Simultaneous Interpreting Output Assessment of Earnings Conference Call: Case Analysis of Tencent 2022 Q3 Result Announcement

Quality in simultaneous interpreting is a frequently discussed concept. In the enterprise setting, earnings conference call remains a rarely explored field. This thesis offers a descriptive study on assessing interpreting quality from perspectives of fidelity, fluency, and appropriacy. As the corpus, Tencent 2022 Third Quarter Result Announcement provides an ideal transcript to the author to conduct its analysis. Interpreting is frequently done without bearing in mind the multitude of factors that can affect the quality of interpreting. Drawing a conclusion that the interpreter does make a lot of omissions, pauses and hesitations posing a negative effect on the fidelity, fluency and accuracy of the interpreting, the present author suggests that more preparation should be done for improving performance, such as terminologies, company background information, a reasonable speech rate, good image and acoustic quality, and so on.

Effects and Causes of VR-Supported Interpreting Learning Environment on the Interpretation Classroom Anxiety of Student Interpreter

Interpreting activity is considered a high-anxiety activity due to its immediacy, multitasking, complexity of cognitive processing, and uncertainty of cognitive processing. Research has shown that interpreting anxiety, as the biggest emotional obstacle in the interpreting process, is the main emotional factor that leads to individual differences in interpreting. Students often claim to have fear or anxiety behaviors in interpreting exams, interpreting competitions, and interpreting classes. However, the research on interpreting teaching attaches importance to the cultivation of language knowledge, cultural knowledge, and interpreting skills, and does not pay enough attention to emotional factors such as motivation and anxiety in interpreting learning, which makes it difficult for the cultivated interpreters to meet the requirements of professional practice. In recent years, virtual reality technology (VR) has been gradually applied in the field of foreign language and interpreting teaching for creating a real, interactive and experiential language learning environment. Situated Learning Theory stresses that the fundamental mechanism for learning to take place is for individuals to participate in the real context in which knowledge is generated, and to realize the construction of knowledge through the interaction with the community of practice and the environment. Virtual reality technology can satisfy the needs of language learners for real contexts by providing learners with immersive, imaginative and interactive scenario simulations, and has a certain positive effect on alleviating learning anxiety. Therefore, relying on the virtual simulation course “United Nations Kubuqi International Desert Ecological Science and Technology Innovation International Volunteer Language Service Practical Training System”, this paper adopts a combination of quantitative and qualitative analyses to investigate the interpretation anxiety level of the interpreter trainees and the factors affecting them in the VR situation to help them discover effective responses to interpreter anxiety.

Problems and Countermeasures in the Training of Master of Translation and Interpreting Talents in the Context of Technological Empowerment

This paper aims to explore the problems and countermeasures in the training of Master of Translation and Interpreting(MTI)talents in the context of technological empowerment.Through methods such as questionnaires and literature review,the study analyzes the problems in the training of MTI talents under technological empowerment and,by examining the feasibility and applicability of technologies,proposes countermeasures to these problems.The research finds that there are still problems in four areas:training concept,teaching content,research in the current training of MTI talents,and mutual empowerment of technology and education.Four countermeasures are suggested to address these problems,including utilizing technology to promote personalized training and optimize teaching content.This study provides valuable references for future educators to leverage technology in the training of MTI talents.

CYP2D6、CYP2C19、CYP2B6、SLC6A4、HTR2A基因型和5-羟色胺再摄取抑制剂抗抑郁药:2023年CPIC指南解读

2023年临床药物基因组学实施联盟(CPIC)更新并扩展了2015年CYP2D6和CYP2C19基因型与选择性5-羟色胺再摄取抑制剂(SSRIs)治疗指南,在涉及基因型与药物部分分别增加了CYP2B6、5-羟色胺转运体SLC6A4、5-羟色胺2A受体HTR2A以及5-羟色胺与去甲肾上腺素再摄取抑制剂(SNRIs)、类SSRIs活性的5-羟色胺(5-HT)调节剂相关内容,并更新了基于基因多态性的5-HT再摄取抑制剂类药物应用建议。本文对该指南相关内容进行解读,以期为我国5-HT再摄取抑制剂类药物个体化药物管理提供参考。

