This study focuses on the analysis of the Chinese composition writing performance of fourth,fifth,and sixth grade students in 16 selected schools in Longhua District,Shenzhen during the spring semester of 2023.Using L...This study focuses on the analysis of the Chinese composition writing performance of fourth,fifth,and sixth grade students in 16 selected schools in Longhua District,Shenzhen during the spring semester of 2023.Using LIWC(Linguistic Inquiry and Word Count)as a text analysis tool,the study explores the impact of LIWC categories on writing performance which is scaled by score.The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students;while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students.Process word categories have a positive influence on the composition scores of all three grades,but the impact of complex process word categories increases as the grade level rises.展开更多
Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instr...Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instruction-brief and instruction-category, and constructing test suite. Consequently, this approach is adopted to test oven embedded system, and detail process is deeply discussed. As a result, the factual result indicates that the “instruction-category” approach can be effectively applied in embedded system testing as a black-box method for conformity testing.展开更多
BACKGROUND An association between cardiorespiratory fitness(CRF)and insulin resistance in obese adolescents,especially in those with various obesity categories,has not been systematically studied.There is a lack of kn...BACKGROUND An association between cardiorespiratory fitness(CRF)and insulin resistance in obese adolescents,especially in those with various obesity categories,has not been systematically studied.There is a lack of knowledge about the effects of CRF on insulin resistance in severely obese adolescents,despite their continuous rise.AIM To investigate the association between CRF and insulin resistance in obese adolescents,with special emphasis on severely obese adolescents.METHODS We performed a prospective,cross-sectional study that included 200 pubertal adolescents,10 years to 18 years of age,who were referred to a tertiary care center due to obesity.According to body mass index(BMI),adolescents were classified as mildly obese(BMI 100% to 120% of the 95^(th)percentile for age and sex)or severely obese(BMI≥120% of the 95^(th)percentile for age and sex or≥35 kg/m^(2),whichever was lower).Participant body composition was assessed by bioelectrical impedance analysis.A homeostatic model assessment of insulin resistance(HOMA-IR)was calculated.Maximal oxygen uptake(VO_(2)max)was determined from submaximal treadmill exercise test.CRF was expressed as VO_(2)max scaled by total body weight(TBW)(mL/min/kg TBW)or by fat free mass(FFM)(mL/min/kg FFM),and then categorized as poor,intermediate,or good,according to VO_(2)max terciles.Data were analyzed by statistical software package SPSS(IBM SPSS Statistics for Windows,Version 24.0).P<0.05 was considered statistically significant.RESULTS A weak negative correlation between CRF and HOMA-IR was found[Spearman’s rank correlation coefficient(rs)=-0.28,P<0.01 for CRF_(TBW);(r_(s))=-0.21,P<0.01 for CRF_(FFM)].One-way analysis of variance(ANOVA)revealed a significant main effect of CRF on HOMA-IR[F(2200)=6.840,P=0.001 for CRF_(TBW);F_((2200))=3.883,P=0.022 for CRF_(FFM)].Subsequent analyses showed that obese adolescents with poor CRF had higher HOMA-IR than obese adolescents with good CRF(P=0.001 for CRF_(TBW);P=0.018 for CRF_(FFM)).Two-way ANOVA with Bonferroni correction confirmed significant effect of interaction of CRF level and obesity category on HOMA-IR[F_((2200))=3.292,P=0.039 for CRF_(TBW)].Severely obese adolescents had higher HOMA-IR than those who were mildly obese,with either good or poor CRF.However,HOMA-IR did not differ between severely obese adolescents with good and mildly obese adolescents with poor CRF.CONCLUSION CRF is an important determinant of insulin resistance in obese adolescents,regardless of obesity category.Therefore,CRF assessment should be a part of diagnostic procedure,and its improvement should be a therapeutic goal.展开更多
Food additives, whether natural or artificial substances, are widely used around the world to improve the sensory quality of products, extend their shelf life and make them more competitive. However, the abusive and u...Food additives, whether natural or artificial substances, are widely used around the world to improve the sensory quality of products, extend their shelf life and make them more competitive. However, the abusive and uncontrolled consumption of food additives is the cause of numerous illnesses and diseases such as poisoning, allergies, diabetes and numerous cancers. So, in addition to setting up control and regulatory bodies, it is becoming essential to keep a constant watch on the presence of food additives on the market. The aim of this study is to highlight the main categories of food additives in food products frequently sold on the Senegalese market. The methodology of the study is based on the identification of food additives from the information given on the labels of food packaging. Information was collected in markets in two (2) major regions of Senegal: Dakar and Saint-Louis. The results of our study show the presence of 153 food additives on the labels of 514 samples collected. Moreover, the frequency and diversity of additives depended on the food category. On the other hand, beyond their important technological and functional roles, some additives such as aspartame and monosodium glutamate have been implicated in pathologies, and others, such as titanium dioxide, are the subject of much controversy and even withdrawal in certain legislations for their impacts deemed potentially negative on consumer health.展开更多
Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality ...Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.展开更多
Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradien...Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.展开更多
For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net...For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.展开更多
文摘This study focuses on the analysis of the Chinese composition writing performance of fourth,fifth,and sixth grade students in 16 selected schools in Longhua District,Shenzhen during the spring semester of 2023.Using LIWC(Linguistic Inquiry and Word Count)as a text analysis tool,the study explores the impact of LIWC categories on writing performance which is scaled by score.The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students;while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students.Process word categories have a positive influence on the composition scores of all three grades,but the impact of complex process word categories increases as the grade level rises.
