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。

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery： A Case Study on Hilly Loess Plateau Region, China

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

Temporal sequence Object-based CNN(TS-OCNN) for crop classification from fine resolution remote sensing image time-series

Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.

Object-based Classification of Baltic Sea Ice Extent and Concentration in Winter 2011

The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland and the Gulf of Riga. Sea ice plays an important role in dynamic and thermodynamic processes and also has a strong impact on the heat budget of the sea. Also a large part of transport goes by sea, and there is a need to create ice charts to make the marine transport safe. Because of high cloudiness in winter season and small amount of light in the northern part of the Baltic Sea, radar data are the most important remote sensing source of sea ice information. The main goal of the following studies is classification of the Baltic sea ice cover using radar data. The ENVISAT ASAR （Advanced Synthetic Aperture Radar） acquires data in five different modes. In the following studies ASAR Wide Swath Mode data were used. The Wide Swath Mode, using the ScanSAR technique provides medium resolution images （150 m） over a swath of 405 kin, at HH or VV polarization. In following work data from February 13th, February 24th and April 6th, 2011, representing three different sea ice situations were chosen. OBIA （object-based image analysis） methods and texture parameters were used to create sea ice extent and sea ice concentration charts. Based on object-based methods, it can separate single sea ice floes within the ice pack and calculate more accurately sea ice concentration.

Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using WorldView-2 Satellite Imagery

The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.

Integration of SAR Polarimetric Features and Multi-spectral Data for Object-Based Land Cover Classification

An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.

Object-Based vs. Pixel-Based Classification of Mangrove Forest Mapping in Vien An Dong Commune, Ngoc Hien District, Ca Mau Province Using VNREDSat-1 Images

Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.

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。

OBH-RSI:Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland

With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.

Object-based Analysis for Extraction of Dominant Tree Species

As forest is of great significance for our whole development and the sustainable plan is so focus on it. It is very urgent for us to have the whole distribution,stock volume and other related information about that. So the forest inventory program is on our schedule. Aiming at dealing with the problem in extraction of dominant tree species,we tested the highly hot method-object-based analysis. Based on the ALOS image data,we combined multi-resolution in e Cognition software and fuzzy classification algorithm. Through analyzing the segmentation results,we basically extract the spruce,the pine,the birch and the oak of the study area. Both the spectral and spatial characteristics were derived from those objects,and with the help of GLCM,we got the differences of each species. We use confusion matrix to do the Classification accuracy assessment compared with the actual ground data and this method showed a comparatively good precision as 87% with the kappa coefficient 0. 837.

Object-based image analysis for mapping geomorphic zones of coral reefs in the Xisha Islands, China

Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.

Geographic Object-Based Image Analysis of Changes in Land Cover in the Coastal Zones of the Red River Delta (Vietnam)

The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.

Object-Based Analysis of Multispectral RS Data and GIS for Detection of Climate Change Impact on the Karakoram Range Northern Pakistan

Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect not only the local environment but also the whole country with frequent and heavy floods. Remote sensing （RS） from Satellites and Airplanes used in Geographical Information Systems （GIS） are technologies that can aid in understanding the on-going environmental processes. Furthermore, help researchers to observe, understand, forecast and suggest response to changes that occur. It can be natural disasters or man-made disasters and human induced factors. Still analysis accuracy issues play a vital role for the formulation of any strategy. To achieve better results, object based analysis methods have been tested. Various algorithms are developed by the analysts to calculate the magnitude of land cover changes. However, they must be evaluated for each environment that is under observation as mountainous areas. Here were object-based methods evaluated in comparison with pixel based. Landslides, soil moisture, soil permeability, snow cover and vegetation cover can be effectively monitored by those methods.

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。