Since the concept of carbon neutrality was put forward, it has gradually become clear that technological progress in related fields is the fundamental measure to achieve this goal, both internationally and domestically. In this context, the latest progress and prospect of carbon neutrality technology in China is studied. The connotation of carbon neutrality technology is defined based on existing literature, representative carbon neutrality technology classifications and 14 key carbon neutrality technology fields sorted out, the domestic demand for carbon neutrality technology and technological paths summarized, the latest progress in carbon neutrality technology in key areas in China in recent years listed, and the current difficulties in the development of carbon neutrality technology in China discussed. Seven countermeasures and suggestions are proposed to further promote the development of carbon neutrality technology in China, including the development of multi-domain technology integration.

Tumor metastasis is an important link in the process of tumor development, and it is also one of the main reasons for cancer deterioration and treatment failure. Taking tumor metastasis as the background, a study is conducted on the generative model of tumor lymphatics based on the interaction between tumor and extracellular matrix (ECM). First, mathematical language is used to sort out the biological principles of tumor lymphangiogenesis, and then assumptions made and mathematical models established and qualitative analysis carried out. The proof of the uniqueness of the existence of local solutions of the model is mainly carried out by means of approximation methods, the qualitative theory of partial differential equations and Banach's immovable point theorem, as well as the uniqueness of the existence of the overall solution of the model with the help of the regularity estimate of the local solution and the embedding inequality. Finally, the difference numerical method is used to carry out numerical simulation to illustrate the reliability and accuracy of the model. This research is of great significance for in-depth understanding the mechanism of tumor metastasis, guiding cancer treatment, and promoting related research.

Identifying potential adverse reactions of drugs can help doctors make clinical medication decisions. In view of the high-dimensional sparse features of previous studies and low prediction accuracy in constructing an independent prediction model for each adverse reaction, a prediction model of adverse reactions based on knowledge graph embedding and deep learning is developed, which can uniformly predict the adverse reactions covered by the experiment. On the one hand, knowledge graph and its embedding technology can fuse the correlation information between drugs and alleviate the deficiency of high-dimensional sparse feature matrix. On the other hand, the efficient training ability of deep learning can improve the prediction accuracy. In the study, drug characteristic data is used to construct a knowledge graph of adverse drug reactions; by analyzing the embedding effect of different embedding strategies, the best embedding strategy is selected to obtain the sample vector. Then a convolutional neural networks model is constructed to predict adverse reactions. The results show that the convolutional neural networks model has the best prediction effect under the DistMult embedding model and the 400-dimensional embedding strategy. The mean values of accuracy, F₁ score, recall and Area Under Curve were 0.887, 0.890, 0.913 and 0.957, respectively, which are better than those reported in the literature. The prediction model has good prediction accuracy and stability, which can provide an effective reference for safe medication.

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease of the world, and the doctors need a lot of time to read the abdominal CT images for COPD pre-evaluation. To improve the pre-evaluation efficiency, a deep network based on slice correlation information was proposed for COPD auxiliary diagnosis. First, by using a grouping approach, the architecture of the deep network is divided into several network branches, each of which aims to extract the local intra-slice association information of the CT images. Then, the outputs from multiple network branches are integrated via a BiLSTM to extract the global inter-slice association information between the adjacent CT slices. To further improve the ability of local feature extraction for each network branch, the enhanced multi-headed convolutional attention is designed by embedding the ConvNeXt into the existing multi-headed convolutional attention. Experimental results show that the proposed deep network achieves promising effectiveness for CT image classification on auiliarily diagnose of COPD, and the accuracy, sensitivity, specificity and AUC of the proposed network reach to approximately 92.15%, 94.17%, 91.17% and 95.33%, respectively.

In view of the characteristics of long course and mild disease development of breast cancer, a multi-task recurrent neural network banded regression model is proposed to analyze individual survival of breast cancer. First, a multi-task banded regression model based on recurrent neural network is proposed to optimize the individual survival analysis of patients by identifying the difference in the effects of various pathological features on different patients. Then, the form of the banded check matrix is expanded and its effect on the hazard distribution of patient is investigated. Finally, the survival analysis on the real datasets of breast cancer shows obvious differences among different patients, which verifies the validity of the model. The survival analysis on two real datasets of breast cancer shows that the C-index of the multi-task banded regression model based on recurrent neural network is greatly improved compared with the Cox regression model commonly used in medicine, and has a smaller 95% confidence interval.

Causal effect analysis is an important and popular method in clinical statistics, which typically conducted based on the observational data. However, the analysis based on observed data may be affected by unobserved variables, which may produce bias, leading to estimate the causal effects inaccurately. Existing methods ignore the unobserved variables or are unable to find appropriate proxy variables to weaken this bias, which fail to provide reliable estimation of the causal effects. To address this problem, this paper proposes the instrumental variable method, a more accurate computational method than traditional approaches in the realm of clinical statistic for drug efficacy analysis. This method incorporates the effect of unobserved into the error term, to estimate the accurate causal effect. Under some mild assumptions, the variables in the observational data is considered as instrumental variables. Then, the proposed method calculates the effects of butylphthalide (i.e., a drug) on patients with acute ischemic stroke (AIS) in the presence of unobserved variables. The causal effect of monthly prognosis and the confidence interval of this causal estimator is estimated. The study results show that the butylphthalide has a significant positive effect on the prognostic recovery of patients with acute ischemic stroke.

