Aiming at the shortcomings of forensic technology based on Docker container, we proposed an investigation and forensics model based on Docker host, and gave a targeted data forensics method according to the different states of the Docker host in 
the forensic model. The experimental results show that the forensics  can obtain relevant electronic evidence more specifically by using the model.

Firstly, aiming at the problems that the existing technology could not automatically extract software repository, mark data, build quality analysis model and analyze software quality, we proposed a just-in-time quality analysis framework GIF for Python projects. Secondly, based on GIF, the top 10 most popular Python projects on GitHub were extracted and labeled, and three classical classifiers (logistic regression, naive Bayes and random forest) were used for experimental verification on the evaluation indexes of AUC and F₁ values. The experimental results show that GIF framework can identify software defects in Python projects immediately and effectively, it is an easy-to-use just-in-time software quality analysis framework.

Aiming at the problems  of low target segmentation rate and feeble correlation of image context feature information under multi-scale categories in image semantic segmentation, we proposed an image semantic segmentation model that fused attention mechanism and multi-scale features. The model used the improved  atrous spatial pyramid pooling to increase the segmentation of multi-scale targets, used the attention refinement module to capture context information to guide feature learning, and added feature fusion  based on attention mechanism to  supervise the learning of important channel features, guide the fusion of high-order and low-order features, so as to improve  the generalization capability of the model. The simulation results on the Cityscapes dataset show that the mean intersection over union of the model is 1.14% higher than that of DeepLab v3+, which proves that the model has good robustness.

Mesenchymal stem cell exosomes (MSC-Exos) are a class of nanoscale vesicles with great potential in experimental research  and clinical applications. They contain a variety of biomolecules,  including miRNA,  mRNA,  proteins and lipids,  and have the function of  mediating  cell signaling and participating in regulation of receptor cells. Based on the important role of  MSC-Exos, we review the significant effects of  MSC-Exos in promoting tissue repair,  immune regulation and neuroprotection from the research progress and clinical applications,   especially in the treatment of autoimmune diseases,  neurodegenerative diseases,  cardiovascular diseases and tumors. We analyze a series of unsolved problems and application popularization challenges in its clinical application,  including elucidation of mechanism of action,  separation,  extraction and purification technology,  formulation of standardized production rules,  determination of dosage and administration route,  enhancement of stability,  and reduction of immunogenicity. This provides a basis for addressing these limitations to achieve widespread clinical application of MSC-Exos.

Aiming at the problems of high input dimensionality and long training time in traditional game intelligent  algorithm models, we  proposed a novel deep reinforcement learning game intelligent  guidance algorithm that integrated state information transformation and reward function shaping techniques. Firstly, using  the interface provided by the Unity engine to directly read game backend  information effectively compressed  the dimensionality of the state space and reduced the amount of input data. Secondly, by finely designing  the reward mechanism, the convergence process of the model was accelerated. Finally, we conducted comparative experiments between the proposed algorithm model and existing methods  from both subjective qualitative and objective quantitative perspectives. The experimental results show that this algorithm not only significantly improves the training efficiency of the model,  but also markedly enhances the performance of the  agent.

Aiming at the problems existing in few-shot learning at present, we designed a new network structure and its training method to improve the few-shot learning. The  convolution network and multi-scale slide pooling method were used to enhance feature extraction in the feature embedding part of the network. The main structure  of the networks was the Siamese network  to facilitate learning semantics from small sample data through comparison between samples. The training method  of the framework adopted nested level parameter updating to ensure the stability of convergence. Compared with the common visual model and 
few-shot learning methods, the experimental results  on two classical few-shot learning datasets show that the method significantly improves the  accuracy of  few-shot learning, and  can be used as a solution  under the condition of insufficient sample.

The purpose of mobile Internet information accessibility (MIIA) was to ensure that mobile application content was equally accessible, convenient, and barrier-free for all users, including those with visual impairments.  We systematically review the latest research progress in the field of mobile Internet information accessibility, focusing on the analysis and summary of research achievements in  semantic representation and understanding of mobile GUI, accessibility detection and layout repair. The analysis shows that from traditional heuristic rule methods to deep learning-driven automated tools, related technologies have gradually improved detection accuracy and adaptability, while also revealing challenges in addressing complex dynamic interactions and diverse user needs. We have provided an outlook on  future research directions.  MIIA technologies have significantly improved  the digital experience for visually impaired users, but they still need continuous innovation and optimization  to achieve a truly inclusive digital society.

