Patients’ postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The purpose of this study was to evaluate the accuracy of artificial neural networks in the prediction of the postoperative facial swelling following the impacted mandibular third molars extraction. The improved conjugate grads BP algorithm combining with adaptive BP algorithm and conjugate gradient BP algorithm together was used. In this neural networks model, the functional projective relationship was established among patient’s personal factors, anatomy factors of third molars and factors of surgical procedure to facial swelling following impacted mandibular third molars extraction. This neural networks model was trained and tested based on the data from 400 patients, in which 300 patients were made as the training samples, and another100 patients were assigned as the test samples. The improved conjugate grads BP algorithm was able to not only avoid the problem of local minimum effectively, but also improve the networks training speed greatly. 5-fold cross-validation was used to get a better sense of the predictive accuracy of the neural network and early stopping was used to improve generalization. The accuracy of this model was 98.00% for the prediction of facial swelling following impacted mandibular third molars extraction. This artificial intelligence model is approved as an accurate method for prediction of the facial swelling following impacted mandibular third molars extraction. In addition, the incidence of facial swelling is related to the type of third molar, the degree of impaction, and ease of extraction operations8. As far as a specific patient is concerned, most of oral surgeons could predict the incidence of the facial swelling solely based on their personal experience. The first attempt to create a model of prediction was published by MacGregor7, who established a multivariate model based on radiographic findings. Berge and Bøe8 attempted to predict the extent of postoperative morbidity following third molar surgery through multiple regression analysis. Although a number of efforts at determining a model for this assessment have been made, none of them could be praised for universal reliablility1. Obviously, the main reason was the difficulty of quantification of the magnitude of the contributions of the different categories of variables.The extraction of impacted mandibular third molars is one of the most common surgical events. Patients’ postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The severity of the facial swelling is depending on the case and the facial swelling originated in an inflammatory process that initiated by surgical trauma. The facial swelling is more likely occurring in those patients who received impacted mandibular third molars extraction with utilizing flap operation and bone removal. The factors thought to influence the incidence of facial swelling after third molar removal include patients’ age, gender, physique and oral hygiene12. The applications of neural networks have received a great deal of attentions in clinical medicine. By properly choosing neural networks structures and training the weights, researchers could use neural networks to perform medical outcome prediction18. Neural networks have the capacity to “learn” how to make a diagnosis through the information presented to them21.Artificial nerve networks principle can be used to analyze the multiple factors and the relationships between the factors which have unclear relationships, and it is a system based on the imitation of human brain structure and function. Neural networks method is a branch of biomedical engineering known as artificial intelligence (AI). Artificial neural networks, due to their excellent ability of non-linear mapping, generalization, self-organization and self-learning, have been proved to be of widespread utility in digital signal processing, system modeling, automatic control, and others22. 5-fold cross-validation is used to get a better sense of the predictive accuracy of the neural network and early stopping is used to improve generalization. The purpose of this study was to evaluate the accuracy of this improved neural networks model in predicting.In this present study, an AI model was established with improved conjugate grads backpropagation (BP) algorithm to predict facial swelling following impacted mandibular third molars extraction. The software we used for the experiments is MATLAB’s Neural Network ToolboxThe study protocol was approved by the Ethics Committee of the School and Hospital of Stomatology, Wuhan University, Wuhan, China. All patients were informed of the clinical study, and they provided signed informed consent. The study was conducted in full accordance with ethical principles, including the Declaration of Helsinki. All methods were performed in accordance with the relevant guidelines and regulations. The 400 patients, who received impacted mandibular third molars extraction in Hospital of Stomatology, Wuhan University, were randomly selected in this study. There were 300 patients assigned as the training samples, which is the design set,and another 100 patients were made as the test samples. All samples were selected from a pool of patients admitted for mandibular third molars extraction between January 2014 and December 2016.These selected patients had no known immune impairment, no contraindications for oral surgery and no medication taking. The surgical procedure was standardized and uniformly performed by one same oral surgeon, and patients strictly followed post-extraction instructions. Antibiotics were used for three days after extraction to prevent infection in all patients. A single examiner performed all clinical measurements prior to surgery as well as 72 hours after the operation.In this study, the parameters, which related to the occurrence of facial swelling following impacted mandibular third molars extraction, were considered as the networks input, including 15 factors. These 15 factors were consisted of patient’s age, gender, physique and oral hygiene as personal factors; relation of the third molars to the mandible ramus and second molar, relative depth of the third molars in bone, relationship of the long axis of the third molars in relation to the long axis of the second molar, relation of the third molars in mandibular dental arch, number of root as anatomy factors of third molars; and type of incision, location and quantity of bone removal, section into pieces or not, root fracture condition, fracture of lingual bone plate or not, surgical time as factors of surgical procedure. All the parameters and the data normalization are shown in Table According to Table 24. Differences between the measurements prior to surgery and those made 72 hours after the operation at distances were recorded, and the average of the five differences value was calculated. When the average of the five differences value was less than 10 mm, the patient was classified as mild swelling; 10–20 mm was moderate swelling; more than 20 mm was severe swelling. All postoperative data were recorded by the same independent blinded investigator in order to avoid observer bias.The facial swelling measurements were taken using a 2–0 nylon thread and a millimeter ruler before the surgery and 72 hours after the operation. To evaluate the swelling, markings with permanent marker were made prior to the surgery on the following facial regions: the angle of the mandible, the tragus, the labial commissure, the nasal border, laterally to the external corner of the eye, and on the soft pogonion. The following measurements were taken: Distance I (from the angle of the mandible to tragus); Distance II ; Distance III ; Distance IV ; Distance V (from the angle of the mandible to the soft pogonion)The neural networks model based on the improved conjugate grads BP algorithm combining adaptive BP algorithm and conjugate gradient BP algorithm is shown in Fig. th input neuron and the nth hidden-layer neuron, respectively, and Ok is the output neuron for kth set of input data. These artificial neurons passed information from one layer to the other, in a process that imitated a human synapse, and the processed information produced an output signal. The number of interactions or learning cycles of the network was established upon trial and error in all the interactions, and used the reduction of the mean square error (MSE) as learning criterion.The neural network used in this experiment was a one-hidden-layer perception with the improved conjugate grads BP algorithm that had 15 neurons in the input layer, 15 in the hidden layer, and 1 in the output layer. Figure Like conventional BP algorithm, conjugate gradient BP algorithm has the same process of propagating the input forward through the networks. The main difference between them is the process of propagating the error backward through the networks. The conjugate gradient BP algorithm reduces the amount of computation in the training process, thus there is no need to calculate the search direction in every step of the iterative process in the conventional BP algorithm.−3. At the second step, propagating the input forward through the networks, and judging whether MSE of sample meets the required error precision (10−3). If it does, then the learning will end. At the third step, it is judgment that whether the networks training enters extreme point. If it does, then the conjugate gradient method will be used in the backpropagating of networks. Otherwise, the adaptive BP algorithm will be used in the backpropagating of networks. At the fourth step, they are propagating the optimized weights forward through the networks, and judging whether the MSE of sample meets the error precision. If it does not, then it will return to the third step. After the above four steps, one epoch of the improved BP Algorithm is completed, in which epoch is an iteration of subset of the samples for training algorithm.The process of the improved BP Algorithm is as follows. At the first step, the networks weights are initialized by random values, and the training sample values are inputted, in which the required error precision is set as 10At the third step of the algorithm process, it is necessary to judge whether the networks training enters extreme point. In the present study, 10 epochs training is the interval, and the difference value of training errors in the intervals is evaluated. If the difference value is less than the judging value of extreme point, the networks training is considered as entering the extreme point. The judging value of extreme point will influence the speed of networks training. If the judging value of extreme point is too high, error function of the sample will not meet the conditions to further use conjugate gradient method, which will have trouble with overflow. If the judging value of extreme point is too low, conjugate gradient method will be used until networks training is trapped in the local minimum for a long time, which will then result in remarkably slow networks training speed.When adaptive BP algorithm was applied, the learning curve would be apt to be trapped in infinitesimal, and there would be still no convergence until training epoch was over 100000. When conjugate gradient BP algorithm was used in networks training of the same sample, it would be apt to overflow. However, when improved BP algorithm was used, the problem of local minimum would be avoided effectively by networks training, and the speed of networks training would be improved greatly. The networks training epoch would be 13 when networks training of this sample converged to the condition that MSE was 0.001.−3, networks training epochs were represented as different judging values of extreme point  is divided into two subsets. the first subset (300 samples) is the designing dataset, which is used for training and validating the neural network, the second subset (100 samples) is the test dataset.5-fold cross-validation and early stopping technique were used in order to get a better sense of the predictive accuracy and avoid overfitting. 