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
DNA testing for biological relationships is becoming increasingly common in Burkina Faso. STR analysis remains the most reliable technique for resolving disputes or claims in court regarding biological relationships. ...DNA testing for biological relationships is becoming increasingly common in Burkina Faso. STR analysis remains the most reliable technique for resolving disputes or claims in court regarding biological relationships. This study aimed to establish the links of biological relationships between subjects by analyzing 21 STR loci. The participants were 14 individuals referred to CERBA by the Justice in 2022. Oral or blood samples were taken for each subject. DNA was extracted, and the analysis of DNA polymorphism by PowerPlex® 21 Kit (Part No. DC8902) was performed by capillary electrophoresis on the ABI PRISM 3130 sequencer. DNA profiles were compiled using the GeneMapper IDX software version 1.2. Of the fourteen subjects examined, twelve of these samples had complete genetic profiles, while the other two had partial and absent profiles. The results confirmed the inclusion of three cases of Paternity, one case of maternity, one case of a relationship of brotherhood, and the exclusion of one case of maternity and one case of a relationship of brotherhood. DNA tests improve the resolution of filiations, but they require ethical and cultural awareness and a strengthened legal framework to prevent and protect society.展开更多
Fluorescence-based imaging applications have been benefiting greatly from donor-acceptor(D-A)/donor-π-acceptor(D-π-A)fluorescent probes owing to their intramolecular charge transfer(ICT)nature and self-assembly beha...Fluorescence-based imaging applications have been benefiting greatly from donor-acceptor(D-A)/donor-π-acceptor(D-π-A)fluorescent probes owing to their intramolecular charge transfer(ICT)nature and self-assembly behavior.In this study,we design and synthesize a hydrophilic D-A fluorescent probe,namely CHBA,which would self-assemble into interlaced textures down to nanoscale but disassemble by trace amount of water in fingertip area.Upon finger-pressing,it enables fingerprint imaging and covers level-1/2/3 fingerprint information,wherein the sweat pores can be mapped in both bright field model and fluorescence mode,capable of naked-eye-based similarity analysis for personal identity verification(PIV).Spectroscopic analysis and morphology study show that the working mechanism can be attributed to the selective water-erosion effect on the solid-liquid interphase under physical contact.The sweat pore information can be digitized by polar coordinate conversion,further allowing machine-learning-based analysis for PIV application.The final PIV accuracy reaches 100%for all the involved machine-learning models,with no erroneous judgements.A prototype of PIV system is constructed by integrating CHBA with artificial intelligence hardware,wherein the sweat pore imaging,data processing and the decisionmaking could be run in parallel,suggesting high feasibility in real-world application.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
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
Terpenoids,as crucial plant secondary metabolites in honey,possess various bioactivities.This study aimed at investigating terpenoids of longan,litchi,and schefflera honey.Our results reveal that these three species o...Terpenoids,as crucial plant secondary metabolites in honey,possess various bioactivities.This study aimed at investigating terpenoids of longan,litchi,and schefflera honey.Our results reveal that these three species of honey are characterized by abundant terpenoids,with eight terpenoids and one phenylpropanoid identified.Callunene,3,4,5-trimethoxyphenylpropanal,schefflerin(4-2'-(3',4'-dihydroxy-5'-methylfuran)-3,5,5-trimethyl-2-cyclohexenone)or abscisic acids may be as the characteristic components of longan,litchi,or schefflera honey.Abscisic acids were the most abundant components,average contents reached up to 53.67,96.89 and 21.11 mg/kg,respectively,and the content ranges in three types of honey were 13.75-85.40,19.93-177.31,14.69-28.38 mg/kg,respectively.To our knowledge,the presence of schefflerin,(6R,9S)-vomifoliol,and 3,4,5-trimethoxyphenylpropanal in honey is a novel finding.We distinguish Thailand from Chinese mainland and Taiwan longan honeys,based on the ratio of abscisic acid and callunene content.Overall,our proposal to combine terpenoids with standard fingerprints seems to provide a novel and reliable solution for evaluating the authenticity of commercial honey.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
Generally,gaining fundamental insights into chain processes during the combustion of flame-retardant polymers relies on the qualitative and quantitative characterization of key chain carriers.However,polymer combustio...Generally,gaining fundamental insights into chain processes during the combustion of flame-retardant polymers relies on the qualitative and quantitative characterization of key chain carriers.However,polymer combustion processes based on conventional solid-fuel combustion strategies,due to the high coupling of pyrolysis,combustion,soot formation and oxidation,exhibit relatively high complexity and poor flame stability,and lead to a huge obstacle to the use of optical diagnostics.Herein,a spatial-confinement combustion strategy,which can produce a special staged flame with multi-jets secondary wave,is devised to provide a highly decoupled combustion environment.Glowing soot particles are therefore decoupled from main chemiluminescence region and confined to the flame tip to provide a well-controlled,opticalthin test environment for combustion diagnostic.Based on this strategy,a multi-nozzle-separation(MNS)burner is designed and fabricated,and the combustion processes associated with four model compounds,PVC,PS,PP/TBBA blends and PP/RP blends are investigated by spontaneous spectral diagnosis,and the chemiluminescence fingerprint of key diatomic/triatomic intermediates(such as OH,CH,C_(2),ClO,Br_(2),and PHO)are clearly observed.This encouraging result means that the strategy of spatial-confinement combustion we proposed shows promising prospect in many subjects associated with combustion chain regulation,such as efficient design of flame retardants.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