To solve the problem that the interference between millimeter wave radars will cause the weak vital sign signal obtained by bioradar to be submerged, resulting in the inability to accurately measure respiration and heartbeat, a method is proposed based on improved singular spectrum analysis to suppress the interference between radars, and the target beat signal is reconstructed from the interfered signal through correlation calculation to suppress the interference and eliminate the background noise. Furthermore, an ensemble empirical mode decomposition method based on information entropy is proposed to eliminate the residual phase noise of the beat signals, and the respiration and heartbeat signals are selected from the intrinsic mode function components after ensemble empirical mode decomposition through information entropy calculation to suppress the residual noise. Experimental results show that the proposed method can effectively recover the respiration and heartbeat signals from the interfered signals, and improve the signal-to-noise ratios of respiration and heartbeat. Therefore, the methods proposed in this research improve the anti-interference ability of bioradar and enhance the practicability of bioradar.

Traditional deep learning-based methods have achieved promising performance for low-illumination image enhancement. However, these methods usually need to be trained on the pair-wise datasets, which are difficult to collect. Moreover, most existing enhancement methods have the problems of imperfect enhancement effect and image noise in real low illumination image enhancement. To address this, a unsupervised generative adversarial network is designed for low-illumination image enhancement, which has no requirement of training on the pair-wise datasets. The proposed network consists of two subnetworks: attentional mechanism network and enhancement network. The attentional mechanism network is used to distinguish the low-light region from the bright region of the low-illumination image, and the residual enhancement network is used to enhance the image by combining with the global-local discriminator. By doing this, a low-illumination image can be well enhanced. Extensive experimental results show that the proposed method outperforms the baseline Enlighten-GAN and Cycle-GAN for low-light image enhancement.

In order to improve the accuracy of video anomaly behavior detection, a new detection method based on generative adversarial networks for video foreground areas is proposed. First, the foreground and background masks of the ground truth video frame are extracted, to determine the foreground areas of the output video frames from generative adversarial networks. For the foreground areas under consideration, the foreground area peak signal-to-noise ratio (F-PSNR) is used to calculate the detection score of anomaly behaviors. The experimental results show that the proposed method can effectively improve the detection accuracy of video anomaly behaviors with a reduced detection time for the Avenue dataset, UCSD-Ped1 dataset and UCSD-Ped2 dataset.

In short text classification, too short text length, fewer keywords and underutilization of the label information leads to the severe problems of sparse features and ambiguous semantics, which can affect the performance of short text classification. Agraph convolution network model based on tag semantic embedding is proposed for the problem. Firstly, according to TF/IDF, a new word frequency method is proposed, which comprehensively considers the inter-class and intra-class distribution of words in the global corpus. Then, through By label embedding method, each training text with the corresponding label is mapped into one feature space in the text graph. After filtering and aggregation in one feature space, synonyms embedded of label information can highlight the category representation. Finally, the text graph is input into the graph convolution neural network to learn new feature. Both the learned new feature Both the learned new feature and the features from the pre-training model can improve the classification accuracy of short texts and the generalization ability of the whole model. We choose four short text datasets such as MR, web_snippets, R8 and R52, to evaluate the performance of our proposed algorithm and fourteen benchmark models. The experimental results show that the proposed model in this paper is superior to others in classification accuracy, recall ratio and F₁-score.

Twin support vector machine learns two nonparallel hyperplanes by calculating two quadratic programming problems to solve the binary classification problems. However, in practical applications, the data usually contain uncertain information, making it difficult to construct the classification model. This paper proposed a new and efficient uncertain-data-based least squares twin support vector machine (ULSTSVM) method to address the problem of data uncertainty. Firstly, since the data may contain uncertain information, a noise vector was introduced to model the uncertain information of each example. Secondly, the noise vectors were incorporated into the least squares TWSVM. Finally, to solve the derived learning problem, we employed a two-step heuristic framework to train the least squares TWSVM classifier and updated the noise vectors alternatively. The experiments showed that our proposed ULSTSVM outperforms the baselines in training time and meanwhile achieves comparable classification accuracy. In sum, ULSTSVM adopts a noise vector to model the uncertain information and transforms the quadratic programming problems of TWSVM into linear equations, such that better classification accuracy and higher training efficiency can be obtained.

