Try to Save Mistakes

We can make mistakes at any age.Somemistakes we make are about money.But mostmistakes are about people.“When I got thatjob,did Jim really feel good about it as afriend?Or is he envious(嫉妒)of my luck?”“And Paul—Why didn't I find that he wasfriendly just because I had a car?”When welook back,thinking about these,that can makeus feel bad.But when we look back.it's too late.

What to Do When You Make Mistakes in Talking

The best way of learning a language is by using it.The best way of learning English is talking in English as much as possible.Sometimes you will get your words mixed up(混淆) and people will not understand you.

Correction:Nanomaterial-Based Repurposing of Macrophage Metabolism and Its Applications

Following publication of the original article[1],the authors reported an error in the last author’s name,it was mistakenly written as“Jun Den”.The correct author’s name“Jun Deng”has been updated in this Correction.

Some mistakes in the algorithm for solving underflow problem in Baum-Welch algorithm and their corrections

Baum-Welch algorithm most likely results in underflow in practice. In some literatures, such as 'Scaling' algorithm was introduced to solve the problem. In applications, however, some mistakes were found in the equations presented in these literatures. The practical calculations show that the original algorithm often results in poor or even none convergence and rather higher error rate in speech recognition. The mistakes in these literatures and brings forward the correct equations are analysed. The speech recognition system using the revised algorithm can converge well and has lower error rate.

Erratum to:Review on biomass metallurgy:Pretreatment technology,metallurgical mechanism and process design

Erratum to:International Journal of Minerals,Metallurgy and Materials Volume 29,Number 6,June 2022,Page 1133 https://doi.org/10.1007/s12613-022-2501-9 The original version of this article unfortunately contained a mistake.The spelling of the surname of one of the co-authors was incorrect.The name should be Alberto N.Conejo.

Erratum

For the paper,Identification and epitope mapping of anti-p72 single-chain antibody against African swine fever virus based on phage display antibody library,published in Journal of Integrative Agriculture 2023,Vol.22,No.9,on page 2834-2847,the authors regret to apprise about a post-publication mistake in Fig.7 of the article.There are errors in the flow cytometry analysis.The new corrected Fig.7 is provided in this corrigendum to match with the corresponding text.

The Most Common Mistakes in English Usage

美国宾夕法尼亚州西切斯特州立学院(West Chester State College)英语教授Tho-mas Elliott Berry所著的The Most CommonMistakes in English

Seven Money Mistakes Everyone MakesBy Gary Belsky

接读此篇来稿,编者竟时时发现自己的影子。此文的标题可译:七个花钱的误区。可毫不夸饰地说,此文是篇消费心理学的上乘之作,三读不厌！同时,可使自己的消费心理更趋成熟。最值得提及的是全文的七个小标题,它们分别与段中某句相呼应,可读可品。如:1.We fear loss more than we should. -peo-ple place about twice as much significance on a loss as on a gain.2.We forgetabout inflation—always think in terms of your buying power.3.We follow thecrowd.-set your own financial guidelines.4.We are overconfident -If youremember only your victories,you tend to dramatically underestimate theprospect of deteat.5.We hear what we want to hear—A different perspectivemight show where you’ve been kidding yourself.6.We value some dollars lessthan others.一With found money,wait a while before you make any spendingdecisions.7.We bite off more than we can chew--Break every package deal intoits parts and make sure that you want every feature.就表达言,第 5,6,7三条的小标题最可玩味。本文的一个成功之处是:设置“情景”,让读者自己体验,引起共鸣,尔后再作分析,提?

Let’s Not Copy Japan’s Mistakes

In 1953 a small start-up company in Japan asked government officials at the Ministry of International Trade and Industry (MITI) for permission to buy transistor-manufacturing rights from Western Electric for $25,000. MITI was not impressed with the new technology or the young Japanese firm,and didn’t want to squander scarce foreign currency on either. Permis-

Correction:Nonlinear dynamics in Divisia monetary aggregates:an application of recurrence quantification analysis

Following publication of the original article(Andreadis et al.2023),the authors reported that they mistakenly omitted the affiliations 1,2 and 3 for Ioannis Andreadis,Athanasios D.Fragkou and Theodoros E.Karakasidis.The correct affiliations for each author should read.

