Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources  at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks. The Internet of Vehicles (IoV) is an emerging concept in intelligent transportation systems that aims to improve traffic safety and passenger comfort through integration with the Internet of Things (IoT), and it is an important implementation of the IoT . Based oAs the number of vehicles on the road continues to increase, the IoV continues to evolve, smart vehicles accounting for an increasing share of Internet-connected devices. In the IoV paradigm, smart vehicles are equipped with computing units and communication technologies that provide services such as intelligent control, traffic management and interactive applications for vehicles. The edge-side computing architecture for autonomous vehicles (AVs) relies on the communication infrastructure and services provided by the edge-cloud collaboration and the (Long-Term Evolution/5th Generation Mobile Communication Technology) LTE/5G. The edge side mainly refers to on-board edge computing units, RSUs, Mobile Edge Computing (MEC) servers, etc. AVs are equipped with a large number of sensors that collect data for different types of traffic systems and navigation applications, etc. . CurrentSince MEC servers operate at the edge of the radio access network, connecting to RSUs and performing transmission tasks with their help, their service area may be limited by the radio coverage of the RSUs. Due to the high mobility of the nodes, moving vehicles may pass through multiple RSUs and MEC servers during task offloading and can offload their computational tasks to any MEC server they have access to that provides computational resources for the tasks offloaded by the mobile nodes , as showA centralized offloading strategy has been used in some studies, where vehicles can request information about MEC servers available for offloading along the road from a centralized server located at a higher level . The cenSuch problems are known as Optimal Stopping Theory (OST) problems. A basic question about OST is that nodes autonomously decide when to act based on sequentially observed random variables with the goal of increasing expected benefits or decreasing expected costs . The SecThe remainder of this paper is organized as follows: we provide a summary of the related work and outline our contributions in In dealing with the issue of how to offload computational tasks and data to edge nodes, various pieces of research have emphasized whether data or tasks should be offloaded to servers or computed locally. The offloading decision has two main goals: minimization of computational delay and energy consumption.Ref.  proposedRef.  used DevIn , OST wasIn ,17, the In , the autIn this work, we consider the case of dividing the tructured tasks into multiple subtasks . To solvWe consider the multi-task offloading problem based on OST, where structured tasks are divided into multiple subtasks and offloaded to the MEC servers with the goal of minimizing the total task processing time.We propose a time-optimized function to simulate the situation where a mobile node needs to perform offloading as far as possible before the task data become obsolete in a realistic scenario and inject it into the decision model.We propose a time-optimized multi-task offloading model based on OST aiming at maximizing the probability of offloading to the optimal servers for general distribution scenarios.We propose a better time-optimized multi-task offloading model based on OST with the objective of minimizing the total task processing time for some realistic road scenarios where the CPU utilization may have an approximately uniform distribution.We provide an experimental comparison, performance evaluation and sensitivity analysis of the models in this paper and a series of models in other research using simulated and real data sets.In summary, our contributions are:Consider a vehicle networking application scenario studied in ,10: mobied by t1 . So, thesubtasks .The MEC server can operate at the edge of the network with the help of the RSU, so its communication range will be limited by the RSU communication range . Since tith server processing time observed. When a node generates tasks locally and needs to offload, the node can observe n MEC servers; the mobile node uses the network and server analyzer to check each MEC server it passes and obtains the X values. Assuming that K blocks of tasks need to be unloaded, in n observation durations, then K unloading decisions need to be made.The mobile node observes the MEC servers along the route in order at this point, and K optimal servers; (2) minimize the expected value of the total X for K tasks. We first propose an offloading model that is generalizable to the distribution of X for the first objective, and then, we propose a better offloading model with the second objective  for certain road scenarios where there may be a uniform distribution of X. The current goal is to optimize the decision to offload multiple targets to available servers. Overall, the offloading objectives are twofold: (1) maximize the probability of offloading to n can be defined by the user or estimated by the application based on the deadlines of the tasks to be offloaded. Each node can only check the servers in a sequential and random order and will rank them relatively based on the previously observed servers. At each observation, the node should decide whether to select the current candidate server, and once selected, it cannot revoke its decision. The node should maximize the probability of selecting the best candidate among n probability of choosing the best among the candidates. This is a best choice problem ; X is the server utilization or CPU utilization. For example, In the previous experiments, the random variable X following a uniform distribution, and the K-Best model also achieves a more desirable expected processing delay, which is significantly better than the p-model and the Random model, as can be seen in As shown in X obeys a normal distribution, and the case when the random variable X obeys a uniform distribution is shown in X.As shown in As shown in We also consider real data sets to evaluate our models. The purpose of this evaluation is to see how our models perform when dealing with real data sets. We use the CABS data set provided by the Shenzhen Smart City project  to simulThe processing time of the servers is provided by the actual CPU utilization obtained in the Alibaba Cluster Tracking Program . There aAt the beginning of the experiment, the mean and the standard deviation were taken once for the whole servers’ utilization data set to feed the models . In a realistic scenario, the mobile nodes do not know the mean and standard deviation of a particular MEC server, but they can obtain the information about the historical data of the MEC servers in one area in a specific time with the help of MEC servers operators. Therefore, we take this information once when we start the experiment.K servers for mobile nodes to offload to minimize the total offload delay. The total delay is expressed in terms of the total server utilization.Similar to the experiments based on simulated data, the results of each model are aggregated and compared in the experiments based on real data sets. Each model selects p-model with We use the number of successful offloading for different threshold requirements as a performance metric for the models. The number of successful offloading is the number of offload decisions suggested by each model that satisfy a specific requirement. Suppose we have three different MEC applications x, y, and z, all with specific requirements. For example, application x requires a total CPU utilization ≤0.4, application y requires a total CPU utilization ≤0.6, and application z requires a total CPU utilization ≤0.8.X distribution and a time-optimized multi-task offloading model that performs better for the case of uniform X distribution are proposed, and detailed evaluations are provided. The experimental evaluations show that the OST-based models outperform other offloading methods, achieve more desirable expected processing latencies, are efficient to use in mobile nodes, and do not require significant resources. In addition, the OST-based models are suitable for scenarios where mobile nodes need to make local and independent decisions in the MEC environment. In realistic scenarios, mobile nodes can obtain the required information with the help of MEC service providers and thus can make decisions autonomously. As future work, it is considered to study the K-Best model when K keeps increasing and may give a feasible closed-form solution. Since the observed recall of the MEC server is allowed in different mobility models, the optimization of the mult-itasking offloading model based on OST theory can be further investigated in the recallable server as well as in the multi-node competition scenario.In this paper, we focus on a time-optimized multi-tasking offloading model based on OST in an IoV environment. A time-optimized multi-task offloading model adapted to a general


