After an intensive few years of development,ACG Kinna Automatic and ACG Nystrom–members of TMAS,the Swedish textile machinery association–have delivered the first microfactory for the production of fully finished fi...After an intensive few years of development,ACG Kinna Automatic and ACG Nystrom–members of TMAS,the Swedish textile machinery association–have delivered the first microfactory for the production of fully finished filter bags to a major international filtration industry customer,in cooperation with JUKI Central Europe.展开更多
Objective:This study aimed to examine the causal model of eating behaviors among pregnant women working in industrial factories.Methods:This cross-sectional study was conducted on 210 participants,attending 4 healthca...Objective:This study aimed to examine the causal model of eating behaviors among pregnant women working in industrial factories.Methods:This cross-sectional study was conducted on 210 participants,attending 4 healthcare centers,at a tertiary care hospital in Chonburi province,Thailand.Data were collected using 7 questionnaires:demographic form,eating behavior questionnaire,perceived benefits of the healthy eating questionnaire,perceived barriers to the healthy eating questionnaire,perceived self-efficacy questionnaire,social support questionnaire,and accessibility to healthy foods questionnaire.Descriptive statistics and path analysis were used for data analysis.Results:The participants had relatively high mean scores for eating behaviors.The final model fitted well with the dataχ^(2)=12.86,df=10,P=0.23;χ^(2)/df=1.29;comparative fit index(CFI)=0.98;goodness-of-fit index(GFI)=0.98;adjusted goodness-of-fit index(AGFI)=0.95;root mean square error of approximation(RMSEA)=0.04.Four factors-perceived benefits(β=0.13,P<0.05),perceived self-efficacy in healthy eating(β=0.22,P<0.001),pregnancy planning(β=0.28,P<0.001),and accessibility to healthy foods in the factory(β=0.12,P<0.05)-positively affected eating behavior,while only perceived barriers to healthy eating had a negative effect on eating behavior(β=−0.24,P<0.001).All the above factors explained 27.2%of the variance in eating behaviors.Conclusions:Nurses or healthcare providers can apply these findings to create an eating behavior modification program,focusing on pregnancy planning,behavior-specific variables,and interpersonal and situational influence,to promote the nutritional status of pregnant women working in industrial factories.展开更多
Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning techn...Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.展开更多
文摘After an intensive few years of development,ACG Kinna Automatic and ACG Nystrom–members of TMAS,the Swedish textile machinery association–have delivered the first microfactory for the production of fully finished filter bags to a major international filtration industry customer,in cooperation with JUKI Central Europe.
文摘Objective:This study aimed to examine the causal model of eating behaviors among pregnant women working in industrial factories.Methods:This cross-sectional study was conducted on 210 participants,attending 4 healthcare centers,at a tertiary care hospital in Chonburi province,Thailand.Data were collected using 7 questionnaires:demographic form,eating behavior questionnaire,perceived benefits of the healthy eating questionnaire,perceived barriers to the healthy eating questionnaire,perceived self-efficacy questionnaire,social support questionnaire,and accessibility to healthy foods questionnaire.Descriptive statistics and path analysis were used for data analysis.Results:The participants had relatively high mean scores for eating behaviors.The final model fitted well with the dataχ^(2)=12.86,df=10,P=0.23;χ^(2)/df=1.29;comparative fit index(CFI)=0.98;goodness-of-fit index(GFI)=0.98;adjusted goodness-of-fit index(AGFI)=0.95;root mean square error of approximation(RMSEA)=0.04.Four factors-perceived benefits(β=0.13,P<0.05),perceived self-efficacy in healthy eating(β=0.22,P<0.001),pregnancy planning(β=0.28,P<0.001),and accessibility to healthy foods in the factory(β=0.12,P<0.05)-positively affected eating behavior,while only perceived barriers to healthy eating had a negative effect on eating behavior(β=−0.24,P<0.001).All the above factors explained 27.2%of the variance in eating behaviors.Conclusions:Nurses or healthcare providers can apply these findings to create an eating behavior modification program,focusing on pregnancy planning,behavior-specific variables,and interpersonal and situational influence,to promote the nutritional status of pregnant women working in industrial factories.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.2021R1C1C1013133)funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.5199990914048)supported by the Soonchunhyang University Research Fund.
文摘Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.
