subgraphs, it is necessary to select the edge with the greatest weight from the edges removed by the division. We retrieved this from each subgraph.A and B. This can be done by deleting some edges so that A and B. It can further be defined as A and all of the nodes V in graph G can be recorded as The normalized-cut algorithm is a graph segmentation algorithm. A weighted undirected graph n method  is givenNote that minimizing the calculation result ensures that the subgraphs have a small degree of correlation and that the subgraphs have a large degree of correlation.G is divided into several subgraphs We used ecessors  used 100Each subgraph must have overlapping vertices to complete the camera pose synthesis and subsequent reconstruction work between subgraphs. Therefore, the subgraphs after division must be expanded accordingly. Each subgraph does not need to overlap with all of the other subgraphs. However, the coincidence ratio between the subgraphs must be guaranteed to ensure that each subgraph can be merged. This is not affected by the relationship between the subgraphs. We define the consistency constraint asWhen all of the removed edges of multiple subgraphs were restored, the overall expansion ratio was lower, resulting in insufficient matching relationships. In this case, we performed a secondary expansion on all subgraphs. We defined the ratio of all new vertices to the total number of original vertices as the expansion consistency constraint asEquation . If the The camera poses in each subgraph can be obtained by executing the local SFM for each subgraph. In this section, we provide a fast and accurate averaging method to merge and optimize the previously calculated camera poses of each subgraph. We obtain global camera poses for subsequent triangulation and bundle adjustment.i, respectively; j; and i and j, respectively. A similar transformation method to that in  was usedThese equations represent the conversion relationship between the poses of the two cameras. The camera poses in the world coordinate system of the two cameras can be used to estimate the relative rotation and translation between the two cameras. The camera pose was merged using a similar transformation method. We optimized the merged camera pose by nonlinear optimization to obtain an accurate global camera pose.We denote the repeated cameras Except for the fixed camera pose, all other camera poses belonging to the subgraphs need to be calculated. This results in some coincidence points that are repeatedly calculated for the pose. These repeated calculations were used as a benchmark for error. We first selected a fixed camera pose, which we believed was the most accurate reference camera. We then set the error between the camera poses of all calculated coincidence points and the original pose as the smallest one. Finally, we used this camera as the base of the camera’s global rotation registration.The rotations and translations of the camera poses in each subgraph have their own scales. These scales do not affect the abovementioned method of global rotation registration. However, the registration of the global translation registration needs to calculate the difference scale value As shown in Equation , withoutThe camera poses in each subgraph can be obtained by executing the local SFM for each subgraph. The relationship between the local translation within two subgraphs can be obtained after executing the local SFM. We combined all of the coincident points In the global rotation registration, we selected the most accurate reference camera, and in the global translation registration work, we used the same camera as the reference. We marked the translation of the reference camera in the current coordinate system and the target coordinate system as We first calculated the relative translation Equation . We thenEquation .In this section, we discuss how the camera pose is merged between the two sub-images. We can also merge new subgraphs through continuous iterations and finally obtain a unified global camera pose. Our method does not require overlap between each subgraph; it only needs to ensure that there is sufficient overlap relationship, so that a new subgraph can be merged repeatedly through iteration. As each new subgraph only needs to have an overlap relationship with the merged group of pictures, the number of overlapping points is at least 2.P takes 2 to represent the Calculation of the translation scale among the coordinate systems can be guaranteed to be accurate by averaging it with the selections from the reference camera. A relatively complete reconstruction result can be obtained by directly applying these camera poses to the subsequent reconstruction work. However, the details of the reconstruction result are greatly reduced. In this section, we give more consideration to the relationship between the sub-images and further optimize the previously calculated camera pose to obtain a more refined reconstruction result. We keep the relative rotations and translations obtained when local SFM is executed on each subgraph and record them as Equation , dR reprThese local camera poses are obtained from each subgraph, which can ensure that the relationship between them is accurate because of the advantage of normalized-cut. Therefore, we used the camera pose as input to avoid calculating the relationship among all camera poses. This can also completely optimize the overall global camera pose. After optimizing the above camera pose, a robust and accurate global camera pose was obtained for subsequent triangulation, and hence, a sparse point cloud with an accurate position could be obtained.We implemented GNSS neighbor computing, camera clustering, and global camera pose merging and optimization on a single computer. The server system was Ubuntu 16.04, g++ 9.2.0 and was configured with an Intel(R) Xeon(R) CPU E5-2680 v4 at 2.40 GHz 28-core 96 GB memory. We also performed image matching and local camera pose estimation on a distributed computing system consisting of eight computers. In this section, we use three aerial datasets to validate our method. Aerial dataset 1 is an area of Tiangong University, which contains 1510 images. Dataset 2 is the entire campus of Tianjin University of Technology and contains 8876 images. Dataset 3 is the Tianjin Xuefu Industrial Zone, which contains 25,726 images. To verify the effectiveness of our image matching method, we performed a time-consuming analysis on these three datasets. We used a single computer configuration to verify the accuracy of the camera clustering results. We further applied our method to a large-scale scene to verify the effectiveness of the method. As described in We compared our approach with state-of-the-art sparse point cloud generation methods in different scenarios. We carried out experimental verification on the two types of internet public datasets and large-scale datasets.Time efficiency evaluation: We compared our approach with two incremental SFM (COLMAP  and Theijth point as seen by the ith camera. If the image measurements are noisy then the equations Accuracy and performance evaluation: To verify the accuracy and efficiency of our approach, we compared our method with several traditional methods on public internet datasets. Bundle adjustment is generally used as a final step in generating sparse point clouds. It is known as the estimation involving minimizing the reprojection error. Consider a situation in which a set of 3D points In the final generation of sparse point cloud, we evaluated our algorithm on several public datasets ,72. For The sparse point cloud reconstruction results are shown in Effectiveness evaluation: We used the large-scale scenes in Performance evaluation: Similar to the performance evaluation in Parameters: Although our method involves many parameters, most of these parameters are insensitive to different large-scale scenes. We used the same parameter values for all scenes throughout our experiments. All parameter values were specified in each step.Robustness: We generated a sparse point cloud of a large-scale scene to evaluate the robustness of our method. Calculating all camera rotation and translation matrices at one time consumes a considerable amount of time and surpasses the memory limit of the computing nodes. We proposed a divide-and-conquer framework to achieve global camera pose registration for large-scale scenes. Limitations: Although our method uses the divide-and-conquer strategy to solve the registration of camera poses in large-scale scenes, it does not improve the subsequent bundle adjustment, which still consumes a lot of computing resources. Another limitation is that the poor-quality extended cameras are discarded after the local camera pose estimation due to incomplete capture of large-scale scenes. In addition, the proposed workflow does not work when there is no available camera spatial location information.We proposed a distributed 3D sparse point cloud generation method for reconstruction of large-scale scenes. By calculating the GNSS spatial neighborhood of aerial images, the square time complexity of traditional image matching was reduced to approximately linear. The divide-and-conquer framework was used to solve the distributed SFM problem. We divided the camera graph into several subgraphs and ensured that the subgraphs met the enforcement and consistency constraints. Local camera poses were merged and optimized to obtain the global camera poses. Finally, we used traditional triangulation and bundle adjustment to obtain a sparse point cloud. Compared with the traditional state-of-the-art sparse point cloud generation methods, our approach could effectively generate sparse point cloud results and the corresponding 3D models. Our method could complete the 3D sparse point cloud reconstruction of a real scene with an area of 10 km


