Improving creep strength of the fine-grained heat-affected zone of novel 9Cr martensitic heat-resistant steel via modified thermo-mechanical treatment

The infamous type Ⅳ failure within the fine-grained heat-affected zone (FGHAZ) in G115 steel weldments seriously threatens the safe operation of ultra-supercritical (USC) power plants.In this work,the traditional thermo-mechanical treatment was modified via the replacement of hot-rolling with cold rolling,i.e.,normalizing,cold rolling,and tempering (NCT),which was developed to improve the creep strength of the FGHAZ in G115 steel weldments.The NCT treatment effectively promoted the dissolution of preformed M_(23)C_(6)particles and relieved the boundary segregation of C and Cr during welding thermal cycling,which accelerated the dispersed reprecipitation of M_(23)C_(6) particles within the fresh reaustenitized grains during post-weld heat treatment.In addition,the precipitation of Cu-rich phases and MX particles was promoted evidently due to the deformation-induced dislocations.As a result,the interacting actions between precipitates,dislocations,and boundaries during creep were reinforced considerably.Following this strategy,the creep rupture life of the FGHAZ in G115 steel weldments can be prolonged by 18.6%,which can further push the application of G115 steel in USC power plants.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Experimental Study on the Effect of Fine-Grained Soil Content on the Freezing Strength of Aeolian Sand-Cement Interface

In cold regions,understanding the freezing strength of the interface between soil and structure is crucial for designing frost-resistant foundations.To investigate how the content of cement powder in aeolian sand affects this strength,we conducted direct shear tests under various conditions such as different fine-grained soil content,normal stress,and initial moisture content of the soil.By analyzing parameters like soil properties,and volume of ice content,and using the Mohr-Coulomb strength theory to define interface strength,we aimed to indirectly measure the cementation strength of the interface.Our findings revealed that as the particle content increased,the interface stress-strain curves became noticeably stiffer.We also observed a positive linear relationship between freezing strength and silt content,while the initial moisture content of the soil did not significantly impact the strengthening effect of fine-grained soil on freezing strength.Moreover,we discovered that as the powder content increased,the force binding the ice to the interface decreased,while the friction angle at the interface increased.However,the cohesion force at the interface remained relatively unchanged.Overall,our analysis suggests that the increase in freezing strength due to fine-grained soil content is primarily due to the heightened friction between aeolian sand and the interface.

热敏灸治疗颈型颈椎病的Meta分析及GRADE证据等级评价

目的系统评价热敏灸及其综合疗法用于颈型颈椎病的临床效果,为热敏灸治疗颈型颈椎病提供更好的证据来源。方法收集国内外各大数据库热敏灸及其综合疗法用于颈型颈椎病的随机对照试验(RCT),通过Revman 5.3软件进行Meta分析,按照GRADE标准对结局指标进行证据质量评价。结果纳入9篇文献共855例患者,Meta分析结果提示:热敏灸及其综合疗法组在提高总有效率(RR=1.19,P<0.01)和治愈率(RR=1.73,P<0.01)、降低视觉模拟量表(VAS)评分(SMD=-0.73,P<0.01)、简化McGill疼痛问卷(SF-MPQ)评分(SMD=-1.28,P<0.05)以及NPQ颈痛量表评分(SMD=-0.81,P<0.05)方面都优于对照组;GRADE证据等级评价提示:前三者为低质量证据,后二者为极低质量证据。结论热敏灸及其综合疗法用于颈型颈椎病在提高临床效果、镇痛以及改善患者生活质量方面更具优势。

Fine-grained gravity flow sedimentation and its influence on development of shale oil sweet sections in lacustrine basins in China