基于LightGBM与SHAP的空腔积水深度可解释性机器学习模型

传统的机器学习模型主要围绕如何提升模型预测精度进行研究,从而忽略了预测结果的可解释性.本研究基于LightGBM (Light Gradient Boosting Machine)建立了预测掺气设施空腔积水深度的黑箱模型,与常用的机器学习模型如RF (Random Forest)、SVM (Support Vector Machine)及XGBoost (Extreme Gradient Boosting)对比结果表明LightGBM拥有较高的预测精度.进一步通过贝叶斯优化技术对LightGBM的四个超参数进行优化,较大程度上提升了模型的R^(2)(决定系数)得分.应用SHAP (Shapley Additive Explanation)事后解释方法对LightGBM的预测结果进行全局解释和局部解释.全局解释结果表明:流速、水舌冲击角、坎高及流量是影响空腔积水形成的主要因素,特征交互解释可以用来解释特征之间的复杂非线性关系,局部解释则可以显示单个样本各特征的影响大小.研究建立的基于LightGBM-SHAP的空腔积水深度可解释性机器学习模型在掺气设施体型优化及模型试验方案优化方面有很好的应用前景.

用者为本,技之大道--评《以口译员为中心的技术可用性研究:以SmarTerp为例》

在口译学科“技术转向”的时代背景下,口译技术人性化和可持续发展的重要性日益凸显。《以口译员为中心的技术可用性研究:以SmarTerp为例》一书立足时代需求,介绍了全球首项使用可用性工程方法开发计算机辅助口译工具的案例研究,旨在反映口译领域技术变革,确保口译技术的公平可持续发展。该书通过采用人机交互的理论框架和方法论框架评估计算机辅助口译工具SmarTerp界面的可用性,弥补了当前计算机辅助口译实证研究的不足,对口译技术的研发和应用具有重要的启发意义和借鉴价值。

HealthMeasures患者报告结局报告清单的解读

美国匹兹堡大学和HealthMeasures指导委员会及PROMIS健康组织标准委员会共同发表了HealthMeasures患者报告结局报告清单,旨在促进不同患者报告结局测量工具的标准化报告,帮助研究人员提高工具报告质量及结果解释的准确性,进一步改善跨研究间的比较分析。本文对HealthMeasures患者报告结局报告清单的主要内容进行介绍和解读,以期为中国患者报告结局研究者提供参考和借鉴。

基于STL-XGBoost-NBEATSx的小时天然气负荷预测

小时天然气负荷预测受外部特征因素与预测方法的影响,为提高其预测精度并解决其他深度学习类模型或组合模型可解释性差、训练时间过长的问题,在引入“小时影响度”这一新特征因素的同时提出一种基于极端梯度提升树(extreme gradient boosting tress,XGBoost)模型与可解释性神经网络模型NBEATSx组合预测的方法;以XGBoost模型作为特征筛选器对特征集数据进行筛选,再将筛选降维后的数据集输入到NBEATSx中训练,提高NBEATSx的训练速度与预测精度;将负荷数据与特征数据经STL(seasonal and trend decomposition using Loess)算法分解为趋势分量、季节分量与残差分量,再分别输入到XGBoost中进行预测,减弱原始数据中的噪音影响;将优化后的NBEATSx与XGBoost模型通过方差倒数法进行组合,得出STL-XGBoost-NBEATSx组合模型的预测结果。结果表明:“小时影响度”这一新特征是小时负荷预测的重要影响因素,STL-XGBoost-NBEATSx模型训练速度有所提高,具有良好的可解释性与更高的预测准确性,模型预测结果的平均绝对百分比误差、均方误差、平均绝对误差分别比其余单一模型平均降低54.20%、63.97%、49.72%,比其余组合模型平均降低24.85%、34.39%、23.41%,模型的决定系数为0.935,能够很好地拟合观测数据。

基于ISM-AHP-Entropy航空公司风险因素研究

针对航空公司风险因素研究,引入ISM-AHP-Entropy综合评价方法对风险因素进行评价。构建了风险因素指标体系,利用解析结构模型(ISM)将因素指标分为表层和深层致因,构建结构模型;根据问卷结果,用AHP-Entropy求出综合权重,结果表明人因和管理因素对风险影响最为显著;进一步运用AHP-Entropy综合法分别计算表层和深层致因权重并分析,结果表明表层致因中机组资源管理水平、签派员应急处置能力影响最为显著,深层致因中安全规章方针的制定情况、应急管理培训与实施情况影响最为显著;从多个现实角度出发,结合CBTA理念,为航空公司降低风险、提高安全意识与水平提出多条建议,可供航空公司参考采纳。