The small size of the feline mandible makes its manipulation difficult when fixing dislocations of the temporomandibular joint or mandibular fractures. In both cases, non-invasive techniques should be considered first. When not possible, fracture repair with internal fixation using bone plates would be the best option. Simple jaw fractures should be repaired first, and caudal to rostral. In addition, a ventral approach makes the bone fragments exposure and its manipulation easier. However, the cat mandible has little space to safely place the bone plate screws without damaging the tooth roots and/or the mandibular blood and nervous supply. As a consequence, we propose a conceptual model of a mandibular prosthesis that would provide biomechanical stabilization, avoiding any unintended (iatrogenic) damage to those structures. The improvement of imaging techniques and a patient-specific prosthesis made of full biocompatible material are part of the future trends to improve patients’ recovery.The cat mandible is relatively small, and its manipulation implies the use of fixing methods and different repair techniques according to its small size to keep its biomechanical functionality intact. Attempts to fix dislocations of the temporomandibular joint should be primarily performed by non-invasive techniques (repositioning the bones and immobilisation), although when this is not possible, a surgical method should be used. Regarding mandibular fractures, these are usually concurrent with other traumatic injuries that, if serious, should be treated first. A non-invasive approach should also first be considered to fix mandibular fractures. When this is impractical, internal rigid fixation methods, such as osteosynthesis plates, should be used. However, it should be taken into account that in the cat mandible, dental roots and the mandibular canal structures occupy most of the volume of the mandibular body, a fact that makes it challenging to apply a plate with fixed screw positions without invading dental roots or neurovascular structures. Therefore, we propose a new prosthesis design that will provide acceptable rigid biomechanical stabilisation, but avoid dental root and neurovascular damage, when fixing simple mandibular body fractures. Future trends will include the use of better diagnostic imaging techniques, a patient-specific prosthesis design and the use of more biocompatible materials to minimise the patient’s recovery period and suffering. As seen in the first part (The cat mandible (I) ), a deepHence, the present review will be focused on how to solve different pathological situations of the feline mandible and temporomandibular joint, such as luxation or fractures. In addition, we will insist on the importance of keeping the integrity of the tooth roots and the mandibular canal neurovascular bundle, as they could be damaged during fracture repair.The temporomandibular joint (TMJ) is characterised by the presence of a whole intra-articular disc, although is very thin in the cat and in the dog. Many elements are related to this synovial condylar joint and gaining a comprehensive knowledge of this joint is required to correctly interpret its diagnostic images. This ability is essential in order to make a diagnosis and to achieve good results in the management of different conditions, minimising the incidence of surgical iatrogenic lesions. While TMJ caudal luxation might occur with fractures in the retroarticular process, mandibular fossa or mandibular head, rostral mandible movement is more common . UnilateOpen-mouth jaw locking is characterised by an inability to close the mouth that usually results from fixed mandibular coronoid process displacement lateral to the ipsilateral zygomatic arch and abnormal contact pressure between these two structures . The hisMandibular fractures are commonly seen in practice, comprising up to 6% of all fractures in dogs, and 11–23% of all fractures in cats, according to Glyde and Lidbetter . They ocn = 517). Symphyseal fractures were the most common (73.3%), followed by fractures of the body (16%), condyle (6.7%) and coronoid process (4%). Complications developed more commonly in cats with multiple or open fractures. Clinical union occurred after an average of 6 weeks (range 3–12 weeks) for symphyseal fractures, 10 weeks (range 8–16 weeks) for body fractures, 6 weeks for coronoid fractures and 6 weeks (range 4–8 weeks) for condylar fractures [Independently of the aetiology, in cats, and according to Umphlet and Johnson , mandiburactures . Umphletractures  also repractures . Nonetheractures .Accordingly, Little  reportedA non-invasive approach should receive primary consideration, and an invasive option is employed only if non-invasive treatment is insufficient or impractical . Tape muIn the cat, when the jaw is immobilised to allow healing of the fracture, the mouth should be kept open no less than 5 mm and no more than 10 mm  to allow for the tongue to protrude and lap water and a slurry diet. If the mouth is open too far, it will result in difficulty in swallowing . This imSoutherden et al.  reportedRegarding mandibular biomechanics, as reported by Spodnick and Boudrieau , a contiMandibular symphysis fractures are the simplest and are best treated with cerclage wire . PalpablParasymphyseal fracture is a common iatrogenic injury during extraction of the mandibular canine tooth in cats . The fraWhen assessing the mandibular body for fractures, the direction of the fracture and the location of the fracture in relation to the dental roots should be evaluated. From a biomechanical point of view, fractures could be simple (in which the fracture line is perpendicular to the long axis of the mandible), or oblique fractures, that may be described as favourable or unfavourable according to the difficulty of immobilisation. This distinction results from the forces that the muscles of mastication place on the mandible as they either compress (favourable) or distract (unfavourable) the fracture segments ,21 Figu. Hence, As far as fracture repair is concerned, it is extremely important to take notice of a large percentage of the mandibular body being occupied by deep dental roots—between 50% and 70% of its dorsomedial depth —and the As stated by Lantz , the priMandibular body fractures may be treated in various ways. Favourable body fractures could heal using conservative methods to get stabilization of the tension surface ,21. InteAnother option to treat unfavourable fractures is by using internal fixation with conventional bone miniplates and screws. The advantages of internal rigid fixation in the treatment of mandibular fractures are the accurate restoration of normal anatomy and occlusion and rapid return to normal function . NeverthThe mandible is not an easy bone in which to use plating techniques for stabilisation of fractures. According to Higgins , at leasThe article by Greiner et al.  biomechaPlate placement on the ventral (compression) border of the mandibular body, which avoids the neurovascular structures of the mandibular canal, increases the load on the plate. Mono-cortical application of bone plates to the mid-buccal surface of the mandible has been recommended to reduce the risk of iatrogenic damage . ConsequIn the case of comminuted or open fractures, in which soft tissue wounds prevent the use of internal fixation, an external fixation with pins and a mandibular bumper bar is appropriate for their reduction .According to Spodnick and Boudrieau , the manFractures of the mandibular ramus are relatively stable because the surrounding muscles usually prevent gross displacement of the fracture segments . Condylarami dentales) entering the apical foramina and the bone itself [It should be taken into account that more than the upper two-thirds of the body of the mandible is occupied by dental roots. The ventral third includes the mandibular canal, containing the inferior alveolar nerve and associated blood vessels, and the inferior alveolar artery and vein. The inferior alveolar nerve provides sensory innervation for the teeth and leaves the bone through three mental foramina as the mental nerves. These nerves are sensory to the soft tissues of the rostral part of the mandible. The blood vessels in the mandibular canal supply all the teeth by the way of small dental branches  model was built starting from the three-dimensional (3D) model of a real jaw. A cloud of points, attained after laser scanning a real jaw, has been post-processed to obtain the 3D model of the cat mandible used. Meshlab and Cloud Compare software were used to post-process the data obtained by laser scanning.The current prosthesis design proposal was made by using the design program SolidworksThe proposed prosthesis  is a conWe think it could achieve good results in further TRL levels as it has three fixation points with small screws and the fourth is like a folded tab for fastening the ventral edge, therefore avoiding drilling the mandibular canal. The design with a two-sided ‘Y’ was chosen in a way that the screw positions do not imply any risk of perforating dental roots or damaging the neurovascular support. This shape also allows keeping safe the mental nerve that goes through the main mental foramen and also avoiding the caudal mental foramen.The suitable material to implement this prosthesis, given its high requirements of resistance and biocompatibility, is titanium. The optimal manufacturing method would be ‘additive manufacturing’ , i.e., metal 3D printing, since the prosthesis is a unique piece of small size, and up to now, this is the only method that makes production economically viable. In addition, given that its geometric complexity does not pose problems for its manufacture, this method is considered the best option.In order to promote good adhesion of the prosthesis to the bone, and trying to avoid damage to the bone, the surface of the prosthesis should have a high roughness . Thus, the effective area of contact with the bone  would be minimised while increasing the friction force of the prosthesis with respect to the bone, which greatly reduces the possible relative slipping that may exist between the elements.Considering that at the time of application of the prosthesis, the jaw is immobilised, and that once the bone has healed the stresses of the prosthesis are greatly reduced, the function of the prosthesis at the beginning consists fundamentally in ensuring that the bone fragments are correctly and firmly positioned. A calculation of resistance based on a bite force of 10 kg is considered conservative.M) of:F is the biting force (N)d is the distance between the point of application of the force and the point of evaluation  (mm)Considering this force and that the central part of the prosthesis is the weakest part , which is placed 2 cm away from the canines, it is calculated that it supports a moment , as is shown in the next equation , with a Equation (1): Maximum traction tension considering only flexion efforts.b corresponds to thickness value (mm)h corresponds to height value (mm)M corresponds to flexure moment value (N·mm)σ corresponds to tension value (MPa)where:The value of the bending moment resistance is obtained considering a squared section of flexure; if the design of the prosthesis changes, as a result of an irregular section chosen, the finite element method (FEM) would be needed to calculate the maximum bending tension.As it can be seen, the stress obtained is considerably lower than the elastic limit of titanium. Thus, it could be considered a valid measurement. However, a balance should be struck between material resistance and physiology. Regarding the field of material resistance, it is advisable to make the prosthesis with a width as large as possible to achieve greater rigidity. However, this is inadvisable from the physiological point of view. Therefore, it is a question of achieving a balance between good rigidity and comfort.Screws with a diameter of 1 mm have been considered, and taking into account the previous data and the conservative hypothesis that all the force is supported by one of them, the following shear stress is obtained :(2)τmax=Equation (2): Maximum shearing tension considering bite forceτ corresponds to shear tension in screws (N/mm)F corresponds to bite force (F)r corresponds to screw radius (mm)where:The resulting stress is considerably lower than the shear strength of titanium, which means that the diameter of the screws can be even smaller.Depending on the shape or position of the screws, a deeper calculation based in the FEM would be needed.As previously indicated, the prosthesis shown is purely conceptual, and although an idea of the thickness and width that might be needed has been given, the calculations must be reviewed in practical clinical cases. Moreover, it would be convenient to undertake a more detailed study based on calculation software using the finite element method to obtain a more precise notion of the stresses to which the prosthesis is subjected. A topological optimisation of the geometry using suitable software could also be useful.The current proposal would be equivalent to a TRL2 prototype according to the well-known TRL scale applied to technology, whose value (from 1–9) gives an indication of the level of maturity of the product, which implies that it is the formulation of a concept that has not yet been validated in the laboratory. Therefore, our proposal considers the mandibular prosthesis concept like an embryo, without practical validity, although its correct evolution and development could mean important advances in the field of intervention for mandibular fractures, not only in cats, but also in other species, by avoiding complications caused by damaging the integrity of the tooth roots and neurovascular supply when placing the screws to fix the jaw prosthesis.This prosthesis model aims to visually reveal the proposals and ideas expressed throughout this review, without delving into the details about main dimensions, materials, manufacturing process and other aspects that may affect its implementation. Prostheses of these characteristics should be designed specifically for each specimen, adapted in a 3D model of the cat patient’s jaw. In this way, not only will the size be adjusted, but the surfaces will also adapt much better, and thus a better grip will be ensured with all the advantages that this entails for fracture healing.On one hand, the use of virtual surgical planning (VSP) and CAD/CAM (Computer-Aided Design/Computer-Aided Manufacturing) technology has added a new dimension to surgical planning, especially in the areas of craniomaxillofacial trauma, orthognatic surgery and reconstructive maxillofacial surgery . This teOn the other hand, a comparison of three cone-beam computed tomography (CBCT) methods with dental radiography was analysed by Heney et al. . They foLiptak et al.  were oneOn one hand, the mandibular prosthesis size should be adapted to the small size of feline patients. It seems that most pet prostheses are designed for bigger specimens , they must have no sharp edges, and finally, they should have plenty of holes to place the screws and for muscle or tissue attachment. However, the screws used are usually too long, far surpassing the contralateral cortical bone.On the other hand, materials used in osteosynthesis must be more biologically acceptable because soft tissue is not going to attach to metal , so a deTo close this section, we cannot agree more with Vaughan , who staIn the body of the cat mandible, dental roots and the mandibular canal  occupy most of the volume. Therefore, in mandibular fractures , it makes it challenging to apply a plate with fixed screw positions without invading dental roots or neurovascular structures. Otherwise, it would be a very painful process, including the failure of fixation due to chronic infection and inflammation at the fixation site. In the face of all these difficulties, we proposed a suitable prosthesis design, produced by additive manufacturing, that would provide acceptably rigid biomechanical stabilisation and avoid damage to any of those structures when fixing a mandibular body fracture. The future depends on the improvement of diagnostic images and Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM) technology to manufacture custom-designed prostheses made of highly biocompatible material.


