One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN's weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism. Currently, Deep Learning (DL) is the base for many cutting-edge artificial intelligence (AI) applications –3. DL caDL principle can be employed for the design of a variety of neural networks, among which Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolution Neural Network (CNN) are the most popular. There are also generative and hybrid models of DL. For the former, some examples are Deep Belief Network (DBN) and Boltzmann Machine (DBM), and for the latter referring to a combination of discriminative and generative models, a well-known example is pretrained deep CNN using DBN. Between the various models of DL, the focus of this study is on CNN , 21.DL has a great ability for resolving learning problems. However, this method is challenging to be trained for producing the optimal results. A CNN model learns many patterns through many weights and biases inside the convolutional layers. These weights and biases obtain their best possible values through a learning process with a large number of data. Actually, in a CNN model, the number of the training samples plays a crucial role in the obtaining best possible solution –24. To aRecently, metaheuristic methods have been employed to resolve complex problems such as scheduling  and deteSwarm based methods act based on animal social behavior like PSO , 41, WOAEvolutionary methods act based on a natural evolutionary process like Genetic algorithm (GA) Biological based optimizers like Satin Bowerbird Optimizer (SBO) Human-based algorithms inspired from human behavior such as Life Choice Based Optimizer System-based algorithms inspired by natural ecosystems such as Artificial Ecosystem-based Optimizer (AEO) Physics-based methods that mimic the physical phenomenon in nature like Equilibrium optimizer  and SimuMetaheuristic algorithms can be categorized into the following subgroups –39:SwarmIn this paper, to overcome the problem of overfitting and convergence, we optimize the values of weight and biases of a LeNet-5 by an optimization algorithm. From the various types of metaheuristic algorithms, we used the Shuffled frog-leaping algorithm (SFLA) to optimize the performance of the LeNet-5 CNN . This isThis paper is organized as follows: Neural networks or artificial neural networks are computing systems that have been inspired by the human brain. In other words, these networks mimic the biological functions that are transmitted the signal to other neurons such as synapses in a biological brain , 50–52. The convolutional layer is a critical component of CNN that uses the information of adjacent pixels as a linear operation to extract features. Each location of the tensor is calculated through an element-wise product between input tensor, which is an array of numbers and kernel or filter, while its output is summed up in order to obtain a single value in the corresponding location of output tensor, which is known as a feature map. Several various kernels should be applied to represent a different characteristic of the input tensor in order to achieve variant feature extractors such as a horizontal edge detector or a vertical edge detector , 56. TheA pooling layer provides alternative and more robust downsampling, as well as avoiding overfitting and a lot of computation by representing abstracted feature maps. This layer is operated on each feature map independently to make a new set of the same number of pooled feature maps and reduce the number of subsequent learnable parameters. Specifically, filter size, stride, and padding are also applied as hyperparameters in pooling operations. There are two common functions in pooling operation, average pooling, which is computed average value for each patch of the feature map, and max pooling, which is calculated the maximum value. In all cases, polling supports that the value of pooled features is remained almost invariant by translating the small amount of input , 59, 60.The linear output of convolution operation is passed through nonlinear activation function, which is considered as a fundamental component in order to learn the complex patterns and the ability to add nonlinearity into the network. There are various types of activation functions such as sigmoid or hyperbolic tangent function, which are taken into account as smooth nonlinear functions and rectified linear unit (ReLU) function, which is recently the most widely used activation function. This is due to the fact that sigmoid and tanh activation functions are commonly saturated and really sensitive to modify around their mid-point of their input , 61.Typically, the output of the final convolution and pooling layer is transformed to a one-dimension array of numbers, which is known as flattening. The output of flattening is considered as the input of one or more fully connected layers, in which every neuron in one layer is connected to every neuron in the other layer. In other words, the nonlinear combination of high-level features, which is the output of the convolution layer, is learned by a fully connected layer in order to map the final output of the network such as the probability of each class in the classification task , 62. It The cost function is another essential component in Neural Networks. Actually, cost and loss functions are synonymous; the only difference is that the single training batch uses the loss function, while the cost function is referred to apply the loss function over the entire training set. The loss function is evaluated by the compatibility between the predicted value and the ground truth label, in which the higher output of loss means the incapable performance of the model. Another hyperparameter that is required for assigning is selecting an appropriate loss function with respect to the performed task. Since the problem is an optimization problem, Gradient descent is usually applied as an optimization algorithm to minimize the loss function , 65, 66.The type of CNN employed in this study is LeNet-5, which is one of the earliest CNN models  Figure . It is akth feature map Mijk with the weights Wk  and bk  is achieved by applying the tanh function as follows:Essentially, convolution layers are connected to several feature maps, kernels, and correlated to the prior layers. Each extracted feature matrix (feature map) is generated as a consequence of a sum of convolution from extracted feature matrices of the earlier layer, their corresponding mask (kernel), and a linear filter. Moreover, a bias value is summed to the extracted feature matrix and subsequently; it is applied to a nonlinear function. The tanh function is employed for this purpose. The Through a subsampling layer, the size of each extracted feature matrix is reduced in relation to one of the extracted feature matrices of the former layer. This pooling strategy decreases the resolution of the extracted feature matrix. The pooling layers summarize the features present in an area of the feature map created by convolution layers. Also, a pooling layer diminishes the number of parameters for learning and the amount of computation performed in the network.The classification task is carried out through the classification layer. This layer is placed after all the convolution and subsampling layers. In the classification layer, the output of each neuron is given to a single class label, and in the case of Oxford17, Oxford 102, Caltech/UCSD birds, and Caltech 101 airplanes dataset, this layer is composed often neurons corresponding to their labels.Shuffled Frog-Leaping Algorithm (SFLA) is a memetic metaheuristic approach that is employed to find a global solution through an informed heuristic search by employing a heuristic function . This al(1)p” of the potential number of solutions, controlled by a set of virtual frogs (n).The algorithm contains a population “(2)m). The memeplexes can be considered as a set of parallel frog cultures trying to achieve some goals.The population is split into subsets denoted as memeplexes ((3)i is shown by Xi =  in which S indicates the number of variables.Frog (4)Xb and Xw.Within each memeplex, each frog culture searches the space in different directions and exchanges ideas independently. The frogs with best and worst fitness are denoted as (5)Xg.Frog with global best fitness is identified as (6)The frog with the worst fitness is modified based on the following equation:SFL algorithm is a population-based approach composed of frogs of the same attributes. Each frog can be considered as a solution. The total population of frogs is divided into numerous subgroups known as memeplexes. Diverse subgroups can be appreciated as dissimilar frog memes. Each memeplex is responsible for a limited exploration. At each memeplex, other frogs might affect the behavior of each frog, and the evolution will take place through the process of memetic evolution , 71. AftDi is the size of the leaping step of the frogi and Dmax is the maximum value permitted to adjust frog position. If the value of fitness Xw is better than the current value of Xw, it will be accepted. If the fitness value is not modified, then the calculation is repeated by replacing Xb with Xg. If there is no potential for enhancement, a novel Xw will be created arbitrarily. This shuffling process and the local search continue until defined convergence criteria provide satisfactory results ,  results , 70, 71.Problem representation is the foremost step in training a CNN employing metaheuristics algorithms. To train a CNN, the problem should be formulated in a suitable way for metaheuristics. The most important variables to training this type of network are weights and biases. In the trainer, the best biases and weights values are found to provide the highest classification, approximation, and prediction accuracy for the network. Thus, biases and weights are trainable variables. It means by changing the values of biases and weights of all neurons the output results of the network can be varied. So, controlling the process of applying new weights and biases by an optimizer approach leads to reaching higher accuracy. As the SFL procedure takes the variables in the format of a vector, the variables