The article shows the usage of swarming algorithms for reconstructing the heat transfer coefficient regarding the continuity boundary condition. Numerical calculations were performed using the authors’ own application software with classical forms of swarm algorithms implemented. A functional determining error of the approximate solution was used during the numerical calculations. It was minimized using the artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO). The considered in paper geometry comprised a square (the cast) in a square (the casting mold) separated by a heat-conducting layer with the coefficient  These models demonstrate improved performance and increased passenger security when integrated with adaptive safety systems.AI has been instrumental in addressing various complex issues, with road safety being a prime example. Adaptive safety systems have been developed to enhance the security of moving vehicles, often requiring rapid prediction of potential dangers to avert accidents or traffic incidents. Meier et al.2 presented the concept of intelligent swarming, emphasizing the adaptability of these algorithms to various constraints, including space size and independence of the number of variables.Swarm algorithms, inspired by biological observations of animals such as bee swarms, ant colonies, bird flocks, and worm groups, have made significant advancements in AI. HackwoodAs we transition into Industry 4.0, the appropriate application of AI algorithms becomes increasingly crucial.3. The author formulated this physical problem as an optimization challenge, using heuristic algorithms, such as genetic algorithms, to search for improved solutions. These algorithms utilize evolutionary mechanisms and natural selection principles to navigate available solutions.One such application involving the heat conduction problem with an unknown heat source was discussed by Karaboga4 proposed the Artificial Bee Colony (ABC) algorithm, a model based on the food-seeking behavior of honey bees. The model encompasses three main elements: employed bees, food sources, and non-employed bees. Additionally, bees use an extended dance technique to enhance communication about food sources with other individuals.The swarming algorithms were modeled on the intelligence of bees. KarabogaIn the conventional approach, optimization problems are typically addressed through gradient-based methods. Gradient-based algorithms and bee/ant swarm algorithms represent distinct methodologies for optimization. The first of them leverage the derivatives of a function to find the value where the function attains its smallest value. Whereas the second one, to ascertain the most optimal solution, employs natural evolution as a source of inspiration, drawing from the evolutionary processes observed in living species within the natural environment. No gradient counting is needed in ABC and ACO algorithms.6.Gradient-based algorithms are suitable for solving problems where the objective function has a smooth profile, and the derivatives of the function are known since they can quickly and efficiently find the local minimum of the objective function. Bee and ant swarm algorithms are better suited for solving problems where the objective function has an irregular profile or only the function values at the mesh nodes are knownSwarm algorithms used in the article are more robust to errors  relative to gradient-based algorithms. In addition, ACO and ABC algorithms are more scalable than gradient-based algorithms. Gradient-based algorithms can be difficult to apply to complex problems because they require calculating gradients for all optimized variables.7.Swarm algorithms are more efficient than gradient methods because all individuals in the population can solve the problem simultaneously, while gradient-based algorithms can be time-consuming for large problemsUltimately, the choice of algorithm depends on the specific problem to be solved. If the problem has many local minima, bee and ant swarm algorithms are better than gradient methods. If the problem has a smooth profile and is easy to solve, then gradient-based algorithms are a better choice.8.Local minimums resilience refers to the ability of an optimization algorithm to avoid local minimums that are not global. Gradient optimization algorithms are susceptible to local minimums because they track the directions of the greatest decrease in the objective function, which means that these algorithms will move in the direction in which the value of the objective function decreases the fastest. If the solution space has multiple local minima, the gradient optimization algorithm may get stuck in one of them, skipping the global minimum. ABC and ACO algorithms are less prone to local minimums because they do not rely on differentiation of the objective function, which means these algorithms randomly explore the solution space, increasing the chances of finding a global minimum. However, robustness to local minimums is one of many factors to consider when choosing an optimization algorithm. Other important factors include the algorithm’s complexity and run time. Note, however, that not all inverse problems are the same, and in some cases, gradient optimization algorithms may be a better option8.Both swarm algorithms (ABC and ACO) and gradient methods solve optimization problems, but the first is evolutional, and the second one is analytical. ABC and ACO algorithms are more effective than gradient methods in tackling high-complex optimization problems due to their ability to explore the solution space. Furthermore, gradient approaches can become stuck in local minima, whereas ABC and ACO algorithms can get around this problem. The time consumption of bee and ant swarm algorithms depends on many factors, such as the size of the problem, the number of individuals in the swarm, and the values of the input parameters controlling the algorithm. In general, bee and ant swarm algorithms are considered linear complexity algorithms. Gradient-based algorithms are decision algorithms that guarantee to find the optimized problem’s global minimum. However, they can be time-consuming, especially for large problems (be careful when translating). As with swarm algorithms, the time consumption of gradient algorithms depends on factors such as the size of the problem or the number of variables in the problem. In general, gradient algorithms are considered algorithms of quadratic complexity. On the other hand, ABC and ACO algorithms tend to be slower than gradient methods. This is because ABC and ACO algorithms have to explore the solution space randomly, while gradient methods can quickly use information about the objective function to find a solution. In conclusion, swarm algorithms are considered more efficient but generally slower than gradient methods in solving optimization problems of high complexity9, Komar demonstrated a better ACO efficiency than standard navigation to find the shortest route between two points.Finding the shortest path in a city between two points spaced at a given distance from each other is a different problem. Ant Colony Optimization (ACO) algorithm was used to solve it. This algorithm makes full use of the capabilities of distributed and large-scale systems. In a paper11 applied selected swarm intelligence algorithms to address the inverse heat conduction problem, focusing on the third kind of boundary condition. They reconstructed the temperature distribution within the considered area and identified the thermal conductivity parameter as part of the problem-solving process. The accuracy of the approximate solution was assessed by minimizing the functional in the heat conduction problem. The authors emphasized the efficacy of swarm algorithms in solving inverse problems, particularly when considering input errors and parameter selection.In their research, Hetmaniok et al.14. Therefore, the authors use FEM in the numerical part of their study.The finite elements method (FEM) for numerical calculations of many phenomena in computer simulations is the most widely used method, for example, in the continuous casting of steel and many othersConductivity-radiation transient problems are often encountered in engineering applications such as thermal insulation design and heat transfer analysis in combustion chambers. Many unknown parameters, such as thermal conductivity, emissivity, and absorption, typically characterize these problems. Inverse analysis is the process of estimating unknown parameters from experimental data.15 present a new inverse analysis method for conductivity-radiation transient problems. The method proposed is based on the Boltzmann method on a grid (LBM) and the finite volume method (FVM). The LBM method is a numerical method for solving transport equations, such as the thermal conductivity equation. The FVM method is a numerical method for solving partial differential equations, such as the radiation equation. The Genetic Algorithm (GA), a stochastic optimization algorithm, was also used to find optimal values for unknown parameters. The authors demonstrated the applicability of artificial intelligence to engineering problems.The authors inThe thermal properties of materials are often difficult to measure accurately, especially for materials with variable thermal conductivity, such as composites and porous materials. Inverse analysis is a powerful tool that can be used to recover the thermal properties of materials from experimental data.16 present a new inverse analysis method for recovering the thermal properties of materials in transient conductivity-radiation problems. The presented method is based on the finite element (FEM), a powerful numerical method for solving partial differential equations. The authors also used the genetic algorithm (GA), a stochastic optimization algorithm, to search for optimal values of thermal properties. It was shown that the presented method can accurately estimate unknown parameters from experimental data.The authors in17, the authors present an inverse analysis method for parameter estimation in non-Fourier’s law. The method presented is based on the finite element method (FEM), a powerful numerical method for solving partial differential equations. For calculations, the authors also used the genetic algorithm (GA), a stochastic optimization algorithm, to find optimal parameter values. The authors demonstrated the effectiveness of the method. The authors apply it to the two-dimensional non-Fourier problem of conductivity and radiation. They showed that the method can accurately