铜铟镓硒薄膜光伏组件中电池与封装材料界面的光学特性对组件性能的影响

根据测试数据,分析模拟了铜铟镓硒(CIGS)薄膜光伏组件中电池的活性区域、非活性区域与封装材料之间界面的光学特性对组件的短路电流产生的影响。根据组件结构建立了光学模型,从光学模拟结果分析组件内的反射与吸收。发现电池前电极透明导电氧化物薄膜(TCO)与封装材料界面的反射不可忽视,提出通过在透明导电氧化物薄膜与封装材料之间添加减反射层,并以MgO作为膜层材料以降低活性区域的界面反射;模拟了在非活性区域一次反射光角度与二次反射的关系,由此分析了非活性区域反射面倾角、镜面反射与漫反射比例对光利用的影响。模拟结果显示,活性区域的减反层结构可降低透明导电氧化物薄膜表面的反射率1%以上,而通过在非活性面积区域制备光反射结构,理论上能够利用非活性区域光照超过50%。     

紧凑型可重复运行的高功率纳秒脉冲源

讨论了一种低阻抗、高储能密度、可输出中等高压的百ns脉冲形成技术,其输出波形质量较好;采用磁感应电压叠加技术将该脉冲形成装置输出的中等高压脉冲叠加到应用需求的高电压高功率脉冲。研究表明单个感应模块可在2.8 Ω的负载上获得脉冲宽度为220 ns,前沿为50 ns的中等高压脉冲。     

Predicting the Classification of Home Oxygen Therapy for Post-COVID-19 Rehabilitation Patients Using a Neural Network

Objective: We evaluated the accuracy of a neural network to classify and predict the possibility of home oxygen therapy at the time of discharge from hospital based on patient information post-coronavirus disease (COVID-19) at admission. Methods: Patients who survived acute treatment with COVID-19 and were admitted to the Amagasaki Medical Co-operative Hospital during August 2020–December 2021 were included. However, only rehabilitation patients (n = 88) who were discharged after a rehabilitation period of at least 2 weeks and not via home or institution were included. The neural network model implemented in R for Windows (4.1.2) was trained using data on patient age, gender, and number of days between a positive polymerase chain reaction test and hospitalization, length of hospital stay, oxygen flow rate required at hospitalization, and ability to perform activities of daily living. The number of training trials was 100. We used the area under the curve (AUC), accuracy, sensitivity, and specificity as evaluation indicators for the classification model. Results: The model of states at rest had as AUC of 0.82, sensitivity of 75.0%, specificity of 88.9%, and model accuracy of 86.4%. The model of states on exertion had an ACU of 0.82, sensitivity of 83.3%, specificity of 81.3%, and model accuracy of 81.8%. Conclusion: The accuracy of this study’s neural network model is comparable to that of previous studies recommended by Japanese Guidelines for the Physical Therapy and is expected to be used in clinical practice. In future, it could be used as a more accurate clinical support tool by increasing the sample size and applying cross-validation.     

感应电压叠加器模块对方波脉冲的响应

为了解感应电压叠加器（IVA）对馈入脉冲的响应特性,尤其对上升前沿、平顶的影响,从理论和实验两个方面对IVA模块进行了研究。介绍了IVA的工作原理,利用集总参数方法建立了相应的电路模型,通过拉普拉斯变换分析了感应电压叠加器对方波脉冲上升前沿和平顶的响应,并在一个特定的IVA模块上进行了实验研究。选择输出阻抗约1.2 Ω、脉宽约1 μs的脉冲形成网络作为馈源,在匹配负载上得到的波形与输入波形在幅值、上升前沿方面达到了很好的吻合,平顶出现略微的顶降,与理论预期相一致。     