of a CNN denoted for this technique are as follows:n is the size of the input, Wij  denotes the connection weight between layers ith and jth, and θj is the bias (threshold) of the jth hidden node.In which m indicates the output numbers, the preferred output of the ith input unit when the kth training data samples are utilized is denoted by dik, and the actual output of the ith input unit when the kth training data appear in the input is oik.Once the variables are defined, defining the objective function for the SFL technique is the next goal. Mean Square Error (MSE) is a common metric for the evaluation of networks. Once a set of training samples are given to the CNN, this equation measures the difference between the obtained output values and the desirable values through the following equation:To design an effective CNN, the network should be adapted to the whole set of training samples. As a result, CNN performance is assessed according to the average of MSE with respect to the training samples as the following equation:As, at each iteration, the weights and biases move towards having the best CNN, the probability of an improved CNN increases gradually. However, due to stochastic nature of SFL algorithm, there is no guarantee that the optimal CNN is obtained. On the other hand, with sufficient number of iteration, the SFL algorithm finally reaches to a solution that works more efficient than random preliminary solutions. The following section assesses the advantages of the SFL algorithm in training a CNN practically.In this part, the suggested SFL-based CNN is investigated by employing four standard classification datasets from –77, OxFoThe classification datasets were intentionally selected with the diverse test/training data and difficulty levels to efficiently evaluate the performance of our SFL-based CNN. We employed a Hewlett-Packard (hp) computer with the processor of Intel (R) Core (TM) i7-6500U CPU @ 2.50 GHz 2.60 GHz, installed memory (RAM) of 8.00 GB, System type of 64-bit Operating System, and Windows 10 Home. For data processing, we used MATLAB and Statistics Toolbox Release 2019a. The SFL assumptions and other techniques are shown in To generate the results, the datasets are solved 50 times by applying each technique. As illustrated in 4This study presented Shuffled Frog-Leaping Algorithm (SFLA) as one model of metaheuristic algorithms to optimize one type of Convolutional Neural Network. At first, the training problem of a CNN was formulated for the SFL technique. This method was then applied to define the optimum values for biases and weights. Theoretically, the SFLA is similar to the particle swarm optimization (PSO). However, the values of weights and biases can be exchanged among local searches through a shuffling technique, thereby obtaining global optimum. The proposed SFLA was employed to train four standard classification datasets . To verify the performance of SFLA, the results were compared to three other stochastic optimization trainers: WO, BFSO, and ACO. The resulting outcomes demonstrated that the suggested technique can effectively train the CNN. It improves the probability of finding optimal values for biases and weights for CNNs. Our optimization strategy can obtain noticeable accuracy for classifying objects in four standard classification datasets.For future study, finding the proper parameters for SFL algorithm needs to be investigated. Moreover, by exploring the optimal values for number of batches in the training, type of pooling, number of neurons in the hidden layer, and number of kernels in the convolutional layers, we can obtain more noticeable results. Further optimal tuning of this method is worth further research using different datasets such as CKP and facial expression datasets, as well as ImageNet and ORI.


Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of PSNR = 33.2642 and SSIM = 0.9206 on JSRT datasets and PSNR = 35.1086 and SSIM = 0.9328 on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding. The appearance of noise is random and inevitable, which is closely related to image quality assessment. Since the radiation-sensitive property of medical images, various noises occur during the acquisition process, especially when radiation dose reduces. As a fundamental step of image processing, image denoising needs to remove the noise and preserve image details. In general, image denoising methods can be divided into two categories: traditional methods and deep learning methods, including local and nonlocal methods .The common traditional methods deal with noise according to various filters. Discrete wavelet , 3 with With the development of deep learning, some methods have outperformed traditional image analysis and computer-aided diagnosis technologies. Deep learning methods work well for uncertain noise types robustly with their outstanding ability of high-level feature representation. Deep convolutional neural networks have made great achievements in the field of image denoising such as deep convolutional neural network (DCNN) . The netIn this paper, a novel medical image denoising method based on CGAN is proposed. Different from traditional CGAN, the proposed method contributes to preserving image context relationship and structural information. It is well known that super-resolution reconstruction can improve the quality of images and recover the details. To make full use of the information and relationship of each layer in CGAN, some residual dense blocks (RDBs) are embedded in the generator, which are used for superresolution reconstruction. The noise image and its corresponding gradient image are merged as conditional information to input into the proposed model, which enhanced the noise information in noise image. In addition, the reconstruction loss and WGAN loss are combined as the objective loss function.This work introduces a novel medical image denoising method based on CGAN, which contributes to preserving image context relationship and structural informationWe construct a super-resolution generator by embedding some residual dense blocks (RDBs), which makes full use of the information and relationship of each layer in CGANIn this paper, the noise image and its corresponding gradient image are integrated as conditional information to input into the network. By this way, the noise information is enhanced. Besides, this paper employed a structural hybrid loss comprising reconstruction and WGAN losses to train the model efficientlyIn order to verify the performance of the proposed method, ablation experiments and comparison experiments are conducted on JSRT and LIDC datasets. Besides, the residual images and two indicators are employed to evaluate the denoised images. Simulation results demonstrate that the proposed model achieves higher performance while preserving more structural and contrast informationHere are main contributions of this paper:To clearly describe and demonstrate the proposed method, this paper is organized as follows: x ∈ ℝN×N represent a noise image, and y ∈ ℝN×N is the corresponding normal image. Usually, the relationship between x and y can be formulated asNoise reduction for medical images in this paper can be modelled as follows. Let f to satisfyThe task of noise image denoising is to find a function f and map the noise image to a normal image. This optimization problem can be solved by different objective functions with different models.The purpose of the denoising process is to find an adaptive function G and the discriminator network D. The generator G tries to produce a synthetic sample according to the real data distributions, which usually come from low-dimensional random noise. The discriminator D with the output of a score plays a role of classification between a synthetic sample and real sample. The generator tries to deceive the discriminator with an optimization method, while the discriminator is trained to distinguish synthetic samples from the real samples. Therefore, a GAN is such a game process: if G generates a sample and gets a high score in D, which proves that G is trained well, and if D can distinguish easily between the synthetic and real samples, the effect of G is insufficient. This pair of networks trained alternately until the samples generated by G is almost indistinguishable from the real samples. Mathematically, the process of the game between G and D can be formulated as a two-player minimax game asPdata and Pz) denotes the probability which x subjects to the real data, and z is the random noise which is used as the input of G.Generative adversarial network (GAN)  as a powy is condition information.In order to guide the generation of GAN, the conditional GAN (CGAN) is introduced . As showBoth the basic GAN and the CGAN methods can recover noise image and improve the quality in vision, while they ignore the image structure preserving. Medical images illustrate the location, appearance, and relationship of tissues and lesions, which are obliged to accurate diagnosis and treatment. In general, the type of noise is always unknown for image denoising. Especially, a lot of quantum noise and some other kinds of noise are commonly generated in medical image acquisition. Therefore, medical image denoising is required to maintain the consistency of both vision and content between the recovered image and the real image. Inspired by this, we proposed a novel medical image denoising method based on CGAN.G has 4 convolution layers and 6 residual dense blocks (RDBs) which extract abundant context features to generate a synthetic (denoised) image close to the real image. Each convolution layer has a Leakey ReLU after instance normalization. Then, D with the fully connected layer maps the feature vector to a confidence value to distinguish synthetic image and real image in terms of structure consistency. Some modules in the proposed framework will be introduced in the following subsections.The overall architecture of the proposed network is designed as in Different from traditional algorithms, the proposed method employs a mechanism that incorporates gradient information. In this work, the noise image and its corresponding gradient image are merged as conditional information to input into the proposed