Effect of Chitosan Coating on Properties of Polyactic Acid Thread-Embedding Material

Thread-embedding materials buried into acupuncture points to produce a long-time stimulation is a method which has been widely applied in the acupuncture and moxibustion therapies for diseases treating and they should have excellent properties of good biocompatibility,biodegradability and moderate rigidity.Polyactic acid(PLA) as the thread-embedding material had some shortcomings such as poor hydrophilicity and slowly biodegradable speed.In this paper,PLA thread-embedding material was modified by chitosan coating which could improve rigidity,hydrophilicity and moisture absorption and thus produce better stimulation effect.The variation tendency of diameter,mass,hydrophilicity,and initial modulus of the sample under the change of chitosan coating concentration,coating time and coating times were studied respectively.The results showed that the hydrophilicity,mass,and initial modulus after coating rose in a certain range when the coating time,coating times and coating concentration increased,while the changing of coating had little impact on fiber diameter.

Clinical study on the treatment of bronchiectasis in remission period by embedding thread combined with Jianpi Qushi Huayu plaster

Objective:To explore the clinical effect of embedding thread combined with Jianpi Qushi Huayu ointment in the treatment of bronchiectasis in remission period,and to observe the effect on the level of inflammatory cytokines.Methods:150 patients with bronchiectasis in remission period were divided into three groups(50 cases in each group),western medicine group(treated with salmeterol fluticasone and low dose erythromycin),combined group(treated with Jianpi Qushi Huayu plaster),observation group(treated with western medicine+Jianpi Qushi Huayu plaster+embedding thread),and the clinical treatment of the three groups were compared.Result:before and after treatment,the improvement of TCM syndrome score,the improvement of pulmonary ventilation index,the improvement of inflammatory cytokines,and the change of adverse reactions.Results:the treatment efficiency of the observation group and the combination group was significantly higher than that of the western medicine group P The peak level of expiratory flow velocity,the maximum expiratory flow,the forced expiratory volume in one second and the ratio of forced expiratory volume in one second to vital capacity in the observation group and the combined group were significantly better than those in the western medicine group and the pre-treatment level(P<0.05),and the improvement in the observation group was better than that in the combined group After treatment,the expression of IL-13,IL-10 and IL-4 in the combined group was significantly better than that in the western medicine group and before treatment(P<0.05),and the improvement in the observation group was better than that in the combined group;there was no significant difference in the incidence of adverse reactions among the three groups(P>0.05).Conclusion:catgut embedding combined with Jianpi Qushi Huayu ointment can significantly alleviate the related clinical symptoms and signs,effectively control the further aggravation of the disease,improve the lung function of patients,regulate the level of inflammatory cytokines to reduce the degree of inflammatory reaction,and do not significantly aggravate the adverse reactions of drugs.

A Linked List Encryption Scheme for Image Steganography without Embedding

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

TCM-HIN2Vec:A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment

Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Thin-Shell Wormholes Admitting Conformal Motions in Spacetimes of Embedding Class One

This paper discusses the feasibility of thin-shell wormholes in spacetimes of embedding class one admitting a one-parameter group of conformal motions. It is shown that the surface energy density σis positive, while the surface pressure is negative, resulting in , thereby signaling a violation of the null energy condition, a necessary condition for holding a wormhole open. For a Morris-Thorne wormhole, matter that violates the null energy condition is referred to as “exotic”. For the thin-shell wormholes in this paper, however, the violation has a physical explanation since it is a direct consequence of the embedding theory in conjunction with the assumption of conformal symmetry. These properties avoid the need to hypothesize the existence of the highly problematical exotic matter.

Roller Screw and Threaded Chain Speed Reducer: An Experimental Evaluation

We are developing a speed reducer that can be considered a transformation of a worm gear reducer: the worm is replaced by an inverted roller screw, and the gear is replaced by a threaded chain drive. This configuration lessens wear, increases load capacity, and improves efficiency. The threaded chain consists of nut-shaped links. This paper presents the results of tests carried out on a prototype with a reduction ratio of 46.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

OBSERVATION OF REMOTE AND RECENT THERAPEUTIC EFFECTS OF ACUPOINT-THREAD EMBEDDING, WESTERN MEDICINE OR ALLIED THERAPY ON PERENNIAL ALLERGIC RHINITIS

Objective: The comparison of the remote and recent therapeutic effects on perennial allergic rhinitis was carried on among single application of acupoint-thread embedding, western medicine (certirizine+flixonase) and allied therapy. Method: Ninety cases were randomized into thread-embedding group, western medicine group and embedding+western medicine group (allied-therapy group), 30 cases in each one. In thread embedding group, Yingxiang (迎香 LI 20) and Feishu (肺俞 BL 13) were selected for thread embedding, once a week, totally 4 treatments were required. In western medicine group, certirizine tablet 10 mg in combination with flixonase sprayer was applied once per day, continuously for 30 days in treatment. In allied-therapy group, both of the above methods were adopted continuously for 30 days in treatment. The therapeutic effects were in statistics at the end of treatment and 1 year later. Results: At the end of treatment, in thread-embedding group, 21 cases were remarkably effective, 3 cases effective and 6 cases no effect. In western medicine group, 20 cases were remarkably effective, 3 cases effective and 7 cases no effect. In allied-therapy group, 23 cases were remarkably effective, 2 cases effective and 5 cases no effect. It was shown in statistics that there was no significant difference in therapeutic effects among three groups ( P>0.05 ). But one year after the end of treatment, it was indicated that in thread-embedding group, 3 cases were remarkably effective, 10 cases effective and 17 cases no effect; in western medicine group, 2 cases were remarkably effective, 9 cases effective and 19 cases no effect; in allied-therapy group, 7 cases were remarkably effective, 11 cases effective and 12 cases no effect. The significant difference in the therapeutic effects had not been presented in the comparison between thread-embedding group and western medicine group (P>0.05), but that had been presented between allied-therapy group with either of the groups with single therapy applied (P<0.05). Conclusion: Quite good recent therapeutic effect of either acupoint-thread embedding or western medicine (certirizine+flixonase) was achieved on perennial allergic rhinitis. The remote therapeutic effect of allied therapy was more advantageous than that of single application of either thread embedding or western medicine.

