Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me...Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.展开更多
针对现有低功耗蓝牙(BLE)欺骗攻击检测技术准确率低的问题,提出了一种基于异常指纹的BLE欺骗攻击检测技术,将攻击者的射频指纹作为异常数据,把欺骗攻击检测建模为异常检测问题;设计了一种基于深度支持向量描述(Deep Support Vector Data...针对现有低功耗蓝牙(BLE)欺骗攻击检测技术准确率低的问题,提出了一种基于异常指纹的BLE欺骗攻击检测技术,将攻击者的射频指纹作为异常数据,把欺骗攻击检测建模为异常检测问题;设计了一种基于深度支持向量描述(Deep Support Vector Data Description,DeepSVDD)的异常指纹检测模型——RFFAD_DeepSVDD,并使用残差单元构建网络模型,有效缓解了机器学习异常检测算法非线性特征提取不足的问题。采用预训练自编码器获取最优初始化参数,极大增强了模型边界决策能力。在异常检测实验中,该模型准确率达到95.47%,相比基于机器学习的异常检测模型平均提升8.92%;在欺骗攻击检测实验中,该方法相比现有欺骗攻击检测技术在攻击节点运动与静止状态下均表现出更好的性能,能够准确检测并识别出中间人攻击、冒充攻击、重连接欺骗攻击3种欺骗攻击。展开更多
This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passi...This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.展开更多
Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial fea...Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.展开更多
The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier,providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinti...The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier,providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinting of phytotoxins as a cutting-edge approach.This study explores the dynamic intersection of phytochemistry and forensic science,highlighting how the unique phytochemical profiles of toxic plants and their secondary metabolites,serve as distinctive markers for forensic investigations.By utilizing advanced techniques such as Ultra-High-Performance Liquid Chromatography(UHPLC)and High-Resolution Mass Spectrometry(HRMS),the detection and quantification of plant-derived are made more accurate in forensic contexts.Real-world case studies are presented to demonstrate the critical role of plant toxins in forensic outcomes and legal proceedings.The challenges,potential,and future prospects of integrating phytochemical fingerprinting of plant toxins into forensic science were discussed.This review aims to illuminate phytochemical fingerprinting of plant toxins as a promising tool to enhance the precision and depth of forensic analyses,offering new insights into the complex stories embedded in plant toxins.展开更多
With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.Th...With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.展开更多
Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose signifi...Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.展开更多
A leading position in the areas of testing,instrumentation and machine control has been established by members of the British Textile Machinery Association(BTMA)and a number of new developments in these fields will be...A leading position in the areas of testing,instrumentation and machine control has been established by members of the British Textile Machinery Association(BTMA)and a number of new developments in these fields will be showcased at this year’s ITMA Asia+CITME exhibition,which takes place in Singapore from October 28-31.“Many of our members are currently developing new technologies,either in-house or increasingly through joint projects,and there will be much to reveal by the time of ITMA Asia in Singapore,”says BTMA CEO Jason Kent.“Some of the most recent developments are really going beyond what has previously been possible.”展开更多
Fraudulent website is an important car-rier tool for telecom fraud.At present,criminals can use artificial intelligence generative content technol-ogy to quickly generate fraudulent website templates and build fraudul...Fraudulent website is an important car-rier tool for telecom fraud.At present,criminals can use artificial intelligence generative content technol-ogy to quickly generate fraudulent website templates and build fraudulent websites in batches.Accurate identification of fraudulent website will effectively re-duce the risk of public victimization.Therefore,this study developed a fraudulent website template iden-tification method based on DOM structure extraction of website fingerprint features,which solves the prob-lems of single-dimension identification,low accuracy,and the insufficient generalization ability of current fraudulent website templates.This method uses an im-proved SimHash algorithm to traverse the DOM tree of a webpage,extract website node features,calcu-late the weight of each node,and obtain the finger-print feature vector of the website through dimension-ality reduction.Finally,the random forest algorithm is used to optimize the training features for the best combination of parameters.This method automati-cally extracts fingerprint features from websites and identifies website template ownership based on these features.An experimental analysis showed that this method achieves a classification accuracy of 89.8%and demonstrates superior recognition.展开更多