estimate parameters in the non-Fourier law from experimental data.Fourier’s law of thermal conductivity is a commonly used model for heat transfer in solids. However, there are many situations in which Fourier’s law is not valid, such as in materials with large temperature gradients or materials with inhomogeneous properties. In such cases, it is necessary to use a more general heat transfer model, such as Non-Fourier’s law. In the articleAI algorithms and their applications in areas such as heat conduction showcase the potential for innovative solutions in various fields. The seamless integration of AI methodologies, including swarm algorithms, with mathematical models in heat transfer and other domains will continue to drive progress and open new avenues for research and development.11. Our approach shows that swarm algorithms can reconstruct the The conducted research aimed to reproduce the value of the In the subsequent section, the authors explore the influence of parameters such as finite element mesh density and input parameters on computer simulations. This analysis is conducted within the context of ABC and ACO calculations as they relate to the reconstruction of the heat conduction coefficient between the cast and the casting mold. The outcomes of these calculations were then compared across different iterations, two distinct tessellations, and a varying number of individuals. The findings present interactions between AI methodologies and mathematical models in heat transfer.Q–the capacity of internal heat sources T–temperature [K], c–specific heat 18:Transient heat conduction occurs during heating and cooling bodies as long as they strive to achieve temperature equilibrium with the environment in which they find themselves. The heat transfer between the parts of bodies in direct contact with each other is defined as heat conduction. The following formula defines the mathematical model of conduction in a single body Boundary conditions are essential for solving the heat transfer equation. With a suitable set of them, the solution can be clearly determined. The chosen type of boundary condition depends on the specific problem. In some cases, only one type of boundary condition can be used. In other cases, using more than one type of boundary condition may be necessary. We distinguish four types of boundary conditions associated with heat transfer:One of the boundary conditions in the casting process is the Dirichlet condition. In this case, the temperature at the surface of the casting is known.Boundary condition of the first type (Dirichlet)—on the boundary Boundary condition of the second type (von Neumann)—on the boundary T is the temperature at the boundary between the body and q denotes the heat flux inputting (Boundary condition of the third type (Newton’s or Robin’s)—on the boundary The continuity boundary condition (the fourth kind) is used when heat flows between two areas . It can be applied in two cases: ideal contact or through a layer separating the cast and the casting mold . The boundary conditions significantly affect the temperature distribution in the casting, so they must be carefully selected to ensure that the desired properties of the cast are achieved.ideal contact between areas,19.lack of ideal contact—heat exchange through the separation layer describe The numerical case described in the article20 presents a more complex casting model; the model presented in the reviewed article is much simpler. In the current article, the model does not consider mechanical interactions and uses simplified geometry. However, it is free to move on to realistic problems, as the swarm algorithms used do not impose any restrictions on the numerical model:modeling casting of metal alloys—in this case, many factors must be taken into account, such as the temperature of the alloy, the temperature of the mold, the mechanical properties of the alloy and the mold, and the solidification speed of the alloy,modeling the hardening process of steel—in this case, also needs to consider many factors, such as the temperature of the steel, the temperature of the quenching oil, the cooling rate of the steel, and the mechanical properties of the steel.The proposed optimization methods with ABC and ACO algorithms can successfully solve these problems with high accuracy.Boundary condition of the fourth type (continuity condition)—on the boundary 21. Metaheuristics make obtaining results close to the optimum possible without requiring expert knowledge of a specific optimization problem. These algorithms quickly adapt to constraints and the solution space size while not depending on the number of variables. The authors, B. Gerardo et al., in the paper22 and S. Hackwood, in the paper2, presented the concept of swarm intelligence. The inspiration for the development of these algorithms came from observations of biological systems such as herds of birds, swarms of ants, colonies of worms, or just swarms of bees.The ABC and ACO algorithms are classified as swarming algorithms and belong to metaheuristics algorithms. A metaheuristic is a general computational problem-solving method used to solve any problem described by the terms defined by this algorithm. They are often based on analogies to real-world processes  that can be interpreted in terms of optimizationThe artificial bee and ant colony are two of the most popular swarm-based optimization algorithms. They are easy to understand and implement and perform well in many optimization problems. There are many other swarm-based optimization algorithms , but these algorithms are more complex and harder to implement.scalability to large problems,resistance to local minimum narrowing,ease of adaptation to other problems,ease of implementation in various programming languages.In general, bee and ant artificial colony algorithms are powerful tools for solving optimization problems, are easy to understand and implement, and have outstanding computational performance.Additional reasons why ABC and ACO were chosen are:The artificial bee colony that makes up the ABC algorithm comprises two groups of bees. The first half of the colony consists of worker bees. The second half of the colony comprises equal parts of non-employed bees.One of the assumptions of the bee algorithm is that the number of non-employed bees is equal to employed bees. It means only one bee belongs to one food source in a given surround. If a food source explored by employed bees becomes exhausted, such bees become unemployable.P population (number of food sources) and SN solutions (the number of the explored food sources).A possible solution of the problem in the ABC algorithm is the positions of the food sources. The amount of nectar present in the food source determines the quality of the solution (efficiency). The first step of the ABC algorithm is randomly generating the initial Each time a solution Information is exchanged between the worker bees and the non-employed bees. Updating the change in solution by the employed bee occurs depending on local information, and the new source is tested depending on the amount of nectar. The new position of the food source is remembered only if the amount of nectar in the next iterations exceeds the value of the previous iterations. Otherwise, the previous position is remembered. Employed bees, after the foraging process, share information about the amount of nectar, which is calculated from the formula:23:D parameter and denotes the number of optimization parameters in the ABC algorithm. The variable k must be different from i.The probability value The ACO algorithm is used when considering problems of finding the shortest path in a graph and is inspired by real ants. The search is done by finding the shortest path between the anthill and the food. The ants randomly choose the food direction, leaving a pheromone passage on their way back to the anthill. This passage left on a specific path gradually evaporates if other ants do not frequent the path. Ants are more likely to choose shorter routes because, on such routes, the pheromone passage steams much more slowly than on a longer path. It is interesting to note that when ants discover a better path, more of them will start using it, which is known as the positive feedback phenomenon.The artificial ants cooperate in searching for the most favorable solution to difficult combinatorial problems. In the search for this solution, there is a relationship between the ants and the accumulated experience they use. Ants work out a common set of solutions over time in the form of shortest paths that lead to their goal. However, there are differences between artificial and real ants. Artificial ants move within the input graph’s edges, while ants can choose any route in nature. The quality of the solution in the ACO algorithm is related to the pheromone passage. One characteristic of an artificial ant colony is that each ant finds the expected solution in each passage. As a result of the algorithm, the best solution is obtained, which is found by the best ant. The pheromone passage is updated along the route when the route found by the artificial ant turns out to be better than the one generated so far. Through this, subsequent ants choose certain edges in the graph more willingly. The trace-reinforced ants’ process is influenced by the distance of the anthill from the foraging area (the path length in the graph). There is a higher probability that the next ant will follow the trail of its predecessor if the pheromone passage is stronger.M—a number of ants. The probability jth node by the ant being in ith node is defined by the equation:kth ant’s decision, G is a path in the graph traversable by kth ants, t—time step iteration, R—node in the graph.The passage routes of all ants follow the rules: at the beginning, the nodes across which the ant will pass are determined at random kth ant on the movement path, The best way to remember a path is when the new one is better than the previous one. When all ants have passed through all paths, then the pheromone array is updated under the formula:25.During the algorithm operation, a pheromone steaming is added to preserve against the unlimited growth of the pheromone passage. The roulette wheel method randomizes nodes on the paths for each ant during the first iterations. The probability calculated from the equation Eq.  is consiThe paper deals with an issue that needs an association of two different subject areas: thermomechanics (heat conduction) and computer science . The analyzed physical heat conduction processes