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关键词： 凝血酶原时间   肝硬化疾病   血小板检验   疾病类型   人体消化系统   诊断数据   发病因素   弥漫性  

摘要： 肝硬化是临床人体消化系统经常出现的疾病,发病因素是由多种因素反复作用导致的弥漫性损害,从而诱发肝硬化疾病。肝硬化严重时容易产生出血问题,也是导致死亡的恶性疾病之一。有针对性对患者的出血与疾病发展多加观察,有助于病情的准确判断,一定程度可对疾病发展进行控制,有助于预后的良好改善^([1])。为使肝硬化能够在早期得到治疗,提高诊断是唯一之举。因为只有准确诊断出疾病,方才能够按照疾病类型制定治疗方案控制病情,及时挽救患者生命。为探索一类有效的诊断方法,本研究对纳入的研究样本实施凝血酶原时间与血小板检验,观察诊断数值,诊断数据如下所示。

关键词： 产业智能化   供需匹配   消费回流   消费升级  

摘要： 新一轮的智能化科技革命和产业变革,为缓解国内供需错配、促进消费升级提供了机遇。本文通过构建产业智能化指数,理论分析并实证检验了产业智能化对消费升级的影响效果及作用机制。结果表明:产业智能化程度的提升可显著带动消费升级;产业智能化可通过驱动国内产品供需匹配、拉动海外消费回流,以及通过国内产品供需匹配拉动消费回流三条中介路径促进消费升级;同时,产品质量优化还会调节产业智能化对消费升级的影响效果,生产效率提升的调节作用则不显著。为更好地发挥产业智能化对消费升级的推动作用,应进一步借助智能化产业发展机遇优化产品质量、推动国内产品供需匹配、加强实感体验以拉动消费回流,协同推进企业技术创新、产业智能化和消费升级。

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： 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： 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： 生产检测检验   矿山   安全生产  

摘要： 随着安全生产理念在各行各业的不断深入,矿山安全生产也得到了社会各界的广泛关注和重视。为了能够进一步保障矿山作业的安全生产,为施工监管工作的顺利开展提供技术支持,目前生产检测检验技术已经得到了矿山作业的广泛应用。基于此,文章对生产检测检验在矿山安全生产中的作用进行了全面分析。

关键词： Random Forest   Samples cleansing   Training sample   Agricultural mapping   Food security   LAND-COVER CLASSIFICATION   RANDOM FOREST   TIME   NOISE   ALGORITHMS   ACCURACY   SPACE   AREAS  

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： 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： Role of the teacher   Inquiry learning   Learner guidance   Instructional support   Geography education   Kikan-Shido   SMALL-GROUP WORK   INTERVENTIONS   GEOGRAPHY   STUDENTS   SUPPORT  

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

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.