An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

东海OBN和三维DAS-VSP数据的联合采集与处理方法研究

近年来,光纤传感技术已经应用于地面地震数据、海洋地震数据、井中地震数据和井地联合地震数据的采集,推动了光纤传感技术在地球物理特别是地震数据采集中的应用。井地或井海联合地震勘探是陆地或海洋三维地震与三维DAS-VSP勘探相结合形成的三维立体地震勘探方法,利用井海联采的三维DAS-VSP数据,可以获得地下井周围准确的时深关系、地层速度、反褶积算子、球面扩散补偿因子、吸收衰减因子、各向异性参数和井筒周围的高分辨率构造成像,这些参数可以基于井驱处理提高陆地或海洋三维地震数据的处理质量。在中国东海某OBN数据勘探中首次开展了井下套管内铠装光缆同步采集的三维DAS-VSP数据的处理方法研究以及成像处理。首先,采用常规的三维VSP数据成像处理技术对三维DAS-VSP数据进行常规处理,具体处理流程包括:观测系统定义、预处理、初至拾取、静校正、振幅补偿、反褶积、波场分离、速度分析与建模和利用上行反射波进行井周围地层的构造成像;然后,再根据海上三维DAS-VSP数据下行多次波的特点,研发了海上三维DAS-VSP数据的下行多次波成像技术,扩展了三维DAS-VSP数据成像范围,提高了三维DAS-VSP数据成像的整体质量。与本工区早期的三维OBC数据成像结果相比,本次三维DAS-VSP数据的下行多次波成像结果表明,井周围三维构造成像质量得到了显著改进,大幅度扩展了成像范围;新采集的OBN数据和三维DAS-VSP数据的成像结果展示了更为详细和较高分辨率的构造成像,基于新的成像资料,对储层顶部和储层内流体的识别与追踪变得更加容易和清晰。井海联采技术生产效率高且成本低,既能快速获得三维DAS-VSP数据及成像,还能对三维海洋地震数据进行井驱提高分辨率处理。此外,三维OBN或OBC数据和三维DAS-VSP数据还能够进行融合处理,实现井地或井海同步采集数据的联合偏移成像,可以大幅度提高三维海洋地震数据的成像品质,值得在有条件的地方推广应用。

基于“脑肠轴理论”探讨龟羚帕安丸对PD大鼠纹状体DA及血清CCK、VIP水平影响的研究

目的:基于“脑肠轴理论”探讨龟羚帕安丸对PD大鼠纹状体裂解液多巴胺(Dopamine,DA)、血清胆囊收缩素(cholecystokinin,CCK)及血管活性肠肽(vasoactive intestinal peptide,VIP)水平的影响。方法:采用6-羟基多巴胺注射法制作PD大鼠模型,随机分为模型组、西药组、中药高、中、低剂量组,每组15只,另设15只假手术组。模型组及假手术组给予等容积生理盐水灌胃,其余组给予相应药物灌胃,连续给药4周。酶联免疫吸附测定法(ELISA)检测纹状体裂解液DA及血清CCK、VIP水平。结果:模型组大鼠纹状体DA、血清CCK及VIP水平显著降低;中药各剂量组、西药组大鼠纹状体DA、血清CCK及VIP水平明显均增加(P<0.01)。结论:龟羚帕安丸具有明显神经保护作用,作用机制与激活脑肠轴、增加血清CCK及VIP水平、增加中脑黑质纹状体DA的水平、发挥改善肠道功能作用有关。

采用非稳相偏振滤波的DAS-VSP数据P/S波分离方法及其应用

分布式光纤声学传感(DAS)因成本低、易布设以及高密度采样等优势正成为重要的地震观测技术,尤其是越来越多地与垂直地震剖面(VSP)结合,用于主动地震勘探或被动地震监测.DAS传感器通过感知弹性波场产生的轴向应变或应变速率来观测地震波场振动.然而,目前单分量DAS-VSP数据未完整地记录地下弹性波场的三维矢量振动信号,因此如何从中分离出P波或S波用于后续地震成像与参数反演是重要且很有挑战的课题.以弹性波传播理论为基础,本文根据P波和S波的频散关系估算接收点处各自的偏振方向,通过随频率和空间位置变化的偏振滤波实现DAS-VSP数据的P/S波分离.理论模型合成数据与东海实际DAS-VSP数据实验结果表明,该方法能有效地将P波和S波信号从单分量DAS-VSP数据中分离出来,可为后续纵横波速度反演、PP与PS波成像提供关键的数据预条件处理.