族以人重：明清之际魏塘钱氏的家族网络与政治变动

晚明江南地方社会的发展进程中,地方上势家大族起了很多积极作用。通过这些大族地方生活的轨迹,可以知晓其最重要的姻亲网络及核心家族间的链接关系。在浙江嘉善县,堪称权势阶层主流的钱氏等家族,对国家保持着高度忠诚,没有在地方上发展为垄断州县行政的“宗族社会”。在清兵南下后,这些大族的变化与家族成员的人生遭际,呈现出比较复杂的样态,也折射出王朝统治变化进程中地方的政治脉动和士人的家国情怀。从钱氏族人的生死往事、已逝的生活世界,可以探求和理解明清之际士人生活的状态及其生存之道,他们处于交互编织的网络中,既包括社会身份,也包括本土秩序,形塑了地方社会形态,影响了地方历史的主流变化。钱家在长期的发展过程中,已构造出了相对稳定的生活方式、丰富的家族遗产、接近一致的信仰以及大族共有的文化氛围。在以亲属关系为基础的社会结构中,钱家的那些代表人物,渗透至社会与文化生活的各个方面,都有所谓“链接性角色”的作用,建立起一种无限复杂的“微权力”网络。因此也可以解释,在一个具体的生活环境与地域社会中,因为有了这样具有“结构”性力量的社会阶层存在,晚明以来足以表现地方的人物,就是那样一些官绅士人而非其他的原因。     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.     

Investigation Driven by Network Pharmacology on Potential Components and Mechanism of DGS,a Natural Vasoprotective Combination,for the Phytotherapy of Coronary Artery Disease

Phytotherapy offers obvious advantages in the intervention of Coronary Artery Disease (CAD), but it is difficult to clarify the working mechanisms of the medicinal materials it uses. DGS is a natural vasoprotective combination that was screened out in our previous research, yet its potential components and mechanisms are unknown. Therefore, in this study, HPLC-MS and network pharmacology were employed to identify the active components and key signaling pathways of DGS. Transgenic zebrafish and HUVECs cell assays were used to evaluate the effectiveness of DGS. A total of 37 potentially active compounds were identified that interacted with 112 potential targets of CAD. Furthermore, PI3K-Akt, MAPK, relaxin, VEGF, and other signal pathways were determined to be the most promising DGS-mediated pathways. NO kit, ELISA, and Western blot results showed that DGS significantly promoted NO and VEGFA secretion via the upregulation of VEGFR2 expression and the phosphorylation of Akt, Erk1/2, and eNOS to cause angiogenesis and vasodilation. The result of dynamics molecular docking indicated that Salvianolic acid C may be a key active component of DGS in the treatment of CAD. In conclusion, this study has shed light on the network molecular mechanism of DGS for the intervention of CAD using a network pharmacology-driven strategy for the first time to aid in the intervention of CAD.     

Role of Natural Compounds and Target Enzymes in the Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurological condition. The rising prevalence of AD necessitates the rapid development of efficient therapy options. Despite substantial study, only a few medications are capable of delaying the disease. Several substances with pharmacological activity, derived from plants, have been shown to have positive benefits for the treatment of AD by targeting various enzymes, such as acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), β-secretase, γ-secretase, and monoamine oxidases (MAOs), which are discussed as potential targets. Medicinal plants have already contributed a number of lead molecules to medicine development, with many of them currently undergoing clinical trials. A variety of medicinal plants have been shown to diminish the degenerative symptoms associated with AD, either in their raw form or as isolated compounds. The aim of this review was to provide a brief summary of AD and its current therapies, followed by a discussion of the natural compounds examined as therapeutic agents and the processes underlying the positive effects, particularly the management of AD.     

中药复杂组效关系的变结构神经网络辨识方法

针对中药复杂组效关系的辨识问题,研究了变结构多层前馈神经网络,推导出一种新型的变结构网络学习算法,成功地应用于中药川芎药效活性预测计算.该方法从一个规模较小的网络出发,当网络无法达到预定的学习精度时,自动增加隐含层神经元个数,并在原有学习结果的基础上确定新的网络参数,自适应地确定前馈神经网络结构,可用于处理复杂化学模式信息.计算机仿真实验结果表明,该方法能有效地确定多层前馈神经网络的最佳结构,提高网络学习效率和函数逼近精度,解决复杂非线性函数映射关系准确建模问题.     

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.