碳中和背景下配电网差异化节能降耗潜力评估分

为了提高配电网差异化节能降耗效果，解决现有潜力评估方法存在的应用性能差的问题，提出碳中和背景下配电网差异化节能降耗潜力优化评估方法。根据配电网的空间结构，构建相应的等值电路模型。在该模型下，从设备损耗和运行附加损耗2个方面计算配电网的损耗量。根据损耗量计算结果，确定配电网差异化碳中和节能降耗方式。从静态和动态2个角度设置潜力评估指标，通过指标数据处理、指标权重求解等步骤，得出配电网差异化节能降耗潜力的综合量化评估结果。将设计潜力评估方法应用到配电网的差异化节能降耗改造工作中，能够有效降低配电网的实际线损量、降低区域损耗费用，并具有较高的应用价值。     

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。     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

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。     

Electronic synaptic characteristics and simulation application of Ag/CeO2/Pt memristor

With the development of science and technology, artificial neural networks (ANNs) have become a research spot. Furthermore, two-terminal oxide memristors with adjustable resistance have attracted extensive attention due to their simple structure, low power consumption, and easy integration, among other attractive features. Additionally, among many oxides, ceria has exhibited good performance, such as longer retention and better stability in resistive devices; however, it was currently rarely used in artificial neural synapses. In this work, a self-designed Ag/CeO₂/Pt memristor was found to realize the slow transition between the high-resistance state (HRS) and low-resistance state (LRS) at a very small working voltage. It was also found to exhibit very good retention performance and cyclic characteristics. The conductivity of the device was analyzed by the current-voltage (I–V) characteristics curve. Furthermore, its artificial synaptic function was explored, and a series of neuromorphic systems simulations were carried out. Additionally, the relationships between the pulse sequence parameters and the resistance state of the device were explored, and an electrical signal simulation of Pavlov's dog experiment was designed. The findings demonstrated that the device could be used to realize the application of artificial neural synapse simulation.     

某福利院一起肠炎沙门菌食源性疾病的调查分析

目的 对某福利院一起肠炎沙门菌食源性疾病进行调查和溯源,为研究相关食源性疾病提供参考。方法采用流行病学、食品卫生学和脉冲场凝胶电泳(PFGE)同源性分析等方法,分析本次食源性疾病事件。结果 确认本次事件中病例23名,患病率5.65%(23/407)；现场采集病例肛拭子15份和厨房工作人员肛拭子3份、留样菜品3份、水果2份以及冰棒1份,其中从11份病例肛拭子中检出肠炎沙门菌,PFGE结果显示11株肠炎沙门菌的DNA条带图谱相似性为96.4%,聚类分析为同一型,结合流行病学调查,初步判断菌株来自同一克隆系。结论 综合流行病学、食品卫生学和实验室检测结果,确定为一起肠炎沙门菌引起的食源性疾病,福利院应加强对特殊人群的饮食安全管理,制定相应的食源性疾病突发事件应急处理预案,防止此类事故再发生。     

Flash joining of alumina ceramics under a small current density

The ceramic joining using electric field (E-field) has garnered significant research attention due to the decreased joining barrier and enhanced reliability. However, the underlying mechanism of E-field assisted joining remains unclear. Herein, we report the rapid joining of alumina ceramics using a small current. Moreover, the E-field is applied in both perpendicular and parallel directions to the faying surfaces, demonstrating a significant difference in terms of joint strength. These results indicate that the E-field generates defects and promotes the joining process by facilitating ionic diffusion.     

人工智能赋能在线实验教学行为分析

教学行为分析作是教学质量分析的重要组成部分，也是教学引导与反馈机制的重要依据。文章阐述了人工智能（AI）的含义及发展历程，重点分析总结了教学行为分析方法及AI在教学行为分析上的应用。文章以东南大学电工电子在线实验为研究平台，探索分析了AI技术在实验教学行为分析上的可行性，梳理了基于专家系统的在线实验分析系统的设计思路，充分探讨了“智能”教育在实验教学中的深刻内涵。     

Beyond “Big Eaters”: The Versatile Role of Alveolar Macrophages in Health and Disease

Macrophages act as immune scavengers and are important cell types in the homeostasis of various tissues. Given the multiple roles of macrophages, these cells can also be found as tissue resident macrophages tightly integrated into a variety of tissues in which they fulfill crucial and organ-specific functions. The lung harbors at least two macrophage populations: interstitial and alveolar macrophages, which occupy different niches and functions. In this review, we provide the latest insights into the multiple roles of alveolar macrophages while unraveling the distinct factors which can influence the ontogeny and function of these cells. Furthermore, we will highlight pulmonary diseases, which are associated with dysfunctional macrophages, concentrating on congenital diseases as well as pulmonary infections and impairment of immunological pathways. Moreover, we will provide an overview about different treatment approaches targeting macrophage dysfunction. Improved knowledge of the role of macrophages in the onset of pulmonary diseases may provide the basis for new pharmacological and/or cell-based immunotherapies and will extend our understanding to other macrophage-related disorders.     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.