Sparse Reconstructive Evidential Clustering for Multi-View Data

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

Metal-free two-dimensional phosphorene-based electrocatalyst with covalent P-N heterointerfacial reconstruction for electrolyte-lean lithium-sulfur batteries

The use of lithium-sulfur batteries under high sulfur loading and low electrolyte concentrations is severely restricted by the detrimental shuttling behavior of polysulfides and the sluggish kinetics in redox processes.Two-dimensional(2D)few layered black phosphorus with fully exposed atoms and high sulfur affinity can be potential lithium-sulfur battery electrocatalysts,which,however,have limitations of restricted catalytic activity and poor electrochemical/chemical stability.To resolve these issues,we developed a multifunctional metal-free catalyst by covalently bonding few layered black phosphorus nanosheets with nitrogen-doped carbon-coated multiwalled carbon nanotubes(denoted c-FBP-NC).The experimental characterizations and theoretical calculations show that the formed polarized P-N covalent bonds in c-FBP-NC can efficiently regulate electron transfer from NC to FBP and significantly promote the capture and catalysis of lithium polysulfides,thus alleviating the shuttle effect.Meanwhile,the robust 1D-2D interwoven structure with large surface area and high porosity allows strong physical confinement and fast mass transfer.Impressively,with c-FBP-NC as the sulfur host,the battery shows a high areal capacity of 7.69 mAh cm^(−2) under high sulfur loading of 8.74 mg cm^(−2) and a low electrolyte/sulfur ratio of 5.7μL mg^(−1).Moreover,the assembled pouch cell with sulfur loading of 4 mg cm^(−2) and an electrolyte/sulfur ratio of 3.5μL mg^(−1) shows good rate capability and outstanding cyclability.This work proposes an interfacial and electronic structure engineering strategy for fast and durable sulfur electrochemistry,demonstrating great potential in lithium-sulfur batteries.

Effect of nano-CaO particle on the microstructure,mechanical properties and corrosion behavior of lean Mg-1Zn alloy

The effects of nano-CaO contents on the microstructure,mechanical properties and corrosion resistance of lean Mg-1Zn alloy were investigated.The results showed that the addition of nano-CaO significantly refined the grain size and improved mechanical properties of the Mg-1Zn alloy.At the same time,CaO reacted with molten Mg in situ to form nano-MgO,whose corrosion product in SBF solution was the same with the degradation product of Mg matrix,resulting in the enhanced compactness of the Mg(OH)_(2) layer and reduced corrosion rate of matrix.The Mg-1Zn alloy had lower corrosion resistance due to excessively large grain size and shedding of corrosion products.The composite with 0.5 wt.%CaO had the best corrosion resistance with a weight loss of 9.875 mg·y^(-1)·mm^(-2)due to the small number of Ca_(2)Mg_(6)Zn_(3) phase and suitable grain size.While for composites with high content of CaO(0.7 wt.%and 1.0 wt.%),they had lower corrosion resistance due to the coexistence of large number of Ca_(2)Mg_(6)Zn_(3) and Mg_(2)Ca at grain boundaries,especially for 1.0 wt.%CaO composite,resulting from the strong micro-galvanic corrosion.

Design Strategies for Leaning-Type Arch Bridges

This paper presents a study on the design strategy of leaning-type arch bridges.The main characteristics of leaning-type arch bridges are first introduced;Kunshan Yufeng Bridge is taken as an example to discuss different aspects of a design strategy,which includes self-system optimization,selection of beam length and bridge deck position,and other aspects.This paper can be used as a reference to further improve and develop bridge design.

