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—W...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.展开更多
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 ...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.展开更多
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.
Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mix...Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.展开更多
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 mis...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.展开更多
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,...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.展开更多
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.
文摘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.
基金Hunan University of Arts and Science provided doctoral research funding for this study (grant number 16BSQD23)Fund of Geography Subject ([2022]351)also provided funding.
文摘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.
文摘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.
文摘Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.
文摘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.
文摘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.
文摘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.
