Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

NIC X-FILE和DENCO Pre-Shaper镍钛器械对模拟弯曲根管成形的影响

背景:根管预备是根管治疗的关键步骤,近年来随着新型镍钛器械的迅速发展,根管成形能力得到了显著提高,然而不同种类的镍钛系统成形能力亦不同,目前国内外关于R相热处理机用镍钛器械NIC X-FILE和DENCO Pre-Shaper对弯曲根管成形能力的影响鲜见报道。目的:比较NIC X-FILE和DENCO Pre-Shaper机用镍钛锉预备树脂模拟弯曲根管的成形效果。方法:选取树脂模拟弯曲根管50个,随机分为2组(n=25),分别使用NIC X-FILE、DENCO Pre-Shaper镍钛根管锉进行根管预备,记录根管预备时间、采集根管预备前后图像。使用图像处理和分析软件测量各观测点的根管内、外侧壁树脂去除量,运用统计学软件,对比研究两种机用镍钛锉的根管预备时间和中心定位能力。结果与结论:①DENCO Pre-Shaper组平均根管预备时间为(1.58±0.02)min,NIC X-FILE组平均根管预备时间为(2.22±0.03)min,两组间比较差异有显著性意义(P<0.05);②DENCO Pre-Shaper组在距离根尖孔4,5,7,8,9,10 mm处的内侧树脂去除量多于NIC X-FILE组(P<0.05),在距离根尖孔3,5,6,7,8 mm处的外侧树脂去除量少于NIC X-FILE组(P<0.05);③在距离根尖孔2,4,5,6,7,8,9 mm处,NIC X-FILE组的中心定位能力优于DENCO Pre-Shaper组(P<0.05),其中在距离根尖孔4 mm处的中心定位能力最佳;④结果表明,DENCO Pre-Shaper根管锉较NIC X-FILE镍钛根管锉具有较高的机械效率,但其中心定位能力不如NIC X-FILE镍钛根管锉。

NRT FILES镍钛根管锉预备对弯曲根管应力分布的仿真分析

背景:在根管治疗中,镍钛合金器械常因应力超过金属塑性变形的弹性极限或在根管弯曲处重复弯曲导致镍钛合金疲劳断裂。目的:探索新型镍钛锉NRT FILES在作为根管预备材料时对根管内壁存在的应力分布,明确应力集中区域。方法:利用Solidworks软件对NRT FILES进行三维建模,模拟还原器械在真实口腔环境中存在的状态。建立6组根管简化模型:①弯曲角度30°、弯曲半径5 mm、根尖弯曲;②弯曲角度30°、弯曲半径5 mm、根中弯曲;③弯曲角度30°、弯曲半径2 mm、根尖弯曲;④弯曲角度30°、弯曲半径2 mm、根中弯曲;⑤弯曲角度45°、弯曲半径5 mm、根尖弯曲;⑥弯曲角度45°、弯曲半径2 mm、根尖弯曲。将根管简化模型与根管锉模型进行组配,对根管内壁受力点及应力分布情况进行分析。结果与结论:①-⑥组根管锉最大应力分别为1 185,1 354,1 371,1 678,1 335,2 557 MPa,最大应变分别为0.010 36,0.013 54,0.013 79,0.016 48,0.013 45,0.022 03;根管弯曲部位的不同会影响根管锉所承受的应力大小,因此在根管治疗中,根管锉沿着根管越往下,根管锉与根管接触的半径越大、刚性越差,根管锉所承受的应力越大越容易发生断裂;根管弯曲半径越小,根管锉承受的应力越大;根管弯曲角度越小,根管锉所承受的应力越大。在根尖弯曲部位,根管锉在根管弯曲半径小的弯曲根管需要承受更大的应力,根管锉更容易发生断裂;同理,在根中弯曲部位,根管锉在根管弯曲半径小的弯曲根管亦承受更大的应力,因此要格外注意根管的弯曲程度,以此来选择合适的根管锉进行根管治疗。

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

基于序列挖掘的Dockerfile规则自动提取工具

Dockerfile定义了一组构建容器镜像的指令,这些指令指示了容器化的应用程序该如何构建.最近的研究表明Dockerfile存在相当多的质量问题.在本文中,我们提出了一种新的工具DMiner(Dockerfile Miner)来提取高质量Dockerfile中的隐含规则,这些规则将有助于提升Dockerfile的质量.DMiner主要分为3个模块,分别负责Dockerfile的采集、过滤,Dockerfile的解析处理以及Dockerfile规则的挖掘提取,DMiner将Dockerfile解析成统一的序列表示并使用序列模式挖掘算法来提取规则.本工具对现有的Dockerfile数据集进行了扩充,同时新提取出了9条在其他工作未曾出现的规则,在真实数据集上的大量实验证明了该工具的有效性和高效性.