5-fold cross-validation splits the designing dataset into five parts at random. It trains five new neural networks, each one on four parts of the designing dataset, it then examines the predictive accuracy of each new neural network on the data not included in training dataset, which is named the validation set. The method for improving generalization is called early stopping. In this technique, the error on the validation set is monitored during the training process. The validation error normally decreases during the initial phase of training, as does the training set error. However, when the network begins to overfit the data, the error on the validation set typically begins to rise. When the validation error increases for a specified number of iterations, the training is stopped, and the weights and biases at the minimum of the validation error are returned. In this case, every training fold contains roughly 4/5 of the designing dataset and every validation fold contains roughly 1/5 of the designing dataset.The test dataset which is not used during training, but it is used to compare different models.22.When the neural network output value was lower than 0.4, it meant mild or no swelling; when it was higher than 0.6, it meant severe swelling; when it was between 0.4 and 0.6, it meant moderate swelling. When 5-fold cross-validation and early stopping technique are used, the test performance of each training network in 5-fold cross-validation is showed in Fig. The test dataset (the diagnosis 100 samples) was performed with prediction test by the fourth trained network of 5-fold cross-validation. The confusion matrix of the test dataset, the validation dataset, the test dataset and all of the dataset was shown in Fig. 8. All clinicians stress the need for swelling control in patients who undergo third molar surgery, and many investigations focused on reduction of the swelling27. However, there is no special intelligent method to predict the swelling. The inaccuracy in the prediction of the swelling response always affects the quality of medical services and the follow-up treatment plan.Postoperative facial swelling is a common complaint after impacted third molar removal, and the swelling degree is different among the patients. The swelling differs depending on the patients’ characteristics, preoperative difficulty index, and intraoperative factors28.The factors that contribute toward the postoperative facial swelling following third molar surgery are sophisticated. In this study, 15 variables were recorded as the parameters in the artificial neural networks. The major advantage of artificial neural networks is their ability to synthesize a considerable amount of factors (or variables) without requiring statistical modeling of the problem. In addition, artificial neural networks learn to recognize patterns from entered data through training. Once it has been trained on entered data, the network is able to recognize similarities when presented with a new input pattern, which will result in a predicted output pattern29.It is worth mentioning that we have employed computerized system based on artificial neural networks to predict the swelling following impacted mandibular third molars extraction, which shows satisfying results. The improved conjugate grads BP algorithm combining adaptive BP algorithm and conjugate gradient BP algorithm together can converge to a global optimal solution in certain conditions, which not only avoid the problem of local minimum effectively, but also improve networks training speed greatly30, in which the neural networks based on adaptive BP algorithm is a new widely used diagnosis and prediction method. Adaptive BP algorithm, however, may generally be trapped in a local minimum. Another variation on BP algorithm, the conjugate gradient BP algorithm allows the searching the direction of deterioration as a certain probability in each step, because each iteration is not a single negative gradient direction but a conjugate direction, Hence, conjugate gradient BP algorithm can converge to a global optimal solution in certain conditions, which can solve the local minimum problem of adaptive BP algorithm. However, conjugate gradient method requires the error function is convex, and the direction of each iteration requires exact linear search. The practical problems often can not meet the conditions. In general, error function of extreme value shows characteristics of convex function, and the conjugate gradient method being applied near the extreme value will improve convergence speed and learning accuracy. Therefore, the algorithm combining adaptive BP algorithm and conjugate gradient BP algorithm together may be an ideal neural networks model, which is able not only to avoid the problem of local minimum effectively, but also to improve networks training speed greatly. We believe this improved conjugate grads BP algorithm could serve as a valid neural networks model to predict the swelling following impacted mandibular third molars extraction. In this present study, the improved algorithm combining adaptive BP algorithm and conjugate gradient BP algorithm is used to establish a valid neural networks model applied in the prediction of facial swelling following impacted mandibular third molars extraction.The ‘feed-forward, back propagation’ neural networks usually trained by BP of errors is perhaps the most popular networks architecture todayIn this study, 5-fold cross-validation and early stopping were used to get a better sense of the predictive accuracy and improve generalization. The prediction accuracy of this method is 98.00%, which can meet the needs of actual diagnosis or prediction in clinic. There are two reasons may explain the fact that the prediction unable to achieve higher accuracy. First, there are too many related parameters (n = 15) involved in this study. In the follow-up study, some minor parameters should be abandoned, in order to optimize the networks training and improve the prediction accuracy. Second, the normalization process of these parameters data is lack of objective evidence. In future, the mathematical formulae of normalization for these parameters data should be sought to improve the scientificity and accuracy of sample.In conclusion, this artificial neural networks based on the improved conjugate grads BP algorithm has a high prediction accuracy, which is a new developed system that can help to predict the swelling following impacted mandibular third molars extraction.


This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation around the hole and, consequently, a displacement field. DIC measures this displacement field by comparing digital images taken before and after the hole-drilling. The stress level in the concrete structure is then determined by solving an optimization problem, designed to minimize the difference between the displacement fields from DIC and the one from a numerical model. Compared to the pointwise measurements by strain gauges, the full-field displacement obtained by DIC provides a larger amount of data, leading to a more accurate estimation. Our theoretical results were experimentally validated using concrete specimens, demonstrating the efficacy of the proposed method. In full-scale civil engineering structures, the actual stress distribution due to self-weight and external loads allows a better understanding of the current structural status. For example, the monitoring of stress levels during construction can provide useful information for both design and construction verification, which can be helpful in assuring structural safety. In addition, the deterioration of prestressed concrete bridges, particularly due to loosened tendons, can be tracked by measuring the decreasing compressive stress in concrete girders ,2,3. DesThe stress relaxation method (SRM)  is one oPrevious studies involving the core-drilling method contained a couple of critical limitations that impeded the accurate measurement of stress in concrete structures. The core-drilling method involves the infliction of a relatively large amount of damage to the concrete structure, hence it is usually not the preferred method of practitioners and researchers. In literature, the size of the cores used for the SRM varied between 50 mm and 150 mm ,8,9; theDeformation measurements during the SRM can be enhanced by DIC ,9,10 thaThis study proposes a stress estimation method, based on a combination of stress relaxation and DIC. The proposed method initially measures the displacement field around a small drilled hole using DIC; this measurement is subsequently used to estimate the current stress level in the concrete structure by comparing the measured displacement field to that from a finite element model. DIC provides a full-field displacement at all points around the hole, which allows the collection of sufficient displacement information and an effective reduction in the measurement errors caused by the large uncertainty in concrete. The proposed method was validated through laboratory-scale experiments performed on concrete specimens.The proposed method introduces the DIC technique into the SRM, with the aim of estimating the static stress in large concrete structures. Rather than using a few strain or displacement data points, we used the two-dimensional displacement field obtained from the DIC, improving the stress estimation quality. As shown in Firstly, the two-dimensional displacement field data is obtained from the DIC: A Gaussian pattern, considered the best choice for the DIC technique , is applThe reference displacement field, which becomes relaxed when the concrete is under a reference uniform stress, is generated through the FEM. Assuming that the concrete structure is within the linear elastic range, the amount of displacement relaxed by drilling the hole is linearly related to the amount of static stress. The product between the static stress and the reference displacement yields the same displacement field acquired by the DIC. Thus, the reference displacement field is established through the FEM, using the specimen information .FEMV and DICV are the displacement from the DIC and the FEM,  is the new coordinate system,  and  are the positions within the original coordinate systems, and  and  are the coordinates of the hole center in the coordinate system of the FEM and the DIC, respectively. Consequently, the static stress can be estimated by comparing those two displacement fields in the same coordinate system.The next step is to match the origin of the coordinate system between the two displacement data sets. Because the only point shared by the DIC and the FEM is the hole center, the two coordinate systems are aligned to let the hole center be the common origin. In order to identify the hole center, the best-fitting circle is searched using a circular Hough transform . The cooDICV and FEMV represent the displacement at the hole center, σ is the static stress to be estimated, and σ0 is a reference stress. The displacements from the DIC and the FEM are subtracted by DICV and FEMV, in order to disregard the rigid body motion of the whole specimen, as the displacement at the hole center represents the motion of the whole structural body. When the rigid body motion is neglected, DICV equals FEMV but is amplified by the amount of static stress. Using the two-dimensional displacement fields (DICV and FEMV), the static stress can be estimated as follows:+ is the Moore-Penrose inverse,  are the coordinates of the k-th point, and n is the number of points used. The optimal static stress of the concrete structure, denoted by σ, can hence be obtained.The final step is to estimate the stress, by comparing the DIC and the FEM in the same coordinate system. The two displacement results have a linear relationship:The proposed static stress estimation method is validated through laboratory-scale experiments. A total of five concrete specimens are prepared; their size, elastic modulus, and Poisson’s ratio are summarized in The displacement fields around the hole are calculated from the DIC. Ncorr  is adoptThe reference displacement is obtained using commercial FEM software, ABAQUS. A finite element (FE) model of the specimen shown in The displacement fields obtained by the DIC and the FEM are shown in estimatedσ and appliedσ are the estimated and applied stress, respectively. The MSE represents the regression quality between the DIC and the FEM displacement fields, with smaller MSE values indicating more accurate stress estimations. Hence, the static stress can be estimated by using the proposed method and the estimation reliability can also be checked by evaluating the MSE.The experimental results are summarized in FEMV and DICV are adjusted using the estimated stress and rigid body motion in Equation (4), as follows:FEMV’ and DICV’ with respect to their data index for Tests 1 and 5. During Test 1, the stress is more accurately estimated  and the FEMV’ and DICV’ datasets agree quite well with each other  and the stress relaxation method (SRM). The static stress in concrete structures can be released through the hole-drilling method. Firstly, the redistributed displacement field generated by the hole-drilling is measured through DIC. Secondly, a reference displacement field is calculated by using a finite element method (FEM), with given structural information. Lastly, the static stress is estimated through the least-square method, using the displacement fields from DIC and the FEM.The proposed method was validated through laboratory-scale experiments on five concrete specimens, loaded by a universal testing machine (UTM). The best-fitting case resulted in a 5.67% larger stress, showing the smallest mean squared error (MSE) between the DIC and FEM displacement fields. The results with small MSE values provided better estimations than those with large MSE values. We conclude that the proposed method can be used to estimate the static stress in concrete structures; furthermore, the MSE values provide a quality check of the stress estimation.