基于改进DEMATEL-ISM的钢桁梁桥施工风险影响因素研究

为提高钢桁梁桥施工过程中的安全管理,对钢桁梁桥施工中的风险因素进行分析,提出了一种基于改进DEMATEL-ISM的评价方法,首先引入三角模糊数对DEMATEL法中的直接影响矩阵进行改进,从而去模糊化以减少专家评价的主观性,再通过DEMATEL法分析各影响因素的影响度、被影响度、中心度与原因度并加以排序,对得到的主要影响因素进行初步分析,利用ISM法对筛选出的影响因素进行层次划分,得到直接层影响因素、过渡层影响因素与本质影响因素,并针对分析得到的5个本质影响因素提出深入的预防预警措施,可为钢桁梁桥施工方向的安全管理提供针对性措施,为事故预防提供一定的参考依据。

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

基于MultiRes-Unet神经网络的三维断层识别研究

三维地震数据中的断层规模从米级到千米级不等,断距从米级到数十米级不等。断层通常表现为同相轴错断、突然增减、消失或扭曲等显著地震响应特征。断层在三维地震数据体中占的体积比例很小,使得利用常规断层识别方法得到的断层识别结果存在不连续、识别率低等问题。针对断层的多分辨率特征,充分考虑了断层点或线在整个地震数据的占比小等特点,提出了一种三维神经网络MultiRes-Unet3D断层识别方法,该方法在网络学习过程中使用加权交叉熵损失函数解决了普通交叉熵损失函数不同项之间的平衡问题,使得神经网络具有了较为可靠的断层识别能力。首先,利用正演模拟方法生成三维合成地震数据集和断层标签,然后基于Tensorflow搭建、训练与测试MultiRes-Unet3D神经网络,再将训练好的网络模型迁移到实际三维地震数据的断层识别中。该神经网络断层识别方法在实际地震数据中的应用表明,断层识别结果空间连续性好,识别结果客观,断层边界更为准确,网络模型泛化性能良好,适用于具有不同断层构造特征的实际地震数据,节约了断层解释的时间成本与人工成本。

基于DEMATEL-AISM的矿山救护指挥员能力素质研究

为探究矿山救护指挥员能力素质要素及相互间的关系,促进矿山应急救援水平的进一步提升,通过文献研究和界值法分析,从政治素质、身体素质、业务素质、心理素质和管理素质5个方面筛选能力素质的核心要素,建立矿山救护指挥员能力素质指标体系,采用决策实验室算法和对抗解释结构模型联用的方法,构建因果关系和对抗多层拓扑结构,分析了矿山救护指挥员能力素质的多要素重要程度和耦合关系。结果表明,应急救援理论和知识、心理承受能力、纪律意识、对党忠诚是有效提高矿山救护指挥员能力素质的重要因素,避免队伍自身伤亡能力、临危不乱、应急救援决策能力和灾害事故研判能力是关键要素,避免队伍自身伤亡能力为帕累托最优集。

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

结合STPA和DEMATEL-ISM的民机起落架收放系统风险研究

为从系统整体角度完成对起落架收放系统的风险辨识和影响分析,将系统理论过程分析(Systematic Theory Process Analysis,STPA)与决策实验室分析-解释结构模型(Decision Making Trial and Evaluation Laboratory Interpretive Structural Modeling,DEMATEL-ISM)相结合来开展分析。首先,定义事故和系统级危险,以民机进近阶段放下起落架为例,运用STPA完成对风险因素的系统化辨识;其次,基于最大平均熵减(Maximum Mean De-entropy,MMDE)算法帮助DEMATEL-ISM模型确定阈值,完成对风险因素影响的重要性分析并识别可能引发系统级危险的风险传递路径,据此挖掘关键致因场景,以给出风险预防建议。结果显示:线路性能退化或失效、位置作动控制组件(Position Action Control Unit,PACU)核心处理器故障为关键原因因素,收放作动筒作动异常、机组成员操作不当、起落架指示灯显示异常、起落架液压选择阀作动异常、PACU信息接收有误为关键结果因素,这些因素均涉及多条可能引发系统级危险的风险传递路径,应予以重点控制。