A Multi-Level Circulant Cross-Modal Transformer for Multimodal Speech Emotion Recognition

Speech emotion recognition,as an important component of humancomputer interaction technology,has received increasing attention.Recent studies have treated emotion recognition of speech signals as a multimodal task,due to its inclusion of the semantic features of two different modalities,i.e.,audio and text.However,existing methods often fail in effectively represent features and capture correlations.This paper presents a multi-level circulant cross-modal Transformer(MLCCT)formultimodal speech emotion recognition.The proposed model can be divided into three steps,feature extraction,interaction and fusion.Self-supervised embedding models are introduced for feature extraction,which give a more powerful representation of the original data than those using spectrograms or audio features such as Mel-frequency cepstral coefficients(MFCCs)and low-level descriptors(LLDs).In particular,MLCCT contains two types of feature interaction processes,where a bidirectional Long Short-term Memory(Bi-LSTM)with circulant interaction mechanism is proposed for low-level features,while a two-stream residual cross-modal Transformer block is appliedwhen high-level features are involved.Finally,we choose self-attention blocks for fusion and a fully connected layer to make predictions.To evaluate the performance of our proposed model,comprehensive experiments are conducted on three widely used benchmark datasets including IEMOCAP,MELD and CMU-MOSEI.The competitive results verify the effectiveness of our approach.

Improved Speech Emotion Recognition Focusing on High-Level Data Representations and Swift Feature Extraction Calculation

The performance of a speech emotion recognition(SER)system is heavily influenced by the efficacy of its feature extraction techniques.The study was designed to advance the field of SER by optimizing feature extraction tech-niques,specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients(MFCC).This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches.Ultimately,the primary objective was to elevate both the intricacy and effectiveness of our SER model,with a focus on augmenting its proficiency in the accurate identification of emotions in spoken language.The research employed a dual-strategy approach for feature extraction.Firstly,a rapid computation technique for MFCC was implemented and integrated with a Bi-LSTM layer to optimize the encoding of MFCC features.Secondly,a pretrained ResNet model was utilized in conjunction with feature Stats pooling and dense layers for the effective encoding of Mel-spectrogram attributes.These two sets of features underwent separate processing before being combined in a Convolutional Neural Network(CNN)outfitted with a dense layer,with the aim of enhancing their representational richness.The model was rigorously evaluated using two prominent databases:CMU-MOSEI and RAVDESS.Notable findings include an accuracy rate of 93.2%on the CMU-MOSEI database and 95.3%on the RAVDESS database.Such exceptional performance underscores the efficacy of this innovative approach,which not only meets but also exceeds the accuracy benchmarks established by traditional models in the field of speech emotion recognition.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

长庆安塞油田剩余油开发区DAS井地联采技术探索与实践

中国石油集团东方地球物理勘探有限责任公司结合长庆安塞油田开发区“三低”和“双复杂”的典型特点,面对老油田剩余油预测突出问题,从井地、井震协同一体化研究思路出发,开展国内首次面向开发区剩余油预测的多井DAS三维井地联采技术探索与实践,通过井地联合勘探实现地面地震测量和井中地震测量的优势互补,取得了良好成果。(1)通过井震同步井驱处理,三维地震资料保真度和分辨率明显提升,有效频带拓宽8%以上。(2)通过井地、井震一体化油藏静态描述剩余油预测研究,减少了单一地面地震解释成果多解性,可分辨更小地质单元,为油田精细开发提供了可靠的资料依据。(3)DAS三维井地联采配套技术支撑了安塞油田新增地质储量,并通过低产低效井组挖潜措施调整,提产效果明显。通过本次研究得出,井地联合立体探测是提升油田开发过程中微幅构造刻画和储层、流体预测精度的有效技术手段,形成的技术可推动物探技术向油田开发延伸、向井地立体探测发展,可为老油田稳产增效、推进高效开发提供支撑。