Traffic flow prediction is an important issue of the intelligent traffic control and management systems. However, traffic flow data has nonlinear and complex characteristics in both time and space, making it challenging to accurately predict it. In this regard, this paper proposes a Graph temopral recurrent independent mechanisms (G-tRIM) model, which uses Graph attention networks (GAT) to effectively capture the spatial dependencies of traffic flow data, and uses Recurrent independent mechanisms (RIM) to accurately characterize the latent state of traffic flow data. We conduct experiments on the Beijing and Guizhou datasets, and the experimental results show that our proposed G-tRIM outperforms the baseline models on both datasets in terms of MSE and MAE.

In order to make full use of the computing resources of Mobile Edge Computing (MEC) system, this paper designs an optimization scheme of collaboration between two MEC servers and joint computing and communication resources. The scheme proposes the optimization problem of dual-server collaborative multi-user task calculation, where the weighted sum between system computing delay and user energy consumption is minized. The multi-user computing unloaded transmitting power and task segmentation are optimized in the proposed scheme. A joint design scheme with low computational complexity is proposed. The original problem is decoupled into two sub-problems of computational offload optimization and computational task segmentation design, both of which can be solved by interior point method and simplex method respectively. The simulation results show that the system performance of the proposed scheme is better than the existing heuristic benchmark algorithm scheme. And the joint optimization algorithm scheme can get the similar system performance as compared with the basic scheme of the optimal Lagrange multiplier method with less computation time.

Due to the low complexity and flexible construction, polar code has become one of the most popular channel codings in wireless communication. However, the conventional successive cancellation (SC) decoder suffers from the modest performance. To deal with this issue, some improved decoders, such as successive cancellation stack (SCS) and successive cancellation list (SCL) , are developed with significant improvement of bit error ratio. However, the performance improvement of these methods is at the cost of high complexity, especially in the procedure of path selection. In this work, we propose a new hardware architecture of path selection by combining the monotone sorting of groups with the parallel comparison, which enhances the performances of hardware efficiency and resource utilization. By exploiting our proposed architecture, the results of the implementation on field programmable gate array (FPGA) verify that the hardware consumptions of the look up table (LUT), register and block random access memory (BRAM) are reduced by 24.06% , 56.42% and 39.29% respectively. And the throughput is improved by 24.38% as compared with the existing architectures.

Due to the fact that most existing process similarity metrics focus on a single dimension of the process and lack comprehensive consideration of process information, the accuracy of process retrieval needs to be improved and the application focuses on a single scenario. In this paper, an efficient and multidimensional similarity measure of string sequences of weighted Petri nets is proposed based on the structural and behavioral information. First, the proposed method weights the event log information to Petri nets. Then, the proposed method converts the weighted Petri net model into a string sequence using breadth-first traversal, and further divides the sequence into a set of immediately adjacent variation pairs with weights and a structural sequence and calculates the similarity value separately. Finally, the similarity value between processes is obtained by weighting. The experimental results show that the metric has a high accuracy rate of metric is 99.51% with a low time complexity.

In order to deeply understand the physical properties of strained heterojunction quantum well structures and improve the design of wide-bandgap nitride semiconductor devices, in this paper, based on the six-band stress-dependent k·p Hamiltonian and the self-consistent Schrodinger-Poisson equation, the valence subband model of wurtzite GaN/AlN quantum well with polar (0001) , semi-polar ($10\bar 12 $) and non-polar ($10\bar 10 $) orientations under field confinement was established. The subband energy dispersion relations between GaN/AlN quantum well with different crystal orientations under biaxial and uniaxial stresses were also given. According to the influence of stress on the valence band structure of quantum well, the microcosmic physical relationship between stress and hole effective mass was studied comprehensively. The results show that the valence band structure heavily depends on the modification in crystal orientation. The biaxial stress has little effect on the improvement of effective mass. However, uniaxial compressive stress can obtain more holes in the region of low effective mass by reducing the energy in the vertical channel direction, such that the hole effective mass can be effectively reduced. And it is reduced by about 90% in the structure of different crystal orientations.

Behavioral option pricing has been one of the hottest frontiers in the area of international finance. Stochastic volatility model has become the de facto standard model of international derivatives pricing, but it is not accurate enough in the pricing of short-term options, especially for OTM. One of the reasons is that the traditional option pricing methods ignore the irrational psychological and behavioral factors in the real market. To solve this problem,prospect theory is brought into the traditional option pricing framework, the different value judgments of investors facing gains and losses are described by introducing the subjective value function, the subjective decision weight function is used to modify the probability density function of the asset price path described by Heston model, and then the cash flow in the two periods of signing and executing European option contracts is regarded as two separate mental accounts. Prices of European options under Heston model are derived under the condition of market equilibrium. The empirical results of SSE 50ETF option show that the Heston stochastic volatility model considering the prospect theory can significantly improve the pricing accuracy of short maturity OTM option. The model parameter correction results show that the improvement of pricing performance is due to the behavioral parameters representing irrational psychology and emotion included in Heston model. Relatively speaking, investors' risk attitude towards ITM options is nearly neutral, so the improvement of behavior parameters on its pricing accuracy is limited.