Fingerprint authentication based on deep convolutional descent inversion tomography.

Fingerprint authentication is widely used in various areas. While existing methods effectively extract and match fingerprint features, they encounter difficulties in detecting wet fingers and identifying false minutiae. In this paper, a fast fingerprint inversion and authentication method based on Lamb waves is developed by integrating deep learning and multi-scale fusion. This method speeds up the inversion performance through deep fast inversion tomography (DeepFIT) and uses Mask R-CNN to improve authentication accuracy. DeepFIT utilizes fully connected and convolutional operations to approach the descent gradient, enhancing the efficiency of ultrasonic array reconstruction. This suppresses artifacts and accelerates sub-millimeter-level fingerprint minutia inversion. By identifying the overall morphological relationships of various minutia in fingerprints, meaningful minutia representing individual identities are extracted by the Mask R-CNN method. It segments and matches multi-scale fingerprint features, improving the reliability of authentication results. Results indicate that the proposed method has high accuracy, robustness, and speed, optimizing the entire fingerprint authentication process.

Investigation of Mechanical and Shrinkage Performance for Large-Size Cement-Stabilized Aggregates.

Cement-stabilized macadam materials are widely utilized as semi-rigid base materials in road construction. However, conventional cement-stabilized macadam (CCSM) bases often develop shrinkage cracks during early construction and maintenance due to variations in humidity and temperature. Shrinkage cracks can subsequently result in reflective cracks in the asphalt pavement, significantly reducing the overall service life of the road. This study systematically evaluates the shrinkage and mechanical properties of large-size cement-stabilized macadam (LSCSM). Initially, the mix proportion for LSCSM is determined using the Bailey method. Subsequently, an experimental design based on the response surface method is implemented to comprehensively investigate various properties, including unconfined compressive strength, compressive rebound modulus, flexural strength, and the durability aspects of early drying shrinkage and temperature shrinkage through laboratory experiments. Further, the performance differences between CCSM and LSCSM are analyzed comparatively. The findings reveal that the compressive strength of LSCSM surpasses that of CCSM, albeit with comparatively lower compressive rebound modulus and flexural strength. LSCSM demonstrates a unique blend of characteristics, exhibiting traits of both semi-rigid and flexible materials. Furthermore, LSCSM exhibits favorable crack resistance properties, as evidenced by lower dry shrinkage strain, average dry and temperature shrinkage coefficient compared to CCSM. The proposed LSCSM in this study effectively reduces cement dosage and enhances the crack resistance performance of base materials.

Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning.

Deep learning method is applied to spectral detection due to the advantage of not needing feature engineering. In this work, the deep neural network (DNN) model is designed to perform data mining on the laser-induced breakdown spectroscopy (LIBS) spectra of the ore. The potential of heat diffusion for an affinity-based transition embedding model is first used to perform nonlinear mapping of fully connected layer data in the DNN model. Compared with traditional methods, the DNN model has the highest recognition accuracy rate (75.92%). A training set update method based on DNN output is proposed, and the final model has a recognition accuracy of 85.54%. The method of training set update proposed in this work can not only obtain the sample labels quickly but also improve the accuracy of deep learning models. The results demonstrate that LIBS combined with the DNN model is a valuable tool for ore classification at a high accuracy rate.

Creep Characteristics of Layered Rock Masses after Water Absorption Due to Structural Effects.

Affected by the "three highs and one disturbance" (high ground pressure, high ground temperature, high permeability pressure, and strong mining disturbance), deep layered rock mass roadways often display large deformations, resulting in accidents and disasters from time to time. This paper aims to study creep characteristics of layered rock masses after water absorption due to structural effects, combined with acoustic emission energy and dominant frequency value analysis. Experimental results show that as the water content decreases, the long-term strength of the rock sample increases, and the damage becomes more severe. Under the same water content state conditions, the rock samples with bedding angles of 0°, 30°, and 90° have high long-term strength and undergo severe failure, whereas rock samples with bedding angles of 45° and 60° have low long-term strength and undergo mild failure. Under the same water content, the initial energy release increases with the bedding angle. Under the same water content, the energy release during failure decreases first and then increases with the increasing bedding angle. The initial energy, the cumulative energy, the initial main frequency, and the main frequency at the time of failure tend to decrease with the increase in water content.

A planar ultraviolet objective lens for optical axis free imaging nanolithography by employing optical negative refraction.

