A comparative study on pyrolysis reactivity and gas release characteristics of biomass and coal using TG-MS analysis

Pyrolysis reactivity and gas release characteristics of biomass and coals were comparatively investigated using TG-MS. In addition, chemical compositions and structures of tested samples were determined to correlate pyrolysis gas release characteristic. It was found that pyrolysis reactivity and gaseous product release were hardly varied with pyrolysis heating rate. Furthermore, pyrolysis rates of two biomass samples were greater than those of coals, which were mainly attributed to the chemical compositions and the gaseous product evolution process. The result also showed that gaseous product release during pyrolysis process was related to the presence of different functional groups in samples.     

强变形工艺对2519A铝合金组织与性能的影响

通过硬度测试、拉伸性能测试、透射电镜观察等分析手段研究了不同强变形工艺下2519A铝合金的力学性能与微观组织。结果表明,经50%的冷轧变形和165 ℃人工时效后,2519A合金的力学性能明显提高,其抗拉强度、屈服强度和伸长率分别为522 MPa、468 MPa和8.5%。而在冷变形前添加165 ℃×2 h预时效处理,合金的力学性能进一步提高,其抗拉强度、屈服强度和伸长率分别达到535 MPa、497 MPa和8%。预时效处理可以提高合金中θ′相的密度,使析出相分布更加均匀,有助于提高合金的力学性能。     

基于大数据的五年制高职护生在线学习行为要素研究

在线学习已逐渐成为高职院校一种重要学习形式,该领域也成为当前研究热点之一。对在线学习进行介绍,同时对大数据技术在在线学习平台中的应用进行分析,重点对五年制高职护生在线学习行为要素进行研究分析,为更好地开展在线学习评价提供参考,促进高职院校更好地开展在线学习,提高在线学习质量。     

低活化马氏体钢热变形行为及机理型本构建模研究

利用Gleeble-3500热模拟试验机研究了低活化马氏体钢在变形温度为850～950 ℃、应变速率为0.001～1 s^-1条件下的热变形行为。建立了流变应力本构方程，并评估了该方程的预测能力。绘制了低活化马氏体钢在不同应变下的热加工图。结果表明：在较高的应变速率条件下，该材料主要发生动态回复，在较高变形温度和较低应变速率下具有明显的动态再结晶特征；本构方程的预测结果与实验结果符合良好；变形温度870～930 ℃、应变速率0.001～0.01 s^-1和变形温度920～950 ℃、应变速率0.3～1 s^-1分别是真应变为0.4和0.6下最优的热加工区域。     

基于模拟退火优化BP神经网络的变形监测数据预测方法

针对BP神经网络模型用于变形监测数据处理时容易陷入局部极小值并且收敛速度慢的问题，提出一种基于模拟退火(Simulated Annealing,SA)算法优化BP神经网络的建筑物变形预测方法，利用SA的全局寻优能力对BP神经网络的模型参数进行优化，使参数迭代过程始终保持较高的“温度”和“能量”，从而确保BP神经网络能够得到全局最优解的同时具备较高的预测精度和收敛速度。采用实际算例对所提SA-BP方法在低信噪比和小样本条件下的预测精度进行验证，结果表明所提方法相对于传统BP神经网络法和小波方法能够获得更高的预测精度，并且在小样本和低信噪比条件下优势更加明显。     

Improved one-dimensional beam model for unbalanced composite single-lap joint

An analytical nonlinear solution was provided for unbalanced composite single-lap joint (CSLJ) using an improved one-dimensional beam model, which incorporated the effect of bending–tension coupling. The bending–tension coupling stiffness was introduced to characterize the coupling bending and tension behavior induced by the un-symmetric stacking sequence of composite laminates. The governing differential equations captured the bending–tension coupling behavior and the geometrically nonlinear features were constructed based on the displacement compatibility conditions of flexible interface. The transverse deformation in overlap region, edge moment factors and adhesive stress distributions for the unbalanced CSLJ with inflexible, intermediate flexibility and flexible adhesive can be determined by the present one-dimensional beam model. The accuracy of the present model was validated by the comparison with nonlinear finite element model. The effect of bending–tension coupling on edge moment factors and peak values of adhesive stresses was shed light on with the present model.     

综放切顶沿空留巷围岩变形特征分析及防治

为了掌握高地应力条件下综放沿空留巷围岩变形与破坏特征，以古汉山矿1604工作面为工程背景，采用理论分析的手段研究分析了综放沿空留巷围岩变形破坏形成机制，并提出采用预裂爆破切缝、强帮护顶等围岩控制措施，总结了综放沿空留巷技术原理，确定了沿空留巷支护参数，最后在现场进行了工业性试验。研究结果表明:沿空留巷巷道整体稳定性好，顶底板最大移近量为240~320 mm，两帮最大移近量为360~410 mm，支护效果良好，成巷效果较好；解决了采掘接替紧张、巷道维护困难的难题，满足了安全生产需要，提高了煤矿生产效率。     

基于大数据挖掘的用电行为分析

得益于智能电网的发展,电力大数据的价值与作用逐渐被挖掘。通过对电力大数据的深入挖掘与研究,可促进电网企业管理水平的进一步提升,为我国智能电网的发展提供助力。而依托于大数据挖掘的用户用电行为分析,可实现当前企业电网调控与需求侧的充分融合,为电力系统的稳定性运行提供保障。基于此,文章针对大数据挖掘的用电行为分析进行深入探析,以期为智能电网发展提供帮助。     

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.     

Compressive and flexural properties of novel polylactic acid/hydroxyapatite/yttria-stabilized zirconia hybrid nanocomposite scaffold

The mechanical property is a crucial factor in the design of bone tissue engineering scaffolds. In the current study, novel PLLA (Poly-L-lactic acid)–Hydroxyapatite (HA)–yttria-stabilized zirconia (YSZ) nanocomposite scaffold with various compositions was prepared and characterized. The effect of HA and YSZ contents on the mechanical behavior of the resultant composites was investigated. TEM micrograph revealed that HA particles are needle-like in shape and nano in size. Scanning electron microscopy (SEM) micrograph also showed that YSZ powder is in granule form and submicron size. SEM disclosed that all scaffolds had a highly interconnected porous structure and X-ray diffractometry revealed that there were some molecular interactions between PLA (Polylactic acid), HA, and YSZ in the composites. The results depicted that introducing YSZ to the nanocomposite leads to a significant increase in compressive strength, modulus, and densification strain. In addition, flexural strength and modulus showed an upward trend by adding YSZ particles to scaffolds. It should be noted that PLA–20%HA–20%YSZ indicates the highest strength and modulus in both compression and bending tests, though, it did not demonstrate the proper strain compared to other scaffolds. Thus, PLA–15%HA–15%YSZ has been reported as the best candidate due to appropriate strength and strain. Also, energy absorption in nanocomposites showed an upward trend by increasing the amount of YSZ particles. It was found that the strength of samples was declined after being soaked in simulated body fluid. However, scaffolds with HA underwent more decrease in strength compared to samples containing YSZ.