The COVID-19 pandemic has affected education and assessment programs and has resulted in complex planning. Therefore, we organized the proficiency test for admission to the Family Medicine program as a proctored exam. To prevent fraud, we developed a web-based supervisor app for tracking and tracing candidates’ behaviors.We aimed to assess the efficiency and usability of the proctored exam procedure and to analyze the procedure’s impact on exam scores.The application operated on the following three levels to register events: the recording of actions, analyses of behavior, and live supervision. Each suspicious event was given a score. To assess efficiency, we logged the technical issues and the interventions. To test usability, we counted the number of suspicious students and behaviors. To analyze the impact that the supervisor app had on students’ exam outcomes, we compared the scores of the proctored group and those of the on-campus group. Candidates were free to register for off-campus participation or on-campus participation.Of the 593 candidates who subscribed to the exam, 472 (79.6%) used the supervisor app and 121 (20.4%) were on campus. The test results of both groups were comparable. We registered 15 technical issues that occurred off campus. Further, 2 candidates experienced a negative impact on their exams due to technical issues. The application detected 22 candidates with a suspicion rating of >1. Suspicion ratings mainly increased due to background noise. All events occurred without fraudulent intent.This pilot observational study demonstrated that a supervisor app that records and registers behavior was able to detect suspicious events without having an impact on exams. Background noise was the most critical event. There was no fraud detected. A supervisor app that registers and records behavior to prevent fraud during exams was efficient and did not affect exam outcomes. In future research, a controlled study design should be used to compare the cost-benefit balance between the complex interventions of the supervisor app and candidates’ awareness of being monitored via a safe browser plug-in for exams. The COVID-19 pandemic has heavily affected education and assessment programs. Flemish (Belgian) universities were, as those in many other countries, not sufficiently prepared to fluently switch from analogous teaching and testing to digital teaching and testing. Moreover, the COVID-19 measures have also necessitated physical distancing before and during exams, which has resulted in a lower number of candidates participating in exam sessions. This situation has resulted in complex planning for managing human resources, locations, and adequate equipment.The challenge of reorganization is even more impressive in interuniversity collaborations for education. The Advanced Master of Family Medicine program in Flanders, Belgium, is formally organized and is offered by 4 Flemish universities. This collaboration comprises a common administration, common courses and examinations, common residencies, and separate registers of residents for the university of their choice. In the three phases of the advanced master program, we have over 900 residents in training and in education. The planning of examinations is therefore a complex logistic and administrative matter. To address this logistic challenge, we built (more than 1 decade ago) an intelligent, comprehensive, and interactive assessment platform. From other authors, we have learned that  students are in favor of computerized exams, and these exams may enhance learning experiences and effects without affecting final test outcomes [In general, it is not desirable to postpone or reschedule high-stakes tests or assessments ,6. Such The combination of planning an off-schedule exam and offering exams on campus  while adhering to the original exam format has forced us to opt for a creative solution. We decided to prioritize the format of this high-stakes test and organize—in addition to the original on-campus exam—a proctored off-campus exam. Web-based exams (or remote exams) for high-stakes assessments are considered user-friendly and cost-friendly and are flexible in terms of planning, but their validity is questionable if fraud cannot be ruled out ,8-10. ThTo minimize the occurrence of fraudulent events during the exam, we developed a web-based supervisor app for tracking and tracing candidates’ behaviors during the exam. The technology we have built and applied goes beyond that of traditional proctored exam systems that focus on tracing sounds and images. In addition to real-time supervision (image and sound), all student behavior is recorded and logged for posttest reassessment in case of suspiciousness. In this paper, we report on the efficiency and usability of our antifraud measures and the impact that a proctored exam has on exam outcomes.Through collaboration with the developers of the assessment platform and through discussions with the coordinators and exam supervisors  of the Advanced Master Education program, we determined the criteria and conditions for designing the supervisor app. The application operated on the following three levels: the recording of actions, analyses of behavior, and live supervision. First, during the exam, the system recorded data from three sources—the computer screen, the camera, and the microphone. These recordings were immediately encrypted and saved on a secured server. Second, the supervisor app used a pattern recognition algorithm for response, clicking behavior, and time stamp analyses . The appEventsBlur page (suspicious)This means that the user switched to another browser tab or another program.Focus page (suspicious)This means that the user returned their focus to the page.Focus page events can be used (after blur page events) to determine how long the user had been gone.Close page (suspicious)This means that the user closed the current tab or opened a different page in the same tab.Load pageThis event occurs when the user loads a page.Load page events can be used (after close page events) to determine how long the user had been gone.Keyboard eventsLogged with a basic key loggerConnected to the internet Disconnected from the internet (suspicious)The user can stop their internet connection to stop video recordings. This data were sent to the server when the user reconnected to the internet.Mouse clicksIncludes click location coordinatesSound detected (suspicious)The app tracks any noise above certain volume levels with a duration of >1 ms.Administrator started watchingThis is marked in blue on the tracking page.Administrator started talkingThis is marked in blue on the tracking page.Finalization of the test componentThis is needed because when a user completes the test component, a close page event is triggered.The third level of supervision allowed for optional human oversight during the examination. The human monitor could immediately join the live feed of each candidate to obtain more information or to send a warning to candidates in private. In case the supervisor app crashed, the affected candidates were able to switch to the Safe Exam Browser (Eidgenössische Technische Hochschule Zürich), which allows only the exam interface to remain accessible on a user’s computer.In addition to the technical solutions, we expected the application to have a preventive effect on candidates’ behaviors. Candidates were comprehensively briefed in advance on how to install the application, on how to test it, and on the specific features of the supervising technology. We used an animation video and a written instruction form for the briefing.A voluntary panel that consisted of experienced teachers and exam supervisors tested the supervisor app in 2 sessions. These test participants were asked to behave in a suspicious manner . After the first session, we made the following adjustments to the application and the procedure: we isolated the suspicious sound events from the other suspicious events, and we increased the overall suspicion rating from 0.5 to 1.The comparison group was composed of the on-campus candidates who were supervised under the usual circumstances and conditions. To assess efficiency, we logged technical issues and the interventions that were used during the exam. Technical issues were defined as those reported by students, and interventions referred to the active responses of the team. To analyze the impact that the supervisor app had on students’ exam outcomes, we compared the scores of the proctored group to those of the on-campus group. To test usability, we counted the number of suspicious students and behaviors.All students who applied for admission to the advanced master program were included in this study. To take the exam, candidates were free to register for off-campus participation or on-campus participation. On campus (n=4), a human supervisor was present, and candidates used the campus’ gear. For off-campus monitoring, we had an experienced supervising team of 6 staff members. A team of 2 developers was also fully available during the exam. The off-campus supervisors were able to send notifications or warnings to candidates who were behaving suspiciously, and these supervisors intervened in cases involving technical issues.The actual exam and the associated procedures were set up in the same manner as those that were conducted before the COVID-19 pandemic; all candidates completed the same exams, and candidates who failed the machine-assisted test  were invited to a jury exam after 1 week. Candidates who were flagged as those exhibiting suspicious behaviors during the exam or in postexam analyses were also invited to the jury exam.Ethical approval was not required for this study. Consent for the administration of the proctored exam was obtained from the Permanent Education Commission of the Faculty of Medicine of KU Leuven.The complete data set is available upon simple request and can be sent as a link to a Google Drive directory.P=.15).A total of 593 candidates subscribed to the exam. Of these candidates, 472 (79.6%) used the supervisor app for off-campus exams and 121 (20.4%) were present on campus . The tesOverall, we registered and solved 15 technical issues that occurred during the off-campus exam . Of thesOf the 593 candidates, the application detected 22 (3.7%) with a suspicion rating of >1. All cases were the result of 1 or more noise events (background noise). These students received an immediate but nonoffensive request to stop all background noise without further consequences or impacts on the progression of their exams. Additionally, the supervisor sent a request to all students to reduce background noise. All other noncritical events included