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In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S25−50). To date, however, S25−50 temperatures have not been used in patternbased attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S25−50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S25−50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25−50 signal and noise patterns are accompanied by large cooling of S25−50 (1 to 2 ◦C over 1986 to 2022) and low S25−50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere. [ABSTRACT FROM AUTHOR]

Human Merkel cells (MCs) were first described by Friedrich S. Merkel in 1875 and named "Tastzellen" (touch cells). Merkel cells are mainly located in the basal layer of the epidermis and are concentrated in touchsensitive areas. Their density varies among different anatomical sites. Increased concentration was observed in the palms of hands with a predominance in the finger pads and also in the soles and toes. They can be classified according to the function as mechanoreceptive, endocrine, and chemo-sensitive cells. In the development of primary ridges which establish the future fingerprint patterns is assumed that Merkel cells have a significant importance in this process. At about the 7th week EGA, they first time appear in the volar skin and start to occupy the place of future primary ridges at 10 weeks EGA. It will be interesting to study their presence or absence in individuals suffering with abnormal dermatoglyphics and also to study whether the skin diseases associated with altered dermatoglyphics display some deviation regarding the distribution and density of MCs in primary ridges. [ABSTRACT FROM AUTHOR]

A painting, like human skin, develops cracks on the surface as it dries and ages. The painting cracks, also known as craquelure, are often considered analogous to human fingerprints; these have been regarded as a unique signature reflective of the painting’s characteristics and are important in art authentication. Intriguingly, studies in other fields, such as geology, have observed the presence of distinctive characteristics in soil desiccation cracks. These cracks exhibit self-similarity, forming patterns that suggest broader geological processes at work. In light of this connection, the primary objective of this study is to investigate whether the painting cracks also exhibit a self-similar nature. By delving into this, we seek to shed light on the underlying properties of the painting cracks. This study also aims to investigate whether the characteristic self-similar trait of the cracks can serve as an identifier in relation to the provenances of the paintings. To this end, this study adopts the methodology originally designed to characterize the phenotypic traits of 3D particle geometries in granular materials research. This study develops a 2D equivalent concept, focusing on the phenotypic traits of the individual islands enclosed by cracks within paintings. The results successfully demonstrate that the phenotypic trait of painting cracks exhibits a self-similar nature, which can reveal characteristics associated with the provenances of paintings. The findings will offer valuable insights into the scientific examination of artworks based on painting cracks. [ABSTRACT FROM AUTHOR]

Objectives GCA is an inflammatory disease following a chronic, relapsing course. The metabolic alterations related to the intense inflammatory process during the active phase and the rapid impact of steroid treatment remain unknown. This study aims to investigate the serum metabolome in active and inactive disease states. Methods A total of 110 serum samples from 50 patients (33 GCA and 17 PMR) at three time points—0 (V1: active disease), 1 and 6 months (V2 and V3: remission)—of treatment with glucocorticoids (GCs) were subjected to NMR-based metabolomic analysis. Multi- and univariate statistical analyses were utilized to unveil metabolome alterations following treatment. Results Distinct metabolic profiles were identified between activity and remission, independent of disease type. N-acetylglycoproteins and cholines of bound phospholipids emerged as predictive markers of disease activity. Altered levels of 4 of the 21 small molecules were also observed, including increased levels of phenylalanine and decreased glutamine, alanine and creatinine in active disease. Metabolic fingerprinting discriminated GCA from PMR in remission. GCA and PMR patients exhibited characteristic lipid alterations as a response and/or adverse effect of GC treatment. Correlation analysis showed that several identified biomarkers were further associated with acute phase reactants, CRP and ESR. Conclusion The NMR profile of serum metabolome could identify and propose sensitive biomarkers of inflammation. Metabolome alterations, following GC treatment, could provide predictors for future steroid-induced side effects. [ABSTRACT FROM AUTHOR]

Owing to the ultralong afterglow, room temperature decay phosphorescence nanomaterials have aroused enough attention. In the work, by simple one-pot solid-state thermal decomposition reaction, aggregate carbon dots (CDs) was prepared from trimesic and boric acid. Based on the intermolecular hydrogen bonds and intramolecular π-π stacking weak interaction from precursors, CDs was encapsulated in boron oxide matrix and formed aggregation. The aggregate state of CDs facilitated the triplet excited states (Tn), which could induce the room temperature decay phosphorescence properties. By careful investigation, under different excitation wavelengths at 254 and 365 nm, the aggregate CDs showed > 15 s and > 3 s room temperature phosphorescence emission in the naked eye, which was associated with 1516.12 ms and 718.62 ms lifetime respectively. And the aggregate CDs exhibited widespread application in encoding encryption, optical anti-counterfeiting and fingerprint identification etc. The interesting aggregate CDs revealed unexpected ultralong-afterglow room temperature decay phosphorescence properties and the work opened a window for constructing ultralong-afterglow room temperature decay phosphorescence aggregate CDs nanomaterials. [ABSTRACT FROM AUTHOR]