Objective:To evaluate the quality of Lygodium japonicum(Thunb.)Sw.(L.japonicum,Hai Jin Sha)by comparing its components without stewed(W)and stewed(S)using ultra-high-performance liquid chromatography(UHPLC)and chemome...Objective:To evaluate the quality of Lygodium japonicum(Thunb.)Sw.(L.japonicum,Hai Jin Sha)by comparing its components without stewed(W)and stewed(S)using ultra-high-performance liquid chromatography(UHPLC)and chemometric analysis.Additionally,network pharmacology was employed to investigate the possible mechanisms of action of L.japonicum in the urinary calculi(UC)treatment.Methods:A fingerprinting method was established to identify components through UHPLC-tandem mass spectrometry.Chemometric techniques were used to compare the L.japonicum extraction methods.Furthermore,various network pharmacological approaches were used to identify and analyze the potential targets of the identified components in relation to UC.Results:The W and S extracts were distributed into two distinct clusters.Significant differences in the levels of protocatechuic aldehyde,caffeic acid,and p-coumaric acid were observed between S and W.Network pharmacology analysis revealed that the primary targets of L.japonicum in the UC treatment were serum albumin and epidermal growth factor receptors,with potential active components including protocatechuic acid and caffeic acid.Conclusion:This study comprehensively examined the therapeutic components of L.japonicum before and after boiling,shedding light on its potential mechanisms of action in UC treatment.These findings offer valuable insights into the development and utilization of L.japonicum resources.展开更多
In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative pro...In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative processing methods for recognition are often required.The use of Eu^(3+)-doped phosphors,which emit red-orange light,presents an effective approach for enhancing the visibility of LFP.Eu^(3+)-doped Ba_(2)LuSbO_(6)(BLSO)phosphors were synthesized using a high-temperature solid-state method,aiming to improve the recognition of LFP for enhanced fingerprint identification.The phase structure of the fluorescent powder samples was analyzed through X-ray diffraction(XRD)combined with Rietveld refinement,as well as scanning electron microscopy(SEM)and X-ray photoelectron spectroscopy(XPS).Photoluminescence spectra of Ba_(2)LuSbO_(6):xEu^(3+)phosphor samples exhibit orange-red emission peaks at595 and 618 nm when excited by 250 nm deep ultraviolet light.Concentration quenching is observed at a doping concentration of 20 mol%.The Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample demonstrates exceptional thermal stability,with value of 92.50% at 423 K.The quantum efficiency of the Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample was evaluated using an integrating sphere,yielding an IQE of 79.34%.In order to visualize latent fingerprints(LFPs),hydrophilic BLSO:0.2Eu^(3+)phosphors were transformed into hydrophobic BLSO:0.2Eu^(3+)@OA phosphors through a coating of oleic acid(OA).The BLSO:0.2Eu^(3+)@OA phosphor proves capable of generating dependable LFP fluorescence images with superior contrast and resolution.These findings underscore the significant potential for application of BLSO:0.2Eu^(3+)products LFP visualization.展开更多
