With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety. In recent years, infrared detection technology has been widely applied in military, remote sensing, civil, and other fields, such as infrared reconnaissance and early warning, infrared space detection, automotive navigation, medical infrared detection, and many other application scenarios. As a critical technology in the field of infrared early warning detection, infrared object detection algorithms adapted to different complex scenes have been widely studied by researchers. Under the situation that the spatial resolution of the optical system is complex to further improve, it is of great significance to study the infrared object detection algorithm with a low false alarm rate and strong adaptability, which is suitable for different scenes.However, the detection of infrared images also has many challenges. First, the object has fewer features available. Secondly, the signal-to-noise ratio of the image is low. Finally, the real-time performance of infrared image object detection is limited. These factors indicate that designing an object detection network with high accuracy and good real-time performance in infrared images is challenging. We can see from the current research interests that the most popular object detection methods mainly focus on visible scenes, such as Single Shot Detection (SSD) , You OnlAlthough there are challenges for infrared target detection, many methods have been tried, and these methods have achieved relatively good results. For example, the YOLO-FIRI  algorithAs we all know, producing visible images requires compensation for external illumination when the illumination conditions are poor. Infrared cameras can produce infrared spectral images throughout the day, but infrared spectral images lack details such as texture and color. Moreover, in infrared images, the critical factor determining the object’s visibility is the temperature difference between the object and the environment. For example, the car object is brighter than the background ,18. HoweAccording to the above analysis, some researchers began to try to make complementary detection between infrared and visible images. For example, MFFN  proposesWe propose the Dual-YOLO method based on the above observations on visible image object detection and infrared and visible image fusion detection. This method effectively solves the problems of low accuracy, feature loss, too many fused redundant features, and slow detection speed in infrared image object detection. Compared with the general target detection, our proposed Dual-YOLO is more suitable to solve the problem of target detection based on RGB UAV imagery. We can also see from  that tar(1) Based on the current YOLOv7  network (2) We propose the attention fusion module, which added the Inception module and SE mutual attention module in the infrared and visible feature fusion process. So that infrared and visible images can achieve the best feature complementarity and fusion effect without increasing the number of parameters.(3) We propose the fusion shuffle module, which adds dilated convolution in the infrared and visible feature fusion process and increases the receptive field for feature extraction of the fusion module. In addition, we add the channel shuffle module to make the infrared and visible features more uniform and reduce redundant features. In addition, we design a feature fusion loss function to accelerate the convergence of Dual-YOLO.(4) Our method achieves state-of-the-art results on the challenging KAIST multispectral pedestrian dataset and the DroneVehicle  remote sThe rest of this paper is structured as follows: In The overall network structure we have designed is shown in Thermal infrared images have strong feature contrast properties in environments with low light levels. However, visible images have a unique texture feature under normal light conditions. This textural feature can compensate for the lack of recognition of objects in thermal infrared images. Therefore, we add a visible feature extraction branch to the backbone design. The structure of the visible feature extraction branch is the same as that of the infrared feature extraction branch. In the neck’s design, we elicit feature map vectors from the backbone’s P3, P4, P5, and P6. The structure of this type of FPN already covers small, medium, and large objects in the infrared image, thus reducing the probability of missing detection. We design the novel Dual-Fusion (D-Fusion) module in the fusion features section, where the structure and characteristics of D-Fusion are described amply in In the design of the neck section, we refer to the structure of YOLOv7. Three up-sampling operations are performed in the deck to eliminate the problem of gradual loss of features due to convolution. At the same time, four detection heads are designed to preserve the small object features in the convolution, where the detection head can cover all object sizes.The design of this module is derived from several sets of experiments we have conducted on the effectiveness of network detection for a single data source. Before designing Dual-YOLO, we complete the following groups of experiments, as shown in In the feature fusion module, we feed the visible and infrared images into a two-branch backbone and perform shared learning of features at the FPN layer. This architecture is used to achieve the fusion of mixed modal features of infrared and visible images. In the fusion module, we add the batch normalization (BN) operation to the double branch’s features to improve the network’s generalization ability. In addition, we add the SE attention module in the independent branches, which multiplies the attention feature vectors obtained from the two feature calculations with the corresponding branches. Moreover, we use the deep separable convolution instead of the conventional 3 × 3 convolution to reduce the number of parameters in the network with less network performance. The structure of the feature fusion module we designed to incorporate the attention mechanism is shown in We can understand the attention fusion structure intuitively in After the infrared features are fused with the visible features, we add the process of fusion shuffle. The purpose is to allow the network to learn more mixed features of the infrared and visible images, thus allowing the network to adapt to both modes of features. So we take the module’s design for feature enhancement from  and imprIn In the design of the loss function, we divide the loss of Dual-YOLO into four parts. The first is for the D-fusion module. In the overall structure of the network, we design four fusion modules for visible and infrared images based on the feature pyramid structure. Furthermore, the corresponding fusion is carried out according to deep and shallow features. Assuming that the feature matrix of the visible image is quations  and 5)..Zvis andEquation .(6)Lfusb is the predicted box, c is the diagonal length of the smallest enclosing rectangle of the predicted box and the GT box, The value of Equation .(7)LboxFor the object coordinate position error, we choose the Smooth Binary Cross Entropy (Smooth BCE) loss with logits function to increase numerical stability, which is calculated as shown in Equation .(10)LobB is the number of bounding boxes predicted for each cell. The value of j-th bounding box of the i-th cell. We use 1 if it exists, 0 otherwise. For the total loss function design, we add up the loss function of the head part with the loss of the D-fusion. The total loss value of the network is finally obtained, which is calculated as shown in Equation LclEquation . Where λTo test the performance of the infrared image object detection models Dual-YOLO proposed in this paper, we use the public DroneVehicle, KAIST, and FLIR infrared pedestrian datasets.The DroneVehicle dataset  is a larThe KAIST dataset  is a mulThe FLIR dataset  containsWe utilize the YOLOv7 network as the main framework. Each image is randomly horizontally flipped with a probability of 0.5 to increase the diversity. The whole network is optimized by stochastic gradient descent (SGD) optimizer for 300 epochs with a learning rate of 0.005 and a batch size of 16. Weight decay and momentum are set to 0.0001 and 0.9, respectively. We implement our codes with the PyTorch framework and conduct experiments on a workstation with two NVIDIA GTX3090 GPUs. We summarize the setting of experimental environment and parameter as shown in Precision, Recall, and mean Average Precision (mAP) are used to evaluate the detection performance of different methods. In the experiments of this paper, we mainly use the values of precision and recall to measure the network’s performance, which are calculated as shown in Equations  and 15)15).(14)For example, in the FLIR dataset for detecting persons and cars, TP (True Positive) represents the number of cars (or persons) correctly recognized as cars (or persons). FP  means the number of samples that identified non-car instances (or non-person instances) as cars (or persons), and FN  indicates the number of samples that identified cars (or persons) as non-car instances (or non-person instances).Average Precision (AP) refers to the area value of the P-R curve surrounded by coordinates. The closer the AP value is to 1, the better the detection effect of the algorithm. The calculation process of AP can be summarized as follows:The mAP indicates each class’s average value of AP, which is used to measure the performance of multi-class object detection tasks fairly. Therefore, the mAP is also adopted to evaluate the detection accuracy in our experiments. The mAP measures the quality of bounding box predictions in the test set. Following , a prediTo verify the detection effectiveness of our proposed Dual-YOLO method on small infrared objects, we conduct a series of experiments on the DroneVehicle dataset. The experimental results are shown in In To further verify the effectiveness and robustness of our proposed Dual-YOLO, we conduct experiments on the challenging KAIST dataset. After comparing with some popular methods, our experimental results are shown in From We also conduct a series of experiments on the FLIR dataset to prove the effectiveness of the proposed method. Furthermore, we compare the performance with some popular methods, such as SSD, RetinaNet, YOLOv5s, and YOLOF. The final experimental results are shown in In the structure shown in The experimental results obtained according to the position of the shuffle are shown in We conduct the following ablation study to test the function of the attention fusion module proposed in From To overcome the problem of accuracy loss caused by the loss of texture features of infrared objects, we propose the Dual-YOLO object detection network with infrared and visible image fusion based on YOLOv7. In the infrared image feature extraction, we design the infrared and visible image feature fusion module named D-fusion. Furthermore, we obtain simplified and useful fusion information in feature extraction through attention fusion and fusion shuffle design. This method reduces the impact of redundant information on network accuracy reduction. Finally, we design the fusion module loss function in the network training process to accelerate the network’s convergence. Through experimental verification on the DroneVehicle, KAIST, and FLIR datasets, we prove the effectiveness of Dual-YOLO in improving the accuracy of infrared object detection. The proposed method is expected to be applied in the fields of military reconnaissance, unmanned driving, agricultural fruit detection, and public safety. Meanwhile, further research will include infrared and visible image fusion for semantic segmentation and infrared object tracking. In addition, we will do more optimization work in terms of parameter compression and acceleration of the model. Through these optimization strategies, the proposed infrared small target detection model Dual-YOLO is more suitable for embedded platform.


Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements  are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods  are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice. Proteins are crucial components of cell and tissue structures and are cornerstones used by an organism to maintain normal life activities. Due to the different roles each protein plays in the life activities of organisms, proteins are divided into essential proteins and nonessential proteins. The deletion or elimination of essential proteins may result in normal cellular function disorders or diseases and may even affect the development and survival of organisms , 2. PrevTraditional biological experiments can only be carried out in a limited number of species and are expensive and time consuming . FortunaPPI networks provide a comprehensive view of the global interaction structure of an organism’s proteome. Initially, the key proteins were predicted by measuring topologic properties. In 2001, Jeong  pointed PPI networks retrieved from high-throughput techniques are incomplete and inherently noisy . The relTo overcome the influence of false positive data in PPI networks, two categories of methods have been developed to improve the performance of identifying essential proteins. The first category identifies essential proteins by combining the topological properties of PPI networks with various biological data, such as Gene Ontology (GO) annotation data –27, geneThe methods in the second category predict key proteins based on refined networks by filtering the false positive interactions in the original network. For instance, Kim et al.  designedGO annotation is a system of uniform and normative descriptions of the genes and gene products of all species. A GO annotation collects information on the molecular function (MF), biological process (BP), and cellular component (CC) of different organisms. The GO-based semantic similarity metric (SSM) is a numerical measure that is used to estimate the semantic intimacy between two terms and is widely used for measuring the functional similarities between proteins –39. FiveIn this paper, we comprehensively discuss the aforementioned five semantic similarity measurements in combination with three subontology  terms on the identification of essential proteins. Six centrality methods  are applied on refined GO-PPI networks and the results are compared with those of the same methods on the original PPI networks. Extensive comparisons have been conducted under different conditions, and the simulation results offer a reference to biologists when investigating the essential proteins of PPI networks.In this part, six conventional centrality methods  are reviewed briefly. Then, refined PPI network construction methods are described in detail. Additionally, the utilized datasets and evaluation metrics are presented.PPI = , where P is composed of proteins and E represents the set of interactions between proteins. PPI networks are stored as adjacent matrices. The six centrality calculation methods are calculated as follows.PPI networks are abstracted into graph structures, which are denoted as Sp represents the number of shortest paths between protein i and j that go through protein p and S represents the number of shortest paths between protein i and protein j. Considering the global characteristics of PPI networks, this method can identify some nodes whose degrees are not high but play a vital role in the connection of the given network.BCp) represents the number of proteins connected to p directly, which is called the degree of p. And ap,u ∈ A represents the interactions between proteins p and u.DCα is a eigenvector of the adjacency matrix A and αmax(p) is the pth component of the eigenvector belonging to the maximum eigenvalue λmax.ECNp and Nu represent the neighboring sets of proteins p and u, respectively. ECC is the edge clustering coefficient. This method characterizes the connection relationships between a node and its neighbors; that is, the similarity of the relationship between two proteins is described by calculating the number of common neighbor nodes.NCμl(p) represents the number of loops whose starting and ending proteins are p and the lengths of these loops are l. In complex networks, essential proteins tend to form dense subgraphs. The shorter the loop is, the more likely the protein is to be in a dense subgraph and to be a key protein.SCNp represents the set of protein p’s neighbors. The significance of a protein is measured by its neighbors.aveNCThere are two kinds of measures used to record confidence scores for a PPI network. One relies on interaction data , and theFive widely used methods, Jiang, Lin, Rel, Resnik, and Wang, are applied to compute semantic similarities based on the GO terms between proteins, and these are denoted as confidence scores. Wang determines the confidence scores between two proteins according to the locations of their corresponding GO terms in the GO graph and their ancestor terms’ relationships. The other four methods are based on information content (IC), which depends on the probabilities of the two GO terms involved and their closest common ancestor terms in the corpus of the GO annotation information.The details of the five SSMs (semantic similarity metric) are shown as follows:Resnikm and v in this method is denoted asC represents the set of common ancestors of m and v. The IC mentioned above is denoted as IC(t) = −lnp(t), where p(t) represents the probability of occurrence in the GO corpus and IC is used to express the specificity of a protein.Resnik believes that information content (IC) is the most informative common ancestor (MICA) . The simLin and JiangIt seems that the performance of Resnik is valid for calculating the similarity of two terms, but it cannot distinguish between terms that have the same MICA. To tackle this problem, Lin and Jiang developed new methods with comprehensive consideration of the ICs between protein pairs and their MICAs. The similarity of two proteins based on the Lin and Jiang methods is defined asRelShortcomings still exist in the approaches developed by Lin and Jiang. The similarity between two terms is overestimated when a protein is an ancestor of another. In addition, these approaches ignore the specificities of the two terms. By combining Resnik and Lin, Rel presented a novel measure to capture the similarity between two terms. The similarity between two proteins is defined asWangGv =  is a GAG for a GO term v, where Pv contains the ancestor terms of v including itself, and Ev is composed of edges that connect the GO terms in Gv. Other terms closer to v in Gv contribute more to its semantics. The contribution of a protein u to the semantics of protein v in Gv is defined as the S-value of u and is calculated asωe(0 < ωe < 1) is the semantic contribution factor for edge e ∈ Ev that links term u with its child term u′. And SV(v) is used to compare the semantics of two GO terms, and SV(v)is defined asWang is a hybrid method that combines the number of common ancestors with the locations of these ancestors in the GO graph when calculating the similarity between two terms. GO terms are presented as directed acyclic graphs (DAGs). Suppose that m and v is denoted asThe semantic similarity between protein pairs In this article, we apply five GO-based semantic similarity measurements to measure the reliability of protein pairs. For each SSM, we first compute the confidence scores for all of the protein pairs, and then construct refined PPI networks by filtering the interactions with low confidence scores. The refined PPI networks we obtain by measuring the GO semantic similarity are named GO-PPI for short, and the network