We established a method based on liquid  chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS) to detect A1 and A2 β-casein in milk. Caseins were extracted from milk by isoelectric point precipitation method,  after trypsin hydrolysis,  the hydrolysates were analyzed by LC-HRMS/MS method. The peptide containing varied amino acid moiety at the 67th position was selected as characteristic species to  distinguish A1 and A2 β-casein. Casein was fully dissolved in Tris-HCl buffer solution. The  acetonitrile was added to the enzymatic hydrolysis system to improve the enzymtic hydrolysis efficiency.  High resolution mass spectrometry and tandem mass spectrometry were used to eliminate the interference signal of impurity. Four kinds of common milk  and two kinds of  A2 milk  were tested by this method. The results show that the content of A1 β-casein is about 2—4 times that of  A2 β-casein in common milk,  while a small amount of  A2 milk samples contain trace A1 β-casein. This method can detect A1 and A2 β-casein in milk with advantages of high reliability and sensitivity,  which provides support for A2 milk source screening and quality control.

Aiming at the problem that most of the existing task assignment in spatial crowdsourcing only considered unilateral benefits, short-term benefits and single scenario, we proposed a spatial crowdsourcing task assignment algorithm based on multi-agent deep reinforcement learning. Firstly, a new spatial crowdsourcing scenario was defined, in which workers could freely choose whether to cooperate with others. Secondly, a multi-agent deep reinforcement learning model based on the attention mechanism and A2C (advantage actor-critic) method was designed for task assignment in the new scenario. Finally, simulation experiments were carried out, and the performance of the algorithm was compared with other latest task assignment algorithms. The experimental results show that the proposed algorithm can achieve higher task completion rate and worker profitability rate simultaneously, which proves the effectiveness and robustness of the algorithm.

We consider a variant of the classic problem of  the traveling salesman problem (TSP) in combinatorial optimization problem: 
 the close enough traveling salesman problem (CETSP).  Firstly, we comprehensively introduce the history, solving methods, and algorithms for both TSP and CETSP, including exact algorithms (such as branch and bound method, linear programming) and heuristic algorithms (such as particle swarm optimization, greedy algorithms, etc.). The TSP requires finding the shortest path to visit each city  once and return to the starting point given a list of cities and distances. CETSP is a generalization of TSP, allowing the visiting point for each target to be chosen from within a specified neighborhood, rather than  exact location. It is  suitable for practical applications that can  tolerate errors, such as logistics distribution, intelligent transportation, and wireless sensor networks, etc. CETSP has higher flexibility and adaptability, which can significantly reduce computational resources and time consumption, particularly for large-scale problems with greater advantages. Secondly, we introduce  the potential  of CETSP in practical applications, especially in logistics, industrial manufacturing, traffic planning, information and communication, offering effective solutions for improving efficiency, reducing costs, and promoting intelligent decision-making. Finally, we have identified some future research directions for CETSP.

With the rapid depletion of fossil fuels and increasing pollution,  the development and utilization  of clean energy are becoming increasingly important. Photocatalytic technology that  converts solar energy into clean hydrogen energy  is  an effective solution. It is necessary to solve the contradiction between  the bandgap of photocatalysts and the intensity of sunlight  due to limitations in water splitting electrode potential. Therefore,  it is highly significant to develop and utilize photocatalysts with visible light  response capability. We review  the development and principles of photocatalysts,  discuss their immense potential for advancement, and introduce the most  common photocatalysts and  current research progress.

Aiming at the problem that the YOLO_v4 model would be gradually  diluted due to the features of serial connection of the neck network, which affected the performance of the model, we proposed a YOLO_v4 object detection method with improved multi-scale features. The method  reconstructed the YOLO_v4 neck network structure   by introducing an intermediate layer, and then used the intermediate layer to participate in the subsequent feature fusion to realize the cross-level connection of features, and used the parameters that could be learned through the network as the balance factor between the features to perform  feature weighted fusion. The experimental results on the VOC-2007 and VOC-2012 datasets show that the proposed  method  can improve the average accuracy of the model by 1.3%, which can effectively improve the detection ability of the model for different targets.

We combined the traditional two memory-based collaborative-filtering methods and proposed a data-based personalized mixed recommendation method for GitHub projects. The method could not only calculate the similar users dynamically to ensure the personalized recommendation, but also obtain the recommendation quality comparable to the item-based method with only small scale of similar users. At the same time, the method solved the data sparsity and cold boot problems of the original method in the face of GitHub, a data set of users and projects of an order of magnitude but with low degree of crossover to some extent by establishing inverse table and using K-means classification. By comparing with the
traditional method, we verified the effectiveness and superiority of the proposed method.