With the increasing demand for information security, current anti‐counterfeiting methods not only suffer from issues such as low information density and vulnerability to forgery, but also inherently involve a trade‐off between information capacity and readout methods. This paper reports a high‐security solution using hierarchical pattern with perfect combination of micro self‐wrinkle and nano phase‐separation as physical unclonable function (PUF) labels, which is generated through self‐organization of anthracene‐functionalized poly(styrene‐block‐butadiene‐block‐styrene) (SBS‐CAN) under UV exposure. The double‐layer morphologies formed by the wrinkle and phase separation are adjustable, independent, and stable. The obtained hierarchical PUF labels exhibit random and unique features similar to the minutiae of fingerprints at both micro and nano scales, ensuring a well‐balanced bit uniformity (>0.492), high uniqueness (>0.496), and outstanding reliability (>96%). As a consequence of the multi‐layered combination of morphologies, the designed PUF label possesses an information density about 1010 times higher than that of human fingerprints. The PUF labels can be quickly obtained through simple visual scanning and exhibit sufficient security. To cope with various application environments, the advanced authentication pipeline designed LPLA guarantees robust label recognition capability in real‐world scenarios. A practical integrated anti‐counterfeiting authentication system is developed by combining hierarchical PUF labels and authentication pipeline. [ABSTRACT FROM AUTHOR]

Tactile texture sensors are designed to evaluate the sensations felt when a human touches an object. Prior studies have demonstrated the necessity for these sensors to have compliant ridges on their surfaces that mimic human fingerprints. These features enable the simulation of contact phenomena, especially friction and vibration, between human fingertips and objects, enhancing the tactile sensation evaluation. However, the ridges on tactile sensors are susceptible to abrasion damage from repeated use. To date, the healing function of abraded ridges has not been proposed, and its effectiveness needs to be demonstrated. In this study, we investigated whether the signal detection capabilities of a sensor with abraded epidermal ridges could be restored by healing the ridges using polyvinyl chloride plastisol as the sensor material. We developed a prototype tactile sensor with an embedded strain gauge, which was used to repeatedly scan roughness specimens. After more than 1000 measurements, we observed significant deterioration in the sensor's output signal level. The ridges were then reshaped using a mold with a heating function, allowing the sensor to partially regain its original signal levels. This method shows potential for extending the operational lifespan of tactile texture sensors with compliant ridges. [ABSTRACT FROM AUTHOR]

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment. [ABSTRACT FROM AUTHOR]

Crystalline α‐Gd2(MoO4)3 is synthesized by sintering at 800 °C using a coprecipitation method to prepare the precursor. The resulting α‐Gd2(MoO4)3 displayed a monocrystalline structure with a strong main peak (‐221) in the X‐ray diffraction signal. To develop a light‐emitting material, rare earth ions are added during synthesis. By doping with Tb3+ and Eu3+, the Gd2(MoO4)3 down‐conversion phosphor shows potential for use in UV–LED chips, counterfeit money prevention, and fingerprint identification. Additionally, co‐doping with [Er3+]/[Yb3+] and [Ho3+]/[Yb3+] ions produce green and red emissions when applied to a 980 nm LED chip, useful for anti‐counterfeit ink. The magnetic properties of gadolinium are leveraged to confirm magnetic resonance imaging luminescence. A flexible composite for heat detection and explored various applications for its use is also developed. [ABSTRACT FROM AUTHOR]

[Objectives] To establish the HPLC-DAD fingeiprint 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 mobile phase, gradient elution; flow rate; 1.0 mL/min; column temperature 30 °C; The injection volume is 10 jjlL. The quality of XXC samples produced by different manufacturers was evaluated by similarity evaluation and cluster analysis. [Results] In the HPLC-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 results 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 powerful reference for further improving the quality evaluation system of XXC. [ABSTRACT FROM AUTHOR]

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods. [ABSTRACT FROM AUTHOR]

Bound states in the continuum (BICs) are an excellent platform enabling highly efficient light–matter interaction in applications for lasing, nonlinear generation, and sensing. However, the current focus in implementing BICs has primarily been on single sharp resonances, limiting the extent of electric field enhancement for multiple resonances. In this study, experimental demonstrations are conducted to showcase how metasurfaces can enable the control of symmetry‐broken and Friedrich–Wintgen BICs by leveraging the asymmetry of split resonant rings. This approach allows for the existence of multiple free‐control BIC resonances and tailored enhancement of controlling light–matter interactions. Further experiments are conducted to validate the effectiveness and performance of meta‐sensors for the identification of the distinct fingerprint of α‐lactose with high sensitivity using only one single metasurface. These findings present a novel and efficient platform for the development of miniaturized and chip‐scale photonics devices with intense light–matter interaction. [ABSTRACT FROM AUTHOR]