considered the continuity boundary condition (of the fourth type). The obtained results were tested regarding the influence of input parameters in swarming algorithms on computer simulations of heat conduction. The work concerned the value reconstruction of the 11. The main parameters in our calculations, respectively, are: food sources—the number of bees in the population,search area—the range of the kappa parametera number of iterations—the rerun number of food source searching processes by individuals, consistent with the results presented in the calculations.for the algorithm ABC:number of iterations—the rerun number of food source searching processes by individuals, consistent with the results presented in the calculations,the initial number of ant population—the smallest number of individuals as a fixed parameter of the algorithm,the number of ants in the population—the actual number of individuals as an input parameter of the algorithm,Intensification Factor (Selection Pressure)—the value of 0.5, the fixed default parameter of the algorithm,Deviation-Distance Ratio—value 1, the fixed default parameter of the algorithm search area—the range of the kappa parameter.for the algorithm ACO:Geometrical model and finite element meshes were created in the GMSH26 program. The TalyFEM27 library and algorithms implemented in C++ were used in numerical calculations. TalyFEM is a tool that employs the finite element method of the selected physical phenomena simulation. It taps data structures from the PETSc28 library, including vectors, matrices, or ready-made solvers.The parametric settings associated with the ABC and ACO algorithms were taken from literature items29, the authors used the same FEM model to simulate the temperature distribution in a DC motor with permanent magnets. The model included a rotor, a stator, and a winding. The authors used various boundary conditions, including constant rotor and stator surface temperatures and heat flow through the bearings. The authors compared their results with those obtained from an experiment on the heat conduction problem. This validation indicates that the FEM model is accurate and can be used to simulate the temperature distribution.These libraries were used to implement the numerical model. InThe tests were performed on a computer with the following parameters: Intel (R) Core (TM) i5-4590 CPU @ 3.30 GHz processor, x86_64 architecture, using the Linux operating system in Ubuntu distribution.30. The error of the approximate solution was minimized using the bee and ant algorithms, respectively. The reference temperature values and the temperatures obtained during the simulation were generated with the constant reference heat transfer coefficient Swarming algorithms have been implemented in Python and adapted to the possibility of combining them with the TalyFEM18. The initial temperatures were respectively The tests used two different finite element mesh density variants for the same geometrical model. Simulations were carried out for one mentclass2pt{minimmentclass2pt{minim2D finite element meshes are described by a full tessellation, including element nodes, interface, surfaces, edges, and vertices sets of the domain. Two meshes of 111 and 576-nodes tessellation are considered in our paper.i is the number of nodes in the FEM mesh, j is the number of time steps, 31.For the calculations presented in the article, the number of iterations  was the criterion for finishing the calculations. The convergence control of the algorithm was evaluated on the basis of the value of the functional:The obtained results concern the layer separating the cast and the casting mold for two different tessellations Fig. . The nodLimitations of the ongoing research are related to the need for more time records for the calculations, the somewhat simple design, or the limited hardware resources. However, these limitations are temporary and easily eliminated by exploiting the scalability properties of the algorithms and parallelizing the calculations.For each tessellation, calculations were performed for the ABC and ACO algorithms for populations of 15, 17, and 20 individuals and 2 and 6 iterations. In order to decrease the search area and get correct results, a feature that characterizes heuristic algorithms, i.e. the need for multiple runs, was used. For the purpose of the calculations performed in each case, the algorithms were run three times. The 0In the case of our calculations, disturbances were introduced into the temperature values.32.In general, the measurement of temperatures with a thermocouple during the experiment is characterized by good accuracy. It is assumed that, for thermocouples, the measurement accuracy is within In our research, we used a uniform distribution for disturbance represented by the random.uniform function available in Python. The range of disturbance is symmetric, meaning that for a 5Figure entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaFigure Figure Figure Table Authors picked up that better, in most cases, results come from using the ABC algorithm for the finite element mesh of 111 nodes. For the 1For a finite element mesh divided into 576 nodes, generally better results are obtained with ACO than with ABC. Only for two iterations with 2kappa does not differ from the expected value of more than 0.09For a 15 individuals population, the best results are obtained by the ABC algorithm for the tessellation of 111 nodes. In contrast, for the tessellation of 576 nodes, the ACO algorithm proved to be better. The value of the coefficient }% Table . NeverthTable For a tessellation equal to 111 nodes at 0The presented results show that for a finite element mesh divided into 576 nodes at 0The results of numerical simulations obtained for 17 individuals using ABC and ACO give almost identical results, as evidenced by the tiny or zero relative error. The values obtained for the two algorithms differ slightly from the expected value of the }% Table .Table 4RTable The results presented for 111 nodes in the finite element mesh showed that ACO at 0For the tessellation of 576 nodes, the ACO algorithm also proved slightly better. However, in this case, the results obtained had the same trends as previous calculations for a smaller number of individuals in the population. Correspondingly, the results closest to the expected value were obtained for 0During the calculations, both algorithms obtain results for both tessellations very close to each other and at a satisfactory level. In neither case, even in the worst case, the relative error between the expected and obtained values exceeded 0.015kappa heat conduction coefficient in the layer separating the cast and the casting mold. The numerical experiment used bee and ant colony optimization algorithms. The studied parameters were different tessellations, varying numbers of individuals, percentage perturbation of benchmark temperature values, and different the numbers of iterations.This paper presented an analysis of input parameters for reconstructing the value of As a result, researchers concluded that all examined parameters’ effect on reconstruction 33.Generally, it can be said that six iterations in the vast majority of computational cases give a more successful result in reconstructing the coefficient Although the numerical case described in the reviewed article is somewhat simple, the proposed schemes, due to their scalability, can be used to solve more realistic problems with high accuracy.34, swarm algorithms have been successfully used to optimize the production planning problem or resource allocation process in a rapidly developing field, and swarm algorithms are one of many digital technologies that can be used in this sector.This article is a noteworthy contribution to the field of research on the application of swarm algorithms. Swarm algorithms possess the capability to enhance production processes, conduct data analysis, and facilitate data-driven decisions. According to the literatureThe study described in the article demonstrates the potential of utilizing optimization algorithms as effective artificial intelligence tools. These algorithms have shown promise in practical applications, particularly in situations where it is necessary to replicate experimental settings quickly, albeit with some degree of approximation, through computer simulations.carry out calculations for a more complicated shape and for the case of kappa dependent on temperature,take into account a larger number of optimized parameters for swarm algorithms,implement parallelized swarm algorithms in conjunction with numerical modeling of casting solidification and cooling,experimentally verify the results of calculations for complex shapes.As part of future research, we intend to:


A 45-year-old male patient, with a case of cirrhosis with portal hypertension, massive ascites, and a large umbilical hernia with ulceration presented to the hospital with sudden rupture of the hernia with drainage of ascitic fluid from the abdomen. The spontaneous rupture of an umbilical hernia with a sudden gush of fluid is known as flood syndrome. This syndrome is an uncommon consequence of chronic ascites and end-stage liver disease and is named for the surge of fluid that follows the spontaneous rupture of an umbilical hernia. An abrupt rise in intra-abdominal pressure that is accompanied by coughing, vomiting, straining, or getting up from a chair may be followed by rupture. Umbilical hernia rupture is typically preceded by the development of cutaneous ulcerations (80% of the time). The development of cellulitis, peritonitis, and sepsis are complications of umbilical hernia rupture, as well as bowel imprisonment, and hypotension brought on by large-volume spontaneous paracentesis. Urgent surgical referral should be made if an umbilical hernia develops an ulcer or necrosis over it as this is a serious sign that indicates an impending rupture. In the present case, the patient’s condition worsened during the course of the hospital as he developed high-grade fever and hypotension, his blood parameters showed an elevated white blood cell count (WBC) of 18,000/cu.mm and he was started on intravenous antibiotics and vasopressors. The patient was taken up for Transjugular Intrahepatic Portosystemic Shunt (TIPS) after which the patient became hemodynamically stable. Abdominal hernia repair was postponed owing to his poor medical condition. The patient improved symptomatically upon the treatment of his massive ascites and was discharged in stable condition with the advice of regular follow-up.