A device is defined as a memristor if it exhibits a pinched hysteresis loop in the current–voltage plane,and the loop area shrinks with increasing driven frequency until it gets a single-valued curve.However,the expl...A device is defined as a memristor if it exhibits a pinched hysteresis loop in the current–voltage plane,and the loop area shrinks with increasing driven frequency until it gets a single-valued curve.However,the explaination of the underlying mechanism for these fingerprints is still limited.In this paper,we propose the differential form of the memristor function,and we disclose the dynamical mechanism of the memristor according to the differential form.The symmetry of the curve is only determined by the driven signal,and the shrinking loop area results from the shrinking area enclosed by driven signal and the time coordinate axis.Significantly,we find the condition for the phase transition of a memristor,and the resistance switches between the positive resistance,local zero resistance,and local negative resistance.This phase transition is confirmed in the HP memristor.These results advance the understanding of the dynamics mechanism and phase transition of a memristor.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
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
Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoo...Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.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
Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classificati...Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.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
Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint featur...Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and fur-ther reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power ampli-fiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and ampli-tude of the signal kurtosis (FA-SK) is further proposed. Simula-tion and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.展开更多
The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral...The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.展开更多
The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influenc...The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings.To understand the reason for this discrepancy,this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific(EPA),western Pacific(WPA),Atlantic(ATL),Africa(AFR),the Indian Ocean(IDO),and residual area(RA),based on six reanalysis datasets and 13 CMIP6 models.In reanalysis data,the trends in regional HCI over EPA and ATL(WPA and AFR)contribute to(partially offset)the increasing trend in GZM HCI,while the trends in regional HCI over IDO are different in different reanalysis data.The CMIP6 models skillfully reproduce the trends in regional HCI over EPA,ATL,WPA,and AFR,but simulate notable decreasing trends in regional HCI over IDO,which is a key reason for the opposite trends in GZM HCI between reanalysis data and models.The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models.Optimal fingerprint analysis indicates that anthropogenic(ANT)and non-greenhouse gas(NOGHG)forcings are the dominant drivers of the HCI trends in the EPA and ATL regions.In the WPA(AFR)region,NOGHG(ANT)forcing serves as the primary driver.The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.展开更多
This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic(PV)array D...This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic(PV)array DC arc fault location methods based on electromagnetic radiation(EMR)signals.Initially,a comprehensive analysis of the time–frequency characteristics of series arc EMR signals is carried out to pinpoint effective data sources that reflect fault features.Subsequently,a multi-kernel domain-adversarial neural network(MKDANN)is introduced to extract domain-invariant features,and a feature extractor designed specifically for fingerprint matching is devised.To reduce inter-domain distribution differences,a multi-kernel maximum mean discrepancy(MK-MMD)is integrated into the adaptation layer.Moreover,to deal with dynamic environmental changes in real-world situations,the support-class passive aggressive(SPA)algorithm is utilized to adjust model parameters in real time.Finally,MKDANN and SPA technologies are smoothly combined to build a fully operational fault location model.Experimental results indicate that the proposed method attains an overall fault location accuracy of at least 95%,showing strong adaptability to environmental changes and robust interference resistance while maintaining excellent online learning capabilities during model migration.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DNA testing for biological relationships is becoming increasingly common in Burkina Faso. STR analysis remains the most reliable technique for resolving disputes or claims in court regarding biological relationships. This study aimed to establish the links of biological relationships between subjects by analyzing 21 STR loci. The participants were 14 individuals referred to CERBA by the Justice in 2022. Oral or blood samples were taken for each subject. DNA was extracted, and the analysis of DNA polymorphism by PowerPlex® 21 Kit (Part No. DC8902) was performed by capillary electrophoresis on the ABI PRISM 3130 sequencer. DNA profiles were compiled using the GeneMapper IDX software version 1.2. Of the fourteen subjects examined, twelve of these samples had complete genetic profiles, while the other two had partial and absent profiles. The results confirmed the inclusion of three cases of Paternity, one case of maternity, one case of a relationship of brotherhood, and the exclusion of one case of maternity and one case of a relationship of brotherhood. DNA tests improve the resolution of filiations, but they require ethical and cultural awareness and a strengthened legal framework to prevent and protect society.