High-grade ovarian epithelial cancer (HGOEC) is considered to be among the most fatal gynecological cancers, and it is associated with poor response to treatment and adverse prognosis, possibly due to marked intratumoral heterogeneity. The aim of this study is to present a novel technique that can assess intratumoral cellularity based on quantitative features extracted from medical images, denoted as radiomics, advanced image processing and artificial intelligence algorithms, in an attempt to offer biomedical engineers and health professionals a tool for personalized medicine. According to our results, the average accuracy rating of the proposed method in our study population (−3 mm2/s). Results: The average recorded accuracy of the proposed automated classification system was equal to 0.86. Conclusion: The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.Purpose: Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity. Methods: Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10 High-grade ovarian epithelial cancer (HGOEC), also known as high-grade serous ovarian carcinoma, is considered to be a particularly aggressive form of ovarian cancer accounting for 207,252 deaths in 2020, according to . It is tThe presence of intratumoral heterogeneity, or the diversity of cell types and genetic mutations within a single tumor, is considered to play a significant role in the development and progression of cancer . IntratuIn patients with HGOEC, magnetic resonance imaging (MRI) may be used to assess the presence of intratumoral heterogeneity, as it can provide useful information to evaluate the imaging characteristics in the different areas of a tumor. MRI scanning can also help the medical doctor to identify areas of high cellularity, cellular atypia, or necrosis, which may be indicative of tumor heterogeneity. Moreover, functional techniques, such as diffusion-weighted imaging (DWI), are a way to obtain information since highly cellular tissues usually exhibit lower apparent diffusion coefficient (ADC) values than healthy tissue because the water diffusion is restricted by the high cellularity and the disorganized architecture of the tumor ,8.The assessment of MRI data can be based on radiomics by providing a more comprehensive and objective characterization of the tumor, allowing for a more accurate diagnosis and prognosis, as they express features of medical images as a series of quantitative data ,10. RadiIn the fairly recent past, radiomics-based analysis has been employed in order to correlate ovarian cancer phenotype, primarily from Computed Tomography (CT) imaging data, with specific gene patterns and survival rates. Based on various studies performed by The Cancer Genome Atlas (TCGA) Research Network, the classification of ovarian cancer (CLOVAR) model was proposed, which relies on microarray transcriptomic profiles, which in turn were used as a prognostic algorithm for high-grade serous ovarian cancer (HGSOC) . MoreoveThe aim of this study is to present a novel classification method for assessing imaging data and depicting highly cellular tissue within the cancerous tumor based on radiomic features extracted from multiple magnetic resonance imaging sequences. The proposed system identifies intratumoral areas with low ADC values based on the hypothesis that marked tumor heterogeneity may potentially affect prognosis and treatment, as the scope of imaging informatics is to improve efficiency, accuracy and reliability of services among the medical society .Between March 2019 and March 2022, pelvic MRI scans were performed on 22 women . The data were acquired at the Department of Radiology of the National and Kapodistrian University of Athens in a 3.0-T MRI scanner . The particular individuals were considered potential HGOEC cases, according to clinical examination and laboratory findings. At the time, none of them had received treatment for HGOEC or had any other conditions requiring an MRI scan.T2-weighted DIXON sequences in the axial plane;Diffusion-weighted imaging (DWI) in the axial plane.MR images created a database of 11,095 images with pelvic cancerous regions, with a size of 448 × 448 pixels. According to the acquisition protocol designed for this study, those images came from the following sequences:Image registration: The process of aligning two or more medical images of the same or different modalities  to analyze them. In our case, the T2-weighted and the DWI images are aligned.Image segmentation: The process of dividing an image into multiple regions or segments, each of which corresponds to a different anatomical structure or tissue type. The goal of medical image segmentation is to detect the outline of the cancerous region.Image extraction: The process of extracting the cancerous region. The image extraction is needed for both the machine learning pipeline itself and the validation of the whole model through the comparison with the ADC values derived from the DWI sequence.Feature extraction: The process of extracting quantitative characteristics, called radiomic features, from the above-mentioned extracted images.Feature selection: The process of identifying the subset of the most relevant and useful features from the extracted radiomic features.Classification: A machine learning technique that involves training a model to assign a class label to the input data, namely the set of T2-weighted images, on a pixel-by-pixel basis, in an attempt to characterize each region of the data as potentially dangerous or non-dangerous.The goal of this study is to locate the regions of the tumor with low ADC values. This is achieved through a combination of image processing techniques and artificial intelligence. The overall procedure can be summarized in the following steps, which are performed sequentially:The overall processing flow of the proposed system is presented in As can be seen in 2 for automatically identifying the whole tumor [Once both the DWI and ADC image series are spatially matched, it is possible to detect the anatomical region of the tumor from the DWI series. For this purpose, the K-Means clustering algorithm is applied to the images acquired with a b value equal to 1200 s/mmle tumor ,23. Two −3 mm2/s according to the radiologists’ personal experience and data obtained from the literature [After distinguishing tumorous and non-tumorous areas, it is possible to define the outline of the cancerous region. In each case, the particular region was extracted as a new image. At the same time, the low ADC region of the tumor was automatically extracted from the images derived from the MRI and used as a “golden standard” to validate the proposed model. The ADC cut-off selected to define the “highly cellular tumorous area” was 0.85 × 10terature ,25,26.n of the feature space F in order to overcome the risk of overfitting by recursively evaluating our classification process. To produce a feature ranking, we tested two optimization methods. First of all, a support vector machine (SVM) was used as a type of supervised learning algorithm for the classification of the pixels. The learning method used by SVM is based on the concept of maximal margin classification, which involves finding the hyperplane with the largest possible margin (the distance between the hyperplane and the nearest examples) that separates the classes. This can be formulated as an optimization problem, where the goal is to find the hyperplane that maximally separates the classes while minimizing the number of misclassified examples. To solve this optimization problem, SVMs use the concept of kernel functions, which map the input data into a higher-dimensional space, where it may be easier to find a hyperplane that separates the classes. The kernel function is chosen based on the characteristics of the input data, and it can be used to transform the data into a space where it is linearly separable, even if it is not linearly separable in the original space.In order to assess the heterogeneity of the neoplasm itself, in search of the low ADC region within the tumor, a feature extraction technique was employed to extract the quantitative characteristics of the region of interest. The pre-processed image series, along with their corresponding masks, that is, pixels with useful information versus background, were used, and feature extraction was performed in each slice of the volumetric imaging data, providing a total of 1242 features. After removing the constant features, which are the features that do not differ from image to image, an optimal subset of the original feature vector is selected through a recursive feature elimination cross-validation process . In the The second method employed during training was stochastic gradient descent (SGD), where the goal is to learn the parameters of the model that minimize the loss function, which measures the difference between the model’s predictions and the true class labels of the examples in the training set. SGD involves iteratively updating the model parameters in a direction that minimizes the loss function. At each step, SGD uses a randomly chosen subset of the training examples  to compute an estimate of the gradient of the loss function with respect to the model parameters. The model parameters are then updated in the opposite direction of the gradient, with the step size determined by a learning rate hyperparameter.Ten-fold cross-validation was implemented in order to minimize the risk of overfitting and the limited data sample . Ten-folWavelet filter: A wavelet filter is a mathematical tool used, in our case, to analyze and process image data signals. It is based on the concept of wavelets, which are small, localized functions that can be used to represent signals at different scales and resolutions. This filter applies a wavelet filter to the input image and yields the decompositions and the approximation.Laplacian of Gaussian filter: The particular filter applies a Laplacian of Gaussian filter to the input image and yields a derived image for each sigma value specified. A Laplacian of Gaussian image is obtained by convolving the image with the second derivative (Laplacian) of a Gaussian kernel.Square filter: Takes the square of the image intensities and linearly scales them back to the original range. It is used to enhance certain features or attributes of the T2-weighted image, such as sharpness and contrast.Square root filter: The square root filter is a type of image processing technique that is used to enhance the contrast and texture of the input image. It takes the square root of the absolute image intensities and scales them back to the original range. The square root filter works by raising each pixel value in the image to the power of 0.5 and has the effect of enhancing the contrast of the image, increasing the visibility of small structures.Logarithm filter: The logarithm filter works by applying a logarithmic transformation to the pixel intensity values in the image. This transformation maps the pixel values to a new scale, which can help to stretch out the differences between pixels with relatively small intensity values and those with relatively large intensity values. This can make it easier to distinguish between different tissue types or structures within the image.Exponential filter: The exponential filter considers the exponential absolute intensity. It works by applying a weighted average to the pixel values in a local region around a central pixel, with the weighting decreasing exponentially as the distance from the central pixel increases.Gradient: The gradient returns the magnitude of the local gradient. The gradient is a measure of the local intensity change or slope of an image. It is calculated by taking the derivative of the intensity values in