The problem of privacy in social networks is well documented within literature;users have pri- vacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third partie...The problem of privacy in social networks is well documented within literature;users have pri- vacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third parties. While numerous causes of poor behaviour have been suggested by re- search the role of the User Interface (UI) and the system itself is underexplored. The field of Per- suasive Technology would suggest that Social Network Systems persuade users to deviate from their normal or habitual behaviour. This paper makes the case that the UI can be used as the basis for user empowerment by informing them of their privacy at the point of interaction and remind- ing them of their privacy needs. The Theory of Planned Behaviour is introduced as a potential theoretical foundation for exploring the psychology behind privacy behaviour as it describes the salient factors that influence intention and action. Based on these factors of personal attitude, subjective norms and perceived control, a series of UIs are presented and implemented in con- trolled experiments examining their effect on personal information disclosure. This is combined with observations and interviews with the participants. Results from this initial, pilot experiment suggest groups with privacy salient information embedded exhibit less disclosure than the control group. This work reviews this approach as a method for exploring privacy behaviour and propos- es further work required.展开更多
Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data...Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.展开更多
A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work.Inspired by the anisotropic friction mechanism of natural wheat awns,a bioinspired mechanism is...A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work.Inspired by the anisotropic friction mechanism of natural wheat awns,a bioinspired mechanism is used to achieve the designed driving strategy based on the asymmetric-mass control method that mimics bidirectional motion characteristics of wheat awn.A lumped parameter theoretical model is established,and the numerical simulation results have verified the designed bionic working principle and revealed the key system parameters.Experimental results show that the prototype has the bi-directional motion ability inherited from anisotropic friction of wheat awn,with theoretically infinite stroke and can easily obtain the required step displacement and velocity by conveniently adjusting the voltage.It can achieve a resolution of 0.7μm,and a forward and backward maximum velocity of 12.7μm/s and 90.72μm/s respectively.In addition,the actuator also has the advantages of good stability,control convenience,and ease of integration.Besides,the actuator is capable of adjusting motion direction via voltage,providing a significant advantage in precise bidirectional control.This study confirms that the proposed mass on-line control type actuator embodies a successful bionic translation from plant morphology to precision engineering,and adds a new member to the family of impact inertial piezoelectric actuators,which completes the last piece of the puzzle for the impact inertial driving mechanism.It promotes the further development of inertial precision driving and control technology and is expected to expand the scope of application.Future work will focus on optimizing performance and developing applications.展开更多
This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federat...This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning.The framework integrates blockchain technology,the InterPlanetary File System(IPFS)for distributed storage,and a dynamic differential privacy mechanism to achieve collaborative security across the storage,service,and federated coordination layers.It accommodates both multimodal data classification and object detection tasks,enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection.This effectively mitigates the single-point failures and model leakage issues inherent in centralized architectures.A dynamically adjustable differential privacy mechanism is introduced to allocate privacy budgets according to client contribution levels and upload frequencies,achieving a personalized balance between model performance and privacy protection.Multi-dimensional experimental evaluations,including classification accuracy,F1-score,encryption latency,and aggregation latency,verify the security and efficiency of the proposed architecture.The improved CNN model achieves 72.34%accuracy and an F1-score of 0.72 in object detection and classification tasks on infrared surveillance imagery,effectively identifying typical risk events such as not wearing safety helmets and unauthorized intrusion,while maintaining an aggregation latency of only 1.58 s and a query latency of 80.79 ms.Compared with traditional static differential privacy and centralized approaches,the proposed method demonstrates significant advantages in accuracy,latency,and security,providing a new technical paradigm for efficient,secure data sharing,object detection,and privacy preservation in smart grid substations.展开更多
With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these i...With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these issues,we propose Federated Dynamic Prototype Learning(FedDPL)for malware classification by integrating Federated Learning with a specifically designed K-means.Under the Federated Learning framework,model training occurs locally without data sharing,effectively protecting user data privacy and preventing the leakage of sensitive information.Furthermore,to tackle the challenges of data heterogeneity and the lack of category information,FedDPL introduces a dynamic prototype learning mechanism,which adaptively adjusts the clustering prototypes in terms of position and number.Thus,the dependency on predefined category numbers in typical K-means and its variants can be significantly reduced,resulting in improved clustering performance.Theoretically,it provides a more accurate detection of malicious behavior.Experimental results confirm that FedDPL excels in handling malware classification tasks,demonstrating superior accuracy,robustness,and privacy protection.展开更多
The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re...The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