Enhanced Best Fit Algorithm for Merging Small Files

In the Big Data era,numerous sources and environments generate massive amounts of data.This enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future changes.Hadoop is used to process this kind of data.It is known to handle vast volumes of data more efficiently than tiny amounts,which results in inefficiency in the framework.This study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm(EBFM)that merges files depending on predefined parameters(type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same range.Its primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established system.This procedure takes place before the files are available for the Hadoop framework.Additionally,the files generated by the system are named with specific keywords to ensure there is no data loss(file overwrite).The proposed approach guarantees the generation of the fewest possible large files,which reduces the input/output memory burden and corresponds to the Hadoop framework’s effectiveness.The findings show that the proposed technique enhances the framework’s performance by approximately 64%while comparing all other potential performance-impairing variables.The proposed approach is implementable in any environment that uses the Hadoop framework,not limited to smart cities,real-time data analysis,etc.

Distributed File System Based on a Relational Database

Working with files and the safety of information has always been relevant, especially in financial institutions where the requirements for the safety of information and security are especially important. And in today’s conditions, when an earthquake can destroy the floor of a city in an instant, or when a missile hits an office and all servers turn into scrap metal, the issue of data safety becomes especially important. Also, you can’t put the cost of the software and the convenience of working with files in last place. Especially if an office worker needs to find the necessary information on a client, a financial contract or a company’s financial product in a few seconds. Also, during the operation of computer equipment, failures are possible, and some of them can lead to partial or complete loss of information. In this paper, it is proposed to create another level of abstraction for working with the file system, which will be based on a relational database as a storage of objects and access rights to objects. Also considered are possible protocols for transferring data to other programs that work with files, these can be both small sites and the operating system itself. This article will be especially interesting for financial institutions or companies operating in the banking sector. The purpose of this article is an attempt to introduce another level of abstraction for working with files. A level that is completely abstracted from the storage medium.

RCache: A Read-Intensive Workload-Aware Page Cache for NVM Filesystem

Byte-addressable non-volatile memory(NVM),as a new participant in the storage hierarchy,gives extremely high performance in storage,which forces changes to be made on current filesystem designs.Page cache,once a significant mechanism filling the performance gap between Dynamic Random Access Memory(DRAM)and block devices,is now a liability that heavily hinders the writing performance of NVM filesystems.Therefore state-of-the-art NVM filesystems leverage the direct access(DAX)technology to bypass the page cache entirely.However,the DRAM still provides higher bandwidth than NVM,which prevents skewed read workloads from benefiting from a higher bandwidth of the DRAM and leads to sub-optimal performance for the system.In this paper,we propose RCache,a readintensive workload-aware page cache for NVM filesystems.Different from traditional caching mechanisms where all reads go through DRAM,RCache uses a tiered page cache design,including assigning DRAM and NVM to hot and cold data separately,and reading data from both sides.To avoid copying data to DRAM in a critical path,RCache migrates data from NVM to DRAM in a background thread.Additionally,RCache manages data in DRAM in a lock-free manner for better latency and scalability.Evaluations on Intel Optane Data Center(DC)Persistent Memory Modules show that,compared with NOVA,RCache achieves 3 times higher bandwidth for read-intensive workloads and introduces little performance loss for write operations.

Protaper、MTwo、K File根管锉在隐裂性牙髓炎患者预备根管中的应用效果比较

目的:比较Protaper、MTwo、K File根管锉在隐裂性牙髓炎患者预备根管中的应用效果。方法:回顾性分析2019年5月至2021年3月该院收治的165例(198颗)隐裂性牙髓炎患者的临床资料,根据不同预备根管器械将其分为Protaper组61例(68颗,根管总数111根)、MTwo组58例(73颗,根管总数125根)、K File组46例(57颗,根管总数101根)。比较三组临床疗效、根管填充情况、疼痛程度[视觉模拟评分法(VAS)]评分、约诊间痛发生率、治疗后1年患牙牙根纵裂发生率。结果:三组治疗总有效率比较,差异无统计学意义(P>0.05);Protaper组根管适充率与MTwo组、K File组比较,差异均无统计学意义(P>0.05);MTwo组根管适充率高于K File组,差异有统计学意义(P<0.05);MTwo组与K File组VAS评分比较,差异无统计学意义(P>0.05);MTwo组约诊间痛发生率与Protaper组、K File组比较,差异无统计学意义(P>0.05);Protaper组VAS评分均低于MTwo组、K File组,约诊间痛发生率低于K File组,差异有统计学意义(P<0.05);治疗后1年,三组患牙牙根纵裂发生率比较,差异无统计学意义(P>0.05)。结论:Protaper、MTwo、K File根管锉应用于隐裂性牙髓炎患者预备根管中治疗总有效率相当,但Protaper降低VAS评分、MTwo提高根管填充质量的效果明显。