This review paper initially summarizes the latest developments in impact testing on polymer matrix composites collating the various analytical, numerical, and experimental studies performed since the year 2000. Subsequently, the scientific literature investigating nanofiller reinforced polymer composite matrices as well as self-healing polymer matrix composites by incorporating core-shell nanofibers is reviewed in-depth to provide a perspective on some novel advances in nanotechnology that have led to composite developments. Through this review, researchers can gain a representative idea of the state of the art in nanotechnology for polymer matrix composite engineering, providing a platform for further study of this increasingly industrially significant material, and to address the challenges in developing the next generation of advanced, high-performance materials. Laminated polymer matrix composites are well-known materials for diverse applications. They have fibre reinforcements ingrained in a matrix of either thermoplastic or thermosetting polymers. The fibre diameters may range from a few millimeters to the sub-nanometer range . The advThe paper is basically divided into four major sections pertaining to the response of composite materials to impact loading. The first section deals with the basic study of impact on composite materials, including a brief review of the various historical events associated with high-velocity impact studies. The second section discusses the use of electrospun nanofiber interleaved polymer matrix composites and various property enhancements achieved through this technique. The third section deals with the addition of nanofillers into composites and their response to impact loading. Finally, the fourth section details the self-healing of polymer matrix composites using core-shell nanofibers.Impact studies on composite laminates have always been an area of concern for various researchers globally ,20,21,22The ballistic limit velocity is a benchmark for measuring the material’s nature in withstanding a projectile impact. It is calculated as an average of the maximum velocity that does not penetrate and the minimum velocity leading to perforation.Moving cone formed on the back face of the target.Shear plugging of the projectile into the target.Tensile failure of the primary fibers.Elastic deformation of the secondary fibers.Matrix cracking and delamination and frictional energy absorbed during penetration.During a ballistic impact, the energy of the projectile is absorbed by the target through :Moving cUnder high-velocity impact conditions, most of the available energy is dissipated over a small zone near the point of contact . The besAn extensive literature review has been carried out in the field of high-velocity impact events using the Scopus database, from the year, 2000, through to 2018 ,37,38,39There is no doubt that composites are a class of materials receiving much attention world-wide as an alternative for various existing structural and lightweight, high strength materials. The synergistic properties of their components make them superior in numerous applications due to advantages, such as high specific strength and modulus, facile fabrication, high design flexibility, good resistance to fatigue and corrosion, desirable thermal expansion characteristics, and economic efficiency ,41,42. TThinness and lightness: They can range from few microns to even subnanometric ranges and are as light as a few grams per square meter, making the impact to weight and thickness of the final manufactured negligible.Porosity: They are highly porous, allowing the matrix to impregnate through them while still maintaining a solid bonding between the two layers of fibers encapsulating them.Tiny volume: The actual volume of these nanomats is negligible owing to the fact that they accumulate with the resin.Mechanical properties: They show highly improved properties compared to their bulk counterparts.Electrospinning is one of the most commonly used methods to produce nanofibers ranging from several micrometers to the sub nanometric ranges . NanofibThere have been property improvements for interleaved nanofibers of various materials, with Impact on nanofiber interleaved polymer matrix composites has also been studied. Due to high-velocity impacts, a cone shape forms on the composites. Depending on the nature of the impact, such a cone can form from the top to the bottom of the laminate (⋁ shaped), or from the bottom to the top (⋀ shaped). The main aim of interleaving is to prevent the formation of such cones and to strengthen the structure. More research in the field is required to study these mechanisms and develop analytical models on the impact events.The dynamic response and energy absorption characteristics of composite laminates fabricated from glass fiber/epoxy composites with and without nanofillers in quasi-static and impact loading have been studied by many researchers ,56,57,58IIC, on glass/epoxy specimens using the end notched flexural test and they observed that laminates with clay up to 5% showed a higher energy release rate than neat epoxy specimens. The enhancement in the strain energy release rate for the glass/epoxy with 5% clay was about 25% when compared to the neat epoxy specimen.Balaganesan et al. conducted experiments to identify the quasi-static properties of nanocomposite specimens. The tensile and flexural properties were predicted for the specimens with and without filler dispersion. They observed that filler dispersion up to 4% in the matrix had a uniform, exfoliated structure. It was also observed that the specimens of epoxy with 3% clay showed enhancements in tensile strength, tensile modulus, flexural strength, flexural modulus, and impact strength when compared to neat epoxy specimens of 13%, 17% and 15%, 11%, and 22%, respectively. They also prepared specimens with glass fiber as the primary reinforcement and nanofillers as the secondary reinforcement. The specimens of glass/epoxy with 3% clay showed an enhancement in tensile strength, which was 32% higher when compared to neat epoxy specimens. The authors conducted a test to find the strain energy release rate, GThe authors prepared glass/epoxy laminates of three, five, and eight layers with and without nano fillers as a secondary reinforcement to predict the perforation resistance under a quasi static punch test. It was observed that the specimens with clay up to 5% offered higher resistance when compared to neat epoxy specimens. The enhancement in energy absorption was 60% for three layer laminate with 5% clay when compared to neat epoxy specimens.Velmurugan and Balaganesan used modal analysis on pre-impacted and post impacted nanocomposite specimens. The NF and DF were predicted using the impulse hammer technique for un-impacted and post-impacted laminates, with all the edges under clamped boundary conditions.The NF values and DF values were obtained for the first five modes. The change in NF for three, five, and eight-layer nanocomposite laminates was observed in all the modes due to the presence of nano clay up to 5% by weight in all the modes. The frequency response function (FRF) plot for the nanocomposite specimen with 3% clay is shown in The damage of the laminate due to fiber failure and delamination changes the vibration parameters, NF and DF, of the specimens. The authors observed that the DF values were high in post-impacted specimens, which was due to the interaction between layers separated in delamination. For post-impacted specimens, the NF decreased, which was due to a decrease in the stiffness, and DF increased with an increase in the impact velocity.The authors also conducted an elaborate study on damage scenarios between specimens with and without filler. They observed a decrease in the delamination area for specimens with filler when compared to specimens without filler during impact loading below ballistic velocities. This was due to the nano filler, which acts as a secondary reinforcement and supports better bonding between layers. It clearly indicates that as the impact velocity increases, the delamination area increases, due to which the NF values of post-impacted laminates decrease. However, in nano-filled composites, the decrease in NF is less as the filler reduces the area of delamination.Balaganesan et al. worked on the dynamic response of nanocomposite laminates during impact loading. They used a shock accelerometer with a 100 kg value. They obtained an acceleration-time response and frequency response of the specimens with and without nano fillers. The time response curve was plotted between the acceleration and the period of vibration for different impact velocities. The frequency response was plotted for NF values in various vibration modes of the laminates. The acceleration-time response for the nanocomposite specimen with 3% clay based on the authors’ research work is shown 2. The maximum amplitude of acceleration of the specimen with the same configuration when subjected to an impact velocity of 135 m/s was 4892 m/s2.They observed that the maximum amplitude of acceleration for impact velocities that are below the ballistic velocity was 10 times higher than that for velocities that are above the ballistic limit. In this particular case, for specimens with 3% filler, the maximum amplitude of acceleration when subjected to an impact velocity of 35 m/s was 48,522 m/sIt was also observed in another work  that theThe authors also observed that specimens of fewer thickness values dissipated energy at higher modes of vibration. This was observed by the authors from the signal of the accelerometer. In their study, they conducted impact tests on three, five, and eight layer glass/epoxy laminates with and without nano fillers. Three and five layer specimens showed higher amplitude values at higher NF values and the eight layer specimen showed the same amplitude value in all the modes of vibration. Balaganesan and Velmurugan  studied The conclusions from the study  of energThe authors presented that the kinetic energy of the projectile is absorbed by the specimens in the form of deformation under strain and then vibration, failure due to delamination, and matrix cracking. It was also observed that the energy absorbed by the laminate in vibration is the energy absorption in vibration for impact velocities that are below the ballistic limit, but higher than the energy absorbed due to delamination and matrix cracking. The presence of clay in the matrix enhanced the interaction between the clay and polymer within the elastic limit, which reflects an increase in energy absorption due to vibration. It was observed that the specimen with 3% clay showed a higher increase in energy absorption during vibration when compared to the neat epoxy specimen, which is due to the exfoliated structure formation within the matrix.The ballistic limit of a three-layer glass/epoxy laminate is about 100 m/s. The authors conducted an impact test at the velocity of 82 m/s in a three-layer glass/epoxy specimen and the energy absorption in various failure modes is shown in It was observed by the authors that clay dispersion in the matrix decreases the energy absorbing capability of specimens with clay up to 5% in the failure modes of delamination and matrix cracking for below ballistic impact velocities . A threeVelmurugan and Balaganesan studied the ballistic limit and energy absorbed by nanocomposite specimens subjected to the impact of a target at above ballistic velocities. Fibers that are in direct contact with the projectile are termed primary fibers and those that are not in contact are termed secondary fibers. Secondary fibers were subjected to elastic strain energy in the deformation mode in the direction of the projectile movement. In this case, the kinetic energy of the projectile before striking the target specimen was dissipated by the failure of the specimens in elastic deformation of the secondary fibers, delamination, matrix