砂土含水率对DAS振幅响应影响的试验研究

利用分布式声波传感(DAS)技术和地下通讯光纤网进行周界安防(入侵)监控、管道泄漏监测、交通状况评估等是城市安全动态监测的新方向。为探究DAS监测过程中暗光纤周围岩土介质含水率变化对DAS振幅响应特性的影响,设计了小球撞击圆盘与小球直接撞击砂土面二种激振方式下五种质量含水率0%、5%、10%、15%和20%砂土的DAS振幅响应试验。试验结果表明:(1)小球自由下落撞击砂土面激发的振动信号和传感光纤圆环布设可提高DAS信噪比,试验装置可靠性高、试验结果重复性好;(2)受砂土似黏聚力和声波传播衰减两个因素共同影响,DAS信号振幅随砂土含水率变化存在一个临界含水率。当砂土含水率小于临界含水率时,DAS信号振幅随含水率增大而减小,而当砂土含水率大于临界含水率时,DAS信号振幅随含水率增大而增大;(3)因砂土似黏聚力作用和小球撞击时能量转化的差异,小球与砂土的接触形式对DAS振幅响应有显著影响。研究结果为城市地下工程安全动态DAS精细监测提供理论依据。

Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning,which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates.In recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior.In this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable Detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter(HatEval)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.

基于Fe^(3+)-DA-APS自催化体系的Fe_(3)O_(4)/PAA水凝胶的制备及性能研究

以Fe_(3)O_(4)纳米粒子为磁性组分,基于AA(丙烯酸)与部分Fe3O4反应产生的Fe3+、多巴胺(DA)构建双重自催化过硫酸铵(APS)的自由基聚合体系,在低温下制备了Fe_(3)O_(4)/聚丙烯酸(PAA)水凝胶,并对其进行表征。研究结果表明:Fe_(3)O_(4)/PAA水凝胶具有良好的力学性能,断裂伸长率、拉伸强度分别为900%、251.1 kPa;同时,其可较好粘附不同基材,在钢材上粘附-剥离循环20次后粘附强度仍稳定在30.7 kPa左右;此外,其还可感应极小形变,并在166 ms内快速响应。该Fe_(3)O_(4)/PAA水凝胶综合性能良好,具备应用于柔性传感器等领域的潜力。

Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

基于DA多重插补法和电力物联网的电能数据缺失修复方法

针对电力物联网中电能数据量过多,缺失电能数据修复难度较大的问题,研究基于DA多重插补法和电力物联网的电能数据缺失修复方法。电力物联网利用感知层的电能数据采集终端采集电能数据,所采集电能数据利用通信层传送至应用层,应用层的电能数据缺失修复模块,利用EM插补算法计算电能数据缺失值的初始插补值;将所获取的电能数据插补值作为DA多重插补法的初始值,DA多重插补法利用局部加权回归模型,通过调整电能数据缺失值的预测误差,获取最终电能数据缺失修复结果。实验结果表明,该方法修复电力物联网电能数据的观测误差方差低于0.2,对于短期电能数据与长期电能数据,均具有良好的修复结果。

Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state.The examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior assessment.Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs.Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals effectively.Although these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some limitations.So this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human emotions.The utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over time.The next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster convergence.Then the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram sequentially.The attitude of feature selection is to retain only dominant features by excluding the irrelevant ones.In this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features selection.Finally,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short Term Memory(Bi-LSTM)network to discern emotions.Since the deep Bi-LSTM can hierarchically learn complex features and increases model capacity by achieving more robust temporal modeling,it is more effective than a shallow Bi-LSTM in capturing the intricate tones of emotional content existent in speech signals.The effectiveness and resilience of the proposed SER model were evaluated by experiments,comparing it to state-of-the-art SER techniques.The results indicated that the model achieved accuracy rates of 90.92%,93%,and 92%over the Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS),Berlin Database of Emotional Speech(EMO-DB),and The Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets,respectively.These findings signify a prominent enhancement in the ability to emotional depictions identification in speech,showcasing the potential of the proposed model in advancing the SER field.

HPLC多指标成分联合PCA和OPLS-DA法的复方儿茶胶囊综合质量评价

目的建立HPLC法同时检测复方儿茶胶囊中儿茶素、表儿茶素、槲皮素和山奈酚的含量,并结合主成分分析(PCA)、正交偏最小二乘法-判别分析(OPLS-DA)法构建其质量控制体系。方法采用Waters Symmetry C18色谱柱(5μm,250 mm×4.6 mm),流动相乙腈-0.5%冰醋酸,梯度洗脱;检测波长分别为280 nm(检测儿茶素和表儿茶素)、360 nm(检测槲皮素和山奈酚);用外标法计算各成分含量,并联合PCA、OPLS-DA进行品质分析。结果4种成分分别在各自范围内线性关系良好(r≥0.9992);平均加样回收率96.85%~99.54%(RSD<2.0%);经PCA法得2个主成分的累积贡献率达到87.463%;OPLS-DA法显示12批复方儿茶胶囊样品聚为3类;儿茶素和表儿茶素是影响复方儿茶胶囊产品质量的主要潜在标志物。结论所建方法结果准确、操作便捷,可用于复方儿茶胶囊的综合质量评价。