Most of the current optical image watermarking schemes are designed for robust watermarking, primarily aimed at protecting image copyrights. However, these robust optical watermarking schemes cannot determine if an image has been tampered with. To address this issue, we propose an optical fragile watermarking scheme based on single-pixel ptychography and variational image decomposition. In our proposed fragile watermarking scheme, the complex watermark image is encoded by the single-pixel ptychography system and embedded into the high-frequency detail component of the color host image obtained through variational image decomposition. Our proposed fragile watermarking scheme is highly sensitive to common image tampering, image processing and geometric attacks. Additionally, by incorporating human fingerprint keys during the encoding and encryption process of the watermark image, the scheme associates the watermarking process with user identity, thereby enhancing security. Extensive numerical simulations validate the feasibility, security and fragility of our proposed optical fragile watermarking scheme. [ABSTRACT FROM AUTHOR]

Background: Recent advances have increased the importance of the human microbiome, including the skin microbiome. Despite the hand microbiome research, the factors affecting the composition of the hand microbiome and their personal characteristics are incompletely known. Objectives: Despite changing environmental factors and personal variation, we aimed to indicate the interpersonal distinction between skin microbiota using simple and rapid molecular methods. Methods: Over a non‐consecutive 10‐day period, samples were taken from 10 adult individuals, and ribotyping analysis of the 16S and 23S genes of S. epidermidis was performed on each skin sample. Additionally, EcoRI and HindIII enzyme reactions and variable number tandem repeat (VNTR) reactions of S. epidermidis obtained from DNA samples were performed. The skin microbiomes of individuals were evaluated along with the microbiome profiles left on the surfaces they touched. Results: In the environmental samples taken, it has been observed that people preserve their core skin microbiota characters and carry them to their environment. It was determined that the highest similarity rate was 77.14%, and the lowest similarity rate was 31.74%. Conclusion: Our study showed that the core skin microbiota retains its characteristics and leaves traces in environments. The fact that the personal microbiome remains unchanged despite environmental differences and has characteristic features has shown that it can be used in forensic sciences to distinguish individuals from each other. These results with simple and rapid methods further increased the importance and significance of the study. The findings indicate that personal skin microbiota can provide a significant contribution to criminal investigations by increasing accuracy and reliability, especially in forensic analyses. [ABSTRACT FROM AUTHOR]

We previously described NMR based fingerprint matching with peptide backbone resonances as a fast and reliable structural dereplication approach for Pseudomonas cyclic lipodepsipeptides (CLiPs). In combination with total synthesis of a small library of configurational CLiP congeners this also allows unambiguous determination of stereochemistry, facilitating structure‐activity relationship studies and enabling three‐dimensional structure determination. However, the on‐resin macrocycle formation in the synthetic workflow brings considerable burden and limits universal applicability. This drawback is here removed altogether by also transforming the native CLiP into a linearized analogue by controlled saponification of the ester bond. This eliminates the need for macrocycle formation, limiting the synthesis effort to linear peptide analogues. NMR fingerprints of such linear peptide analogues display a sufficiently distinctive chemical shift fingerprint to act as effective discriminators. The approach is developed using viscosin group CLiPs and subsequently demonstrated on putisolvin, leading to a structural revision, and tanniamide from Pseudomonas ekonensis COR58, a newly isolated lipododecapeptide that defines a new group characterized by a ten‐residue large macrocycle, the largest to date in the Pseudomonas CLiP portfolio. These examples demonstrate the effectiveness of the saponification‐ enhanced approach that broadens applicability of NMR fingerprint matching for the determination of the stereochemistry of CLiPs. [ABSTRACT FROM AUTHOR]

Fingerprint recognition systems hold a pivotal role across various modern applications, such as criminal investigation, civil identification, and access control. Fingerprint segmentation is commonly the first stage of fingerprint recognition systems, focusing on extracting the foreground from captured fingerprint images. This helps to reduce extracting false minutiae, speed up the extraction process, and hence, improve the overall system performance. In this paper, we introduce a novel fingerprint segmentation method by first proposing new frequency and intensity features with employing fuzzy-c-means and genetic algorithm, accompanied by proposing a new cluster assignment strategy that involves features weighting and cluster assignment probabilities thresholding which helps to improve the accuracy compared to the standard cluster assignment method. Finally, we introduce a new morphological post-processing technique in order to minimize the misclassified pixels and obtain the final mask. Furthermore, we carry out a comprehensive performance evaluation against leading fingerprint segmentation methods on various known databases. As a result, the proposed method achieves an average error rate of 2.52%, which is much lower than the corresponding errors obtained in the literature. By these experimental results, we show that the proposed method outperforms the best existing methods. [ABSTRACT FROM AUTHOR]