refined by using the Resnik metric under the BP annotation term is named Resnik-BP GO-PPI for short. The main idea of constructing a refined GO-PPI network is shown in To compare the performance of these centrality methods under different combinations of strategies, we choose the well-studied Saccharomyces cerevisiae PPI data for experiments, as they are widely applied for testing the performance of new methods. The datasets include the YDIP dataset composed of 5093 proteins and 24743 interactions, the new DIP dataset, which includes 4928 proteins and 17201 interactions, the Krogan dataset containing 7123 interactions among 2708 proteins, and the Krogan Extended dataset, which consists of 3672 proteins with 14317 interactions. A summary of these datasets is presented in The GO annotation information of each protein is downloaded from the Saccharomyces Genome Database, which was released on September 10th, 2020.http://www.sequence.stanford.edu/group/).The benchmark of a known essential protein dataset including 1285 proteins is collected from four different databases  are applied in the experiments. We compare the numbers of key proteins identified properly and the prediction precisions under different types of strategies. The threshold for GO semantic similarity is set to 0.33 for filtering the unreliable links in the PPI networks.The number of interactions in a network influences the speed of calculation for identifying essential proteins. The lower the number of interactions, the less time is required for the calculation. Therefore, we compute the number of interactions and the portions of key proteins under the original PPI network and refined the GO-PPI network for the YDIP dataset. As shown in In the meantime, we study the interactions that rank among the top 600. As the numbers of interactions are different for the original network and the reconstructed GO-PPI network, we compute the proportions of the interactions between essential protein pairs (Ess-ess), essential and nonessential protein pairs (Ess-noness), and nonessential pairs (Noness-noness), and the results for the YDIP dataset under the BP subontology are shown in In this part, we do a systematic evaluation of the performance of the newly constructed networks on the four test datasets. For each dataset, we adopt five SSMs to calculate confidence scores for the protein pairs in the PPI network under the three GO annotation terms  and obtain fifteen kinds of refined GO-PPI networks. Six centrality methods are applied to predict the key proteins of the newly constructed GO-PPI network and the original PPI network.To verify the superiority of the newly proposed strategy, we calculate the number of key proteins identified correctly by each method under three GO subontology terms for the reduced DIP PPI dataset, the Krogan dataset, and the Krogan Extended dataset. The calculation results are listed in Tables For the new DIP PPI dataset, the comparison results are shown in However, for the MF and CC subontologies, some of the centrality methods perform poorly under the refined GO-PPI network, such as the BC method under the Jiang-CC (the Jiang method under the CC subontology) PPI network and the DC method under the Resnik-MF (the Resnik method under the MF subontology) PPI network. The maximum number of essential proteins predicted by the NC method in all five newly constructed PPI networks under the MF subontology is 306, which is compared to the 318 correctly predicted essential proteins under the original PPI network. Considering the number of interactions under the refined GO-PPI network in the MF subontology , this isAs seen from the results obtained using the Krogan dataset in Similar results are obtained on the Krogan Extended dataset and listed in To further investigate the performance of the six centrality methods under the newly refined networks, we take the network constructed by using the Resnik metric with BP subontology for the YDIP dataset as an example. We calculate the numbers of key proteins predicted by these centrality approaches among the top 100, 200, 300, 400, 500, and 600 ranked candidates. As shown in To validate the advantage of the reconstructed GO-PPI network in predicting key proteins intuitively, six centrality approaches  are taken to predict key proteins under the original PPI network and reconstructed GO-PPI network.To further exhibit the performance of proposed strategy, we compared the ROC curves of different methods under original PPI networks and corresponding GO-PPI networks. The top 600 ranked proteins predicted by each method are assumed as essential, the rest proteins are non-essential. For the gold-standard essential proteins in GO-PPI is obtained from the original true essential protein sets and filtered the proteins that are not in GO-PPI network. The rank value of each protein in original PPI network and GO-PPI network are normalized, and the true positive rate as well as false positive rate is calculated by using the threshold value varies in . We draw the ROC curve by using the obtained true positive rate and false positive fate. AUC means the area under the ROC curve and calculated by using trapz function in Matlab. The comparison of ROC curves as well as AUC value under original new DIP PPI and YDIP PPI network are shown as following Figs Since the new GO-PPI network is constructed by filtering the unreliable links in the original PPI network, we need to choose an appropriate threshold to distinguish false positive data and real interactions. However, the threshold value is related to the SSMs and the quality of a given PPI network, and different thresholds should be set for different SSMs to achieve the best performance.To investigate the effect of the threshold on the performance of the methods in identifying essential proteins, we plot the true numbers of key proteins identified among the top 100, 200, 300, 400, 500, and 600 candidates as functions of the threshold value for the YDIP Jiang-BP network in Predicting essential proteins by developing computational methods from PPI networks has been a hot topic in recent years. However, the PPIs obtained by high-throughput technology at present have high false positive rates. False interactions in PPI networks have great effects on the performance of computational methods in terms of predicting key proteins. Semantic similarity measures have been shown to be useful for assessing the confidence scores between linked protein pairs. The best of the five current widely used semantic similarity measurements for selecting appropriate metrics to measure the reliability of interactions remains unclear.This paper presents a comparison between GO-PPI networks newly constructed by five semantic similarity methods with three GO annotation terms and corresponding original PPI networks. The six topological-based centrality methods  are used to calculate the numbers of correct predictions and the precisions for the 600 top-ranked candidate proteins under the newly constructed GO-PPI networks and original networks. The comparison results suggest that the prediction accuracies under each of the newly constructed GO-PPI networks are consistently higher than those under the original PPI network. In particular, the networks constructed by using the semantic similarity metrics of Resnik and Wang under the BP annotation term are most reliable for predicting essential proteins among these topological-based centrality methods. These results suggest that constructing a new PPI network by using the Resnik and Wang metrics under the BP annotation term can filter out some false positive data effectively and improve the quality of the network, which is also the direction of future research.


Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis. Conventional hip stems are made of dense metals such as cobalt-chromium, stainless steel, and titanium. The stiffness of these dense stems is 5–15 fold higher than that of the cortical bone and 50–100 fold higher than that of the cancellous bone . This hiMachine learning extends to a large variety of fields and can assume different approaches. However, one simple and clear definition was presented by Murphy  stating In medicine, several researchers have turned to ML techniques as a computationally cheaper alternative to Finite Element (FE) model simulations. Villamor et al.  made comWithin this context, this study aims to address stress shielding in dense hip implants by introducing a porous hip implant. ML techniques were used to validate finite element results of porous hip stems with different designs. Six types of algorithms were used to investigate errors. Then, a new data set was created with semi-porous hip stems which have outer dense layers with different thicknesses and inner porous cellular structures with different porosities. The most efficient validated algorithm was used to predict the outcomes of new implant designs, which are stiffness of the implants, stresses in dense layers and porous cellular structures, and factor of safety of the implants. Using predictive machine learning techniques to validate the FEA-based models presented by Mehboob et al.  to reducComparing multiple machine learning algorithms to determine the best-performing method for the chosen model.There are multiple stem designs that use finite element analysis (FEA) to investigate biomechanical performance, as mentioned above. One of these unique designs that help alleviate stress shielding was proposed by Mehboob et al. . AccordiQuick prediction of the designs of the stems using machine learning will be readily available for printing as a personalized implant which will decrease the complications of revision of surgery and reduce the burden on the health system. In addition, the lower cost achieved through machine-learning-aided designs will potentially reduce the cost of the implant on patients. Moreover, designing the semi-porous implants and printing using additive manufacturing will further reduce the wastage of the material. The remainder of this paper is organized as follows. a is stress amplitude, σs is mean stress, Se is endurance limit, Sy is the yield strength of the material and n is the factor of safety. The factor of safety is calculated to determine the safety of the structure under a certain load  and y = . A shuffle split is then horizontally implemented to divide the dataset into training which accounts for 70% and validation which accounts for the remaining 30% of the dataset. The resulting training and validation matrices are of dimensions 21 × 6 and 9 × 6, respectively. The vertical split is shuffled using a NumPy seed . It is noted that a normalization of range  is problematic due to the upcoming calculations of prediction accuracies. The MinMax normalization is applied using the following equation.mPy seed  to allowAs mentioned earlier, machine learning techniques are generally categorized into classification and prediction algorithms. The machine learning algorithms used in this paper are generally prediction based as the output to be predicted is numerical. Six different algorithms are used, namely decision tree regression (DTR), linear regression, ridge regression (RR), lasso regression (LSR), elastic net (EN), and multilayer perceptron (MLP) regression. Each of the previously mentioned techniques is implemented using its default hyperparameter settings, as shown in Decision trees can be used for both classification and regression tasks . More spOne of the most commonly used machine learning predictors is linear regression. It has been shown to aid in the prediction of output variables based on input variables by means of best-fit linear relationship navigation  which isu et al. , and forRidge regression is an extension of linear regression  where ℓ2Another extension of linear regression is lasso regression  where ℓ1The third and final extension of linear regression that is utilized in this paper is elastic nets. It utilizes both rization . It is uAn artificial neural network, also called a multilayer perceptron, can be utilized in its most basic form for the purposes of supervised learning. It nonlinearly maps inputs to outputs by utilizing nodes and their associated weights . The weiA is the actual value and P is the predicted value. RMSE and MAPE results are reported in the results and discussion section where a comprehensive comparison is shown across the six utilized machine learning algorithms and their associated samples. An average, standard deviation, maximum, and minimum result from Equations (7) and (8) is also shown for the purposes of clearly determining the lowest validation prediction error.Each of the machine learning predictors is used to fit the data 10 times according to the NumPy seed in the range of  which creates a sampling with a replacement scheme. The algorithms are trained on the 70% training portion and validated on the 30% validation portion of the dataset. This approach creates 60 different runs that were implemented on Python 3 . The valAdditionally, the trained algorithms are tested against an unseen test set. The test set inputs are shown in After using the trained and validated models to predict the outputs of the test set, the trend is observed, and the best algorithm is chosen based on a voting system. The voting system utilizes the proportional trend validity for each algorithm and produces a score out of 100. The best algorithm is then used to showcase its trend results for each of the four output variables. The criteria for choosing the best algorithm depend on two factors. The first factor is the monotonicity of the prediction where no two consecutive predicted outputs are repeated. To clarify, monotonicity means always increasing or always decreasing. The second factor is the nonexistence of negative value predictions as some negative values are wrongly predicted by some algorithms where no negative values exist in the original data. This study investigates the biomechanical performance of semi-porous hip stems to address issues such as stress shielding, which mainly occurs in Gruen Zone 7 due to a solid dense metallic stem. Designing porous and semi-porous implants can address the complications of stiff dense implants. However, in silico, in vitro, and in vivo investigations of the newly designed porous and semi-porous complex structure are time-consuming and costly; therefore, the results of computer simulations were validated using machine learning in this study. After validation, the algorithms were trained and employed to predict the biomechanical performance of newly designed semi-porous hip stems. Finite element analysis of implants showed that by increasing the thickness of the outer layer, the stresses in the layer were decreased, as discussed in a previous study . In addiFollowing the application of the machine learning methodology in The lowest average MAPE in terms of percentage is the decision tree regression algorithm. However, since observing a reasonable trend is the desired outcome, DTR is excluded from being the algorithm of choice. In fact, all algorithms, after training and validation, were used to predict the test set, and the trend was observed. It is evident from As shown in The following figure, It is clear that the trends of the predicted test set, as shown in v3 to v6, stiffness of stems, maximum stresses in a dense layer, maximum stresses in a porous section, and factor of safety. For the purposes of comparison, DTR was also tested on the same test set. As can be seen in v3 to v6. It is evident that the trend produced by the ridge regression predictions of the test set followed the original dataset’s trend in terms of the output variables The framework of this study has great potential to aid new stem designs, and thus may be applicable for clinical use. It allows for the rapid exploration of the biomechanical performance of various designs of hip stems to be used and applied to the personal conditions of patients. Thus, this study has reduced the computational cost and time for the design process of stems. In general, machine learning models show robust performance in predicting the biomechanical performance of different designs of hip stems; however, the prediction efficiency can always be improved by investigating a larger dataset. Finite element simulations are computationally expensive as compared with machine learning algorithms, but give more accurate results. The validation of finite element results was successfully achieved using machine learning algorithms and this validation of finite element results would be far too expensive in in vivo experimentations.Using the simulated data, a variety of machine learning algorithms were utilized for the purposes of predicting the stiffness of stems, maximum stresses in a dense layer, maximum stresses in a porous section, and factor of safety. The ridge regression algorithm was shown to produce the most accurate test set prediction in terms of trend as compared with the original dataset’s trend. Thus, the trend of biomechanical performance of new stem designs was successfully predicted using trained machine learning algorithms. Future research potential depends on further optimizing machine learning algorithms to achieve a better prediction scheme for the biomechanical performance of different stem designs. An example of further optimization could depend on using grid search to optimize the selected hyperparameter values. Another avenue of future research could include utilizing different test sets to facilitate machine-learning-informed implant design without the need for running computer simulations. In addition, future research can also focus on using machine learning techniques in the designing of high-performing hip implants rather than simply predicting design parameters.One of the limitations of this paper is the relatively small dataset that was used to train the machine learning models. Accordingly, the accuracy of the predictive models was affected. The use of a larger dataset may improve the accuracy of the prediction. Another limitation is that these results are predicted based on finite element analysis and machine learning approaches; however, the biomechanical performance should be investigated using in vivo experiments. Another limitation is the fact that ML model complexity was not considered when evaluating the models used in this research. More complex models are usually expected to perform better but they are less interpretable. The current algorithms are not trained to predict biochemical effects, including biocompatibility, stress shielding, and bone remodeling, which are gradual changes in the peri-stem bone changes and are important in assessing the secondary stability of implants. Hip stems can be optimized considering changes in bone density to maximize the life of total hip arthroplasty. Once the implant design is optimized, a biochemical study should be carried out to study the effect of material and design on biological response.