cracking, and tensile failure of the primary fibers.When the laminates were subjected to an impact velocity above the ballistic limit, the presence of clay in the epoxy matrix enhanced the impact resistance of the nanocomposites when compared to neat epoxy specimens . The enhThe authors conducted experiments for various initial velocities of the projectile and obtained residual velocities for each case of the initial velocity. The authors also considered various other impact studies ,62,63 anThere is a limited amount of research that has focused on developing theoretical models to understand impact phenomena. In this regard, Balaganesan et al.  developeFrom the model, they observed that 70% of the projectile energy was absorbed by the specimens due to elastic deformation of fibers that are not in contact with the projectile. This is due to the higher area of the target that is not in direct contact with the projectile. It was observed that the increase in energy absorption was observed for specimens with clay up to 5%. The authors validated the model results with experiments based on their earlier study.Projectile energy during perforation and various failure modes of the specimen are shown in Core-shell nanofibers containing healing agents have been employed to improve the mechanical properties of laminated composites and to induce self-healing ability in these types of composites . Table 3In 2013, Wu and his coworker encapsulated DCPD in PAN nanofiber via coaxial electrospinning . The preA comprehensive series of studies on the development of self-healing composites using nanofibers was carried out by Neisiany and his coworker from 2016 to 2018 ,68,69. FNeisiany et al. firstly encapsulated an epoxy and amine-based curing agent within the PAN nanofibers  and incoZanjani et al. encapsulated healing agent in the core-shell nanofibers via tri-axial electrospinning  and emplThere is no doubt that polymer matrix composites are an emerging class of materials for innumerable applications, thus replacing their traditional counterparts. The present paper provided a brief review of the advancements in nanoengineered polymer matrix composites. This review indicates that significant knowledge has been achieved in the area through extensive research conducted by various researchers. The high-velocity impact studies from 2000 till present were reviewed and important events were tabulated. Though laminated polymer matrix composites are already superior in regards to their properties compared with their conventional counterparts, they are further enhanced by the addition of or modification with other materials. In this review, the nanofillers and nanofiber interleaved composites were introduced and reviewed. More importance is given to the high-velocity impact response of nanofiber interleaved composites. Self-healing composites, via the interleaving of nanofibers that encapsulate healing agents, were reviewed as the technique provides solutions for a long standing concern of composite repair. Though various reviews have been published in the considered topic, there remains a significant gap in information regarding the use of nanoparticles and nanofibers while considering their effect on composite laminates. The study of high velocity impacts on nanofiber reinforced composites is another untouched area. This paper described various studies on nanoparticle modified composites, but various issues, like the agglomeration of these particles, uniform distribution during fabrication, the alignment of nanoparticles, etc., need to be addressed. The core-shell approach has gained popularity over the last few years and more applications in various other fields are currently being tested. From this review, we can conclude that increasingly more research is being carried out on the incorporation of nano level particles and materials to improve the properties of fiber reinforced polymer matrix composites. Furthermore, significant scope for further development still exists. This paper provides insight and a brief review of the state-of-the-art in nano-engineered polymer matrix composites.


Sci Rep 7:39328 (2017), Mesnage et al. Sci Rep 6:37855 (2016).Two -omics studies on genetically modified maize and Roundup-fed rats, recently published in the journal Scientific Reports, contain serious flaws in the experimental design, methodology and interpretation of results, which we point out here. The use of -omics technologies are of increasing importance in research, however we argue for a cautious approach to the potential application in food safety assessments as these exceptionally sensitive and complex methods require a thorough and detailed evaluation of the biological significance of obtained results. Arising from: Mesnage  We here point these out and discuss why caution about the far-reaching conclusions presented in the two studies is necessary. The first study  claims that genetic transformation process caused metabolic disturbances in genetically modified (GM) NK603 Roundup-tolerant maize, questioning the compositional similarity of this cultivar to a non-GM control cultivar1. The second study  reports that exposure of rats to an ultra-low dose of the glyphosate-based herbicide Roundup were found to cause significant metabolome and proteome changes allegedly indicative of liver dysfunction2. Both reports are based on samples collected from an earlier two-year study4.Two studies recently published in Scientific Reports1) is no guarantee that the two cultivars are genetically near-identical. The appropriate control comparator should have been a known near-isogenic, or, preferably, a progenitor line. Lack of standardised protocols such as randomised block field design precludes the evaluation of possible confounding environmental factors, which are often found to have a greater impact on variation than transgenesis6. The lack of spatially separated biological replicates does not conform to standard experimental design, and the use of only two temporal replicates undermines statistical power. It is not clearly described how the technical replicates were obtained, neither is the merging of the two biological replicates for the metabolome analysis explained . The authors cite one study  which reported that approximately 3% of metabolites in NK603 maize kernels differed compared to isogenic lines, however without mentioning that the cited study itself explained that natural variability and environmental factors were the dominant parameters driving the variability of the maize metabolite profiles. The authors do acknowledge that further experiments made under different environmental conditions are needed  but nevertheless proceed with over-sensationalist claims that the transformation process caused a general disturbance and a metabolic imbalance in energy and carbohydrate metabolism in the GM plants . There is also no reference to the expected natural variation in the levels of selected proteins and metabolites among maize cultivars, severely hampering any conclusion about the observed differences. The polyamines cadaverine and putrescine are highlighted in the GM maize report as being of potential negative health concern, however maize kernels normally contain a wide range of these substances, which may even be beneficial at moderate levels7. In our opinion, the study lacks a suitable experimental design to prevent the influence of the environment, and lacks references to the natural variation of these compounds in maize. Thus, the study does not allow the authors to argue, as they do , that the nutritional quality has been hampered by metabolic imbalances nor that the observed differences are due to the transformation process and the resulting expression of a transgenic protein. In conclusion, it is not possible from this study to determine the cause of the observed differences between the two maize varieties, or to link these results to any effect of biological relevance.The GM maize report does not provide evidence in support of the claim that the transformation process has caused the observed metabolic differences. First, no evidence is presented to prove that DKC2678 (Roundup-tolerant NK603) is near-isogenic to DKC 2675. Similar identity numbers in a seed catalogue  by looking at the 55 metabolites that were significant at the more lenient p-value threshold (p < 0.05) instead of correcting for multiple comparisons as in the proteome analysis, while the Benjamini-Hochberg False Discovery Rate (FDR) analysis only yielded three significantly different metabolites at a threshold of q < 0.05 . The p-value threshold (p < 0.05) implies that 34 (5%) of the 673 analysed compounds could be obtained by chance alone in two identical situations and thus could be false positives, making the conclusions unreliable without further testing and validation. Though not completely incorrect, we would emphasise that this approach calls for careful treatment and interpretation of the results. In conclusion, the choice of sample material for the experiments, the statistical analysis and the interpretations are questionable. The authors do acknowledge that small sample size and aged animals give little statistical power , but nonetheless go ahead with speculative conclusions .In the Roundup report, it is relevant to note that the analysed liver samples came from the same rat material used for a several years old study11. The evaluation was later updated to include a total of 60 GM crop profiling -omic studies that reinforced the original conclusion12. More recent studies14, along with a consensus in the scientific community15, also confirm this general perspective. The authors seem to misinterpret the Substantial Equivalence concept, which recognizes that differences between varieties will always be observed, providing a starting-point of the food safety evaluation rather than an end-point in itself. Any observed differences indicated by molecular profiling are not necessarily biologically meaningful in a health and safety context, and rather than using a single comparator it would be more appropriate to develop a reference class model covering variety, growth location, replications and other sources of variation16.Both reports claim disproportionate biological implications based on the presented -omics data. We recommend the authors to acknowledge the wealth of studies with contradictory results. Of relevance to -omics research related to GM crops, an article was cited in the GM maize report that evaluated 44 publications containing -omic profiling studies, of which twelve were on GM maize. It concluded that natural variability, conventional breeding techniques and environmental factors consistently have a higher impact on gene expression, protein and metabolite levels than transgenesisBoth GM crops and glyphosate are currently subjects of political discussion in the European Union. Since the accumulated scientific body of evidence can (and should) be used to guide policy decisions, it is imperative that the research is solid and indisputable. While we support the research and development of cost-effective and accurate methods for safety assessment of food and feed crops, we disagree with the claims of the GM maize and Roundup reports since the problems with the experimental design and data interpretation prevent the experimental evidence to support the conclusions. Ultimately, the wider discussion concerns the standards of scientific publication. Researchers are constantly balancing between the urge to publish generated data and the requirements to further validate, or reject, their hypotheses through repetition and potential generalisation. One important guideline is that the more spectacular a finding is, the more rigorous are the requirements for validation. It is against this background that we wish to question the claims of the two reports criticised here and weigh their presented findings against the accumulated body of evidence in the research area.