Aiming at the problem that most traditional methods for multidimensional time series analysis relied on manually establishing temporal dependencies to explore the  implicit rules  in historical data, we proposed  an autoregressive neural network method. Firstly, the neural network composed of convolution neural network (CNN) and bidirectional long short-term memory (LSTM) was used to capture the complex dependencies existing in multidimensional input features and time series, and the linear relationship was extracted by combining the traditional autoregressive method. Secondly,  compared with several classical models on two datasets in different domains, the experimental results showed that the model had the best prediction performance and could  successfully capture the repeated patterns in the data. Finally, the  ablation experiments verified the efficiency and stability of the model framework.

Aiming at the problem of the loss of features and precision degradation in the analysis process of neural network classifier prediction model based on dimension reduction, we proposed a multi-layer perceptron (MLP) regression prediction model optimized by random forest algorithm. The optimization model added an optimization mechanism between the full connection layer and the logistic regression layer of MLP regression network, the random forest algorithm was used to optimize the state of hidden layer, so as to solve the problem of losing some data features in the process of dimension reduction of neural network. The experimental results on the information data set of the borrowing customers show that the model can guarantee the main features and greatly improve the prediction accuracy, which proves that the model has high practicability in feature engineering.

Aiming at the shortcomings of existing methods in the image segmentation performance of small and medium-sized organs in the abdomen, we proposed  a network model based on local and global parallel coding  for multi-organ image segmentation in the abdomen. Firstly, a local coding branch was designed to extract multi-scale feature information. Secondly, the global feature coding branch adopted the  block Transformer, which not only captured the global long distance dependency information but also reduced the computation amount through the combination of intra-block Transformer and inter-block Transformer. Thirdly, a feature fusion module was designed to fuse the context information from two coding branches. Finally, the decoding module was designed to realize the interaction between global information and local context information, so as to better compensate for the information 
loss in the decoding stage. Experiments were conducted on the Synapse multi-organ CT dataset, compared with the current nine advanced methods, the average Dice similarity  coefficient  (DSC) and Hausdorff distance (HD) indicators achieve the best performance, with 83.10% and 17.80 mm, respectively.

Aiming at the complexity and diversity of current network intrusion, the traditional model was insufficient to extract traffic characteristics, and had low accuracy, we proposed an intrusion detection method based on CR-BiGRU hybrid model improved by merging residual network. Firstly, the dataset was normalized and one-hot encoding treatment in the model. Secondly, the convolutional neural network based on the residual network was used to extract the spatial features. Finally,   the bidirectional gated neural network was used to extract the temporal features,  complete the training of the model and realize the intrusion detection of the abnormal network. In order to illustrate the applicability of the model, comparative analysis experiments were conducted based on NSL-KDD and UNSW-NB15 datasets. The results show that the accuracy of the method based on the above datasets is 99.40% and 83.79% respectively, which is obviously superior to the classical network intrusion detection algorithm, and can effectively improve the accuracy of network intrusion detection, so as to  better ensure the  communication security of network data.

Firstly, we reviewed the latest research and application progress of several main types of nano-ferrite based core-shell structured composite absorbing materials, introduced the corresponding preparation methods and their advantages and disadvantages. Secondly, we summarized the main problems in the research field of nano-ferrite based core-shell structured composite absorbing materials. Finally, we looked forward to the future research and development direction.

Aiming at the problems of the limitation of obtaining global features in image recognition and the difficulty in improving recognition accuracy, we proposed  an image recognition method based on the lightweight model of random augmentation Swin-Tiny Transformer.  The method combined the random data augmentation based enhancement (RDABE) algorithm to enhance image features in the preprocessing stage, and adopted the Transformer’s self-attention mechanism to obtain more comprehensive 
high-level visual semantic information. By optimizing the Swin-Tiny Transformer model and fine-tuning the parameters on a maize disease dataset, the applicability of the algorithm was verified on maize diseases in the agricultural field, and more accurate disease detection was achieved. The experimental results show that the lightweight Swin-Tiny+RDABE model based on stochastic 
enhancement has an accuracy of 93.586 7% for maize disease image recognition. The experimental results compared with the excellent performance lightweight Transformer and convolutional neural network (CNN) series models with consistent parameter weights show that  the accuracy of the improved model is higher than that of the  Swin-Tiny Transformer, Deit3_Small, Vit Small, 
Mobilenet_V3_Small, ShufflenetV2 and Efficientnet_B1_Pruned models by 1.187 7% to 4.988 1%, and can converge rapidly.