The geological conditions and processes of fine-grained gravity flow sedimentation in continental lacustrine basins in China are analyzed to construct the model of fine-grained gravity flow sedimentation in lacustrine basin,reveal the development laws of fine-grained deposits and source-reservoir,and identify the sweet sections of shale oil.The results show that fine-grained gravity flow is one of the important sedimentary processes in deep lake environment,and it can transport fine-grained clasts and organic matter in shallow water to deep lake,forming sweet sections and high-quality source rocks of shale oil.Fine-grained gravity flow deposits in deep waters of lacustrine basins in China are mainly fine-grained high-density flow,fine-grained turbidity flow(including surge-like turbidity flow and fine-grained hyperpycnal flow),fine-grained viscous flow(including fine-grained debris flow and mud flow),and fine-grained transitional flow deposits.The distribution of fine-grained gravity flow deposits in the warm and humid unbalanced lacustrine basins are controlled by lake-level fluctuation,flooding events,and lakebed paleogeomorphology.During the lake-level rise,fine-grained hyperpycnal flow caused by flooding formed fine-grained channel–levee–lobe system in the flat area of the deep lake.During the lake-level fall,the sublacustrine fan system represented by unconfined channel was developed in the flexural slope breaks and sedimentary slopes of depressed lacustrine basins,and in the steep slopes of faulted lacustrine basins;the sublacustrine fan system with confined or unconfined channel was developed on the gentle slopes and in axial direction of faulted lacustrine basins,with fine-grained gravity flow deposits possibly existing in the lower fan.Within the fourth-order sequences,transgression might lead to organic-rich shale and fine-grained hyperpycnal flow deposits,while regression might cause fine-grained high-density flow,surge-like turbidity flow,fine-grained debris flow,mud flow,and fine-grained transitional flow deposits.Since the Permian,in the shale strata of lacustrine basins in China,multiple transgression-regression cycles of fourth-order sequences have formed multiple source-reservoir assemblages.Diverse fine-grained gravity flow sedimentation processes have created sweet sections of thin siltstone consisting of fine-grained high-density flow,fine-grained hyperpycnal flow and surge-like turbidity flow deposits,sweet sections with interbeds of mudstone and siltstone formed by fine-grained transitional flows,and sweet sections of shale containing silty and muddy clasts and with horizontal bedding formed by fine-grained debris flow and mud flow.The model of fine-grained gravity flow sedimentation in lacustrine basin is significant for the scientific evaluation of sweet shale oil reservoir and organic-rich source rock.

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.

Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models

A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,respectively.The performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(IOS),and index of agreement(IOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone models.The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN models.However,the LSTM models outperformed the GPR models in predicting the compaction parameters.The sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.

刮痧改善腰椎间盘突出疼痛症状的系统评价及GRADE证据质量评价

目的系统评价刮痧干预腰椎间盘突出疼痛症状的疗效与安全性,为其临床治疗提供循证依据。方法计算机检索自建库到2022年8月29日前公开发表在中国知网、万方、维普、中国生物医学文献服务系统、PubMed、Embase、Web of Science、The Cochrane Library数据库,收集关于刮痧干预腰椎间盘突出的随机对照试验(Randomized controlled trial,RCT)。文章由两名研究者独立筛选,根据Cochrane Reviewers Handbook 6.2的偏倚风险评估工具对纳入文献进行质量评价,应用RevMan 5.4软件对纳入文献的结局指标进行Meta分析,并采用GRADE系统对结局指标进行证据质量等级评价。结果最终纳入10篇RCT,共1087例患者。Meta分析结果显示:刮痧试验组治疗腰椎间盘突出患者在疼痛VAS评分[MD=-1.11,95%CI(-1.58,-0.64),P<0.00001]、日本骨科协会(JOA)评分[MD=4.47,95%CI(3.76,5.17),P<0.00001]、临床有效率[relative risk(RR)=1.13,95%CI(1.08,1.19),P<0.00001]和护理满意度[RR=1.21,95%CI(1.09,1.34),P=0.0005]方面,明显优于对照组,差异均有统计学意义(P<0.00001)。本文结局指标的证据质量为极低。结论当前证据表明,刮痧治疗腰椎间盘突出患者的疼痛症状具有良好临床疗效,并有助于提高临床护理满意度。由于部分结局证据质量较低,有待严谨的多中心、大样本随机对照试验来进一步验证其有效性,提升临床证据。

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network

Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.

