Flexible Osteogenic Glue as an All-In-One Solution to Assist Fracture Fixation and Healing

Osteogenic glue that reproduces the natural bone composition represents the final frontier of orthopedic adhesives with the potential to revolutionize surgical strategies against comminuted fractures. However, it is difficult to achieve an all-in-one formula, which could provide flexible and reliable adhesiveness while avoiding interfering with or even promoting the healing of glued fractures. Herein, an osteogenic glue characterized by inorganic-in-organic integration between amine-modified mesoporous bioactive glass nanoparticles (AMBGN) and bioadhesive gelatin-dextran network (GelDex) is introduced as an all-in-one tool to flexibly adhere and splice bone fragments and subsequently guide fracture healing during degradation. Relying on such integration, a 4-fold improvement in cohesiveness is presented, followed by a nearly 5-fold enhancement in adhesive strength in ex vivo porcine bone samples. The reversible and re-adjustable adhesiveness also enables glue to effectively splice intricate fragments from highly comminuted fractures in the rabbit radius in an in vivo environment. Moreover, well-preserved organic–inorganic integrity during degradation of the glue guides sustained interfacial osteogenesis and achieve satisfying healing outcomes in glued fractures, as observed by the 2-fold improvement in biomechanical and radiological performance compared with commercially available cyanoacrylate adhesives. The current findings propose an all-in-one solution for the fixation of bone fragments during surgery.     

Stacked Pyramid Attention Network for Object Detection

Neural Processing Letters - Scale variation is one of the primary challenges in object detection. Recently, different strategies have been introduced to address this challenge, achieving promising...     

Hydrogeochemical Processes and Inverse Modeling for a Multilayer Aquifer System in the Yuaner Coal Mine,Huaibei Coalfield,China

Coal mining can dramatically change hydrogeological conditions and induce serious environmental problems. Fifty groundwater samples were collected from the main aquifers in the Yuaner coal mine (Anhui Province, China). The results show that the main hydrogeochemical processes in the mine include dissolution, precipitation, pyrite oxidation, desulfurization, and cation exchange. The Neogene porous aquifer is affected by groundwater flow conditions; its main hydrogeochemical processes are dissolution of carbonate minerals and gypsum, and cation exchange. The Permian coal measure’s fractured sandstone aquifer was confirmed to be controlled by the region’s geological structure; its main hydrogeochemical processes are desulfurization and cation exchange. The Carboniferous Taiyuan limestone aquifer was determined by both groundwater flow conditions and regional geological structure; its main hydrogeochemical processes are dissolution of carbonate minerals and gypsum, pyrite oxidation, and cation exchange. Additionally, hydrogeochemical inverse modeling of the groundwater flow path confirm the hydrochemistry results and principal component analysis.

     

基于ERNIE-BiGRU模型的摘要语步自动识别研究

学术文献的摘要是对文献主要内容的浓缩,摘要不同部分的语步具有不同的信息,语步的自动识别和抽取对于学术摘要的后续研究有着重要的应用价值,而目前语步识别的研究相对较少,并且相关算法的效果还需要提高。针对上述问题,该文提出了一种基于ERNIE-BiGRU模型的语步识别算法。该算法首先结合中文句法分析理论提出基于句法依存关系的多语步结构拆分法,对学术文献摘要多语步结构进行自动拆分,获得多个单语步结构;然后构建用于训练的单语步结构语料库,并利用知识增强语义表示预训练模型,训练出句子级词向量;最后将训练出的单语步结构词向量信息输入双向门限循环单元(BiGRU)进行摘要语步自动化识别,取得了良好的效果。实验结果表明,该算法具有较好的鲁棒性和较高的识别精度,在结构化和非结构化摘要上的识别准确率分别达到了96.57%和93.75%。     

CDBN‑IKELM的轴承变工况故障诊断方法

针对现有方法在轴承变工况方面存在的诊断精度低、人工提取特征不充分等问题，提出了基于卷积深度置信网络（convolutional deep belief network，简称CDBN）与改进核极限学习机 （improved Kernel?based extreme learning machine，简称IKELM）的滚动轴承故障智能识别方法。首先，由卷积深度置信网络对原始信号内的故障特征进行深层自适应提取；其次，利用等距特征映射对提取的多维特征进行降维，去除冗余特征信息；然后，采用改进的核极限学习机对特征进行分类，使用粒子群（particle swarm optimization， 简称PSO）对模型重要参数进行优化，实现滚动轴承变工况下的故障识别；最后，将所提方法应用于不同工况下多种轴承故障的诊断。实验结果表明，该方法能够智能有效地识别变工况的轴承故障，诊断结果优于已有的智能故障诊断方法。     

ZnO·2.7Al2O3 nanocomposite with high optical transparency

Here we report a transparent dual-phase ZnO·2.7Al₂O₃ ceramic. The composite is pore-free and consists of thin nanosheets with a spinel phase and a hexagonal phase, while the two phases match closely in both lattice and refractive index. Such features result in excellent optical transmittance (maximum value >80% in the visible spectrum) at comparable phase volume. This work may provide a new thought for the rational structural design of optical nanocomposites.     

基于虚拟元-有限元耦合的隧道内道床干缩裂缝细观研究

为解释道床与盾构隧道管片产生脱空的现象，研究新混凝土在浇筑后由干燥收缩产生的裂缝机理。基于虚拟元推导任意多边形骨料的刚度矩阵格式，利用UEL子程序实现有限元与虚拟元的耦合计算，纯弯梁数值试验结果表明耦合法较有限元法计算效率明显提升，且具有较好的稳定性。在此基础上，建立细观尺度下新老混凝土的干缩数值模型，并通过圆盘干缩模型验证了模型参数。研究骨料和砂浆的应力分布、内部湿度与裂缝发展的对应关系以及新老混凝土表面和界面的应力发展。结果表明：①收缩过程中，骨料主要承受压应力作用，砂浆沿骨料边界的切线方向受骨料约束作用易形成收缩裂缝，在新混凝土表面首先出现大量微小裂缝，随后界面两端发生剥离; ②受表面下骨料的约束作用，新混凝土表面拉应力呈现“驼峰”分布。开裂后表面被划分为多个收缩区，收缩区边界受拉应力，内部受压应力；③界面的拉伸和剪切应力由两侧开始增加，并且随着界面剥离的增加不断内移。在界面内部未剥离区域内存在由界面附近骨料约束导致的局部压应力和剪切应力增大。     

Sparse spectrum fitting algorithm using signal covariance matrix reconstruction and weighted sparse constraint

Multidimensional Systems and Signal Processing - Although sparse spectrum fitting algorithm has good multi-target resolution, the performance is decreased under the influence of strong...     

盾构隧道施工物料无人运输发展现状及关键技术

盾构隧道施工物料无人运输有助于提升运输效率、降低运输成本、减少运输安全事故率。与公路汽车无人驾驶、港口车辆无人驾驶、轨道交通无人驾驶等逐步成熟的无人驾驶技术不同，在隧道场景下实施施工物料无人运输存在运输物料种类繁多、运输调度困难、地下定位信号拒止、狭窄车道频繁会车、行车路面工况复杂、地上地下联动响应慢等诸多挑战。从实现盾构隧道施工物料无人运输的关键问题分析入手，综述当前盾构隧道施工物料运输方式、运输需求、无人运输发展现状及存在的挑战，提出盾构隧道施工物料无人运输的五大关键技术：多种类物料智能货控能效管理技术，无人化垂直装卸门机控制技术，多传感器融合同步定位与地图构建技术，隧道复杂环境路径规划与自主避障技术，隧-地一体化联动响应的高可靠性无线通信技术。