In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-Net is proposed in this paper. This proposed method replaces the basic convolution in the U-Net model with the improved convolution combining shear transformation and deformable convolution, making the network more sensitive to border features and more suitable for prostate segmentation tasks. Experiments show that DSU-Net has higher accuracy than other existing traditional segmentation methods. With the acceleration of the modernization process, people’s quality of life has been greatly improved in all aspects, but the increasing pressure of life and work has also caused a certain threat to health. The prostate is a substantial gland located at the root of the urethra that surrounds the urethra and has a slightly flattened chestnut shape. Prostate cancer is one of the most common cancers among men ,2, and iTRUS-guided biopsies are one of the most commonly used and effective methods for diagnosing of prostate cancer ,6, but aComputer vision technology  in the field of image processing and deep learning, which is in continuous development, has been widely used in organ segmentation tasks to assist doctors in diagnosis. However, at present, most segmentation deep learning algorithms are aimed at medical images such as CT and MRI with good image quality, while there is relatively little research on segmentation methods for ultrasound images with poor quality based on deep learning. In recent years, scholars have proposed some ultrasound image segmentation methods based on deep learning. Among them, Yang et al.  proposedAccurately segmenting the prostate in TRUS can also guide physicians in the diagnosis and surgical treatment of prostate cancer. It is impractical that large-scale manual labeling for segmentation requires a lot of time and labor costs. Therefore, the prostate segmentation algorithm based on TRUS images can automatically segment the prostate region of TRUS images, which obtains more accurate segmentation results.The fully convolutional network (FCN) was proposed by Jonathan et al.  in 2015.Ronneberger et al.  proposedAt present, the U-Net network model is widely used in the semantic segmentation tasks of various medical images, and its segmentation performance is excellent, but in the segmentation tasks of prostate TRUS images, there will still be certain traditional network segmentation problems. The common problem is that the accuracy of the segmentation results for prostates with asymmetrical, irregular, and blurred edges is not high enough. Therefore, in response to these problems, the DSU-Net prostate segmentation method proposed in this paper is based on an improvement in the U-Net network model structure, and the segmentation effect obtained by the improved model has been improved.Convolutional neural network training requires a certain amount of data as the basis; sufficient prostate TRUS image data and corresponding labeling data are the necessary prerequisites for prostate segmentation using computer vision technology. Before data preprocessing, we made sure of the following three things: (1) all samples were of the same size as well as the number of channels; (2) the images were eventually to be fed to the convolutional network as a tensor; (3) normalization was performed for each channel of each image. These three steps were designed to make the convolutional neural network run smoothly and to make the training process smoother.In the preprocessing stage, due to the small number of publicly available prostate TRUS image datasets, one is limited by issues such as patient privacy. Therefore, the data augmentation of prostate TRUS datasets is performed with the aim of helping the network to train properly and improve the robustness to avoid overfitting ,16. FurtIn the convolution operation of the same layer of the CNN, the receptive fields of all activation units are the same, and the convolution kernels of the same size always sample the input features at a fixed position and perform pooling operations on the feature map at a fixed ratio. The formula expression of the traditional convolution structure is shown in Equation (1), where R defines the size and expansion of the receptive field with a 3 × 3 convolution kernel of expansion size 1. Formula (2) represents the calculation of each position Deformable convolution  is an opThe calculation method of each position Shear transformation  (ST) is Shear transformation includes the horizontal direction and vertical direction. The horizontal direction is denoted as The image transformation formula for when the direction is horizontal is shown in Equation (4):The image transformation formula for when the direction is vertical is shown in Formula (5):The image can be restored to the original image by applying its inverse transformation. The inverse shear transformation also includes horizontal and vertical directions, and the horizontal direction is denoted as In this paper, the shear transform with lateral parameters of 0.3 and 0.6 shown in b,c; FiguThe DSU-Net model proposed in this paper replaces the original 3 × 3 convolution with the deformable convolution and performs shear transformation and inverse shear transformation before and after the deformable convolution. Firstly, the shear transformation is added before the regular 3 × 3 convolution; secondly, bias learning of convolution is added to generate a deformable convolution kernel with irregular shape; and finally, the inverse shear transformation is added and, then, the feature map is restored to the original size so that the next convolution layer can extract the image features. At the stage of the encoder and decoder, the model fits the boundary information of different scales based on deformable convolution by learning local adaptive receptive fields. During the training process, the deformable convolution obtains the offset through a convolution operation on the same input feature map, and then simultaneously learns and obtains the convolution kernel and offset used to generate the output feature map.The prostate segmentation network based on DSU-Net is shown in The experiments in this paper were created, trained, and tested on the Pytorch platform. The hardware of the experimental environment are as follows. CPU: Intel(R) Core(TM) I7-7800X CPU @ 3.50 ghz, GPU: GeForce GTX 2080Ti, operating system: 64-bit, and memory: 64 GB. The software information is Ubuntu 16.04, PYTHon3.6.2, pyTorch1.1.0. The specific experimental parameters are shown in In order to verify that the method proposed in this paper can effectively segment the prostate, all of our data were provided by Shanghai Ruijin Hospital and were obtained by doctors using specialized medical equipment to obtain ultrasound images of the prostate. We acquired a total of 1057 prostate TRUS images with a resolution of 800 × 608. All data were guided by professional radiologists, using 3DSlicer medical image processing tools to mark the prostate area of TRUS images, and then checked and confirmed by professional radiologists. In this paper, the training data are 780 cases, the verification data are 123 cases, and the test data are 154 cases. In the experiment, the TRUS image and the corresponding label were cropped to an image size of 581 × 477 which was the prostate image after removing part of the prostate image information.In this paper, two quantitative indexes are used to analyze the results of prostate segmentation, which are the commonly used Dice coefficient and Jaccard similarity coefficient.The Dice coefficient is defined as the similarity or overlap degree of two samples, which is a calculation function to evaluate the similarity between samples. The calculation formula is as follows:GT is the real label of the image and SR is the actual segmentation result obtained by the model. For the calculation of Dice coefficients (as shown in Equation (6)):First, |The Jaccard coefficient is used to compare similarities and differences between finite sample sets, that is, the intersection ratio of two sets. The numerator is the intersection of two finite sample sets, and the denominator is the union of two finite sample sets.The Jaccard coefficient is calculated as follows: the metric related to the Jaccard coefficient is called the Jaccard distance, which is used to describe the dissimilarity between sets. The larger the Jaccard distance, the less similar the sample is.The Jaccard coefficient calculation formula is as follows (the meaning of GT and SR has been explained in this chapter):In this section, the DSU-Net segmentation method that combines shear transformation and deformable convolution to segment the prostate in TRUS images is proposed. The results were compared with the original DeepLabv3+  network The results of prostate segmentation obtained with this method are shown in It can be seen from In this work, we proposed a prostate segmentation method, which combines shear transformation and deformable convolution. The method in this paper solved the problem of inaccurate segmentation results caused by asymmetric and irregular shapes of the prostate in TRUS images and fuzzy edges. By replacing the original convolution with improved convolution-assisted network training in U-Net segmentation network, more continuous boundary information was extracted, so as to be more sensitive to image boundary information. The effectiveness of the proposed model was verified by conducting comparison experiments with other competitive methods. Furthermore, the results showed that it was closer to the ground truth and the segmentation effect of the boundary region was more accurate. However, some possibilities still remain to be explored. In the near future, more effort will be invested into exploring the causes of unclear boundary regions in image segmentation.


Studies that particularly look into how the pandemic has affected Saudi Arabian patients' dental preferences are scarce. The majority of the research done so far has focused on the pandemic's broad effects on oral health rather than the specific changes in patient preferences for dental care. Therefore, the primary goal of the study was to examine how patients at a private university dental hospital underwent dental procedures both before and after the pandemic. The study is a retrospective analysis of patient data from March 2019 to February 2022 obtained from electronic hospital records (Dentoplus). The data extracted information pertaining to the number of patients and their periodic visits. The data was examined via statistical tests. At 0.05, the significance level was chosen. According to the research, compared to non-lockdown times, lockdown times saw a considerable drop in the number of patients scheduling appointments. There was also a significant decrease in the number of first-time patients during lockdown periods. The study also revealed a significant increase in the number of cancelled appointments and patients who discontinued treatment during lockdown periods. The age and gender of the patients did not have a significant effect on these findings. The study's findings suggest that dental professionals need to be aware of these changes in patient behavior and adapt their services accordingly. Dental clinics may need to focus on promoting their services and ensuring that patients feel safe and comfortable seeking dental care during the pandemic. The COVID-19 pandemic began in late 2019 and quickly spread across the world, affecting millions of people. Governments and healthcare systems worldwide worked to contain the spread of the virus and provide care to those who fell ill .The COVID-19 pandemic had a profound impact on the field of dentistry. Dentists, dental hygienists, and other dental professionals were among the healthcare workers who faced significant challenges during the pandemic . Due to In light of the possibility of a continuation of the pandemic with different kinds of variants, it is necessary to conduct studies over a period to devise and put into practice unique procedures to educate the general public about the safety of dental care during the pandemic period. Moreover, it is important to analyze the pattern of dental care provided by patients during pre-pandemic and pandemic days. So that necessary arrangements could be made for the effective delivery of dental care at the institutional level.Most of the studies that have been conducted so far have focused on the impact of the pandemic on oral health in general without looking at the specific changes in patients' dental preferences in this part of the world. Secondly, there is a need for studies that compare the impact of the pandemic on the dental preferences of patients in private university hospitals versus public hospitals in Saudi Arabia. This is important because private and public healthcare systems may have different resources, policies, and patient populations, which could affect the impact of the pandemic on patients' dental preferences. Also, there is a need for studies that investigate the barriers to accessing dental care during a pandemic in Saudi Arabia, including financial constraints, fear of contracting COVID-19, and travel restrictions. This is important because understanding the challenges faced by patients in accessing dental care during a pandemic can help healthcare providers develop strategies to improve access to care and ensure that patients