Sclerotinia sclerotiorum (Lib.) de Bary is a worldwide and necrotrophic phytopathogenic fungi with a wide host-range. Sclerotinia stem rot (SSR) caused in soybean and rapeseed by S.sclerotiorum has caused huge economic losses to agricultural production. The pathogenic mechanism of S.sclerotiorum is complicated,  which not only has a necrotrophic phase that directly kills cells,  but also includes a short biotrophic phase that needs to suppress plant immunity. S.sclerotiorum has a wide variety of pathogenic factors,  including key regulatory factors that mediate the formation of infection structure or stress resistance,  hydrolytic enzymes that degrade plant cell components,  oxalic acid,  effector that induce plant cell death or inhibit plant immunity,  etc. We have reviewed the infection model of S.sclerotiorum, summarized  the roles of various pathogenic factors,  especially effector proteins,  in the pathogenesis of S.sclerotiorum. Combined with the latest research,  we have prospected the new pathogenic mechanism of S.sclerotiorum,   providing theoretical basis for the prevention and control of crop Sclerotinia diaease.

In view of the skin lesions with different scales and irregular shapes, the traditional U-Net method lacked robustness to analyze targets from different scales, and lost some spatial context information when extracting high-level semantic features of the image, which affected the accuracy of subsequent segmentation, the author proposed a medical image segmentation method based on U-Net with multi-scale and multi-dimensional feature fusion. Firstly, the spatial context information from different scales was fused by atrous convolution. Secondly, the weight information of each channel of the feature map was extracted by the channel context information fusion module. Finally, the multi-scale and multi-dimensional information in the feature map was fused to preserve more spatial context information. Experimental results show that the proposed method can segment the skin lesion on the skin lesion dataset, and the segmentation effect is good.

A smooth activation function with a parameter was constructed for the deep learning neural networks. The online correction formula for this parameter was established based on the error back propagation algorithm, which avoided the problems of gradients losing, nonsmooth and overfitting. Compared with some popular activation functions, the results show that the new activation function works well on many data sets.

 Based on gene therapy as a fundamental treatment for diseases, it brings new ideas and methods for the treatment of rheumatoid arthritis (RA). We review  the relevant research advances  of gene therapy for rheumatoid arthritis,  including small interfering RNA (siRNA), micro RNA (miRNA),  DNA,  CRISPR/Cas9 system,  deoxyribonuclease and some other technologies,   providing reference ideas for the application of  gene therapy in the field of RA and offering more  effective and targeted treatment plans for the patients with RA. 

Aiming at the problem that existing multilingual models were inefficient in utilising multilingual datasets in the pre-training process, which led to a more insufficient cross-language contextual learning ability and thus language bias, we proposed a cross-language text similarity model based on the alternating language data  reconstruction method. This method formed reconstructed pre-trained text pairs by symmetrically replacing Chinese and English words in the parallel corpus, and used the above text pairs to perform targeted pre-training and fine-tuning processing based on data reconstruction for the multilingual large model mBERT (BERT-based-multilingual). In order to verify the feasibility of the model, experiments were conducted on the United Nations parallel corpus dataset, and the experimental results show that the similarity checking accuracy of this model outperforms that of mBERT and the other two baseline models. It can not only  further improve the accuracy of cross-language information retrieval, but also  reduce the research cost of multilingual natural language processing tasks.

We studied  the antioxidant active components and mechanism of  dandelion based on  high performance liquid chromatograph (HPLC) method and network pharmacology method.   The effective components of Chinese herbal medicine dandelion were extracted by  organic reagents,  such as petroleum ether,  ethyl acetate,  dichloromethane and n-butanol,  and their antioxidant effects were studied. Multiple online databases of network pharmacology were used to obtain  common targets of dandelion antioxidant construct PPI network,  and conduct GO enrichment analysis and KEGG signal pathway enrichment analysis. The results show that the  extracts of each phase of dandelion have a certain scavenging ability to hydroxy radical (.OH),  superoxide anion radical (O^2-.) and 1,1-diphenyl-2-picrylhydrazyl radical (DPPH.),  the ethyl acetate phase extract of dandelion and the dichloromethane phase extract of dandelion have better scavenging effects,  and the main antioxidant active components are isorhamnetin and oleanolic acid. There are 137 dandelion antioxidant targets screened from the online database. The common targets are mainly concentrated on membrane rafts,  which are resistant to the response of cells to inorganic substances,  nitrogen compounds,  and nutrient levels oxidation. The common targets are closely related to cancer signal pathway,  NF-kappa B signal pathway,  and PPAR signal pathway. 