基金the financial support from National Natural Science Foundation of China(No.51703135)the technical support from Beijing Key Laboratory of Optical Materials and Photonic Devices。
文摘Fluorescence-based imaging applications have been benefiting greatly from donor-acceptor(D-A)/donor-π-acceptor(D-π-A)fluorescent probes owing to their intramolecular charge transfer(ICT)nature and self-assembly behavior.In this study,we design and synthesize a hydrophilic D-A fluorescent probe,namely CHBA,which would self-assemble into interlaced textures down to nanoscale but disassemble by trace amount of water in fingertip area.Upon finger-pressing,it enables fingerprint imaging and covers level-1/2/3 fingerprint information,wherein the sweat pores can be mapped in both bright field model and fluorescence mode,capable of naked-eye-based similarity analysis for personal identity verification(PIV).Spectroscopic analysis and morphology study show that the working mechanism can be attributed to the selective water-erosion effect on the solid-liquid interphase under physical contact.The sweat pore information can be digitized by polar coordinate conversion,further allowing machine-learning-based analysis for PIV application.The final PIV accuracy reaches 100%for all the involved machine-learning models,with no erroneous judgements.A prototype of PIV system is constructed by integrating CHBA with artificial intelligence hardware,wherein the sweat pore imaging,data processing and the decisionmaking could be run in parallel,suggesting high feasibility in real-world application.
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.
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supported by the Modern Agro-industry Technology Research System(CARS-45-KXJ19)the Agricultural Science and Technology Innovation Program from the Ministry of Agriculture of the People’s Republic of China(CAAS-ASTIP-2019-IAR)。
文摘Terpenoids,as crucial plant secondary metabolites in honey,possess various bioactivities.This study aimed at investigating terpenoids of longan,litchi,and schefflera honey.Our results reveal that these three species of honey are characterized by abundant terpenoids,with eight terpenoids and one phenylpropanoid identified.Callunene,3,4,5-trimethoxyphenylpropanal,schefflerin(4-2'-(3',4'-dihydroxy-5'-methylfuran)-3,5,5-trimethyl-2-cyclohexenone)or abscisic acids may be as the characteristic components of longan,litchi,or schefflera honey.Abscisic acids were the most abundant components,average contents reached up to 53.67,96.89 and 21.11 mg/kg,respectively,and the content ranges in three types of honey were 13.75-85.40,19.93-177.31,14.69-28.38 mg/kg,respectively.To our knowledge,the presence of schefflerin,(6R,9S)-vomifoliol,and 3,4,5-trimethoxyphenylpropanal in honey is a novel finding.We distinguish Thailand from Chinese mainland and Taiwan longan honeys,based on the ratio of abscisic acid and callunene content.Overall,our proposal to combine terpenoids with standard fingerprints seems to provide a novel and reliable solution for evaluating the authenticity of commercial honey.
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.
基金supported by the National Natural Science Foundation of China(No.51827803)the Fundamental Research Funds for the Central Universities,111 Center(No.B20001)Institutional Research Fund from Sichuan University(No.2021SCUNL201).
文摘Generally,gaining fundamental insights into chain processes during the combustion of flame-retardant polymers relies on the qualitative and quantitative characterization of key chain carriers.However,polymer combustion processes based on conventional solid-fuel combustion strategies,due to the high coupling of pyrolysis,combustion,soot formation and oxidation,exhibit relatively high complexity and poor flame stability,and lead to a huge obstacle to the use of optical diagnostics.Herein,a spatial-confinement combustion strategy,which can produce a special staged flame with multi-jets secondary wave,is devised to provide a highly decoupled combustion environment.Glowing soot particles are therefore decoupled from main chemiluminescence region and confined to the flame tip to provide a well-controlled,opticalthin test environment for combustion diagnostic.Based on this strategy,a multi-nozzle-separation(MNS)burner is designed and fabricated,and the combustion processes associated with four model compounds,PVC,PS,PP/TBBA blends and PP/RP blends are investigated by spontaneous spectral diagnosis,and the chemiluminescence fingerprint of key diatomic/triatomic intermediates(such as OH,CH,C_(2),ClO,Br_(2),and PHO)are clearly observed.This encouraging result means that the strategy of spatial-confinement combustion we proposed shows promising prospect in many subjects associated with combustion chain regulation,such as efficient design of flame retardants.