receive the dental treatment they need even during a pandemic. Therefore, the main aim of the study was to analyze the behavior of patients with regard to their use of dental services at a private university dental hospital before and during the pandemic. We also compared the number of patients who sought dental treatment, the types of treatments sought, and the frequency of visits before and during the pandemic to assist clinicians in identifying strategies to reduce patients' fear of contracting COVID-19-related infections and improve communication with patients about the importance of maintaining oral health during a pandemic.Ethical approvalThe research proposal had been submitted to the research and innovation center of Riyadh Elm University (REU), and ethical approval had been obtained after verification from the Institution Review Board (IRB), with the number FPGRP/2022/699/800/774 being assigned to this investigation. The investigators had been responsible for keeping ethical considerations, patient privacy, and identity confidential. Confidentiality had been maintained by assigning each participant a unique identification number. The data had been filled in by one of the investigators (AO) and had been double-entered into a computer database using the unique identification numbers. The data had been validated using appropriate computer software. Discrepancies had been handled by reviewing the relevant database. The data and unique numbers had been securely stored and accessible only to study personnel.Null hypothesisThe null hypothesis was that "There is no significant difference in the behavior of patients with regard to their use of dental services before and during the pandemic." Study designThe study design was a retrospective analysis of patient data obtained from the electronic records of hospitals (Dentoplus) from March 2019 and February 2022.Data extraction protocolThe dental clinic staff meticulously documented patients' activity records, such as appointment details, cancellations, treatment discontinuations, and service preferences for each month. The impact of lockdowns on patient engagement with dental services was evaluated using this data, and the age and gender demographics of patients were also considered. The records were analyzed in a comparative framework, juxtaposing pre- and post-lockdown periods to explore how these restrictions affected the interest and engagement of patients toward dental services. The outcomes were scrutinized with careful attention to detail, identifying potential variations and patterns in patients' preferences and behavior toward oral health management.Statistical analysisThe normality of the data had been assessed using Shapiro-Wilk’s test and Kolmogorov-Smirnov tests. Chi-square and Fisher’s exact test had been used to compare categorical variables. Statistical significance had been set at p<0.05.Table Table Table Table The findings show that there were 188,592 total appointments during the mentioned period, out of which 39,436 were first-time patients, and the number of cancelled appointments was 7,484. Restorative dentistry was the most commonly sought-after service, while pedodontics and oral and maxillofacial surgery had the least number of patients. Patients aged between 19 and 35 years were the most frequent visitors to the dental clinics. The study also found that the COVID-19 pandemic led to a decrease in the total number of appointments and the number of first-time patients, but there was no significant difference between males and females in terms of seeking dental services. Moreover, the statistical analysis presented in Table In order to determine the indirect influence of the COVID-19 pandemic on patient behavior undergoing treatment in a dental facility, a number of studies have so far been done . The recThe study conducted has several limitations that must be acknowledged. First, since the study was conducted in a laboratory setting, the results may not accurately reflect the real-world behavior of individuals. The artificial environment of the laboratory may have influenced the behavior of the participants, and thus the results may not be generalizable to other contexts. Additionally, the sample size of the study was relatively small, which may have limited the statistical power of the analysis. Furthermore, the study only focused on a specific age group and population, which was university students, which may not be representative of the broader population. The study also only examined a specific type of behavior, namely procrastination, and did not consider other factors that may influence academic performance, such as motivation, self-esteem, or anxiety. Another limitation of the study is that the data collected was self-reported, which may be subject to response bias. Participants may have provided socially desirable responses or may not have accurately recalled their behavior, leading to inaccurate data. Also, the study only examined the immediate effects of procrastination rather than the long-term consequences on academic performance and mental health.The influence of the COVID-19 pandemic on patients' interest in dental care was revealed by this study's analysis of patient data collected from March 2019 to February 2022. According to the report, patients' use of dental care was significantly impacted by the pandemic and related lockdowns. Particularly, when compared to pre-pandemic periods, the number of appointments made, the number of new patients, and the number of patients who had stopped treatment each month all reduced. During the pandemic, patients' preferences for a specific kind of service also changed.


Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson’s Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier’s performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter. PD according to the World Health Organization is a degenerative condition of the brain, associated with motor symptoms  as well as non-motor symptoms  . The symThe number of PD cases has been increasing in recent decades, at a faster pace as compared to other neurological diseases. In the United States, nearly 90,000 people are diagnosed with PD every year and according to the Parkinson’s Foundation, there will be 1.2 million people with PD in the US by 2030  whilst wFoG is recognized as one of the most critical debilitating motor symptoms of advanced PD, presents a higher rate of occurrence in aged people while, as elaborated previously, its episodes are random in time and subjective to each person at that occasion, and under those circumstances it manifests. Thus, the inherent difficulties and the random nature of FoG manifestation, in tandem with the need for an experienced clinician’s presence (while data acquisition occurs) to verify and annotate the data (time point of occurrence), exhibit the limitations in collecting large volumes of FoG-related data. It is apparent that data augmentation via synthetic data creation for prediction/classification purposes is more than critical towards robust and generic model development. Such tools, given the technological evolution, can be provided by computer science, i.e., AI methods. The main hindrance to such technologies is the limited availability of data in order to be sufficiently robust and efficient.The availability of data in the healthcare domain is crucial and, in many cases, due to many reasons such as privacy legislation, there is difficulty in data gathering, with data being scarce, unstructured or of low quality. Especially in PD, another limitation concerns the patients who cannot provide daily or periodically unbiased and exact data in a systematic way due to motor impairment and other symptoms coupled with the frequency the patients visit and report to their doctors. Also, the wide spectrum of data that can be collected for PD patients imposes another difficulty for computer scientists to develop tools to detect PD in early stages. The heterogeneity and the scarcity of PD data are a major concern for state-of-the-art technologies as they can hinder such “smart” solutions due to inefficient training. This leads to the necessity of alternative ways for data augmentation by engaging state-of-the-art technology such as GANs.Ian Goodfellow et al. , back inThe shortage of data in the health domain, due to the sensitive and private nature of healthcare data which is accompanied with strict ethical and legal regulations governing their acquisition and usage is, in general, a well-known bottleneck for efficient model training, because it is hard to acquire. This can result in limited sample sizes, restricting dataset diversity and representativeness. The lack of quality data significantly impacts the development of AI technologies in multiple ways: (i) affecting the model’s performance, (ii) introducing biases leading to inaccurate and unfair predictions, (iii) raising ethical concerns regarding patient privacy, (iv) causing delays in AI model development, and (v) impacting validation compliance, as adequate data are vital for model efficacy. Thus, the motivation and the contribution of this work is the development of a novel yet efficient methodology for synthetic data creation. In this work, we present a novel approach for realistic synthetic data creation with the exact characteristic properties of the data fed in the FoGGAN model, that its application can lead to data augmentation and mitigation of bias and unbalances by creating data using the under-represented origin of the original data. This highlights the potential of GANs in mitigating data scarcity issues by generating data that preserve essential and similar-looking statistical and structural characteristics of the limited original input dataset.The remainder of the paper is organized as follows: There are many examples in the current scientific literature regarding the use of GANs for the generation of synthetic data  related to the health domain. In , HargreaYang et al.  proposedGANs are also particularly useful for augmenting time series data as well as health-related signals. Esteban et al.  proposedKiyasseh et al.  proposedPrivacy is a major concern in applications encompassing data augmentation with GANs. Torfi et al.  proposedThere are many GAN-based applications used specifically in the domain of PD diagnosis and treatment. Kaur et al.  proposedVoiceprints used for distinguishing PD patients and healthy individuals were utilized by Xu et al. . More spContrary to most methodologies for classifying healthy individuals and patients with dementia which focuses on one type of dementia only, Noella and Priyadarshini  proposedIn , Zanini Kaur et al.  demonstrGANs can also be used in applications relevant to the FoG symptom. Ramesh and Bilal  presenteThe ‘data_daphnet_combined’ dataset  was base0: not part of the experiment. For instance, the sensors were installed on the user or the user was performing activities unrelated to the experimental protocol, such as debriefing;1: experiment, no freeze ;2: freeze.The dataset contained a total of 1.92 million records. Each record contains a value  for each of the nine attributes collected. Also, for each record, there is the annotation column which can have a 0, 1, or 2 value. These annotations mean the following:To refine the dataset and optimize the generation processes, we executed targeted preprocessing procedures. First, we excluded data instances categorized under class 0. To simplify the classification task, that will be detailed in Thus, each data record in the dataset included detailed information about time, the values from the sensors placed to ankles, upper legs and the trunks as well as the annotation and the data file containing the record. Generative Adversarial Networks (GANs) are a category of algorithms, which encompass a dual neural network framework characterized by adversarial competition, hence the term “adversarial”. This architectural solution comprises two distinct neural networks, specifically referred to as the generator and the discriminator, collaborating to generate synthetic data. In 2014, Ian Goodfellow and his colleagues introduced  advancedThe primary objective of the generator is the creation of top-tier synthetic data, with the specific intent of deceiving the discriminator. It takes a random noise vector as input in order to produce high-quality, similar-looking data resembling the provided content.In contrast, the discriminator is tasked with distinguishing between real and synthetic data. The model is implemented as a sequential deep neural network, comprising dense and dropout layers, with the task of classifying input data samples as either real  or fake (generated). Its effectiveness in distinguishing real from fake data samples is, then, utilized to optimize and enhance the overall performance of the GAN, encompassing both the generator and the discriminator. Calculating generator and discriminator losses is of paramount importance during the training processes of Generative Adversarial Networks (GANs). These loss functions serve as pivotal metrics to optimize the performance of both networks. They facilitate the adversarial learning process by quantifying the generator’s ability to deceive the discriminator and the discriminator’s capability to distinguish between real and generated data samples. As previously mentioned, and demonstrated in The pivotal aspect of the