Various novel conjugated polymers(CPs)have been developed for organic photodetectors(OPDs),but their application to practical image sensors such as X-ray,R/G/B,and fingerprint sensors is rare.In this article,we report...Various novel conjugated polymers(CPs)have been developed for organic photodetectors(OPDs),but their application to practical image sensors such as X-ray,R/G/B,and fingerprint sensors is rare.In this article,we report the entire process from the synthesis and molecular engineering of novel CPs to the development of OPDs and fingerprint image sensors.We synthesized six benzo[1,2-d:4,5-d’]bis(oxazole)(BBO)-based CPs by modifying the alkyl side chains of the CPs.Several relationships between the molecular structure and the OPD performance were revealed,and increasing the number of linear octyl side chains on the conjugated backbone was the best way to improve Jph and reduce Jd in the OPDs.The optimized CP demonstrated promising OPD performance with a responsivity(R)of 0.22 A/W,specific detectivity(D^(*))of 1.05×10^(13)Jones at a bias of-1 V,rising/falling response time of 2.9/6.9μs,and cut-off frequency(f_(-3dB))of 134 kHz under collimated 530 nm LED irradiation.Finally,a fingerprint image sensor was fabricated by stacking the POTB1-based OPD layer on the organic thin-film transistors(318 ppi).The image contrast caused by the valleys and ridges in the fingerprints was obtained as a digital signal.展开更多
The identification of germplasm is an important step for effective utilization of the available germplasm. In previous studies, isoenzyme, RAPD and SSR techniques had been used to conduct the genetic identification of...The identification of germplasm is an important step for effective utilization of the available germplasm. In previous studies, isoenzyme, RAPD and SSR techniques had been used to conduct the genetic identification of watermelon ( Citrullus lanatus (Thunb.) Mansf.), but their effectiveness was limited due to the extremely narrow genetic background among watermelon genotypes. In this research, amplified fragment length polymorphism (AFLP), which was reported as a reliable technique with high efficiency in detecting polymorphism, was used to conduct genetic analysis and variety identification of thirty genotypes of watermelon core collection that represent a wide range of breeding and commercially available germplasm. As a result, a DNA fingerprint based on 15 bands amplified with four primer combinations was developed. In this fingerprint, each genotype has its unique fingerprint pattern and can be distinguished from each other. Furthermore, in or der to facilitate the utilization of AFLP marker in practice, one specific AFLP band of genotype 'PI296341' coming from fragment amplified by primer combination E-AT/M-CAT was successfully converted into a sequence characterized amplified region (SCAR) marker.展开更多
A simple and accurate high-performance liquid chromatography（HPLC）method to generate the fingerprints of crude Pu-erh tea（CPT）and ripened Pu-erh tea（RPT）is described.A suitable chromatographic system was establi...A simple and accurate high-performance liquid chromatography（HPLC）method to generate the fingerprints of crude Pu-erh tea（CPT）and ripened Pu-erh tea（RPT）is described.A suitable chromatographic system was established using a linear gradient elution with acetonitrile and water containing 0.6% formic acid as the mobile phase and a detection wavelength of 300 nm.HPLC analysis identified 24 common peaks among RPT and 21 common peaks among CPT.This study revealed that crude Pu-erh tea and ripened Pu-erh tea contained some similar major constituents,but with distinctive peaks,which may be caused by the difference in the fermentation process.This HPLC fingerprint method could be used to evaluate and authenticate crude Pu-erh tea and ripened Pu-erh tea.展开更多
A new, simple and reliable method using HPLC-UV-ELSD was developed to generate the fingerprint of Ophiopogonis Radix. Homoisoflavonoids and steroidal saponins were determined simultaneously in a single run. A total of...A new, simple and reliable method using HPLC-UV-ELSD was developed to generate the fingerprint of Ophiopogonis Radix. Homoisoflavonoids and steroidal saponins were determined simultaneously in a single run. A total of 27 Ophiopogonis Radix samples were analyzed, and 18 reference substances were used for the identification of the common peaks. The fingerprint was further analyzed by chemometrics methods including similarity analysis （SA）, hierarchical clustering analysis （HCA） and principal component analysis （PCA）. The results indicated that the combination of chromatographic fingerprint and chemometrics analysis could be used for the geographical differentiation and quality evaluation of Ophiopognnis Radix.展开更多
