心理素质诊断模型的建立与应用

综合运用国内外多种测量量表对大学生的心理素质进行诊断与测试 ,并应用多元统计分析方法对其心理素质结构主因素进行定量分析 ,在此基础上建立了心理素质诊断模型 ,并运用该模型对实际问题进行了分析 .     

基于单片机控制的实验线路故障诊断系统

介绍了基于单片机控制技术的强电实验线路故障自动诊断系统的结构及功能,阐述了系统硬件设计、软件设计的基本原理．     

Railway wheel fault diagnosis using a fuzzy-logic method

A simple method for diagnosis of railway wheel defects using fuzzy-logic is proposed. The method is based on vibration measurements at different train speeds on healthy wheels and wheels with defects known a priori. The measured vibrations are statistically analyzed and confidence intervals for healthy and defective wheels depending on train speed and frequency of analysis are established. To facilitate the implementation of the method a fuzzy-logic is adopted. The fuzzy-logic model stores the obtained experience in a database and performs the decision making on damage extent and consequently the need of preventive maintenance. In view of the obtained results, the advantages and the limitations of the proposed approach as well as suggestions for further improvements are presented and discussed.     

Recent progress in two-photon small molecule fluorescent probes for enzymes

This review aims to provide a summary of the progress in TP small molecule fluorescent probes for enzymes in recent years and displays the main fluorescent mechanisms that have been applied to design probes.     

Progress on the application of electrochemiluminescence biosensor based on nanomaterials

In this review, we check out the number of published literature in the field of ECL biosensors trends during nearly a decade, and compare the research status of four different types of biosensors; summarize the application forms of nanomaterials in ECL biosensor. We have an overview of the building patterns and application example of the four main types of biosensors in the paper.     

微量元素锌在治疗Bell麻痹中的作用

为探讨急性早期应用补锌和综合疗法治疗Bell麻痹 ,将 1 5 8例Bell麻痹患者 ,随机分成两组 :(1 )补锌组 (补锌 +综合疗法 ) ;(2 )对照组 (单纯综合疗法 )。对其进行了观察对比治疗 ,同时检测了所有患者治疗前后自身对照发锌值 ,还对所有患者进行了定位诊断、面神经电反应测试、并对面肌功能进行评分。结果表明 ,对定位诊断其病变在D段、面肌功能评分在 1 1分以下、面神经无电反应患者 ,即使给予早期综合治疗 ,效果仍较差 ,而早期给予补锌加综合治疗效果优于对照组 ,其它段内患者治疗次数和治愈天数也均优于对照组。可见微量元素锌在发挥免疫应答、免疫调控中的重要作用     

锌与小儿神经系统及代谢性疾病

综述了锌在小儿神经系统疾病和内分泌代谢性疾病中的作用及临床意义，尤其是对癫痫的发病起重要作用，大量资料证明高锌与癫痫发作有关，其血锌值明显高于健康儿童，因此在治疗中不要轻易补锌。还综述了锌与智能发育障碍、生长发育障碍、多动症、胎儿畸形、遗传性感音神经性聋等疾病的关系及在治疗糖尿病、肝豆状核变性等内分泌和代谢性疾病时要注意维持锌的正常动态平衡。     

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble “mystery boxes”. Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.     

Synthetic and structural carboxylate chemistry of neurotoxic aluminum in relevance to human diseases

The contact of Al(III) with biological components in human physiology has increased significantly over the years, due to a number of factors, prominent among which stands the rapid acidification of the environment and the concomitant introduction of that abundant metal ion in human biological fluids. As a result, pathophysiological aberrations in humans have arisen due to Al(III) (neuro)toxicity. Among the efforts targeting the elucidation of the factors responsible for Al(III) toxicity is the exploration of the requisite Al(III)-carboxylate chemistry in aqueous media, and its relevance to soluble, potentially bioavailable species capable of exerting toxic effects. A detailed synthetic, structural, and spectroscopic account of the Al(III)-carboxylate complexes, purported to exist as components in aqueous Al(III)-carboxylic acid speciation, is presented. The structures described are classified as mononuclear, dinuclear, trinuclear, tetranuclear, and polynuclear species, arising from various aqueous and non-aqueous Al(III)-carboxylate ligand reactions. Moreover, the solution chemistry and kinetic behavior of the so far reported complexes is given, with the specific aim of comparing their solid state and solution chemical and structural properties. In this sense, a comprehensive picture on the Al(III) speciation, in the presence of various physiological or biologically relevant carboxylate ligands, appears to emerge, which is expected to contribute to the understanding of Al(III) (neuro)toxicity and its consequence(s) in a multitude of human diseases. Carboxylate containing low and high molecular mass components stand prominent in their chemical preference to react with Al(III) in biological fluids. In this context, factors considered to influence the aqueous low molecular mass Al(III)-carboxylate chemistry, thus affecting the solubility and possibly the bioavailability of the resulting species, are discussed as potential research links to the ultimate manifestation of Al(III) toxicity at the cellular level.     

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.