model. In the noise images, one noise point is always different from the surrounding pixels. Hence, the gradient information will be larger than normal pixels.gx and gy are the horizontal gradient and vertical gradient, respectively. gx and gy can be formulated as Equations ,,(5)Mx,y=By calculating the gradient on each pixel, the gradient map is obtained as in Developing efficient and adaptive denoising models with prominent structure preserving plays an important role in medical imaging, which helps clinicians accurately interpret medical images. In addition, it facilitates improving the ability of feature recognition in medical images. Some studies have shown that the application of image restoration methods based on ResNet is helpful to the preservation of organs and fine structural details .The residual dense network (RDN) was firstly proposed by Zhang et al. for image superresolution . More prnth Conv layer of dth RDB is formulated asFd−1 and Fd are the input and the output of the dth RDB, respectively. Wd,n is the weight of the nth convolution layer.In this research, the superresolution reconstruction that can improve the quality of images and recover the detail is considered. To make full use of information in each convolutional layer, a super-resolution method is employed to the generator network. In this paper, the generator with 6 residual dense blocks (RDBs) is utilized to extract the context information among layers. The structure of RDB is designed as in The definition of loss function is critical for the performance of GAN-based image denoising method. To a great extent, the loss function of deep learning influences the noise image restoration process . Many reℒ1-norm and formulated asIraw is the original raw image and Inoise is the image with artificial noise.Some previous studies have found that it is beneficial to introduce a more traditional loss to the GAN objective , 29. As The above reconstruction loss focuses on structure preservation but ignores image details. To conquer this dilemma, a WGAN loss is added to provide detailed information.On the basis of standard GAN loss, WGAN loss  introducG and D also can be formulated as a two-player minimax game asThe process of the game between x = {Igrad_aug, Iraw}, x′ = {Igrad_aug, Idenoised}, Igrad_aug is the augmented image, Iraw is the original raw image, and Idenoised generated by the generator G denotes the denoised image.With Wasserstein distance and conditional information, the WGAN loss can learn a generative model which can fit the distribution of the real samples and prevent overfitting effectively. In Equation , this paLRecon and WGAN loss LW‐GAN, defined asλ1 is a hyperparameter.During the training process of the model, the total loss between the normal image and the denoised image is computed, which can be back-propagated for the proposed model to update the parameters. The final structural loss function of the proposed network consists of reconstruction loss In the experiments, the raw X-ray images from the public Japanese Society of Radiological Technology (JSRT) dataset  were adoAnother dataset used in this paper was the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI or LIDC) . The LIDμX1 and μX2 were the sample means of patch X1 and patch X2, respectively. σX12 and σX22 denoted the sample variances of patch X1 and patch X2, respectively. σX1X2 was the crosscovariance between the two image patches; C1, C2 were the stable constants. When X1 and X2 were more similar, the value of SSIM was closer to 1.The experimental results were evaluated in terms of peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These two indicators were defined as in Equations  and 14)14), respℒ1-norm was employed as the objective loss function. The hyperparameters were set as follows: λ1 was 0.5, batch size was 1, learning rate was 2e-3, and epoch was 1500.As the foundation of the proposed method, the images with random artificial noise were used as the condition of the CGAN to input into the network. For the super-resolution generator, 6 residual dense blocks (RDBs) were embedded in, to make full use of the information in each convolutional layer and extract the context information among the convolutional layers. Different from the traditional conditional GAN, image patches were extracted. The proposed method cut the input into the image patch with the size of 70∗70 at random. In addition, the integration of Wasserstein distance and In the training stage, a pair of images from the training set were inputted to both the generator and discriminator, where the generator would produce a denoised image and the discriminator would map the image into a confidence value. With the method of Adam, the generator would be optimized to produce a better result of denoised image which should be close to the ground truth to earn the confidence of the discriminator. Finally, the trained network tested the test noise images and output the denoised results. The overall process of the proposed algorithm is described in Generally speaking, the methods based on super-resolution reconstruction can improve the quality of images and recover the details. The 6 residual dense blocks (RDBs) were used for superresolution reconstruction in the generator in this paper. To prove its superiority, an ablation experiment without residual dense block (RDB) was implemented on the JSRT dataset. By adding some unknown noise to the dataset, the SSIM of each result is shown in In conditional GAN, the noise image and its corresponding gradient image were integrated as conditional information to input the network in this paper. In noise images, the isolated noise is different from the surrounding pixels; therefore, its gradient will be larger than normal pixels. Besides, the method of thresholding was adopted to ignore the edge and texture information in the gradient maps. Therefore, adding gradient information is beneficial to enhance noise in theory. To test our idea, this paper also made an ablation experiment without gradient enhancement. In this experiment, only the noise image was adopted as conditional information for comparison. The superparameter epoch was set as 500; the coefficient of reconstruction loss was 0.5. The denoised results are shown in Previous studies have proved that reconstruction loss can focus on structure preserving, but at the same time, it also ignores the image details. Moreover, the WGAN loss attempts to learn a generative model, which is aimed at fitting the distribution of the real samples and preventing overfitting effectively. As a result, the WGAN loss is added to supplement detailed information for reconstruction loss in this paper.From some ablation experiments with different objective loss functions, we found that perceptual loss will damage the structural information in images, which is described in We compared the performance of the proposed method with several state-of-the-art methods and ablation experiments on the datasets of In From the comparative experiments of The residual images of Figures We develop a novel medical image denoising model based on conditional GAN. Instead of focusing on the complex network structure construction, this paper is dedicated to image context exploration and structure preservation. Firstly, a generator with super-resolution reconstruction is used to improve the quality of denoised image against other generators. Secondly, different from traditional denoising GAN models, this paper combines the noise image with its corresponding gradient image as conditional information of conditional GAN, which enhanced the noise information. Thirdly, the model is trained based on residual calculation by combining synergistic loss functions so that the denoised results are as close to the ground truth as possible. Finally, residual images and evaluation indicators are used to quantify the denoised results on JSRT and LIDC datasets. Compared with different denoising models, the proposed model not only improves the quality of denoised images but also maintains the detailed structure consistent with the lossless images.


The on-going COVID-19 pandemic and consequent lockdowns cast significant impacts on global economy in the short run. Their impact on stability of global electric vehicles (EVs) supply chain and thus our climate ambition in the long run, however, remains hitherto largely unexplored. We aim to address this gap based on an integrated model framework, including assessing supply risks of 17 selected core commodities throughout the EV supply chain and further applying the supply constraints to project future EV sales until 2030. Our model results under three pandemic development scenarios indicate that if the pandemic is effectively contained before 2024, the global EV industry will recover without fundamentally scathed and thus can maintain the same growth trend as in the no-pandemic scenario by 2030. We suggest that fiscal stimulus in the postpandemic era should be directed more toward upgrading the quality of battery products, rather than expanding the production capacity.  •Lithium is the most critical commodity for the electric vehicle industry•Short-term COVID-19 pandemic will not cause constraints on the production side•Long-term pandemic can seriously jeopardize electric vehicle market development•Postpandemic policies should focus more on improving battery quality Electrochemical energy engineering; Energy transportation; Transportation engineering The COVID-19 pandemic has cumulatively caused 300 million confirmed cases worldwide, with three million deaths reported by January 7th, 2022 . Most coAnother crisis faced by humankind is the climate change, which persists longer and has a more extensive impact . To addrA number of studies have evaluated the impacts of the pandemic on human society from the perspectives of energy demand and associated environmental implications , criticaPrevious literature provided static evaluation for the existing impacts of the pandemic on EV industry. However, potential dynamic impact of lockdown measures on supply-demand balance of global EV industrial chain in the long run has hitherto not been fully investigated. To fill this gap, we developed an integrated model to explore whether the supply delays caused by the COVID-19 pandemic will be a bottleneck on the growth target of global EV market. The integrated model structure is shown in Here we show that lithium mineral is the most critical raw material that will likely form restrictions on the EV industry due to the pandemic. If the pandemic could be gradually contained in the short term, the growth trajectory of EV market will not be affected by lockdown measures. Current excess production capacity of LIBs and relevant raw materials will provide a large buffer for supply disruption constraints on consumption growth. However, if the pandemic spread is not controlled in the short term, the development of EV industry could be seriously jeopardized before 2030. The results imply that the postpandemic recovery of EV industry should seek the window of opportunity created by this pandemic to upgrade the quality rather than the quantity of production capacity.The current EV supply chain covers thousands of relevant components and raw materials . ConsideIn 2019, a total of 63 countries and regions were linked to the EV supply chain. Among these countries, Turkey, the USA, Brazil, Argentina, and Spain were the top five countries with the highest RCSI , whereas China (mainland), Taiwan, Australia, Madagascar, Burma, and Ghana were the five regions with the lowest RCSI . These variations between countries reflect mainly the differences in their regional infection rate.Supply of lithium mineral, EV, cobalt ore, nickel ore, and lithium carbonate were the greatest risk sources for EV industry during the pandemic, with the highest CCI of 1622, 1436, 1297, 1171, and 1159, respectively. The high CCI of lithium mineral was ascribed to the large production proportion of Chile and Argentina , as As the EV industry is still at the phase of rapid growth, the supply structure of EV-related commodities would change substantially in the future due to different investment strategies of various enterprises and countries. Therefore, the production data for 2019 may not accurately reflect the dynamic risks of EV supply chain in the next decade. We collected the information about production layout of relevant suppliers for the 17 selected commodities see . AlthougThe COVID-19 pandemic affects the supply-demand balance in multiple dimensions, including for example factory shutdown, logistics disruption, business failures, and purchasing power decline. In this study, quantification of the impacts of the pandemic on EV market begins on the supply side. As the information about long-run supply planning of lithium minerals is not available and lithium is currently essential for all EV battery technology routes, we mapped the probability of supply disruption of lithium minerals into LIB supply constraints and ignored the impacts of nonbattery applications of lithium. We developed the Battery Supply Projection Model based on the production capacity planning of LIB suppliers and two key parameters: (1) release efficiency of existing production capacity, quantifying the influence of the production disruption caused by the lockdown measures on the annual output of commodities; (2) construction efficiency of new production capacity, quantifying the influence of suspension of new plant construction on annual growth rate of production capacity. Model details are described in the There is still high uncertainty about the future development of the COVID-19 pandemic. Therefore, we designed three scenarios to explore the possible consequences of different pandemic development pathways. The main difference between the various scenarios lies around the assumptions about the duration of the pandemic. Scenario assumptions and corresponding model parameters are shown in The end-use applications of LIBs were divided into three sectors: EVs , stationary energy storage systems, and other electric devices . We assumed that the EV sector would be the first to be affected by LIB supply constraints due to its large share in consumption, high technical requirements, and high substitutability . The future consumptions of stationary energy storage systems and other electric devices were modeled consistently under these three scenarios. Then we can get the available LIB production capacity for EVs by subtracting the demand for other two sectors from the total production capacity. Further we applied the available LIB production capacity for EVs into our developed TIM as the constraint condition to calculate prospective sales of LIB-powered EVs.Prospective production capacity and end-use consumption of LIBs are shown in On the demand side, LIB used in the nonvehicle applications will grow from 85 GWh in 2017  to 290 GWh  in 2030, which are the same in all three scenarios due to our assumption. Consumption for LIBs in the EV sector will be the same under the BAU and STR scenarios, which will grow steadily to 2993 GWh in 2030 from 145 GWh in 2020. The penetration rate of EV will reach 42% by 2030 with annual sales of 2.1 million plug-in hybrid electric vehicles and 65.2 million battery electric vehicles. Under the LTC scenario, more restrictive production capacity will depress the consumption of LIBs in EV sector to 2051 GWh in 2030. The penetration rate of EVs will reach 27% by 2030 with sales of 1.4 million plug-in hybrid electric vehicles and 41.7 million battery electric vehicles per year. Detailed results of EV sales by vehicle type  are shown in the One significant finding of our model results is that the losses of LIB production capacity caused by COVID-19 pandemic will have little impact on the EV demand growth if the pandemic could be gradually contained before 2024. The underlying reason for this circumstance is the existing overcapacity for production of LIB in the past. From 2017 to 2019, the ratio of actual production to production capacity of LIBs kept decreasing from 75% to 51%. These excess production capacities form a large buffer for the shock of the pandemic on supply. Nevertheless, if the pandemic persists for long run, production capacity constraints will start to cast a devastating impact on the development of the EV industry.In the integrated model developed in this study, the model parameter with the highest uncertainty is the duration of strict lockdown measures (hereinafter referred to as lockdown duration). This parameter directly affects the construction efficiency of new facilities and release efficiency of existing facilities. We made a strong assumption for lockdown duration that this parameter would be consistent with the historical data during each wave of future outbreak, whereas factors such as the severity of future outbreaks and the speed and willingness of governments to respond could be very different from the past. Even for the same country, the lockdown duration is likely to change dramatically. In addition, there are great differences in the enforcement of pandemic prevention policies by national governments. We distinguish the impact of the lockdown duration on the opening rate at the country level based on the RCSI. But for countries with similar indexes, enforcement and focus of lockdown measures could vary greatly. The lockdown measures in some countries only stay at the level of controlling social distance in order to maintain the operation of the economy. Some other countries may suspend all economic activities in an effort to quickly contain the pandemic.Another major source of uncertainty is the weight coefficient of the two parameters, infection rate and growth rate of COVID-19 confirmed cases, used to calculate RCSI. Due to the insufficient information supporting the weight set, we assumed that the two parameters contribute equally to the regional supply risk. The changes of weight coefficient may lead to the different relative size of the country’s RCSI value.We consequently performed uncertainty analysis to test the impact of the uncertainty of lockdown duration and weight coefficient on the model output. The observed model output is the cumulative global EV sales from 2020 to 2030. The results of uncertainty analysis are shown in the This study investigated the potential impacts of COVID-19 pandemic on global EV industry using an integrated model framework that combines supply chain risk assessment, LIB supply constraint calculation, and EV market projection. Our model results could provide insights to inform policy for improving security of global EV supply chain.Although the COVID-19 pandemic has caused a huge shock to the world economy, the pace of the energy transition has not been hindered, with countries strengthening the emission reduction regulations and adopting corresponding fiscal stimulus policies. Global total vehicle sales in 2020 reached 78.03 million units, with a 13% decrease from 2019. Global sales of EVs, however, rose by 43% to 3.2 million units. Our model results suggest that this development trajectory is promising to continue if the pandemic could be effectively controlled in the short-term because the quantity of production capacity is more likely not to be a constraint. However, it does not mean that the current overcapacity for LIB production is worth advocating; this is particularly true considering that the automotive power battery market presents an oversupply of low-grade batteries and inefficiency of high-grade batteries . The marIt should be noticed that the above conclusion is drawn in reference to current EV penetration expectation. The focus of this study is to explore the impact of different pandemic development scenarios on production capacity and then to see if they will cause constraints on the original target. However, the impacts of the pandemic will be cast not only to the supply side but also to the demand side. For example, many studies suggest that stimulus funding in the postpandemic era should be spent on green industries . In caseOur results also suggest that the performance of countries in the pandemic should be incorporated into considerations on capacity planning. China’s low RCSI and large production share are the major reasons for its construction efficiency and release efficiency to remain over 80% under the LTC scenario. During the COVID-19 pandemic, government policies and executive forces vary