Fingerprint Features Research Articles

A Dynamic Hybrid Luminescent Display for Multilevel Anticounterfeiting

AbstractIncorporating specific motifs to provide tangible evidence of authenticity is a direct and effective measure against counterfeiting. However, achieving multi‐level anticounterfeiting through multiple motifs requires utilizing numerous stimulus sources to configure region‐specific emissions with customizable colors, which is complex, costly, and inaccessible. To address this challenge, a hybrid luminescent display is developed by integrating an internal down‐conversion layer with an organic light‐emitting diode (OLED). The internal down‐conversion layer can effectively extract the substrate and waveguide modes, resulting in a very rare outcome where the efficiency of a hybrid down‐conversion white OLED exceeds that of the pump light source. More importantly, by combining voltage‐modulated color‐tunable electroluminescence (EL) with EL‐induced down‐conversion luminescence, this unique design can precisely create dynamic and configurable multi‐color display patterns using only electrical stimulation. For anticounterfeiting purposes, the further amalgamation of the dynamic hybrid luminescent display device with the Internet of Things for digital authentication, and with fingerprint features for physical unclonable functions showcases unprecedented security. These results herald a new generation of multilevel luminescent anticounterfeiting technology.

Diagnosis and Biomarker Screening of Endometrial Cancer Enabled by a Versatile Exosome Metabolic Fingerprint Platform.

Exosomes have emerged as a revolutionary tool for liquid biopsy (LB), as they carry specific cargo from cells. Profiling the metabolites of exosomes is crucial for cancer diagnosis and biomarker discovery. Herein, we propose a versatile platform for exosomal metabolite assay of endometrial cancer (EC). The platform is based on a nanostructured composite material comprising gold nanoparticle-coated magnetic COF with aptamer modification (Fe3O4@COF@Au-Apt). The unique design and novel synthesis strategy of Fe3O4@COF@Au-Apt provide the material with a large specific surface area, enabling the efficient and specific isolation of exosomes. The exosomes captured Fe3O4@COF@Au-Apt can be directly used as the laser desorption/ionization mass spectrometry (LDI-MS) matrix for rapid exosomal metabolic patterns. By integrating these functionalities into a single platform, the analytical process is simplified, eliminating the need for additional elution steps and minimizing potential sample loss, resulting in large-scale exosomal metabolic fingerprints. Combining with machine learning algorithms on the metabolic patterns, accurate discrimination between endometrial patients (EGs) and benign controls (CGs) was achieved, and the area under the receiver operating characteristic curve of the blind test cohort was 0.924. Confusion matrix analysis of important metabolic fingerprint features further demonstrates the high accuracy of the proposed approach toward EC diagnosis, with an overall accuracy of 94.1%. Moreover, four metabolites, namely, hydroxychalcone, l-acetylcarnitine, elaidic acid, and glutathione, have been identified as potential biomarkers of EC. These results highlight the great value of the integrated exosome metabolic fingerprint platform in facilitating low-cost and high-throughput characterization of exosomal metabolites for cancer diagnosis and biomarker discovery.

Quantum Physical Unclonable Function Based on Multidimensional Fingerprint Features of Single Photon Emitters in Random AlN Nanocrystals

AbstractPhysical unclonable function (PUF) has emerged as a unique physical'fingerprint' that is inherently difficult to replicate. It shows tremendous application value in various hardware security areas such as identity authentication, chip anticounterfeiting, communication encryption, blockchain, etc. However, with the rapid development of 3D nanoprinting, classical PUFs constructed with disordered micro‐nanostructures face tremendous threats from physical cloning attacks. Herein, this study proposes and demonstrates the utilization of room‐temperature single‐photon emitters derived from atomic defects in randomly distributed pyramidal aluminum nitride (AlN) nanocrystals as a novel quantum PUF to resist physical cloning attacks. The fabrication of this quantum PUF on silicon (Si) wafers enables seamless integration with silicon photonic integrated circuits. The multidimensional fingerprint features of the single‐photon emitters are highly sensitive to the lattice parameters of the uneven AlN nanocrystals. Furthermore, each single photon emitter can work as a quantum random number generator to ensure the fundamental unpredictability of PUFs. The subatomic precision requirement coupled with unpredictable quantum emission behavior makes it practically impossible to attack the proposed quantum PUF, providing a promising solution for information security in the post‐quantum era.