Gait recognition has become an important biometric feature for human identification, in addition to data such as face, iris, and fingerprint. The goal of human gait recognition is to identify people based on walking images. Artificial intelligence technologies have revolutionized the field of gait recognition by enabling computers to automatically learn and extract intricate patterns. These techniques examine video recordings to determine key features in an individual's gait, and these features are used to identify the person. This paper examines the existing appearance-based gait recognition methods that have been published in recent years. The primary objective of this paper is to provide an informative survey of the state-of-the-art in appearance-based gait recognition techniques, highlighting their applications, strengths, and limitations. Through our analysis, we aim to highlight the significant advance that has been made in this field, draw attention to the challenges that have been faced, and identify areas of prospective future research and advances in technology. Furthermore, we comprehensively examine common datasets used in gait recognition research. By analyzing the latest developments in appearance-based gait recognition, our study aims to be a helpful resource for researchers, providing an extensive overview of current methods and guiding future attempts in this dynamic field. [ABSTRACT FROM AUTHOR]

The Middle Permian Lucaogou Formation is the most significant source rock in the eastern Junggar Basin. Previous studies have confirmed its excellent hydrocarbon-generating potential in the Jimsar Sag. However, its potential in other areas of the eastern Junggar Basin remains uncertain. Based on total organic carbon and pyrolysis, organic petrology, hydrocarbon simulation experiments, basin simulation, and combined wellseismic coupling interpretation, this study systematically compares the hydrocarbon-generating potential of the Lucaogou source rock in the Jimsar Sag with other areas of the eastern Junggar Basin. It discusses the sedimentary environment of high-quality source rocks and depicts the distribution of practical source kitchens. The Lucaogou source rocks in the eastern Junggar Basin are oil-prone, dominated by type I–II kerogen, and generally classified as good to excellent source rocks. Nowadays, the area of the Lucaogou source rocks that have entered the main oilgenerating window is approximately 11 × 10³ km² . Except for the bulge area, the Lucaogou source rocks in the eastern Junggar Basin successively entered the hydrocarbon-generating threshold during the Jurassic and the main oil-generating window in the Cretaceous. The Lucaogou source rocks in the Jimsar Sag and other parts of the eastern Junggar Basin share similar biomarker fingerprints, characterized by relatively low ratios of Pr/Ph, Pr/n-C17, Tm/C30 hopane, C19/C20 tricyclic terpene, and C24 tetracyclic terpene/C26 tricyclic terpene, and high β-carotene content, gammacerane index, and Ts/Tm ratios. These characteristics reflect deposition in a strongly reducing brackish lacustrine environment with parental sources dominated by lower organisms such as algae and bacteria. Generally, the Lucaogou source rocks in the eastern Junggar Basin have an oil-generating intensity of more than 3 × 106 t/km² . Several oil-generating centers with an intensity of more than 5 × 106 t/km² have developed in the front of the Bogda Mountain, Jimsar Sag, Dongdaohaizi Sag, Wucaiwan Sag, and Shazhang Fault Zone, covering a total area of approximately 12,500 km² . These characteristics of the Lucaogou source rocks promise favorable potential for forming large and medium oil fields. The results further consolidated the oil and gas resources in the eastern Junggar Basin and provided valuable references for exploring future oil and gas fields. [ABSTRACT FROM AUTHOR]

We present a neural network (NN) potential based on a new set of atomic fingerprints built upon two- and three-body contributions that probe distances and local orientational order, respectively. Compared with the existing NN potentials, the atomic fingerprints depend on a small set of tunable parameters that are trained together with the NN weights. In addition to simplifying the selection of the atomic fingerprints, this strategy can also considerably increase the overall accuracy of the network representation. To tackle the simultaneous training of the atomic fingerprint parameters and NN weights, we adopt an annealing protocol that progressively cycles the learning rate, significantly improving the accuracy of the NN potential. We test the performance of the network potential against the mW model of water, which is a classical three-body potential that well captures the anomalies of the liquid phase. Trained on just three state points, the NN potential is able to reproduce the mW model in a very wide range of densities and temperatures, from negative pressures to several GPa, capturing the transition from an open random tetrahedral network to a dense interpenetrated network. The NN potential also reproduces very well properties for which it was not explicitly trained, such as dynamical properties and the structure of the stable crystalline phases of mW. [ABSTRACT FROM AUTHOR]