[Objective] The experiment aimed to study chromatographic fingerprint in volatile components of acacia honey and provide scientific evaluation and effective control on quality of acacia honey.[Method] Using solid-phas...[Objective] The experiment aimed to study chromatographic fingerprint in volatile components of acacia honey and provide scientific evaluation and effective control on quality of acacia honey.[Method] Using solid-phase microextraction method to separate and detect volatile components and construct chromatographic fingerprint.[Result] The honey was preheated for 15 min in water bath at 40 ℃ and solid-phase microextraction 85 μmPA was used to extract in overhead air about 30 min,then put it into the injector and desorpted 3 min,which is in 230 ℃.The Supelco WaxTM10 30 m×0.25 mm×0.25 μm column and gradient heating program was the best method to separate volatile components from honey.83 fingerprint peaks were constructed,among which 17 common fingerprint peaks were comprised of chromatographic fingerprint of volatile components of acacia honey.[Conclusion] The chromatographic fingerprint could provide reference for quality control of acacia honey.展开更多
Sedum aizoon L.（SA） has been widely used for treatment of various hemorrhages, insomnia, pain and trauma？ however, its anti-inflammatory activity is unknown. In this study, we firstly investigated the anti--inflamm...Sedum aizoon L.（SA） has been widely used for treatment of various hemorrhages, insomnia, pain and trauma？ however, its anti-inflammatory activity is unknown. In this study, we firstly investigated the anti--inflammatory activity of SA extracts by petroleum ether（PE）, ethyl acetate（EtOAc）, and H2O in LPS-stimulated RAW 264.7 cells. Results showed that the EtOAc extract rich in phenolics and flavonoids significantly inhibited LPS-induced NO, TNF--α, and IL-6 production in RAW 264.7 cells（P0.01）, suggesting that this extract possessed potent anti--inflammatory activity. The phytochemical profile of the effective extract was subsequently analyzed by HPLC fingerprint with 11 standards. The results indicated that gallic acid, protocatechuic acid, p--hydroxybenzoic acid, ethyl gallate, iriflophene, 5,7-dihydroxy chromone, quercitrin, quercetin, luteolin, kaempferol, and isorhamnetin were present in this extract, which might contribute to its anti-inflammatory activity. Thus, the Et -OAc extract of SA showed anti-inflammatory activity and could be used as a potential natural anti--inflammatory agent.展开更多
基金supported by Macao Science and Technology Development Fund,Macao SAR,China(Grant No.:0043/2023/AFJ)the National Natural Science Foundation of China(Grant No.:22173038)Macao Polytechnic University,Macao SAR,China(Grant No.:RP/FCA-01/2022).
文摘Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
文摘针对现有低功耗蓝牙(BLE)欺骗攻击检测技术准确率低的问题,提出了一种基于异常指纹的BLE欺骗攻击检测技术,将攻击者的射频指纹作为异常数据,把欺骗攻击检测建模为异常检测问题;设计了一种基于深度支持向量描述(Deep Support Vector Data Description,DeepSVDD)的异常指纹检测模型——RFFAD_DeepSVDD,并使用残差单元构建网络模型,有效缓解了机器学习异常检测算法非线性特征提取不足的问题。采用预训练自编码器获取最优初始化参数,极大增强了模型边界决策能力。在异常检测实验中,该模型准确率达到95.47%,相比基于机器学习的异常检测模型平均提升8.92%;在欺骗攻击检测实验中,该方法相比现有欺骗攻击检测技术在攻击节点运动与静止状态下均表现出更好的性能,能够准确检测并识别出中间人攻击、冒充攻击、重连接欺骗攻击3种欺骗攻击。
基金supported in part by the National Key Research and Development Program under Grant(2021YFB2900300)by the National Natural Science Foundation of China(NSFC)under Grants 61971127,61871122by the Southeast University-China Mobile Research Institute Joint Innovation Center,and by the Major Key Project of PCL(PCL2021A01-2).
文摘This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
基金supported by the National Defense Basic Scientific Research Program of China(No.JCKY2023602C026)the funding of Key Laboratory of Mobile Application Innovation and Governance Technology,Ministry of Industry and Information Technology(2023IFS080601-K).
文摘Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.