GANs’ evolvement lies in the utilization of loss functions, a collection of mathematical Equations (1)–(3) guiding each network’s improvement after every training epoch. These equations are provided later in the text. The discriminator and generator possess their respective loss values. Across successive epochs, these networks learn by striving to minimize their respective loss functions.G represents the generator, D represents the discriminator, and V represents the value function of the min-max game. In greater detail, the process begins by establishing the generator’s data distribution, denoted as gp(x), which operates under the assumption that the input noise variables, zp(z) have been already defined. Once these noise variables are defined, a mapping to the data space is articulated as G, where G represents a differentiable function instantiated as a multilayer perceptron, characterized by parameters gθ. Simultaneously, a second multilayer perceptron, denoted as D, is introduced. This perceptron returns an output of a singular scalar value. Specifically, D(x) quantifies the probability that the data point x originates from the actual data distribution rather than being generated by gp. Consequently, the training procedure involves dual objectives: Firstly, the discriminator is trained to maximize the likelihood of correctly classifying both original and generated samples. Secondly, the generator is trained to minimize the negative logarithm of (1 − D(G(z))).More specifically, the generator and discriminator losses are computed independently and then integrated through a min-max game as described below by Equation (1) . In thisAdditionally, the losses for both the generator and the discriminator can be computed independently using Equations (2) and (3). Both generator and discriminator losses will ultimately converge to a stable state as they undergo an adequate number of training epochs.In this study, we implemented the so-called Freeze of Gait GAN (FoGGAN) model architecture, designed specifically for generating one-dimensional (1D) synthetic data from the dataset previously described in Diagrams prove to be an efficacious tool for comparing and visualizing similarity scores between real and synthetic datasets generated by a GAN (FoGGAN in our study) model. These scores offer crucial insights into the quality and precision of the synthetic dataset, aiding researchers in pinpointing areas where improvements to the GAN model are needed to produce more similar-looking synthetic data. The choice of diagram type depends on the data’s inherent characteristics as well as on the specific objectives of the research.In the context of the current study, we employed the developed FoGGAN architecture  diagrams provide a visual representation of the cumulative sum for both the real and generated datasets. In the context of assessing similarity scores and comparing datasets using the FoGGAN model, the cumsum diagram offered an effective way to visualize the accumulation of the similarity scores computed between the original and generated datasets gradually.Logarithmic (Log) mean and standard deviation (STD) diagrams usually serve as tools for comparing similarity scores between the original dataset and the one generated by a GAN (the ‘FoGGAN’ in our study). A Log mean diagram provided a visual representation of the average or mean similarity score between the original and generated dataset(s) for each training epoch. This depiction enabled an assessment of how the similarity score evolves over time, revealing whether the generated dataset’s similarity to the real dataset is increasing or decreasing during the training process. Conversely, a standard deviation diagram (STD) illustrated the variability in similarity scores between the real and generated datasets for each training epoch. This visualization assessed the consistency of the similarity score and identifies significant fluctuations in similarity between epochs.Principal Component Analysis (PCA) diagrams are employed as a valuable tool in comparing similarity scores between the original and generated datasets. These diagrams offer a graphical representation of how the under-examination dataset’s dimensions align and diverge. By visualizing the distribution of similarity scores through PCA, it was possible to distinguish patterns and trends in the relationship between the compared datasets, illuminating the evolution of their similarity as the GAN model underwent training.Distribution diagrams for individual features are instrumental in comparing similarity scores between the original and generated datasets, on a feature-specific level. These diagrams provided a focused view of how each feature’s distribution evolved over time during the FoGGAN model’s training procedure. By analyzing these distributions separately, we gained insights into the similarity fluctuations for each feature, aiding in a more detailed assessment of the synthetic data generation process.For this purpose, five distinct types of diagrams, outlined below, served as effective means to represent these similarity scores. Each figure presented in the subsequent section incorporated the following elements:Through the examination of these diagrams that illustrated and compared both the real and synthetic datasets, it became feasible to gauge the FoGGAN model’s effectiveness in producing synthetic data that closely mirror the attributes, in terms of quality, of the authentic data.The visual representation of Subsequently, examining the results presented in the (sub)figures included in The insights depicted in The insights drawn from In The primary focus of this study lies in the evaluation of the data generated by the FoGGAN model in the ‘Daphnet’ dataset context, both detailed in To further evaluate the effectiveness of the generated samples, we employed a complementary Deep Neural Network (DNN), a commonly used Deep Learning (DL) architecture in the realm of classification, as the additional arbitrator of the data authenticity. The evaluation processes on this task involved the examination of the generated data using the DNN model. The DNN classifier was initially trained with the original dataset, as detailed in It is noteworthy that the DNN model utilized for this study was not optimized in terms of accuracy and loss, as the primary focus lay in assessing the quality of data generated by GANs rather than the performance of the deep learning model itself. The extracted results of the freeze of gait (FoG) classification using the Deep Neural Network (DNN) classifier underscored some interesting trends, particularly in addressing data limitations in sensitive medical domains such as Parkinson’s Disease (PD) datasets. Specifically, when evaluating the model’s accuracy across different datasets, it was evident that the classifier achieved a high accuracy rate across the board. First, when evaluated with an unseen sample of data instances from the original dataset, the model achieved an accuracy of 90.29%. This demonstrated the classifier’s ability to effectively generalize to familiar data. Remarkably, when applied to a dataset generated using the FoGGAN, the model’s accuracy further improved to 92.09%. This highlighted the potential of GAN models in enhancing the dataset’s diversity and aiding the classifier in making more accurate predictions on unseen data. Furthermore, evaluating the model with a mixed dataset containing (unseen) data instances from both the original and the generated datasets yielded an accuracy of 90.66%. This result showcased the utility of the FoGGAN in augmenting the original dataset, thereby contributing to the classifier’s overall performance and robustness. The higher accuracy achieved when evaluated on the generated dataset alone highlights that the synthetic data created by the FoGGAN contributed essentially to the classifier’s performance. This is particularly valuable in scenarios where acquiring a large and diverse dataset can be challenging. In summary, the DNN classifier exhibits strong classification performance , with the highest accuracy achieved when evaluated on the generated dataset. Moreover, the extracted findings underscored the significance of data augmentation techniques like GANs architectures in enhancing the classifier’s accuracy and its potential in real-world applications, such as FoG classification.Access to healthcare data is often restricted in order to protect the patient’s privacy, thus hindering the reproducibility of existing results and limiting new research. In order to surpass this problem for robust and efficient AI model development, synthetically generated healthcare data have become one of the major tools . This waData Source Availability: The availability of high-quality, annotated FoG datasets remains a challenge. Gathering and labeling datasets, especially for rare medical conditions like FoG, is time-consuming and resource-intensive. This limitation hinders the scalability and widespread applicability of our approach.Clinical Validation: While promising, the high-accuracy results obtained using the FoGGAN-generated data for classification purposes need further clinical validation. Real-world clinical trials and expert assessments are necessary to validate the clinical utility of the synthetic data.Data Generalization: The effectiveness of the FoGGAN architecture in generating synthetic data relies on the quality and representativeness of the input dataset. If the initial dataset has limitations or biases, these may also be reflected in the generated data. Careful curation of the input dataset is necessary to mitigate this issue.The current research focuses on leveraging Generative Adversarial Models (GANs), specifically FOGGAN as introduced, to address data scarcity challenges in the healthcare domain, including the freezing of gait (FoG) dataset related to Parkinson’s Disease (PD), offering promising insights. Nonetheless, it is essential to acknowledge several limitations inherent to our study:The current study acknowledges these limitations as part of our commitment to transparency and responsible research. Addressing these challenges is crucial for the continued development and deployment of GAN-based data augmentation solutions into the healthcare domain, ultimately contributing to optimized and improved patient care and medical research.The main goal of this study was to present a GAN architecture for generating almost identical medical data for PD and specifically for FoG cases. The data which were used as input for the GAN deployed in this study were from the ‘data_daphnet_combined’ dataset. Based on this kind of data input, specifically in the tabular data format, the FoGGAN architecture has been shown to be able to generate almost identical data to the real dataset as indicated by the metrics presented in To assess the added value of the proposed data generation process using the FoGGAN model, we trained a Deep Neural Network (DNN) classifier using the original dataset as detailed in The FoGGAN architecture presented in this work can be used as a very useful tool for data augmentation in the context of PD in many ways apart from the type of data showcased, i.e., FoG-related. It can be used in PD research but not only there, since the issue of data shortage is apparent in most neurodegenerative diseases , where FoGGAN can play a significant and multifaced role in terms of data augmentation. The most obvious one is the creation of additional data with the same statistical properties, inherent information, and predictive power as the data fed into the FoGGAN as we already presented. Another perspective is focused data augmentation for data that are underrepresented in the original/real dataset. For example, considering a bias in data due to the participants’ age, where the age group of 40–45 years old is small compared to older people, one can create additional data for that age group . Thus, FoGGAN can also be considered a bias mitigation method. Further, in the same way, unbalanced datasets can be balanced, boosting the robustness and generalization of the classification or prediction model to be developed.The future direction that this study aims to follow is to explore and evaluate different GAN architectures in order to conclude if the quality of the generated data is sufficient. The different GAN architectures that are planned to be studied in the future are the hybrid models which engage autoencoders and GANs. Moreover, future studies can also examine the impact of hyperparameters of a GAN model on the generated data. Another future prospect of the current study is to expand the data input to also include other medical data such as data from different symptoms or different diseases or even different data formats like images or videos. In this way, there will be a generalization of the capabilities of the presented GAN architectures in different health applications or other domains as well as of the data formats that will be used as inputs. Following the paradigm of the constant learning and data expansion, and based on the GAN solution proposed in this study, future research can also include lifelong learning techniques. In this way, there will be a continuous update process of the generated datasets leading to an adaptable and expandable solution about the data scarcity issue that is present in many domains including medical science domains.


