Improving water retention of chicken breast meats by CaCl2 combined with pulsed electric fields

Frozen poultry meat is the most widely consumed animal-based food. However, water loss often leads to quality loss of poultry meat. Therefore, the present study sought to investigate the combined effect of calcium chloride (CaCl₂) and pulsed electric fields (PEF) treatment on chicken breast meats and the mechanisms underlying protein degradation. The results showed that the synergistic effect was superior to the single treatment. Compared with the untreated group, the combination of CaCl₂ and PEF increased water holding capacity of chicken breast meats by 16.61% and decreased cooking loss by 28.93%. Low-field nuclear magnetic resonance (LF-NMR) results indicated that the synergistic treatment promoted water molecules' binding capacity in myofibrils of poultry meat, which exhibited higher immobilised water. Additionally, the combination of CaCl₂ and PEF led to increased degradation of proteins of high-molecular weight and surface hydrophobicity of myofibrillar protein. Furthermore, the extension of the protein molecule and microenvironmental changes promoted interaction between protein and water. In conclusion, the synergistic treatment of CaCl₂ and PEF enhanced water retention and improved physicochemical properties of the myofibrillar protein in chicken breast meats.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

Magnetic field-enhanced photocatalytic nitrogen fixation over defect-rich ferroelectric Bi2WO6

Photocatalytic N₂ fixation is a promising and sustainable manufacturing process of ammonia (NH₃); however, the NH₃ production rate by this method is very low, thus severely restricting further application of this sustainable technology. Therefore, developing an efficient photocatalyst for N₂ fixation under mild conditions is urgently required. Herein, ferroelectric Bi₂WO₆ materials with different surface oxygen defects were prepared, and the concentration of corresponding defects was controlled by adjusting the thermal reduction time. The abundant oxygen defects in Bi₂WO₆ can provide more reactive sites to promote the effective adsorption of N₂, and the photogenerated charge carrier can be efficiently separated benefiting from the internal electric field. These would weaken the N₂ triple bond and reduce the activation energy barrier for the conversion of N₂ to NH₃ under mild conditions. In the absence of sacrificial agents and cocatalysts, the optimized Bi₂WO₆ with oxygen defects shows an indigenous NH₃ yield of 132.175 μmol·g^-1·L^-1·h^-1, which is more than two times higher than that of the original Bi₂WO₆. Surprisingly, the Bi₂WO₆ with oxygen defects produced more than eight times NH₃ (471.13 μmol·g^-1·L^-1·h^-1) than that of the original Bi₂WO₆ when assisted by an external magnetic field, thus providing a new perspective for further enhancing the N₂ fixation performance.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

条形药包爆破震动场的试验研究

通过时条形药包周围实测爆破震动数据采用数值计算方法进行处理，作出了条形药包周围的爆破震动场，使我们对条形药包的爆破地震效应有了全面的认识，从而在实际工程爆破中能够有针对性地进行安全防护。分析研究认为：条形药包周围的震动场由一系列同心椭圆形等震线组成。在爆破近区由于位置因素的影响，等震线的长轴与药包轴线重合；在远区的震动主要受波动干涉作用的影响，致使长轴与药包轴线垂直，因此不能采用集中药包的计算方法。     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

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。     

基于卷积神经网络的5G蜂窝网络无线定位方法

5G蜂窝网络发展迅猛，其覆盖面积将逐渐增大，因此使用5G蜂窝网络进行定位是有研究潜力的研究方向。本文提出一种新的深度学习技术来实现高效、高精度和低占用的定位，以代替传统指纹定位过程中繁重的指纹库生成以及距离计算。该方法建立了一个特殊的卷积神经网络，并根据5G天线信号的接收信号强度指示、相位和到达角等特征量，选择合适的输入数据格式构造样本组建训练集，对该卷积神经网络进行训练。训练得到的卷积神经网络可以替代指纹定位中的庞大指纹库，非常有利于直接在5G移动设备端实现定位。虽然卷积神经网络在训练过程中需要大量时间，但在训练完毕后直接进行分类定位的速度非常快，可以保障定位实现的实时性。本文所实现的卷积神经网络权重与偏置所占内存不到0.5 MB，且能够在实际应用环境中以95%的定位准确率以及0.1 m的平均定位精度实现高精度定位。     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。