高熵氧化物的制备及应用研究进展

高熵氧化物作为近几年发展起来的新型氧化物体系，打破了传统掺杂氧化物的设计理念，由五种及以上氧化物以等摩尔或近等摩尔构成，因其具有简单的结构和优异的性能等受到国内外研究人员的广泛关注。高熵氧化物由于多主元且主元之间混乱排列，易形成岩盐型、氟化钙型、尖晶石型或钙钛矿等固溶体结构，从而表现出优异的性能，尤其在能源存储材料和磁性材料方面有十分广阔的应用前景，但目前对高熵氧化物应用研究较少。本工作介绍了国内外高熵氧化物的制备方法，主要包括固相法、热解法、共沉淀法、水热合成法和液相燃烧合成法等，比较了各方法的优缺点和发展前景；归纳了高熵氧化物作为锂离子电极材料、巨介电材料、磁性材料和催化材料等方面的应用；指出了高熵氧化物目前研究存在的问题，讨论了解决措施，展望了高熵氧化物未来的发展趋势。     

基于CFD-DEM方法的净化器流场模拟与结构优化

针对空气净化器能耗高的问题，使用离散元方法(DEM)在吸附滤网中建立随机堆积柱形活性炭模型，采用计算流体力学(CFD)方法对空气净化器内部流场进行数值模拟，在模拟与实验验证的基础上，考察了压降最小、流场最均匀的吸附滤网结构。结果表明，空气净化器压降主要发生在轴向，活性炭吸附滤网中回流、沟流现象严重，流体阻力是其他两种滤网的3倍。边数对多边形填充孔结构吸附滤网内压降与流场均匀性无影响，当孔结构改为圆形时，压降减小约52 Pa，节能18.4%(49 W)；当孔直径由8 mm增至12 mm，压降减小约48 Pa，节能19.4%(45 W)；滤网间距对空气净化器压降无影响，圆形、小孔径的吸附滤网内流场最均匀。     

马脊梁煤矿井下巷道贯通测量方法分析

在煤矿测量工作中,贯通测量是其中重要工作之一,确保贯通点的误差在设定的范围内,决定了贯通测量的成败。本文将马脊梁煤矿副立井与王村回风立井的贯通作为研究对象,选择使用井下控制测量、竖井联系测量及陀螺定向技术等,设定了贯通测量的方案,开展了误差预计,通过对比预计结果和要求,保证了贯通测量设计的可行性。     

Comparison of methods to develop an emotional lexicon of wine: Conventional vs rapid-method approach

The development of emotional lexicons can be seen as a time- and resource-consuming activity. Several rapid methods have been developed for time-restricted studies, but the suitability of these methodologies in the development of emotional lexicons compared to conventional ones has not been investigated.The aim of this study was to test the suitability of a rapid method, as it is the Sorting Task, for emotional lexicon development compared to a conventional procedure. For this purpose, two different approaches (conventional vs rapid-method) were tested to evaluate wines.222 consumers participated in the lexicon development procedure. A common phase of generation and filtering of the terms was carried out for the development of both lexicons. Then, two approaches were used, one applying the procedures proposed in the literature (conventional, herein as Lexicon I) and the other applying Sorting Task (rapid-method, herein as Lexicon II). To test both lexicons, consumer studies (n = 185) were conducted using a set of seven commercial wines.Both methods were helpful in discriminating emotional responses. Lexicon I reported a wine effect for 10 of the 13 emotional categories, while the Lexicon II discriminated the samples in all 15 categories. Although multifactorial analysis showed a high agreement between configurations (RV = 0.985), the emotional map of both lexicons did not follow the theoretical circumplex model of emotions. SADNESS and NOSTALGIA feelings were related to the second axis.In conclusion, both methods provided similar maps of wines, but Lexicon II was more efficient in terms of time and resources during the lexicon development.     