Polymers find widespread applications in various industries, such as civil engineering, aerospace, and industrial machinery, contributing to vibration control, dampening, and insulation. To accurately design products that are able to predict their dynamic behavior in the virtual environment, it is essential to understand and reproduce their viscoelastic properties via material physical modeling. While Dynamic Mechanical Analysis (DMA) has traditionally been used, innovative non-destructive techniques are emerging for characterizing components and monitoring their performance without deconstructing them. In this context, the Time–Temperature Superposition Principle (TTSP) represents a powerful empirical procedure to extend a polymer’s viscoelastic behavior across a wider frequency range. This study focuses on replicating an indentation test on viscoelastic materials using the non-destructive Viscoelasticity Evaluation System evolved (VESevo) tool. The primary objective is to derive a unique temperature–frequency relationship, referred to as a “shift law”, using characteristic curves from this non-invasive approach. Encouragingly, modifying the device setup enabled us to replicate, virtually, three tests under identical initial conditions but with varying indentation frequencies. This highlights the tool’s ability to conduct material testing across a range of frequencies. These findings set the stage for our upcoming experiment campaign, aiming to create an innovative shift algorithm from at least three distinct master curves at specific frequencies, offering a significant breakthrough in non-destructive polymer characterization with broad industrial potential. Viscoelastic materials are a class of substances that exhibit both viscous (flow-like) and elastic (reversible deformation) properties when subjected to mechanical stresses. These materials play a pivotal role in various applications, ranging from aerospace engineering to biomedical devices. One area where their properties have an exceptionally profound impact is the automotive industry, specifically in the design and performance of tires ,2. TiresTherefore, knowing the properties of these materials is of considerable importance although it is not as simple as it might seem, given their unique response that involves a blend of purely elastic and viscous processes ,5. MoreoDynamic mechanical analysis  is a typDepending on the field of pneumatic tire application and the range of angular velocities generated by the wheel, it becomes useful to extend the knowledge of the compound mechanical behavior by analyzing the evolution of its temperature-dependent response in a higher-frequency range compared to the reference one ,16. The There are several shift laws describing the variation in the shift factors with temperature, such as the Arrhenius law or other methodologies based on the use of the first derivative of elastic modulus curves , but amoHowever, a persistent challenge that remains is the assessment of the WLF coefficients using non-invasive techniques. Currently, these coefficients are typically determined through destructive experiments at various frequencies using the DMA methodology ,23, whicThis simulation study, validated by appropriate experimental tests, illustrates the feasibility of obtaining these curves by changing the values of the indenter masses, demonstrating that VESevo could serve as a valid tool for identifying a novel shift algorithm without any component alteration.Polymers can be classified as viscoelastic materials due to their particular behavior, which is halfway between an elastic solid and a viscous fluid. Typically, the viscoelastic properties of these materials are investigated with dynamic experiments imposing a sinusoidal oscillating input force (or deformation), causing a sinusoidal stress (or strain) to be applied to the sample, which generates a sinusoidal strain (or stress) that can be measured .For this class of materials, a signal shift between input and output occurs with a phase angle between 0 and 90, as shown in The input solicitation and the output response are related to each other via a transfer function that physically contains all the information about the viscoelastic properties of the sample under analysis. This peculiar function is called a complex dynamic modulus and is mathematically defined as:In other words, this dynamic viscoelastic function is characterized by real and imaginary parts, each of which has a specific physical meaning . The fir modulus ,28,29,30It is defined as the loss factor, that is, a dimensionless measure of the viscoelastic damping of polymeric materials, which describes their capability to dissipate energy, as highlighted in a typical stress–strain curve b. The arThese viscoelastic properties of polymers, such as the tire tread compound, are typically experimentally evaluated via dynamic mechanical analysis. This characterization technique allows for obtaining much information about the polymeric sample, subjecting it to bending or torsional forces at different temperatures within a frequency range from 0.1 Hz to 100 Hz [Generally, two kinds of test modes can be carried out to characterize the viscoelastic behavior of polymers: temperature sweep and frequency sweep tests. The former provides temperature-dependent viscoelastic properties since the frequency of the oscillating input is kept constant; the latter is useful to investigate frequency effects on viscoelastic materials at a specific temperature. A typical output of these two test modes is illustrated in Polymeric materials exhibit a constant frequency response in the borderline cases of very low (rubbery plateau region) and high-frequency (glassy region) values, whereas a strong frequency dependence occurs in the linear viscoelastic region where peaks in loss factor and loss modulus curves emerge . The samTo overcome the limitations of the conventional destructive characterization techniques for viscoelastic materials, the Vehicle Dynamics Research Group of the Department of Industrial Engineering at the University of Naples Federico II developed the innovative device VESevo. This device represents a valid solution capable of testing tire tread compounds and almost any material that exhibits viscoelastic behavior in a completely non-destructive way. It allows one to characterize the material’s viscoelastic properties and their variations due to cooling/heating thermal cycles as well as monitor their time evolution as a result of wear and aging phenomena without altering the tested component .The VESevo device houses a rod–spring mechanism that consists of a steel rod with a semi-spherical indenter and a spring element. A suitable guide allows the steel rod to slide and bounce freely once it hits the sample surface. An innovative patented unhooking system is implemented to guarantee that the motion of the rod always starts from the same initial position. The rod displacement is measured by means of an optical laser sensor whereas a compact-size IR pyrometer monitors the tread surface temperature a.The acquisition phase consists of lifting the trigger of the device, which allows the rod to be raised to the release point. The unhooking system then drops the rod, which slides along the guide and impacts and bounces on the surface of the compound sample, while the optical sensor measures the rod displacement. In The displacement curve exhibits three different phases that are essential for the evaluation of specific physical magnitudes related to the viscoelastic behavior: the initial monotonically decreasing curve branch reflects the free fall of the steel rod followed by the sinking of the indenter, corresponding to the minimum point of the acquisition curve. In the second phase, the rod reaches the maximum point of the curve after the first indentation as a result of its interaction with the tread surface. The last phase is characterized by a stabilized rod displacement value corresponding to indenter–sample contact when the rebound phenomenon is completely extinct.The experimental setup is straightforward, requiring the VESevo device, access to an electrical network, and a computer for connecting the device. After positioning the sample or object to be tested, an operator manually lifts the trigger, releasing the rod automatically. Following each test, the rebound curve is displayed on the PC’s graphical interface. After completing all tests, the data are processed to obtain the desired properties. Additional tools for heating or cooling the sample, as well as adapters for addressing specific shape-related challenges in individual samples, can be employed as needed.Several acquisitions at ambient temperature;Cooling to −30 °C employing a climatic cell or a freezing spray and then performing VESevo acquisitions during the natural heating process up to ambient temperature;Forced heating up to 100 °C through a thermal blanket or a professional heating gun and then carrying out acquisitions down to ambient temperature.In order to investigate the temperature dependence of the compound viscoelastic response, the displacement curves are acquired over the entire operating temperature range. Typically, the VESevo test session is carried out using the following standard testing procedure :Several T is the compound temperature, and The rod displacement raw signals, acquired throughout the temperature range, contain all the information necessary to extrapolate, through a sophisticated post-processing algorithm, a series of physical temperature-dependent parameters with which it is possible to define specific mathematical relationships for the evaluation of the viscoelastic properties and the construction of the full master curves. Particularly, the storage and loss moduli can be defined using the following functional relations:The loss factor is calculated as the ratio of The TTSP represents a powerful empirical procedure to extend the knowledge of the viscoelastic behavior of the polymers in a frequency range wider than the experimentally investigable one. However, the actual configuration of the VESevo device, based on a single indenter mass, does not allow us to uniquely identify the temperature–frequency relation of the material under analysis. In fact, it provides the master curve at only one reference frequency of 1 Hz. Therefore, a preliminary simulation study was conducted to perform a sensitivity analysis in order to investigate