Recently, studies focusing on identifying user’s current location for use in a wide variety of differentiated location-based and localization services have steadily increased. In particular, location awareness using wireless communication is gaining attention in indoor environments composed of many obstacles, where GPS signals cannot reach. Previously, studies have focused mostly on location precision, which resulted in using many beacon nodes, not considering the initial installation and maintenance costs, communication robustness, or power consumption. This makes it difficult to apply existing methods to various fields, especially in mobile nodes  with limited battery capacity. In this paper, we propose a hybrid situation-aware indoor localization (SAIL) system for real-time indoor localization using a combination of low frequency (LF) and Bluetooth Low Energy (BLE) 4.0. This approach allows us to work with limited battery capacity mobile devices, and identify tagged mobile nodes and their current location in relevance to the anchor node. In our experiment, we attached one anchor node at the entrance to indoor areas such as office or factory settings. Using our hybrid SAIL system, we were able to detect the passing of a mobile node through the entrance and recognize whether the node is entering or exiting the room by calculating the direction of movement as well as the distance from the entrance. This allowed us to distinguish the precise position in an indoor environment with the margin of error being 0.5 m. The signal attenuation due to obstacles is overcome by using LF communication in the 125-kHz band. This approach enables us to reduce the number of initially installed anchor nodes as well as the power consumption of the mobile node. We propose an indoor position recognition system, namely, the hybrid SAIL system, that can be applied to mobile nodes with limited battery capacity by reducing the system complexity and power consumption. Location awareness technology using wireless communication channels have been proposed as a promising method in the location-based service (LBS) field and lends to a variety of applications and differentiated services based on current location . Mostly,These attempts have been aimed at efficient and accurate indoor positioning even in indoor environments with many obstacles. Recently, various studies have been conducted to satisfy these scenarios, proposing positioning methods such as Angle of Arrival (AOA), Time Difference of Arrival (TDOA), Received Signal Strength-based, and cell-based methods using a variety of communication technologies such as infrared (IR), ultrasonic, wireless local area network (WLAN), ultra wide band (UWB), and BLE ,8. Each Many of the current studies did not satisfy the requirements of real-world location recognition services. Typically, to improve accuracy, a method using a number of beacon devices is not suitable for an actual LBS system ,10. SeveIn addition, the conventional localization method requires periodic scanning and arithmetic processing for the mobile node ,10,11,12In this paper, we propose a system for real-time indoor location recognition using a mixture of LF and BLE which can be adapted even in a mobile device with limited battery capacity. The proposed system, namely the hybrid SAIL system, detects mobile nodes passing through the entrance and recognizes the current situation by calculating the direction of movement and distance from the entrance to identify the precise position in the indoor environment. This system uses a hybrid of LF and BLE, taking in the robust characteristic with high transmittance to obstacles of LF and common use and low-power implementation characteristic of BLE. This can reduce the number of initially installed anchor nodes and reduce the power consumption of the mobile node. In addition, the system can be extended to an entire building with a wireless sensor network (WSN) using adjacent anchor nodes. By presenting a location-aware system that can be used in battery-constrained mobile nodes, our system can be applied to a variety of LBS applications such as mobile asset management and tracking, smart factory systems, smart hospital systems and large-scale building guidance systems.The remainder of this paper is organized as follows. A brief literature review for studies related to localization using WSN is provided in In There have been numerous efforts to solve the location identification problem of cluttered indoor environments. These technologies include IR, computer vision, WLAN, UWB, Ultra High Frequency (UHF), Radio Frequency Identification (RFID), etc. ,8. In reAmong them, RSSI-based location awareness systems are widely used due to their relatively low operating cost and easy implementation compared to other technologies. RSSI indicates a measurement of the power present in a received radio signal. Localization performance of RSSI-based techniques are affected by the measurement accuracy of RSSI. LF communication and BLE communication are typical examples of RSSI-based wireless communication technologies. In the case of indoor location recognition systems such as the hybrid SAIL system, many studies have been conducted to improve the stability of RSSI even in difficult environments. To compensate for the advantages and disadvantages of each communication characteristic, additional fusion sensors or mixed-type position sensing studies have attracted a growing interest from the research community ,14,15,16BLE is the most commonly used wireless communication technology in RSSI-based localization technology, due to its low-cost and low-power properties. In addition, it has the advantage of high accessibility because various devices including smartphones support BLE communication. Therefore, BLE is traditionally used in most location-based services such as asset tracking and access control. Examples of indoor positioning research can be found in the form of using the triangulation method ,18 and tThere is a problem in that the accuracy of the position recognition is very low in a non-line-of-sight (NLoS) situation where an obstacle exists between communication nodes. This is because the permeability to an obstacle is relatively low at an ultra-high-frequency band. Since the received signal’s transmittance is low, the received signal is often reflected by surrounding object and not received directly from the source. Thus, the BLE RSSI value is not only inaccurate but also unpredictable. This is called the multipath propagation phenomenon. As the BLE signals are especially affected by multipath propagation and NLoS conditions, it is difficult to obtain accurate distance estimations using this method in cluttered indoor environments . To solvThe above problems are generalized problems in BLE based location aware systems. To solve this problem, many studies have considered adding additional sensors or using hybrid algorithms. A hybrid Distance Vector (DV)-Hop Algorithm  is a mixRFID is a very interesting technology for indoor positioning because of its low operating cost and low power consumption characters. To achieve high localization accuracy with RFID technology, ways to improve the reliability of RSSI in challenging environments need to be studied. In particular, using low frequency (125 kHz band) of RFID (LF-RFID) shows low power consumption and high accuracy even in the presence of obstacles, which can be used to our advantage. LF-RFID can be operated with low power  because Multiple studies on several position recognition have been carried out, recognizing the above characteristics. Research of LF-RFID based indoor positioning systems  considerThe system can recognize and monitor the accurate indoor location by using a mixture of the RSSI of LF and BLE signals between the anchor node and mobile node. We used the LF signal of the 125-kHz band for situation recognition. The LF transmitter is designed to be located at the anchor node and the receiver at the mobile node. The anchor node is attached to the door, and two LF antennas are installed inside and outside. The LF range in We propose hybrid SAIL system that can recognize the precise position using hybrid signal composed of LF and BLE. Basically, it is based on RSSI signal and uses the triangulation method and distance conversion method. The mobile node receives the LF signal and recognizes the access state (entry or exit) and the distance to the entrance by comparing the RSSI values of the received LF signals. The mobile node transmits the recognized information to the anchor node using the beacon signal. Unlike the previous information processing method of similar systems, the hybrid SAIL system is composed of distributed computing environments between anchor nodes. An anchor node is connected to other anchor nodes located in nearby rooms through BLE communication to form a distributed network. In this way, only a specific area where a mobile node is located is activated, which can lead to reducing not only the power consumption but also the information throughput. In hybrid SAIL system, anchor node networks with neighboring anchor nodes use BLE communication. Thus, it is possible to synchronize and monitor received information, thereby expanding the recognition range.The mobile node incorporates an LF receiver and receives periodic LF signals transmitted from the anchor node. The LF antennas are installed inside/outside the entrance door. The mobile node can compare the RSSI values of the LF signals transmitted from two antennas and clearly distinguish whether the location is inside or outside relative to the entrance door. That is, it can be determined whether the mobile node has moved in or out. In addition, it is possible to obtain information of the present location. The mobile node can obtain the distance between the anchor node and the mobile node through the algorithm of receiving the LF signal and converting the RSSI into distance. That is, with the anchor node attached to the entrance door, the mobile node can determine its distance from the doorway. Since the LF signal has a long wavelength, it is possible to estimate a relatively accurate distance even in a room that contains many obstacles. In summary, the mobile node can use the LF signal to determine information about the access area, the entry/exit situation, and the distance from the entrance. However, if only the LF signal is used, it has a limitation in that it cannot recognize the exact position in the room because it knows the distance from the door but does not obtain information about the direction. To solve these limitations, we propose a more precise position recognition method using BLE signals based on situation and distance information. To derive more accurate localization, the mobile node shares the location information by transmitting the obtained information to the anchor node through the BLE advertising signal. First, the mobile node advertises its own location, situation, and distance from the door using the LF signal, as shown in RSSI distance transform equation. The Tx power of Equation (1) means transmitted power of BLE antenna and we use 4 dBm. RSSI refers to the strength of the received signal when an anchor node receives the BLE advertising signal from the mobile node. N is the correction constant, which we set as 2. We want to determine the precise location, which we cannot distinguish by the LF signal alone. We accomplish this by estimating the distance between the mobile and neighboring anchor nodes through the D is the inter-device distance of the proposed