The hydrogel was prepared by free radical polymerization and freeze-thaw cycle using hydroxypropyl acrylate， ι-carrageenan，  polyvinyl alcohol and calcium chloride as raw materials. We tested and analyzed the mechanical property，  self-recovery property，  swelling property，  self-healing property，  anti-fatigue property and the conductivity property of the hydrogel. The results show that the hydrogel has high mechanical strength, the fracture stress is 625.77 kPa， the fracture elongation is 604.48%, the swelling rate of hydrogel is low, and has excellent self-recovery property，  anti-fatigue property  and  conductivity property.

Aiming at the  characteristics of  subtle, uneven, imperceptible inter-class differences between classes and real-world data distribution in  fine-grained image classification, we proposed a fine-grained image classification model based on attention mechanism. Firstly, the preliminary feature extraction of the image was carried out  by introducing the fusion of a two-way channel attention and residual network. Secondly,  the multi-head self-attention mechanism was applied to extract fine-grained relationships between  deep feature data. Thirdly, the training of loss function measurement system was designed by combining cross entropy loss and center loss. The experimental results show that the test accuracy of the model on two standard datasets 102 Category Flower and CUB200-2011 is  94.42% and 89.43%, respectively. Compared with other mainstream classification models, the classification effect is better.

A complex symmetric  operator refers to a linear operator with a symmetric matrix representation  on a  Hilbert space. We review the main research  advances  and  several open problems of complex symmetric operators in recent years, involving special complex symmetric operators, reducing subspaces, the norm closure problem, and algebraic properties  and so on.

In order to study the key genes and key metabolic pathways after the sgf73 gene was knocked out in Schizosaccharomyces pombe,  the wild\|type yeast strains and sgf73Δ  gene-deficient strains were sequenced and bioinformatics analyzed by RNA-Seq sequencing technology, and the GO and KEGG functional enrichment analysis were carried out.  The results show that in the sgf73Δ gene-deficient strains,  there are 1 834 highly expressed genes,  including 6 extremely highly expressed genes,  and 1 714 differentially expressed genes,  of which 934 genes are up-regulated and 780 genes are down-regulated. The  sgf73 gene knockout leads to  abnormal changes in cellular metabolism and transmembrane transport. The  down-regulation of cki1,cki2 and cdc25 genes involved in cycle regulation in the MAPK signaling pathway leads to prolongation of mitotic time in sgf73Δ strain,  the down-regulation of regulatory cytoskeleton rgf2,rho1 and stt4 genes leads to abnormal contraction  of actin ring of  sgf73Δ strain.

Aiming at the problem of  the navigational challenges for visually impaired users on diverse webpages, we proposed a method for automatically identifying navigation bars to improve  webpage accessibility. Firstly, by designing heuristic rules, elements within the navigation bars were  autonomously extracted based on the ordered element arrangement within the navigation bar, as well as rules such as hyperlinks and succinct textual content within sub-elements. Secondly, a decision tree binary classification algorithm was used to categorize elements with pronounced feature disparities in the navigation bars. Finally, the identified navigation bar elements were subject to the injection of Landmark attributes. In experimental evaluations of  100 websites, the method successfully identified  92.6% of navigation bar elements, and the infusion of Landmark attributes significantly improves website accessibility, thereby ameliorating the user experience for visually impaired individuals.

SWEET (sugars will eventually be exported transporters) proteins are a novel class of sugar transporter proteins that mediate the bidirectional transmembrane transport of sugars in cells and play important functions in plant growth and development,  including phloem loading,  phytohormone transport,  flower,  fruit and seed development,  interactions between plants and pathogen, and symbiosis between plants and microorganisms.  SWEET proteins are important participant in the process of plant-pathogen interactions. We summarize the response mechanisms of SWEET proteins in biotic stresses, as well as the metabolic characteristics,  regulatory pathways and specific defense responses of SWEET genes when plants are infected with different pathogens (bacteria,  fungi,  nematodes and virus). We also discuss  the use of gene editing tools to edit SWEET genes to enhance plant resistance to pathogens and their application in agriculture. The aim is to provide a reference for in-depth research on the mechanism of  SWEET proteins involvement in plant-pathogen interactions and the use of SWEET genes for disease resistance breeding.

