非英语专业大二学生英语写作错误分析

本文运用错误分析理论对非英语专业大二学生在一次习作中出现的语言错误进行分析,发现学生一方面受母语思维、表达方式影响,另一方面对目标语——英语在词汇、句子、篇章各层面的掌握欠准确性,并据此提出英语写作教学意见。     

浅谈英语词语搭配错误

英语词语搭配错误是学生作文中常见的错误之一。是否能够正确使用词语搭配影响到学生的词汇能力和英语表达水平。本文对造成学生词语搭配错误的三个方面进行了分析，并对提高学生词语搭配能力提出了一些建议。     

经济新闻英译中的翻译问题及对策——以诺德文本分析为指导

克里斯蒂安·诺德首次提出文本分析理论以此来回应批评家对目的论的质疑,并归纳出四种常见的翻译错误。她对于四种常见的翻译错误的分析对于实用文本的翻译证明是有成效的。本文试图分析在经济新闻英译汉的过程中一些中国特色词汇或其他新词的翻译,提出一些相关的解决方法,以期对未来更深入的研究有所帮助。     

兽医临床上常见的几个错误

兽医临床上常见的几个错误李兴如（陕西省子长县畜牧站子长７１７３００）１错误地用抗菌素治疗蹄叶炎蹄叶炎是马类畜常见的一种疾病，因有炎症表现，许多人常用抗菌素来治疗，但笔者认为，蹄叶炎是蹄叶组织的一种非细菌感染，用抗菌素治疗是无效的。而用地塞米松、柴胡、...     

牛、羊疾病治疗中常见用药误区

在牛羊疾病的预防和治疗中,有很多养殖户及兽医存在一些习惯性的用药方式错误,这种错误的用药方式对牛羊的疾病恢复,以及牛羊的身体健康都带来了严重的影响,严重情况下,还有可能会造成牛羊的死亡。因此,必须对牛羊疾病的常见用药错误进行纠正,这样才可以充分保证牛羊养殖的质量。该文重点对牛羊疾病常见几种习惯性用药错误进行了分析和研究,并且提出了问题的解决方法。     

纠正牧区使用兽药的常见错误

牧区牲畜多，疾病也多，牧户多自备药物治疗畜病。但因缺乏基本的用药知识，经常造成错误用药，轻者造成浪费，重者导致家畜死亡。为了牧区畜牧业的健康发展，很有必要纠正牧区错误用药的现象。     

地面测报月报表审核错误解析

通过对内蒙古呼和浩特地区6个台站近几年的质量分析,发现地面气象测报业务的技术规范性错误占有很大的比重.这些错误出现的原因主要有:对《地面气象观测规范》掌握不够,对月数据文件的记录格式不清楚,对OSSMO 2004测报软件掌握不好,对各传感器及采集器的原理、维护缺乏必要的了解等.为了保证地面气象月报表出门合格率,提高地面气象信息化资料的审核质量,根据呼和浩特市5个旗(县)自动站的运行情况,对地面气象月报表中常见的错误和其他常见问题及相应处理方法进行了探讨.     

兽药使用的10种错误

兽药在畜牧业养殖中应用广泛,在使用过程中往往出现用药错误,对养殖户造成了不必要的损失,笔者结合工作经验,现谈谈兽药使用中常出现的错误。     

从错误走向正确——浅析《机械制图》课程的教法

机械制图教学过程中,学生通常会遇到一些困难,产生一些错误。怎样进行课堂教学,才能达到良好的教育教学目的。在教学中适当"制造"一些"错误",由"错误"引起学生的怀疑,由怀疑激发学生的学习兴趣,激起学生们的反驳和反思热情,从而引导学生由错误走向正确,使学生的学业不断取得进步。     

数据类型对输出的影响

C语言提供了多种数据类型用于处理数据,这些数据类型如果定义不恰当,就会造成数据输出错误,本文就常见的由于数据类型不匹配造成的输出错误进行分析。     

Common laboratory artifacts caused by inappropriate sample collection and transport: how to get the most out of a sample