the extent of the effects that variations in the main design parameters of the VESevo device have on the rod dynamics. In particular, the aim was to discern whether it is possible to acquire noticeable distinct dynamic indenter responses, thereby enabling the generation of a minimum of three viscoelastic master curves, which is fundamental for the development of an innovative shifting algorithm.The simulation model was developed, starting from the components CAD (Computer Aided Design), in the Simulink environment and allows for virtually replicating the real VESevo device and changing the physical parameters of different device components without making any modifications to the actual device. Thus, with a simple personal computer and a relatively low computational cost, it is possible to build several VESevo configurations. (center of gravity) represent the body blocks, which are rigid bodies with a specific mass, coordinates of their center of gravity, and other user-defined properties. These bodies can be rigidly connected, or they can move with respect to each other with translational motion; in the first case, a weld block was used (it represents 0 degrees of freedom (DOF)), while in the second case, a prismatic block was employed . Each body block was identified using a label showing the name of the real VESevo component that the body block virtually replicates.The virtual model is schematically shown in Two other important block elements are the “Body Spring & Damper” and the “Translational Hard Stop” blocks. The first one simulates the rod–spring system of the VESevo device. It includes three important parameters to be set: the spring constant, the spring natural length, and a damping coefficient; the latter takes into account the friction phenomena between the rod and the relative guide.The Translational Hard Stop block recreates the physical phenomenon by which the rod bounces on the sample surface when it comes into contact with the surface itself; this interaction is simulated using a slider (the rod) and a hard stop . The hard stop is represented as a spring–damper system, taking into account the elastic interaction between the slider and the stop and the dissipative phenomena. Therefore, it is necessary to set a contact stiffness and a damping coefficient; these two parameters reproduce the elastic response and the dissipative properties of the material under analysis (represented with a simple linear viscoelastic model). Finally, two joint blocks have been implemented: a joint initial condition that simulates the initial drop height of the rod and a joint sensor that can measure the position and velocity of the rod and provide the typical output signals, displacement, and velocity signals.The constructed model is parametric, meaning it allows for adjustments in various parameters to replicate the design characteristics of the real system without the need for any physical alterations. Additionally, it offers the capability to replicate various material responses that change the hard stop characteristics.In this research, the approach used to study how the different features influence the system dynamics involves systematically adjusting individual model parameters and observing the corresponding system responses. However, the focus was mainly on investigating the effects of the indenter mass on the rod’s rebound curve and, consequently, the viscoelastic properties determined through the VESevo device’s algorithm.Ultimately, to guarantee the fidelity of the virtual model and, by extension, the ability to draw valid real-world conclusions, an experimental testing campaign was conducted. This campaign involved an analysis of the outcomes generated via the newly designed components and their subsequent validation against the simulated data.As mentioned above, the analysis was conducted by varying the indenter mass while the rod drop height, spring constant, and material characteristics were held constant. The initial challenge encountered in this research endeavor was the discerning selection of mass values, a task that entailed a thorough evaluation of the results obtained when varying mass values, with a particular emphasis on indentation frequency, alongside a comprehensive analysis of overall dimensions and cost factors. Subsequently, after careful consideration, three specific mass values were meticulously chosen for the study, denoted as The displacement raw signals obtained from the simulation, as illustrated in The raw signals were then processed with the VESevo data processing algorithm in order to obtain the typical physical parameters. As shown in Since these physical parameters play an important role in the mathematical formulation of dynamic viscoelastic functions, their variations affect the storage modulus and loss factor trends, as depicted in Consequently, a challenge arises in achieving the uniform viscoelastic characterization of materials, as it depends on the three masses that strongly influence the impact velocity.This issue stems from the fact that, in these experiments, the elastic component of the rod–spring system in the virtual VESevo remained consistently constant. In order to solve this problem, the VESevo spring was redesigned to determine the optimal spring constant value, ensuring a constant impact velocity regardless of the indenter mass used. To this end, the simulation model was used to obtain several spring constant values for each indenter mass and to build impact velocity vs. VESevo stiffness (From these curves, the The normalized values were calculated by dividing each indenter mass by By employing these new spring constant values and performing the analysis again, the impact-free surface and velocity remained constant and independent of the indenter mass, as depicted in With these changes, the energy contribution of the indenter impacts the tread surface; thus, the impact velocity is kept constant, regardless of the employed indenter mass, ensuring complete and correct viscoelastic characterization.Finally, in order to evaluate if the different rod dynamics responses have a noticeable effect on the frequency of the indentation phenomena, the normalized frequencies corresponding to the tests carried out with the three different masses were compared. The results shown in All obtained results were additionally validated with the real device. To do this, the masses and springs designed following the simulation results were realized and assembled to obtain three different sensor modules . These cThe results presented in this study align consistently with the fundamental physical principles governing the VESevo instrument, which relies on the concept of a body experiencing free fall under the influence of gravity and subsequently rebounding off a surface. Let us first examine the influence of mass on rebound curves a. AssumiThe impact of mass on speed follows a similar trend b. Lower The physical consistency of the results, as well as the agreement with the experimental results shown in the previous section, validate the virtual model’s accuracy in replicating the VESevo device’s behavior and confirm that the insights drawn from the simulations accurately represent the device’s real-world performance. Therefore, the next step will be to set up a robust experimental test campaign in which, for given polymeric materials, different temperature sweeps will be carried out by employing three different indenter masses. In this regard, one initial limitation of the newly developed system is that the use of three different masses and springs results in extended testing times. This requires replacing the corresponding sensor module each time a test with a different indentation frequency is desired. In the future, more easily interchangeable modules could be designed to assess this challenge. Furthermore, the choice of masses is constrained by overall dimensions. It is crucial to assess whether the selected masses and the consequent indentation frequencies are sufficient, also with experimental data, for establishing clearly separate master curves, from which shift factors can be derived to construct a valid shift law applicable across the entire frequency domain. The expectation is to obtain three distinct master curves, two of which can be appropriately shifted in a specific frequency, chosen as the reference, in order to uniquely identify the temperature dependence of the shift factors for the material under analysis.The current configuration of the VESevo device, based on a single indenter mass, lacks the capability to distinctly establish the temperature–frequency relationship of the analyzed viscoelastic material. In order to address this limitation and overcome the problem of a destructive characterization with the DMA, a preliminary simulation study on a validated model was conducted to assess the feasibility of achieving at least three distinct indentation frequencies with the device.The study confirmed that this objective is achievable, provided that modifications are made to the spring element of the rod–spring system in the VESevo device. These adjustments ensure consistent energy conditions upon impact, ensuring uniform viscoelastic characterization, regardless of the indenter masses employed.With these promising simulation results, the next step involves conducting a robust experimental testing campaign to characterize viscoelastic materials using three different indenter masses. If the new device setups prove effective in identifying three separate master curves at distinct frequencies, it would create the opportunity to develop a novel shift algorithm based only on non-destructive testing, marking a significant advancement in the characterization of viscoelastic materials. By offering the ability to test polymers in real-time under diverse operating conditions and reconstruct comprehensive master curves, it opens the door to a wide range of applications. The derived viscoelastic properties, in terms of storage modulus and loss factor, can be harnessed for various purposes, such as the development of predictive models for tire grip and wear or to make predictions about the life cycle of a polymer component subject to temperature variations. This methodology not only advances the understanding of polymer behavior but also has the potential to support the development of highly accurate and individually tailored engineering solutions across multiple industries.