Examiner consistency in perceptions of fingerprint minutia rarity

Friction ridge examiners (FREs) identify distinctive features (minutiae) in fingerprints and consider how rare these observed minutiae are in their decisions about both the value of a fingerprint and whether there is enough correspondence between two fingerprints to support an “identification” or “exclusion” decision. But subjective perceptions about the frequency of events and features tend to be inconsistent and dynamic, which means that variable perceptions of minutia frequency may contribute to inconsistencies in FREs’ opinions about fingerprint evidence. We surveyed expert FREs at two time points (NTime 1 = 132; NTime 2 = 99) to establish how rare FREs believe different minutia types to be and to determine the variation in examiners’ perceptions—both between different examiners and across time for the same examiner. We observed significantly less variation in FREs’ perceptions of minutia frequency for three minutiae: the two most common minutiae and the minutia perceived to be the least common. We also observed increases in FREs’ estimates of minutia frequency over time and when they reported recent sightings of the rarest minutiae. FREs reported frequently using this information in their fingerprint comparison decisions. We present practical recommendations for using these consensus-based frequency estimates (until more objective data are available) to increase consistency in FREs’ use of base rates when examining fingerprint evidence, which may consequently increase the repeatability and reproducibility of decisions made by FREs.

Luminescence, temperature sensing and Judd-Ofelt analysis of Bi2Mo3O12:Eu3+ phosphors and the application in latent fingerprint visualization

Eu3+-activated materials have garnered significant attention due to their outstanding optical characteristics. In this work, the sol–gel method was successfully used to prepare Bi2Mo3O12 phosphors doped with different amounts of Eu3+. The generated samples were identified as orthorhombic Bi2Mo3O12 with a scheelite-like structure by X-ray diffraction analysis of the crystal structure. The sample’s morphology and size were examined using scanning electron microscopy and transmmission electron microscopy, which revealed irregular block morphology and tens to hundreds nanometers scale dimension. From the analysis of the concentration-dependent luminescence intensity of Eu3+, it was confirmed that the exchange interaction was responsible for the quenching of 5D0 fluorescence of Eu3+. When excited at 374 nm, the phosphor emitted brilliant red light, with the highest emission occurring at 616 nm (5D0→7F2 transition), and the calculated color coordinates of the sample were (0.663, 0.336). By examining the temperature dependence of the emission spectra, the temperature sensing performance of the sample and the thermal quenching behavior of Eu3+ luminescence was explored. Furthermore, the optical transition property of Eu3+ was investigated by using the emission spectra and fluorescence lifetime within the context of Judd-Ofelt theory. Ultimately, latent fingerprint visualization of the sample on various object surfaces was studied, thanks to the intense luminescence and small particle size of the Bi2Mo3O12:Eu3+ phosphor. The results indicated that the sample can clearly display the different hierarchical features of fingerprint on different object surfaces.

Molecular characterization of oxidized organic nitrogen in the polluted urban atmosphere of Beijing

Oxidized organic nitrogen (OON) serves as a crucial link between volatile organic compounds (VOCs), nitrogen oxides, ozone, and secondary organic aerosol (SOA). However, the comprehension of the molecular composition, formation mechanism, and implications of OON remains limited, particularly in urban environments influenced heavily by anthropogenic emissions. This study presents the field measurements of OON conducted at an urban site in Beijing using chemical ionization mass spectrometry with nitrate as the reagent ion. The molecular characteristics, dominant species, and oxidation states of OON were investigated. Positive matrix factorization analysis disentangled the diverse OON into factors characterized by unique fingerprint features and formation pathways. A modified workflow was used to classify the majority of OON as terpene (3.0 %), isoprene (15.2 %), aliphatic (38.1 %, containing dinitrate), and aromatic (36.0 %, containing aromatic ring retaining) OON. This highlights the significant impact of anthropogenic sources and underscores the need for stringent controls on anthropogenic VOC emissions to mitigate OON formation. Volatility estimates further indicate that aromatic and aliphatic OON, with relatively low volatility, are expected to be the primary contributors to SOA formation by condensation or gas-particle partitioning in urban Beijing. In addition, hazy weather conditions may facilitate multi-generation reactions, leading to the production of large amounts of semi-volatile dinitrate OON and promoting its conversion to SOA.