system, which is the distance from the source to the destination based on the subscripts m being the mobile node and A being the anchor node attached to each door. Since a plurality of nodes are used, the applicable device is referred to using the number The anchor node periodically transmits the LF signal indicating the inside and outside of the entrance through two LF antennas. The mobile node attached to the moving object collects all LF signals generated from the anchor node through the LF receiver. When the received LF signal is present, information of the zone where the LF signal is currently located can be known by using the strongest RSSI value based on the LF signal value. In addition, since the inside and outside information can be obtained, the mobile node can recognize its current situation of entry or exit.After obtaining the information of the zone where the mobile node has entered, the RSSI value of the LF signal received from the anchor node installed in the corresponding area is applied to the distance estimation algorithm. This allows the mobile node to determine its distance from the door. To share the location recognized by the mobile node, the mobile node advertises the BLE beacon signal with information on the area where the mobile node is located. Not only the anchor node in the access area but also the adjacent anchor nodes receive the beacon signal of the mobile node. In this process, the position of the mobile node becomes more precise. Each anchor node receives an RSSI value when it receives a BLE beacon signal. The distance between the mobile node and the neighboring anchor node can be derived through Equation (1). The neighboring anchor nodes transmit the derived internode distance information to the anchor nodes of the entrance area again. The anchor node of the entry area collects all distance information between the mobile node and the anchor nodes so that the distance and direction of the mobile node can be derived by applying the algorithm used in Equation (3). By adopting this scheme, the networks of adjacent anchor nodes are continuously connected, and the entire network configuration is largely connected to one network in a relay protocol. Since all networks are interconnected, we can increase the data transmission and processing speed of the proposed system. In addition, if an anchor node has a problem and fails, this can be detected in real time, which is advantageous for maintenance. Combining the characteristics of our distributed hybrid SAIL system and the network topology of anchor node network, we show the scalability of our system. Actual implementation of this larger scenario and high-level applications such as navigation services will be explored in our future work.To evaluate the performance of the hybrid SAIL system, three different localization tests (Experiments 1–3) were carried out. This section describes the environment, equipment used, setup, and procedures used during these experiments.For the experiments, the hybrid SAIL system was tested in an environment that includes NLoS conditions, with the presence of various obstacles, both metallic and not and humans. The testing environment, which consisted of indoor office spaces with a total area of around 65Experiment 1 evaluated how well the moving mobile node can detect its current situation. This experiment examined the entry/exit situation with a 1-m-wide door. Two LF antennas were installed inside and outside at 50 cm apart from the entrance door. The LF signal intensity was analyzed by moving about 12In conclusion, we have shown that the position recognition of the proposed method has an accuracy of less than 50 cm in terms of average margin of error regardless of the position and obstacles. The hybrid SAIL system proposed in this study shows that accurate indoor location is possible using one node, while existing indoor location awareness research uses at least 4–8 beacon nodes. Thus, we claim that it is possible to obtain a relatively accurate indoor mobile node position while greatly reducing the initial installation cost. Cumulative ratio for margin of errors of the mobile node positions for two different conditions are shown in We implemented a mobile node localization service using the hybrid SAIL system in We have confirmed that the time taken to recognize the location of the 20 mobile nodes is within 1 s. Although the total recognition time increases gradually as the number of mobile nodes increases, it is possible to recognize the location of all mobile nodes in a relatively short time even in a concentrated situation of one anchor node and to provide stable service. In this paper, we proposed the hybrid SAIL system, a situation aware indoor localization system which uses a combination of LF and BLE signals to locate mobile nodes and their current situation (entry/exit). LF is used to effectively measure the distance from the anchor node, while BLE is used to determine the direction of the mobile node. Unlike previous studies that advertised simple information in anchor nodes and calculated precise locations in mobile nodes, this study changed the method in which a mobile node transmits a simple advertisement and the anchor node calculates the precise position, leading to effective application of mobile nodes with limited battery capacity while considering efficiency, installation cost, and power consumption as well as the accuracy of indoor location awareness. In addition, as a distributed computing system, the system scalability is increased. Eliminating unnecessary information throughput helps reduce the system complexity as well.Through performance evaluation, we established that the situation of entry/exit of a mobile node can be determined in real-time and that we can also accurately recognize the direction of travel as well as the indoor location of the mobile node. We also confirmed that it is possible to recognize similarly precise positions , even though we reduced the number of nodes by installing one anchor node per entrance. We also show that, even when many mobile nodes congest a given area, the time it takes to determine user location only diminishes minutely (average time for 5 nodes and 20 nodes difference is less than 0.5 s).Our proposed system can be applied to a wide variety of LBS applications such as mobile asset management, smart factories and patient monitoring systems since it is more applicable by reducing the power consumption for mobile nodes as well as the number of anchor nodes. In the future, we will examine the robustness of the system by increasing the number of experimental mobile nodes. We also plan to conduct experiments that cover larger areas, such as an entire building as well as high-level application scenarios such as navigation services.


Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides. Although the Earth’s surface is dynamic and landslides are part of the dynamic processes, they are sometimes triggered by human intervention, especially for infrastructure construction purposes. Constructing a dam is an important interference to the topography and other nature characteristics of a region. In addition, proper site selection for such an expensive investment is crucial for its construction costs and life time. Landslides can be triggered by dams, and in return, they may affect the life time of dams. Therefore, producing accurate landslide inventories and reliable susceptibility maps are needed prior to site selection.During the last three decades, thousands of landslide susceptibility maps have been produced and published in international literature. Several auto-production methods ,2,3 for Among several landslide susceptibility map production methods proposed in the literature, artificial neural network (ANN)  were employed. The area within the unique lithology was evaluated. Almost all of the landslides observed in the field are observed within this unit. As mentioned, the Solhan volcanics are prone to landslides in terms of their rock weathering and structural properties. The approach is that, when lithology is accepted as unique, the landslide susceptibility is controlled by the topographic parameters that vary depending on rock weathering and structural properties. In other words, the topography represents the rock mass strength characteristics that vary depending on rock weathering and structural properties. The Solhan volcanics are located in the Bingol complex and the East Anatolian Fault Zone. For this reason, these parameters were not considered when performing landslide susceptibility assessments. Among the topographic derivatives, slope, aspect, altitude, stream power index, distance to channel, distance to ridge, stream power index, general, plan, and profile curvatures were considered as conditioning parameters during the landslide susceptibility analyses.s is the specific catchment area (m2 m−1), β is the slope gradient (°).The parameter the stream power index (SPI) simply defines erosive power of flowing water based on the assumption that discharge is proportional to specific catchment area . In ordeTopographic wetness index (TWI) defines the effect of topography on the location and size of saturated source areas of runoff generation . The parSlope curvatures are the second derivatives of DTMs. The curvature parameters define the rate of change of the first derivatives slope gradient and slope aspect in a certain direction . In addiLR, ANN, and RF are widely used supervised learning algorithms in machine learning. Supervised learning methods in principle use data samples and estimate the model parameters based on those data samples. Particularly for landslide susceptibility mapping, the model learns from actual landslide areas and no-landslide areas and gives predictions for the future landslides, known as probability of occurrence of landslide. On the other hand, one can consider landslide susceptibility mapping as a binary classification in supervised learning where each input pixel is actually labeled as 0 and 1. This classification output refers the landslide occurrence probability of each pixel in the whole dataset.x shows feature vector, The method of LR relies on well-known sigmoid function and gives the probability of landslide occurrence in the range of 0 and 1. Many references can be found on the details of LR method ,41. The Using independent variablesANN is a more complex model in comparison to LR and aim at iterative determination of the weights with respect to the given true data samples. In the training stage, the network predicts each sample’s label using their weights and bias values. A cost function finds the distinction between the calculated label and true label. At the backpropagation stage, each weight receives an update according to gradient of the cost function. This process continues until a convergence is met or a maximum number of epoch is reached in the training. More details on the ANN method can be found in . In FiguAccording to A few factors influence the success of the neural network applications. The first one is the dimensionality of the data. With a higher number of dimensions, more complexity occurs in the network although it helps to solve more complex classification problems. The second factor is the numbers of hidden layers and the number of neurons in the hidden layers. According to the heuristic approaches, doubling the number of features is a good choice for neural networks with one hidden layer. The third and the most important factor is the normalization of the data. In other words, all data