文摘Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instruction-brief and instruction-category, and constructing test suite. Consequently, this approach is adopted to test oven embedded system, and detail process is deeply discussed. As a result, the factual result indicates that the “instruction-category” approach can be effectively applied in embedded system testing as a black-box method for conformity testing.
文摘BACKGROUND An association between cardiorespiratory fitness(CRF)and insulin resistance in obese adolescents,especially in those with various obesity categories,has not been systematically studied.There is a lack of knowledge about the effects of CRF on insulin resistance in severely obese adolescents,despite their continuous rise.AIM To investigate the association between CRF and insulin resistance in obese adolescents,with special emphasis on severely obese adolescents.METHODS We performed a prospective,cross-sectional study that included 200 pubertal adolescents,10 years to 18 years of age,who were referred to a tertiary care center due to obesity.According to body mass index(BMI),adolescents were classified as mildly obese(BMI 100% to 120% of the 95^(th)percentile for age and sex)or severely obese(BMI≥120% of the 95^(th)percentile for age and sex or≥35 kg/m^(2),whichever was lower).Participant body composition was assessed by bioelectrical impedance analysis.A homeostatic model assessment of insulin resistance(HOMA-IR)was calculated.Maximal oxygen uptake(VO_(2)max)was determined from submaximal treadmill exercise test.CRF was expressed as VO_(2)max scaled by total body weight(TBW)(mL/min/kg TBW)or by fat free mass(FFM)(mL/min/kg FFM),and then categorized as poor,intermediate,or good,according to VO_(2)max terciles.Data were analyzed by statistical software package SPSS(IBM SPSS Statistics for Windows,Version 24.0).P<0.05 was considered statistically significant.RESULTS A weak negative correlation between CRF and HOMA-IR was found[Spearman’s rank correlation coefficient(rs)=-0.28,P<0.01 for CRF_(TBW);(r_(s))=-0.21,P<0.01 for CRF_(FFM)].One-way analysis of variance(ANOVA)revealed a significant main effect of CRF on HOMA-IR[F(2200)=6.840,P=0.001 for CRF_(TBW);F_((2200))=3.883,P=0.022 for CRF_(FFM)].Subsequent analyses showed that obese adolescents with poor CRF had higher HOMA-IR than obese adolescents with good CRF(P=0.001 for CRF_(TBW);P=0.018 for CRF_(FFM)).Two-way ANOVA with Bonferroni correction confirmed significant effect of interaction of CRF level and obesity category on HOMA-IR[F_((2200))=3.292,P=0.039 for CRF_(TBW)].Severely obese adolescents had higher HOMA-IR than those who were mildly obese,with either good or poor CRF.However,HOMA-IR did not differ between severely obese adolescents with good and mildly obese adolescents with poor CRF.CONCLUSION CRF is an important determinant of insulin resistance in obese adolescents,regardless of obesity category.Therefore,CRF assessment should be a part of diagnostic procedure,and its improvement should be a therapeutic goal.
文摘Food additives, whether natural or artificial substances, are widely used around the world to improve the sensory quality of products, extend their shelf life and make them more competitive. However, the abusive and uncontrolled consumption of food additives is the cause of numerous illnesses and diseases such as poisoning, allergies, diabetes and numerous cancers. So, in addition to setting up control and regulatory bodies, it is becoming essential to keep a constant watch on the presence of food additives on the market. The aim of this study is to highlight the main categories of food additives in food products frequently sold on the Senegalese market. The methodology of the study is based on the identification of food additives from the information given on the labels of food packaging. Information was collected in markets in two (2) major regions of Senegal: Dakar and Saint-Louis. The results of our study show the presence of 153 food additives on the labels of 514 samples collected. Moreover, the frequency and diversity of additives depended on the food category. On the other hand, beyond their important technological and functional roles, some additives such as aspartame and monosodium glutamate have been implicated in pathologies, and others, such as titanium dioxide, are the subject of much controversy and even withdrawal in certain legislations for their impacts deemed potentially negative on consumer health.
基金sponsored by the National Natural Science Foundation of China(Grants:62002200,61772319)Shandong Natural Science Foundation of China(Grant:ZR2020QF012).
文摘Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.
文摘Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.
基金Supported by the National Key Research and Development Program of China(No.2018YFB1702601).
文摘For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.