significantly from country to country . It tookDespite the current progress of the vaccination is optimistic, it is still necessary to normalize the prevention and control of the coronavirus. Preparing for extreme scenarios such as the LTC is highly essential. The corresponding measures to maintain the stability of supply should be laid out in advance, such as establishing regular material inventory and maintaining the promotion of recycling . Our resThere are some limitations and uncertainties in our model structure and results. First, the pandemic data used in this study are adopted from public information. Because of inadequate testing, statistical biases, and delay in information disclosure, the actual number of infections may be different from the number recorded. In addition, as the pandemic was still under development when the study was finished, the final quantitative results of the study do not fully reflect the severity of the pandemic in each country.Second, in the prediction of future LIB production and consumption, due to the deviation of model structure, model assumptions, and basic data, the results may be different from the actual situation. Future potential changes in energy technology and policy might remodel the EV supply chain significantly. The enterprise capacity planning will also keep changing either because the original capacity planning fails to reach the target or because the market performance changes. Model output of future EV market balance could be adjusted with updated information.Third, this study only considered the risks transmitted from the LIB supply chain on EV industry. However, the supply disruption of other upstream commodities is also possible due to the spread of coronavirus. The supply of automotive-grade chips, for example, is already in serious trouble, and such situation is likely to continue until 2023 . Becausehao@tsinghua.edu.cn).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Han Hao (This study did not generate new unique reagents.The integrated model is consisted of four sub-model: (1) Supply Chain Risk Assessment Model; (2) Battery Supply Projection Model; (3) Non Vehicle Demand Model; (4) Transport Impact Model. The functions, specific composition and calculation logic of each sub-model are described as follows.The Supply Chain Risk Assessment Model is used to quantify the possibility of supply disruption in the EV supply chain due to COVID-19 pandemic. The EV supply chain is consisted of over a thousand types of commodities. Considering the importance of these commodities to the performance of EVs and the availability of data , 17 key RCSIi is the RCSI of country i;IRi is the infection rate of COVID-19 in country i ;IRmin is minimum infection rate among all countries;IRmax is maximal infection rate among all countries;GRi is the average daily growth rate of the cumulative number of confirmed cases;GRmin is minimum average daily growth rate among all countries;GRmax is maximal average daily growth rate among all countries.We used the RCSI to quantify the severity of each country affected by the COVID-19 pandemic, as RCSI is consisted of two sub-indexes: (1) infection rate and (2) average daily growth rate of cumulative COVID-19 confirmed cases at the country level. The infection rate of COVID-19 confirmed cases reflects the current status of regional pandemic diffusion. As the coronavirus is still spreading in many countries, the average daily growth rate of cumulative COVID-19 confirmed cases is used to reflect the future regional pandemic diffusion potential. The two sub-indexes are normalized respectively and then aggregated to calculate the RCSI. Here we assumed that the contribution of the two parameters is equal to the final pandemic severity and set the weight coefficients of the two parameters to the same size.CCIj is the CCI of commodity j;POi is the production of commodity j of country i.Ij is the impact of commodity j on EV industry.Further the CCI was calculated to quantify the possibility of supply constraints on EV market of each kind of commodities, as RCSI are used to calculate the probability of supply disruption of the commodity due to the pandemic. The more severe the pandemic is in the major producers of a commodity, the more likely the commodity’s supply will be disrupted. The impact of commodities on EV industry is quantified by proportion of EV sector associated with the commodity’s total demand. For example, all EVs are powered by LIBs, thus the impact index of LIB is 100%. 25% of LIBs use LCO as the cathode material, thus the impact index of LCO is 25%. Detail explanations and results of RCSI, impact of various commodities on EV industry, and CCI are shown in The commodity supply structure and the The COVID-19 data comes from the Center for Systems Science and Engineering at Johns Hopkins University . The temPCt is the production capacity of LIBs in year t (kWh);REt is the release efficiency of existing production capacity in year t;CEt is the construction efficiency of new production capacity in year t;t0 is initial time of model input, which is set to be 2019;EGR is the expected growth rate of production capacity of LIBs.The Battery Supply Projection model is used to estimate the future LIB production capacity, which is the maximum amount of LIBs that can be produced. It is modeled based on the expected growth rate of production capacity, release efficiency of existing production capacity, and construction efficiency of new production capacity, which is calculated as EGR1 is the expected growth rate in phase 1 (2020–2025);EGR2 is the expected growth rate in phase 2 (2025–2030);PCi,2019 is production capacity of LIBs of company i in 2019 (GWh);PPC2025 is the planned production capacity of LIBs of company i by 2025 (GWh);PPC2030 is the planned production capacity of LIBs of company i by 2030 (GWh).Expected growth rate of production capacity is the growth rate under the BAU scenario. We assume that the growth of production capacity is not affected by the COVID-19 pandemic in this scenario. We collected the information about announced capacity planning of LIB suppliers . The capRelease efficiency of existing production capacity is estimated based on the current pandemic data and information about lockdown measures of each country. The average duration of a single lockdown period is 64 days . The aveDue to the difference of national governance framework, the operation situation of factories in different countries will be very discriminative during the period when the lockdown measures are taken. However, it is difficult to obtain the specific information of each country, and there is great uncertainty about whether the policy will remain the same after the outbreak of the pandemic again in the future. Here we use the RCSI to estimate the operation rate of each country during the lockdown period, which is assumed to be: 100% (RCSI: 0–10); 80% (RCSI: 10–15); 60% (RCSI: 15–20); 40% (RCSI: 20–25); 20% (RCSI: 25–30); 0% (RCSI>30). As stated in the The calculation of construction efficiency of new production capacity is similar to that of release efficiency of existing production capacity. The difference is the operation rate of each country during the lockdown period, which is assumed to be: 100% (RCSI: 0–5); 50% (RCSI: 5–10); 0% (RCSI>10). The reason for this setting is that construction requires far more manpower than production activities. Based on the schedule of worldwide LIB production capacity layout , we can Except for EVs, the LIB applications can be divided into two categories: stationary energy storage system, and other electric device. The LIB demand in these two sectors are modeled independently.LCSESS,t is the LIB consumption in stationary energy storage systems in year t (GWh).The estimation of future LIB demand in stationary energy storage systems is based on the report of Lux Research . This reLCOED is the LIB consumption in other electric devices in year t (GWh).Other electric devices include cell phones, wireless headsets, smart watches, cameras, notebook computers, tablets, robots, drones, power tools, etc. Currently the consumer electronics is the major sector of LIBs used in other electric devices. From 2016 to now, sales of mobile phones and computers have been nearly unchanged. Future growth of LIBs used in other electric devices will come from increased demand for electricity from 5G applications and sales of other devices. We have predicted this demand in our previous published study . In thatLCLDV,t is the LIB consumption in EVs in year t (GWh);SAi,t is the vehicle sales in year t in country i (unit);MPt is the EV market penetration in year t;BCi,t is the battery capacity per EV in year t in country i (kWh/unit).The LIB consumption in the EV sector is modeled based on the Transport Impact Model and output of the Battery Supply Projection Model. Vehicle types considered in the model include passenger vehicle, light-duty commercial vehicle, heavy-duty commercial vehicle. Transport Impact Model is a technology-rich, country-level, flow-driven approach model, developed by China Automotive Energy Research Center of Tsinghua University , 2019b. The total vehicle sales is the product of country-level economy and population development forecast see . The basERt,i is the electric range of vehicle sold in year t, in country i (km);t, in country i (MJ/km), which is the function of vehicle weight, aero and rolling resistance, and vehicle powertrain efficiency;RFt,i is the aero and rolling resistance factor of vehicle sold in year t, in country i;EDt,i is the battery energy density of vehicle sold in year t, in country i (kg/MJ);CWt,i is the vehicle curb weight (excluding battery weight) of vehicle sold in year t, in country i (kg);PEt,i is the powertrain energy efficiency of vehicle sold in year t, in country i (%);αandβare the characteristics parameters, which reflect the rationale of vehicle energy consumption;γis the percentage of battery energy that can be actually used out of the total battery capacity (%).The battery capacity per EV is obtained by solving the non-linear equations, as The key input data, including country-level EV sales, EV electric range, and battery technology evolvement in four categories of powertrain systems and three categories of vehicle models, are shown in the