A rapid and simple liquid chromatography method with on-line solid phase extraction was developed and validated for the quantitative determination of cyclophosphamide in rat plasma.The plasma sample was first extracte...A rapid and simple liquid chromatography method with on-line solid phase extraction was developed and validated for the quantitative determination of cyclophosphamide in rat plasma.The plasma sample was first extracted on an Acclaim？ Polar Advantage II C18 guard column（PA II C18,10 mm×4.6 mm,5 μm）,which was also the on-line Extraction Cartridge SPE column,by washing with 100% H2O for 1 min.The extracted sample was then eluted onto a PA II C18 column（150 mm×4.6 mm,5 μm） and separated by isocratic elution with acetonitrile-water（40：60,v/v）.The mobile phase was run at a flow rate of 1.0 mL/min,and the UV detector was set at 195 nm.Retention time of cyclophosphamide was 4.3 min and the total run-time was 6 min.The linear range of the standard curve was from 1.0 to 200 μg/mL（r2 = 0.9999）,and the limits of quantification and detection were 1.0 μg/mL（RSD10%,n = 5） and 0.3 μg/mL（RSD13%,n = 5）,respectively.Both intra-and inter-day variations were less than 5.6%.The developed method can be used for the therapeutic drug monitoring of cyclophosphamide in the clinic.展开更多
The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sens...The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.展开更多
This paper introduces the development and industrial application of an on-line corrosion monitoring device for condenser tubes. Corrosion sensors are made up of representative condenser tubes chosen by eddy current te...This paper introduces the development and industrial application of an on-line corrosion monitoring device for condenser tubes. Corrosion sensors are made up of representative condenser tubes chosen by eddy current test, which enable the monitoring result to be consistent with the corrosion of actual condenser tubes. Localized corrosion rate of condenser tubes can be measured indirectly by a galvanic couple made up of tube segments with and without pits. Using this technology, corrosion problems can be found in time and accurately, and anticorrosive measures be made more economic and effective. Applications in two power plants showed the corrosion measurements are fast and accurate.展开更多
The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through...The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through on-line solution and off-line aging treatment but decreases after off-line solution treatment. By X-ray diffractometry, optical microscopy, differential thermal analysis, scanning electron microscopy and X-ray energy dispersive spectroscopy, it is found that the microstructures of the die-cast AZ91D magnesium alloy before and after on-line solution and off-line aging are similar, consisting of α-Mg and β-Al12Mg17. The precipitation of Al element is prevented by on-line solution so that the effect of solid solution strengthening is enhanced. The β-Al12Mg17 phases precipitate from supersaturated Mg solid solution after off-line aging treatment, and lead to microstructure refinement of AZ91D alloy, so the effect of precipitation hardening is enhanced. The β-Al12Mg17 phases dissolve in the substructure after off-line solution treatment, which leads to that the grain boundary strengthening phase is reduced significantly and the hardness of die cast AZ91D is reduced.展开更多
We have developed an on-line detection method using acridine orange as the fluorescence probe and applied this method to rapidly identify active compounds in herbal medicines. This on-line method was equipped with a h...We have developed an on-line detection method using acridine orange as the fluorescence probe and applied this method to rapidly identify active compounds in herbal medicines. This on-line method was equipped with a high-performance liquid chromatography tandem diode array detector, electrospray ionization-ion-trap time-of-flight mass spectrometry and DNA- acridine orange fluorescence detection （HPLC-DAD-MSn-DNA-AO-FLD）. A large amount of information could be simultaneously obtained during one run, which included HPLC fingerprint, ultraviolet spectra, total ion chromatograms, MSn data of high-resolution mass spectrometry and activity profile of each compound binding with DNA. The method also provided information on structureactivity relationships and mechanism of interaction. We used this on-line method to identify five DNA-binding activity components from Lithospermum erythrorhizon sample for the first time. The result showed that the parent nucleus of shikonin derivatives could bind with DNA. The structure-activity relationship showed that the parent nucleus of shikonin derivatives plays a major role in DNA binding, not the carboxyl group on the side chain. This simple, rapid, high precision and good stability on-line method should be useful for compound separation, structural identification and screening of DNA-binding compounds in herbal medicines.展开更多
文摘The problem of privacy in social networks is well documented within literature;users have pri- vacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third parties. While numerous causes of poor behaviour have been suggested by re- search the role of the User Interface (UI) and the system itself is underexplored. The field of Per- suasive Technology would suggest that Social Network Systems persuade users to deviate from their normal or habitual behaviour. This paper makes the case that the UI can be used as the basis for user empowerment by informing them of their privacy at the point of interaction and remind- ing them of their privacy needs. The Theory of Planned Behaviour is introduced as a potential theoretical foundation for exploring the psychology behind privacy behaviour as it describes the salient factors that influence intention and action. Based on these factors of personal attitude, subjective norms and perceived control, a series of UIs are presented and implemented in con- trolled experiments examining their effect on personal information disclosure. This is combined with observations and interviews with the participants. Results from this initial, pilot experiment suggest groups with privacy salient information embedded exhibit less disclosure than the control group. This work reviews this approach as a method for exploring privacy behaviour and propos- es further work required.
