Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Characteristics,classification and ordination of riparian plant communities in the Three-Gorges areas

Sixteen different vegetation types of grassland and shrubland were selected to study the component and diversity of plant species of riparian plant communities along main channel in the Three-Gorges areas. Species richness (s), Simpson index (D), and Shannon-Weiner index (H) were used to study the biodiversity and the hierarchical classification was carried out by the methods of TWINSPAN and DCA ordination. The results showed that the components of flora were complex and dominated by the temperate type in the riparian plant communities. Species diversity was not different between the communities, but Shannon-Weiner indexes of different layers in some grassland were significantly different. TWINSPAN and DCA indicated that riparian plant communities distributed along the gradient of moisture.

Classification and Integration of Storage and Transportation Engineering Technologies in Potato Producing Areas of China

Considering the development of potato （Solanum tuberosum） industry in China, the existing technologies of potato storage and transportation in the produc- ing area were analyzed through investigation on four main potato production areas. Unear classification was used to conduct the technology classification. According to the technical attributes and characteristics, the potato technologies of storage and transportation in producing area were classified with large classes, middle classes, small classes and subclasses, into the agricultural production area processing and storage engineering technology system, to reveal the structure and functions. Mean- while, the widely used technologies were integrated and summarized into 5 principal technology integration programs, which could be used for the technology integration of the new management subjects such as planting professional cooperatives, family farms, enterprises and so on.

Object oriented land cover classification using ALS and GeoEye imagery over mining area

An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.

The Building Extraction Based on Object Oriented Classification Method in High Vegetation Coverage Area

Quickly extraction of building information technology is an important application in urban development planning, electronic information, national defense and others. This paper takes Landsat-8 multispectral and panchromatic data as data source, using the local variance method to select the optimal segmentation scale, normalized difference vegetation index (NDVI) and the normalized building index (NDBI) and panchromatic brightness value of an object oriented classification rule extraction. The high vegetation coverage area of buildings, and through the spatial relationships and distinguishing feature of collections of buildings independent buildings and villages. The results showed that Google earth high resolution image analysis and accuracy evaluation. the results of the extraction based on the overall accuracy of village extraction was 83%, the accuracy of extraction of independent buildings was 70%, according to the L8 remote sensing data, object oriented classification method can quickly and accurately extract the high vegetation coverage area of the building.

Reflections on the New Classification of Tumors by the WHO and Changes in Esophageal Cancer in a High-risk Area

ABSTRACT In year 2000, a book entitled the Pathology and Genetics of Tumors of the Digestive System was published by the WHO, presenting some new diagnostic criteria and treatment principles. I have analyzed the epidemiologic change of tumors in over 30 years in the high-risk area with esophageal cancer. The following phenomenon was found： accompanied by the sharp decrease in the incidence and mortality of esophageal cancer, there was an increase in the incidence and death rate of stomach cancer involving cardiac cancer. This fact should be considered when analyzing the sharp decrease in esophageal cancer incidence and mortality rate. More attention was given to diagnosis of cardiac cancer; at the same time it is more practical to improve the early screening of cancers. To observe the development of high and low - grade intraepithelial neoplasms will be an urgent task for esophageal cancer research in the high risk area, according to WHO＇s new classification.

Remote Sensing Image Classification Based on Decision Tree in the Karst Rocky Desertification Areas: A Case Study of Kaizuo Township

Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and unsupervised classification are often used to classify the remote sensing image.But they only use pixel brightness characteristics to classify it.So the classification accuracy is low and can not meet the needs of practical application.Decision tree classification is a new technology for remote sensing image classification.In this study,we select the rocky desertification areas Kaizuo Township as a case study,use the ASTER image data,DEM and lithology data,by extracting the normalized difference vegetation index,ratio vegetation index,terrain slope and other data to establish classification rules to build decision trees.In the ENVI software support,we access the classification images.By calculating the classification accuracy and kappa coefficient,we find that better classification results can be obtained,desertification information can be extracted automatically and if more remote sensing image bands used,higher resolution DEM employed and less errors data reduced during processing,classification accuracy can be improve further.

Classification of Emergency Responses to Fatal Traffic Accidents in Chinese Urban Areas

Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management.Therefore,it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly.The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas.This study used a multilevel hierarchical structural model to determine emergency-response classification.In the model,accident attributes,urban road network vulnerability,and institutional resilience were used as classification criteria.Each evaluation indicator was selected according to importance ranking and independence screening and was given an interpretation and a quantitative criterion.The Fuzzy Delphi Method was used to rank the importance of the evaluation indices and the combined weight of each index was calculated using the G1 method.Finally,the case of a fatal traffic accident was used to validate the model.The results showed that the multilevel hierarchical structural model,Fuzzy Delphi Method,and G1 method can effectively address the problem of emergency-response classification.Because of its simplicity and adaptability,the approach presented here could be useful for decisionmakers and practitioners for determining emergency-response classifications.