Update in lean metabolic dysfunction-associated steatotic liver disease

BACKGROUND A new nomenclature consensus has emerged for liver diseases that were previously known as non-alcoholic fatty liver disease(NAFLD)and metabolic dysfunction-associated fatty liver disease(MAFLD).They are now defined as metabolic dysfunction-associated steatotic liver disease(MASLD),which includes cardiometabolic criteria in adults.This condition,extensively studied in obese or overweight patients,constitutes around 30%of the population,with a steady increase worldwide.Lean patients account for approximately 10%-15%of the MASLD population.However,the pathogenesis is complex and is not well understood.AIM To systematically review the literature on the diagnosis,pathogenesis,characteristics,and prognosis in lean MASLD patients and provide an interpretation of these new criteria.METHODS We conducted a comprehensive database search on PubMed and Google Scholar between January 2012 and September 2023,specifically focusing on lean NAFLD,MAFLD,or MASLD patients.We include original articles with patients aged 18 years or older,with a lean body mass index categorized according to the World Health Organization criteria,using a cutoff of 25 kg/m2 for the general population and 23 kg/m2 for the Asian population.RESULTS We include 85 studies in our analysis.Our findings revealed that,for lean NAFLD patients,the prevalence rate varied widely,ranging from 3.8%to 34.1%.The precise pathogenesis mechanism remained elusive,with associations found in genetic variants,epigenetic modifications,and adaptative metabolic response.Common risk factors included metabolic syndrome,hypertension,and type 2 diabetes mellitus,but their prevalence varied based on the comparison group involving lean patients.Regarding non-invasive tools,Fibrosis-4 index outperformed the NAFLD fibrosis score in lean patients.Lifestyle modifications aided in reducing hepatic steatosis and improving cardiometabolic profiles,with some medications showing efficacy to a lesser extent.However,lean NAFLD patients exhibited a worse prognosis compared to the obese or overweight counterpart.CONCLUSION MASLD is a complex disease comprising epigenetic,genetic,and metabolic factors in its pathogenesis.Results vary across populations,gender,and age.Limited data exists on clinical practice guidelines for lean patients.Future studies employing this new nomenclature can contribute to standardizing and generalizing results among lean patients with steatotic liver disease.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

基于E-leaning的体育课程网络化教学改革

E-leaning可以优化教育资源、提高学习效率、降低学习成本,对于体育课程教学而言,其灵活便捷的教学方式能够解决"学训矛盾",方便学生自主学习。学习过程记录方便、教学管理和课程资源及时有效的更新,既方便了教学管理,也为体育远程教育创造了条件。E-leaning借助网络跨越了校园空间的限制,给学习者提供了一个全新的学习环境,促使了学校与学习者之间距离的缩短,让学校教育向更广泛的地区辐射。借助E-leaning进行体育精品课程和网络课程整合,开展体育远程教育有助于现有体育精品课程和网络课程的可持续发展,有利于体育教育资源共享,是未来学校体育教育和终身体育教育的重要途径。

《Leaning to do》的启示:把价值观教育与TVET结合起来

这几个模块相关联的核心价值是健康,与自然和谐,达到人的身体、智力、情感、社会和精神的全面发展,实现一种人与人、人与自然环境和谐的状态.要对自身健康负责,对所有形态的生命关爱,成为一个环境的看护者.

新时代学习方法的主流——M-Leaning

在现今知识信息的新时代,随着学习需求的与日俱增,现有的教育体系、模式与人们对教育的需求之间的矛盾也逐渐暴露出来,人们渴望更新、更灵活的学习模式的出现。因此,在任何时间(Anytime)和任何地点(Anywhere)利用手持的、无线移动通讯设备(如手机、PDA)学习任何内容(Anything)的M-leaning的出现是一种必然趋势。

Boosting Lean Electrolyte Lithium-Sulfur Battery Performance with Transition Metals: A Comprehensive Review

Lithium–sulfur(Li–S) batteries have received widespread attention, and lean electrolyte Li–S batteries have attracted additional interest because of their higher energy densities. This review systematically analyzes the effect of the electrolyte-to-sulfur(E/S) ratios on battery energy density and the challenges for sulfur reduction reactions(SRR) under lean electrolyte conditions. Accordingly, we review the use of various polar transition metal sulfur hosts as corresponding solutions to facilitate SRR kinetics at low E/S ratios(< 10 μL mg~(-1)), and the strengths and limitations of different transition metal compounds are presented and discussed from a fundamental perspective. Subsequently, three promising strategies for sulfur hosts that act as anchors and catalysts are proposed to boost lean electrolyte Li–S battery performance. Finally, an outlook is provided to guide future research on high energy density Li–S batteries.

基于Lean管理理念的门诊医疗服务质量提升研究

目的分析在Lean管理理念基础上实施门诊护理管理对门诊医疗服务质量提升的影响。方法选取2021年1月—2022年12月在南京医科大学附属逸夫医院门诊工作的34名护理人员作为研究对象,2021年1—12月实施常规管理(实施前组),2022年1—12月实施Lean管理理念(实施后组)。比较实施前后护理质量、不良事件发生率及交接班质量评分。结果实施后组护理质量评分高于实施前组,差异有统计学意义(P<0.05)。实施后组护理差错、缺陷、纠纷发生率均低于实施前组,差异有统计学意义(P均<0.05)。实施后组护理人员交接班质量评分高于实施后组,差异有统计学意义(P<0.05)。结论Lean管理理念在门诊护理管理中的应用能有效提升护理质量,降低护理差错发生率,减少护患纠纷和投诉发生率,对提升门诊医疗服务质量具有积极意义。

Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.

ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

Relational graph location network for multi-view image localization

In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.

成果导向下“精益”教学资源库建设模式的构建与实践

针对高职专业教学资源库建设中资源与岗位对接度不够、资源利用率不高、学习达成度评价体系不全等问题,提出了一种成果导向下的“精益”建设模式。“精益”建设模式以成果为导向,反向设计资源模块,项目式串联建设可复用颗粒化资源,精准化设置学习目标达成评价体系,教师可以基于学情数据,利用资源库资源搭建在线课程,满足不同学习者的学习需求,实现分层次差异化教学。实践表明该模式能有效服务于专业人才培养需求,高效益建设资源库资源,提高学生学习达成度,更好的服务于教师的教学需求,为职业教育资源库提供了一种参考模式。

回流区稳定燃烧的近熄火特性理论分析

基于火焰稳定在回流区剪切层中的假设,建立了预混火焰近贫燃熄火极限特性的理论分析模型.一旦火焰稳定在剪切层中,由于剪切层同时从两侧卷吸流体,所以进入火焰的流体将是自由流与回流区流体的混合物.如果是贫燃火焰,回流区流体由燃烧产物和多余的氧化剂组成而没有燃料,因此进入剪切层火焰的混合物当量比将低于自由流的当量比.如此一来,即使自由来流是可燃的,剪切层中混合物的当量比也可能超出可燃极限.分析表明,对于回流区稳定的贫燃火焰,进入剪切层火焰的混合物有效当量比将低于自由来流的当量比.根据理论分析建立了近贫燃熄火极限的回流区稳定燃烧模式图,其中涉及4个参数:自由来流当量比、剪切层卷吸比、吹熄极限和再点火极限.分析揭示出4种可能的燃烧模式:超稳定火焰、亚稳定火焰、振荡火焰和熄火.特别地,在参数空间发现了一个振荡区,它可能为近吹熄火焰引入一种新的固有不稳定性机制.在这种机制中,剪切层中的当量比振荡实际上是由燃烧过程本身驱动的,因为火焰的位置/振荡影响着进入回流区的流体成分进而影响剪切层中的当量比.因此,当量比振荡与燃烧过程之间的反馈循环由于周期性的火焰吹熄和闪回/再稳定而得以封闭.

基于DMAIC模型的妊娠合并脑血管病急诊就诊流程优化研究

目的利用精益六西格玛(lean six sigma,LSS)管理中的界定、测量、分析、改进和控制(define,measure,analyze,improve,control;DMAIC)模型,优化妊娠合并脑血管病患者就医流程,提高患者就医效率,保障母婴安全。方法2021年12月,首都医科大学附属北京天坛医院根据DMAIC模型,对危重孕产妇的就诊流程进行优化:梳理就诊流程,明确到院—医嘱开立、采血—送检、医嘱开立—影像学检查为院内延误的关键环节,对上述环节进行流程跟踪及分析,找出延误原因,并采取改进措施,优化流程。本研究回顾性纳入流程优化前(2019年1月—2021年12月)的妊娠合并脑血管病患者为优化前组,流程优化后(2022年1月—2023年12月)的患者为优化后组。比较两组患者就诊流程中的到院—医嘱开立、采血—送检、医嘱开立—影像学检查、到院—办理住院的时间。结果急诊就诊流程优化后,妊娠合并脑血管病患者的总体就诊效率提高。到院—医嘱开立[24.0(13.5~38.5)min vs.39.0(17.5~98.0)min,P=0.027]、医嘱开立—影像学检查[48.0(10.0~73.0)min vs.65.5(22.7~90.7)min,P=0.025]以及到院—办理入院总时间[120.0(93.0~149.0)min vs.218.0(123.0~382.7)mi n,P<0.001]均较优化前缩短,差异有统计学意义;采血—送检时间有缩短趋势,但优化前后差异无统计学意义。结论使用DMAIC模型能够明确流程优化的关键环节,优化妊娠合并脑血管病患者的急诊就诊流程。

DIP医保支付方式改革下医院多维联动精益运营管理研究

按病种分值付费(Diagnosis-Intervention Packet,DIP)医保支付方式改革的实施与落地对医院运营管理模式造成深远影响,使得医院的运营方式和管理内涵急需适配与优化。深度剖析DIP医保支付方式对于医院运营管理造成的影响,分别从病种分值政策的实施、医保成本核算控制、医疗服务质量提升、医保事后分析、病种管理等多个维度,对全生命周期视角DIP医保支付方式改革下医院运营管理模式的构建策略进行有力探索,以期更好地促进医院健康持续发展。