穴位埋线治疗功能性便秘疗效与安全性Meta分析及GRADE评价

目的:系统评价穴位埋线治疗功能性便秘(FC)的疗效和安全性及纳入证据的质量,以期为临床决策提供循证医学证据。方法:检索中国知网(CNKI)、万方知识服务平台(WANFANG)、维普中文期刊数据库(VIP)、中国生物医学文献服务系统(SinoMed)、Embase、PubMed和Cochrane Library数据库,搜集穴位埋线治疗FC的随机对照试验(RCT),检索时间均从建库至2023年10月16日。由2名研究者独立完成文献筛选、资料提取及偏倚风险评估,采用RevMan 5.4软件进行Meta分析,并采用GRADE评价方法对各项结局指标进行证据质量评价。结果:最终共纳入19项RCT,包括1673例患者。Meta分析结果显示,与对照组比较,穴位埋线治疗FC可提高总有效率[RR=1.23,95%CI=(1.17,1.29),P<0.00001],增加完全自发排便(CSBM)次数[MD=0.61,95%CI=(0.11,1.10),P=0.02],改善粪便性状[MD=0.53,95%CI=(0.31,0.76),P<0.00001]及排便困难[MD=-0.71,95%CI=(-1.03,-0.40),P<0.00001],缩短排便时间[MD=-0.50,95%CI=(-0.72,-0.29),P<0.00001],改善便秘生活质量评估量表(PAC-QOL)评分[MD=-7.72,95%CI=(-12.83,-2.61),P<0.003];两组不良反应发生率比较,差异无统计学意义[RR=0.37,95%CI=(0.10,1.38),P=0.14]。GRADE证据级别评价结果显示:总有效率为中级别证据质量,不良反应发生率、粪便性状评分、排便困难评分为低级别证据质量,CSBM次数、排便时间、PACQOL评分为极低级别证据质量。结论:穴位埋线治疗FC具有一定的疗效,可改善便秘相关症状,且未增加不良反应发生率。但纳入研究质量总体偏低,上述结论尚需开展更多高质量的研究予以验证。

腕踝针干预术后疼痛疗效的Meta分析和Grade评价

目的运用循证医学方法对腕踝针干预术后疼痛的疗效和安全性进行系统评价和Grade评价。方法计算机检索中国知网、万方、维普、中国生物医学文献数据库、PubMed、Embase、Web of Science、Cochrane Library中关于腕踝针干预术后疼痛的随机对照试验,检索时限为建库至2023年10月。采用RevMan 5.4软件进行Meta分析。结果纳入23篇文献,共计1968例患者,Meta分析结果显示,与常规治疗相比,腕踝针能够提高术后疼痛患者的总有效率[OR=4.42,95%CI(2.60,7.50),P<0.001],术后镇痛泵药量使用减少[MD=-9.03,95%CI(-12.09,-5.98),P<0.001],术后疼痛评分降低[MD=-1.39,95%CI(-1.68,-1.09),P<0.001],可减少不良反应发生率[RR=0.40,95%CI(0.32,0.48),P<0.001]以及临床满意度[OR=3.94,95%CI(2.40,6.48),P<0.001]。Grade证据分级结果显示:总有效率、不良反应发生率和临床满意度3项结局指标为中等质量证据,VAS评分指标为低质量证据,镇痛泵药量使用指标为极低质量证据。结论腕踝针可提高总有效率,减少术后镇痛药用量,不良反应少,安全性高,为患者提供了一种安全有效的镇痛方式。

医院药学指南制定中的证据与推荐意见GRADE系统评级方法及应用

目前,国际上存在多种证据与推荐意见分级系统,对临床医务工作者阅读指南证据和推荐意见造成困扰。证据推荐分级的评估、制定与评价(GRADE)系统目前是全球使用最为广泛的证据与推荐意见分级系统,本文简单地介绍GRADE系统证据质量评级及影响推荐意见形成的4个主要因素,并结合中国医院药学界发起制定的、在国际上发表的几部药学指南举例阐述,以帮助临床医务工作者了解GRADE系统,以批判性思维运用指南证据和推荐意见指导临床实践行为。

乌梅丸加减治疗帕金森病的Meta分析及GRADE评价

目的 系统评估乌梅丸加减治疗帕金森病(parkinson's disease, PD)的疗效和安全性。方法 计算机检索Cochrane Library、EMBASE、Web of Science、PubMed、CNKI、CBM等中英文数据库及临床试验中心,关于乌梅丸治疗PD的临床随机对照试验(randomized controlled trial, RCT),检索时限从建库至2023年4月。进行资料整理及数据提取等工作后,最终纳入8项研究,治疗组(乌梅丸)和对照组分别纳入226例。使用RevMan 5.3及stata14软件进行荟萃分析,使用GRADEpro GDT评估各项结局指标的证据质量。结果 治疗组降低PD统一评分量表(unified Parkinson's disease rating scale, UPDRS)的各项量表积分较对照组明显,其中在改善UPDRS Ⅰ、Ⅲ、Ⅳ方面具有统计学意义,结果分别为[MD=-0.769,95%CI(-1.213~-0.324),P=0.001]、[MD=-2.211,95%CI(-2.713~-1.708),P=0]、[MD=-0.526,95%CI(-1.041~-0.012),P=0.045]。2组在改善UPDRS总分方面差异无统计学意义[MD=3.964,95%CI(-9.465~17.392),P=0.563]。治疗组能显著提高治疗有效率,差异具有统计学意义[RR=1.709,95%CI(1.361~2.145),P=0]。各项研究均未发现不良事件发生及与治疗有关的检验指标异常。GRADE证据质量分级结果为UPDRS Ⅰ、UPDRS Ⅲ、UPDRS Ⅳ及有效率方面均为中等级别,UPDRS Ⅱ为低等级,UPDRS总分为非常低。结论 乌梅丸加减的应用对PD的治疗有一定效果,且具有良好的安全性。