文摘The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier,providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinting of phytotoxins as a cutting-edge approach.This study explores the dynamic intersection of phytochemistry and forensic science,highlighting how the unique phytochemical profiles of toxic plants and their secondary metabolites,serve as distinctive markers for forensic investigations.By utilizing advanced techniques such as Ultra-High-Performance Liquid Chromatography(UHPLC)and High-Resolution Mass Spectrometry(HRMS),the detection and quantification of plant-derived are made more accurate in forensic contexts.Real-world case studies are presented to demonstrate the critical role of plant toxins in forensic outcomes and legal proceedings.The challenges,potential,and future prospects of integrating phytochemical fingerprinting of plant toxins into forensic science were discussed.This review aims to illuminate phytochemical fingerprinting of plant toxins as a promising tool to enhance the precision and depth of forensic analyses,offering new insights into the complex stories embedded in plant toxins.
文摘With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.
基金supported by the National Natural Science Foundation of China(Nos.62002100,61902237)Key Research and Promotion Projects of Henan Province(Nos.232102240023,232102210063,222102210040).
文摘Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.
文摘A leading position in the areas of testing,instrumentation and machine control has been established by members of the British Textile Machinery Association(BTMA)and a number of new developments in these fields will be showcased at this year’s ITMA Asia+CITME exhibition,which takes place in Singapore from October 28-31.“Many of our members are currently developing new technologies,either in-house or increasingly through joint projects,and there will be much to reveal by the time of ITMA Asia in Singapore,”says BTMA CEO Jason Kent.“Some of the most recent developments are really going beyond what has previously been possible.”
基金This research is a phased achievement of The National Social Science Fund of China(23BGL272).
文摘Fraudulent website is an important car-rier tool for telecom fraud.At present,criminals can use artificial intelligence generative content technol-ogy to quickly generate fraudulent website templates and build fraudulent websites in batches.Accurate identification of fraudulent website will effectively re-duce the risk of public victimization.Therefore,this study developed a fraudulent website template iden-tification method based on DOM structure extraction of website fingerprint features,which solves the prob-lems of single-dimension identification,low accuracy,and the insufficient generalization ability of current fraudulent website templates.This method uses an im-proved SimHash algorithm to traverse the DOM tree of a webpage,extract website node features,calcu-late the weight of each node,and obtain the finger-print feature vector of the website through dimension-ality reduction.Finally,the random forest algorithm is used to optimize the training features for the best combination of parameters.This method automati-cally extracts fingerprint features from websites and identifies website template ownership based on these features.An experimental analysis showed that this method achieves a classification accuracy of 89.8%and demonstrates superior recognition.
基金supported by Ministry of Industry and Information Technology of the People's Republic of China 2022 Industrial Technology Basic Public Service Platform Project-Traditional Chinese Medicine Whole Industry Chain Quality and Technology Service Platform(2022-230-221)Foshan Nanhai District Key Area Science and Technology Research Project[Nanke(2023)20-18].
文摘Objective:To evaluate the quality of Lygodium japonicum(Thunb.)Sw.(L.japonicum,Hai Jin Sha)by comparing its components without stewed(W)and stewed(S)using ultra-high-performance liquid chromatography(UHPLC)and chemometric analysis.Additionally,network pharmacology was employed to investigate the possible mechanisms of action of L.japonicum in the urinary calculi(UC)treatment.Methods:A fingerprinting method was established to identify components through UHPLC-tandem mass spectrometry.Chemometric techniques were used to compare the L.japonicum extraction methods.Furthermore,various network pharmacological approaches were used to identify and analyze the potential targets of the identified components in relation to UC.Results:The W and S extracts were distributed into two distinct clusters.Significant differences in the levels of protocatechuic aldehyde,caffeic acid,and p-coumaric acid were observed between S and W.Network pharmacology analysis revealed that the primary targets of L.japonicum in the UC treatment were serum albumin and epidermal growth factor receptors,with potential active components including protocatechuic acid and caffeic acid.Conclusion:This study comprehensively examined the therapeutic components of L.japonicum before and after boiling,shedding light on its potential mechanisms of action in UC treatment.These findings offer valuable insights into the development and utilization of L.japonicum resources.