the image with respect to position. The gradient can provide information about the edges and boundaries of objects in an image, as well as the texture and shape of these objects.Local Binary Pattern: This is a simple and efficient method for extracting texture features from images. It works by dividing an image into small cells or neighborhoods and comparing the intensity values of each pixel to the center pixel value. Based on the comparison, the pixel is assigned a binary value (0 or 1). These binary values are then concatenated to form a binary pattern, which is a unique descriptor of the texture in the neighborhood.In addition to the original images themselves, several other qualitative features were used for classifying the tumorous regions. Those features were extracted by applying the following filters ,30,31,32Nineteen features from first-order statistics. First-order statistics are a type of feature that can be extracted from medical images using radiomics techniques. These features describe the overall distribution of intensity values within an image and can provide information about the shape, symmetry and intensity range of the structures in the image. Examples of first-order statistics include the mean, median and standard deviation of the intensity values within an image.Shape-based (2D) features. Shape-based features in radiomics involve the measurement and characterization of the shape of structures in the images. These features can include measures of size, shape and spatial relationships, such as the volume, surface area and compactness of a structure. These features are independent of the gray level intensity distribution in the ROI.Features from the gray-level cooccurrence matrix describe the second-order joint probability function of an image region. One of these features is autocorrelation. Autocorrelation is a quantitative measure of fineness or coarseness of the texture, which expresses the average intensity difference between a center point and the points of the whole region, indicating the spatial rate of change.Sixteen features from the gray-level run-length matrix. This matrix corresponds to the continuous image points that have the same intensity value. One example of such a feature is the run percentage (RP). RP measures the coarseness of the texture by taking the ratio of the number of runs and the number of voxels in a region of interest.Sixteen features from gray-level size zone that quantifies gray-level zones in an image. An example of these features is large area emphasis (LAE). LAE is a measure of the distribution of large area size zones, with a greater value indicative of larger size zones and more coarse textures.Fourteen features from the gray-level dependence matrix (GLDM) that quantifies gray-level dependencies in an image. An example of these features is dependence non-uniformity (DN). DN measures the similarity of dependence throughout the image, with a lower value indicating more homogeneity among dependencies in the image.δ. An example of these features is coarseness, which has been described above.Five features from the neighboring gray-tone difference matrix, which quantifies the difference between a gray value and the average gray value of its neighbors within a distance For the above-mentioned T2-weighted images, the following categories of features were extracted :NineteenIn order to evaluate the performance of the proposed classification systems, four well-established metrics were employed: accuracy, balanced accuracy, sensitivity and specificity ,35,36. AIn other words, it refers to the percentage of correct predictions made by a classifier out of all the predictions made. It is a common metric used to evaluate the performance of a classifier, and it is calculated by dividing the number of correct predictions by the total number of predictions. The basic element of the metric is every pixel of the tumorous area. Each unit has the same weight, and they contribute equally to the accuracy values. .Balanced accuracy is a metric for evaluating the performance of a classifier, specifically when the classes in the data are imbalanced. It is calculated by taking the average of the recall for each class, where recall is defined as the number of True Positive predictions made by the classifier divided by the total number of instances of the class in the data. It is used because our initial set of data is unbalanced .Sensitivity  is the fraction of True Positive elements divided by the total number of positively classified units. Sensitivity measures the model’s predictive accuracy for the positive class. Specificity, respectively, is the proportion of the True Negatives correctly identified by a diagnostic test. It suggests how good the test is at identifying normal (negative) conditions. In all cases, the results of the classification system were compared against the ADC-based regions, focusing on the highly cellular tumor area.All image processing routines were developed using the Insight Toolkit (ITK) . The resData from all 22 MRI datasets were processed to produce respective 3D models of the tumors in each case. Initially, the estimated 3D models were assessed qualitatively by means of visual inspection of the extracted tumors. After the extraction of the tumorous regions from the 3D models and the generation of the training data, the proposed classification system was applied to the extracted regions of interest in order to classify their pixels. The validation was performed using the respective ADC values per point derived from the diffusion-weighted images.On average, the classification processing pipeline required about 10 min for feature extraction and classification of each patient (depending on the processed datasets). All tests were performed on a common reference system .The first step of the proposed study was to form a database of MRI imaging data with images from patients suspicious of HGOEC. Those images were used to extract radiomic features. Radiomic features enable us to quantify image characteristics and create a self-learning model for differentiating high and low cellularity areas. The alignment and segmentation techniques applied to develop the model were evaluated quantitatively and qualitatively, while the whole artificial intelligence classification system was tested for possible overfitting over the entire available dataset from the patients recruited during the study.The presented quantitative and qualitative results demonstrate that the proposed system provides a good prediction performance concerning the highly cellular tumor area. By examining However, the risk of a possible biased result due to the small number of patients in our dataset is eliminated through the implementation of the specific ten-fold cross-validation method concerning models based on a limited data sample .Another area for potential future work is to use this algorithm to assess the whole tumor or even to study a correlation between biomarkers automatically derived from imaging data and clinical, genomics and/or proteomics data, especially in highly cellular tumors, as radiomics offer huge opportunities to better capture tumor behavior .Identification of intratumoral genetic heterogeneity, or the presence of genetic variations within a single tumor, may help identify areas of unexpected, more aggressive tumor growth. This is because tumors with a high degree of genetic heterogeneity are more likely to contain subclones or subpopulations of cells that have acquired genetic mutations that promote the growth and spread of cancer .One way that intratumoral genetic heterogeneity can be identified is through the use of genomic sequencing techniques, such as whole-genome sequencing or targeted sequencing of specific genes. These techniques can reveal variations in the DNA sequence of different cells within a tumor, allowing for the identification of subclones or subpopulations of cells with distinct genetic profiles. In addition, the spatial distribution of genetic variations within a tumor can also be analyzed through techniques such as single-cell sequencing and spatial transcriptomics, which can help map the location of genetic variations within the tissue.The knowledge of the presence of subclones with specific genetic characteristics within a tumor can help guide treatment decisions. For example, if a subclone with a specific genetic mutation is identified, targeted therapies that target that mutation may be more effective in treating that particular area of the tumor .Similar studies could be tested on different cancer subtypes where the system can classify different subtypes of cancer based on imaging, genetic, molecular and clinical characteristics. Therefore, another possible area for the presented model to be tested is on different types of abnormalities or lesions in images such as CT or MRI scans.Consequently, in summary, while similar classification systems could be applied to various medical data, a good assessment of the tumor through the proposed model offers an opportunity for an intratumoral genetic heterogeneity study and hence the identification of areas with potentially more aggressive tumor growth. The identification of specific genetic mutations or subclones within a tumor can also guide treatment decisions and personalized medicine approaches ,44,45 (pThe intuitive application programming interface allows for the fast building of medical image segmentation, visualization of the tumor fused in the T2W sequence, pipelines including data I/O, pre-processing, metrics, a library with state-of-the-art feature extraction model and model utilization such as training, tumor area classification as well as fully automatic evaluation.We propose an effective classification system for assessing highly cellular tumorous areas with the use of radiomics, extracted through a series of pre-processing steps that consist of registration, segmentation and feature extraction on imaging data. The proposed system may be potentially useful for biomedical engineers as a guide to improve the prediction performance based on classification and could even help medical professionals to classify patients into different risk groups that can lead to personalized medicine. Accurate preoperative classification of HGOEC patients into risk categories may be associated with early customization of treatment, optimizing the therapeutic outcome and thus ultimately improving patients’ survival rates .


Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information—in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy. L, where the  plane contains the focal points of the views, and the  plane means image plane. L can be viewed as an assignment of an intensity value to the ray passing through  and  16], LF_OSideboard scene. The edge of the red box was more accurate than in other algorithms, and the depth estimation effect was improved greatly.(2)Sideboard scene. Other scene indexes were better than the traditional LF image processing algorithm, and the comprehensive index was the best.Quantitative Results: Quantitative comparisons of BP > 0.07 and MSE are shown in Furthermore, the algorithm in this paper was also close to the true value at the edge of shoes and basketballs in the (1)Sideboard scene, and the edge depth effect was very close to the true value. The edge of EPIRefocusNet and SPO algorithms had a little noise, the edge of EPNosgc and EPI-orm algorithms had error estimation, and the edge of EPI-shift algorithm had a little gap. At the chandelier region in the scene, the algorithm in this paper predicted its depth edge closer to the true value. In the region where multiple chandeliers block each other, the SPO and EPI-orm algorithms had obvious noise in the edge prediction. The EPNosgc and EPIRefocusNet algorithms had error estimations at the chandeliers, which are close to each other. The algorithm in this paper is basically accurate in predicting the edges of the chandeliers. In the Cotton scene, the algorithm proposed in this paper was very close to the true value in the portrait hair region prediction, and there was basically no noise in the background region—indicating that the comprehensive effect is better. In the Boxes scene, although the edge results of the proposed algorithm were good, there was a little noise on the box, indicating some disadvantages when compared with other algorithms. In the Dino scene, the proposed algorithm also had obvious effect advantages at some edges and obtained better depth results.Visual comparison on estimated depth map: Detailed experimental comparisons are shown in For verifying the performance of the algorithm in this paper under the same class of algorithms, the experimental results are compared with the mainstream EPI deep learning LF depth estimation algorithms EPIRefocusNet , EPI-ORM(2)Comparison on BP, MSE and Q25 indexes: Moreover, this paper makes three types of index correlation images based on the subjective depth map, which reflects the details of the algorithm results, as shown in It can be seen from the above that the algorithm proposed in this paper is more accurate in estimating the depth detail region and can achieve good subjective results.Boxes scene, which consists of occlusions with depth discontinuity, and the Sideboard scene, which had complex shape and texture—our approach always achieved the best effect. This shows that our algorithm is more advantageous for occlusion and complex scenes.BP measures the percentage of wrongly estimated pixels. It can reflect the edge preservation ability of the algorithm. As we can see in Cotton sequence containing smooth surfaces and textureless regions, but it is not dominant for noisy scenes. This is the common shortcoming of applying EPIs to the CNN-based method. The reason is that noise may lead to the false slope estimations in EPI patches. Therefore, one of the future works could introduce global constraints into our model.MSE reflects the smoothness of the reconstructed depth map. As shown in Q25 visualization depicts the accuracy for those pixels that fall into the Q25, i.e., the regions with the 25% best accuracy for each algorithm. As we can see in We drew the intuitive radar chart as shown in We further compared the proposed method with two advanced subaperture image-based methods: epinetfcn  and lfatAlthough EPI LF contains the depth information of each point in the scene, it discards many pixel features inside the subaperture image and contains less spatial information compared with other LF representation forms, which will have an impact on the accuracy of the depth estimation. Although the directional relationship model and multiviewpoint attention mechanism designed in this paper can accurately extract the EPI slope features, it is inevitable that it will lose EPI slope features due to the small number of pixels in the EPI and the occlusion of important EPI slope information. Therefore, we deduced that a multimodel LF depth estimation algorithm combining the LF EPI with other LF representation forms, such as subaperture image and focal stack images, would be designed to improve the accuracy of the depth estimation.In this paper, in consideration of EPI LF’s characteristics of multidirectional relationship and pixel consistency, we proposed an EPI LF depth estimation method based on the directional relationship model and multiviewpoint attention mechanism. The EPI directional relationship model was used to establish the directional relationship of two EPIs. The EPI low-level features were extracted by using multiviewpoint attention mechanism, and the EPI slope high-level features are extracted by combining channel attention and the spatial attention mechanism. The feature redundancy was eliminated by using the residual module.Boxes and Sideboard. In future works, we could introduce global constraints to enhance the antinoise ability. We will also try to combine the EPI with subaperture images or focal stack images to improve the accuracy of the depth estimations.We demonstrated the effectiveness of our approach on the 4D LF benchmark. It can reconstruct the smooth surface and the region with sharp depth discontinuity. Especially, it is able to predict more accurate disparity maps in some challenging scenes such as


Vibration exercises on a platform , widely used in rehabilitation, sports medicine, and fitness, is an alternative to strength effort. The presented study assessed the effect of a 12-week cycle of vibration training on the serum concentrations of brain-derived neurotrophic factor (BDNF) and cortisol in young women . Volunteers were assigned to three groups: performing exercises on a vibrating platform (n = 17), performing identical exercises without a platform (n = 12), and passive control group (n = 17). The concentration of BDNF and cortisol was assessed four times: before the first training session, 5 min after it, also before, and 5 min after the last training session. There were no statistically significant changes in the groups or among groups for both substances. WBV in the presented form did not increase the secretion of BDNF and is not a stressful stimulus. Whole-body vibration (WBV) is used in rehabilitation, sports medicine, and fitness and is an alternative to traditional strength effort. The essence is mechanical vibrations generated by a vibrating platform and transferred deep into the body through the myofascial and skeletal systems of the person subjected to vibration. The biomechanical characteristics of the resulting sinusoidal oscillations are frequency, amplitude, and acceleration. During WBV, static exercises (holding a given position) or dynamic exercises (combined with limb movement) can be performed ,2. The mechanism of the beneficial effects of WBV is combined with the tonic vibration reflex in which the activation of mono- and polysynaptic neural pathways in response to vibration of a given intensity is observed. In the next stage, there is efferent impulsion from the spinal cord and the contraction of muscle cells. The tonic vibration reflex active during WBV generates alternating muscle contractions and relaxation. Mechanical vibrations also generate remodeling in the skeletal system . The infOn the other hand, this indicates that improperly applied vibrations may distress the human body. The first known type of such action was vibration syndrome . It is nThere are considerable differences in the regional amounts of the brain-derived neurotrophic factor (BDNF) protein. Its expression also takes place outside the central nervous system. BDNF belongs to the neurotrophin family and increases neuronal survival and neuroplasticity. In a healthy population, the concentration of this factor may be highly varied . HoweverThe cortisol release is associated with psychological and physical stress . There aWBV, due to the activation of the tonic vibration reflex  and otheThe presented hypothesis assumed that the proposed training with an additional vibration stimulus would substantially impact the plasma level of BDNF than training without a vibration platform. A 12-week vibration training series can increase this neurotrophy’s concentration, which can be seen by comparing the levels before the first and the last training. The first training session can be a stress factor, which can be illustrated by an increase in the plasma cortisol concentration of the subjects. Regular training, however, should have an adaptive effect. It was expected that the last training session would no longer increase the cortisol levels. Regular exercise could also lower the steady-state levels of this hormone.Young, untrained women (n = 50) who met the following inclusion criteria were qualified to participate in the study. The following inclusion criteria were used: age from 18 to 25 years, body mass index (BMI) within the range of expected values (18.5–24.9), diet consistent with the recommendations of the Institute of Food and Nutrition , a levelParticipants were informed about the purpose, methods used, and the possibility of withdrawing from the study at any time. The subjects gave their written consent and agreed to stick to the current diet, not to take any medications or alcohol, and to follow the current level of physical activity throughout their participation in the