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Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems. [ABSTRACT FROM AUTHOR]

In Moroccan higher education, the integrity of examinations is paramount, yet it faces the persistent challenge of identity impersonation. This form of academic dishonesty not only undermines the credibility of educational institutions but also contributes to the graduation of incompetent students. In an era where artificial intelligence is revolutionizing various sectors, its application to upholding academic integrity is both timely and essential. This paper proposes an advanced deep learning-driven fingerprint verification model specifically designed to combat impersonation in university examinations. Unlike traditional methods, our model leverages the power of Siamese neural networks (SNN), renowned for their effectiveness in learning distinct features and similarities. The model, trained, validated, and tested using the SOCOFing dataset, demonstrated high accuracy and effectiveness in fingerprint identification, crucial for verifying identities in educational exam settings. It achieved an accuracy of 99.29%, and an F1 score of 99.27%, surpassing other systems and significantly contributing to examination integrity in Moroccan higher education. [ABSTRACT FROM AUTHOR]

The utilization of analytical techniques in deciphering forensic investigation cases represents a significant advancement. Investigators are adept at scrutinizing minute details of evidence, including latent fingerprints, DNA, and bloodstain patterns. Fingerprints stand as paramount biometric evidence for personal identification, widely employed by law enforcement agencies globally due to their uniqueness. This distinct characteristic arises from the presence of various minutiae within the fingerprint patterns, necessitating specialized personnel for meticulous development. Thus, there arises a need for analytical phenomena like luminescence and fluorescence to facilitate the visualization of latent fingerprint patterns (LFPs). Currently, various organic and inorganic fluorescent compounds are used by forensic investigators for fingerprint development. These fluorescent compounds function by binding to specific components within sweat pores located on frictional ridges of fingerprint patterns, thus rendering them visible. This review discusses materials with fluorescence, and additional properties such as aggregation-induced emission (AIE) and highlights the properties, applications, and notable drawbacks of these materials. The primary aim of this review is to spotlight existing techniques, their limitations, and the latest research endeavours in the quest for the most optimal material for fingerprint development. Such material should possess all essential features while minimizing constraints, thereby bolstering forensic investigations on a global scale. [ABSTRACT FROM AUTHOR]

With the development of science and technology, forensics has become a more attractive scientific subject to study, and the field of nanoforensic research has seen a lot of interest in nanomaterials. Fingerprints are crucial pieces of evidence at each crime scene, and future fingerprint investigations could greatly benefit from nano-based procedures. In terms of tangible evidence for proving a person's identity, fingerprints have historically been and now are considered to be the most valuable sort. The prints left behind at a crime scene are typically latent (invisible) fingerprints; therefore, the distinctive ridge details of fingerprints can be obtained by coating various object surfaces with various nanoparticles, with gold being the most popular choice. These nanoparticles include silver, zinc oxide, silicon dioxide, aluminum oxide, gold, carbon, and silica. Latent fingerprints can be made better and more stable using nanoparticles. This review study concentrates on the employment of various nanomaterials in the creation and identification of latent fingerprints as a consequence. [ABSTRACT FROM AUTHOR]

Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular representation learning method called MvMRL, which can incorporate feature information from multiple molecular representations and capture both local and global information from different views well, thus improving molecular property prediction. Specifically, MvMRL consists of four parts: a multiscale CNN-SE Simplified Molecular Input Line Entry System (SMILES) learning component and a multiscale Graph Neural Network encoder to extract local feature information and global feature information from the SMILES view and the molecular graph view, respectively; a Multi-Layer Perceptron network to capture complex non-linear relationship features from the molecular fingerprint view; and a dual cross-attention component to fuse feature information on the multi-views deeply for predicting molecular properties. We evaluate the performance of MvMRL on 11 benchmark datasets, and experimental results show that MvMRL outperforms state-of-the-art methods, indicating its rationality and effectiveness in molecular property prediction. The source code of MvMRL was released in https://github.com/jedison-github/MvMRL. [ABSTRACT FROM AUTHOR]

