探讨加强质量监督管理提高电网安全水平

电网的安全水平主要取决于其使用产品的质量是否过关。如果产品的质量较好，则能建造出优质的电网，电网的安全水平便会随之提高。因此，应加强对产品质量的监督管理，从而提高电网的安全水平。我们可建立质量监督管理系统，及时抽检出质量不过关的产品，从而消除电网运行过程中潜在的安全隐患。     

气管上段腺样囊性癌的临床病理

目的　探讨气管上段腺样囊性癌的临床病理特征及诊断和鉴别诊断要点。方法　收集2000年1月至2014年2月在北京协和医院确诊的4例气管上段腺样囊性癌病例，通过光镜、免疫组织化学及组织化学染色分析其临床病理特征、免疫组织化学特点、诊断及鉴别诊断要点。结果　4例气管上段腺样囊性癌患者中，男1例，女3例，平均年龄47岁(38~57岁）；既往均无腺样囊性癌病史，1例患者既往有结节性甲状腺肿手术史。镜下检查4例均为筛状/管状型腺样囊性癌，3例累及甲状腺组织，3例累及神经组织，未见淋巴结受累。免疫组织化学染色示4例P16、CD117、BCL2、P63、SMA均阳性，Ki67指数平均8%，TTF1和P53均阴性；4例中基底膜样物胶原Ⅳ阳性；组织化学染色示AB/PAS阳性。术后均接受总剂量为48~56 Gy的放疗，随访6~120个月，平均72.5个月，1例术后96个月复发，3例随访期间无复发及转移。结论　气管上段腺样囊性癌是罕见的原发于气管的低度恶性肿瘤，肿瘤生长缓慢，就诊时多数已侵及甲状腺组织，需要与原发于甲状腺的恶性肿瘤相鉴别，特别是在甲状腺穿刺及术中冰冻检查时。结合电子喉气管镜下表现、典型的形态学及免疫组织化学和组织化学染色有助于准确诊断。手术难以切除干净，术后放疗对延缓疾病复发有一定帮助。     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

杭州湾南岸滩涂沉积物中HCHs与DDTs污染特征分析

摘 要：目的 了解六六六（hexachlorocyclohexanes,HCHs）与滴滴涕(dichloro diphenyl trichloroethanes,DDTs)在杭州湾南岸滩涂沉积物中的污染现状。方法 于2018年4月，采集杭州湾南岸地区4个采样点不同深度（0~80 cm）滩涂沉积物，利用气相色谱-串联质谱法（gas chromatography tandem mass spectrometry, GC-MS/MS）测定了该区域中HCHs与DDTs的含量，对其分布规律进行分析，使用共识沉积物质量基准法对其生态学风险进行了评估。结果 所有样品中均检出，HCHs与DDTs含量分别为4.20~6.24 μg/kg与3.37~4.73 μg/kg，主要以β-HCH、δ-HCH以及DDE为主，其污染主要来源于历史性工业品，整体污染处于较低水平；20.3%的沉积物样品中DDEs含量以及28.1%的样品DDTs总量介于阈值效应浓度(threshold concentration, TECs) 和可能效应浓度(probable effect concentration, PECs)之间。结论 杭州湾南岸沉积物中HCHs与DDTs的污染处于较低水平，具有较小的生态学风险。     

A New NTRU-Type Public-Key Cryptosystem over the Binary Field

As the development of cloud computing and the convenience of wireless sensor netowrks, smart devices are widely used in daily life, but the security issues of the smart devices have not been well resolved. In this paper, we present a new NTRU-type public-key cryptosystem over the binary field. Specifically, the security of our scheme relies on the computational intractability of an unbalanced sparse polynomial ratio problem (DUSPR). Through theoretical analysis, we prove the correctness of our proposed cryptosystem. Furthermore, we implement our scheme using the NTL library, and conduct a group of experiments to evaluate the capabilities and consuming time of encryption and decryption. Our experiments result demonstrates that the NTRU-type public-key cryptosystem over the binary field is relatively practical and effective.     

ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis

Frequency band selection (FBS) in rotating machinery fault diagnosis aims to recognize frequency band location including a fault transient out of a full band spectrum, and thus fault diagnosis can suppress noise influence from other frequency components. Impulsiveness and cyclostationarity have been recently recognized as two distinctive signatures of a transient. Thus, many studies have focused on developing quantification metrics of the two signatures and using them as indicators to guide FBS. However, most previous studies almost ignore another aspect of FBS, i.e. health reference, which significantly affect FBS performance. To address this issue, this paper investigates importance of a health reference and recognize it as the third critical aspect in FBS. With help of the health reference, the frequency band where the fault transient exists could be located. A novel approach based on classification is proposed to integrate all three aspects (impulsiveness, cyclostationarity, and health reference) for FBS. Classification accuracy is developed as a novel indicator to select the most sensitive frequency band for rotating machinery fault diagnosis. The proposed method (coined by accugram) has been validated on benchmark and experiment datasets. Comparison results show its effectiveness and robustness over conventional envelope analysis, the kurtogram, and the infogram.     

Matrilin-3-Primed Adipose-Derived Mesenchymal Stromal Cell Spheroids Prevent Mesenchymal Stromal-Cell-Derived Chondrocyte Hypertrophy

Adipose-derived mesenchymal stromal cells (Ad-MSCs) are a promising tool for articular cartilage repair and regeneration. However, the terminal hypertrophic differentiation of Ad-MSC-derived cartilage is a critical barrier during hyaline cartilage regeneration. In this study, we investigated the role of matrilin-3 in preventing Ad-MSC-derived chondrocyte hypertrophy in vitro and in an osteoarthritis (OA) destabilization of the medial meniscus (DMM) model. Methacrylated hyaluron (MAHA) (1%) was used to encapsulate and make scaffolds containing Ad-MSCs and matrilin-3. Subsequently, the encapsulated cells in the scaffolds were differentiated in chondrogenic medium (TGF-β, 1–14 days) and thyroid hormone hypertrophic medium (T3, 15–28 days). The presence of matrilin-3 with Ad-MSCs in the MAHA scaffold significantly increased the chondrogenic marker and decreased the hypertrophy marker mRNA and protein expression. Furthermore, matrilin-3 significantly modified the expression of TGF-β2, BMP-2, and BMP-4. Next, we prepared the OA model and transplanted Ad-MSCs primed with matrilin-3, either as a single-cell suspension or in spheroid form. Safranin-O staining and the OA score suggested that the regenerated cartilage morphology in the matrilin-3-primed Ad-MSC spheroids was similar to the positive control. Furthermore, matrilin-3-primed Ad-MSC spheroids prevented subchondral bone sclerosis in the mouse model. Here, we show that matrilin-3 plays a major role in modulating Ad-MSCs’ therapeutic effect on cartilage regeneration and hypertrophy suppression.     

物理化学课程的创新教学模式研究及其效果分析

物理化学课程创新教学模式是以学生学习为中心,吐故纳新,优化精选,以新颖或变化的教学方式进行教学。调查表明创新教学模式受到绝大多数学生的肯定和认可,有效地调动学生的积极性,提高了学生对物理化学的学习和探究兴趣,学生提问问题的广度和难度发生显著变化。该教学模式能够逐步提升学生的创新意识和创新能力。     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.