基金Project supported by the Scientific Research Project of Jilin Provincial Department of Education(JJKH20230821KJ,JJKH20230823KJ,JJKH20230822KJ)。
文摘In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative processing methods for recognition are often required.The use of Eu^(3+)-doped phosphors,which emit red-orange light,presents an effective approach for enhancing the visibility of LFP.Eu^(3+)-doped Ba_(2)LuSbO_(6)(BLSO)phosphors were synthesized using a high-temperature solid-state method,aiming to improve the recognition of LFP for enhanced fingerprint identification.The phase structure of the fluorescent powder samples was analyzed through X-ray diffraction(XRD)combined with Rietveld refinement,as well as scanning electron microscopy(SEM)and X-ray photoelectron spectroscopy(XPS).Photoluminescence spectra of Ba_(2)LuSbO_(6):xEu^(3+)phosphor samples exhibit orange-red emission peaks at595 and 618 nm when excited by 250 nm deep ultraviolet light.Concentration quenching is observed at a doping concentration of 20 mol%.The Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample demonstrates exceptional thermal stability,with value of 92.50% at 423 K.The quantum efficiency of the Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample was evaluated using an integrating sphere,yielding an IQE of 79.34%.In order to visualize latent fingerprints(LFPs),hydrophilic BLSO:0.2Eu^(3+)phosphors were transformed into hydrophobic BLSO:0.2Eu^(3+)@OA phosphors through a coating of oleic acid(OA).The BLSO:0.2Eu^(3+)@OA phosphor proves capable of generating dependable LFP fluorescence images with superior contrast and resolution.These findings underscore the significant potential for application of BLSO:0.2Eu^(3+)products LFP visualization.
基金funded by the National Research Foundation(NRF)of Korea(2020M3H4A3081816,RS-2023-00304936,and RS-2024-00398065).
文摘Various novel conjugated polymers(CPs)have been developed for organic photodetectors(OPDs),but their application to practical image sensors such as X-ray,R/G/B,and fingerprint sensors is rare.In this article,we report the entire process from the synthesis and molecular engineering of novel CPs to the development of OPDs and fingerprint image sensors.We synthesized six benzo[1,2-d:4,5-d’]bis(oxazole)(BBO)-based CPs by modifying the alkyl side chains of the CPs.Several relationships between the molecular structure and the OPD performance were revealed,and increasing the number of linear octyl side chains on the conjugated backbone was the best way to improve Jph and reduce Jd in the OPDs.The optimized CP demonstrated promising OPD performance with a responsivity(R)of 0.22 A/W,specific detectivity(D^(*))of 1.05×10^(13)Jones at a bias of-1 V,rising/falling response time of 2.9/6.9μs,and cut-off frequency(f_(-3dB))of 134 kHz under collimated 530 nm LED irradiation.Finally,a fingerprint image sensor was fabricated by stacking the POTB1-based OPD layer on the organic thin-film transistors(318 ppi).The image contrast caused by the valleys and ridges in the fingerprints was obtained as a digital signal.
文摘The identification of germplasm is an important step for effective utilization of the available germplasm. In previous studies, isoenzyme, RAPD and SSR techniques had been used to conduct the genetic identification of watermelon ( Citrullus lanatus (Thunb.) Mansf.), but their effectiveness was limited due to the extremely narrow genetic background among watermelon genotypes. In this research, amplified fragment length polymorphism (AFLP), which was reported as a reliable technique with high efficiency in detecting polymorphism, was used to conduct genetic analysis and variety identification of thirty genotypes of watermelon core collection that represent a wide range of breeding and commercially available germplasm. As a result, a DNA fingerprint based on 15 bands amplified with four primer combinations was developed. In this fingerprint, each genotype has its unique fingerprint pattern and can be distinguished from each other. Furthermore, in or der to facilitate the utilization of AFLP marker in practice, one specific AFLP band of genotype 'PI296341' coming from fragment amplified by primer combination E-AT/M-CAT was successfully converted into a sequence characterized amplified region (SCAR) marker.