Clinicians are frequently presented with laboratory test results that are not consistent with preconceived expectations for a given case. One important reason for such results is the occurrence of preanalytical errors. In this article preanalytical errors are discussed in 2 parts. The first part covers the steps of sample collection, preparation, and transportation, in which preanalytical errors often occur. This part would be most useful if read in full before collecting a sample. The second part of this article includes a systematic review of preanalytical errors divided according to individual analytes or parameters or, when appropriate, groups of analytes and parameters that represent the same biological system. This part will hopefully serve the clinician as a quick and user friendly guide for identification of possible pitfalls when presented with unexpected laboratory test results for a given case. This article is limited to errors that can affect the complete blood count, chemistry, and coagulation panels.     

Determining cause and effect in herds.

This article presents components of the logical process for deter-mining cause and effect and lists common cognitive errors of the medical decision-making process. Individuals who are aware of these errors may be better able to avoid committing them. The first section provides the concepts used in considering cause and effect relationships, the second section provides a logical basis for evaluating cause and effect relationships, and the third section illuminates potential reasoning errors.     

Identification of errors and factors associated with errors in data from electronic swine feeders

Electronic swine feeders are used to automatically measure individual feed intake on group-housed pigs, but the resulting data contain errors caused by feeder malfunctions and animal-feeder interactions. The objectives of this study were to 1) develop criteria to identify errors in data from an electronic feeder that is predominant in the United States; 2) evaluate the frequency of errors in data from three consecutive experiments using the same feeders; and 3) identify factors associated with errors. Across experiments, data included 1,878,321 feed intake records (visits) on 1,721 pigs and 124 pens. Sixteen criteria were developed to detect errors in seven variables related to feed trough weights and times. Logistic regression was used to identify factors associated with the presence or absence of each error type in identified visits (visits where the feeder recognized a transponder) using a model that included the fixed effects of replicate, sex, linear and quadratic effects of day on test, and random effects of feeder within replicate, pig within feeder within replicate, test day within replicate, and week within feeder within replicate. Frequencies of error types in identified visits varied considerably within and between experiments. Errors in feed trough weights were more frequent than errors in time. Percentage of identified visits and of daily feed intake records with at least one error ranged from 4.3 to 18.7% and from 17.2 to 50.0%, respectively, and decreased from the first to the last experiment, reflecting the increasing ability of the managers to operate the feeders. Replicate, sex, test day, feeder within replicate, pig, and day within replicate affected the number of errors that occurred, but their effect varied among error types. Week-to-week variation within a feeder and replicate had the largest effect on number of errors, which was likely associated with feeder management. Results indicate that the frequency of errors in data from electronic swine feeders is substantial, but visits with errors can be identified and their frequency can be decreased by proper feeder management.     

Preconditioning of calves

A high frequency of diseases in the veal industry is caused by diseases which are induced by several factors which are caused or at least influenced by errors of management. In order to comply with the political goal of a diminution of the use of antimicrobials in the veal industry (ban of growth promoters), errors of housing and of nutrition, damaging influences of the microclimate and hazardous commercial substances should be eliminated. Further measures of prophylaxis are discussed.     

Accuracy and repeatability of clinical examinations postpartum in dairy cattle

A variety of diagnostic tests are utilized during herd health examinations at the herd and individual cow level. While most of these tests are based on the best available evidence it has to be considered that each diagnostic test generates type 1 and 2 errors. Such diagnostic errors can lead to treatment errors with relevant clinical and pharmacological as well as ethical and economical consequences. The objectives of our studies were to generate science-based information on the repeatability and accuracy of relevant diagnostic methods utilized in postpartum monitoring of dairy cows. Both rectal thermometry and visual examination of vaginal discharge by means of a speculum are not perfect. Results can be biased by a variety of factors. Diagnostic performance (accuracy and repeatability), however, is adequate for cow side tests in the practice. The visual assessment of rumen fill is an inadequate method to monitor dry matter intake in cows.     