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 under Grant Nos.62071496,62061008the Research and Innovation Project of Graduate of Central South University under Grant No.2023ZZTS0168.
文摘A device is defined as a memristor if it exhibits a pinched hysteresis loop in the current–voltage plane,and the loop area shrinks with increasing driven frequency until it gets a single-valued curve.However,the explaination of the underlying mechanism for these fingerprints is still limited.In this paper,we propose the differential form of the memristor function,and we disclose the dynamical mechanism of the memristor according to the differential form.The symmetry of the curve is only determined by the driven signal,and the shrinking loop area results from the shrinking area enclosed by driven signal and the time coordinate axis.Significantly,we find the condition for the phase transition of a memristor,and the resistance switches between the positive resistance,local zero resistance,and local negative resistance.This phase transition is confirmed in the HP memristor.These results advance the understanding of the dynamics mechanism and phase transition of a memristor.
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.
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 the National Key Research and Development Program of China(No.2022YFB4300902)。
文摘Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.
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.
文摘Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.
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.
基金supported by the Youth Science and Technology Innovation Award of National University of Defense Technology (18/19-QNCXJ)the National Science Foundation of China (62271494)
文摘Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and fur-ther reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power ampli-fiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and ampli-tude of the signal kurtosis (FA-SK) is further proposed. Simula-tion and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.
基金financially supported by Quanzhou high-level Talents Project Fund(No.2022C033R)the National Natural Science Foundation of China(Nos.21925401,52221001)+2 种基金the Fundamental Research Funds for the Central Universities(No.2042022rc0004)the Postdoctoral Innovative Research of Hubei Province of China(No.211000025)the interdisciplinary innovative talents foundation from Renmin Hospital of Wuhan University。
文摘The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.
基金the National Key Research and Development Program of China[grant number 2022YFF0801704].
文摘The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings.To understand the reason for this discrepancy,this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific(EPA),western Pacific(WPA),Atlantic(ATL),Africa(AFR),the Indian Ocean(IDO),and residual area(RA),based on six reanalysis datasets and 13 CMIP6 models.In reanalysis data,the trends in regional HCI over EPA and ATL(WPA and AFR)contribute to(partially offset)the increasing trend in GZM HCI,while the trends in regional HCI over IDO are different in different reanalysis data.The CMIP6 models skillfully reproduce the trends in regional HCI over EPA,ATL,WPA,and AFR,but simulate notable decreasing trends in regional HCI over IDO,which is a key reason for the opposite trends in GZM HCI between reanalysis data and models.The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models.Optimal fingerprint analysis indicates that anthropogenic(ANT)and non-greenhouse gas(NOGHG)forcings are the dominant drivers of the HCI trends in the EPA and ATL regions.In the WPA(AFR)region,NOGHG(ANT)forcing serves as the primary driver.The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.
基金financially supported in part by the Natural Science Foundation of Fujian,China,under Grant 2021J01633.
文摘This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic(PV)array DC arc fault location methods based on electromagnetic radiation(EMR)signals.Initially,a comprehensive analysis of the time–frequency characteristics of series arc EMR signals is carried out to pinpoint effective data sources that reflect fault features.Subsequently,a multi-kernel domain-adversarial neural network(MKDANN)is introduced to extract domain-invariant features,and a feature extractor designed specifically for fingerprint matching is devised.To reduce inter-domain distribution differences,a multi-kernel maximum mean discrepancy(MK-MMD)is integrated into the adaptation layer.Moreover,to deal with dynamic environmental changes in real-world situations,the support-class passive aggressive(SPA)algorithm is utilized to adjust model parameters in real time.Finally,MKDANN and SPA technologies are smoothly combined to build a fully operational fault location model.Experimental results indicate that the proposed method attains an overall fault location accuracy of at least 95%,showing strong adaptability to environmental changes and robust interference resistance while maintaining excellent online learning capabilities during model migration.