leaving the webpage, closing a page, or typing text. Live monitoring and a postexam review of the recordings confirmed that all of these events occurred unintentionally and without fraudulent intent. In the on-campus group, we did not detect suspicious or fraudulent students.The monitoring supervisors flagged two candidates who were typing more than the expected amount (in a multiple-choice exam). After the review of the recordings, it was found that these two candidates were using the “control find” function to search for words in the reading text. During the exam, the supervisors intervened 8 times due to technical issues, warned 2 candidates to stop talking to themselves, and sent a group message to ask candidates to reduce background noise.This pilot observational study demonstrated that a supervisor app that records and registers behavior was accurate because it was able to detect all suspicious events without having an impact on exam performance. The efficiency of the proctored supervisor system seemed low, as shown by the availability of a 12-person bystander team, which monitored and solved a relatively low number of technical issues.Students have shown certain preferences for different exam formats depending on how much confidence they have in their techniques, the organization, exam procedures, and exam outcomes and how confident they are with technological aspects and issues ,12,14,15The major weakness of proctored exam systems lies in the occurrence of technical or technological failures and in a lack of user experience ,4,12,16.During the entire exam, a team of 12 persons was constantly monitoring and maintaining contact with the candidates. Although we did not perform a cost-benefit analysis, we assume that the whole procedure does not save costs, since the on-campus exam opportunity also remained available .On average, candidates induced 3 events, of which the noise and sound event was the most prevalent and critical. The development team increased the threshold sound level during the exam, since the application immediately (in the first few minutes) flagged all candidates. Sound is indeed difficult to avoid and control  ,12.A very small number of candidates was flagged as suspicious by the application, but due to the live monitoring of the exam, they were exempted from suspicion. The supervisors reviewed the recordings of two candidates who were flagged by the system and via the live monitoring of the exam. The records did not reveal any fraudulent behavior. During the exam, the team was constantly watching the students, and the system continuously logged behaviors. Additionally, students were very aware of the presence of the supervisors. They were also aware of the fact that they were being watched and screened. Further, the exam was performed within a restricted time span; therefore, students did not have time to cheat. Consequently, students were probably very reluctant to cheat, and this might have increased their stress levels ,13.Prior to the exam, we thoroughly instructed the candidates on the registration of suspicious events. This single intervention might have been decisive in stopping candidates from committing fraud during this high-stakes exam ,4,10. A During the exams, supervisors limited the number of interventions and warnings to avoid distracting the candidates. Technical issues were solved, and supervisors sent a nonoffensive warning to only two candidates who were talking to themselves. After the exam, we only received reports of technical issues; we received no complaints regarding supervisors’ interventions or interruptions caused by the supervisors.A weakness of the intervention is that we did not question the candidates about their experiences. This group of graduation candidates was, at the time of writing this report, very hard to reach. However, the staff and the formal faculty administration did not receive comments or objections from students in the period following the exam. Other reports have shown a relatively high satisfaction rate among master program candidates, and complaints have typically been about technical issues . A seconA major strength of this intervention is the fact that we were able to use the application for a large group of candidates taking a high-stakes exam . AdditioA sophisticated supervisor app that registered behaviors and recorded sounds and images to prevent fraud during exams proved to be efficient and did not affect exam outcomes. However, live monitoring and having a team on standby for solving technical issues resulted in a high amount human workload. In future research, a controlled study design should be used to compare the cost-benefit balance between the complex interventions of the supervisor app and candidates’ awareness of being monitored via a safe browser plug-in for exams.


Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study. Structural monitoring of primary and secondary structural elements is generally a complex, costly and time-consuming process. All structural components are inspected at regular intervals, using different non-destructive techniques (NDTs) that increase more and more the total duration of the downtime.Such inspections for health-condition assessment are of the utmost importance for the safe and efficient operation of structures. A damage that cannot be reliably detected could not be repaired on time, leading to eventually catastrophic failures.To overcome these problems, in view to ensure the safety of the structure, Structural Health Monitoring (SHM) systems able to monitor the actual structural integrity become necessary ,2,3. TheIn the execution of SHM strategies, the most commonly used approach is that based on guided (or Lamb) waves (GW) ,5,6 becaSuccessful damage identification by using a GW-based SHM system can be performed with transducers in a sparse configuration and using well-calibrated signal-interpretation techniques. Some essential steps are the following: (i) activating the desired diagnostic Lamb wave signal, using an appropriate transmitter and capturing the damage-scattered wave signals using a sensor or a sensors network in accordance with either the pitch-catch or the pulse-echo configuration; (ii) extracting and evaluating the characteristics of the captured wave signals with appropriate signal post-processing tools; (iii) establishing quantitative and/or qualitative connections between the extracted signal characteristics and the damage parameters ; and (iv) figuring out the damage parameters of interest in terms of captured signals, based on the quantitative connections established in Step (iii) .However, the main challenge of guided waves is that they propagate with multiple modes and exhibit a strongly dispersive behavior when the excitation frequency increases . Thus, tWith the latest advances in neurosciences and high-capability computing devices, machine learning (ML) algorithms based on Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage/defects identification and localization problem ,10. VariRecent research has focused on damage quantitative estimation by using Lamb wave signals features in combination with ANN and FEM in order to ensure the accuracy of ANN (strictly related to the data used to train the network) ,16. A LaObviously, the performance of an ANN strictly depends on the size and complexity of the obtained dataset. A very large or high-dimensional dataset could affect the accuracy of the classification or clustering and make the model computationally expensive. Employing data-preprocessing methods when dealing with high-dimensional sensory data is also of great importance and impacts the ANN performance .In this paper, a machine learning approach based on ANN is proposed in order to demonstrate and discuss the usability of artificial intelligence for the purpose of detecting and localizing a damage and its coordinates. A physical experiment and numerical simulations on an aluminum plate were carried out. The aluminum plate was equipped with four piezoelectric transducers to activate and receive the wave response in baseline (no damage) and actual (damaged) configurations of the structure.As the first step, the development and assessment of the Finite Element modeling technique was addressed. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data in terms of ®  code that automatically extracts the Once established with respect to GW propagation, the numerical data were used for the training and validation process of the ANN. In particular, starting from the ver path . This waThen, the developed ANN was verified on a CFRP (carbon fiber-reinforced polymer) plate. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.The trained algorithm could be directly applied to experimental measurements without the need of retraining.The layout of the paper is as follows. Details of the case study are outlined in GW propagation mechanisms were investigated in a simple flat isotropic panel. For isotropic materials, Lamb waves travel with the same velocity omni-directionally, and the wavefronts form a circle. This case study, although simple, represents a starting point for the development of an ANN useful for damage detection.The plate under investigation has a square shape with dimension  ta=2 mm . The meche edges . The adhIn order to reduce the number of experiments to carry out, a chirp signal was used. The chirp signal is given as follows:mb waves , was themb waves  to allowA In this section, at first, FE modeling techniques are compared in order to prove the efficiency of 2D-Shell elements ,26 in siOnce established against experiment or analytical data, the FE model can be used to predict wave-propagation mechanisms in a damaged configuration of the plate, and to get a useful dataset for the training of the ANN.In this work, numerical data were compared to the experimental ones, using sensor 1 as actuator and the others as receivers. Such a comparison, although simple and well-studied, offers the opportunity to validate the numerical models and evaluate their convergence by using an isotropic material.