Advanced synthesis and forensic application of CoPr(x)Cr(2-x)O4 nanoparticles for latent fingerprint detection

The production, characterisation, and use of CoPr(x)Cr(2-x)O4 (x = 0.00, 0.01, 0.03, and 0.05) nanoparticles for improved latent fingerprint (LFP) detection are investigated in this study. CoCr2O4 containing different amounts of Praseodymium(Pr3+) were prepared by solution combustion process using 1:1 M ratio of oxidizer and fuel. X-ray diffraction (XRD) was used to examine the structural characteristics of these nanoparticles, and the results showed that the crystallite sizes and lattice parameters varied while the spinel structure remained constant. The effective synthesis of the intended nanoparticles was indicated by the confirmation of CoCr2O4 presence in the spinel structure by Fourier Transform Infrared Spectroscopy, or FTIR. In particular, the study showed how these nanoparticles can be used in forensic science to identify LFP on a variety of surfaces, including steel, paper, and OHP sheets. In daylight, the CoPr0.03Cr1.97O4 (CoPr-3)variation with x = 0.05 performed better, allowing for a clear viewing of Level I–III fingerprint features. 3D interactive plots and high-resolution images demonstrated that the nanoparticles stuck to fingerprint remnants, improving visibility and detail. The study also looked at how stable these nanoparticles were under UV irradiation and aging, and it discovered that CoPr-3 phosphor was remarkably resistant to UV degradation and retained its efficacy for 60 days. These results demonstrate the potential of CoPr(x)Cr(2-x)O4 nanoparticles as a reliable and effective material for forensic fingerprint analysis, providing notable enhancements in LFP detection sensitivity and resolution.

A multi-view feature representation for predicting drugs combination synergy based on ensemble and multi-task attention models

This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The ‘MVME’ model is assessed using the O’Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC).

Thermal stable NaMgLaTeO6:Dy3+ double perovskite yellow phosphors for w-LEDs and latent fingerprint visualization

The novel Dy3+-doped NaMgLaTeO6 (NaMgLaTeO6:Dy3+) phosphors were produced through a high-temperature solid-state reaction. X-ray diffraction (XRD) analysis and Rietveld refinement manifest that the pure phosphor was committed to the space group (P21/m (11)) and features the double perovskite structure. The band gap (Eg) of the direct semiconductor NaMgLaTeO6 is calculated to be 2.065 eV. Under 388 nm excitation, the 4F9/2→6H13/2 energy level transition contributes to the maximum emission peak at 572 nm. Other properties of the phosphor include optimal concentration (x = 5 mol %), high thermal stability, activation energy (Ea = 0.31 eV), and internal quantum efficiency (IQE = 31.44 %). The relevant color coordinate of the white light emitting diode (w-LED) prepared by the phosphor is (0.298, 0.311). The features of the latent fingerprint (LFP) created by NaMgLaTeO6:Dy3+ can be clearly reflected on different surfaces. In summary, NaMgLaTeO6:Dy3+ phosphor has proven high potential in w-LEDs production and LFP detection.

Intelligent Fault Diagnosis Method for Constant Pressure Variable Pump Based on Mel-MobileViT Lightweight Network

The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis.

Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network

Specific emitter identification is a challenge in the field of radar signal processing. Its aims to extract individual fingerprint features of the signal. However, early works are all designed using either signal or time–frequency image and heavily rely on the calculation of hand-crafted features or complex interactions in high-dimensional feature space. This paper introduces the time–frequency multimodal feature fusion network, a novel architecture based on multimodal feature interaction. Specifically, we designed a time–frequency signal feature encoding module, a wvd image feature encoding module, and a multimodal feature fusion module. Additionally, we propose a feature point filtering mechanism named FMM for signal embedding. Our algorithm demonstrates high performance in comparison with the state-of-the-art mainstream identification methods. The results indicate that our algorithm outperforms others, achieving the highest accuracy, precision, recall, and F1-score, surpassing the second-best by 9.3%, 8.2%, 9.2%, and 9%. Notably, the visual results show that the proposed method aligns with the signal generation mechanism, effectively capturing the distinctive fingerprint features of radar data. This paper establishes a foundational architecture for the subsequent multimodal research in SEI tasks.

Highly efficient Dy3+ activated Sr9Al6O18 nanophosphors for W-LEDs, optical thermometry and deep learning-based intelligent system for personal identification applications

A novel single-phase Dy3+ activated Sr9Al6O18 nanophosphors (NPs) was synthesized using a solution combustion method with urea as the fuel. When phosphors are excited at 350 nm, their photoluminescence (PL) spectra exhibit strong emissions at 484 and 574 nm, which are associated with the 4F9/2→6H15/2 and 4F9/2→6H13/2 transitions. These emissions are attributed to magnetic and electric dipole allowed transitions, correspondingly. It is found that dipole–dipole interactions are the main source of concentration quenching and energy transfer among the activator ions. The prepared NPs show thermal stability up to 480 K having an activation energy (Ea) of 0.2437 eV, as determined by temperature-dependent photoluminescence (TDPL). With a remarkable internal quantum efficiency (IQE) of 66.14 %, the optimized phosphor achieves notable relative sensitivity (Sr) and absolute sensitivity (Sa), estimated to be 2.65 % K−1 and 1.38×10−3 K−1 at 300 K, respectively. Thus, Sr9Al6O18:Dy3+ is a promising material for non-contact optical thermometry applications. Latent fingerprints (LFPs) and latent lip prints (LLPs) can be efficiently produced on a variety of surfaces by using the optimized NPs with the simple powder dusting technique. The prints are made visible under UV light with a 365 nm wavelength. The You Only Look Once version 8 (YOLOv8x) approach analyzes the overall morphological correlations of different minutiae to identify essential minutiae that indicate the identities of people from FPs. Splitting and matching multi-scale fingerprints (FPs) features improves the validity of the authenticating findings. The results show that the recommended method, which improves the entire FP authentication process, offers exceptional accuracy, resilience and speed. The findings provide strong support for the implementation of the optimized NPs in photonic devices, optical thermometers, and advanced forensic applications.

Predicting odor from vibrational spectra: a data-driven approach

This study investigates olfaction, a complex and not well-understood sensory modality. The chemical mechanism behind smell can be described by so far proposed two theories: vibrational and docking theories. The vibrational theory has been gaining acceptance lately but needs more extensive validation. To fill this gap for the first time, we, with the help of data-driven classification, clustering, and Explainable AI techniques, systematically analyze a large dataset of vibrational spectra (VS) of 3018 molecules obtained from the atomistic simulation. The study utlizes image representations of VS using Gramian Angular Fields and Markov Transition Fields, allowing computer vision techniques to be applied for better feature extraction and improved odor classification. Furthermore, we fuse the PCA-reduced fingerprint features with image features, which show additional improvement in classification results. We use two clustering methods, agglomerative hierarchical (AHC) and k-means, on dimensionality reduced (UMAP, MDS, t-SNE, and PCA) VS and image features, which shed further insight into the connections between molecular structure, VS, and odor. Additionally, we contrast our method with an earlier work that employed traditional machine learning on fingerprint features for the same dataset, and demonstrate that even with a representative subset of 3018 molecules, our deep learning model outperforms previous results. This comprehensive and systematic analysis highlights the potential of deep learning in furthering the field of olfactory research while confirming the vibrational theory of olfaction.

