声波团聚净化尿素造粒塔尾气实验研究*

尿素造粒塔大多采用水洗工艺处理尾气粉尘,存在较为严重的细颗粒粉尘溢出和水汽拖尾问题,因此深入探究声波团聚技术在造粒塔尾气处理方面的应用。通过实验研究了声波与喷雾对除尘效率的影响,发现声波单独作用时在3500 Hz达到最佳除尘效率,为74.49%,协同喷雾时2600 Hz即可实现相近的除尘效率,为73.83%。进一步探究其中机理,采用以粒径测试为依据的团聚效率作为评价指标,结果显示,2600 Hz声波与喷雾单独作用时团聚效率分别为30.37%和28.82%,协同作用时团聚效率大幅提升至57.95%。该研究可为后续造粒塔工程改造提供理论与数据支持。     

Facile Synthesis of Tricyclic 1,2,4-Oxadiazolines-Fused Tetrahydro-Isoquinolines from Oxime Chlorides with 3,4-Dihydroisoquinoline Imines

A mild and efficient strategy for the synthesis of tricyclic 1,2,4-oxadiazolines-fused tetrahydro-isoquinolines derivatives via [3 + 2] cycloaddition reaction is reported. The reactions provided the functionalized tricyclic 1,2,4-oxadiazolines in high yields (up to 96%). This protocol is simple and easy to handle. Moreover, a gram-scale experiment further highlights the synthetic utility. The chemical structure of the product was determined by X-ray single-crystal structure analysis. A possible mechanism for this transformation is proposed to explain the reaction process.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

用光学多道分析器测定RH的不确定度评定

本文使用光学多道分析器测定了氢原子光谱的计Hα、Hβ、Hγ、Hδ,谱线波长,利用最小二乘法处理测量数据：建立了测量数学模型,得到氢原子光谱里德伯常量RH,按国家计量技术规范JJF1059—1999标准对测量的不确定度进行了分析与评定。     

Luminescence interference-free lifetime nanothermometry pinpoints in vivo temperature

Luminescence nanothermometry makes non-invasive and real-time temperature readings possible in living animals. However,the spectral fluctuation in tissues and fluids, as well as the interaction between fluorophores and environment hinders accuracy of the thermometry. Here, we report a luminescence lifetime-based nanothermometry which specifically addresses this problem. A temporal based calibration(lifetime sensing) in the NIR range, an endogenous thermal response as well as a polymer encapsulation evading environmental factors, altogether help to pinpoint temperature in vivo. Thanks to the highly condensed NdYb ions in a well-protected tiny core-shell nanocrystal(overall 11 nm), a temperature sensitivity about 2.07% K~(-1)(with 5% Yb~(3+) doped nanoparticles) and an accuracy of 0.27 K(with 25% Yb~(3+) doped nanoparticles) in biological fluids are achieved.Hopefully, combining thermally activated energy transfer nanothermometer with anti-interference lifetime thermometry would provide a more accurate temperature measurement for biological and preclinical studies.     

埋地热油管道正常运行的数值模拟研究

对高凝原油管道输送的水力热力问题进行分析研究,掌握管线运行规律,保证管线安全经济运行有着重要意义.本文建立了埋地热油管道正常运行的数学模型,采用非结构化网格和有限容积法对该问题进行了研究,计算结果与实验测量值吻合良好.     

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.     

Surfactant-Assisted Assembly of Dipeptide Forming a Broom-like Structure

Understanding the influence of surfactants on the assembly of peptides has a considerable practical motivation. In this paper, we systematically study the anionic surfactant-assisted assembly of diphenylalanine (FF). FF forms broom-like structures in a concentration of sodium cholate (NaC) around the CMC, and assembles into linear and unidirectional rods in the presence of low and high surfactant concentrations. FF’s improved hydrogen bonding and controlled assembly rates are appropriate for other anionic surfactants. At this stage, the use of FF as the simplest protein consequence can be helpful in the investigation of further protein–surfactant interactions.