The chemical analysis of fingermarks (FMs) has attracted considerable attention in the realm of forensic investigations. Techniques based on direct ionization of a sample by laser irradiation, specifically matrix-assisted laser desorption ionization–mass spectrometry (MALDI–MS), have provided excellent figures of merit for analyzing high molecular–weight compounds. However, it can be challenging to analyze low molecular–weight compounds using MALDI–MS owing to potential interference produced by the organic matrices in the low molecular–weight region, which can impede the detection of small molecules (m/z < 700 Da). Alternately, surface-assisted laser desorption/ionization–mass spectrometry (SALDI–MS) has shown great promise for small molecules analysis owing to the unique properties of the nanostructures used, particularly, minimal chemical background in low m/z region improved the production of ions involved in this method. The advancement of MALDI–MS and SALDI–MS has propelled their application in the analysis of FM components, focused on gaining deep insights into individual traits. This review aims to outline the current role of MALDI–MS and SALDI–MS in the chemical analysis of FMs. It also describes the latest achievements in forensic intelligence derived from fingermark analysis using these powerful methods. The accomplishments include the understanding of certain characteristics and lifestyles of donors. The review offers a comprehensive overview of the challenges and demands in this field. It suggests potential enhancements in this rapidly expanding domain to bridge the gap between research and practical police casework. [ABSTRACT FROM AUTHOR]

A user's devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user's devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user's personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident's consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user's device, UTCID PPA offers actionable recommendations enhancing the user's control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control. [ABSTRACT FROM AUTHOR]

The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain's organization in a multidimensional connectivity gradient space. However, how the brain's morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a "fingerprint" of an individual's brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance. There is much interest in how the brain's morphometric organization changes throughout the human lifespan to support cognition. By analyzing structural MRI data from subjects aged 8-89, this study reveals that networks within the brain become more dispersed with age, and that these effects may underlie age-related changes in cognitive flexibility and executive function. [ABSTRACT FROM AUTHOR]

Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future. [ABSTRACT FROM AUTHOR]

This paper introduces a design of ternary bistale ring physical unclonable function (BRPUF) based on carbon nanotube field effect transistors (CNTFETs). PUFs can extract unique chip features from process variations and assign every chip an inherent fingerprint to be used for identification or authentication of devices. As the main point of strength for PUF security, the size of challenge-response pairs (CRPs) can be increased by adopting multi-valued logic designs. The proposed ternary BRPUF blends higher entropy into CRPs and is tested to produce unbiased response digits, thus making them more unpredictable and more resistant against machine learning attacks. The instinctive features of CNTFETs, such as intensive process variations and high tolerance against temperature fluctuations and noises, enable our design to function robustly in a wide range of operating conditions. The proposed design has been implemented using standard 32-nm CNTFET model libraries and tested by HSPICE simulations. The results elaborate the superiority of our work compared with the traditional binary BRPUFs. [ABSTRACT FROM AUTHOR]

Existing copyright protection schemes for 3D models of oblique photography predominantly utilize digital watermarking technology, most of which suffer from shortcomings such as the potential false accusation of legitimate users and the inability to effectively trace unauthorized distributors. In response to these challenges, this article proposes a homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography. Initially, the fingerprints to be embedded are collaboratively generated by the transacting parties, with the publisher remaining unaware of the user's actual fingerprint. This approach ensures the effective implementation of the asymmetric nature of the fingerprinting scheme. Subsequently, potential geometric and non‐geometric attacks are considered prior to fingerprint embedding. Robustness is enhanced for the proposed solution through relative coordinate transformations and normalization procedures. Ultimately, the embedding and extraction of fingerprints are achieved through the application of an effective bit parity check method, in conjunction with the vertex‐paradigm projection length mapping mechanism and the Paillier homomorphic encryption system, minimizing embedding distortion while ensuring data security. Experimental results demonstrate that the proposed solution exhibits satisfying robustness against common manipulations, such as cropping, simplification, and geometric attacks. Additionally, the scheme effectively safeguards legitimate users from false accusations and accurately traces maliciously authorized users. [ABSTRACT FROM AUTHOR]

