Volatile Compounds Fingerprints for White Duck down and White Goose down Determined by Gas Chromatography-Ion Mobility Spectrometry

This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined with optimized chemometric techniques, namely PCA. The detection method for down samples was established by using GC-IMS. Meanwhile, the reason of unpleasant odors caused by WDD was explained on the basis of the characteristic volatile compounds identification. GC-IMS fingerprinting can be considered a revolutionary approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. A total of 22 compounds were successfully separated and identified through GC-IMS method, and the significant differences in volatile compounds were observed in three parts of WDD and WGD samples. The most characteristic volatile compounds of WGD belong to aldehydes, whereas carboxylic acids from WDD were detected generated by autoxidation reaction. Meanwhile, the main reason of unpleasant odor generation was possibly attributed to the high concentration of volatile carboxylic acids of WDD. Therefore, the constructed model presents a simple and efficient method of analysis and serves as a basis for down processing and quality control.

Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Simultaneous purification of minor components in natural products using twin-column recycling chromatography with a step solvent gradient

The isolation of minor components from complex natural product matrices presents a significant challenge in the field of purification science due to their low concentrations and the presence of structurally similar compounds.This study introduces an optimized twin-column recycling chromatography method for the efficient and simultaneous purification of these elusive constituents.By introducing water at a small flowing rate between the twin columns,a step solvent gradient is created,by which the leading edge of concentration band would migrate at a slower rate than the trailing edge as it flowing from the upstream to downstream column.Hence,the band broadening is counterbalanced,resulting in an enrichment effect for those minor components in separation process.Herein,two target substances,which showed similar peak position in high performance liquid chromatography(HPLC)and did not exceed 1.8%in crude paclitaxel were selected as target compounds for separation.By using the twin-column recycling chromatography with a step solvent gradient,a successful purification was achieved in getting the two with the purity almost 100%.We suggest this method is suitable for the separation of most components in natural produces,which shows higher precision and recovery rate compared with the common lab-operated separation ways for natural products(thin-layer chromatography and prep-HPLC).

Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm:A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.

Certis: Cloud Asset Management & Threat Evaluation Using Behavioral Fingerprinting at Application Layer

This paper introduces Certis, a powerful framework that addresses the challenges of cloud asset tracking, management, and threat detection in modern cybersecurity landscapes. It enhances asset identification and anomaly detection through SSL certificate parsing, cloud service provider integration, and advanced fingerprinting techniques like JARM at the application layer. Current work will focus on cross-layer malicious behavior identification to further enhance its capabilities, including minimizing false positives through AI-based learning techniques. Certis promises to offer a powerful solution for organizations seeking proactive cybersecurity defenses in the face of evolving threats.

High throughput and rapid isolation of extracellular vesicles and exosomes with purity using size exclusion liquid chromatography

Extracellular vesicles(EVs)have emerged as potential biomarkers for diagnosing a range of diseases without invasive procedures.Extracellular vesicles also offer advantages compared to synthetic vesicles for delivery of various drugs;however,limitations in segregating EVs from other particles and soluble proteins have led to inconsistent EV retrieval rates with low levels of purity.Here,we report a new high-yield(88.47%)and rapid(<20 min)EV isolation method termed size exclusion–fast protein liquid chromatography(SE-FPLC).We show SE-FPLC can effectively isolate EVs from multiple sources including EVs derived from human and mouse cells and serum samples.The results indicate that SE-FPLC can successfully remove highly abundant protein contaminants such as albumin and lipoprotein complexes,which can represent a major hurdle in large scale isolation of EVs.The high-yield nature of SE-FPLC allows for easy industrial scaling up of EV production for various clinical utilities.SE-FPLC also enables analysis of small volumes of blood for use in point-of-care diagnostics in the clinic.Collectively,SE-FPLC offers many advantages over current EV isolation methods and offers rapid clinical translation.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Fingerprint Study of Polygonati Rhizoma with Steaming and Exposing to the Sun Alternatively for Different Times

[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.

Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

UV,five wavelengths fusion and electrochemical fingerprints combined with antioxidant activity for quality control of antiviral mixture

Aiming to ensure the consistency of quality control of Traditional Chinese Medicines(TCMs),a combination method of high-performance liquid chromatography(HPLC),ultraviolet(UV),electrochemical(EC)was developed in this study to comprehensively evaluate the quality of Antiviral Mixture(AM),and Comprehensive Linear Quantification Fingerprint Method(CLQFM)was used to process the data.Quantitative analysis of three active substances in TCM was conducted.A fivewavelength fusion fingerprint(FWFF)was developed,using second-order derivatives of UV spectral data to differentiate sample levels effectively.The combination of HPLC and UV spectrophotometry,along with electrochemical fingerprinting(ECFP),successfully evaluated total active substances.Ultimately,a multidimensional profiling analytical system for TCM was developed.

