Individual consistency in spatiotemporal characteristics of migratory Whimbrels in the East Asian-Australasian Flyway

Many migratory birds exhibit interannual consistency in migration schedules,routes and stopover sites.Detecting the interannual consistency in spatiotemporal characteristics helps understand the maintenance of migration and enables the implementation of targeted conservation measures.We tracked the migration of Whimbrel(Numenius phaeopus)in the East Asian-Australasian Flyway and collected spatiotemporal data from individuals that were tracked for at least two years.Wilcoxon non-parametric tests were used to compare the interannual variations in the dates of departure from and arrival at breeding/nonbreeding sites,and the inter-annual variation in the longitudes when the same individual across the same latitudes.Whimbrels exhibited a high degree of consistency in the use of breeding,nonbreeding,and stopover sites between years.The variation of arrival dates at nonbreeding sites was significantly larger than that of the departure dates from nonbreeding and breeding sites.Repeatedly used stopover sites by the same individuals in multiple years were concentrated in the Yellow Sea coast during northward migration,but were more widespread during southward migration.The stopover duration at repeatedly used sites was significantly longer than that at sites used only once.When flying across the Yellow Sea,Whimbrels breeding in Sakha(Yakutia)exhibited the highest consistency in migration routes in both autumn and spring.Moreover,the consistency in migration routes of Yakutia breeding birds was generally higher than that of birds breeding in Chukotka.Our results suggest that the northward migration schedule of the Whimbrels is mainly controlled by endogenous factors,while the southward migration schedule is less affected by endogenous factors.The repeated use of stopover sites in the Yellow Sea coast suggests this region is important for the migration of Whimbrel,and thus has high conservation value.

Differentiating consistency of meningioma based on grey-level histogram analysis of T2WI

Objective To observe the value of grey-level histogram analysis based on T2WI for differentiating consistency of meningioma.Methods Data of 109 patients with meningioma were retrospectively analyzed.The patients were divided into hard group(n=71)and soft group(n=38)according to the consistency of tumors.Tumor ROI was outlined on axial T2WI showing the largest tumor section,gray levels were extracted and histogram analysis was performed.The value of each histogram parameter were compared between groups.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficiency for differentiating soft and hard meningioma.Results P 1,P 10,P 50,P 90,P 99 and the mean grey levels on T2WI in soft group were all higher than those in hard group(all P<0.05),while the variance,the kurtosis and the skewness were not significantly different between groups(all P>0.05).The differentiating efficiency of P 1,P 10,P 50,P 90,P 99 and the mean grey levels on T2WI were all fine,with AUC of 0.774 to 0.833,and no significant difference was found(all P>0.05).Conclusion Parameters of grey-level histogram analysis such as P 1,P 10,P 50,P 90,P 99 and the mean values based on T2WI were all valuable for differentiating soft and hard meningioma.

Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

Separation work analysis of cohesive law and consistently coupled cohesive law

An appropriate coupled cohesive law for predicting the mixed mode failure is established by combining normal separation and tangential separation of surfaces in the cohesive zone model （CZM） and the cohesive element method. The Xu-Needleman exponential cohesive law with the fully shear failure mechanism is one of the most popular models. Based on the proposed consistently coupled rule/principle, the Xu-Needleman law with the fully shear failure mechanism is proved to be a non-consistently coupled cohesive law by analyzing the surface separation work. It is shown that the Xu-Needleman law is only valid in the mixed mode fracture when the normal separation work equals the tangential separation work. Based on the consistently coupled principle and the modification of the Xu-Needleman law, a consistently coupled cohesive （CCC） law is given. It is shown that the proposed CCC law has already overcome the non-consistency defect of the Xu-Needleman law with great promise in mixed mode analyses.