Ternary polymer solar cells (PSCs) are currently the simplest and most efficient way to further improve the device performance in PSCs. To find high-performance organic photovoltaic materials, the established connection between the material structure and device performance before fabrication is of great significance. Herein, firstly, a database of the photovoltaic performance in 874 experimental PSCs reported in the literature is established, and three different fingerprint expressions of a molecular structure are explored as input features; the results show that long fingerprints of 2D atom pairs can contain more effective information and improve the accuracy of the models. Through supervised learning, five machine learning (ML) models were trained to build a mapping of the photovoltaic performance improvement relationship from binary to ternary PSCs. The GBDT model had the best predictive ability and generalization. Eighteen key structural features from a non-fullerene acceptor and the third components that affect the device's PCE were screened based on this model, including a nitrile group with lone-pair electron, a halogen atom, an oxygen atom, etc. Interestingly, the structural features for the enhanced device's PCE were essentially increased by the Jsc or FF. More importantly, the reliability of the ML model was further verified by preparing the highly efficient PSCs. Taking the PM6:BTP-eC9:PY-IT ternary PSC as an example, the PCE prediction (18.03%) by the model was in good agreement with the experimental results (17.78%), the relative prediction error was 1.41%, and the relative error between all experimental results and predicted results was less than 5%. These results indicate that ML is a useful tool for exploring the photovoltaic performance improvement of PSCs and accelerating the design and application with highly efficient non-fullerene materials. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN)-generated image detection is a crucial yet challenging problem. The advancement of diverse generative models makes it simpler to build realistic synthetic images, but it also increases the likelihood of using images maliciously. Recent techniques of fake image detection often overfit to specific GANs, incapable of generalizing to other GANs. Each GAN leaves its distinct fingerprint on the images it generates. We propose a simple and compact attention network that exploits high-frequency and noise residual patterns of images. We design two-channel image representations in the preprocessing phase, with one channel using a bilateral high pass filter (BiHPF) for high-frequency patterns and the other using photo response non-uniformity (PRNU) for noise residuals capturing from GAN-generated and real images. The feature maps pass through the attention network with a dense layer for classification tasks. We assess the generalizability and robustness of the model through extensive experiments. The model is evaluated on a benchmark dataset in various settings, including cross-category, cross-GAN models with varying class settings and different color manipulations. This study demonstrates that the proposed model outperforms state-of-the-art (SOTA) methods by approximately 5% in different experiment settings. • Each GAN leaves a distinctive noise imprint on the images it generates. • A simple and small two-channel CNN framework based on an attention mechanism. • The RGB image is transformed into a 2-channel image by using BiHPF and PRNU. • These features are then used as input to the CNN classifier. • A generalized and robust model evaluated in cross-category and cross-GAN. [ABSTRACT FROM AUTHOR]

Estimation of gender, hand, and finger to minimize the probable suspects list in a fingerprint database search is a very important stride in forensic anthropology. Previous research attempted to estimate the gender, hand, and finger from the fingerprint, but the results were not consistent. In this effort, we proposed gender, hand, and finger estimation based on fingerprints using a deep convolution neural network. The publicly available SOCOFIG dataset which embraces 55222 no of fingerprints, is used for training and evaluation of the proposed procedure. On the aforementioned dataset, the suggested mode of operation achieves 99.38% gender, 99.46% hand, and 97.36% finger prediction validation accuracy. The results are competitive and commendable when compared to the preceding techniques. [ABSTRACT FROM AUTHOR]

Technology has played a critical role in enhancing the effectiveness and development of forensic processes. Digitalization and the use of automated biometric technologies, like the Automated Fingerprint Identification Systems (AFIS) for instance, has been a significant step forward, increasing accountability and transparency, and improving the accuracy and efficiency of evidence processing. Traditionally these systems are search systems only, and require the implementation of additional workflow strategies to support forensic processes in best practice approaches. More recently, the introduction of statistical models represents strategies to strengthen the reliability of forensic science by combining human-based methods with computer-assisted approaches. [ABSTRACT FROM AUTHOR]

Fingerprints hold evidential value for individual identification; a sensitive, efficient, and convenient method for visualizing latent fingermarks (LFMs) is of great importance in the field of crime scene investigation. In this study, we proposed an aggregation-induced emission atomization technique (AIE-AT) to obtain high-quality fingermark images. Six volunteers made over 1566 fingerprint samples on 17 different objects. The quality of fingermark development was evaluated using grayscale analysis for quantitative assessment, combining the fluency of fingermark ridges and the degree of level 2 and level 3 features. Both qualitative and quantitative methods were employed to explore the effectiveness of AIE molecule C27H19N3SO in developing fingermarks, its applicability to objects, and its individual selectivity. Additionally, the stability of the AIE molecule was examined. Comparative experimental results demonstrated the high stability of the AIE molecule, making it suitable for long-term preservation. The grayscale ratio of the ridges and furrows was at least 2, with high brightness contrast, the level 2 and level 3 features were clearly observable. The AIE-AT proved to be effective for developing fingermarks on nonporous, porous, and semiporous objects. It exhibited low selectivity on suspects who leave fingermarks and showed better development effects on challenging objects, as well as efficient extraction capability for in situ fingermarks. In summary, AIE-AT can efficiently develop latent fingermarks on common objects and even challenging ones. It locates the latent fingermarks for further accurate extraction of touch exfoliated cells in situ, providing technical support for the visualization of fingermarks and the localization for extraction of touch DNA. [ABSTRACT FROM AUTHOR]