® CAE environment . Three different element types from the Abaqus® Finite Elements library were considered: (i) 2D-Shell , S4R element with 6 degrees of freedom per node; (ii) 3D-Shell (continuum shell), SC8R element with 3 degrees of freedom per node; and (iii) 3D-Solid (brick element), C3D8R element with 3 degrees of freedom per node.The geometry of the panel under investigation, described in In order to reduce the number of simulations, a chirp excitation signal was modeled in the frequency range This led to the development of different FE models: two 2D-Shell based, providing 10 and 20 NPW respectively; two 3D-Shell based, providing 10 and 20 NPW respectively; and only one 3D-Solid based, providing 10 NPW . In all the numerical analyses, the chirp actuation signal was imposed. This means that, for example, taking into account The wave propagation is a dynamic phenomenon, so the explicit environment was chosen to simulate the actuation, propagation and sensing of GW. For this reason, to ensure the accuracy of the numerical solution, the time step must be less than the ratio of the minimum distance of any two adjacent nodes to the maximum wave velocity  were calculated through Equation (3):eference .This effective displacement was applied on the upper actuator edge see also b after hThe implicit scheme could be used to handle either the wave propagation or mechanical–piezoelectric problems. In fact, there is the option of using electric coupling PZT Finite Elements. That means that the voltage can be directly applied to the terminals of the transducers, and the corresponding strain response in the sensors is acquired through the electro-mechanical coupling of the elements (Equation (3)). Such elements, however, are not available for the explicit procedure used herein to perform the analyses. A comprehensive overview about the modeling of PZT can be found in References ,29,30,31® explicit environment. The test case consists of a multi-frequency analysis with a chirp actuation signal, activated by ‘PZT 1’, due to isotropy of the plate, according to Equation (1). To avoid mode superimposition, low-frequency Lamb waves should usually be chosen for damage detection. In this study, the excited signal is a chirp one limited to the frequency range The wave propagation was simulated in the Abaqus®-based general-purpose tool that interactively allows users to create dispersion curves that simply define the material model and properties. It computes the phase and group velocity dispersion, as well as internal stress and displacement fields (mode shape) of Lamb and shear horizontal waves in isotropic and multilayered composites.Numerical results in the selected frequency range were compared to the experimental ones and also to those obtained by means of the Dispersion Calculator , Augsburg, Germany) . It is aDisplacements field contour plots in ferences ,34.Predicted signals, recorded at sensor locations, were calculated as the average of the in-plane strains, eference ). Then, To validate the Finite Element models, the responses of the transducers were compared for the numerical and experimental results. In particular, for the sake of brevity, in  both 10 a and 20  both 10 b NPW. AcIt is noted that attenuation phenomena were not considered in this study. Additionally, the adhesive layer between the PZT wafer and the panel was modeled as a rigid link. These effects can influence the amplitude of the received waves and justify the slight mismatch existing between measured and predicted data ,34.𝑔) In detail, the 20 NPW 2D-Shell FE modeling approach was chosen for the plate, while the The main idea is to verify if the ANN developed and trained for an isotropic plate is capable of predicting damage also for a composite one. According to the results shown in In this paper, a machine learning approach based on ANN for damage detection and localization was proposed. Specifically, this paper dealt with the development and the assessment of an ANN for the damage detection based on the guided wave propagation method. The ANN was developed and assessed through the Finite Element Method and then used to simulate guided wave propagation in an aluminum plate under different damage configurations. A first step of the research activities herein proposed was addressed to the development of a reliable FE model aimed to calculate the Concerning the development of the ANN, it was trained with respect to an aluminum plate under different damage configurations. Square-shaped damages with a size of The trained ANN showed a good performance in terms of regression . The ANN was found to be able to detect all damages with an acceptable level of accuracy. Because of the proven ANN reliability, its detection capability was also assessed on a different plate made of CFRP laminate, modeled through FEM. It is important to highlight that data achieved by the simulations involving the CFRP plate were not used for the ANN training phase. Nevertheless, the ANN showed its reliability in damage detection yet again.


The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo and achieves the effect of hiding the real target. Traditional deceptive jamming recognition methods need to extract complex features and artificially set classification thresholds, which is inefficient. The existing neural network-based jamming identification methods still follow the pattern of signal modulation-type identification, so there are fewer types of jamming that can be identified, and the identification accuracy is low in the case of low jamming-to-noise ratios (JNR). This paper studies the input of jamming recognition networks and proposes an improved intelligent identification method for chirp radar deceptive jamming. This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). Using a CNN to classify the time–frequency image has realized the recognition of a variety of common deceptive jamming techniques. Similarly, by changing the network input, the original signal is used to replace the echo signal, which improves the accuracy of the jamming recognition in the case of a low JNR. With the development of technologies such as very large-scale integrated circuits (VLSI) and solid-state circuits, active deceptive jamming exhibits features such as high fidelity and high intelligence, which makes the electromagnetic environment of radar work worse and poses a huge threat to the survival of radar. In this context, radar systems have increasingly demanded active jamming identification methods.In ,2, the rTraditional jamming identification methods mostly follow the steps of jamming detection, jamming feature parameter extraction, and jamming pattern recognition. The related algorithms are greatly affected by the JNR and have poor robustness when facing target echo aliasing jamming, especially deceptive jamming with high coherence with the target echo. It is difficult to extract stable feature parameters with a high degree of discrimination from only the time and frequency domains, which will inevitably reduce the accuracy of the perception of jamming. Most traditional classification methods are non-adaptive classification methods such as statistical binary tree classification. The threshold setting of its key nodes is manually determined, and because the radar operator is often required to set it according to the radar display screen or judge based on their experience, the efficiency of the entire jamming identification process is low.With the deepening of artificial intelligence technology research, deep learning networks can effectively uncover deep-level signal information, analyze conventional jamming features, extracting subtle features of jamming that are difficult to quantify, and intelligently identify jamming types. In 1998, Yann LeCun  and his In this article, we study the intelligent identification method of chirp radar active deceptive jamming. Through research on and the analysis of the common active deceptive jamming of radar, a jamming recognition model based on a convolutional neural network is proposed. Through the analysis of the jamming recognition model of the convolutional neural network, it is known that whether a neural network can correctly classify and recognize the jamming of the input depends on the amount of information contained in the inputs of the training and test sets. The existing neural network jamming recognition method has less input information and can only identify a few specific deceptive jamming techniques. Therefore, this paper studies the input of the neural network. By changing the input of the jamming identification network, the neural network recognizes a variety of common radar deceptive jamming techniques, and gains improved recognition accuracy. A short-time Fourier transform time–frequency diagram of the jamming is used as the input of the network to provide the time–frequency distribution information of the jamming. The echo signal is added to the network input so that the input includes the carrier frequency, bandwidth, modulation slope, and other true signal information required for jamming identification. The time–frequency diagram of multi-pulse jamming plus echo is used as the input of the recognition network, so that the input of the recognition network includes the interpulse information of the deceptive jamming. The recognition network is thus able to identify common chirp radar active deceptive jamming techniques, including repeating jamming and towing jamming. Using the original signal instead of the echo signal as the input of the network improves the success rate of the jamming recognition in the case of a low jamming-to-noise ratio (JNR).From the characteristics of radar active deceptive jamming, it can be seen that the way and mechanism of each kind of jamming are different, and their characteristic parameters in the time and frequency domains reflect the mathematical laws of the different dimensions. For linear frequency modulation (LFM) signal, because of its special time–frequency linear relationship, the time–frequency analysis method can more clearly reflect the characteristics of repeater jamming. Short-time Fourier transform (STFT) divides the signal into several time periods by windowing the signal in the time domain. The signal in each time period is regarded as a short-time stationary signal, the Fourier transform method is used to analyze it, and the frequency distribution of the corresponding time period is obtained. Compared to the traditional time-domain and frequency-domain characteristic parameters, the STFT spectrum of radar active deceptive jamming can directly reflect the jamming characteristics, as there is a very clear distinction between various kinds of jamming, which can be used for the subsequent identification of radar active deceptive jamming.ST is the time domain pulse width, f0 is the center frequency, µ = 2πB/ST is the chirp constant, and Radar active deceptive jamming generates false targets by modulating and forwarding the transmitted radar signal so that the real target is hidden in a false target, and the information of the false target is used to disrupt the detection, parameter measurement, tracking, and positioning of the real target. Compared with suppression jamming, the main features of deceptive jamming are that it can obtain a larger signal processing gain and