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关键词： 血液检验   血红蛋白   鉴别诊断   贫血  

摘要： 探析贫血鉴别诊断中血液检验的临床作用。方法:在我院就诊的缺铁性贫血(IDA)患者中选取102例,设为观察组,纳入时间:2018年10月~2020年9月,选取同期健康体检者102例,设为对照组,分析并对比组间检测血红蛋白(Hb)、平均红细胞血红蛋白(MCH)、红细胞体积宽度(RDW)、平均红细胞容积(MCV)、红细胞计数(RBC)。结果:观察组Hb、MCH、MCV、RBC水平较对照组低,RDW较对照组高(P<0.05)。结论:血液检验在鉴别诊断贫血中作用十分关键,可为疾病后续治疗提供有效依据,指导临床治疗工作。

关键词： ABO血型   血型鉴定   检验结果  

摘要： 输血前血型鉴定试验常规要求ABO定型必须同时做正定型和反定型,正、反定型不一致时,常提示标本为疑难血型[1]。对于ABO正、反定型不一致的疑难血型标本,医院输血科或血库工作人员常常是求助于试剂充足、设备完善及经验丰富的血站血型参比实验室,但日常工作中,输血科工作人员可以结合临床资料,尤其是临床检验结果,对正、反定型不一致的原因做出合理的解释,根据检验异常指标有针对性地处理标本干扰,从而正确判定血型。现将3例疑难血型鉴定过程报道如下。

关键词： 技术进步偏向   产业结构优化   要素技术效率   SYS-GMM  

摘要： 在作用机制部分,从要素投入、(全)要素生产率、产出(供给)、需求与消费、国际贸易五条路径深入分析技术进步偏向如何直接或间接作用于产业结构优化,提出了命题假说。利用1998—2017年中国省际面板数据,通过构建标准化系统方程运用非线性似不相关回归(NLSUR),发现我国各地区基本属于资本偏向性技术进步,且劳动技术效率高于资本技术效率。然后通过静态面板模型和动态面板模型进行实证分析,得出结论:劳动增加、劳动技术效率和资本技术效率提高,产业结构趋于合理化、高级化。资本深化不利于产业结构优化,要引导资本向中西部流动,加大技术研发,提高技能劳动水平,加快产业结构优化和区域协调发展。

关键词： Role of the teacher   Inquiry learning   Learner guidance   Instructional support   Geography education   Kikan-Shido  

摘要： The value of effective teacher interaction during inquiry-based learning to catalyse deeper learning is widely endorsed. This project investigated activities that teachers undertake during inquiry-based learning through coding of videoed inquiry, using a Kikan-Shido lens. Inquirybased teaching is often perceived as teacher-as-facilitator, but the teacher used a range of pedagogies, including explicit instruction. Findings showed that the teacher monitored, guided, and redirected student learning during inquiry-based learning. These responsive and multifaceted interventions were targeted to meet the needs of students and their progression through the task. A highly skilled teacher examines students' progress to know when to 'connect' with and when to 'disconnect' allowing the students the space to engage in problem-solving and reasoning together.

关键词： Generalized zero shot classification   Generative adversarial network   Selecting confident unseen samples   Generating unseen samples   Unseen visual prototypes  

摘要： Generalized zero-shot classification (GZSC) is a challenging task to recognize seen and unseen samples from target domain by seen samples in source domain. Since the lack of unseen data, many methods train a generative adversarial network (GAN) to generate unseen samples. However, the GAN model trained by seen samples is not suitable for generating unseen samples. For dealing with this problem, we train the GAN model by generating seen and unseen samples, simultaneously. In order to generate high-quality unseen samples, the visual prototypes of the generated unseen samples are made near to the real unseen visual prototypes. We select the confident unseen samples based on the agreement of the current two unseen classifiers and use them to update the unseen visual prototypes. Through the iteratively generating and selecting method (IGS), we can generate high-quality unseen samples and select the most confident unseen samples. Experimental results on the standard benchmarks show the superiority of the proposed model over the state-of-the-art methods for GZSC tasks.

关键词： Hyperspectral Image Classification (HISC)   Instance Selection   Divide and conquer (D&C) br  

摘要： Hyperspectral Image Classification (HSIC) has gained much attention for several real-world problems in which an appropriate number of labeled training samples are necessary to obtain a good performance which highly depends on the quality (spectral & spatial heterogeneity) of the training samples. For the above reason, this study first identifies a relationship between the misclassification rate and uncertainty of a classifier on a group of very few training samples. We then experimentally prove that uncertain samples significantly help to enhance generalization performance. Finally, we propose a divide and conquer strategy to split the samples based on low, mid, and high uncertain samples to strengthen the classification performance. The proposed pipeline is theoretically explained and experimentally analyzed on benchmark real Hyperspectral Datasets.