Although almost all studies exclude lip conditions because they would affect furrow patterns, some authors maintain that they should be considered temporary or permanent disabilities, all of which require further research in this context.We present a study in which we associate lip conditions with lip print patterns to associate both morphological elements for fieldwork purposes. Fifty-seven women and 48 men aged between 19 and 38 years who resided in Temuco (Chile) were included. The lip conditions and their prints were recorded, and an analysis, comparison, evaluation, and verification protocol (ACE-V) for lip prints was applied. Of the participants, 27.4 % had healthy lips, while 71.7 % had some type of condition. Although patterns related to temporary and permanent lip conditions were recognized in the lip prints in a non-significant way, the diagnosis of "healthy" or "altered" lip status could be made significantly by a calibrated examiner. Although these conditions do not represent identifying variables without empirical studies to validate them, they can affect the quality of the evaluated lip print; therefore, they should be recognized during the analyses considering the prevalence of these conditions. [ABSTRACT FROM AUTHOR]

In several scenarios like forensic and civilian applications, biometric has emerged as a powerful technology for person authentication. Information extracted from different biometric traits is combined by the Multimodal Biometric (MB) solutions, hence showing a high resilience against presentation attacks. Additionally, they offer enhanced biometric performance and increased population coverage that is required for executing larger-scale recognition. By employing Brownian Motion enabled K-Means Algorithm (BM-KMA) and Cosine Swish activation-based Radial Basis Function Neural Network (RBFNN) (CS-RBFNN) methodologies, an MB authentication system centered on overlapped Fingerprints (FPs), Palm Prints (PPs), and finger knuckles (FKs) is proposed here. Primarily, from the publically available datasets, the overlapped FP images and hand images are taken. Next, to separate the PPs and FKs, the Region of Interest (ROI) is estimated for the hand image. Then, pre-processing, feature extraction, and feature reduction are carried out. From the overlapped FP, the noises are removed using BF; after that, the FP's contrast is enriched using SMF-CLAHE for improving the clarity of the minutiae structure of the ridges. Following this, normalization is performed using the Min–Max operation. Minute features are extracted by separating the overlapped FP using BM-KMA, which makes the system from avoidance of system complexity by separating the overlapping. From this, interest features are selected using KRC-PCA. Next, feature fusion is conducted. Finally, CS-RBFNN is wielded to categorize genuine biometrics from imposter ones. Via performance metrics, the proposed system is further affirmed. The outcomes exhibited that the proposed technique surpasses the other prevailing methodologies. [ABSTRACT FROM AUTHOR]

Context: The electronic, discrete water solvation, and vibrational properties of zwitterionic sulfanilic acid were thoroughly investigated using periodic and non-periodic DFT approaches. The periodic-DFT results, obtained by employing the PBE-TS functional (Perdew-Burke-Ernzerhof (PBE) functional with the Tkatchenko and Scheffler (TS) dispersion correction) were first presented in order to analyze the band structures of the studied crystal. An attentive reading of the predicted band structures has shown three lowest gap energies calculated at 4.23, 4.24, and 4.29 eV arising from the Γ→Γ, Γ→Z, and Γ→S transitions, respectively. Then, non-periodic calculations were carried out, at the B3LYP-D3 level of theory (B3LYP functional with the D3 Grimme dispersion correction) in order to optimize the sulfanilic acid-(H2O)10 complex. Starting from the optimized structure, non-covalent interaction calculations were performed and the H-bonding, van der Waals, and steric effect interactions were identified. Finally, the PBE-TS calculations were strengthened by conducting anharmonic B3LYP-D3 calculations in order to achieve a complete decryption of the experimental IR spectrum of sulfanilic acid. The spectral analysis is not limited only to the interpretation of both the NH/CH stretching and fingerprint regions but also extended to the 1800–2600 cm−1 region, which is characterized by a strong anharmonic effect. In the latter wavenumber region, the large experimental IR band centered at 1937 cm−1 is reproduced theoretically employing the anharmonic B3LYP-D3 calculations. The similarity of this band with those usually considered as a fingerprint of zwitterionic amino acids is observed, and its origin is elucidated theoretically. In the vibrational spectroscopy field, the calculations presented in this study are probably the most appropriate for achieving vast analysis and accurate assignments of vibrational spectra of hydrogen bonding compounds recorded in the solid state. Method: The periodic and non-periodic calculations were conducted within the Density Functional Theory (DFT) using the Generalized Gradient Approximation (GGA) at the PBE-TS level of theory and B3LYP-D3 functional with the 6-311++G(d,p) basis set, respectively. The PBE-TS and B3LYP-D3/6-311++G(d,p) calculations were performed using the CASTEP and Gaussian 09 programs, respectively. In addition, The non-covalent interactions were calculated by the Multiwfn 3.8 software. The obtained results for different calculations were visualized by employing the visualization tools in Materials Studio, GaussView, VMD, and Gnuplot programs. [ABSTRACT FROM AUTHOR]