Accurate kinematic modelling is pivotal in the safe and reliable execution of both contact and non-contact robotic applications. The kinematic models provided by robot manufacturers are valid only under ideal conditions and it is necessary to account for the manufacturing errors, particularly the joint offsets introduced during the assembling stages, which is identified as the underlying problem for position inaccuracy in more than 90% of the situations. This work was motivated by a very practical need, namely the discrepancy in terms of end-effector kinematics as computed by factory-calibrated internal controller and the nominal kinematic model as per robot datasheet. Even though the problem of robot calibration is not new, the focus is generally on the deployment of external measurement devices  or mechanical fixtures . On the other hand, we use the factory-calibrated controller as an ‘oracle’ for our fast-recalibration approach. This allows extracting calibrated intrinsic parameters  otherwise not directly available from the ‘oracle’, for use in ad-hoc control strategies. In this process, we minimize the kinematic mismatch between the ideal and the factory-calibrated robot models for a Kinova Gen3 ultra-lightweight robot by compensating for the joint zero position error and the possible variations in the link lengths. Experimental analysis has been presented to validate the proposed method, followed by the error comparison between the calibrated and un-calibrated models over training and test sets. Robots are being widely adopted on the industrial floor nowadays, with the aim to automate more and more manufacturing processes for increased efficiency and production. In addition, in industrial robotic applications, position accuracy and repeatability are the most fundamental attributes for automating flexible manufacturing/assembly tasks . When opPositional accuracy can be defined as the difference between the position of a commanded pose and the barycentre of the attained position . Most maInaccuracies in robots are caused by several factors and in particular, these sources of error can be divided into geometric and non-geometric factors. The most common sources are geometric in nature such as minor axis misalignments from the model which arise during production, errors in joint positions and joint angles . Robots The non-geometric factors contributing to accuracy errors consist of structural deformations such as backlash  and cleaThe kinematic calibration technique presented in this paper was motivated by a very practical need, namely the discrepancy in terms of end-effector kinematics as computed by factory-calibrated internal controller and the nominal kinematic model as per robot datasheet. The proposed approach focuses on the static open-loop re-calibration of the Kinova Gen3 robot arm; however, the procedure is generic enough to be applied to other serial manipulators.Even though the problem of robot calibration is not something new, most of the existing methodologies require external measuring instruments, such as laser trackers or some kind of mechanical fixtures. This not only makes the calibration a laborious procedure in terms of the experimental setup and establishing data synchronization across multiples devices but also introduces challenges under limited robotic workspace (such as factory floor).While many of the methods address the calibration problem by introducing corrections in the final end-effector pose, our approach focuses on correcting the kinematic model itself . As a result, the differentials (and hence the Jacobian) are easily obtained, which otherwise is not available from the factory calibrated feedback. Calibrated models are essential in the dynamic control of robots, where the computation of both forward and inverse dynamics of the robot depends heavily on the underlying model.Hence, the contribution of our work can be summarized as (i) a fast calibration approach without the use of external measuring devices or mechanical fixtures but simply utilizing the factory calibrated kinematics as the ground truth and (ii) a way to calibrate the kinematic robot model itself rather than just the corrected end-effector pose.The rest of the paper is organized as follows. In Robot calibration is performed to improve the positional accuracy of the end-effector by accurately calibrating the kinematic parameters, which are proven to be major contributors of the positioning error ,10. KineThe model-based calibration procedure in general involves developing a model whose parameters accurately represent those of the actual robot then accurately measuring specific features of the real robot followed by the computation of the parameter values for which the model reflects the measurements made . KinematKinematic calibration is usually carried out in following four steps ,6,16:  modelling is the most popular kinematic modelling technique for serial robot arms; however, it is unable to address the singularity issue of two adjacent parallel joints. Ref.  presentsMost calibration methods compare the taught point positions of a robot with measurements relating the end-effector to an external 3D measuring device such as a laser tracker ,24,25,26Even though the problem of robot calibration is widely explored, the focus is generally on the deployment of external measurement devices  or mThe first step towards a typical robot calibration is the modelling of the end-effector/tool pose  with respect to a reference frame, which in general is the robot base frame itself. This can be done in several ways, of which the 4-parameter DH (Denavit–Hatenberg) convention is most widely employed. In this paper, the robot platform used for experimental validation is the Kinova Gen3 ultra-lightweight robot arm and hence we employ the DH representation in order to adhere to the convention followed by the datasheet in building the ideal forward kinematic model.N joints assuming the configuration For a robot manipulator with its robot as FK(q)=T1Here, We can now define the body Jacobian matrix for the manipulator as ,39JbodyThe body Jacobian can be re-written asWith reference to The kinematic model available from the manufacturer  to provide an accurate feedback of the tool pose, in other instances, one can always make use of external measurement systems calibrated to the robot base frame to identify the actual forward kinematics. In our approach to facilitate a fast kinematic calibration, instead of using an external measuring device to set a ground truth for the tool pose, we consider the feedback tool pose from the robot controller  itself as the ground truth.N-joint serial manipulator, consider the forward kinematics as given by the uncorrected  model from Equation (For a Equation (1)FKp+π rad foTo assess the performance of the calibrated model on the training dataset, both the position and orientation errors have been computed before (Equation ) and aftTo validate the proposed approach against a test set, we generated a set of spatial locations spanning the first two quadrants of the robot base frame . The robWe computed the trajectory with the un-calibrated and the calibrated models, which are superimposed on the commanded trajectory for comparison in The position and orientation error plots before and after the calibration are shown in During the validation on the test set, while our approach was successful in bringing down the resultant kinematic error, the corrected model did not perform well in the X and Y axes individually See . HoweverIn this section, we perform the calibration by collecting additional training data samples from the pre-defined operating space of the robot. To that end, we first defined a work volume for the robot within which we assume the robot performs a given task See . We alsoThe calibration preformed on the test dataset yielded a set of calibration parameters as tabulated in To evaluate the performance of Model 2 on the training set, both the position and the orientation errors have been computed and plotted in To populate the test dataset, we generated 20 spatial points in the pre-defined work volume as shown in To evaluate the accuracy along individual axes, the commanded trajectory for the robot, the factory calibrated feedback from the controller, the forward kinematics given by the calibrated and the un-calibrated models are plotted on the X-Y and the X-Z planes as shown in The performance of the calibrated model on the test data is evaluated and both the position and the orientation error are plotted in To perform a quantitative analysis, the error values for each of the axes are plotted in Now to validate our hypothesis regarding the error in the X-Y plane for Model 1, in The work presented in this paper focuses on compensating for the linear and the angular offsets that adversely affect the position accuracy of the robot. However, we do not account for factors such as the compliance in the link or the joint transmission systems. The influence of these components comes into play significantly under the presence of a payload or even simply gravity itself, particularly under configurations which impose relatively higher torques on the links and joints. We observed that during the logging of the training dataset, the robot passes through a total of six such configurations b,d,f at Robot calibration is a necessity in planning and executing both contact and non-contact tasks alike, reliably and safely. This paper presents a fast re-calibration method to improve the robot position and orientation accuracy by compensating for the joint offset error as well as for the discrepancies in the link dimensions. Our approach is based on the assumption that the actual forward kinematics is known with sufficient accuracy, possibly through the factory calibrated feedback from the controller. A set of parameters to account for the linear and angular discrepancies are identified by minimising the mismatch between feedback and the computed forward kinematics. The proposed calibration approach brought about a significant improvement in the forward kinematics in comparison with an un-calibrated model, which is backed up by experimental analysis.


