A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social netw...A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.展开更多
As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocrea...As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.展开更多
Dry corn straw(DCS)is usually used in anaerobic digestion(AD),but fresh corn straw(FCS)has been given less consideration.In this study,the thermophilic AD of single-substrate(FCS and DCS)and co-digestion(straw with ca...Dry corn straw(DCS)is usually used in anaerobic digestion(AD),but fresh corn straw(FCS)has been given less consideration.In this study,the thermophilic AD of single-substrate(FCS and DCS)and co-digestion(straw with cattle manure)were investigated.The results show that when FCS was used as the single-substrate for AD,the methane production was 144 mL·g^(−1)·VS^(−1),which was 7.5%and 19.6%higher than that of single DCS and FCS with cattle manure,respectively.In addition,the structure of FCS was loose and coarse,which was easier to be degraded than DCS.At the hydrolysis and acidification stages,Clostridium_sensu_stricto_1,Clostridium_sensu_stricto_7 and Sporosarcina promoted the decomposition of organic matter,leading to volatile fatty acids(VFAs)accumulation.Methanosarcina(54.4%)activated multifunctional methanogenic pathways to avoid the VFAs inhibition,which was important at the CH_(4) production stage.The main pathway was hydrogenotrophic methanogenesis,with genes encoding formylmethanofuran dehydrogenase(K00200-K00203)and tetrahydromethanopterin Smethyltransferase(K00577-K00584).Methanosarcina also activated acetotrophic and methylotrophic methanogenesis pathways,with genes encoding acetyl phosphate(K13788)and methyl-coenzyme M reductase(K04480,K14080 and K14081),respectively.In the co-digestion,the methanogenic potential of FCS was also confirmed.This provides a scientific basis for regulating AD of crop straw.展开更多
Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination ...Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography(UHR-OCT)imaging data.Methods:A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups:normal(50 eyes),with keratoconus(38 eyes)or with subclinical keratoconus(33 eyes).All eyes were imaged with a Scheimpflug camera and UHR-OCT.Corneal morphological features were extracted from the imaging data.A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes.Fisher’s score was used to rank the differentiable power of each feature.The receiver operating characteristic(ROC)curves were calculated to obtain the area under the ROC curves(AUCs).Results:The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus(AUC=0.93).The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes.Conclusion:The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes.The epithelial features extracted from the OCT images were the most valuable in the discrimination process.This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.展开更多
The granular sludge of microbial fermentation systems includes various biomass-degrading enzymes and different microflora,which have significant impacts on the degradation of biomass and the stability of the system.An...The granular sludge of microbial fermentation systems includes various biomass-degrading enzymes and different microflora,which have significant impacts on the degradation of biomass and the stability of the system.An up-flow anaerobic sludge blanket(UASB)reactor was used to grow hydrogen-producing granular sludge.The results showed that the formation of the granular sludge underwent four stages,i.e.,flocculation of the sludge,formation of the flocculent sludge,swelling of the flocculent sludge,and formation of the granular sludge.The formed granular sludge mostly had regular spherical and ellipsoidal shapes with a fractal dimension of 2.08±0.4;the settling velocities were 0.84 cm/s to 1.96 cm/s in water,the porosity was 0.67-0.95.The shear sensitivity(Kss)of the granular sludge was 0.1152.The granular sludge had a culture cycle of approximately 70 d and a hydrogen yield of 1.09 mol H2/mol glucose.展开更多