Cross-Domain Authentication Scheme Based on Blockchain and Consistent Hash Algorithm for System-Wide Information Management

System-wide information management(SWIM)is a complex distributed information transfer and sharing system for the next generation of Air Transportation System(ATS).In response to the growing volume of civil aviation air operations,users accessing different authentication domains in the SWIM system have problems with the validity,security,and privacy of SWIM-shared data.In order to solve these problems,this paper proposes a SWIM crossdomain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication.The scheme uses a consistent hash algorithm with virtual nodes in combination with a cluster of authentication centers in the SWIM consortium blockchain architecture to synchronize the user’s authentication mapping relationships between authentication domains.The virtual authentication nodes are mapped separately using different services provided by SWIM to guarantee the partitioning of the consistent hash ring on the consortium blockchain.According to the dynamic change of user’s authentication requests,the nodes of virtual service authentication can be added and deleted to realize the dynamic load balancing of cross-domain authentication of different services.Security analysis shows that this protocol can resist network attacks such as man-in-the-middle attacks,replay attacks,and Sybil attacks.Experiments show that this scheme can reduce the redundant authentication operations of identity information and solve the problems of traditional cross-domain authentication with single-point collapse,difficulty in expansion,and uneven load.At the same time,it has better security of information storage and can realize the cross-domain authentication requirements of SWIM users with low communication costs and system overhead.KEYWORDS System-wide information management(SWIM);consortium blockchain;consistent hash;cross-domain authentication;load balancing.

A Data Consistency Insurance Method for Smart Contract

As one of the major threats to the current DeFi(Decentralized Finance)ecosystem,reentrant attack induces data inconsistency of the victim smart contract,enabling attackers to steal on-chain assets from DeFi projects,which could terribly do harm to the confidence of the blockchain investors.However,protecting DeFi projects from the reentrant attack is very difficult,since generating a call loop within the highly automatic DeFi ecosystem could be very practicable.Existing researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing,and no method could promise the non-existent of reentrant vulnerabilities.In this paper,we introduce the database lock mechanism to isolate the correlated smart contract states from other operations in the same contract,so that we can prevent the attackers from abusing the inconsistent smart contract state.Compared to the existing resolutions of front-running,code audit,andmodifier,our method guarantees protection resultswith better flexibility.And we further evaluate our method on a number of de facto reentrant attacks observed from Etherscan.The results prove that our method could efficiently prevent the reentrant attack with less running cost.

Asymptotic Consistency of the James-Stein Shrinkage Estimator

The study explores the asymptotic consistency of the James-Stein shrinkage estimator obtained by shrinking a maximum likelihood estimator. We use Hansen’s approach to show that the James-Stein shrinkage estimator converges asymptotically to some multivariate normal distribution with shrinkage effect values. We establish that the rate of convergence is of order and rate , hence the James-Stein shrinkage estimator is -consistent. Then visualise its consistency by studying the asymptotic behaviour using simulating plots in R for the mean squared error of the maximum likelihood estimator and the shrinkage estimator. The latter graphically shows lower mean squared error as compared to that of the maximum likelihood estimator.

Consistent and Specific Multi-View Functional Brain Networks Fusion for Autism Spectrum Disorder Diagnosis

Functional brain networks (FBN) based on resting-state functional magnetic resonance imaging (rs-fMRI) have become an important tool for exploring underlying organization patterns in the brain, which can provide an objective basis for brain disorders such as autistic spectrum disorder (ASD). Due to its importance, researchers have proposed a number of FBN estimation methods. However, most existing methods only model a type of functional connection relationship between brain regions-of-interest (ROIs), such as partial correlation or full correlation, which is difficult to fully capture the subtle connections among ROIs since these connections are extremely complex. Motivated by the multi-view learning, in this study we propose a novel Consistent and Specific Multi-view FBNs Fusion (CSMF) approach. Concretely, we first construct multi-view FBNs (i.e., multiple types of FBNs modelling various relationships among ROIs), and then these FBNs are decomposed into a consistent representation matrix and their own specific matrices which capture their common and unique information, respectively. Lastly, to obtain a better brain representation, it is fusing the consistent and specific representation matrices in the latent representation spaces of FBNs, but not directly fusing the original FBNs. This potentially makes it more easily to find the comprehensively brain connections. The experimental results of ASD identification on the ABIDE datasets validate the effectiveness of our proposed method compared to several state-of-the-art methods. Our proposed CSMF method achieved 72.8% and 76.67% classification performance on the ABIDE dataset.