should be in the same scale. Although neural networks are able to find a solution with non-normalized data, it takes longer and is not guaranteed to reach the global minimum of the system.Random forests  can be sAll three ML models were implemented in Python programming language using Scikit-learn  ML libraTwo main performance assessment approaches were implemented in this study. The first one is the conventional internal accuracy assessment approach based on the visual analysis of the Receiver Operating Characteristic (ROC) curves and the area under curve (AUC). The ROC curve has been a common way of visualizing a classifier’s performance, and the AUC is one of the best single number indicator . Accordiz-axes may not truly represent the surface-to-surface distance, the Euclidean (3D) distances as provided in LS3D were analyzed here. Although the algorithm is powerful enough to estimate the transformation parameters between two or more 3D surfaces by minimizing the sum of the squares of the Euclidean distances between the surfaces, no surface matching was performed in this study since the absolute orientation of the photogrammetric datasets were very accurate. As the second approach, the performance of the landslide susceptibility maps was assessed externally by analyzing the landslides occurred after the susceptibility maps were produced. For this purpose, the susceptibility maps produced using the 2012 dataset were assessed with the terrain deformation information derived using 2012 and 2018 datasets. A least square surface matching algorithm and tool, i.e., LS3D, developed by  and lateThe ROC curves  and the The landslide susceptibility maps and the surface comparison results between 2012 and 2018 DTMs were provided in the following sub-sections for further visual analysis. As a novel performance assessment, the resulting maps and orthophotos in some parts of the areas were draped on the DTMs in a 3D geovisualization software.The general overviews of the produced landslide susceptibility maps are provided in The surface comparison, as the novel part of the study, was performed between the 2012 and 2018 DTMs. The latter one was produced from the images acquired after the dam reservoir has been filled. Therefore, both construction efforts and landslides cause differences between both terrain models. The Euclidian distance residuals plot of the whole area obtained from LS3D are provided in Considering the geological and geomorphological characteristics of the study area, it is evident that the reservoir area is highly susceptible to landsliding, and a total of 46 landslides were mapped employing orthophotos of 2012. The landslide inventory was checked with extensive field observations. The study area is generally covered by sparse vegetation cover, and the landslides in the study area can be easily observed. Consequently, the reliability of the landslide inventory is high. The performance of a data-driven method is directly based on the quality of the data and the suitability of the method used.Three landslide susceptibility maps were produced by applying three different ML techniques . Considering the ROC assessment, it is possible to say that all maps show good performance. However, the RF method yields the best classification accuracy with an area value of 0.95, where ANN comes second in the performance with an area of 0.84.Based on the surface comparison analysis, the most conservative results were given by LR, while the most representative results were provided by RF. Consequently, the RF is the most appropriate approach for the area considered in the present study. These results are supported by the model performance assessment. In other words, there is a good agreement between AUC values and Euclidean distance calculations introduced in the present study.Performance assessment of landslide susceptibility map is still one of major research areas among the international landslide community. For this reason, such type cases are extremely important for accurate assessment of performances of the existing techniques. In other words, if landslide susceptibility maps are produced before occurring a triggering event, performance assessment can be performed accurately after triggering event using Euclidean distance calculations.In addition to developing technologies and methods, the presence of more and representative historical data makes landslide susceptibility maps more efficient. Therefore, landslide susceptibility maps are becoming more effective and useful tools for reducing losses caused by landslides. Some recent studies ,58 descrRegional landslide susceptibility assessment is one of the hot topics in the international literature since these assessments are used for landslide hazard mitigation efforts. Depending on advancements of the ML techniques, the performances of these assessments have improved significantly. However, during the performance assessment stage, existing landslide inventories have been used as an obligation. Similarly, for the model performance, the landslide inventory before 2012 was used. However, in the present study, landslide susceptibility maps were produced using the DTM of 2012 (before construction of the dam), and the DTM of 2018 (after construction of the dam) was used for comparison. Such comparison provides more realistic assessment because the main purpose of a landslide susceptibility map is to predict future landslides. In the present study, fully completed datasets were used for this purpose. The results and conclusions obtained from the present study can be drawn as follows:


Coastal environments are usually characterized by a brittle balance, especially in terms of sediment transportation. The formation of dunes, as well as their sudden destruction as a result of violent storms, affects this balance in a significant way. Moreover, the growth of vegetation on the top of the dunes strongly influences the consequent growth of the dunes themselves. This work presents the results obtained through a long-term monitoring of a complex dune system by the use of Unmanned Aerial Vehicles (UAVs). Six different surveys were carried out between November 2015 and December 2017 in the littoral of Rosolina Mare . Aerial photogrammetric data were acquired during flight repetitions by using a DJI Phantom 3 Professional with the camera in a nadiral arrangement. The processing of the captured images consisted of the reconstruction of a three-dimensional model using the Structure-from-Motion (SfM). Each model was framed in the European Terrestrial Reference System (ETRS) using GNSS geodetic receivers in Network Real Time Kinematic (NRTK). Specific data management was necessary due to the vegetation by filtering the dense cloud. This task was performed by both performing a slope detection and a removal of the residual outliers. The final products of this approach were thus represented by Digital Elevation Models (DEMs) of the sandy coastal section. In addition, DEMs of Difference (DoD) were also computed for the purpose of monitoring over time and detecting variations. The accuracy assessment of the DEMs was carried out by an elevation comparison through especially GNSS-surveyed points. Relevant cross sections were also extracted and compared. The use of the Structure-from-Motion approach by UAVs finally proved to be both reliable and time-saving thanks to quicker in situ operations for the data acquisition and an accurate reconstruction of high-resolution elevation models. The low cost of the system and its flexibility represent additional strengths, making this technique highly competitive with traditional ones. Beaches are usually characterized by vegetated dune systems performing a variety of functions valuable to the ecosystem, such as protection against the ingression of the sea water during storms and a sand reservoir that supplies sand to eroded beaches during storms and buffers windblown sand and salt spray. In addition, the dunes act as an ecological niche for both flora and fauna.In a natural context, coastal dunes can be classified into three main types: incipient foredunes, established foredunes, and foredune plains ,2. The iFrom a geomorphological point of view, the embryo dunes present the most interesting shapes to be assessed because they are particularly sensitive to even the smallest changes in any of the coastal environment factors. Indeed, they can quickly be destroyed as they are created. Due to this importance, the presence of embryo dunes hence represents a crucial parameter for the final selection of the case study location.Consequently, the monitored site a presentMoreover, the inclusion of this coastal stretch within the “Coastal Botanical Gardens” of Porto Caleri ensures both the complete conservation and the natural evolution of the monitored coastal environment. No significant impact of human activities has been noticed during the last few years preceding the beginning of the surveys; hence the site was found to be particularly suitable for the assessment of the natural geomorphological evolution of dunes, focusing on the embryonic stage.During recent decades, all the southern part of the Rosolina Mare littoral experienced a significant progradation of the shoreline. The reconstruction of Digital Elevation Models (DEMs) represents a primary tool to detect and characterize the morphology of complex systems. Their comparison enables an accurate assessment, especially when the shapes change rapidly in both time and space. Different techniques are currently available for generating DEMs at a variable spatial resolution. Using discrete approaches, a large amount of points may be directly surveyed through GNSS geodetic receivers. When carried out by means of a Network Real Time Kinematic (NRTK) technique, the survey is hence already framed in an official reference system. Alternatively, the same detection can be done in a local reference system by using a total station. Regardless of the instrumentation used, the manual mapping of the shapes is a very time-consuming task and cannot be applied to broad extensions.Further approaches, with a dense  data collection, such as Terrestrial Laser Scanners (TLSs), airborne LiDAR (Light Detection And Ranging) and digital photogrammetry by Unmanned Aerial Vehicles (UAVs), enable the remote sensing of wide areas, still with a good level of accuracy. LiDAR has been perhaps the most common technique used for the purpose of mapping long coastline sections. In fact, the raw data acquired by this technique have high spatial resolutions. However, the subsequent data processing is usually imprinted to provide a resampled model to the users, characterized by a Ground Sample Distance (GSD) of about 0.5–1 m. Moreover, neither the characteristics of the instruments used to acquire the raw data nor the processing procedures (filtering and georeferencing techniques) further applied to them to generate the final products  are actually known for all the LiDAR datasets currently available in the Rosolina Mare area. Despite this particular issue, the LiDAR approach is certainly an effective method for monitoring broad extensions. However, the sandy coastal environment evolves too much rapidly to be assessed by airborne LiDAR recognitions and the alternative use of helicopters has prohibitive costs. For advanced studies in coastal geomorphology, a different method with a great flexibility and able to provide better spatial resolutions at a more reasonable cost is hence needed.A solution is represented by the recent availability of lightweight and compact