Meticulously tailoring phase boundary in KNN-based ceramics to enhance piezoelectricity and temperature stability

Although KNN-based ceramics with high electrical properties are obtained through a variety of strategies, the temperature sensitivity is still one of the key technical bottlenecks hindering practical applications. Here, we use a new strategy, meticulously tailoring phase boundary, to refine the ferroelectric boundary of KNN-based ceramics, leading to high piezoelectricity companied with improving temperature stability. The highest d₃₃ value in this system reaches 501 pC/N with a T_C ∼ 240°C, whereas a large strain of ∼0.134% can be kept with 10% lower deterioration until 100°C. The origin of high piezoelectricity is mainly attributed to the well-preserved multiphase coexistence and the appearance of nanodomains, which greatly facilitate the polarization rotation. Instead of the changed intrinsic thermal insensitivity, the precision phase boundary engineering plays an important role in strengthening the temperature stability of electric-induced strain. This work provides a simple and effective method to obtain both high electrical properties and excellent thermal stability in KNN-based ceramics, which is expected to promote the practical applications in the future.     

Data quality and uncertainty assessment methodology for pavement LCA

In performing pavement life cycle assessment (LCA), users are facing various reports of energy intensity coefficient (EIC) of pavement materials which differ considerably among data sources and therefore alter the LCA results significantly. Instead of selecting a certain EIC without or of little explanation for the current pavement LCA practices, this study proposed a methodology to build probability density function (PDF) for EIC based on available data-set and their qualities. Each data was first evaluated about the data quality indicator (DQI) through data quality pedigree matrix and converted to PDF in modified Beta distribution form. Three weighting methods, the DQI one, coefficient of variation (COV) one and analytical hierarchy process (AHP) one, were developed to assign weightings for different data. Monte Carlo simulation was run with the weighted PDF of each data as input to obtain the ultimate PDF for EIC. A case study to estimate the bitumen’s EIC with eight data samples were performed using the proposed methodology. It is found (1): the estimates by the proposed methodology is of higher reliability (lower COV) compared to any single data due to utilisation of information of the overall data samples; (2) the AHP weighting method is most favoured despite the results of the three weighting methods are close; (3) the central estimates of bitumen’s EIC are between5.4~5.8 MJ/kg. The proposed methodology is helpful in aiding calculating EICs for pavement materials and capturing uncertainties in LCA results in a statistical sense.     

课程群案例嵌入协同教学模式的探索

在课程群的教学中由于每门课程各自独立开展教学,缺乏知识的融合和衔接,导致学生运用综合知识解决问题的能力较弱。在课程群的教学中采用案例嵌入协同教学模式,将完整的工程案例嵌入到课程群各门课程的教学中,协同规划各门课程的教学任务,每门课程再围绕案例展开研究性教学。通过嵌入的工程案例衔接各门课程的知识点,帮助学生建构完整的知识体系,强化工程应用的概念；同时通过研究性教学,培养学生分析问题和解决问题的能力,两部分相结合,提高了学生运用综合知识解决复杂问题的能力。     

工程建设标准客观优先度评价研究

在工程建设标准的制定和修订过程中，如何科学地确定编制顺序一直是困扰标准管理部门的难题，采用传统方式“按需制标”，具有盲目性、主观性，容易造成标准在制定和修订过程中的互相重复、滞后与矛盾。为了保证标准制定和修订工作的协调性，指导标准体系中各标准的制修订顺序，引入复杂网络拓扑结构，将同一标准类型下的标准构成功能团，根据相互之间的关系形成复杂网络，采用剥落算法对其网络节点重要性排序，进而确定标准制修订的优先度。通过对建筑给水排水工程规范标准修订的实证分析，总结工程建设标准的网络拓扑优先度评价的算法模型，实现制定和修订顺序的自动计算，从而更加科学地指导标准的制定和修订工作     

A cathode support structure for use in a magnetron oscillator experiment

A low temperature co-fired ceramic (LTCC) material system has been used to develop a protype field emission cathode structure for use in an experimental magnetron oscillator. The structure is designed for used with 30 gated field emission array (GFEA) die electrically connected through silver metal traces and electrical vias. To approximate a cylinder, the cathode structure (48 mm long and 13.7 mm in diameter) is comprised of 10 faceted plates which cover the GFEA dies. Slits in the facet plates allow electron injection. The GFEA die (3 mm × 8 mm) are placed in axial columns of 3 and spaced azimuthally around a cylindrical support structure in a staggered configuration resulting in 10 azimuthal locations. LTCC manufacturing techniques were developed in order to fabricate the newly designed cathode with seven layers wrapped to form the cylinder with electrical traces and vias. Two different cathode wrapping techniques and two different via filling techniques were studied and compared. Two different facet plate manufacturing techniques were studied. Finally, four different support stand configurations for firing the cylindrical structure were also compared with a square post stand having the best circularity and linearity measurements of the fired structure.     

夹杂物对工程机械用钢Q345FCA疲劳寿命的影响

采用QBWP-6000J型简支梁旋转弯曲疲劳试验机测定了高疲劳寿命工程机械用钢Q345FCA的疲劳寿命;采用扫描电镜(SEM)对疲劳断口形貌进行了观察,并用附带的能谱仪(EDS)寻找断口上的夹杂物;借助夹杂物自动分析系统对钢中的夹杂物进行了分析。通过对试验数据的分析,计算得出了Q345FCA钢和Q345钢夹杂物的表面临界尺寸、次表面临界尺寸和内部临界尺寸。结果表明,Q345FCA钢的疲劳极限为273 MPa,Q345钢的疲劳极限为266 MPa。Q345FCA钢和Q345钢中夹杂物尺寸均小于临界夹杂物尺寸,且断口形貌显示所有疲劳断裂均不是由夹杂物所引起,夹杂物不是疲劳源。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

建筑产业转型升级新时代下高职建筑工程技术特色专业建设问题的新思考

随着建筑业技术进步和转型升级,高职建筑工程技术专业建设应顺应新时代要求,产教融合,办出特色。本文提出了“四个对接一个融合”的特色专业建设新思考。