Evaluation of the impact of errors in the measurement of backfat depth on the prediction of fat-free lean mass

The development of regression equations to predict carcass composition typically assumes that the independent variables, such as backfat depth, are measured without error. However, technological and operator-specific types of measurement errors do exist. To evaluate the impact of measurement error for backfat depth, Monte Carlo simulation was used to model carcass fat-free lean mass (FFLM) in pigs. In the simulation, FFLM was a linear function of carcass weight and actual backfat depth (ABFD). Carcass weight was assumed to be measured without error, but measurement errors were generated such that the correlation (r(BF)) of the measured backfat depth (BFD) and ABFD ranged from 0.70 to 0.95. Two types of measurement errors were simulated: 1) constant variation that was additive to the variance of ABFD, and 2) variation proportional to the ABFD that was additive to the variance in ABFD. A total of 1,000 replications of 1,000 pigs were simulated. Within each type of measurement error, the absolute values of the regression coefficients and R2 values of the equations decreased as r(BF) decreased. The probability of the backfat depth squared (BFD2) being significant (P < 0.05) in the regression equation was increased when the measurement errors were proportional to ABFD. The occurrence of a significant BFD2 variable was 792 times out of 1,000 replications when r(BF) = 0.95 and increased to 996 times out of 1,000 when r(BF) = 0.85 for BFD with type 2 measurement errors. The inclusion of a CW x BFD variable in the regression equations (P < 0.05) increased (270 to 423 times out of 1,000) as r(BF) decreased from 0.85 to 0.70 for BFD with type 2 errors. Equations developed from BFD with measurement errors resulted in biased predictions of FFLM and changes in FFLM per unit change in BFD. The level and type of measurement errors that exist in the independent variables should be evaluated.     

UK surveillance: provision of quality assured information from combined datasets

Surveillance information is most useful when provided within a risk framework, which is achieved by presenting results against an appropriate denominator. Often the datasets are captured separately and for different purposes, and will have inherent errors and biases that can be further confounded by the act of merging. The United Kingdom Rapid Analysis and Detection of Animal-related Risks (RADAR) system contains data from several sources and provides both data extracts for research purposes and reports for wider stakeholders. Considerable efforts are made to optimise the data in RADAR during the Extraction, Transformation and Loading (ETL) process. Despite efforts to ensure data quality, the final dataset inevitably contains some data errors and biases, most of which cannot be rectified during subsequent analysis. So, in order for users to establish the 'fitness for purpose' of data merged from more than one data source, Quality Statements are produced as defined within the overarching surveillance Quality Framework. These documents detail identified data errors and biases following ETL and report construction as well as relevant aspects of the datasets from which the data originated. This paper illustrates these issues using RADAR datasets, and describes how they can be minimised.     

Bias—Is it a problem,and what should we do?

Observational studies are prone to two types of errors: random and systematic. Random error arises as a result of variation between samples that might be drawn in a study and can be reduced by increasing the sample size. Systematic error arises from problems with the study design or the methods used to obtain the study data and is not influenced by sample size. Over the last 20 years, veterinary epidemiologists have made great progress in dealing more effectively with random error (particularly through the use of multilevel models) but paid relatively little attention to systematic error. Systematic errors can arise from unmeasured confounders, selection bias and information bias. Unmeasured confounders include both factors which are known to be confounders but which were not measured in a study and factors which are not known to be confounders. Confounders can bias results toward or away from the null. The impact of selection bias can also be difficult to predict and can be negligible or large. Although the direction of information bias is generally toward the null, this cannot be guaranteed and its impact might be very large. Methods of dealing with systematic errors include: qualitative assessment, quantitative bias analysis and incorporation of bias parameters into the statistical analyses.     

蚕用蜕皮激素水剂含量检测探讨

我省蚕用蜕皮激素水剂含量检测是根据《四川省蚕药标准》(1992年版)制订的紫外分光光度检测法,该方法用于检测蚕用蜕皮激素水剂含量时受水剂中的糖苷,蛋白质、色素等大分子物质影响,测出值与实际值有较大误差,而用高效液相色谱检测法能较好地避免大分子物质的干扰,减少误差,其效果明显优于紫外检测法.