关键词： Random Forest   Samples cleansing   Training sample   Agricultural mapping   Food security  

摘要： Crop type mapping is relevant to a wide range of food security applications. Supervised classification methods commonly generate these data from satellite image time-series. Yet, their successful implementation is hindered by the lack of training samples. Solutions like transfer learning, development of temporal-spectral signatures of the target classes, re-utilization of existing inventories, or crowdsourcing initiatives are commonly applied to generate samples for thematically coarser classifications. These methods are rarely used for generating crop types samples. In this study, we leverage the phenology information of existing data inventories using Time-Weighted Dynamic Time Warping (TWDTW) to address the problem of automatic crop sample generation in two target areas. Resulting labeled samples are refined using proximity measures obtained from Random Forests (RF). Sentinel-2 time-series are used to obtain phenology information from two study areas. The proposed methodology achieved promising results for classes with a reduced inter-classes similarity such as sugar beets (user's accuracy, UA, of 98% and producer's accuracy, PA, of 100%) or grains (UA of 98% and PA of 90%). The crops with a high inter-classes similarity yielded less satisfactory results. Potatoes, for example, obtained a high PA of 95%, but a UA of only 36% because of the spectral-temporal similarity with maize. The methodology works well for areas with balanced crop samples. Yet, it favors prevalent classes in areas with imbalanced crops at the expense of a low accuracy for the minority crops. Despite these shortcomings, the proposed methodology offers a viable option to generate crop samples in regions with few ground labels.

关键词： dialogic interaction   epistemic climate   generative learning environments   knowledge generation   Rasch modeling  

摘要： This study investigates two latent constructs (Engagement and Value) of dialogic interaction to examine the epistemic climate. Since the new reform movement emphasizes creating generative learning environments, it is important to examine whether a classroom promotes students' knowledge generation or limit students' epistemic growth through rote memorization. At this point, it is inevitable to focus on dialogic interaction (one of the epistemic practices), because how students engage with and view dialogic interaction provides meaningful information about the epistemic climate. By employing Rasch modeling, this study tested a statistical validity of two latent constructs of dialogic interaction. The findings in this study highlight that the two latent constructs are theoretically and statistically valid and can be used to gauge the epistemic climate through students' engagement with and value of dialogic interaction. While understanding the epistemic climate cannot solely occur through a single perspective, this study plays a significant role in adding a valid instrument. Such instruments help researchers comprehend the epistemic features of learning environments and provide a practical way to reflect students' lived experiences to learn about the overall atmosphere of the learning environment.

关键词： 美元债   违约   后续处理及建议   恒鼎实业国际  

摘要： 由于国内融资监管严格、审批复杂等原因导致国内融资环境变化,企业在国内融资难度加大。而国外资本环境较为开放,这也就催生了众多企业在海外发行美元债以达到融资目的。随着世界经济竞争越发激烈,国内企业发行美元债后的违约情况逐年递增。由于美元债发行在境外,美元债违约后的处理方式与国内债券违约后的处理方式大相径庭。本文拟以恒鼎实业国际发行美元债违约后的处理方式为例,试图找到相应美元债违约后的处理及建议,给到相应企业以参考。

关键词： 网织红细胞平均血红蛋白含量   缺铁性贫血   临床检验  

摘要： 目的分析网织红细胞平均血红蛋白含量(CHr)检验诊断不同人群缺铁性贫血(IDA)的作用。方法选取80例IDA患者作为实验组,另选取80例健康体检者作为对照组。所有患者及健康体检者均予以CHr检验,对比检验结果。结果实验组成人的红细胞平均血红蛋白量(MCH)、红细胞(RBC)、红细胞平均体积(MCV)、血红蛋白(Hb)、红细胞平均血红蛋白浓度(MCHC)、CHr、血清铁蛋白(SF)、红细胞体积分布密度(RDW)、总铁结合力(TIBC)阳性检出例数均高于对照组成人,差异有统计学意义(P<0.05)。成人缺铁性贫血患者CHr诊断的敏感度97.5%高于MCH、RBC、MCV、Hb、MCHC、SF、RDW、TIBC的75.0%、70.0%、75.0%、80.0%、77.5%、82.5%、80.0%、85.0%,差异有统计学意义(P<0.05)。对照组成人、儿童、孕妇的MCH、Hb、MCV、RBC、MCHC、CHr、SF均高于实验组, RDW、TIBC均低于实验组,差异均有统计学意义(P<0.05)。结论 CHr检验可以检验出不同人群缺铁性贫血,具备较高的临床诊断价值,值得应用与推广。