混合FRP嵌入式加固梁的试验方案设计

嵌入式（NSM）加固法是FRP材料加固混凝土结构的一种新的应用形式,较外贴加固在某些方面具有明显优势.本文对该加固方法进行了分析和探讨.     

玄武岩纤维修复加固损伤木柱的试验方法研究

简述了玄武岩纤维作为加固材料的优势性能和特点,叙述了其修复加固损伤木柱的试验方法,通过试验结果可知,采用该方法对木柱进行加固处理,不仅提高受损木柱的承载力,而且延性得到显著的改善.因此用BFS布包裹修复加固局部受损木柱是行之有效的方法,可用于木结构维修加固的工程实践.     

融合改进 YOLOv7 与 UNet 的编码点定位方法

针对编码点的定位存在误检率高和精准度差的问题,提出了一种融合改进 YOLOv7 与 UNet 的圆形编码点定位方 法。第1阶段使用改进的 YOLOv7 检测编码点的位置,改进的 YOLOv7 首先将DCN-v2 可变形卷积引入 ELAN 模块,提升 特征提取能力;其次把卷积块注意力模块(CBAM) 机制嵌入骨干网络使网络更关注目标特征;然后使用Focal-EloU Loss 提高 收敛速度;最后构建 OD-Cat 模块替换 ConCat 模块以提升网络检测精度。提取出每个圆形编码点的 ROI 后,第2阶段通过 UNet 分割出编码点的中心轮廓后,使用最小二乘法拟合出编码点的中心。实验结果表明,改进后的模型比原 YOLOv7 的精 确率提高了6.33%,平均精度均值(mAP) 提升了5.76%;提出的定位方法验证了在噪声、亮度不足或曝光等复杂环境下可以 准确定位出编码点的中心椭圆轮廓,在实际工业视觉测量中具备鲁棒性。     

空气源热泵在重庆地区学生公寓工程中的应用与实测

结合重庆某高校学生公寓的热水改造工程,介绍了该系统设计中热水负荷、生活热水用量及系统实际运行控制,对该系统冬季平均系统能效进行测试.根据工程的实际情况,针对用水规律提出了进一步优化措施,并对空气源热泵热水系统与太阳能的联合运行提出合理运行模式.     

Rational Design of Three‐Layered TiO2@Carbon@MoS2 Hierarchical Nanotubes for Enhanced Lithium Storage

Here we demonstrate the rational design and synthesis of three‐layered TiO₂@carbon@MoS₂ hierarchical nanotubes for anode applications in lithium‐ion batteries (LIBs). Through an efficient step‐by‐step strategy, ultrathin MoS₂ nanosheets are grown on nitrogen‐doped carbon (NC) coated TiO₂ nanotubes to achieve the TiO₂@NC@MoS₂ tubular nanostructures. This smart design can effectively shorten the diffusion length of Li⁺ ions, increase electric conductivity of the electrode, relax volume variation of electrode materials upon cycling, and provide more active sites for electrochemical reactions. Owing to these structural and compositional features, the hierarchical TiO₂@NC@MoS₂ nanotubes manifest remarkable lithium storage performance with good rate capability and long cycle life.     

面向对象监测系统的软件自动生成

在监测系统的应用领域内对软件自动生成技术进行了研究,提出了一套特定领域内软件自动生成的方法。通过对众多的应用系统进行归纳、抽象,建立了本系统的应用模型;然后根据该模型进行应用描述、参数化和编译连接,从而实现了系统所需的各个功能模块。运用面向对象的分析和设计方法,提出了监测领域软件自动生成的设计方案。     

HLC型乳化炸药连续化生产技术的应用

介绍了HLC型乳化炸药连续化生产线的实际应用情况。针对相配套的德国poly-clip自动灌装系统,提出了化学敏化的乳化炸药成品存在后效问题的解决办法,认为相适应的敏化剂加入量在900～950g·t-1之间。     

Metal–Organic Gel‐Derived FexOy/Nitrogen‐Doped Carbon Films for Enhanced Lithium Storage

The development of cost‐effective and flexible electrodes is demanding in the field of energy storage. Herein, flexible Fe_xO_y/nitrogen‐doped carbon films (Fe_xO_y/NC‐MOG) are prepared by facile electrospinning of Fe‐based metal–organic gels (MOGs) followed by high‐temperature carbonization. This approach allows the even mixing of fragile coordination polymers with polyacrylonitrile into flexible films while reserving the structural characteristics of coordination polymers. After thermal treatment, Fe_xO_y/NC‐MOG films possess uniformly distributed Fe_xO_y nanoparticles and larger accessible surface areas than traditional Fe_xO_y‐NC films without MOG. Taking advantage of the unique structure, Fe_xO_y/NC‐MOG exhibits a superior rate performance (449.8 mA h g^?1 at 5000 mA g^–1) and long cycle life (629.3 mA h g^–1 after 500 cycles at 1000 mA g^–1) when used as additive‐free anodes in lithium‐ion batteries.     

Yolk–Shell MnO@ZnMn2O4/N–C Nanorods Derived from α‐MnO2/ZIF‐8 as Anode Materials for Lithium Ion Batteries

Manganese oxides (MnO_x) are promising anode materials for lithium ion batteries, but they generally exhibit mediocre performances due to intrinsic low ionic conductivity, high polarization, and poor stability. Herein, yolk–shell nanorods comprising of nitrogen‐doped carbon (N–C) coating on manganese monoxide (MnO) coupled with zinc manganate (ZnMn₂O₄) nanoparticles are manufactured via one‐step carbonization of α‐MnO₂/ZIF‐8 precursors. When evaluated as anodes for lithium ion batteries, MnO@ZnMn₂O₄/N–C exhibits an reversible capacity of 803 mAh g^?1 at 50 mA g^?1 after 100 cycles, excellent cyclability with a capacity of 595 mAh g^?1 at 1000 mAg^?1 after 200 cycles, as well as better rate capability compared with those non‐N–C shelled manganese oxides (MnO_x). The outstanding electrochemical performance is attributed to the unique yolk–shell nanorod structure, the coating effect of N–C and nanoscale size.     

Auto-tuning Similarity Search Algorithms on Multi-core Architectures

In recent times, large high-dimensional datasets have become ubiquitous. Video and image repositories, financial, and sensor data are just a few examples of such datasets in practice. Many applications that use such datasets require the retrieval of data items similar to a given query item, or the nearest neighbors (NN or $k$ -NN) of a given item. Another common query is the retrieval of multiple sets of nearest neighbors, i.e., multi $k$ -NN, for different query items on the same data. With commodity multi-core CPUs becoming more and more widespread at lower costs, developing parallel algorithms for these search problems has become increasingly important. While the core nearest neighbor search problem is relatively easy to parallelize, it is challenging to tune it for optimality. This is due to the fact that the various performance-specific algorithmic parameters, or “tuning knobs”, are inter-related and also depend on the data and query workloads. In this paper, we present (1) a detailed study of the various tuning knobs and their contributions on increasing the query throughput for parallelized versions of the two most common classes of high-dimensional multi-NN search algorithms: linear scan and tree traversal, and (2) an offline auto-tuner for setting these knobs by iteratively measuring actual query execution times for a given workload and dataset. We show experimentally that our auto-tuner reaches near-optimal performance and significantly outperforms un-tuned versions of parallel multi-NN algorithms for real video repository data on a variety of multi-core platforms.