Background:To quantitatively assess anterior chamber angle(ACA)structure by anterior segment optical coherence tomography(AS-ocT)and develop a model to evaluate angle width as defined by gonioscopy.Methods:The ACAs of...Background:To quantitatively assess anterior chamber angle(ACA)structure by anterior segment optical coherence tomography(AS-ocT)and develop a model to evaluate angle width as defined by gonioscopy.Methods:The ACAs of each quadrant were evaluated by gonioscopy,classified by the Scheie grading system,and assigned into one of the three grades:small angle(SA),moderate angle(MA),and large angle(LA).The eyes were imaged by AS-OCT,and ACA structural parameters including angle opening distance at the scleral spur(AODSS)and at 750μm anterior to the scleral spur(AOD750),trabecular-iris space area at 750μm anterior to the scleral spur(TISA750),and a newly defined parameter"light intersection distance"(LID),were measured.The ACA structural data were used to construct an ordered logistic regression model for assignment of ACAs to one of the three angle grades.The validity of the model was then tested.Results:A total of 169 quadrants from 53 subjects were included in the analysis,of which 111 quadrants were included in the modeling data and 58 in the testing data.In pairwise comparisons of SA,MA,and LA by ANOVA,the measured parameters were as follows:AOD750(0.174±0.060 vs.0.249±0.068 vs.0.376±0.114 mm;P<0.001),TISA750(0.075±0.035 vs.0.117±0.036 vs.0.181±0.062 mm^(2);P<0.001),and LID(0.300±0.187 vs.0.085±0.170 vs.0.122±0.156 mm;P<0.001).The ACA grading model based on LID showed a relatively high correction rate of 72.4%,and the model efficiency,calculated using the receiver operating characteristic,showed an area under the curve of 0.740.Weighted kappa statistics showed a good agreement for multiple ACA grades(0.772).Conclusions:The AS-OCT-based multiple ACA grades model was demonstrated as a non-contact approach for ACA assessment with high speed and high spatial resolution,providing guidance for diagnosis of angle closure.展开更多
Background:To quantitatively assess anterior chamber angle(ACA)structure by anterior segment optical coherence tomography(AS-OCT)and develop a model to evaluate angle width as defined by gonioscopy.Methods:The ACAs of...Background:To quantitatively assess anterior chamber angle(ACA)structure by anterior segment optical coherence tomography(AS-OCT)and develop a model to evaluate angle width as defined by gonioscopy.Methods:The ACAs of each quadrant were evaluated by gonioscopy,classified by the Scheie grading system,and assigned into one of the three grades:small angle(SA),moderate angle(MA),and large angle(LA).The eyes were imaged by AS-OCT,and ACA structural parameters including angle opening distance at the scleral spur(AODSS)and at 750μm anterior to the scleral spur(AOD750),trabecular-iris space area at 750μm anterior to the scleral spur(TISA750),and a newly defined parameter“light intersection distance”(LID),were measured.The ACA structural data were used to construct an ordered logistic regression model for assignment of ACAs to one of the three angle grades.The validity of the model was then tested.Results:A total of 169 quadrants from 53 subjects were included in the analysis,of which 111 quadrants were included in the modeling data and 58 in the testing data.In pairwise comparisons of SA,MA,and LA by ANOVA,the measured parameters were as follows:AOD750(0.174±0.060 vs.0.249±0.068 vs.0.376±0.114 mm;P<0.001),TISA750(0.075±0.035 vs.0.117±0.036 vs.0.181±0.062 mm^(2);P<0.001),and LID(−0.300±0.187 vs.-0.085±0.170 vs.0.122±0.156 mm;P<0.001).The ACA grading model based on LID showed a relatively high correction rate of 72.4%,and the model efficiency,calculated using the receiver operating characteristic,showed an area under the curve of 0.740.Weighted kappa statistics showed a good agreement for multiple ACA grades(0.772).Conclusions:The AS-OCT-based multiple ACA grades model was demonstrated as a non-contact approach for ACA assessment with high speed and high spatial resolution,providing guidance for diagnosis of angle closure.展开更多
基金supported by the National Natural Science Foundation of China Project(No.62302540)The Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)+2 种基金Natural Science Foundation of Henan Province Project(No.232300420422)The Natural Science Foundation of Zhongyuan University of Technology(No.K2023QN018)Key Research and Promotion Project of Henan Province in 2021(No.212102310480).
文摘A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.
基金the National Natural Science Foundation of China(No.62302540)with author F.F.S.For more information,please visit their website at https://www.nsfc.gov.cn/.Additionally,it is also funded by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)+1 种基金where F.F.S is an author.Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/.The research is also supported by the Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422)for more information,you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html.Lastly,it receives funding from the Natural Science Foundation of Zhongyuan University of Technology(No.K2023QN018),where F.F.S is an author.You can find more information at https://www.zut.edu.cn/.