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

A Consistent Mistake in Remote Sensing Images’Classification Literature

Recently,the convolutional neural network(CNN)has been dom-inant in studies on interpreting remote sensing images(RSI).However,it appears that training optimization strategies have received less attention in relevant research.To evaluate this problem,the author proposes a novel algo-rithm named the Fast Training CNN(FST-CNN).To verify the algorithm’s effectiveness,twenty methods,including six classic models and thirty archi-tectures from previous studies,are included in a performance comparison.The overall accuracy(OA)trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline.Results show that there is a maximal OA gap of 8.35%between the FST-CNN and those methods in the literature,which means a 10%margin in performance.Meanwhile,all those complex roadmaps,e.g.,deep feature fusion,model combination,model ensembles,and human feature engineering,are not as effective as expected.It reveals that there was systemic suboptimal perfor-mance in the previous studies.Most of the CNN-based methods proposed in the previous studies show a consistent mistake,which has made the model’s accuracy lower than its potential value.The most important reasons seem to be the inappropriate training strategy and the shift in data distribution introduced by data augmentation(DA).As a result,most of the performance evaluation was conducted based on an inaccurate,suboptimal,and unfair result.It has made most of the previous research findings questionable to some extent.However,all these confusing results also exactly demonstrate the effectiveness of FST-CNN.This novel algorithm is model-agnostic and can be employed on any image classification model to potentially boost performance.In addition,the results also show that a standardized training strategy is indeed very meaningful for the research tasks of the RSI-SC.

考虑优化调度的微电网频率恢复分布式预测控制策略

为了解决快速频率恢复和缓慢实际功率调度之间的时间尺度分离问题,提出一种考虑优化调度的微电网频率恢复分布式预测控制策略。引入的一致性策略能够在同一时间尺度上处理优化调度和频率恢复,另外提出的实际功率调度方案分布式模型预测控制解决了通信延迟、通信中断和即插即用情景,并且将下垂、实际功率传输和相位角方程作为等式约束,从而预测每个微电网的行为。此外,终端值和不等式约束有助于确定可行解空间的界,从而减少优化时间。在4种测试情景下对控制器的动态性能进行实验评估,结果证明该控制器对负载变化和通信问题具有较强的鲁棒性,并且保证了频率快速回复与高效功率调度。

On the Principle of Consistency of Rights and Obligations in the Constitution of PRC

“The Fundamental Rights and obligations of Citizens”, the title of Chapter II of the current Constitution of PRC, and the stipulation that citizens must fulfill certain obligations while enjoying rights have triggered many debates. Considering the historical origin, constitutional philosophy, and the text and structure of the Constitution, the special provisions of the current Constitution are influenced by the principle of consistency of rights and obligations. The principle of consistency of rights and obligations in the Constitution is of complex connotation. Therefore, although the principle of consistency of rights and obligations effectively connects the public and private spheres, it ignores the diversity and differences of the interests and elements contained in the Constitution, the asymmetry of the normative status of fundamental rights and fundamental obligations,and the right of citizens to self-determination of personal interests.The principle of consistency of rights and obligations should be purposefully narrowed and concretized: In the context of public-private integration and risk society prevention, the principle of consistency of rights and obligations can be used as a supplement to the functional system of the Constitution;in the field of fundamental political obligations, the principle of consistency of rights and obligations should be in line with the requirements of the state to respect and protect human rights;in the field of fundamental social obligations, the exercise of fundamental rights by individuals is protected by the Constitution as long as they comply with the law and do not infringe upon the interests of the social community. The principle of the consistency of rights and obligations is only used as the negative constituents of the determination of rights and the basis for the effect against a third party of fundamental rights.

造纸高浓度磨浆机浓度控制系统设计

造纸工业中的高浓度磨浆机是一个关键的工艺设备,其性能和控制直接影响到纸张的最终质量和生产效率。基于此,从高浓度磨浆机的结构和工作原理入手分析,关注浓度控制系统的设计和关键组成部分,研究数据采集、处理和分析的方法,此外探讨多目标优化控制模型以及力学能耗模型,以确保磨浆机在不同工况下能够实现最佳性能,进而实现可持续和高效的造纸生产过程。