Fine-Grained Features for Image Captioning

Image captioning involves two different major modalities(image and sentence)that convert a given image into a language that adheres to visual semantics.Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences.The Convolutional Neural Network(CNN)is often used to extract image features in image captioning,and the use of object detection networks to extract region features has achieved great success.However,the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation.We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features.First,we extract fine-grained features using a panoramic segmentation algorithm.Second,we suggest two fusion methods and contrast their fusion outcomes.An X-linear Attention Network(X-LAN)serves as the foundation for both fusion methods.According to experimental findings on the COCO dataset,the two-branch fusion approach is superior.It is important to note that on the COCO Karpathy test split,CIDEr is increased up to 134.3%in comparison to the baseline,highlighting the potency and viability of our method.

参芪扶正注射液辅助治疗慢性阻塞性肺疾病的Meta分析与GRADE评价

为了系统评价参芪扶正注射液联合常规治疗作为干预措施对慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者的临床疗效和安全性。检索中国国家知识基础设施(China national knowledge infrastructure,CNKI)、PubMed、万方数据知识服务平台(Wanfang Data)、维普中文科技期刊数据库(Weipu China science and technology journal database,VIP)等数据库,筛选并纳入2023年6月18日以前发表的参芪扶正注射液联合常规疗法治疗COPD患者的随机对照试验(randomized controlled trials,RCT),采用Cochrane风险评价工具及评估、发展和评价建议分级(grading of recommendations assessment,development and evaluation,GRADE)系统进行文献证据质量评价,用RevMan 5.4软件对临床疗效及安全性指标进行Meta分析。结果表明,共纳入16项RCTs,1 486例患者。Meta分析结果显示,参芪扶正注射液辅助治疗可提高患者总有效率和第1秒用力呼气容积/用力肺活量比值(forced expiratory volume in one second/forced vital capacity,FEV1/FVC)指标,与对照组相比具有优势(P<0.000 01、P<0.000 1);不良反应少,无严重不良反应(adverse drug reactions,ADR),两组对比无统计学差异(P=0.32);GRADE评价结果显示,有效率及不良反应指标的证据质量均为中等级,肺功能为低等级。可见参芪扶正注射辅助治疗COPD可以提高患者临床疗效,改善肺功能,且具有良好的安全性。但所纳入研究具有局限性,证据质量不高,仍需结合中药辨证使用特点,规范实验方案,开展更多的高质量RCT研究。

Sedimentology and Paleoenvironmental Characteristics of Fine-grained Sediments in Coal-bearing Strata in the Eastern Ordos Basin:A Case Study of the Exploratory Well in the Zizhou Area

The continuously collected cores from the Permo-Carboniferous coal-bearing strata of the eastern Ordos Basin are essential for studying the hydrocarbon potential in this region.This study adopted sedimentological and geochemical methods to analyze the sedimentary environment,material composition,and geochemical characteristics of the coal-bearing strata.The differences in depositional and paleoclimatic conditions were compared;and the factors influencing the organic matter content of fine-grained sediments were explored.The depositional environment of the Benxi and Jinci formations was lagoon to tidal flat with weakly reduced waters with low salinity and dry-hot paleoclimatic conditions;while that of the Taiyuan Formation was a carbonate platform and shallow water delta front,where the water was highly reductive.The xerothermic climate alternated with the warm and humid climate.The period of maximum transgression in the Permo-Carboniferous has the highest water salinity.The Shanxi Formation was deposited in a shallow water delta front with a brackish and fresh water environment and alternative weak reductiveness.And the paleoclimate condition is dry-hot.The TOC content in fine-grained samples was averaging 1.52%.The main controlling mechanism of organic matter in this area was the input conditions according to the analysis on input and preservation of organic matter.

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop.There is still an opportunity for future enhancement of the classification impact.To solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification tasks.For the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets.For the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features.The parallel network has different depths,considering both high-level and lowlevel semantic information.The model achieves better classification performance in remote sensing ship images with multi-granularity mixing.Finally,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 G.The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.