An applied research on remote sensing classification in the Loess Plateau

Dae to complex terrain of the Loess Plateau, the classification accuracy is unsatisfactory when a single supervised classification is used in die remote sensing investigation of the sloping field. Taking the loess hill and gully area of northern Shaanxi Province as a test area, a research was conducted to extract sloping field and other land use categories by applying an integrated classification. Based on an integration of supervised classification aad unsupervised classification, sampling method is remarkably unproved. The results show that the classification accuracy is satisfactory by the method and is of critical significance in obtaining up-to-date information of the sloping field, which should be helpful in the state key project of converting farmland to forest and grassland on slope land in this area. This research sought to improve the application accuracy of image classification in complex terrain areas.

Groundwater level prediction of landslide based on classification and regression tree

According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.

Reservoir geochemistry of the Tazhong oilfield in the Tarim Basin, China, Part I. Geochemical characteristics and genetic classification of crude oils

According to the assemblage characteristics of saturated hydrocarbon biomarkers in crude oils and their geochemical implications, this study has proposed, for the first time, the criteria for the genetic classification of crude oils in the Tazhong area of the Tarim Basin, China. Crude oils from the area studied are classified as three genetic types: type-I is characterized by the low contents of C29 norhopane, extremely abundant contents of gammacerane, low contents of rearranged sterane and relatively high contents of regular C28 sterane; the geochemical properties of type-II crude oils are opposite to those of type-I crude oils; the parameters for type-III crude oils are intermediate between type-I and type-II. Results of oil correlation indicated that type-I crude oils were derived from Cambrian-Lower Ordovician hydrocarbon source rocks, type-II curde oils originated from Middle-Upper Ordovician hydrocarbon source rocks and type-III crude oils are of mixed origin.

Landform Classification for Community Siting: A case Study in Quxian County, China

This study is to explore a suitable method to classify landform, in order to support the decision making for community siting in mountainous areas.It first proposes the landform classification for community siting(LCCS) method with detailed discussions on its rationality and the chosen parameters.This method is then tested and verified in Quxian county.The LCCS method entails twograde parameters, which uses relative relief as the first grading parameter, slope as the second, followed by a synthesis process to form a suitable landform classification system.By applying the LCCS method in Quxian county, the result shows that its use of watershed to identify geomorphometric units, and its use of the altitude datum concept, can effectively classify landform according to the local cultural traditions, and the economic and environmental conditions.The verification result shows that comparing to the conventional methods, the LCCS method respects to people's daily experience due to its bottom-up approach.It not only help to minimize the disturbance to the nature when choosing locations for community development, but also helps to prepare more precise land management policies,which maximizes agricultural production and minimizes terrain transformation.

A case study on the soil classification of the Yellow River Delta based on piezocone penetration test

Piezocone penetration test(CPTu),the preferred in-situ tool for submarine investigation,is significant for soil classification and soil depth profile prediction,which can be used to predict soil types and states.However,the accuracy of these methods needs to be validated for local conditions.To distinguish and evaluate the properties of the shallow surface sediments in Chengdao area of the Yellow River Delta,seabed CPTu tests were carried out at ten stations in this area.Nine soil classification methods based on CPTu data are applied for soil classification.The results of classification are compared with the in-situ sampling to determine whether the method can provide sufficient resolution.The methods presented by Robertson(based on soil behavior type index Ic),Olsen and Mitchell are the more consistent and compatible ones compared with other methods.Considering that silt soils have potential to liquefy under storm tide or other adverse conditions,this paper is able to screen soil classification methods suitable for the Chengdao area and help identify the areas where liquefaction or submarine landslide may occur through CPTu investigation.

Study on the Present Situation and Strategies of Rural Waste Classification Treatment in Northeast China

The classification treatment of rural domestic waste is not only an important measure to realize the standardized development in rural areas,but also the action support to the concept of"ecological and suitable residence"put forward by the 19th CPC National Congress.This paper summarizes the traditional treatment methods of rural domestic waste in Northeast China,the present situation of rural waste classification treatment in Northeast China and the advantages of waste classification in rural areas of Northeast China.The specific measures to improve the current situation of rural waste classification treatment in Northeast China are put forward in terms of capital policy,classification methods and management means,in order to provide theoretical reference for the treatment of rural domestic waste in Northeast China.

生物质碳材料的孔道分析

生物质碳材料的孔道类型和孔径大小制约着材料有效的活性位点数量,影响材料的性能。孔道分类又是孔径分析的前提条件,因此,建立孔道分类的方法非常有意义。随着生物质碳材料的深入研究,研究者对其孔道分析的要求逐渐提高。由于实际的吸脱附等温线具有不规则性,难以匹配IUPAC规范中的吸脱附等温线,所以,用实际的吸脱附等温线与IUPAC规范中的吸脱附等温线进行匹配对生物质碳材料的孔道进行分类准确度不能得到保证。使用自制生物质碳材料,运用物理吸附仪对其进行表征,采用BET方程(Brunauer-Emmett-Teller)、T-plot方法(Thickness-plot)、DFT方法(Non-local Density Functional Theory)、BJH(Barrett Joyner And Halenda)方法对其孔道进行分析。研究表明,采用孔隙率和比表面积占有率对其进行孔道分类,可以准确地定义出微孔生物质碳材料、介孔生物质碳材料和微介孔生物质碳材料,从而建立了孔隙率和比表面积占有率的孔道分类新方法。用标准样品对孔隙率和比表面积占有率的孔道分类新方法进行论证,结果一致。方法准确可靠、实用性高。