Aiming at  the non-selective expansion  and training deficiencies of the DoubleMix algorithm during data augmentation, we proposed a supervised contrastive learning text classification model based on double-layer data augmentation, which effectively improved the accuracy of text classification when training data was scarce. Firstly, keyword-based data augmentation was applied to the original data at the input layer, while selectively enhancing the data without considering sentence structure. Secondly, we  interpolated  the original and augmented data in the BERT hidden layers, and  then send them to the TextCNN for further feature extraction. Finally, the model was trained by using Wasserstein distance and double contrastive loss to enhance text classification accuracy. The comparative experimental results on SST-2, CR, TREC, and PC datasets show that the classification accuracy of the proposed method is 93.41%, 93.55%, 97.61%, and 95.27% respectively, which is superior to classical algorithms.

The artificially cultivated meat-like Ganoderma lucidum (MGL) extracts were applied to the oxidative damage model of Caenorhabditis elegans  to evaluate their toxicity and antioxidant efficacy.  The ultraviolet (UV) full wavelength scanning method was used to determine the main components of the extracts, the life span and spawning quantity were used to evaluate the toxicity of the extracts, the free radical content was detected by active oxygen detection (DCF) method to evaluate the antioxidant efficacy of the extracts, the potential acting target of the extracts was explored by the transcriptome analysis. The experimental results show that the main components of the MGL extracts are quinones and proteins, the extracts have no obvious toxic effects, 2.5 mg/mL extracts have significant antioxidant capacity against damaged nematodes and can effectively regulate the expression of  related genes to the heat shock protein family (hsp-16.1,16.11,16.48,16.49), superoxide dismutase (sod-5), inner mitochondrial membrane (timm-23), metal sulphurin-1 (mtl-1), and the Skp1 protein family.The artificially cultivated MGL extracts can exert obvious antioxidant efficacy through the life\|regulating signal pathway, and  have the potential ability to regulate fat metabolism and promote protein synthesis.

Aiming at the problem that it was difficult to select the parameters of gradient boosting decision tree (GBDT), we proposed a GBDT regression prediction algorithm based on improved whale optimization algorithm (IWOA). Firstly, an improved whale optimization algorithm was proposed, which initialized the population by using chaotic mapping to improve the diversity of the population, and the inertial weight and the mutation crossover strategy of differential evolution algorithm were introduced to solve the problem that it was easy to fall into the local optimization in the later stage of iteration. Secondly, IWOA was used to optimize the key parameters of the GBDT to avoid the blindness of parameter selection and improve the generalization ability of the regression prediction model. Finally,
 the IWOA-GBDT regression prediction model was established and verified by the UCI dataset. The experimental results show that compared with decision tree, support vector machine, Adaboost and GBDT algorithms, the proposed model algorithm has better fitting effect and certain practical value.

We review  the  floorplanning methods for very large scale integration (VLSI), explore the significance of floorplanning in integrat
ed circuit design, and its impact on chip area, interconnect length, and design cycle. Firstly, we  review the development history of integrated circuit technology, emphasize the role of floorplanning in determining the position, size, and rotation angles of modules. Secondly, we provide a detailed introduction to four main categories of VLSI floorplanning methods: intuitive construction methods, analytical methods, iterative methods and machine learning methods. Thirdly, we discuss two commonly used  MCNC and GSRC benchmark datasets, which are crucial for testing and evaluating floorplanning methods in the VLSI design field. Finally, we summarize the research progress in the field of floorplanning and point out future research directions.

We proposed a  method for automatically identifying components with missing readable text in Android applications to improve the accessibility of these applications. Firstly, We used UI Automator to extract the graphical user interface of the application,  prune  irrelevant components and  complete  component attributes to generate the corresponding view tree. Secondly, we designed three heuristic rules  to identify components with missing readable text in the view tree. Through evaluation experiments on six popular applications, the proposed method successfully identified problematic components with an average accuracy  of 97%. Finally, a generated test report helped application developers  clearly locate and rectify the missing readable text by marking the problematic components in both the source code and screenshots. The  research achievement not only effectively improves the user experience for visually impaired users, enabling them to  interact with the applications more smoothly, but also provides developers with a practical tool to promote the overall accessibility of Android applications. Through  this approach, developers can better understand and address accessibility issues, creating a more user-friendly digital environment for all users.