文摘As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.
基金supported by the Shaanxi Youth Thousand Talents Project(A279021901)the Scientific and Technological Activities for Overseas Researchers in Shaanxi Province(20200002)+3 种基金the Chinese Universities Scientific Fund(2452021112)the Key Research and Development Project of Shaanxi Province(2020NY-114)the Double first-class construction project funded by Northwest A&F University,Northwest A&F University Young Talent Project(Z111021902)the USA Energy Foundation(G-2206-33957).
文摘Dry corn straw(DCS)is usually used in anaerobic digestion(AD),but fresh corn straw(FCS)has been given less consideration.In this study,the thermophilic AD of single-substrate(FCS and DCS)and co-digestion(straw with cattle manure)were investigated.The results show that when FCS was used as the single-substrate for AD,the methane production was 144 mL·g^(−1)·VS^(−1),which was 7.5%and 19.6%higher than that of single DCS and FCS with cattle manure,respectively.In addition,the structure of FCS was loose and coarse,which was easier to be degraded than DCS.At the hydrolysis and acidification stages,Clostridium_sensu_stricto_1,Clostridium_sensu_stricto_7 and Sporosarcina promoted the decomposition of organic matter,leading to volatile fatty acids(VFAs)accumulation.Methanosarcina(54.4%)activated multifunctional methanogenic pathways to avoid the VFAs inhibition,which was important at the CH_(4) production stage.The main pathway was hydrogenotrophic methanogenesis,with genes encoding formylmethanofuran dehydrogenase(K00200-K00203)and tetrahydromethanopterin Smethyltransferase(K00577-K00584).Methanosarcina also activated acetotrophic and methylotrophic methanogenesis pathways,with genes encoding acetyl phosphate(K13788)and methyl-coenzyme M reductase(K04480,K14080 and K14081),respectively.In the co-digestion,the methanogenic potential of FCS was also confirmed.This provides a scientific basis for regulating AD of crop straw.
基金This study was supported by research grants from Key R&D Program Projects in Zhejiang Province(2019C03045)the National Major Equipment Program of China(2012YQ12008004)+1 种基金the National Key Research and Development Program of China(2016YFE0107000)the National Nature Science Foundation of China(Grant No.81570880).
文摘Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography(UHR-OCT)imaging data.Methods:A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups:normal(50 eyes),with keratoconus(38 eyes)or with subclinical keratoconus(33 eyes).All eyes were imaged with a Scheimpflug camera and UHR-OCT.Corneal morphological features were extracted from the imaging data.A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes.Fisher’s score was used to rank the differentiable power of each feature.The receiver operating characteristic(ROC)curves were calculated to obtain the area under the ROC curves(AUCs).Results:The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus(AUC=0.93).The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes.Conclusion:The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes.The epithelial features extracted from the OCT images were the most valuable in the discrimination process.This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.
基金The authors are grateful to the financial support by National Natural Science Foundation of China(Grant No.51506027)“Young Talents”Project of Northeast Agricultural University(Grant No.16QC18).
文摘The granular sludge of microbial fermentation systems includes various biomass-degrading enzymes and different microflora,which have significant impacts on the degradation of biomass and the stability of the system.An up-flow anaerobic sludge blanket(UASB)reactor was used to grow hydrogen-producing granular sludge.The results showed that the formation of the granular sludge underwent four stages,i.e.,flocculation of the sludge,formation of the flocculent sludge,swelling of the flocculent sludge,and formation of the granular sludge.The formed granular sludge mostly had regular spherical and ellipsoidal shapes with a fractal dimension of 2.08±0.4;the settling velocities were 0.84 cm/s to 1.96 cm/s in water,the porosity was 0.67-0.95.The shear sensitivity(Kss)of the granular sludge was 0.1152.The granular sludge had a culture cycle of approximately 70 d and a hydrogen yield of 1.09 mol H2/mol glucose.