program. Allocation of subjects into different groups was completed through volunteer sampling—the participants were recruited via posted advertisement at the university and assigned to the following groups: the experimental group (EG) performing exercises on a vibrating platform; the active control group (AC) performing identical exercises without a platform; and the passive control group (CG) not doing any exercises. During the project, eight women were excluded . Women wMeasurements of weight and body composition were performed twice with the TANITA BC 418 MA (93/42 EEC)  body composition analyzer following the guidelines of Ackland et al.  before aVenous blood was collected four times . Blood samples were collected in the morning (7.00–9.00) considering the daily fluctuations with the use of a BD Vacutainer system . Given the menstruation cycle, the study was designed so that all participants were in the same phase of the cycle  at the time of blood collection.In the obtained serum, the concentrations of human brain-derived neurotrophic factor (BDNF)  and cortisol  were determined using the enzyme immunoassay.The Fitvibe Excel Pro vibrating platform , generating vertical vibrations , was the source of the vibration stimulus. The examined women participated in individual, supervised sessions three times a week for three months. On the platform, in a standing position with slightly bent knees (15%), the participants received vibrations transmitted symmetrically to both sides of the body. To verify the transmission of vibrations to the head and neck of the participants and to monitor the training intensity , they were asked to maintain verbal contact and the body position was also corrected each time by the instructor.Each session included exercises in one series performed in antigravity positions on a vibrating platform . DynamicGreat emphasis was placed on the individual control of each participant while exercising on the vibrating platform. Attention was paid to the correct body posture. This was to limit the transmission of vibrations to the head and neck. During the entire investigation, no withdrawal due to headaches, excessive tension in the muscles, or other disturbing symptoms through training allowed for the presumption that maintaining the correct body position while performing exercise on the vibration platform ensured the safety of the participants. Training Schedule1–4 weeks: Duration of each exercise—1 min; break time between exercises—1 min; vibration parameters—frequency 40 Hz, amplitude 2 mm;5–8 weeks: Duration of each exercise—1 min; break time between exercises—1 min; vibration parameters—frequency 45 Hz, amplitude 2 mm;9–12 weeks: Duration of each exercise—1 min; break time between exercises—1 min; vibration parameters—frequency 50 Hz, amplitude 2 mm.The project was approved by the Ethics Committee (consent number: 224/KBL/OIL/2016) and registered in the clinical trial database . p < 0.05.Statistical analyses were conducted using STATISTICA 13.1 . The results are presented as the mean, standard deviation (SD), median (ME), minimum value (MIN), and maximum value (MAX). The normality was tested with the Shapiro–Wilk and the homogeneity of variance with the Levene tests. Outliers were rejected based on the Grubbs test. The results within one group were compared with one-way ANOVA for repeated measures. The results obtained for the three selected groups were compared with one-way ANOVA for independent measures with Tukey’s post hoc test for unequal counts. The FFM was added as a covariable in order to assess it as a factor differentiating the results between the study groups (ANCOVA model). The level of significance (α) was set at p = 0.029) and fat-free mass (p = 0.013) were found. Women from the EG group were characterized by a lower fat mass and fat-free mass compared to those within the CG. There were no significant differences between group membership regarding the variables: age, height, percentage of body fat, and BMI , the effect of one-time training on the cortisol levels was not indicated, regardless of whether the exercises were performed with or without the use of a vibrating platform. There was also no indication of a change after three months of exercise. A comparison of the plasma cortisol concentrations measured before and after the last training also did not show significant differences, with the last approximately the same as the first session .When analyzing the concentration of BDNF and cortisol in the plasma, similar values were observed . Baselinp = 0.481) or cortisol (p = 0.241). This analysis was repeated for the third measurement (before the last vibration training) with similar results .Significant differences in FFM between the groups indicated the need to analyze the FFM as a variable influencing the concentration of the studied criteria. ANCOVA, for the results obtained before the first training for all selected groups, demonstrated that FFM did not influence the level of BDNF  and fluctuations in the level of this neurotrophin associated with the monthly cycle in women, mainly imitating the profile of sex steroid concentrations . Strict −1. This study’s results were within the physiological reference ranges before and after the training session. On the other hand, the physical effort performed by the women on the vibrating platform as well as the action of the vibrations themselves were not a stress factor for the subjects, which is also important for the recovery of a good postural control pattern and the breathing quality of the exercising women. This was evidenced by the lack of changes in cortisol concentration after a single WBV and the entire program. The cortisol concentration modulates the circadian rhythm. Its highest concentration in the blood was observed, as in the case of BDNF in the morning hours. As a result, the physiological range of concentrations in the morning at rest for cortisol was 150–650 nmol LThe intensity or duration of exercise determines the physical stress and the response of the adrenal cortex. Post-exercise maintenance of cortisol secretion depends on the degree of training, environmental conditions, pre-exercise glycemia, and other variables. The assessment of the effect of WBV on the level of selected hormones including cortisol was investigated by Kvorning et al. . The corp = 0.052) for cortisol levels to rise across time in the vibrated group. Nevertheless, the authors concluded that the observed differences in cortisol levels for young, healthy individuals did not stimulate the neuroendocrine system in a significant way. However, some differences could be observed in the elderly. Hormonal fluctuations were noted in elderly individuals following a single session of WBV. Immediately after the WBV session, the cortisol levels were higher than those observed with static squats alone. Still, by 1 h and 2 h-post-treatment, the cortisol concentrations were reduced below the pre-treatment levels with both WBV plus static squat and static squat alone [In contrast to set forth observations, Kvorning  observedat alone .Despite the gradual increase in the intensity of the vibration stimulus , no significant outcomes were observed. The gradation of the intensity of exercises on the platform should also be applied to obtain beneficial effects. This study only included women with well-defined age and baseline physical activity characteristics that cannot be directly interpolated into the general population. The investigation was solely concerned with the proposed training cycle with specific vibration parameters used in the following weeks of conducting physical exercises on a vibration platform. The change in the device generating the vibration buffer and the modification of the frequency and amplitude may influence the analyzed markers. A significant limitation was related to the size of the cohort group.The meticulous selection of the participants is a strength of this study. Despite the large fluctuations in the plasma concentration of BDNF in the human population, the investigation managed to create favorable conditions for observation by selecting three groups with no differences in the initial plasma concentration of neurotrophins. Another strength is the meticulous control during exercise. Each participant was individually controlled for body posture on a vibrating platform. Additionally, the introduction of the control of transmitting the vibrational stimulus to the head and neck was critical. The 12 weeks of training protocol allowed us to observe the body’s adaptation of the selected cohort group.Future studies should focus on the heterogeneity of the samples including subjects for whom physical exercise activity is contraindicating or challenging to execute. It would be invigorating to look at how people with a reduced baseline BDNF level  react to training with the inclusion of a vibrating platform. The presented observation indicates that the proposed training module is well-tolerated and not overly stressed, which allows it to be offered to people with health loads. The proposed, supervised form of effort on a vibrating platform is well-accepted and not overly stressed. Nevertheless, it cannot be a factor in enhancing neuroplasticity.Neither a single training session nor a 12-week cycle of exercises on a vibrating platform causes changes in the concentrations of BDNF and cortisol in the serum of young, healthy women. The lack of changes in the cortisol level allows us to conclude that the stimulus applied was not stressful for the subjects. The necessary condition, however, seems to be the continuous control of the body posture on the vibrating platform. Comprehensive, cross-sectional, longitudinal research is needed to elucidate and better understand the underlying mechanisms and pathways of interaction engaging WBV and its physiological outcome.