Lithography is one of the most key technologies for integrated circuit (IC) manufacturing and micro/nano-functional device fabrication, while the imaging objective lens plays one important role. Due to the curved surface of the conventional objective lens, the imaging field of view is limited and the objective lens system is complex. In this paper, a planar objective lens based on the optical negative refraction principle is demonstrated for achieving optical axis free and long depth of focus imaging nanolithography. Through employing a hyperbolic metamaterial composed of silver/titanium dioxide multilayers, plasmonic waveguide modes could be generated in multilayers, which results in optical negative refraction and then flat imaging at ultraviolet wavelength. The corresponding imaging characteristics are investigated in simulation and experiment. At the I-line wavelength of 365 nm, the highest imaging resolution of 165 nm could be realized in the 100 nm photoresist layer under the working gap of 100 nm between the objective lens and substrate. Moreover, this planar objective lens has good ability for cross-scale and two-dimensional imaging lithography, and is similar to a conventional projection objective lens. It is believed that this kind of planar objective lens will provide a promising avenue for low-cost nanofabrication scenarios in the near future.

Ultrasonic Guided Wave Inversion Based on Deep Learning Restoration for Fingerprint Recognition.

As an established biometric authentication approach, fingerprint scanning has received considerable attention due to its high accuracy and reliability. In this article, the fingerprint reconstruction at any position is achieved in large physical domains, which monitors wavefield variations of plate-like structures within arrays through the ultrasonic guided wave. Accurate reconstruction and quantitative characterization of fingerprints are obtained using fast inversion tomography (FIT) based on the deep learning convolutional neural network (DLCNN). Parametric optimization is conducted to reveal submillimeter fingerprint minutiae, and a specific DLCNN model is proposed for the artifact removal in FIT reconstructions. The results prove that the FIT based on DLCNN restoration can significantly improve the imaging quality in terms of increased resolution, reduced reconstruction errors, and higher fingerprint matching confidence. The reconstruction also allows an exponential improvement in computational efficiency as a result of much-reduced sensor numbers. Several factors affecting the performance of the proposed reconstruction method are discussed at the end.

Nonlinear Guided Wave Tomography for Detection and Evaluation of Early-Life Material Degradation in Plates.

In this paper, the possibility of using nonlinear ultrasonic guided waves for early-life material degradation in metal plates is investigated through both computational modeling and study. The analysis of the second harmonics of Lamb waves in a free boundary aluminum plate, and the internal resonance conditions between the Lamb wave primary modes and the second harmonics are investigated. Subsequently, Murnaghan's hyperelastic model is implemented in a finite element (FE) analysis to study the response of aluminum plates subjected to a 60 kHz Hanning-windowed tone burst. Different stages of material degradation are reflected as the changes in the third order elastic constants (TOECs) of the Murnaghan's model. The reconstructed degradations match the actual ones well across various degrees of degradation. The effects of several relevant factors on the accuracy of reconstructions are also discussed.

Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors.

Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier's coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters' placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier's maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

Acid-responsive nanoparticles as a novel oxidative stress-inducing anticancer therapeutic agent for colon cancer.

OBJECTIVE: Nanoparticles can efficiently carry and deliver anticancer agents to tumor sites. Mounting evidence indicates that many types of cancer cells, including colon cancer, have a weakly acidic microenvironment and increased levels of reactive oxygen species. The construction of nano drug delivery vehicles "activatable" in response to the tumor microenvironment is a new antitumor therapeutic strategy. METHODS: Cinnamaldehyde (CA) was designed to link directly with dextran to form a polymer through an acid cleavable acetal bond. Herein, a novel pH-sensitive drug delivery system was constructed with co-encapsulated 10-hydroxy camptothecin (HCPT). Dynamic light scattering (DLS) analysis, transmission electron microscopy (TEM) analysis, and release kinetics analysis of HCPT-CA-loaded nanoparticles (PCH) were conducted to investigate the physical and chemical properties. The cellular uptake signatures of the nanoparticles were observed by confocal microscopy and flow cytometry. Cell viability, cell scratch assay, apoptosis assay, and colony formation assay were performed to examine the potent antiproliferative and apoptotic effects of the PCH. The antitumor mechanism of the treatment with PCH was evaluated by Western blotting, flow cytometry, and TEM analysis. The pharmacokinetics of PCH were examined in healthy Sprague Dawley rats within 6 hours after sublingual vein injection. We lastly examined the biodistribution and the in vivo anticancer activity of PCH using the xenograft mouse models of HCT116 cells. RESULTS: Both HCPT and CA were quickly released by PCH in an acidic microenvironment. PCH not only induced cancer cell death through the generation of intracellular reactive oxygen species in vitro but also facilitated the drug uptake, effectively prolonged drug circulation, and increased accumulation of drug in tumor sites. More attractively, PCH exhibited excellent therapeutic performance and better in vivo systemic safety. CONCLUSION: Overall, PCH not only utilized the tumor microenvironment to control drug release, improve drug pharmacokinetics, and passively target the drug to the tumor tissue, but also exerted a synergistic anticancer effect. The acid-responsive PCH has enormous potential as a novel anticancer therapeutic strategy.

Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage.

Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program "e-Bitter" is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Deep subwavelength interference lithography with tunable pattern period based on bulk plasmon polaritons.

Interference lithography based on surface plasmon polaritons has been proven to break the diffraction limit and deliver the high imaging resolution. However, most previously reported studies suffer from the inflexible pattern pitch for a certain structure ascribed to fixed excitation mode, which limits the applications in micro-/nano- fabrications. In this work, the large area deep subwavelength interference lithography with tunable pattern period based on bulk plasmon polaritons (BPPs) is proposed. By simply tuning the incident angle, the spatial frequencies of the selected BPPs modes squeezed through hyperbolic metamaterial changes correspondingly. As a result, the pitch of the interference pattern is continuously altered. The results demonstrate that one-dimensional and two-dimensional periodic patterns with pitch resolution ranging from 45 nm (~λ/10) to 115 nm (~λ/4) can be generated under 436 nm illumination. Additionally, the general method of designing such an interference lithography system is also discussed, which can be used for nanoscale fabrication in this fashion.

Modeling and experimental study of plasmonic lens imaging with resolution enhanced methods.

Plasmonic lens imaging with some resolution enhancement methods are investigated in this paper, mainly by physical modeling and numerical simulations. The imaging model is based on the refined optical transfer function with extra reflection in imaging space and measured in variant magnetic and electric field components. The influences of structured light illumination and mask patterns' modifications are considered as well. As experimental demonstrations, L-shaped slits pattern with a half-pitch of 60 nm is successfully imaged with 50 nm air distance, by using plasmonic cavity lens lithography and off-axis illumination with 365 nm wavelength light. This study is believed to provide the model and methods for the design of high resolution plasmonic lens employed in nano lithography and optical storage etc.

Determination of the binding mode for anti-inflammatory natural product xanthohumol with myeloid differentiation protein 2.

It is recognized that myeloid differentiation protein 2 (MD-2), a coreceptor of toll-like receptor 4 (TLR4) for innate immunity, plays an essential role in activation of the lipopolysaccharide signaling pathway. MD-2 is known as a neoteric and suitable therapeutical target. Therefore, there is great interest in the development of a potent MD-2 inhibitor for anti-inflammatory therapeutics. Several studies have reported that xanthohumol (XN), an anti-inflammatory natural product from hops and beer, can block the TLR4 signaling by binding to MD-2 directly. However, the interaction between MD-2 and XN remains unknown. Herein, our work aims at characterizing interactions between MD-2 and XN. Using a combination of experimental and theoretical modeling analysis, we found that XN can embed into the hydrophobic pocket of MD-2 and form two stable hydrogen bonds with residues ARG-90 and TYR-102 of MD-2. Moreover, we confirmed that ARG-90 and TYR-102 were two necessary residues during the recognition process of XN binding to MD-2. Results from this study identified the atomic interactions between the MD-2 and XN, which will contribute to future structural design of novel MD-2-targeting molecules for the treatment of inflammatory diseases.

Going far beyond the near-field diffraction limit via plasmonic cavity lens with high spatial frequency spectrum off-axis illumination.

For near-field imaging optics, minimum resolvable feature size is highly constrained by the near-field diffraction limit associated with the illumination light wavelength and the air distance between the imaging devices and objects. In this study, a plasmonic cavity lens composed of Ag-photoresist-Ag form incorporating high spatial frequency spectrum off-axis illumination (OAI) is proposed to realize deep subwavelength imaging far beyond the near-field diffraction limit. This approach benefits from the resonance effect of the plasmonic cavity lens and the wavevector shifting behavior via OAI, which remarkably enhances the object's subwavelength information and damps negative imaging contribution from the longitudinal electric field component in imaging region. Experimental images of well resolved 60-nm half-pitch patterns under 365-nm ultra-violet light are demonstrated at air distance of 80 nm between the mask patterns and plasmonic cavity lens, approximately four-fold longer than that in the conventional near-field lithography and superlens scheme. The ultimate air distance for the 60-nm half-pitch object could be theoretically extended to 120 nm. Moreover, two-dimensional L-shape patterns and deep subwavelength patterns are illustrated via simulations and experiments. This study promises the significant potential to make plasmonic lithography as a practical, cost-effective, simple and parallel nano-fabrication approach.