基金"Study on the Active Components and Healthcare Function of Pu-erh Tea"(2007BAD58B04-2)"National Tea Industrial Science and Technology System"from Modern Agriculture Industrial Technology System,Yunnan Agricultural University
文摘A simple and accurate high-performance liquid chromatography（HPLC）method to generate the fingerprints of crude Pu-erh tea（CPT）and ripened Pu-erh tea（RPT）is described.A suitable chromatographic system was established using a linear gradient elution with acetonitrile and water containing 0.6% formic acid as the mobile phase and a detection wavelength of 300 nm.HPLC analysis identified 24 common peaks among RPT and 21 common peaks among CPT.This study revealed that crude Pu-erh tea and ripened Pu-erh tea contained some similar major constituents,but with distinctive peaks,which may be caused by the difference in the fermentation process.This HPLC fingerprint method could be used to evaluate and authenticate crude Pu-erh tea and ripened Pu-erh tea.
基金National Key Technology R&D Program"New Drug Innovation" of China (Grant No. 2012ZX09304-005,2012ZX09301002-002-002)
文摘A new, simple and reliable method using HPLC-UV-ELSD was developed to generate the fingerprint of Ophiopogonis Radix. Homoisoflavonoids and steroidal saponins were determined simultaneously in a single run. A total of 27 Ophiopogonis Radix samples were analyzed, and 18 reference substances were used for the identification of the common peaks. The fingerprint was further analyzed by chemometrics methods including similarity analysis （SA）, hierarchical clustering analysis （HCA） and principal component analysis （PCA）. The results indicated that the combination of chromatographic fingerprint and chemometrics analysis could be used for the geographical differentiation and quality evaluation of Ophiopognnis Radix.
基金Support by Department of Education Science and Technology Research Projects of Hebei Province(2008310)the National Special Fund for the Commonweal Industry(200810345)~~
文摘[Objective] The experiment aimed to study chromatographic fingerprint in volatile components of acacia honey and provide scientific evaluation and effective control on quality of acacia honey.[Method] Using solid-phase microextraction method to separate and detect volatile components and construct chromatographic fingerprint.[Result] The honey was preheated for 15 min in water bath at 40 ℃ and solid-phase microextraction 85 μmPA was used to extract in overhead air about 30 min,then put it into the injector and desorpted 3 min,which is in 230 ℃.The Supelco WaxTM10 30 m×0.25 mm×0.25 μm column and gradient heating program was the best method to separate volatile components from honey.83 fingerprint peaks were constructed,among which 17 common fingerprint peaks were comprised of chromatographic fingerprint of volatile components of acacia honey.[Conclusion] The chromatographic fingerprint could provide reference for quality control of acacia honey.
基金Quality guarantee system of Chinese herbal medicines(Grant No.201507002)Key project of science and technology of Fujian province(Grant No.2014Y0053)youth project of Fujian Provincial Health and Family Planning Commission(Grant No.2015--1--77)
文摘Sedum aizoon L.（SA） has been widely used for treatment of various hemorrhages, insomnia, pain and trauma？ however, its anti-inflammatory activity is unknown. In this study, we firstly investigated the anti--inflammatory activity of SA extracts by petroleum ether（PE）, ethyl acetate（EtOAc）, and H2O in LPS-stimulated RAW 264.7 cells. Results showed that the EtOAc extract rich in phenolics and flavonoids significantly inhibited LPS-induced NO, TNF--α, and IL-6 production in RAW 264.7 cells（P0.01）, suggesting that this extract possessed potent anti--inflammatory activity. The phytochemical profile of the effective extract was subsequently analyzed by HPLC fingerprint with 11 standards. The results indicated that gallic acid, protocatechuic acid, p--hydroxybenzoic acid, ethyl gallate, iriflophene, 5,7-dihydroxy chromone, quercitrin, quercetin, luteolin, kaempferol, and isorhamnetin were present in this extract, which might contribute to its anti-inflammatory activity. Thus, the Et -OAc extract of SA showed anti-inflammatory activity and could be used as a potential natural anti--inflammatory agent.