基于改进模拟退火遗传算法的高速公路服务区自洽能源系统高能效优化

针对高速公路自洽能源系统高能效优化的问题,基于改进模拟退火遗传算法(SA-GA),提出了一种综合考虑经济目标和环保目标的自洽能源系统高能效优化运行策略。首先根据高速公路服务区自洽能源系统中各电力单元特性,建立一种综合考虑系统经济性和环保性的能效目标函数;其次根据高速公路服务区自洽能源系统的分类,建立了不同类型系统对应的约束条件;为避免遗传算法易陷入局部最优的缺陷,引入模拟退火算法,对改进后的SA-GA算法采用降温函数,寻找全局最优解;利用新疆某高速公路服务区自洽能源系统发电和负荷数据,并在传统服务区负荷的基础上进一步考虑新能源汽车充电站的影响。测试函数测试结果表明:SA-GA算法在求解速度和稳定性方面有明显提升,改进SA-GA算法对夏季、冬季典型日能效优化的结果,相较于遗传算法,在寻优精度提升约为20.33%。

企业诚信建设与注册会计师审计意见

在全社会强调传承诚信文化的背景下,已有文献研究了诚信建设带给企业的诸多后果,却鲜有研究从审计市场角度分析企业诚信的影响。本文基于诚信的三个基础因素“言”“行”和“言行不一致”,利用年报文本和监管处罚数据构建了企业诚信指标,分析了企业诚信对审计意见的影响与机制。研究发现,企业诚信降低了注册会计师出具非标意见的可能性,该现象在非国有企业中更加突出。企业诚信通过减少财务报告错报、抑制盈余操纵和加强内部控制的方式,最终影响了其对审计意见的判断。进一步地,当客户的应收账款和存货占比越高、客户越重要时,企业诚信更能减少非标审计意见出具。本研究有助于促进企业从审计市场角度理解诚信建设的重要性。

考虑光储协调的配电网多阶段就地-分布式电压控制策略

针对高渗透率光伏并网点附近线路的电压越限问题,基于对含分布式电源配电网的功率调压原理的分析,提出了考虑光储协调的多阶段电压控制方法。采用切比雪夫多项式滤波方法改进基于一致性原理迭代的分布式控制方式,有效加快了分布式控制的收敛速度;提出了多阶段就地-分布式电压控制策略,包括光伏逆变器就地无功补偿的阶段Ⅰ调压、全网光伏逆变器分布式无功调节的阶段Ⅱ调压、全网储能一致性分布式有功调节的阶段Ⅲ调压。以改进的某实际55节点中压配电系统为算例进行仿真,验证了所提多阶段就地-分布式电压控制策略的有效性和优越性。

轨道交通“网-源-储-车”协同供能技术体系

为降低轨道交通系统牵引能耗,轨道交通“网-源-储-车”协同供能技术通过可再生能源的就地消纳,构建新型协同供能技术体系,实现跨时空高效用能.针对此新型供电系统结构,本文全面分析协同供能系统的物理架构、信息架构和社会架构的基本组成及类型特征;在此基础上,围绕资产能源化的基本概念,总结“荷-源”时空匹配评估方法与优化技术,并从系统角度阐述多源融合技术、保护重构、弹性评估等重要技术体系;重点分析“网-源-储-车”协同的高效能与高弹性的能源自洽技术,并基于人工智能和信息技术构建多层级能量管控系统,实现不同能量流的高效耦合,保障系统安全稳定经济运行.本文系统性地总结了轨道交通“网-源-储-车”协同供能系统的架构特征、评估优化、安全运维及协同运行等关键技术,阐述协同供能系统的技术组成体系,为协同供能系统的工程实践提供相应参考.

MMCSC:一种跨模态的假新闻检测方法

目前基于新闻内容的假新闻检测方法没有考虑到不同模态更高层的语义关联,缺少可以依据的信息对新闻进行判断,从而缺乏对有重要辨别特征的新闻的社交网络信息进行有效使用.针对这个问题,提出了基于新闻内容的假新闻检测方法,通过提取文本、图像和视频等多模态新闻的高层语义特征,分析不同模态高层语义信息,设计跨模态主题一致性和跨模态情感一致性计算方法.在此基础上,设计了一种跨模态内容语义一致性的假新闻检测模型MMCSC(multi-modal feature content semantic consistency).实验证明,相比于传统方法,所提出的MMCSC有较好的检测效果.