Analisis Efektivitas Penggunaan E-Wallet dalam Pembayaran Digital Bagi Mahasiswa Program Studi Manajemen Bisnis Syariah UMSU

This research aims to determine the motives, impacts and experiences of FAI UMSU Sharia Business Management students in using E-Wallets for Digital Payments. Based on the results of observations and interviews based on this title, researchers found that there are many factors that influence students to use E-Wallets for digital payments, including: promos, ease of use, service features, security of the e-wallet digital product. Almost all provinces in Indonesia have touched digital e-wallet applications (OVO, Gopay, Link Aja, Flip, DANA, Shopeepay and so on). E-wallet users are increasing rapidly and are widely used by various groups, including students. Although e-wallet service providers offer a variety of products, not all of them are used by students. E-wallet is a digital payment transaction method that includes features such as balance top-ups, bill payments, inter-bank transfers, money withdrawals, and so on. The use of digital e-wallets is still dominated by payments for online transportation transactions, purchases via e-commerce, and food and beverage delivery services. Security is also one of the factors that makes students interested and interested in using E-Wallet. Meanwhile, the existence of a security verification system makes consumers feel protected regarding the security of their personal data information when using E-Wallet. One of them is by launching fingerprint and facial verification features to validate every transaction.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

Enhanced fingerprint classification through modified PCA with SVD and invariant moments.

This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.

Amplification of intense red emission through integrating Li+ ions to V2O5:Eu3+ phosphors: Optical thermometry, cheiloscopy and extraction of level III features for individual identification

This study presents the synthesis of V2O5:Eu3+ phosphors in concentrations ranging from 1 to 9 mol %, using a solution combustion method with Areca Nut (A.N) as a bio-fuel. The samples exhibit an orthorhombic crystal structure, confirmed by powder X-ray diffraction (PXRD), and rod-like morphology as shown by field emission scanning electron microscopy (FE-SEM). Photoluminescence (PL) studies reveal that the emission intensity of the 5D0→7F2 transition at 619 nm increases with the Eu3+ concentration up to 5 mol %, where it peaks before quenching due to quadruple-quadruple interactions, according to the Van Uitert relation. To improve luminescence, Li+ ions are added, with the V2O5:5Eu3+ co-doped with 4Li+ (V2O5:5Eu3+, 4Li+) ions showing the highest PL intensity, about 5.03 times higher than that of Eu3+ alone. This optimized phosphor has Commission Internationale de l’Eclairage (CIE) chromaticity coordinates of (0.6022, 0.3963), closely matching commercial red-emitting phosphors and demonstrating a colour purity (CP) of 88.69 %. Thermal stability assessments indicate that these phosphors have a high activation energy (Ea) of about 0.224 eV, signifying robust thermal properties. The optimized V2O5:5Eu3+, 4Li+ phosphors efficiently reveal level I–III features in latent fingerprints (LFPs) and accurately identify lip prints (LPs) groove patterns of type I–VI under 365 nm UV light, providing high resolution and contrast. These characteristics make the optimized phosphor an excellent candidate for use in photonic devices, advanced forensic and data security applications.

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions.

The latest advancements in nuclear medicine indicate that radioactive isotopes and associated metal chelators play crucial roles in the diagnosis and treatment of diseases. The development of metal chelators mainly relies on traditional trial-and-error methods, lacking rational guidance and design. In this study, we propose the structure-aware transformer (SAT) combined with molecular fingerprint (SATCMF), a novel graph transformer network framework that incorporates prior chemical knowledge to construct coordination edges and learns the interactions between chelating agents and metal ions. SATCMF is trained on stability data collected from metal ion-ligand complexes, leveraging the SAT network to extract structural features relevant to the binding of ligands with metal ions. It further integrates molecular fingerprint features to refine the prediction of the stability constants of the chelating agents and metal ions. The experimental results on benchmark data set demonstrate that SATCMF achieves state-of-the-art performance based on four different graph neural network architectures. Additionally, visualizing the learned molecular attention distribution provides interpretable insights from the prediction results, offering valuable guidance for the development of novel metal chelators.

Loss of fingerprint features and recognition failure due to physiological factors- a literature survey

Loss of fingerprint features and recognition failure due to physiological factors- a literature survey

Identification of IoT Devices Based on Hardware and Software Fingerprint Features

Identification of IoT Devices Based on Hardware and Software Fingerprint Features