Effect of Chitosan Coating on the Properties of Poly(glycolic acid-lactic acid)Thread-Embedding Material

Poly( glycolic acid-lactic acid)( PGLA) threadembedding material was modified by chitosan coating which could improve the rigidity,hydrophilicity and moisture absorption of the material,and produced better stimulation effect. Thus,this kind of thread-embedding materials which can be buried into acupuncture points to produce a long-time stimulation is popular in the acupuncture and moxibustion therapies. The variation tendencies of diameter,weight, hydrophilicity, and flexibility of the samples under the change of chitosan coating concentration,coating time and coating times were studied respectively. The results showed that the hydrophilicity,weight,and rigidity after coating rose in a certain range with the increase of coating time,coating times and coating concentration. The coating time had little influence on the diameter of fiber.

基于BL602的RT Thread移植方法研究

RT Thread是一款嵌入式实时操作系统,因其易于移植、可裁剪性好等特点备受广大嵌入式开发者的喜爱。BL602芯片内含一个32位的RISC V架构的微处理器,兼具高性能、超低功耗等特点。但目前关于将RT Thread实时操作系统移植到BL602芯片上还没有成熟的方案,针对这一现象,给出了将RT Thread移植到BL602上的详细方案。通过剪裁源码、修改配置文件、修改系统时钟、添加串口输出、采用轮询或中断回调实现串口输入等操作进行移植。实验结果表明,移植到BL602上的RT Thread操作系统可以正常运行,并且具备任务管理、任务调度等功能。本文提出的移植方案不仅可以降低BL602的开发难度、提高CPU的利用率,而且为其他RTOS移植提供了参考。

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

Extrapolation over temporal knowledge graph via hyperbolic embedding

Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.

Word Embeddings and Semantic Spaces in Natural Language Processing

One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.

Summary and clinical application of Zhuang medicine medicated thread moxibustion

Zhuang medicine medicated thread moxibustion is a kind of external therapy with ethnic medicine characteristics that spreads in Guangxi Zhuang region.It has a wide range of clinical application and significant curative effect,and can be used to treat diseases such as internal medicine,surgery,dermatology,gynecology,pediatrics,and five sense organs.Through reviewing and combing the literature on the Zhuang medicine medicated thread moxibustion in recent years,this paper summarizes and prospects its clinical application,with a view to further explaining the basic principles and application rules of Zhuang medicine medicated thread moxibustion and digging its clinical value.

Security Threads and IoT Security

One of the technologies that have attracted the most attention recently across a variety of applications is the Internet of Things (IoT). The Internet of Things (IoT) is the combination of sensor, embedded computing, and communication technologies. The goal of the Internet of Things is to provide seamless services to anything, everywhere, at any time. The internet of things (IoT) technologies plays a vital role everywhere after the internet and information and communication technology, ushering in the fourth disruptive technology revolution (ICT). For real-time processing, communication, and monitoring, the smart items are linked together through wired or wireless connections. Implementing the IoT system presents security and privacy challenges since IoT devices are incompatible with current security standards based on tradition. This paper discusses IoT security strands, mitigation strategies, and privacy issues. This study’s major objective is to get more knowledge about security threats, mitigation techniques, and privacy concerns in IoT devices. The authors also mentioned a few cutting-edge technologies that can address general security problems. This study’s major objectives are to find research gaps in IoT security and match solution paradigms. The advent and rapid growth of the Internet of Things (IoT), which offers innumerable benefits, facilities, and applications including smart grids, smart homes, smart cities, and intelligent transportation systems (ITS), have an impact on everyone’s life. However, the deployment and use of sensing devices exposes IoT-based systems and applications to many security flaws and attacks. Furthermore, the lack of standardization brought on by the diversity of devices and technologies makes integrating security in the IoT a severe problem. The purpose of this review paper is to highlight the numerous security threats, challenges, and attacks that IoT-enabled applications face.

Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

One of the issues in Computer Vision is the automatic development of descriptions for images,sometimes known as image captioning.Deep Learning techniques have made significant progress in this area.The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem.This paper aims to find optimized models for these two subsystems.For the image feature extraction subsystem,the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image.For the caption generation lingual subsystem,this paper tested three different pre-trained language embedding models:Glove(Global Vectors for Word Representation),BERT(Bidirectional Encoder Representations from Transformers),and TaCL(Token-aware Contrastive Learning),to select from them the most accurate pre-trained language embedding model.Our experiments showed that building an image captioning system that uses a concatenation of the two Transformer based models SWIN(Shiftedwindow)and PVT(PyramidVision Transformer)as an image feature extractor,combined with the TaCL language embedding model is the best result among the other combinations.