The novelty and motivation of this research can be emphasized by examining how the heat transfer mechanism of a non-Newtonian Powell-Eyring fluid, which flows because of a stretched sheet, is affected by factors like viscous dissipation, the slip velocity phenomenon, and Joule heating. In addition, the investigation delves into the heat transfer behavior of the fluid flow when it comes into contact with a convectively heated stretched surface that is influenced by varying fluid properties. This analysis also takes into account the influence of changing fluid characteristics and the presence of magnetic field. The numerical solutions of modelled equations that governing the problem are detected using the shooting technique. Also, in order to confirm the validity of the present investigation, a proper comparison with certain published works as a particular case of the present model is presented, and a perfect agreement is noted. With the use of diagrams and tables, the flow problem’s effective parameters are thoroughly discussed. Likewise, through a tabular representation, the values of the local Nusselt number and the skin-friction coefficient are computed and analyzed. Many significant conclusions can be drawn from numerical results. Most importantly, the local Nusselt number rises monotonically with both the surface convection parameter and the slip velocity parameter, but the local skin-friction coefficient has the opposite trend. The results indicate that the nanofluid temperature is enhanced by factors such as the surface convection parameter, magnetic field, and viscous dissipation. On the other hand, the slip velocity phenomenon leads to the opposite effect. Beginning with the pioneer Sakiadis2 studied Blasius type flow over a stretching sheet where the speed was constant and the fluid at rest. Sequentially Crane3 investigated the flow of fluid which has common characteristics with boundary layer of tile Hiemenze flow and the velocity of stretching sheet is proportional to distance of the slit. The analytical solution of flow past stretching surface in the existence of magnetic field in two dimensions was studied by Kumari and Nath4. Abel et al.5 investigated the transfer of the heat in visco-elastic fluid through porous medium over surface where the viscosity was variable with temperature. Sanjayanand et al.6 obtained the analytical solution of zeroth order of stream function. The Mass and heat transfer equations are investigated past continuous stretching sheet to obtain the hypergeometric solutions. Chamkha and Mansour7 studied the transfer of heat past a stretching sheet in the presence of chemical reaction through porous medium and the unsteady free convection as well. The influence of temperature and space on flow which was generated over a stretching surface in the existance of normally applied magnetic field was studied by Ganga et al.8. The generation of entropy on steady incompressible Carreau liquid flow past a stretching sheet with curved shape in two dimensions was studied by Raza et al.9. Megahed et al.10 studied the MHD fluid flow with heat transfer past an unsteady stretching sheet under thermal radiation and heat flux effects. Tawade et al.11 obtained the solution of nonlinear ordinary differential equations using the approach of Runge-Kutta 4th-order with shooting for nanofluid flow which was laminar and unsteady past stretching surface in two dimensions. Some further problems related to the fluid flow under to a stretchable sheet with variables thickness for different conditions can be found in the literature14. Later, there are numerous researchers development about nanofluids flow subject to stretchable sheets are highlighted in Refs.17.Monitoring flow of fluids over stretching surfaces and investigation of their behavior recently occupied a significant regard between researchers in engineering and science aspects due to their major role in several of industrial applications such as plastic manufacturing and rubber as well, photographic films coating by liquids and manufacturing of lubricants etc.20. Powell-Eyring model is one of the non-Newtonian fluid models which is derived from kinetic theory of gases instead of empirical formula. Many researchers used the Powell-Eyring model under different physical conditions. Hayat et al.21 obtained exact solution of magnetohydrdynamic eyring powell fluid flow past a stretching surface and discussed radiative effect as well. The analytic solution for the Powell-Erying fluid flow past a nonlinear stretching sheet was investigated by Panigrahi et al.22. Gaffar et al.23 investigated the Eyring-Powell fluid flow over vertical surface through porous medium. Hayat et al.24 studied flow of Eyring-Powell fluid past an exponentially stretching surface in the Prescence of chemical reactions and heat flux. The influence of convective boundary conditions, viscous dissipation and activation energy on Eyring Powell nanofluid through porous medium was examined by Nazeer et al.25. Patil et al.26 studied the effect of chemical reaction and thermal radiation of unsteady Nano Powell-Eyring fluid flow behind stagnation point. Khader and Babatin27 carried out a numerical solution by using the spectral collocation method the flow of Powell-Eyring fluid in the existence of magnetic field and thermal radiation.Over the years, many researchers have aimed to study the properties of fluids, especially non-Newtonian fluids, which have become extremely popular in various industrial, bio-fluid, foods, and medical fieldsIt’s important to note that earlier research overlooked the heat transfer process of a Powell-Eyring fluid, which is impacted by elements such as viscous dissipation, slip velocity phenomenon, and Joule heating. Additionally, prior studies did not consider a convectively heated stretched surface that is influenced by changing fluid properties, all within the same investigation. Consequently, the novelty and impetus behind the present research are driven by insights from the literature review, urging an exploration into the impacts of some important phenomena like viscous dissipation, slip velocity, Joule heating, and magnetic field on the flow behavior of the convectively heated Powell-Eyring fluid. Notably, this fluid’s properties undergo thermal changes due to the stretching sheet.B are applied to fluid layers, causing them to impart in the transverse direction. In our research, we suppose that a hot fluid exists beneath the stretching sheet’s bottom surface.Consider a Powell-Eyring fluid moving as indicated in Fig. A and m are constants. The viscous dissipation phenomena is also considered for a better understanding of heat transfer mechanism. The energy equation takes into account the effect of a Joule heating. The rules for fluid and heat transport are determined by the foregoing axioms and the usual Boussinesq’s approximation 1:u, v), C are the Powell-Eyring fluid parameters. These parameters (C) are highly significant, as they control the distinct rheological characteristics and behavior of this non-Newtonian material. They directly influence how the fluid behaves and interacts in various situations, offering valuable insights into its response to external factors. Adding slip velocity to the velocity field’s condition adds to the originality. The continuity, momentum, and energy equations are all linked to the following boundary conditions 28:29:M is the magnetic parameter, Ec symbolize to the Eckert number and Pr is the Prandtl number. All of these parameters can be described as follows:This hot fluid is vital in heating the surface of the stretched sheet by convection with the temperature 29. The previous work of Hayat et al.29, which was performed with the homotopy analysis method, can be compared with our current work in the absence of To support the numerical calculations performed in this work, the results of this study were compared to those published in one of the famous journals, which is addressed further down by Hayat et al.mentclass2pt{minimThis portion of the discussion uses many variables that correspond to the flow under consideration to show physical descriptions of velocity and temperature profiles. Thus, Runge-Kutta of order four is used in conjunction with the shooting technique in Mathematica software. We found that the calculated results had significant impacts. Figure In Fig. Figure Figure M are directly related to the generation of increased resistance force, also known as Lorentz force, it acts in a manner perpendicular to the path of fluid flow; hence, as Lorentz force increases, impedance force also increases and attempts to oppose fluid flow, thinning the thickness of the boundary layer. It follows naturally that raising the magnetic parameter improves the temperature of the sheet 30–33. This lends further support to the understanding of how the magnetic field impacts these processes.Figure Photographs of the temperature and velocity patterns in relation to the thermal conductivity parameter Ec. The Eckert number Ec represents the transformation of kinetic energy into stored energy, which is then dispersed as heat, through work performed versus the stresses of the viscous fluid. Thus, an enhancement in the sheet temperature Figure The distributions of velocity and temperature are each subject to the influence of the surface-convection parameter As shown in Table The slip velocity phenomenon weakened the velocity, temperature distributions, and the skin-friction coefficient, in particular.As the magnetic number or the surface convection parameter rises, both the sheet temperature and the temperature distribution within the thermal boundary region rise as well.As the dimensionless material fluid parameters or the viscosity parameter increases, the rate of heat transfer at the wall decreases.Higher conductivity values result in a rise in the temperature profile, however a reversal of this pattern can be seen beside the sheet.By increasing the viscosity parameter’s value, the liquid’s velocity decreases while the temperature field increases slightly.In future research, we aim to build upon this study by exploring heat and mass fluxes. The goal is to regulate the cooling process utilizing nanofluids.A computational model that incorporates viscous dissipation and convective boundary conditions for a slippery, non-Newtonian Powell-Eyring fluid flowing toward a stretching surface has been interpreted. A set of nonlinear ordinary differential equations is produced by incorporating an appropriate dimensionless transformation into the governing model. Significant data are discussed here after hiring shooting approach to analyze the fluid velocity, fluid temperature, the local skin-friction, and Nusselt number. Following are some conclusions that can be derived from the findings of this study:


Evidence confirms that the use of Optical Coherence Tomography (OCT) in oral medicine can be a reliable aid for the diagnosis and management of Oral Potentially Malignant Disorders (OPMDs). Several authors described the ability of this system to detect the structural changes of the epithelia involved by the OPMDs. The purpose of this case series is to provide a suggestion for interpretation of OCT images from different OPMDs, compared to OCT images of healthy tissues.A sample of 11 OPMDs patients was recruited and analyzed with OCT. The images obtained were then compared with an OCT repertoire image. In this work the reflectance degree was considered, together with the analysis of the increased/decreased thicknesses of the various layers. Keratin Layer (KL), Epithelial Layer (EP), Lamina Propria (LP), Basal Membrane (BM) assessment, for each lesion, was performed.OCT measurements of KL, EP and LP layers, together with BM assessing, should aid the physicians to recognize and describe different oral lesions, relating them to the corresponding oral pathology.More studies like this, on larger samples, are needed to validate the results and provide, in the future, a kind of manual that could guide clinicians to correctly interpret the OCT images in relation to the causing pathologies. To date, oral cancer represents the sixth most widespread oncological pathology in the world, with an incidence of approximately 300,000 new cases diagnosed every year . Oral caEarly diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients .To date, the gold standard in the diagnosis of OPMDs is performed by clinical examination and histopathological evaluation, carried out by optical microscope observation of samples taken from patients through surgical biopsy, after staining with hematoxylin–eosin . To imprThe importance of epithelial thickness as a possible predictor of architectural changes was highlighted. A few of study provide that OCT is able to produce measurements that can compete with histopathological standards in a reproducible way, and also indicate how the epithelial thickness, combined with architectural changes, may lead to early differentiation of areas involved or free from cancer/dysplasia . FurtherAn ex vivo study based on an SS-OCT system that used optical attenuation model to identify and classify three types of tissues , showed how an appropriate algorithm can help to improve screening and the diagnosis of oral cancers .As a non-invasive and real-time in vivo imaging technique, OCT can express identical information sufficient for oral cancer screening, but it has not been explored effectively for automatic diagnosis of oral cancer. However, to date the literature shows sufficient evidence regarding the peculiarities and similarities with traditional histopathology: in this way it is possible not always to resort to direct comparison with histological preparations and to avoid, if possible, surgical intervention on the patient .The main purpose of this case series study is to evaluate in vivo discriminatory potential of OCT in a series of patients affected by OPMDs, before traditional biopsy, in order to support a preliminary evaluation by comparation with the site-specific healthy tissue.Patients with clinical appearances of OPMDs were recruited at Oral Medicine Section – CIR Dental School University of Turin, Italy. For each patient, data related to the clinical description were collected, followed by the OCT scan before bioptic sampling. Patients with the following clinical aspects are recruited: white lesions, white-red lesions, and ulcers. Patients with vegetating neoformations and/or with infiltrating characteristics, clinical appearance of Oral Squamous Cell Carcinoma (OSCC), non-cooperative patients, and patients whose sample was not suitable for OCT analysis (presence of artifacts) were excluded from the present work. All the lesions analysed, in addition to the clinical examination, were submitted for confirmation to investigative biopsy with histopathological diagnosis performed by Pathological Unit of Città della Salute e della Scienza di Torino. The present trial was conducted in line with the principles of the Helsinki Declaration of 1975, as revised in 2000 ; it was 2. This probe was installed on the motherboard , was used Fig. . The ligard Fig. . In ordeThe in vivo OCT assessment of pathological tissue must compare with archive images of OCT scans of healthy tissue based on the anatomical site corresponding to the lesion. This is possible considering parameters as Keratinized Layer (KL), Epithelial Layer (EP), Basal Membrane (BM) and Lamina Propria (LP) and related evaluations Table .Table 1OThis is a qualitative research study and adheres to the EQUATOR guidelines for research reporting using the Standards for Reporting Qualitative Research (SRQR) checklist for the collection and processing of instrumental and clinical data . Given tIn the period between October 2021 and September 2022 it was recruited data of 11 patients  , gum, buccal mucosa. These areas were subjected to an observational survey Table .Table 3DThe elementary lesions observed in the 11 patients were classified into: white lesions, white and red lesions, ulcers. Table .Table 4EThe pathologies included in our OPMDs series were: Leukoplakia (LK), Oral Lichen Planus (OLP), Proliferative Verrucous Leukoplakia (PVL), Graft Versus Host Disease (GVHD), Micro invasive carcinoma (K-MICRO) Table .Table 5PAlthough K-MICRO is not considered OPMDs, we decided to include it in this case-series to demonstrate that, whether in early stage cancer processes, OCT is useful in distinguishing between the two different ultrastructural patterns.Below is the detailed description for each site examinated.The dorsum of the tongue was investigated in a 50-year-old male patient. The elementary lesion was a white lesion and the associated disease was Graft Versus Host Disease (GVHD) Fig. c. OCT scThe papillary introflexions of LP towards the epithelial layer are difficult to identify. The basal membrane appears to be assessable.The ventral area of the tongue was investigated in a subgroup of 2 female patients with a mean age of 77 years.The elementary lesions found in this subgroup are a withe lesion and an ulcer. The pathology associated to white lesion was PVL Fig. c and theIn the Fig. The epithelial layer showed in Fig. In the Fig. The tongue border surface was investigated in a subgroup of 3 patients (1 M and 2 F) with an average age of about 73 years. The elementary lesions found in this subgroup are 2 withe lesions Fig. c and 1 uCompared to healthy tissue Fig. a, in theThe Fig. In Fig. The buccal mucosa was investigated in a subgroup of 3 patients, all females, with an average e age of 70 years.The elementary lesions found in this subgroup are 2 white lesions Fig. c and 1 uIn the Fig. In the Fig. The Fig. Gingiva.The adherent gingiva was investigated in a subgroup of 2 patients, a male and a female, with a n average age of 76 years.The elementary lesions found in this subgroup are a white lesion Fig. cand a whThe keratin layer in the Fig. In Fig. Table In white lesions total thickness could decrease: lesions could appear white because there is an increase of the keratin component even if matched with a decreasing of the total epithelial – connective thickness. In this case series, the pathology that most often creates a decrease in thickness is OLP.OLP can also manifest with atrophic-erosive lesions but at the same time, it can also give white lesions with a decrease of the total thickness in the face of an increase in the keratin component.The ability of OCT to distinguish between precancerous lesions and oral cancer is still unclear. A recent study calculated a sensitivity (93–96%) and specificity (74–49%) in ex-vivo OSCC, leukoplakia with hyperplasia and healthy simple tissue by creating computational models of these tissues and employing an artificial neural network analysis algorithm .Another recent meta-analysis reported the trend of OCT in OPMDs to be less sensitive (0.8960) and specific (0.8962) than OSCC (0.9316) .The possible explanation for this result lies in the absence of systematic reviews that analyzed only the OCT patterns only for OPMDs, in the lack of standardization of instrument settings and image measurement methods for the universal use of data. However, the generally high sensitivity and specificity indicate the potential utility of OCT for OPMD follow-up.The use of artificial intelligence will be the real upgrade of use of OCT in Oral Medicine . Hence, Currently, the main limitations of this procedure depend on both the device and the operator. The first concerns the device itself, i.e. the limited diffusion of OCT in the dental field with high-performance intraoral OCT handheld probes and the limited imaging depth . The secKnowledge of OCT healthy tissue is essential for understanding ultrastructural alterations .In the healthy tissue, the EP layer is always hypo-reflective , the LP is always hyper-reflective with an internal non-reflective areas; the difference between these two degrees of reflectance leads to estimate the position of the BM.The data of this study are broadly in agreement with the study of Albrecht et al.  which clFurthermore, the authors affirm that it is possible to identify large vascular structures in the structure of the lamina propria: also, this data is in agreement with our