基于多期相CT图像的深度学习ResNet18和ResNet50模型诊断肾结核的效能比较

目的:探讨基于CT图像构建深度学习模型对肾结核进行鉴别诊断的可行性。方法:回顾性分析2018年9月至2020年8月经解放军总医院第八医学中心收治,并经组织病理学或临床确诊的肾结核、肾肿瘤、肾盂肾炎、正常肾脏、肾囊肿、肾积水患者。共纳入200例患者共400个肾脏的CT图像数据。将400个肾脏CT图像分为结核组(114个)和非结核组(286个),并且按8∶2的比例分为训练集(肾结核:85个;非肾结核:235个)和测试集(肾结核:29个;非肾结核:51个)。基于训练集通过ResNet18和ResNet50网络分别构建肾脏平扫期、皮髓质期、实质期和排泄期的深度学习模型;基于测试集评估所构建模型对肾结核的诊断效能,计算受试者工作特征曲线(ROC)曲线下面积(AUC)、敏感度、特异度、准确率和F1分数。结果:训练集中,结核组平均年龄为(41.27±11.75)岁,明显低于非结核组[(54.05±13.97)岁];测试集中,结核组平均年龄为(44.06±11.95)岁,明显低于非结核组[(56.12±10.73)岁],差异均有统计学意义(t值分别为5.753、3.444,P值均<0.05)。训练集中,结核组男性占66.7%(40/60),女性占33.3%(20/60);非结核组男性占60.9%(78/128),女性占39.1%(50/128);两组差异无统计学意义(χ^(2)=0.009,P=0.924)。测试集中,结核组男性占64.3%(18/28),女性占35.7%(10/28);非结核组男性占58.7%(27/46),女性占41.3%(19/46);两组差异无统计学意义(χ^(2)=0.018,P=0.894)。ResNet18模型肾脏各期相的AUC、敏感度、特异度、准确率和F1分数均高于ResNet50模型。ResNet18模型中以皮髓质期为最佳,AUC、敏感度、特异度、准确率和F1分数分别为0.925、93.1%、86.3%、88.7%和0.857;ResNet50模型皮髓质期AUC、敏感度、特异度、准确率和F1分数分别为0.858、72.4%、84.3%、80.0%和0.724。结论:基于多期相CT图像的ResNet18模型诊断肾结核的效能优于ResNet50模型,ResNet18模型中以皮髓质期诊断效能最佳,有较高的临床应用价值。

一种利用词典扩展数据库模式信息的Text2SQL方法

现有Text2SQL方法严重依赖表名和列名在自然语言查询中的显式提及,在同物异名的实际应用场景中准确率急剧下降.此外,这些方法仅仅依赖数据库模式捕捉数据库建模的领域知识,而数据库模式作为结构化的元数据,其表达领域知识的能力是非常有限的,即使有经验的程序员也很难仅从数据库模式完全领会该数据库建模的领域知识,因此程序员必须依赖详细的数据库设计文档才能构造SQL语句以正确地表达特定的查询.为此,本文提出一种利用词典扩展数据库模式信息的Text2SQL方法,该方法从数据库表名和列名解析出其中的单词或短语,查询词典获取这些单词或短语的语义解释,将这些解释看成是相应表名或列名的扩展内容,与表名、列名及其他数据库模式信息(主键、外键等)相结合,作为模型的输入,从而使模型能够更全面地学习数据库建模的应用领域知识.在Spider-syn和Spider数据集上进行的实验说明了所提出方法的有效性,即使自然语言查询中使用的表名和列名与数据库模式中对应的表名和列名完全不同,本文方法也能够得到较好的SQL翻译结果,明显优于最新提出的抗同义词替换攻击的方法.

中医药团体标准评价体系(SCORE-TCM)解读

本文全面解读了中医药团体标准评价体系(System of Consortium Standards Rating and Evaluation of Traditional Chinese Medicine,SCORE-TCM)。SCORE-TCM是结合定性与定量评价,全面评估中医药团体标准在制定主体、文本编写、技术内容、推广应用和实施效益等几方面特征的综合评价工具。文中详述了SCORE-TCM的构建目的、定义和构建过程,解释了评价指标体系中的各项指标,并对每项指标的评价材料进行介绍。本文旨在帮助中医药团体标准的制定者、第三方评价机构和其他相关方更好地理解SCORE-TCM各评价条目的含义,更有效地运用于中医药团体标准的自评价或第三方评价,SCORE-TCM将为《中医药团体标准管理办法》的贯彻实施,以及中医药团体标准的高质量发展提供技术支持。

基于多元Logistic模型与解释结构模型的乡村旅游可持续发展路径研究

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Why caution should be applied when interpreting and promoting findings from Mendelian randomisation studies

Introduction In their article entitled‘Investigating genetic causal relationships between blood pressure and anxiety,depressive symptoms,neuroticism and subjective well-being’,Cai and colleagues1 presented the results of a two-sample Mendelian randomisation2(MR)study examining associations between blood pressure traits(systolic,diastolic,hypertension and pulse pressure)and psychological traits(anxiety,depression,neuroticism and subjective well-being).After correction for multiple testing.