has strong coherence with the target echo in the time, frequency, and space domains. Common radar active deceptive jamming techniques include range gate pull-off jamming, velocity gate pull-off jamming, range- velocity gate pull-off jamming, velocity false target jamming, range false target jamming, intermittent sampling jamming, and smeared spectrum jamming. The expression of the LFM radar-transmitting signal isThe false distance function generated by range gate pull-off (RGPO)  jamming In order to simplify the formula’s variables, without considering the jammer’s forwarding delay, the signal model of distance towing jamming is obtained asWhen the towing speed of the distance gate towing jamming, g (RFTJ) .The Doppler frequency shift difference between velocity gate pull-off (VGPO)  jamming When the towing velocity of velocity gate pull-off jamming, g (VFTJ) .Range-velocity gate pull-off (R-VGPO)  jamming Interrupted-sampling directly jamming (ISDJ)  is generAccording to the working principle of ISDJ, above, its time domain expression isThe process by which a DRFM jammer generates interrupted-sampling repeater jamming (ISRJ)  is similPartial-pulse dense transmit jamming (PDTJ)  is generSmeared spectrum (SMSP) jamming is a new distance false target jamming invented by Sparrow et al. in 2006 [It can be seen from Equation (12) that a DRFM jammer compresses and replicates the intercepted radar transmission signal to obtain SMSP jamming. The spectrum of SMSP jamming isThe expression of whole-pulse dense transmit jamming (WDTJ)  generateCNN is a multi-level network model. In the field of image processing, its input is image data composed of multiple channels. After the data is subjected to multiple convolutions, pooling, and activation in the network, the features are extracted out, and then output through the fully-connected layer. The basic structure of CNN is shown in the The training prediction process of the jamming recognition model is shown in The jamming signal received by the radar was processed through a series of processing to obtain the input data of the neural network, and the data were divided into a training set, a verification set, and a test set. The training set was input into the training network with the CNN network as its core to obtain the network weights by iterative training. The verification set was used to verify the correct rate of network recognition and find the optimal network weight with the highest test accuracy. In the recognition process, the test set was input into the network model loaded with the optimal network weights for recognition.In order to achieve higher recognition accuracy, this paper uses the ResNet50  network The parameters of the ResNet50 model are shown in The ResNet50 model is shown in The research of neural network and neural network training has been very mature, but the results obtained under different training and test inputs can very different even when processed by the same network. CNNs are widely used in the field of image recognition and classification. The use of a CNN for image classification requires distinct separable features between classes. Recognition based only on the time or frequency domain of deceptive jamming often fails to obtain correct results. Especially for LFM radar, deceptive jamming often has a special time–frequency linear relationship, and joint time–frequency characteristics can clearly reflect the characteristics of deceptive jamming. In this paper, short-time Fourier transform  results,The STFT of signal The following section will simulate and analyze different neural network inputs and neural network recognition results. The frequency resolution of the STFT is limited by the number of Fourier transform points and the maximum frequency, so the signal was transformed to the baseband or low frequency, and then the STFT was performed to better analyze its time–frequency characteristics. The carrier frequency of the simulated LFM signal was 10 MHz, the bandwidth was 15 MHz, the pulse width was 40 μs, and the pulse repetition period was 100 μs. The JNR of the jamming signal was 10 dB, and the jamming signal was subjected to an STFT with a window length of 256 points and a step distance of 40 points.The jamming signal simulation parameters are shown in When the neural network input has only jamming signals, the input is shown in It can be seen from In order to introduce real signal information to the network input, the STFT time–frequency diagram of the echo signal was added to the network input. The simulation parameters were the same as when there was only jamming. The inputs are shown in The darker parts in To correctly identify towing jamming, it is necessary to extract the characteristics of multiple consecutive pulses and analyze their change trend. When a CNN is used in the field of traditional image recognition, when the input image of the network is a color picture, the input contains three channels of red, green and blue (RGB). Using the recognition of color pictures for reference, the time–frequency picture of three consecutive pulses of STFT can be used as if they were the three color channels of a unified image to synthesize a picture, which was then input into the network for jamming recognition. The fusion of the time–frequency diagram of the STFT of the three pulse period signals was used as the input. The input is shown in It can be seen from One of the most difficult problems in spoofing jamming identification is the identification of jamming in the case of a low JNR. Because the echo signal was usually weaker than the jamming signal, in the case of low JNR, the echo will be submerged by the noise first, and thus only the jamming signal can be seen in the picture. The information reflected in the picture is not enough to determine what kind of jamming this is. The same problem can also be encountered in other cases of deceptive jamming.The echo signal can provide the real signal’s carrier frequency, bandwidth, time delay, modulation slope, and other information to enable jamming identification. In an intelligent recognition system for radar jamming based on a CNN, the information of the echo signal needs to be extracted from the time–frequency diagram, thereby increasing the system’s JNR requirements. The only difference between the original signal and the echo signal is that it did not contain time delay information. Through the observation and analysis of the STFT image in As shown in In order to make the model have improved generalized performance, this paper simulated and generated a variety of chirp radar signals and generated a variety of simulation signals of 10 kinds of jamming, under different jamming parameters. The chirp signal parameter settings are shown in The jamming parameter settings are shown in Each of the 10 types of jamming generated 324 jamming signals with different parameters. A total of 35,640 different jamming signals were generated between −13 dB and −3 dB. The STFT of the jamming signal generated a 128 × 244 time–frequency diagram.Four data sets were generated by the jamming signal, jamming plus echo, multi-pulse jamming plus echo fusion, and multi-pulse jamming plus original signal fusion techniques.In each data set, 2000 random pictures from 3240 pictures of each type of JNR were used as the test set, 1100 pictures were used as the training set, and 264 pictures were used as the verification set.Through the analysis of the jamming signal in Tensorflow is an open source, Python-based machine learning framework. It was developed by Google and has rich applications in graphics classification, audio processing, recommendation systems, and natural language processing. It is the most popular machine learning framework at present. An Nvdia CUDA was used to accelerate GPU computing. CUDA is a parallel computing platform and programming model invented by NVIDIA. It can greatly improve computing performance by using the processing power of the GPU. The initial learning rate of the model was 0.0002, BatchSize = 32, epochs = 20, and dropout = 0.5. Fine-tune transfer learning was used to accelerate convergence. The 22,000-picture input set was divided into the test sets input to the trained network, and the confusion matrix to verify the four methods through simulation was obtained.According to the confusion matrix of the four methods in In order to verify the performance improvement of the method proposed in this paper to replace the echo signal with the original signal in the case of a low JNR, the fusion time–frequency graph data set of the multi-pulse jamming plus echo and the fusion time–frequency graph data set of the multi-pulse jamming plus original signal generated in this paper were respectively input to the network model for training. The jamming to signal ratio (JSR) was 3 dB, and the JNR was −13 dB to −3 dB. Under each different JNR, 200 pictures of each jamming type were input into the network for identification, and the accuracy of the two methods under the different jamming types and different JNRs were obtained. The accuracy curves of the two methods are shown in It can be seen from The transformation curve of the total jamming recognition success rate of the two methods under different JNRs is shown in the figure below.It can be seen from Based on neural network input research, this paper proposes a method for identifying deceptive jamming of chirp radar. By analyzing the information needed for interference recognition, the input of the neural network was changed. First, the STFT time–frequency diagram of the jamming signal was used as the input of the jamming identification network, which provided the time–frequency information of the jamming signal for the identification network, and achieved the identification of some types of jamming. Then, adding the time–frequency diagram of the echo signal to the network input provided information about the real signal and the difference between the jamming and the real signal for identification by the network, which increased the number of types of jamming that the network recognized. Then, the fusion of multiple-pulse jamming plus echo time–frequency graphs were used as the input of the network, which provided jamming pulse information for identification network and achieved the identification of all interference types. Finally, by replacing the time–frequency diagram of the echo signal with the time–frequency diagram of the original signal as a part of the network input, the accuracy rate of interference recognition increased under low-JNR conditions. By building a model with ResNet50 network as the core, simulation tested the effectiveness of the jamming identification method and the improved method.


Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (  Despite the scientific advances in the understanding of complex diseases such as cancer, there remains a major gap between the vast knowledge of molecular biology and effective treatments. Next generation sequencing has revealed intrinsic heterogeneity across cancer samples, which partly explain why patients respond differently to the same therapy . For theWith the increasing amount of drug sensitivity screening data, the challenge of translating them into actual drug discovery remains, as recent studies showed that most of clinically approved drug combinations work independently , that thTo initialize the efforts for curating drug combination datasets, and to facilitate a community-driven standardization of evaluation of the degree of synergy and sensitivity of drug combinations, we have provided DrugComb as the very first data portal to harbour the manually curated datasets as well as the web server to analyse them . The oriWith the development of high-throughput screening techniques, the number of data points for drug combinations has been greatly increased. For example, the recent Dream Challenge on drug combination prediction has provided more than 20k drug combinations in cancer cell lines . More reDrugComb portal consists of two major components including a database for harbouring the most recent drug screening datasets as well as a web server to analyse and visualize these datasets or user-uploaded datasets for the degree of sensitivity and synergy. For retrieving the database, users can query by drug names, cell line names as well as study names. For utilizing the web server to analyse user-uploaded datasets, users need to import the data according to the format of an example file, and the results will be shown as both tabular and image displays, which are also downloadable. When users plan a drug combination experiment, they may utilize the web server to predict the sensitivity and synergy and utilize such information to guide the selection of drugs. The drug targets as well as the gene expressions of the signalling pathways for a given cancer cell line can be also annotated as a network model. In the following, we describe how we have improved the coverage of the database as well as the data analysis modules of the web server with a range of algorithms, and the new implementation techniques to accelerate data curation and harmonization efficiency Figure .The initial version of DrugComb consists of four drug combination screening studies, covering 437 923 drug combination experiments. We have curated much more drug combination experiments for cancer cell lines. Furthermore, we have incorporated monotherapy drug screening datasets and considered them as a subset of a drug combination experiment, where the other drug is absent. We have also included the drug screening results from patient-derived cancer samples in haematological malignancies . In addi10-transformed dose-response curves, which has shown enhanced robustness to characterize drug sensitivity  values. RI is the normalized area under the logsitivity . Moreove10-transformed of the combination dose-response curve when one of the two drugs is fixed at its IC50 concentration , that is based on the normalized area under the logntration . CSS andntration . For evantration  and provntration . Drug contration . For exantration . In actuntration . On the ntration .As a unique feature of DrugComb, we visualize the synergy scores of a drug combination at each tested dose. The so-called synergy landscape allows a rich information display to facilitate the interpretation of the data, for which the most synergistic and antagonistic doses can be identified separately Figure . For a gOnce a drug combination experiment has been conducted, for which the results were analysed with the sensitivity and synergy scoring, the next question would be the mechanisms of action of the drug combinations. Network modelling of drug combinations have been recently introduced as an efficient approach for the interpretation of drug combinations, as well as the identification of predictive biomarkers from molecular profiles of cancer . In DrugFor user-uploaded drug combinations or single drugs, ideally the InChiKeys of the drugs should be provided. This allows the web server to query drug STITCH ID from the major drug databases. In case only the drug names are provided, the web server will query from the major drug databases, for which their targets profiles will be visualized in a generic cancer signalling network. In case the cell line names can be matched with the existing gene expression data, their gene expression values will be displayed as coloured nodes. The network modelling results should be interpreted together with the actual drug screening profiles, such that the drug resistance or sensitivity can be related to its target or neighbouring gene expressions Figure .n = 153) across the 39 cell lines  of drug screening data analysis. Meanwhile, the community-based features such as data contribution and quality control can be developed further.The data portal has been designed in a straightforward manner to maximize the user flexibility to retrieve the existing datasets as well as to analyse their own datasets. We provided the API access at Making cancer treatment more effective is what a combination therapy aims to achieve. With the advances of high-throughput drug screening technologies, an increasing number of drug combinations have been tested. However, before we can develop robust machine learning and network modelling algorithms to predict and understand the potential drug combinations, the datasets need to be systematically curated and harmonized. Here we report the major updates of DrugComb, a comprehensive data portal for the drug discovery community to access the concurrent high-throughput drug combination as well as monotherapy drug screening datasets. These datasets have been deeply curated, standardized and harmonized with the data analysis tools including synergy and sensitivity scoring, such that their potential can be maximized within a unified framework. Furthermore, we have updated the network modelling of the drug combinations, such that the transcriptomics profiles of the cancer cell line, and drug target profiles can be integrated in a signalling network where the protein-protein interactions may provide deeper insights on the mechanisms of drugs and drug combinations. In addition, we have provided a machine learning model to predict a given drug combination for a cell line at the single dose level. To the best of our knowledge, this is the first drug combination prediction tool that has been made online with easy accessibility for drug discovery users.The four basic modules of DrugComb, i.e. (i) data curation, (ii) synergy and sensitivity scoring, (iii) network modelling and (iv) machine learning constitute a workflow of network pharmacological approaches based on which we may gain deeper understanding of drug-drug interactions. Currently, DrugComb focuses on small molecule drugs such as cytotoxic and kinase inhibitors, while immunotherapy and gene therapy drugs are largely missing. Furture steps of DrugComb will involve constant improvement on the data coverage, for example, by including drugs from other classes. Moreover, we will include higher-order combinations that involve more than two drugs (e.g. ). In addhttps://openinnovation.astrazeneca.com/. The visualization results for sensitivity, synergy and network models are downloadable as images. The source code for analysing the drug combination datasets is available as the R package SynergyFinder version 2.2.4 (https://bioconductor.org/packages/release/bioc/html/synergyfinder.html). We are committed to open data and welcome any researchers to participate in the development of data curation and harmonization tools for drug discovery.The synergy and sensitivity scores in DrugComb are freely available for download. Larger batch downloads of raw data are permitted by contacting the authors. The AstraZeneca drug combination datasets are proprietary, and a separate agreement is needed, available at gkab438_Supplemental_FileClick here for additional data file.