Erratum to:Development of chloroplast marker for identification of Ulva species

Erratum to:https://doi.org/10.1007/s00343-022-2154-6 The financial supporting information of this article contains a mistake.The correct one is given below.

Corrigendum to“Effects of a symptom management interventionbased on group sessions combined with a mobile health applicationfor persons living with HIV in China:A randomized controlled trial”[Int J Nurs Sci 8(2021)370-379]

The authors regret that the clinical trial registration number appear incorrectly due to their unintentional mistake,which matched another of their clinical trial project.The correct registration number is(ChiCTR2000039097).The authors would like to apologise for any inconvenience caused.

A Consistent Mistake in Remote Sensing Images’Classification Literature

Recently,the convolutional neural network(CNN)has been dom-inant in studies on interpreting remote sensing images(RSI).However,it appears that training optimization strategies have received less attention in relevant research.To evaluate this problem,the author proposes a novel algo-rithm named the Fast Training CNN(FST-CNN).To verify the algorithm’s effectiveness,twenty methods,including six classic models and thirty archi-tectures from previous studies,are included in a performance comparison.The overall accuracy(OA)trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline.Results show that there is a maximal OA gap of 8.35%between the FST-CNN and those methods in the literature,which means a 10%margin in performance.Meanwhile,all those complex roadmaps,e.g.,deep feature fusion,model combination,model ensembles,and human feature engineering,are not as effective as expected.It reveals that there was systemic suboptimal perfor-mance in the previous studies.Most of the CNN-based methods proposed in the previous studies show a consistent mistake,which has made the model’s accuracy lower than its potential value.The most important reasons seem to be the inappropriate training strategy and the shift in data distribution introduced by data augmentation(DA).As a result,most of the performance evaluation was conducted based on an inaccurate,suboptimal,and unfair result.It has made most of the previous research findings questionable to some extent.However,all these confusing results also exactly demonstrate the effectiveness of FST-CNN.This novel algorithm is model-agnostic and can be employed on any image classification model to potentially boost performance.In addition,the results also show that a standardized training strategy is indeed very meaningful for the research tasks of the RSI-SC.

棋下得不好,怪空气?

1 A newly published study shows that chess players perform objectively worse and make more suboptimal(次优的)moves when there is more fine particulate matter(颗粒物)in the air.2“We find that when individuals are exposed to higher levels of air pollution,they will make more mistakes,and they will make larger mistakes.”

Causes for the Fall of Hurstwood in Sister Carrie

This paper has a brief look to the life of Theodore Dreiser(1871-1945),one of the most important writers in American literature and background information of composing his first novel"Sister Carrie".It mainly touches on the causes for the fall of Hurstwood.Three reasons are listed:theft,his crazy infatuation for Carrie and not adapting for the new circumstances.

Study of translation ability with non-English majored postgraduates and its application to translation teaching and learning

The present paper discusses the typical problems in a C-E translation exercise by non-English-major postgraduates. It concludes by providing some implications to translation teaching and learning.

寒区活动层中合成材料的寒冻破坏分析(英文)

In last years, the problem of building s stability in northern areas has become aggravating. The main reasons mass deformations areas follows: 1) mistakes during research, planning, construction and operation of objects; 2) negative changes in frozen ground, bound with reduction of footing s bearing capacity under technogenic impact; 3) premature wear of material of underground constructions under influence of natural and technogenic factors.

An Optimized Deep Learning Model for Emotion Classification in Tweets

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man.Analyzing this data can be critical for any organization.Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society.Sentiment analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter.Resources used for analyzing tweet emotions are also briefly presented in literature survey section.In this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural Network.Furthermore,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used.The main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better results.Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

On Improving the Written English Level of Middle School Students

This paper is an attempt to improve the studen the written English level. There are three parts in this passage. Part one: Analyze the types of mistakes. Part two is "six - step" method in written English. Part three is conclusion.