digital cameras along with the continuous development of newer and more powerful algorithms in computer vision. In addition, the use of small UAVs has revolutionized the classical aerial photogrammetry leading to a new digital photogrammetry based on a Structure-from-Motion (SfM) approach that provides excellent results in terms of spatial resolution at a low cost .structure) starting from a set of two-dimensional images of a scene . The first stages are the detection of features on each image, their characterization through descriptors and the final matching of the established correspondences. The resulting matches, i.e., the tie points, constitute a sparse point cloud: this is a first raw model of the detected object. It is important noticing that no a priori assumption, such as the use of Ground Control Points (GCPs), is needed to reconstruct the geometry, since the well-known collinearity equations [self-calibration [The SfM process  is an adquations  can be sibration . This isTo perform an accurate georeferencing of the point cloud model, a set of GCPs was surveyed by GNSS geodetic receivers through NRTK. The accuracies of such a technique are completely comparable with the Root Mean Square Error (RMSE) of a 2 cm GSD imagery in a SfM approach . To framOnce the three-dimensional geometry has been reconstructed thanks to the tie points detection, a point cloud with higher density can be generated using specialized algorithms capable to reconstruct depth maps and searching for additional features to be matched. For this reason, the final dense cloud represented the starting-point for any further data processing. On average, each dense point cloud in this work contained up to ca. 80 million points.There are increasingly reports in the literature regarding applications of UAV systems, also for the aim of a coastal monitoring . GonçalvThe SfM by UAVs therefore represents an innovative technique able to speed up the acquisition process and it finally provides accurate and reliable products. These considerations, along with its versatility, low cost, effectiveness  and signThe innovative aspect of the present study lies in the use of the SfM technique not only for the simple mapping of a coastal section, but instead for the repetition of the survey with the purpose of monitoring the embryo dunes’ morphological evolution. Indeed, we tested the applicability of these methodologies in the study of morphological changes of a dune system due to natural factors on a seasonal time scale. In particular, we will highlight the accuracy of the UAV-borne surveys and quantify incipient foredune morphodynamics. Such results may also contribute to the application of UAV surveys in coastal management studies.The characterization of the dune system morphology was performed using UAVs , in partSix survey repetitions were carried out respectively on November 2015, March 2016, June 2016, November 2016, May 2017 and December 2017 . For eacTwo aircraft were used: a DJI Phantom 2 Vision and a DJI Phantom 3 Professional, even though the DJI Phantom 2 was actually used for the first survey only (on November 2015), whereas the DJI Phantom 3 was used for all the subsequent survey repetitions. They were equipped respectively with a Panasonic Lumix DMC-GM1 camera (16-megapixel RGB sensor) and a DJI FC300X camera (12-megapixel RGB sensor). Specifications of aircraft and cameras are listed in Thanks to the ability of the combination of SfM and BBA to model any kind of uncalibrated camera through a self-calibration procedure with a very good estimation of all interior parameters and lens distortion, especially whenever a redundant set of images is available, the differences induced by using different cameras on different aircraft were negligible.Generally, the flight plans parameters were imposed as shown in Each survey was framed within the European Terrestrial Reference System (ETRS) horizontal datum, in its ETRF 2000 (2008.0) realization. The National Dynamic Network (RDN) materializes this reference system in Italy, thanks to the same Continuously Operating Reference Stations (CORSs) used to stream corrections for the NRTK approach. Targets were distributed over the ground to almost completely enclose the surveyed area b and werThe need to number each GCP  to be ref, principal point’s coordinates A second alignment was performed once all the GCPs were identified on the images. In this latter alignment, all the camera locations were ignored due to their very poor accuracy. Thus, they were assigned with a very low weight or even simply deleted. A final optimization for reprojecting was performed after this second and final alignment in order to best fit the interior orientation parameters of the camera  was then followed by the creation of a dense cloud. This task has been performed using the same software used for the alignment of the images , through the means of robust densification algorithms already implemented in it. However, the dense cloud represents a model of the outer surface of the detected object, therefore it contains points belonging to the vegetation on the top of the dunes instead of the sand at their bases. This means that it is not helpful to detect and assess any morphological change of the embryo dunes over time. To remove those points from any further data processing (including the creation of DEMs) a filter on a slope detection was used. The filter is a feature already available within Agisoft PhotoScan and it performs a ground points classification. Essentially, it  works tharenaria a and Ech spinosa b.Since outliers were still present after the classification, a further statistical outlier removal (SOR) filter was applied to the dense cloud to remove residual points before proceeding with the creation of a DEM of the sandy terrain. This task was performed using the CloudCompare software. Such a process, from the raw dense cloud to the final integration with GNSS-acquired information, is shown in The DEM of each survey repetition was thus preventively checked  by compaFor a further and last validation, a cross-section profile was also reconstructed through GNSS NRTK during the solely survey on November 2016, to both assess the quality of the profiles reconstructed by a DEM extraction and the effectiveness of the plate under the pole. Once successfully performed the validation of each computed DEM generated, with discrepancies similar to those mentioned above, a series of further information was finally extracted from the DEMs and then compared each other. In fact, the strength of the aerial imageries by UAVs, with respect to any traditional discrete and time-consuming approach based on the use of total stations or GNSS geodetic receivers in NRTK mode, lies in the possibility to make cross sections in any region of the model at any time, thanks to a practically continuous data.In particular, to monitor the changes in the morphology of the studied coastal area, ten cross sections and three longitudinal sections a were usMoreover, an elevation difference was computed generating a DEM of Difference (DoD) for the whole extent of the overlapping area between different surveys. This enabled to compute a sediment budget through the compared area extents in a simple pixel per pixel computation, exploiting the accurate georeferencing of every DEM. Furthermore, A reported in B and C indicate a seaward displacement of the dune system. Indeed, zone B is lower than two years before while zone C represents the area where the interdunal depression itself has been filled. Also, the cross-section in The analysis of the DoD in A, zone B and zone C. For each of these regions, a polygon was selected to be the most representative as possible (shown in Even though a thorough geospatial analysis is certainly beyond the aim of this work, in order to further support the DoD results some basic statistics have been computed on the previously highlighted regions: stable dunes, zone shown in c. NonethA and C are characterized by significantly positive mean values (respectively B also shows a real interdunal depression progradation with a significant lowering of the beach in those intermediate areas.It is worth noting that the stable dunes’ region shows a mean value close to zero, with a discrepancy (From a geomorphological point of view, the definition of control regions such as the ones selected above may be useful for the purpose of monitoring the progradation of the dune system over time, as well as to assess the formation of new embryo dunes near the shoreline.DEMs represent one of best procedures to detect complex system’s morphology, both in terms of high spatial resolution and accuracy. Presently, different techniques can collect data to be processed for finally obtaining DEMs. However, many are the aspects to be considered to adopt the most suitable approach, both in terms of expected results and costs.The need for both high spatial and temporal resolutions to assess the evolution of critical environments subjected to rapid morphological changes, such as complex dune systems in coastal environments, can be certainly achieved with a SfM approach using UAVs as tools for the acquisition of the aerial imageries. This kind of technique represents an excellent compromise between the accuracy and the resolution typical of laser scanning systems with a very low cost, making it possible to repeat surveys also in shorter time intervals. Hence, the SfM approach can be successfully used to reconstruct any three-dimensional geometry. Moreover, it can model both the interior and exterior orientation parameters of the camera, allowing the use of different and even uncalibrated cameras. The BBA can take into account all the non-linearities and enables to achieve the best final accuracies.The effects of the vegetation on any reconstructed surface can be mitigated by following a procedure based on the application of slope detection algorithms and SOR filters. This allows to minimize the influence of these factors on the subsequent DoD analysis. The use of a NRTK GNSS technique speeds up the survey of the GCPs needed for the georeferencing of dense clouds, DEMs, and orthomosaics, with accuracies comparable with the same precision achievable with the SfM approach. Although particular care should be taken for both on-field operations to prevent data biases, e.g., for the sinking of the pole’s tip during the survey, and for the validation stage of the products, the entire process may be applied also from non-specialist geomatic staff, since its workflow is not particularly difficult.The results of this work confirm both the reliability and the affordability of the SfM approach by UAVs and indicates that it is a reliable tool for the interpretation of the change over time of very small shapes by the geologists.The commercial availability of both lightweight UAVs, often very easy to operate, and software implementing SfM algorithms will expand the use of these techniques, not only for coastal environments but wherever rapid acquisition is needed, and high spatial resolution must be achieved. Many types of applications can be already found in the scientific and technical literature, from the mapping of coastal areas  to the dFurther developments of this research may be imprinted to exploit the multispectral data acquired by the Sentera Single sensor shown in Despite a variety of many other alternative surveying methods, the digital photogrammetry represents, at present, an excellent technique able to achieve both high spatial and temporal resolution at a truly affordable cost.