K-means and improved watershed algorithm is proposed. Firstly, Gaussian filter is used to denoise human skeleton image. K-means clustering algorithm is used to segment the denoised image and the connected component with the largest area is extracted as the initial human skeleton region. The initial bone region was morphologically opened and then morphologically closed to eliminate the noise. Morphologically open operation is used to disconnect other human tissues that adhere to the human bone region and eliminate the background noise with small area, while closed operation smoothes the edge of the human bone region and fills the fracture in the contour line. Secondly, the watershed segmentation algorithm is implemented on the image after morphological operation. The similarity degree of two blocks is defined according to the mean difference of gray level of adjacent blocks and the mean value of standard deviation of gray level of pixels in the edge of the block 4-neighborhood. The adaptive threshold T is generated by Otsu method for histogram of gradient amplitude image. If the similarity degree is greater than T, the image blocks will be merged; otherwise, the image blocks will not be merged. The proposed image segmentation algorithm is used to extract and segment the human bone region from 100 medical images containing human bone. The number of blocks segmented by watershed algorithm is 2775 to 3357, but the number of blocks segmented by the proposed algorithm is 221 to 559. The experimental results show that the proposed algorithm effectively solves the oversegmentation problem of watershed algorithm and effectively segments the image target.Watershed algorithm is widely used in image segmentation, but it has oversegmentation in image segmentation. Therefore, an image segmentation algorithm based on  A series of classic image segmentation algorithms—such as watershed segmentation algorithm, active contour growth methods, threshold-based segmentation methods, regional growth methods, clustering algorithms, and artificial neural network segmentation methods—have been applied to image segmentation over recent years . The watC-means algorithm to segment MR images. This algorithm, however, increases the complexity of computation and notably, requires further improvement on its antinoise performance . The Otsu methods selects the threshold T such that interclass variance between C1 and C2 maximizes. The ratio of interclass variance to overall image greyscale variance is a measurement that can divide the image greyscale into two categories [T generated was used to threshold the neighboring segments.After the gradient amplitude of the image is computed, the u method  is emplotegories , and itsσ of Gaussian filter is 0.8. The K-means was applied to carry out initial clustering, with the parameter K = 4, since the medical images of human bones are regularly made up of bones, soft tissues, fats, and background. The effect is shown in The algorithm proposed was compared with the original watershed algorithm in terms of the segmentation performance, where 100 2D medical images of human bones were selected as the experimental subjects. The bone images interfered with salt-and-pepper noise served as the experimental subjected shown as  and denoOtsu thresholding into a binary image, shown as The algorithm proposed was applied to segment the images of human bones. First, Since excessive segmentation is the most critical problem of the original watershed algorithm, the number of segments was used as the index for comparison. 100 human bone images were selected to carry out the target segmentation experiment; the numbers of segments made by the original watershed algorithm, the improved watershed algorithm, and the proposed algorithm were compared. The results are shown in K-means clustering and an improved watershed algorithm in order to crack the excessive segmentation and the noise sensitivity that result from the original watershed algorithm. First of all, K-means initial clustering was carried out upon the removal of gauss noise from images, followed by threshold division using the Otsu method. Then, morphological open and close operations were employed to eliminate human body noises and thereby to extract human bone zones. On the basis of watershed transformation, next, the similarity was computed for segment integration and precise segmentation of human bone zones. After the application of our proposed algorithm to medical segmentation of bone zones, the experimental results demonstrate the good viability of this algorithm in solving the excessive segmentation of the original watershed algorithm and in segmenting the bone zones from other human tissues. Our research findings are of practical significance to medical image segmentation, providing a reliable means to both computer auxiliary therapy and clinical diagnoses. In this article, however, we experimented only on the medical images of human bones. Therefore, further in-depth explorations are required for the ways of applying our algorithm to a wider range of medical images of other tissues, organs, and so forth. Since the target to be segmented in the image is constantly changing, but the number of targets' classification needs to be known when using the K-means algorithm for initial segmentation, the proposed image segmentation algorithm is still not ideal in the number of image blocks generated after image segmentation. All these aspects should be studied in depth. At the same time, the algorithm proposed in the article may be useful for brain imaging detection of mental illness, and further research is needed.We come up with an image segmentation algorithm by combining


How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks?The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity  is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity.We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management , i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy  and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system.Computationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions.The online version contains supplementary material available at 10.1186/s40249-022-00957-1. The emergence of the SARS-CoV-2 Omicron variant B.1.1.529) posts an unprecedented challenge to the control and prevention of the pandemic of COVID-19 .1.529 po. StudiesHow can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks? To answer this question, we must address two technical challenges:How can we foresee the future risks of the Omicron outbreaks in different locations?How can we allocate the limited healthcare resources based on the future risks to reduce the impact of the outbreak?To optimally allocate the healthcare resources to mitigate the spread of the epidemic, especially when the resources are limited, a critical question therefore needs to be answered: To address these two challenging issues, in this study, we propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. As shown in existing studies, spatiotemporal transmission patterns of the virus play a key role in uncovering the dynamism of the disease spread and outbreaks –18, and We validate the effectiveness of the proposed method in forecasting the risks and prioritizing the resources through a comprehensive investigation in Hong Kong, China. The maps of district-level transmission risks show clear spatiotemporal heterogeneity, providing key information to resource allocation. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the effective and flexible testing resource allocation with different levels of capacity insufficiency. As shown in this study, the quantitative characterization of the spatiotemporal transmission patterns of the disease enables us to gain the insights into the dynamism and heterogeneity of the outbreaks as well as the implementation and optimization of the prioritized resource allocation strategies. This is not only of critical importance in preventing the Omicron outbreaks in Hong Kong, China, but also informative in guiding the responses to the potential future outbreaks caused by other new variants and/or in other highly populated countries and regions under serious capacity shortage.We develop the method and conduct the investigation based on the information of reported COVID-19 cases in Hong Kong, China, covering all 18 districts from March 17, 2020 to February 5, 2022. The data are owned and publicly released by the Department of Health, Hong Kong Special Administrative Region (HKSAR) Government [i and j, and the direction of the link is from i to j if the building i appeared before the building j in the case-visited building list provided by the government. We take the difference between the day of visit and the symptom onset day (in terms of the number of days) as the input of the infectiousness profile [i to j, which is introduced by the connection between these two buildings. We then identify the corresponding constituency area that each building belongs to, and accumulate the building-to-building transmission risks to form the transmission matrix at the constituency area level. Based on the 452 We construct each disease transmission matrix (daily) across 18 districts in Hong Kong, China as follows. First, we build the daily transmission matrix at the constituency area level. Specifically, for each confirmed case, if they have visited a series of locations  in the same day, we construct a link between any two building  profile  to get ti, j)-th element denotes the transmission risk from the i-th district to the j-th district. By doing so, we can obtain a series of daily transmission risks for each district in a given period. Then we infer the transmission risks of different districts by adopting the Gaussian process regression model [Based on the constructed daily transmission matrix, we infer the daily transmission risks of different districts as follows. First, we define the daily transmission risk of each district as its transmissibility not only to itself but also to the remaining 17 districts. Therefore, we can obtain such risk of a specific district by summating all elements of the corresponding row in the constructed transmission matrix, in which the  model to simulate the future trend of the disease transmission. The structure of proposed SDNR model is shown in Fig. We construct the daily spatiotemporal transmission matrices in Hong Kong, China during December 27, 2021 to February 5, 2022, which is corresponding to the current wave of Omicron outbreak in Hong Kong, China. Figure From the figure we can observe that edges in the same transmission network  show obvious difference in their intensities, indicating the spatial heterogeneity of the transmission patterns. Moreover, the networks are time-varying, even for two close dates, demonstrating the temporal heterogeneity of the transmission patterns. Such complex spatiotemporal heterogeneity determines the dynamics of the transmission and makes the outbreaks difficult to forecast. By quantitatively characterizing such spatiotemporal transmission patterns, our method can uncover the underlying dynamism of the disease, and thus enables the risk mapping and resource allocation, which will be shown in the following two subsections.Figure Similar to the spatiotemporal heterogeneity of the transmission networks observed in Fig. Another interesting observation is that the districts with more cases may not necessarily indicate the higher transmission risks, and vice versa. For example, on February 2, 2022, the top-3 districts with the highest transmission risks are D17 (Kwai Tsing), D16 (Shatin), and D8 (Wong Tai Sin). In the same day, however, the top-3 districts with the largest number of cases are D2 (Wan Chai), D3 (Eastern), and D6 (Sham Shui Po), which are totally different from the districts with the highest risks. This observation indicates that the number of detected cases might not be the golden criterion to quantify the risks. To provide a more effective characterization, the spatiotemporal transmission patterns should also be taken into consideration, so as to enable the early warning of potential outbreaks and the response planning in advance.To validate the effectiveness of the transmission-guided strategy for allocating resources, we conduct a series of simulations with different levels of testing resource capacity: 300,000, 500,000 and, 700,000 per day, respectively, which are consistent with the current capacity in Hong Kong or expected to be achieved in the near future with the support from the mainland of China . As mentFigure Compared to the scenarios with the baseline and the population-based strategy . This means that we calculate the risk of each district by summating the out-going transmissibility from this district to others, rather the in-coming transmissibility received from other districts. By doing so, we expect to make the characterized risk more foreseeing, i.e., enabling the quantification that how will this district’s risk impact the disease transmission in other districts. This is consistent with our primary goal of actively seeing the future dynamics of the outbreak and effectively planning for disease control.The computational method developed in this study is general and can also be used in guiding public-health responses to the potential future outbreaks caused by other new variants in Hong Kong, China as well as in other densely populated countries and regions. However, when applying the developed method to other countries or regions with similar requirements , we need to adaptively implement the country/region-specific settings , rather than directly using the numerical results obtained in this paper.In this study, we develop a resource allocation strategy for prioritizing limited healthcare capacity based on the computational characterization of spatiotemporal patterns of the disease transmission risks. Compared with the population-based and case-based strategies, the developed strategy foresees the out-going transmission risks of different districts in a more active way, thus enabling the planning of resource prioritization ahead, which is of critical importance in achieving timely outbreak control under insufficient capacity. Through the comprehensive investigation of risk mapping and resource allocation in Hong Kong, China, we gain the insights into the dynamism and heterogeneity of the outbreaks of Omicron as well as the implementation and optimization of the prioritized resource allocation strategies. As a general approach, the proposed transmission-guided strategy can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions.Additional file 1. Model details, experimental settings, and supporting data.


