基于语言特征自动获取的反问句识别方法

反问句是以疑问的形式表达强烈情感的修辞方式,对其有效识别可为自然语言处理中的情感分析任务提供技术支持。该文提出了一种基于语言特征自动获取的反问句识别方法。首先,利用标签注意机制,建立了一个数据驱动的特征抽取模型,用于获取与任务相关的词汇、句法结构、符号标记和话题等语言特征。其次,利用Bi-LSTM模型分别对句子和语言特征进行表示,两者的交互注意被用于获取句子的各个词和符号的注意力权重向量。该权重向量作用于句子的表示,用于构建一个强化语言特征的反问句识别模型。在中文微博数据集上的实验结果表明,提出的方法与之前的工作相比,反问句识别性能有显著提升。     

机翼极限环颤振的CFD/CSD全隐式紧耦合方法

针对轴承振动实时状态监控的需要，从符号动力学角度提出了符号聚合近似与Lempel-ziv复杂度（SAX-LZC）融合的振动监控参数。首先，以Logistic映射和Duffing方程为对象，从理论角度验证了SAX-LZC对动力学结构表征的准确性，并验证了该指标的抗噪能力和计算效率；其次，将SAX-LZC指标与信息熵、样本熵、多分段Lempel-ziv复杂度等动力学参数性能进行了综合对比；最后，从实验角度对轴承早期微弱异常进行了监测，并对轴承典型故障进行了特征提取。理论研究结果表明，SAX-LZC具有动力学结构表征准确、抗噪能力好、计算高效简洁等优点，克服了常规动力学参数工程应用能力弱的问题。实验研究结果表明，SAX-LZC对早期微弱异常有准确的监测，对不同种类故障具有较好的区分度，弥补了时域和频域对轴承微弱故障表征能力不足的缺陷，是一种轴承振动实时状态监控与故障特征提取的有效参数。     

Static pentane extraction as a potential alternative to Soxhlet extraction for polypropylene olive oil overall migration limit measurements

Static pentane extraction of olive oil from polypropylene was evaluated as a safer, higher throughput alternative to pentane Soxhlet extraction for the recovery of olive oil from polypropylene for overall migration limit determinations. Static, ambient temperature, pentane extraction achieved quantitative recovery of olive oil from polypropylene plaques prepared by using the OM‐5 overall migration limit protocol. In addition to validation data for static extractions, a modified esterification procedure is presented as a means to accommodate the larger sample sets that are enabled with static extraction.     

A Review on the Separation of Niobium and Tantalum by Solvent Extraction

Niobium and tantalum are found together in natural minerals. Leaching with concentrated acid solution or alkaline roasting followed by water/dilute acid leaching results in a solution containing these two metals. In this work, we reviewed the extraction and separation of Nb(V) and Ta(V) from leach liquors in the absence and presence of hydrofluoric acid by acidic, neutral extractants and amines. Several solvent extraction systems were compared in the light of the requirement of hazardous chemicals, extraction/separation efficiency, and stability/solubility of extractants. Since the cationic species of Nb(V) and Ta(V) are unstable in the aqueous solution, the application of acidic extractants for extraction and separation of these metals is limited. Amines can extract Nb(V) as well as Ta(V) irrespective of the presence of fluoride ion but the separation of these metals from the loaded amines should be carried out in stripping process, resulting in process complexity. Another disadvantage encountered for amine is related to high viscosity in extraction process. Neutral extractants are considered as promising extractants which are effective to separate Ta(V) and Nb(V) from either non-fluoride or to moderate fluoride ion concentration in terms of extraction, separation and stripping efficiency. Compared to the traditional methyl iso-butyl ketone (MIBK) based system, methyl isoamyl ketone (MIAK) is found to be a promising extractant to separate Nb(V) and Ta(V).     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.