文摘Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.
基金supported in part by the National Natural Science Foundation of China under Grant 52205075in part by the Zhejiang Provincial Natural Science Foundation of China,under Grant LZ24E050008National Undergraduate Training Program on Innovation and Entrepreneurship,under Grant 202410345040.
文摘A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work.Inspired by the anisotropic friction mechanism of natural wheat awns,a bioinspired mechanism is used to achieve the designed driving strategy based on the asymmetric-mass control method that mimics bidirectional motion characteristics of wheat awn.A lumped parameter theoretical model is established,and the numerical simulation results have verified the designed bionic working principle and revealed the key system parameters.Experimental results show that the prototype has the bi-directional motion ability inherited from anisotropic friction of wheat awn,with theoretically infinite stroke and can easily obtain the required step displacement and velocity by conveniently adjusting the voltage.It can achieve a resolution of 0.7μm,and a forward and backward maximum velocity of 12.7μm/s and 90.72μm/s respectively.In addition,the actuator also has the advantages of good stability,control convenience,and ease of integration.Besides,the actuator is capable of adjusting motion direction via voltage,providing a significant advantage in precise bidirectional control.This study confirms that the proposed mass on-line control type actuator embodies a successful bionic translation from plant morphology to precision engineering,and adds a new member to the family of impact inertial piezoelectric actuators,which completes the last piece of the puzzle for the impact inertial driving mechanism.It promotes the further development of inertial precision driving and control technology and is expected to expand the scope of application.Future work will focus on optimizing performance and developing applications.
基金funded by the National Natural Science Foundation of China,grant number 61605004the Fundamental Research Funds for the Central Universities,grant number FRF-TP-19-016A2Guizhou Power Grid Co.,Ltd.2024 first batch of services(2024-2026 technology R&D services for science and technology projects(in addition to national and SGCC key projects)),grant number 060100KC23100012。
文摘This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning.The framework integrates blockchain technology,the InterPlanetary File System(IPFS)for distributed storage,and a dynamic differential privacy mechanism to achieve collaborative security across the storage,service,and federated coordination layers.It accommodates both multimodal data classification and object detection tasks,enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection.This effectively mitigates the single-point failures and model leakage issues inherent in centralized architectures.A dynamically adjustable differential privacy mechanism is introduced to allocate privacy budgets according to client contribution levels and upload frequencies,achieving a personalized balance between model performance and privacy protection.Multi-dimensional experimental evaluations,including classification accuracy,F1-score,encryption latency,and aggregation latency,verify the security and efficiency of the proposed architecture.The improved CNN model achieves 72.34%accuracy and an F1-score of 0.72 in object detection and classification tasks on infrared surveillance imagery,effectively identifying typical risk events such as not wearing safety helmets and unauthorized intrusion,while maintaining an aggregation latency of only 1.58 s and a query latency of 80.79 ms.Compared with traditional static differential privacy and centralized approaches,the proposed method demonstrates significant advantages in accuracy,latency,and security,providing a new technical paradigm for efficient,secure data sharing,object detection,and privacy preservation in smart grid substations.
基金supported by the National Natural Science Foundation of China under Grant No.62162009the Key Technologies R&D Program of He’nan Province under Grant No.242102211065+2 种基金the Postgraduate Education Reform and Quality Improvement Project of Henan Province under Grant Nos.YJS2025GZZ36,YJS2024AL112,and YJS2024JD38the Innovation Scientists and Technicians Troop Construction Projects of Henan Province under Grant No.CXTD2017099the Scientific Research Innovation Team of Xuchang University under Grant No.2022CXTD003.
文摘With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these issues,we propose Federated Dynamic Prototype Learning(FedDPL)for malware classification by integrating Federated Learning with a specifically designed K-means.Under the Federated Learning framework,model training occurs locally without data sharing,effectively protecting user data privacy and preventing the leakage of sensitive information.Furthermore,to tackle the challenges of data heterogeneity and the lack of category information,FedDPL introduces a dynamic prototype learning mechanism,which adaptively adjusts the clustering prototypes in terms of position and number.Thus,the dependency on predefined category numbers in typical K-means and its variants can be significantly reduced,resulting in improved clustering performance.Theoretically,it provides a more accurate detection of malicious behavior.Experimental results confirm that FedDPL excels in handling malware classification tasks,demonstrating superior accuracy,robustness,and privacy protection.