evidence. The same author argues that it is difficult to highlight the basal membrane in the region of the ventral tongue, while we were able to clearly identify it. This could be due to the different position of the intraoral probe: in our study the probe was brought into contact with the mucosa.According to some authors, the hyper-reflectance of the epithelial layer can be understood as a good parameter to differentiate diseased tissues from healthy ones , 23. It The site-specificity in which the lesions appear is the first step in being able to identify the pathological tissue from the healthy one.Panzarella et al.  coded thThe same authors highlight how in dorsum tongue there is no real difference in reflectance that allows us to distinguish KL from EP, therefore the anatomy is not the same as other structures that appear flat.Although the investigated sites are dissimilar, the common aspects of all the OPMDs analyzed were two: increase in epithelial reflectance and decrease in reflectance of the lamina propria . The incFor this reason, ulcers patterns could highlight a superficial hyper-reflectance because epithelial cells increase proliferation starting from the edges of the lesion to try to re-epithelialize the area. In extensive ulcers also the central fibrin panniculus could result in greater reflectance, but this data should be further investigated. The presence of hyper-reflectance area in the residual epithelium of an OLP ulcer is due to a mild hyper-keratosis or hyper-parakeratosis but the loss of the clear difference between the two reflectance degrees  makes it difficult to identify the basal membrane . To dateIt is almost established that the more the lesions evolve towards malignity, the less the layers and the basal membrane are identifiable , 27. TheThe loss of the clear difference between the two reflectance degrees  makes it difficult to identify the basal membrane: the lesions for which it was more difficult to identify the basal membrane were those affecting the buccal mucosa, the adherent gingiva, the border and the ventral tongue.Specifically, it is difficult to identify the basal membrane in: OLP ulcers on the edge of the tongue, ventral tongue and buccal mucosa, spot on the buccal mucosa due to OLP, inhomogeneous white/red lesion on adherent gingiva (K-MICRO) and PVL on adherent gingiva. From the analysis of our images it was possible to notice a characteristic aspect of OLP ulcers: the almost total loss of the epithelial component and a uniform LP hypo-reflectivity of the connective component. This makes it very difficult to estimate the position of the basal membrane. In trauma ulcers, this specific pattern appears not to occur so frequently  probablyA white lesion does not always clinically correspond to an increased epithelial tissue thickness. Even if we are considering keratinized lesions, it is not always possible to assess a KL, because often the keratinization process involves the entire thickness of EP and not only the superficial layer. For this reason, withe lesions on the tissue surface can lead to a decrease in the depth of light penetration, effectively limiting the potential of the OCT system.We conducted our in vivo investigation on different types of lesions without preliminarily knowing the result of the initial histopathological analysis. In our series, an inhomogeneous white-red lesion turned out to be a microinvasive carcinoma and the OCT pattern already describes the typical peculiarities of invasive oral cancer such as the non-determinability of the basement membrane . DespiteAlthough this study did not investigate the possibility of recognizing the presence of dysplasia using the OCT system, it is suggested that dysplasia is also present in perilesional clinically healthy tissue , 34. It To the best of our knowledge this is one of the few studies investigating the in vivo use of OCT in OPMDs.In vivo use of this device by the generalist physician, with adequate training, will improve its diagnostic performances during the long-term follow-up of the patient with potential malignant disorder reserving the biopsy with histopathological traditionally examination if a worsening or malignant evolution pattern is observed.The Oral Medicine specialist should use in vivo-OCT not only for short-medium follow-up for patients most at risk of malignant evolution but also to monitor outcomes of medical or surgical therapy and any recurrences of OPMDs at a non-invasive and ultrastructural level.Further studies with large sample series will be needed to validate the precise values of the thicknesses of different epithelial layers during OCT evaluation to obtain more significant results with reference to the specific sites of the various districts of the oral mucosa.


The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots’ interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method. The proliferation of mobile robots across several industries is a direct consequence of advancements in science and technology. The significance of path planning in enabling mobile robots to achieve autonomous navigation has been widely recognized and has experienced substantial advancements and increased attention in recent times . The priThe issue of path planning can be categorized into two basic components: global path planning and local path planning. These components rely on the robot’s acquisition of information pertaining to its immediate surroundings. Global route planning refers to the entirety of the environment information that is known to the robot, whereas local path planning pertains to the knowledge about the environment that is only partially known or completely unknown to the robot . The priReinforcement learning (RL) is an algorithm characterized by autonomous learning, which has gained significant prominence in recent years. In contrast with conventional algorithms that typically rely on the availability of complete or partial environmental information, reinforcement-learning-based algorithms exhibit the unique characteristic of not requiring prior knowledge of the environmental information. In order to adapt to unfamiliar surroundings, individuals have the ability to modify their activities by continuously receiving feedback from interactions with the environment. From autonomous learning, they are able to make informed judgments that lead to greater rewards, ultimately enabling them to strategize and determine the most ideal path . DuguleaDeep reinforcement learning (DRL) is a more effective way of learning by combining the neural network of deep learning with the reinforcement learning algorithm and using the neural network as a function approximator to represent the value function or the policy function so that the deep reinforcement learning (DRL) so constituted is a more effective way of learning. Yang et al. solved the problem of a slow rate of convergence and high randomness in path planning by adding prior knowledge and rules to the deep-Q network (DQN) algorithm and improved the efficiency of path planning . Yang etIn this study, we provide a novel deep reinforcement learning method based on the soft actor–critic (SAC) framework for the purpose of path planning in settings with unknown characteristics. The algorithm has been modified to accommodate a continuous action space and operates as an offline method. The introduction of maximum entropy is employed to mitigate the issue of local optimality, hence enhancing the system’s resistance to interference.Furthermore, the hindsight experience replay (HER) algorithm is proposed as a solution to address the challenges of reward scarcity and the sluggish training pace seen in goal-oriented reinforcement learning algorithms. HER achieves this by recalculating reward values and efficiently using the knowledge gained from unsuccessful experiences during the training process.Third, the simulation experiments of path planning with environmental maps verify that the new algorithm HER-SAC can effectively perform path planning and improve the training speed and convergence of the algorithm.In this paper, we propose an improved deep reinforcement learning SAC method to apply it to path planning for mobile robots. The main contributions of this paper are summarized as follows:The subsequent sections of this paper are structured in the following manner. The problem of path planning for mobile robots is framed within the context of reinforcement learning. Additionally, an advanced deep reinforcement learning technique called soft actor–critic (SAC) is introduced. This section will be utilized in the subsequent phase to enhance the algorithm.Reinforcement learning, situated between the supervised and unsupervised learning paradigms, enables the acquisition of knowledge through iterative interactions with the environment, devoid of external supervision or direction. The field of reinforcement learning incorporates concepts and principles derived from the field of bionics in order to develop learning algorithms that are both more efficient and cleverer. One application of reinforcement learning is training intelligent systems to acquire optimal behavioral strategies by emulating the reward mechanisms observed in biological systems. Furthermore, reinforcement learning is influenced by the field of bionics in order to develop learning algorithms that exhibit enhanced adaptability in intricate environments and dynamic circumstances. Hence, the utilization of reinforcement learning for addressing path-planning challenges in unfamiliar contexts seems to be a highly efficient approach.Reinforcement learning is an algorithm described using the Markov decision process (MDP), so the path planning of a mobile robot can be reduced to a Markov decision process. The process can be defined as a five-tuple The robot interacts with the environment as it completes its tasks, using a policy to input actions The strategy of a robot is usually denoted by The Bellman expectation equation (BEE) for two value functions is defined as:The purpose of reinforcement learning is to find the optimal strategy, and the corresponding value