In this review, we summarize the recent studies on the metabolic pathway design and implementation of photorespiration alternative pathway aiming to provide clues to efficiently enhance carbon fixation via the modification of photorespiration in plant chassis for bioproduction. These will benefit the development of plant synthetic metabolism for biorefineries via improvement of artificial carbon sequestration cycle, particularly for the mitigation of serious challenges such as extreme climate change, food and energy shortages in the future.Plant chassis has emerged as the platform with great potential for bioproduction of high value-added products such as recombinant protein, vaccine and natural product. However, as the primary metabolic pathway, photorespiration results in the loss of photosynthetically fixed carbon compounds and limits the exploration of plant chassis. People are endeavored to reduce the photorespiration energy or carbon loss based on variation screening or genetic engineering. Insomuch as protein engineering of Rubisco has not resulted in the significant improvement of Rubisco specificity which is linked to the direct CO Tartronic semialdehyde is then converted to glycerate by tartronic semialdehyde reductase (TSR) in the chloroplast, and all the catalytic steps are established in the chloroplast   .2 is shifted and released into the chloroplast, where it could be re-cycled into Calvin-Bension metabolism. Secondly, the NH3 production is greatly avoided. Thirdly, the metabolite transport between organelles is bypassed, whereas between 14 and 18 transport processes are required in natural photorespiration , and the resulting malate is decarboxylated into pyruvate by the malic enzyme (ME) with the first CO2 release. Pyruvate is oxidized into acetyl-CoA by pyruvate dehydrogenase (PDH) with the second CO2 release. The acetyl-CoA re-enters into the bypass by combining with glyoxylate . The by-product H2O2 is also scavenged by CAT     to target enzymes into the chloroplast. CTP recognition is governed by sequence-independent interactions and vectorial-specific recognition domains by a series of eriments . Shen et+ reductase could be introduced into the chloroplast to generate NADH for increasing reducing equivalents. More photorespiratory bypasses that depend on NADH can also be achieved in the chloroplast in the future.The availability of both reducing equivalent and ATP is crucial to bioproduction. In plants, ATP and NADPH are generated in the process of photosynthesis in the chloroplast , and NAD2 and NH3 release.The pathway targeted to the mitochondrion has not been evaluated for the establishment of photorespiratory bypasses. Glycine and serine from photorespiration could serve as the substrates for bypass design in the mitochondrion. Glycine is important for synthesis of collagen, elastin and other protein in mammals . PerhapsIn conclusion, novel and technological solutions must be obtained to further increase the productivity of crops because traditional genetic engineering may reach a plateau . Synthet


Numerous contemporary studies have examined safe sexual behavior among college students. In China, families are reluctant to discuss sexual behavior, thus understanding and exploring the sexual knowledge of college students and the promotion of safe sexual behavior is essential. On the basis of the theory of planned behavior (TPB), a cognitive behavioral theory that is widely used to predict human social behavior, we conducted an in-depth investigation of the factors influencing the sexual behavioral intentions of Chinese college students. We referenced the relevant literature to develop a TPB-based model for analyzing differences in sexual knowledge. Our statistical analysis revealed the following: (1) subjective norms and behavior control are key variables that influence the safe sexual behavior of college students; (2) attitudes and safe-sex behavioral intentions are influential in groups with extensive sexual knowledge; (3) behavioral control and subjective norms influence the differences in the comparative sexual knowledge of students. On the basis of the present results, we propose research recommendations and directions for the development of sex education in China. Sexual health education is a key element in high-quality college education, but it is often neglected. In particular, sexual health education in mainland China has long been conservative, and most parents and teachers explain sex-related topics in a vague manner . Yang etAt the individual level, behavior change theories, such as the theory of planned behavior TPB;  and theo1.Do college students’ attitudes, subjective norms, and behavioral control in relation to safe sexual behaviors affect their safe-sex behavioral intentions?2.Do differences in the level of understanding of sexual knowledge among college students have different effects on their behavioral intentions?Researchers examining personal behavior have previously suggested that human behavior is rational and volitional . HoweverThe term sexual knowledge refers to the individual knowledge and awareness of sex and sexual behavior  was adopted as the main analytical tool for empirical analysis in the present study. The main reasons for adopting SmartPLS are as follows: (1) PLS-SEM is a predictive causality method with strong predictive value when solitary statistical models are used to predict the explanatory power of causal relationships. (2) Compared with traditional covariance-based structural equation modeling, PLS-SEM requires fewer samples to estimate more complex models. (3) In the field of management and psychology, PLS-SEM has been widely applied in social science studies spanning various disciplines . In addiThe reliability and validity of the measurement models used in the present study were analyzed to ensure that the constructs were sufficiently differentiated . The assDiscriminant validity primarily tests the degree to which the items used to measure different constructs are discriminated. According to p < 0.001) and behavior control  of college students would have significant effects on their sexual behavioral intentions were supported; the path coefficient of behavioral control (H3) was revealed to be as high as 0.476. For the effect of attitude  on sexual behavioral intention, a non-significant relationship was detected. Finally, among the control variables, sexual experience (ß = 0.079) and gender (ß = 0.095) were revealed to have non-significant influences on the intention to have safe sex.In the present study, we used the bootstrapping method, which is a resampling method used by t test was conducted to determine if the two sets of path coefficients were significantly different. Through the aforementioned steps, the difference between the path coefficients of safe-sex behavioral intentions and the level of sexual knowledge was obtained. The results pertaining to extensive sexual knowledge (***) was greater than that of extensive sexual knowledge. The students with poor sexual knowledge tended to consider their behavioral ability; by contrast, for the students with extensive sexual knowledge, their understanding of their own ability was influenced by their attitudes and subjective norms. The effect of attitude on behavioral intentions was significant for the students with extensive sexual knowledge (0.18***) but non-significant for the students with poor sexual knowledge (0.022); and the difference in the path coefficients between the two groups was significant. The aforementioned results indicated that attitudes toward safe-sex behavior affected safe-sex behavioral intentions among the students with extensive sexual knowledge but not among the students with poor sexual knowledge.For H4a to H4c, PLS-SEM multiple group analyses were performed to compare the differences between two samples in terms of the path coefficients of the study model. The significance of the differences in path coefficients was assessed per the method suggested by nowledge  indicateThe present study focused on the safe-sex behavioral intentions of Chinese college students. The results indicated that the TPB has favorable predictive power, which is consistent with the findings of numerous studies . AttitudIn general, the difference in sexual knowledge among college students can still be attributed to the implicit nature of sex education in China. Therefore, schools should provide elective sex education courses to help students acquire systematic, comprehensive, and scientific knowledge about sex in a formal classroom setting, where sexual morality and appropriate sexual concepts can be established, and students can be taught methods for identifying and preventing sexually transmitted diseases. This strategy is similar to those proposed in studies conducted outside of China . For curAlthough studies have verified that sexual knowledge is a key concept for understanding sexual behaviors , no studThe present study used an online questionnaire to investigate the safe-sex behavioral intentions of Chinese college students, and this is a method with several limitations. First, the sample was obtained mainly through convenience sampling, and the sampling area was primarily the universities in Zhejiang, China. The lack of samples from schools in other provinces of China could have introduced biases relating to the understanding of sex education. Therefore, our results may only be generalized to developed Chinese provinces. Second, the present study was based only on the TPB. To address this shortcoming, the analysis of the differences in sexual knowledge and their effects on our results can be expanded to allow for greater diversity in the selection of future variables . In particular, gender-, ethnicity-, and province-related differences can be explored to generate more substantive suggestions in relation to our results. Finally, the present study collected quantitative data through a survey and did not employ qualitative methods or other research methods to enhance the strength of the findings. Therefore, mixed-method studies should be conducted to identify the key influences that affect the safe sexual behaviors of college students.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.XW, YJ, and C-LL designed the research and provided guidance throughout the entire research process. MT, QZ, and PH collected the references, did the literature analysis, and wrote the manuscript. XW, MT, TW, and QZ helped with translation and offered modification suggestions. C-LL, YJ, and PH participated in the literature collection, analysis, and organization. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Medical educators are in a continuous quest to close the gap between the needs of medical practice and the rising expectations of the communities in their countries. During the past two decades, competency-based medical education has been evolving as an appealing strategy to close this gap. In 2017, the Egyptian medical education authorities mandated all medical schools to change their curricula to comply with revised national academic reference standards, which changed from outcome-based to competency-based. In parallel, they also changed the timeline of all medical programs for six years of studentship and one-year internship to five years and two years, respectively. This substantial reform involved the assessment of the existing situation, an awareness campaign for the proposed changes and an extensive national faculty development program. Monitoring the implementation of this substantial reform was performed through surveys, field visits and meetings with students, teaching staff and program directors. In addition to the expected challenges, the COVID-19-associated restrictions presented a significant further challenge during the implementation of this reform. This article presents the rationale for and steps of this reform, the challenges faced and how they were addressed.The online version contains supplementary material available at 10.1186/s12909-023-04098-3. Medical education is continually reformed in response to scientific advances and societal needs . During With the start of the millennium came a revolution in undergraduate medical education (UME) –4, whichEgypt has a long and proud tradition as a leading center of medical excellence. Many experts recognize Imhotep, of the twenty-seventh century B.C., as the world’s first physician. Moreover, Egypt is one of the first centers of medical education in the world, having established a medical school in the ninth century A.D. That tradition is being challenged by a burgeoning population and inadequate facilities to meet the demands of twenty-first century medical education. Most medical schools, public and private, exhibit problems of overcrowding and underfunding , 13.While evidence of medical practice in Egypt dates back to 2,500 B.C., medical education was introduced into the university system in 1919. From that date until the turn of the millennium, few national reform initiatives occurred. Thus, until 2008, most medical schools in Egypt still adopted discipline-based curricula taught via didactic large-group lectures and apprenticeship approaches . Only onIn June 2016, the planning and development committee of the medical sector, which overlooks all medical schools in Egypt, under the governance of the Ministry of Higher Education, established a subcommittee named Reform of Undergraduate Medical Program (RUMP) to develop a strategic plan for UME and to implement the developed changes. This committee performed a situation analysis of all undergraduate medical programs and determined that most of them still follow the traditional type of medical education, which is viewed as inadequate for preparing future physicians for medical practice , 16. OnlIn 2017, the Supreme Council of Egyptian Universities (SCU) approved and enforced radical reform, mainly converting the 6-year academic and 1-year internship program into 5 years and 2 years, respectively. Additionally, the discipline-based curriculum was changed to a modular integrated model. In parallel, the National Authority for Quality Assurance and Accreditation in Education (NAQAAE) issued its second version of the National Academic Reference Standards (NARS-Medicine 2017), which was competency-based (CB) to ensure a well-integrated curriculum with early clinical exposure . These sConsequently, the RUMP committee developed a road map and prepared a general framework aligned to the 2017 NARS-Medicine to guide schools in preparation of their new integrated curricula. The developed road map was relevant to the context in Egypt and agreed upon strategies to ensure that medical education is innovative and able to prepare the undergraduates to practice in the changing scenario of medical science.The current article presents the process followed to overhaul the UME curriculum in Egypt’ how monitoring was performed and the challenges encountered during the implementation process.To prepare and implement the intended changes in the medical schools’ curricula, the following are the actions taken and the outcomes achieved  Fig. :Fig. 1ThTo assess the necessity and scope of the change, the RUMP conducted a comprehensive context evaluation, from October to December 2016, to explore the challenges of the traditional program from the stakeholders’ viewpoint. A survey was conducted to explore students’ opinions about the current situation of medical education and how to improve it  in several sessions conducted during the regular medical sector committee and attended by representatives of the RUMP committee members to get their feedback regarding the needed improvements in the existing medical curriculum. Incorporating newer teaching elements in the under-graduation course namely introduction of a foundation course after admission to prepare a student to study medicine effectively; facilitation of horizontal and vertical integration between different disciplines; advocating early clinical exposure right from the first year (viz. case scenarios for classroom discussion/case-based learning); addition of elective courses and methods to enhance self-directed learning, critical thinking and research abilities were the main responses of the academic leaders.To respond to the assessment of the existing situation results, several meetings were conducted by RUMP committee in year 2016 where different international medical curricula were reviewed in order to agree on the proposed curriculum changes. The committee reached a consensus to formulate and adopt a competency based medical education national framework. The committee also recommended effective monitoring of the compliance of medical schools in implementing horizontal and vertical integration, self-directed learning, early clinical exposure, more emphasis on clinical practice and implementation of credit hour/point system..To orient the medical schools about the intended changes in the medical program, an awareness campaign started in December 2016 and ended in January 2017. All governmental and private medical schools all over Egypt at that time were visited. There were 28 medical schools at that time, the public schools constituting the majority. Each visit was conducted by 2–3 RUMP members. The awareness package included information about the rationale for the change to competency-based medical education (CBME), the relevance to NARS-Medicine 2017, the definition, importance, types and levels of integration. Different models of curricula from UK, Europe and Arab countries were discussed as wellThe awareness package was delivered to the stakeholders in each school including students, recent graduates, academic leaders and faculty members. At least 40% of students and 30% of faculty members attended each awareness campaigns.th of October/2017 including modification of the bylaws clause 154, so that the period of undergraduate studies for the medical school in Egypt will be 5 years competency- based integrated program using a credit system followed by two years of internship and a licensing exam before practice. The SCU approved that the new program should be implemented by all medical schools starting from the academic year 2018/2019.A series of meetings and focus group discussions were made between the coordinator of the RUMP, the head of the Medical Sector Committee (MSC) and the secretary general of the Supreme Council of Universities (SCU) and the relevant ministers in the period from June to September 2017 to propose the framework of reform and the required changes in the laws and bylaws. The required changes were approved by the Ministries’ Council on the 4After getting the higher authorities’ approvals, two documents stating a framework for preparing the bylaws and curriculum maps were prepared by the RUMP committee and issued to all schools from the SCU to follow in preparing their curricular maps and new bylaws. Continuous communication occurred between RUMP members and the curriculum committees in each medical school during its work on bylaws and map to be ready for the approval of the new reform. In April to June 2018, RUMP members revised each medical school curriculum map and bylaws according to a pre-set standard checklist to ensure that each program fulfils the updated NARS-Medicine, 2017 requirements and the items stated in a document issued from SCU concerning the framework of the bylaws. RUMP members feedback was given to the schools and modifications were done accordingly. Before the start of the academic year 2018/2019 the bylaws of all medical schools were approved by the SCU which is the higher authority.The framework of the new bylaws covered all the points that should be included in the bylaw such as: information about the College and University, graduate attributes, vision, mission, strategic objectives, educational strategies, departments and committees, admission process, the duration of study, the program structure, credit hours or credit points data and many other important instructions regarding complaints, withdrawal, transfer, etc. The bylaw also defined certain important items that should be emphasized in the curricula which are: 1) description of the integrated modules including types of integration and its level in addition to the used teaching methods that can achieve this integration. 2) the presence of a preparatory period for the students to be oriented with the new program and the presence of a study guide. 3) Self-directed learning and ways of maximizing it. 4) Early clinical exposure. 5) Communication and professionalism and other skills that are mandated in the NARS competencies. 6) Electives 7) Research methodology and research skills to be included in the early years of the program. 8) Including formative and summative assessment in addition to using new objective methods of student assessment that measures higher cognitive domain and practical and professional skills. 9) implementing quality assurance measures on the program.The main curricula changes done by the medical schools in their new curricula were introducing different levels of integration according to the schools’ infrastructures and personal competencies, but all were exceeding level 5 according to Harden ladder . Modern In preparation for implementation, a faculty development program under the title of teaching excellence in medical education was conducted by the Egyptian Knowledge Bank (EKB) in collaboration with the Medical Military Academy  supplemented with semester-vertical courses. Each medical school selected the coordinators for all modules of the new curriculum after an application process. These coordinators had the responsibility of choosing an interdisciplinary planning team of at least six members, including faculty members from non-clinical as well as clinical subjects. These planning teams were charged with selecting the content which would be covered in their module as well as the appropriate learning methods.The RUMP members designed a comprehensive, multicomponent and program-wide monitoring system. The framework expects the quality of the program as comprising four main aspects: curriculum and resources; staff and teaching; student experience; and management support. Key principles of the adopted audit included feedback form students and staff, auditing of teaching, learning and assessments methods. An emphasis on corrective actions following evaluation was included (closing the loop), which was undertaken by teachers, course coordinators, and administrators. An external auditing checklist was prepared by the RUMP committee to be filled by the assigned auditors. The checklist included readiness of the bylaws, program specifications, course specifications, the curriculum matrix, the action plan, teaching and learning methods , self-directed learning (SDL), simulations, field visits and practical/clinical training), as well as details of the assessment process .Four meetings were held with the auditors who include the RUMP committee members in