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
基金National Natural Science Foundation of China(Grant No.81072612)the Natural Science Foundation of Beijing(Grant No.7102107)+1 种基金the Open Foundation of State Key Laboratory of Natural and Biomimetic Drugs(Grant No.K20110109)Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110001110021)
文摘A rapid and simple liquid chromatography method with on-line solid phase extraction was developed and validated for the quantitative determination of cyclophosphamide in rat plasma.The plasma sample was first extracted on an Acclaim？ Polar Advantage II C18 guard column（PA II C18,10 mm×4.6 mm,5 μm）,which was also the on-line Extraction Cartridge SPE column,by washing with 100% H2O for 1 min.The extracted sample was then eluted onto a PA II C18 column（150 mm×4.6 mm,5 μm） and separated by isocratic elution with acetonitrile-water（40：60,v/v）.The mobile phase was run at a flow rate of 1.0 mL/min,and the UV detector was set at 195 nm.Retention time of cyclophosphamide was 4.3 min and the total run-time was 6 min.The linear range of the standard curve was from 1.0 to 200 μg/mL（r2 = 0.9999）,and the limits of quantification and detection were 1.0 μg/mL（RSD10%,n = 5） and 0.3 μg/mL（RSD13%,n = 5）,respectively.Both intra-and inter-day variations were less than 5.6%.The developed method can be used for the therapeutic drug monitoring of cyclophosphamide in the clinic.
文摘The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.
文摘This paper introduces the development and industrial application of an on-line corrosion monitoring device for condenser tubes. Corrosion sensors are made up of representative condenser tubes chosen by eddy current test, which enable the monitoring result to be consistent with the corrosion of actual condenser tubes. Localized corrosion rate of condenser tubes can be measured indirectly by a galvanic couple made up of tube segments with and without pits. Using this technology, corrosion problems can be found in time and accurately, and anticorrosive measures be made more economic and effective. Applications in two power plants showed the corrosion measurements are fast and accurate.
基金Projects (2011BAE22B01, 2011BAE22B06) supported by the National Key Technologies R&D Program During the 12th Five-Year Plan Period of ChinaProject (2010NC018) supported by the Innovation Fund of Inner Mongolia University of Science and Technology, China
文摘The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through on-line solution and off-line aging treatment but decreases after off-line solution treatment. By X-ray diffractometry, optical microscopy, differential thermal analysis, scanning electron microscopy and X-ray energy dispersive spectroscopy, it is found that the microstructures of the die-cast AZ91D magnesium alloy before and after on-line solution and off-line aging are similar, consisting of α-Mg and β-Al12Mg17. The precipitation of Al element is prevented by on-line solution so that the effect of solid solution strengthening is enhanced. The β-Al12Mg17 phases precipitate from supersaturated Mg solid solution after off-line aging treatment, and lead to microstructure refinement of AZ91D alloy, so the effect of precipitation hardening is enhanced. The β-Al12Mg17 phases dissolve in the substructure after off-line solution treatment, which leads to that the grain boundary strengthening phase is reduced significantly and the hardness of die cast AZ91D is reduced.
基金Peking University Comprehensive Platform for Innovative Drug Research and Development(Grant No.2009ZX--09301--010)
文摘We have developed an on-line detection method using acridine orange as the fluorescence probe and applied this method to rapidly identify active compounds in herbal medicines. This on-line method was equipped with a high-performance liquid chromatography tandem diode array detector, electrospray ionization-ion-trap time-of-flight mass spectrometry and DNA- acridine orange fluorescence detection （HPLC-DAD-MSn-DNA-AO-FLD）. A large amount of information could be simultaneously obtained during one run, which included HPLC fingerprint, ultraviolet spectra, total ion chromatograms, MSn data of high-resolution mass spectrometry and activity profile of each compound binding with DNA. The method also provided information on structureactivity relationships and mechanism of interaction. We used this on-line method to identify five DNA-binding activity components from Lithospermum erythrorhizon sample for the first time. The result showed that the parent nucleus of shikonin derivatives could bind with DNA. The structure-activity relationship showed that the parent nucleus of shikonin derivatives plays a major role in DNA binding, not the carboxyl group on the side chain. This simple, rapid, high precision and good stability on-line method should be useful for compound separation, structural identification and screening of DNA-binding compounds in herbal medicines.