function can be used to compare the advantages and disadvantages of the strategy, so the optimal state value function and the optimal state–action value function can be expressed as:The optimal policy can be obtained by maximizing the optimal value function, so the value function and Bellman’s expectation equation are important components of reinforcement learning on which subsequent reinforcement learning algorithms are based.The SAC algorithm is a reinforcement learning method that incorporates deep learning techniques, specifically deep learning (DL), and integrates the maximum entropy framework with the actor-critic (AC) framework through the utilization of offline and stochastic strategies. The SAC algorithm possesses a significant advantage over other reinforcement learning algorithms and deep reinforcement learning algorithms due to its notable exploratory capabilities and its ability to adapt effectively to more intricate challenges.Entropy is used to indicate the degree of randomness of a random variable, and in the calculation, the variable The goal of the SAC algorithm with the introduction of maximizing entropy is to not only maximize the cumulative reward but also to make the strategy more random, i.e., maximize the entropy value of the strategy, with the objective function defined as:Q-value is:The soft strategy iteration is performed alternately between strategy evaluation and strategy improvement. In strategy evaluation, the value of the strategy is calculated based on maximizing the entropy, and for a fixed strategy, the modified Bellman backup operator for the soft Q-value of the policy Ultimately, the soft policy evaluation converges to the soft Q-values, and the strategies, In strategy improvement, the strategy is improved according to the soft strategy enhancement formula: Q-value networks Q structure alleviates the case of overestimation of the Q-value and applies stochastic gradient descent to the objective function. Therefore, the objective function of the soft state value function is defined as:The above soft policy iterative process is for the case of tabular setups, for environments with limited state and action spaces. For continuous space, function approximation needs to be introduced. The SAC network consists of five neural networks: the policy network The gradient is:Q-value function is defined as:The objective function of the soft Of these,The gradient is:The objective function of the strategy update is:Since the SAC algorithm outputs Gaussian-distributed means and standard deviations and the process of sampling the action is not derivable, the actions need to be sampled using a reparameterization technique, denoted as:Since the SAC algorithm requires the robot to obtain the final reward after reaching the goal, the reward is sparse, the sample sampling is less efficient, and the training time is longer. In order to improve the efficiency of sample use, this paper designed the HER-SAC algorithm, which introduces hindsight experience replay (HER)  into theG is sampled, from which The core idea of HER is that in the off-policy algorithm, the initial samples are first collected through the interaction between the robot and the environment. A trajectory sample obtained from an episode interaction By replacing the experience pool of the original SAC with the experience pool constructed with HER, the new HER-SAC algorithm can utilize the experience of failures, thus making the algorithm more learnable as a whole, and the network structure of the algorithm is shown in Q-value); and the V Critic network is used to output the state value function .The relationship between the Q network and the V network can be described by the Bellman equation, which allows for simultaneous updating of the networks and learning of the estimates of the Q-value and the V-value by sharing parameters. The HER-SAC algorithm also includes an important component, the HER module, which is used to extract experience from the experience playback buffer and generate new experience by modifying the target state to take the old experience acquired from the environment and then feeding it back to update the network.The network structure of the HER-SAC algorithm consists of Actor network, two Q Critic networks and two V Critic networks. Among them, the Actor network interacts with the environment and outputs the probability distribution of actions; the Q Critic network is used to output the state action value function  If an obstacle or boundary is encountered, a large negative reward is given  Core(TM) i7-6700 CPU @ 3.40 GHz 3.41 GHz and 8.00 GB of RAM. In this study, it was postulated that the experimental setting consisted of a 20 × 20 two-dimensional planar space. Within this space, various static obstacles of varying sizes were distributed, each occupying a fixed position and being immovable. The mobile robot under consideration was assumed to be a two-dimensional mass point, with no consideration given to its physical appearance or shape. Additionally, in order to simulate real-world conditions, both the robot and the obstacles maintained a certain distance from each other during the process of path planning.As shown in The parameters of the improved HER-SAC algorithm are shown in In order to verify the feasibility of the algorithms, three algorithms, DDPG, SAC, and HER-SAC, were used for path planning and a comparative analysis of the same experimental environment.The paths planned by the three algorithms after learning and training iterations are shown in As depicted in Upon doing a comparative analysis of various settings, it becomes evident that the convergence of the deep deterministic policy gradient (DDPG) exhibits notable improvement, although it remains incompletely converged. In contrast, both soft actor-critic (SAC) and hindsight experience replay–soft actor-critic (HER-SAC) demonstrate considerably stronger convergence in comparison with DDPG.As shown in The comparison of results obtained from the execution of three algorithms in varying environments is presented in Our work focused on the examination of algorithms used in simulation environment route planning. Specifically, the paths, reward curves, and step curves generated by three algorithms, namely, DDPG, SAC, and HER-SAC, were observed and analyzed individually. The efficacy of the three algorithms could be determined by examining the reward curves.Upon carrying out a comparative analysis of the reward curves pertaining to the DDPG and SAC algorithms, it becomes evident that both algorithms exhibit similar convergence rates over a given number of rounds. However, it is observed that the DDPG algorithm demonstrates a slower convergence, whilst the SAC approach showcases a more rapid convergence, ultimately leading to a superior final convergence outcome. To enhance the efficacy of incentive utilization, the HER algorithm was incorporated to optimize the SAC algorithm. Upon analyzing the reward curves of the SAC and HER-SAC algorithms, it becomes evident that the optimized algorithm shows a significant reduction in the number of rounds from the start of convergence to full convergence compared with the pre-optimized algorithm, hence enhancing the pace of convergence. Simultaneously, the ultimate convergence effect is enhanced compared with the pre-optimization state.Based on empirical validation, it can be determined that the optimized algorithm produces superior results in the field of path planning. The effective navigation of mobile robots in real-world settings can be greatly enhanced by addressing the path-planning challenge in complex and unfamiliar environments. From the results obtained from the aforementioned trials, it can be inferred that the method under investigation demonstrates effective path-planning capabilities inside 2D environments. Hence, the proposed methodology can be extended to encompass other 2D environments featuring diverse sorts of obstacles. The algorithm’s effectiveness in real-time applications is diminished due to its reliance on learning from past knowledge and subsequently planning global courses mostly through hindsight experience replay. In the meantime, the subsequent phase can be expanded to encompass the involvement of several robots in the process of multi-intelligent body route planning. This can be achieved via the implementation of collaborative decision making, effective communication and collaboration among the robots, the utilization of group intelligence, and the ability to adapt to dynamic environmental conditions. Hence, the experiment exhibits three primary constraints: firstly, the absence of real-time path planning in the presence of dynamic obstacles; secondly, the absence of intricate obstacle path planning in a three-dimensional environment; and thirdly, the absence of multi-robot path planning. Hence, our forthcoming research endeavors will focus on this particular issue.This study presented a novel approach to address the path-planning challenge encountered by mobile robots operating in unfamiliar and intricate surroundings. The suggested solution leverages deep reinforcement learning techniques to develop an algorithm for efficient path planning. The suggested algorithm, soft actor-critic (SAC), aims to enhance the learning and exploratory aspects of the system. Additionally, the usage rate of samples is improved via the incorporation of the hindsight experience replay (HER) algorithm. The simulation results provide evidence that the proposed algorithm is capable of efficiently determining the shortest path between the initial and target points. Furthermore, it demonstrates an enhanced convergence speed and effectiveness compared with alternative algorithms, resulting in faster and more accurate path finding.In forthcoming times, there will be a greater utilization of algorithms that exhibit enhanced efficiency in the domain of path planning. Furthermore, the task of path planning will be executed across unfamiliar dynamic situations, encompassing both two-dimensional and three-dimensional spaces, in order to enhance its compatibility with real-world surroundings.