基金Supported by research grants from the Natural Science Foundation of Zhejang Province(Grant No.LY18H180008)Optometry Engineering Technology Development Project of Zheijang Eye Hospital(Grant No.GCKF201603).
文摘Background:To quantitatively assess anterior chamber angle(ACA)structure by anterior segment optical coherence tomography(AS-ocT)and develop a model to evaluate angle width as defined by gonioscopy.Methods:The ACAs of each quadrant were evaluated by gonioscopy,classified by the Scheie grading system,and assigned into one of the three grades:small angle(SA),moderate angle(MA),and large angle(LA).The eyes were imaged by AS-OCT,and ACA structural parameters including angle opening distance at the scleral spur(AODSS)and at 750μm anterior to the scleral spur(AOD750),trabecular-iris space area at 750μm anterior to the scleral spur(TISA750),and a newly defined parameter"light intersection distance"(LID),were measured.The ACA structural data were used to construct an ordered logistic regression model for assignment of ACAs to one of the three angle grades.The validity of the model was then tested.Results:A total of 169 quadrants from 53 subjects were included in the analysis,of which 111 quadrants were included in the modeling data and 58 in the testing data.In pairwise comparisons of SA,MA,and LA by ANOVA,the measured parameters were as follows:AOD750(0.174±0.060 vs.0.249±0.068 vs.0.376±0.114 mm;P<0.001),TISA750(0.075±0.035 vs.0.117±0.036 vs.0.181±0.062 mm^(2);P<0.001),and LID(0.300±0.187 vs.0.085±0.170 vs.0.122±0.156 mm;P<0.001).The ACA grading model based on LID showed a relatively high correction rate of 72.4%,and the model efficiency,calculated using the receiver operating characteristic,showed an area under the curve of 0.740.Weighted kappa statistics showed a good agreement for multiple ACA grades(0.772).Conclusions:The AS-OCT-based multiple ACA grades model was demonstrated as a non-contact approach for ACA assessment with high speed and high spatial resolution,providing guidance for diagnosis of angle closure.
基金Supported by research grants from the Natural Science Foundation of Zhejiang Province(Grant No.LY18H180008)Optometry Engineering Technology Development Project of Zhejiang Eye Hospital(Grant No.GCKF201603).
文摘Background:To quantitatively assess anterior chamber angle(ACA)structure by anterior segment optical coherence tomography(AS-OCT)and develop a model to evaluate angle width as defined by gonioscopy.Methods:The ACAs of each quadrant were evaluated by gonioscopy,classified by the Scheie grading system,and assigned into one of the three grades:small angle(SA),moderate angle(MA),and large angle(LA).The eyes were imaged by AS-OCT,and ACA structural parameters including angle opening distance at the scleral spur(AODSS)and at 750μm anterior to the scleral spur(AOD750),trabecular-iris space area at 750μm anterior to the scleral spur(TISA750),and a newly defined parameter“light intersection distance”(LID),were measured.The ACA structural data were used to construct an ordered logistic regression model for assignment of ACAs to one of the three angle grades.The validity of the model was then tested.Results:A total of 169 quadrants from 53 subjects were included in the analysis,of which 111 quadrants were included in the modeling data and 58 in the testing data.In pairwise comparisons of SA,MA,and LA by ANOVA,the measured parameters were as follows:AOD750(0.174±0.060 vs.0.249±0.068 vs.0.376±0.114 mm;P<0.001),TISA750(0.075±0.035 vs.0.117±0.036 vs.0.181±0.062 mm^(2);P<0.001),and LID(−0.300±0.187 vs.-0.085±0.170 vs.0.122±0.156 mm;P<0.001).The ACA grading model based on LID showed a relatively high correction rate of 72.4%,and the model efficiency,calculated using the receiver operating characteristic,showed an area under the curve of 0.740.Weighted kappa statistics showed a good agreement for multiple ACA grades(0.772).Conclusions:The AS-OCT-based multiple ACA grades model was demonstrated as a non-contact approach for ACA assessment with high speed and high spatial resolution,providing guidance for diagnosis of angle closure.