涉海详细规划的主要职能与编制思路

涉海详细规划作为详细规划的重要组成部分,能够深化国土空间总体规划和专项规划各项要求、统筹规划与设计,为海洋空间用途管制提供依据,并助推立体用海发展。结合国内外涉海详细规划研究进展,探索涉海详细规划编制技术路线,提出编制重点区域为海洋保护区、重点开发利用区和海岸带区域,建立三级分区分类体系并进行相邻功能区和用海活动兼容性分析,从生态保护修复、海洋开发利用、陆域开发利用、人居环境提升、市政服务设施建设等角度构建规划管控指标体系,以期为涉海详细规划编制与实施工作提供参考。

基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法

无线传感网络节点体积小,隐蔽性强,节点复制攻击检测的难度较大,为此提出一种基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法。通过信标节点的空间位置数据与相距跳数得出各节点之间的相似程度,结合高斯径向基核函数求解未知节点的横轴、纵轴的空间坐标,确定各网络节点的空间位置;根据网络节点的属性特征与投票机制建立节点复制攻击模型,凭借组合加权k近邻分类法划分节点类型,并将结果传送至簇头节点,由簇头节点做出最后的仲裁,识别出节点复制攻击行为。仿真结果表明,所提方法的节点复制攻击检测率最大值为99.5%,最小值为97.9%,对节点复制攻击检测的耗时为5.41 s,通信开销数据包数量最大值为209个,最小值为81个。

基于高分辨率遥感影像的居民地分类方法研究

随着我国城镇化进程的不断深入,大量乡村居民地已经转化为城镇居民地,为了度量二者实时的转化情况,该文提出一种基于监督分类机制的居民地分类方法,该方法首先采用边缘特征及高斯函数量化影像上的局部特征,然后构建5种城镇及乡村居民地分类规则,其次创建训练样本对各类规则进行学习,最后通过城镇及乡村测试样本验证该文方法的精度。实验表明,该文方法可以对高分辨率遥感影像城镇及乡村居民地进行初级分类,为“城镇化”进程提供一个新的衡量指标。

神经根沉降征四分法分型与腰椎管狭窄症严重程度、疼痛、最小硬膜囊横截面积的相关性

目的:分析神经根沉降征(NRS)四分法分型与腰椎管狭窄症(LSS)严重程度、视觉模拟疼痛量表(VAS)评分、最小硬膜囊横截面积(CSA)的相关性,探讨其对LSS的临床价值。方法:回顾性选择180例LSS患者作为研究对象,均行MRI横断面扫描;采用NRS四分法分型分为阳a型58例,阳b型55例,阳c型31型及阴型36例4组,比较4组最小硬膜囊CSA、VAS评分、Oswestry功能障碍指数(ODI)及腰椎管狭窄相关指标,分析NRS四分法分型与最小硬膜囊CSA、VAS评分、ODI的相关性。结果:4组最小硬膜囊CSA、VAS评分、ODI比较,差异均有统计学意义(均P<0.05)。最小硬膜囊CSA组间两两比较:阳a型、阳b型、阳c型均小于阴型,阳b型、阳c型均小于阳a型,阳c型小于阳b型,差异均有统计学意义(均P<0.05)。VAS评分、ODI组间两两比较:阳a型、阳b型、阳c型均大于阴型,阳b型、阳c型均大于阳a型,阳c型大于阳b型,差异均有统计学意义(均P<0.05)。各型椎管正中矢状径、侧隐窝矢状径差异均无统计学意义(均P>0.05)。NRS分型与VAS评分、ODI呈正相关(r=0.894,0.929;均P<0.001),与最小硬膜囊CSA呈负相关(r=-0.870,P<0.001)。结论:NRS四分法分型越高,LSS症状和狭窄程度越重,这对LSS的诊断和病情评估均有重要价值。

论海洋自然保护地分类分区管控的中国方案

海洋自然保护地分类分区管控既有空间规划的自然科学依据,又有区域治理的社会科学依据,且与精细化建设之间具有手段与目的的关系。分类施策、分区治理,精细化建设,是我国海洋自然保护地分类分区管控的改革方向。海洋自然保护地分类分区管控以新型海洋自然保护区制度、海洋公园制度接替海洋特别保护区制度,与国土空间规划制度、生态保护红线制度等衔接,并将生态功能保护区作为我国新时代自然保护地体系中的类型之一。分类分区管控基于陆海统筹战略提出分区差异化管控、精细化建设的中国方案,并通过完善立法保障其实现。