Above-crossing excavations may cause uplift damages on existing shield tunnels. Therefore, to accurately calculate the deformation of shield tunnels is very necessary for geotechnical engineers. At present, the single-sided elastic foundation beam model is usually used in longitudinal deformation calculations for shield tunnels, which overestimates the uplift of deep shield tunnels. Because of the existence of the ground arch, deep shield tunnels are subjected to two-sided foundation reaction forces. Therefore, this paper proposes a partial missing double-sided elastic foundation beam model and the related fourth-order partial differential equations. In this model, the shield tunnel is subjected to double Winkler foundation springs and is simply considered a Euler–Bernoulli beam. A two-stage analysis method is used to solve the problem. First, the vertical unloading stress due to the above-crossing tunnelling at the tunnel location is calculated through Mindlin’s solution. Second, the deformation response of the beam subjected to an unloading stress is calculated by the finite difference method. Two engineering cases are used to verify the research. The results indicate that the proposed model is more accurate than traditional models in predicting the maximum uplift value, which is basically consistent with the observations. Due to the existence of segment staggering, the longitudinal influence range of the calculation by two models is larger than the actual measurement. In recent years, the overburden depth of subway tunnels has rapidly increased. As a result, various projects involving above-crossing tunnelling are becoming increasingly common. During the construction of tunnelling, the ground stress is released due to the excavation of the stratum, and the balanced ground stress field is broken, which will inevitably induce adverse effects, such as upward deformation and segment stagger, for the existing tunnels. Compared to mine tunnels, a shield tunnel has a weaker longitudinal deformation resistance because there are segmental lining joints. With increasing subway running speed, the deformation requirements of subway tunnels are becoming increasingly demanding. Therefore, serious concerns have been increasingly raised about the deformation response of underlying subway tunnels, especially existing shield tunnels, which are the focus of this study, due to the upper load reduction.At present, scholars have studied the effects of adjacent construction on existing tunnels through various approaches such as field observations, numerical analyses, and theoretical derivations, and have obtained useful conclusions. Based on field observations, He et al. , SimpsonAmong the current theoretical calculation methods, the elastic foundation beam model is most widely used. Its basic principle is to simplify the shield tunnel into an infinitely continuous beam on an elastic foundation. The main difference in these studies is the combination of different foundation models and types of beams. Zhang et al.  and LianHowever, by analysing the forces, it can be found that the beams in the aforementioned models are only subjected to single-sided foundation reaction forces under shield tunnels. Moreover, the calculation results are similar to the observations when the shield tunnels are shallow, but the calculation obviously overestimates the deformation of deep shield tunnels. For example, Zhang et al.  used a mAiming at the shortcomings of previous studies on the longitudinal deformation of deep shield tunnels due to the above-crossing construction, a simplified calculation model based on the basic Winkler foundation and Euler-Bernoulli beam is established, which can be called the partial missing double-sided elastic foundation beam model. The author carefully derives a fourth-order partial differential equation with coefficient variables and provides a scheme to solve the equations suing the finite difference method. Finally, the model is verified by two measured engineering cases.The above-crossing tunnelling will break the balanced ground stress field and cause the heave and additional bending moment of the underlying tunnel. If the deformation is too large, the safety of the tunnel structures and running trains will be seriously threatened. According to the different buried depth, shield tunnels can be divided into shallow tunnels and deep tunnels. These two different types of tunnels have different mechanical characteristics.The force diagram of the double-sided elastic foundation beam model of a deep shield tunnel is established in this study, as shown in Before the above-crossing construction, the shield tunnel is in equilibrium, and the relationship between the foundation reaction forces is as follows:After the above-crossing construction, the shield tunnel deforms, and the foundation reaction forces satisfy the following equation:Taking any tunnel microunit with width From the moment equilibrium condition, the equation can be concluded as:In the mechanics of materials, the relationships of rotation angle Omitting the second-order items in Equation (4), the deflection of the differential equation of the beam on a double-sided Winkler foundation from Equations (1)–(5) is obtained as:x-axis coordinate, and The preceding paragraphs show that if the shield tunnel is shallow, the upper strata do not provide foundation reaction forces. Then, the subgrade modulus coefficient am model ,11,12. FSchematic diagrams of the calculation model associated with the above-crossing tunnelling are established, as shown in After excavation, the upward unloading pressure When the excavation area is under the ground arch, the displacements of the shield tunnel can be calculated by Equation (7). However, Equation (7) is a fourth-order partial differential equation with coefficient variables, and no analytical solution is available. Therefore, this paper uses the finite difference method to obtain the solution. First, the shield tunnel is discretized into Since the shield tunnel is unaffected by tunnelling when the distance from the excavated area tends to infinity, both ends of the infinite beam are free ends, and the bending moments of nodes 0 and By solving Equation (11), the following equations can be obtained:Equation (10) can be written in matrix form, and the expression is:Equation (13) is rewritten as follows:Thus, the process of solving the fourth-order partial differential Equation (7) is transformed into solving a system of linear equations. The vertical displacements from node 1 to node k=1The underground interchange passages are set between the new Caoqiao station of Beijing Daxing International Airport Express and the existing Metro Line 10. The relative positions of the new passages and existing shield tunnels are shown in This paper takes the equivalent bending stiffness of the shield tunnel as 1/6 of the continuous tunnel. The model input parameters are shown in As shown in Chen et al.  reportedThe earliest study of this case is mainly deformation monitoring research, so there is no detailed physical parameterized introduction to the stratum. Therefore, this paper adopts the test results of Quaternary muddy clay, which is widely distributed over the Shanghai area, and obtains the undrained shear strength eference . Accordieference , the YouThe model input parameters are shown in This paper investigates the longitudinal deformation model of deep shield tunnels. The following conclusions can be drawn:(1) A partial missing double-sided elastic foundation beam model is proposed to predict the uplift of deep shield tunnels due to upper load reduction. The model assumes the tunnel to be a Euler–Bernoulli beam and subjected to two-sided foundation reaction forces. According to the model, the authors derive a fourth-order partial differential equation with coefficient variables and provide the matrix formulas to solve the equation by the finite difference method. In contrast with the single-side foundation beam model, it is more suitable for calculating the deformation of deep shield tunnels.(2) In comparison with the measured results of two engineering cases in different cities, the computational maximum displacements are basically consistent with the field tests, which are the results of most concern. The tunnel segment staggering will weaken the deformation transfer, so the longitudinal influence range of the calculation is larger than the actual measurement.(3) Although the deep shield tunnel is only subjected to single-sided foundation forces within the excavated width like a shallow tunnel, the shear stiffness between the segment rings will weaken the tunnel uplift within the excavated width. Therefore, the traditional single-side foundation beam models obviously exaggerate the effect of excavation.