井下安全阀力学性能分析

井下安全阀对安全性和可靠性要求较高。以渤海油田常用油管携带式井下安全阀为分析对象,对其工作压力、地面控制压力、全开启和全关闭压力、最大下入深度和自平衡能力等关键力学性能进行分析。井下安全阀的工作压力值与地面液控压力值相等,其选取依据为井口压力; 全开启和全关闭液控压力值可通过压力曲线法得到; 最大下入深度取决于弹簧安装时的预紧力; 自平衡能力与活塞面积和自平衡孔面积的比值有关,比值越大,自平衡能力越强。     

压裂管汇无损检测方法改进

:按照现有无损探伤检验标准检验合格的压裂管汇在压裂作业时出现了管件刺漏、爆裂等情况。分析了压裂管汇中管件结构的特点和现有的无损检测方法的适用性。增加了管件的检测部位,改进了检测方法,并研制出了能检测不同形状和尺寸管件的超声波探头和比对试件。采用改进的无损检测方法,检测出不合格管件数增加44.9%。新增加检测部位后,检测出不合格管件数增加63.7%。目前已应用改进的无损检测方法1 a多,在压裂作业时未出现高压管件刺漏或爆裂情况。     

New doctors ranking system based on VIKOR method

Nowadays, we can use different websites that help us make decisions about various aspects of our lives. However, privacy protection prevents websites from providing personalised guidelines to users. We propose a novel doctor‐ranking system (DRS) based on multi‐criteria group decision‐making (MCGDM) method to address the problems of privacy protection. The following aspects differentiate our proposed DRS model from previous works: (a) textual information reviews are used to identify user preferences and complementary criteria, (b) criteria weights are determined by term frequency inverse document frequency (TF‐IDF) instead of Delphi method or expert opinion, (c) intuitionistic fuzzy sets (IFSs) are used to replace sentiment analysis to express subjective user criteria, and (d) VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method for MCGDM with IFSs is used to solve the doctor‐ranking problem. We apply our proposed model to datasets from Haodf.com to compare the performance of our method with that of sentiment analysis and technique for order performance by similarity to ideal solution (TOPSIS) methods. The experimental results show that our method provides accurate ranking and increases the reliability of DRS.     

Fluid flow and heat transfer with nail dipping method in mould during continuous casting process

ABSTRACT

The character and cause of sliver defect on IF steel sheet surface are studied by means of SEM, unstable flow in mould could induce surface velocity and level fluctuations, leading to surface defects during continuous casting of steel. The nozzle clogging is a serious problem during the continuous casting of steel, due to its influence on the casting operations and products quality. In this study, the nail dipping method for measuring surface velocity and flow direction in molten steel were employed. The fluid flow in mould of whole casting sequence was investigated, especially during the nozzle clogging conditions. The results showed that when nozzle clogging occurred in the 7th heat, the flow velocities on R and L side of nozzle were 0.280 and 0.402?m/s, respectively. The surface defect ratio of hot-rolled and cold-rolled plates increases with the increase of heat flux deviation on both sides of the mould copper. The different clogging per cent on both sides of the nozzle will lead to asymmetry flow, the surface velocity is higher with the small clogging per cent side compared to that of relatively large per cent clogging side.     

Learning bipartite Bayesian networks under monotonicity restrictions

ABSTRACT

Learning parameters of a probabilistic model is a necessary step in machine learning tasks. We present a method to improve learning from small datasets by using monotonicity conditions. Monotonicity simplifies the learning and it is often required by users. We present an algorithm for Bayesian Networks parameter learning. The algorithm and monotonicity conditions are described, and it is shown that with the monotonicity conditions we can better fit underlying data. Our algorithm is tested on artificial and empiric datasets. We use different methods satisfying monotonicity conditions: the proposed gradient descent, isotonic regression EM, and non-linear optimization. We also provide results of unrestricted EM and gradient descent methods. Learned models are compared with respect to their ability to fit data in terms of log-likelihood and their fit of parameters of the generating model. Our proposed method outperforms other methods for small sets, and provides better or comparable results for larger sets.