addition to a selected group of faculty members from different universities who have qualifications and experience in medical education. One meeting was held at the beginning of the academic year 2018/2019 to standardize the auditing process and discuss the items of the external auditing checklist with the auditors. The other meetings were held before each cycle of monitoring visits. Three in-person monitoring visits were conducted: two in the first year of implementation (once per semester) and the third one in the first term of the academic year 2020/2021. The third visit occurred during the evolving COVID-19 pandemic. Because of the COVID-19 lockdown, auditing in the academic year 2020/2021 was paused. In each auditing visit, two assigned RUMP members visited each school. The auditing visits in 2021 were mostly done online.The filled checklists were included in a final report. Generally, the auditors found that the program specifications were prepared in almost all the faculties by the 2020 audit. Conversely, the alignment matrix between the 2017 NARS-Medicine and program intended learning outcomes (ILOs) were completed in about three-quarters of the faculties visited. The reform action plans that were formulated by the faculties to address the comments of the audit reviewers were implemented in 71% of faculties in the first audit and 80% in the second round of the audit.To assess the students’ and faculty’s points of view after implementation, questionnaires, designed by RUMP committee, was distributed to all medical schools through a survey monkey application. The first student survey  and continued to utilize its options during the pandemic.Conversely, variable approaches occurred for the assessment process during the pandemic. Most faculties used online formative assessments to motivate the students learning. Regarding final written tests, some faculties used online assessments, while other faculties requested that students submit review articles that were uploaded online. Considering the practical skills, they were assessed by objective structured practical exam (OSPE) on campus with the application of all COVID-19 prophylactic procedures.Skepticism and resistance were found from some faculty members who felt that such young students would be unable to adopt self-directed learning. Faculty members of certain disciplines expressed concerns that the new scoring system in the integrated curriculum, which assigned scores to learning outcomes rather than disciplines, would encourage some students to ignore subjects that contributed a small share in some modules. Because the integrated modules followed a common sequence, some faculty members argued that this order was the most suitable one for their disciplines. Some believed that moving the students into the modular system would not provide a good base of the required knowledge in all disciplines. Others saw that the integrated system required that instructors must change the way of teaching, which they had been adopting for a long time. Collaboration with colleagues from other departments would also be needed to plan classes. In other words, they would be required to make an extra effort that they used to do in the original curriculum.Many students complained of marked overload in the content of some modules, apparently because some departments did not do enough effort to tailor the content to the shortened time of their disciplines.Early clinical exposure could not be satisfactorily achieved in some medical schools because of the large number of students and logistic difficulties in securing the required training places.Some RUMP reviewers reported that in some medical schools, the level of horizontal integration was limited to synchronization without actual exchange of material and educational plans to ensure proper sequencing of educational activities.In some schools, examinations, especially in the first year, largely addressed recall with a small percentage of questions devoted to testing higher cognitive levels.Lastly, the COVID-19 pandemic presented an extra burden during the implementation.During the implementation of the new medical curriculum, RUMP explored the challenges by questioning the auditors who met representatives of faculty, students and leaders of medical schools during the monitoring visits. Their answers can be summarized in the following points:In 2016—2018, the governing councils of medical education in Egypt managed to introduce major nation-wide reform in medical curricula. This was done mainly in response to the feedback of medical students and academic leaders about the effectiveness of existing medical curriculum at that time. The expectations of the Egyptian community regarding the quality of medical graduates and the current trends in medical education were also other important driving factors.Introducing such a significant change in the structure and timeline of the medical curriculum, at a national level, is quite challenging and could stumble at any stage. Actually, there was a previous trial of major reform in medical education and practice in Egypt several years ago that did not make it because of much resistance from different parties. Resistance to substantial change in higher education institutions is a frequent encounter despite that these institutes should initiate change in their societies . BecauseThe most important strategy throughout the whole process was to involve the essential stakeholders in every step, especially, the students, teaching staff and decision-makers to convince them about the change. This coincides with the Lewin’s three-step model of change management , 23. ThiExploring the students’ opinions regarding their medical curriculum, before implementing the new program, showed a high level of dissatisfaction (66.6%) due to the high load of theoretical knowledge and the reduced time devoted for skills acquisition. An alarming point, in the narrative part of the questionnaire, was the extent of the problem of private tutoring. Many students reported their need of private tutoring to compensate for some issues in their program, such as a relatively deficient clinical training. In addition to collecting students’ feedback, focus group discussions with the academic leaders in all medical schools also corroborated the main findings of the students’ survey.In response to these important comments and to narrow the gap between medical education and real practice and ensure a better transition from theory to practice, the governing councils adopted the CBME framework which emphasizes the professional skills in the curriculum. To serve the same purpose, 1 year was moved from studentship phase to the internship phase, so that the timeframe became 5 years plus 2 years instead of 6 years plus 1 year. This curriculum duration is consistent with UK General Medical Council (GMC) requirements.Another motive for adopting CBME is the global trend in this direction. During the last two decades, the national medical authorities in several countries, such as Canada , USA 2525, NetheEgypt was no exception in this regard, as it decided in 2016 to start the update of the first version of NARS, issued by NAQAAE in 2009, to be a competency based one. This first version of NARS 2009 was an outcome-based in which the outcomes were categorized as knowledge, cognitive skills, professional skills and general skills. During application, it was obvious that this categorization is rather artificial. Therefore, the second version of NARS-Medicine which was issued in 2017, adopted the competency-based framework  with conBefore implementation of the new curriculum, each medical school was required to go through a mandatory regulatory process to update its bylaws. A general framework of the new bylaws was prepared by the RUMP as a guide and each medical school could modify it according to its own context because medical schools in Egypt vary in terms of the resources, intake capacity and number of the teaching faculty.The awareness campaign at the start of the implementation process was important to create readiness for the reform in the academic environment.Before this curriculum reform, only a few medical schools in Egypt were implementing active student-centered strategies and the premise of the CBME learner-centered approach was unfamiliar. Most programs were mainly relying on the lectures and practical sessions as the principal instructional methods. Since CBME is a learner centered approach, the RUMP emphasized proper implementation of active teaching methods in all medical schools. Thus, all programs introduced small group tutorials, flipped classrooms, students field projects, e-courses or team-based learning from year 1. Previous data of implementing CBME in undergraduate curriculum stated the key role of active learning in this kind of curriculum .In parallel to the change in instructional methods, all medical schools started implementing reform in tools of students’ assessment. Specifically, all schools were required to use portfolios for monitoring students’ progress and to provide training in self-evaluation and reflection. Some programs replaced the printed portfolios first used with an electronic one to facilitate the whole process. This action was essential in the successful implementation of the CBME model, given the key role of continuous assessment and frequent feedback in the development of the planned competencies. Additionally, it improved student-staff communication and facilitated the monitoring of competencies acquisition via different activities and assessment methods. This finding was supported by other studies .One of the important strategies during implementation is organizing an extensive training and support to the faculty. It has been reported that faculty development is one of the strategies to face the resistance to change during the transition to CBME . Thus, fAdditionally, the feedback obtained from the faculty and the students after implementation revealed some strengths such as the relatively large number of faculty who received training and the satisfactory level of students’ awareness of the learning outcomes. Points that need more emphasis in some schools’ curricula were the research, clinical skills and elective modules. Programs are alerted of areas of concern and corrective plans were to be followed-up during future monitoring visits, which will continue on annual or semiannual basis according to the needs of each school.Evidence exists from the literature that extending the duration of well-designed internship training improves acquisition of clinical skills , 35. TheA radical reform was made in the medical education program in Egypt in collaboration with different stakeholders. The reform was done in response to the new advances in medical education and the competency-based framework. Regular monitoring and evaluation of the new implemented program occurs. However, thorough evaluation should be performed after the graduation of the first batch in 2023 to receive an overall idea about the challenges of the implementation and to develop an action plan for improvements.Additional file 1. Needs assessment questionnaire 2016-2017.Additional file 2. Teaching excellence in Egyptian medical education.Additional file 3. Teaching excellence in medical education assessment design.Additional file 4. Student survey 2018-2019.Additional file 5. Student survey 2019-2020.Additional file 6. Faculty perceptions and practices in the new (Integrated) program questionnaire.