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

深度学习下吊装作业工人防护装备及吊钩检测方法

为解决危大工程中吊装作业安全管理的问题,基于深度学习构建目标检测算法(You Only Look Once version 5,YOLOv5)网络模型,针对进入吊装作业区域内人员的防护装备进行多目标融合检测,并对吊钩在施工过程中的状态进行检测。在原始的检测网络模型中引入4种注意力机制,并通过5种训练模型的结果对比分析,进而选择卷积块注意力模块(Convolutional Block Attention Module,CBAM)最优模型。优化后的检测模型对安全帽的平均识别精度达86.5%,对反光衣的平均识别精度达83.0%,对吊钩的状态识别精度达92.0%。将训练好的人员检测模型和吊钩检测模型打包成exe执行文件,应用到施工安全管理人员的中控平台,可帮助管理人员更好地判断吊装作业的工作情况,进而及时进行风险管控。

可撤销属性加密的区块链数据访问控制方法

针对区块链数据共享中存在的粗粒度访问控制问题,提出一种基于属性撤销密文策略属性基加密的区块链数据访问控制方法。在现有方案基础上进行改造,引入预解密过程,结合属性撤销列表实现属性实时撤销;基于非对称群下的DBDH困难问题假设进行安全性证明;基于超级账本Fabric进行系统设计,结合星际文件系统采用链上链下存储方式解决区块链容量不足和系统效率问题。实验结果表明,所提方案撤销属性时无需更新密钥密文重复上链,仅需要6次Pairing操作进行预解密和解密,且在大规模属性集下,预解密时间和解密时间平均保持在百毫秒左右的常量级上,实现区块链数据高效、细粒度的访问控制。

基于FileNet存储的数字文档管理系统设计与实现

在文档体量剧增的背景下,本文从实际问题出发,基于File Net底层存储,结合统一的管控模块,构建上层应用系统。通过成熟稳定的架构,研发一套企业数字文档管理系统。实现勘探开发、生产、钻井、合同等各专业非结构化、半结构化文档的集中、统一管理。近年来,随着公司的发展,油气田勘探、开发业务量增加,勘探、开发生产、工程等各专业形成了大量信息,文档资料的数量在快速增长。业务流程交叉及复杂化、文档存储查询接口的不断新增,传统的数字文档的管理模式无法满足业务需求的增长。

公司濒临破产时董事破产申请义务的立法建构

公司濒临破产时已经或者即将失去债务清偿能力,此时的经营风险将由股东向债权人进行转移。董事破产申请义务是助力债权人应对上述风险的重要支点,因为这一制度能够有效地遏制股东、董事以及债权人内部的机会主义行为,防止公司资产的持续恶化。在学理上,“直接义务论”和“间接义务论”都可以为其立法建构提供逻辑支持,前者强调当公司濒临破产时董事对债权人直接承担信义义务,后者则认为此时董事依然对公司整体承担信义义务,但是应在决策中重点考量债权人利益。从我国公司治理实践来看,也有推进该制度立法建构的现实需求,包括平衡认缴资本制对法定资本规则的弱化、弥补破产程序启动规制的不足、顺应董事会中心主义治理模式的发展以及填补公司法与破产法之间的制度缝隙等。无论是德国和奥地利对该制度的明确规定,还是美国的“加深破产理论”与英国的“不当交易条款”等功能性替代规定,都能够为我国的立法提供参考。就具体的本土方案而言,可以“直接义务论”为指引,将该制度嵌入破产法体系之中。同时,围绕董事破产申请义务触发条件的界定、董事破产申请义务启动主体构成边界的明晰、董事破产申请义务限制期限的规制、董事破产申请义务中重整程序的融入、董事违反破产申请义务的责任承担以及董事违反破产申请义务的债权人追诉机制等相关内容,建构系统化的具体规则。

行政规范性文件备案审查中比例原则的适用

行政规范性文件备案审查是一种事后的合法性监督制度。作为方法论意义上的比例原则引入行政规范性文件备案审查,旨在改善行政规范性文件备案审查的方法,提升行政规范性文件备案审查的实效。适当性原则审查的核心是手段与目的关联性。关联性是指手段能够正向推进目的得以实现的单向关系。必要性原则审查的核心是手段与结果的关联性,最小损害是确定关联性的基本要素。均衡性原则审查的核心是手段与结果的合比例性,利益衡量是确定合比例性的基本方法。在行政规范性文件备案审查中,事实审查有助于提升行政规范性文件的“科学性”。对因紧急状态而制定的行政规范性文件进行备案审查,可以适度降低比例原则的要求。