Rational and Continuous Measurement of Emotional-Fingerprint, Emotional-Quotient and Categorical vs Proportional Recognition of Facial Emotions with M.A.R.I.E., Second Half

Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.

Determination of 14 Organophosphorus Pesticide Residues in Mutton by Gel Permeation Chromatography-Gas Chromatography-Mass Spectrometry(GPC-GC-MS)

[Objectives]This study was conducted to purify mutton samples by gel permeation chromatography(GPC).[Methods]Fourteen organophosphorus pesticide residues in samples were qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry(GC-MS)in selective ion scanning mode(SIM).[Results]The organophosphorus pesticide standard solutions showed good linearity in the mass concentration range of 0.1-10.0μg/ml with correlation coefficients(r)not lower than 0.999,and the detection limits(S=3 N)ranged from 0.01 to 0.05 mg/kg.The average recovery values were in the range of 80.2%-99.7%,with relative standard deviations(RSDs,n=3)in the range of 1.8%-6.3%,at the addition levels of 0.5,1.0 and 2.0 mg/kg.[Conclusions]The method is simple,sensitive and accurate,and can be used for the determination of organophosphorus pesticide residues in mutton.

Study on HPLC-DAD Fingerprint of Xueshuan Xinmaining Capsules

[Objectives]To establish the HPLC-DAD fingerprint of Xueshuan Xinmaining Capsule(XXC).[Methods]The chromatographic conditions for the analysis of XXC solution were as follows:XSelect HSS T3 column;acetonitrile-0.1%phosphoric acid water was used as mo-bile phase,gradient elution;flow rate:1.0 mL/min;column temperature 30℃;The injection volume is 10μL.The quality of XXC samples produced by different manufacturers was evaluated by similarity evaluation and cluster analysis.[Results]In theHPLC-dad fingerprints of 15 batches of XXC,23 common peaks were identified and 9 peaks were identified,and the similarity was greater than 0.95.According to the re-sults of cluster analysis,15 batches of XXC samples could be divided into two categories,S2,S5,S6,S7 and S8 batches belonged to category Ⅰ,and the rest batches belonged to category Ⅱ.[Conclusions]In this study,a representative and universal identification method of Xxc HPLC-DAD fingerprint was established.The method has high precision,stability and repeatability,is simple and reliable,and provides a pow-erful reference for further improving the quality evaluation system of XXC.

Comprehensive analysis of phenolic composition and antioxidant mechanisms in Gymnema sylvestre extracts using LC-MS and column chromatography

Recent studies have highlighted the potential of plant extracts as therapeutic agents for managing oxidative stress and related disorders.This study aims to elucidate the phenolic composition and antioxidant properties of Gymnema sylvestre extracts.Ethanolic reflux extraction followed by column chromatography was employed to isolate phenolic compounds.The total phenolic and flavonoid contents were quantified using the Folin–Ciocalteu and aluminum chloride colorimetric methods,respectively.Antioxidant activities were assessed by DPPH,ABTS scavenging assays and the ferric reducing antioxidant power(FRAP)assay.High-Performance Liquid Chromatography(HPLC)with a C18 column and Thermo TSQ Quantum Access Max(LC-MS)were used to determine the levels of gymnemic acid and identify other potential phenolic compounds.The analysis revealed significant antioxidant activities in the fractions.Fraction A showed the highest DPPH and ABTS scavenging activities,and Fraction C demonstrated the highest ferric reducing power.LC-MS analysis identified several phenolic compounds,indicating that these are major contributors to the antioxidant efficacy of the extract.This study provides a detailed phenolic profile and confirms the strong antioxidant potential of Gymnema sylvestre leaf extract,supporting its therapeutic use and further investigation.

Determination of Benzo[a]pyrene in Edible Oil by High Performance Liquid Chromatography-Fluorescence Detector (HPLC-FL)

In this study, an optimized high performance liquid chromatography-fluorescence detector (HPLC-FL) method for the determination of benzo[a]pyrene in edible oil was established. HPLC was performed with Thermo Fisher Scientific C18 column (250 mm×4.6 mm, 5 μm) as the chromatographic column and acetonitrile and water as the mobile phase, and the excitation wavelength and emission wavelength of fluorescence detector were 286 and 430 nm, respectively. The response was high, and the linear range was 0.5-10.0 ng/ml. The lowest limit of detection was 0.11 ng/ml, and the average recovery was 92.5%. This method is suitable for quantitative analysis of benzo[a]pyrene content in edible oil.