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

HPLC多指标成分联合PCA、OPLS-DA及灰色关联度法的宫瘤消胶囊综合质量评价

目的:采用HPLC法同时检测宫瘤消胶囊(GLX)中13种成分含量,并联合化学计量学及灰色关联度分析(GRA)对GLX质量进行综合评价。方法:以Dikmatech Diamonsil Plus C18为色谱柱,乙腈-0.1%磷酸为流动相,同时测定GLX中党参炔苷、紫丁香苷、鸡矢藤次苷甲酯、车叶草苷酸、车叶草苷、氧化芍药苷、芍药苷、丹皮酚、香附烯酮、圆柚酮、α-香附酮、莪术二酮和莪术醇的含量。采用化学计量学挖掘影响GLX产品质量的主要标记物。基于灰色关联度法分析不同批次GLX各指标成分数据,以相对关联度为测度,对GLX综合质量进行评价。结果:13种成分在各自范围内线性关系良好(r>0.999),平均加样回收率(n=9)在96.81%~100.19%之间(RSD<2.0%)。化学计量学分析显示莪术二酮、香附烯酮、丹皮酚、车叶草苷、党参炔苷、鸡矢藤次苷甲酯、芍药苷和氧化芍药苷是影响GLX产品质量的主要潜在标志物。灰色关联度分析相对关联度0.3302~0.6068,GLX存在一定批间差异。结论:所建立的HPLC多组分定量联合化学计量学及灰色关联度分析方法,操作便捷、结果准确、全面客观,可用于GLX质量的综合评价,为该制剂质量控制提供参考。

QAMS多组分定量联合PCA、OPLS-DA及GRA分析法评价白花蛇舌草的质量

目的:采用高效液相-一测多评(HPLC-QAMS)法联合化学计量学及灰色关联度分析法评价白花蛇舌草质量。方法:采用Alltima C_(18)色谱柱(5.0μm,250.0 mm×4.6 mm),0.2%磷酸-乙腈为流动相梯度洗脱;以芦丁为参照物,建立其与京尼平苷酸、京尼平苷、车叶草苷酸、车叶草苷、2-羟基-3-甲基蒽醌、1,2-二羟基-3-甲基蒽醌、槲皮素、山柰酚、齐墩果酸、熊果酸、豆甾醇和β-谷甾醇的相对校正因子并进行校正因子耐用性考察。同时采用ESM和QAMS法测定收集到的16批白花蛇舌草中该13种成分的含量,再运用统计软件进行化学计量学及灰色关联度分析。结果:13种成分方法学验证均符合2020年版《中华人民共和国药典》要求。京尼平苷酸、京尼平苷、车叶草苷酸、车叶草苷、2-羟基-3-甲基蒽醌、1,2-二羟基-3-甲基蒽醌、槲皮素、山柰酚、齐墩果酸、熊果酸、豆甾醇、β-谷甾醇与芦丁的平均相对校正因子分别为0.9489、0.7033、0.7824、1.1359、0.5845、0.8005、0.8933、1.0683、0.7406、0.8640、0.6745、0.5424。两种方法测定结果比较,差异无统计学意义(P>0.05)。化学计量学方法显示16批白花蛇舌草聚为3类,呈现一定的产区差异。芦丁、车叶草苷酸、京尼平苷和齐墩果酸是影响白花蛇舌草产品质量的主要潜在标志物。GRA法分析结果显示7个省中浙江和江西地区所得白花蛇舌草质量最优。结论:本试验所建立的方法操作便捷、结果准确,结合化学计量学及GRA方法可用于白花蛇舌草质量的综合评价。

胎圈钢丝用钢盘条C82DA的CCT曲线测定及应用

为了研究分析高性能胎圈钢丝用钢盘条C82DA在不同冷却速度下组织转变,达到产品工艺精细化控制的目的,利用Gleeble-3500热模拟试验机和光学显微镜对不同冷却速率的试样进行显微组织分析,绘制连续冷却转变曲线。分析结果显示:开始产生马氏体组织的临界冷速为20℃/s,冷却速度在0.5~10℃/s时组织为珠光体和索氏体,但组织中有明显网状渗碳体;冷却速度在12~20℃/s时组织为珠光体和索氏体;通过对比发现,冷却速率为10~15℃/s时,胎圈钢丝用钢盘条C82DA的综合性能达到最佳。