Diffuse reflectance spectroscopy exploits multiple scattering and absorption of light in superficial tissue to capture characteristic spectral signatures. When combined with machine learning, this approach enables accurate, real-time, non-invasive detection. Conventional drug screening methods, based on biochemical assays of urine, blood, or hair are limited by their invasiveness, long turnaround time, high cost, and poor portability that hinder their use in rapid, large-scale applications. To address these limitations, we propose a fast, efficient, and high-accuracy non-invasive drug screening framework that integrates diffuse reflectance spectroscopy with sex-stratified machine learning models. A skin spectral trace element database was established using data collected from over two thousand individuals. Two sequential classification pipelines were developed: support vector machines for male subjects and an Extreme Gradient Boosting for female subjects. In a case study focused on heroin detection, both models achieved high classification accuracy and demonstrated strong performance in terms of the area under the receiver operating curve. This approach provides a practical solution for preliminary drug screening and demonstrates the promise of combining optical spectroscopy with artificial intelligence for real-world drug surveillance.

Knowledge of the business relationships between Autonomous Systems (ASes) is the basis for studying many aspects of the Internet. Despite the significant progress achieved by the latest inference algorithms, their inference results still suffer from errors on many special or critical links, thus hindering many relationship-related applications. We take an in-depth analysis on the challenges inherent in inferring AS relationships, including complex routing policies, limited and biased vantage point (VP) coverage, as well as a lack of accurate validation data. To address these challenges, we introduce HELA, a framework for inferring AS relationships with a hybrid of empirical and machine learning algorithms. HELA incorporates an array of grouping, voting, and machine learning algorithms and allows flexible substitution of each. We systematically evaluate various combinations of them to determine the most effective one for HELA. Furthermore, we describe the collection of varied validation datasets, including BGP community and RPSL records from Internet Routing Registries (IRRs), as well as OneStep community. Our up-to-date dataset corrects errors in previous published validation sets, contains 95% more labelled links, and exhibits a closer alignment to actual link distribution. Using routing data and validation datasets composed for each month during$2021\sim 2023$, we access HELA’s superiority in both inference accuracy and stability compared to the state-of-the-art inference algorithms, i.e., AS-Rank, ProbLink, and HELA’s predecessor TopoScope. In particular, HELA achieves up to$2.9\times $reduction on error rates across overall datasets, up to$2.6\times $reduction with a$4.5\times $decrease on standard deviation on incomplete and biased datasets, and up to$1.7\times $reduction on various sources of validation datasets.

In recent years, the complex-valued convolutional neural network (CV-CNN) for processing complex data has made great use in the field of SAR data processing. In this paper, a complex-valued activation enhancement method named CRIA is constructed based on the cross-fusion of real and imaginary activation in the activation layer of CV-CNN, the core of which is to cross-combine the real and imaginary parts of the activation output of the two activation functions to enhance the overall processing of complex data, to enhance the ability of the network to parse complex value information. By conducting classification experiments on ship slices in SAR images of complex data, the experimental results show that the CRIA method in the activation layer can accelerate the network convergence speed and enhance the network classification performance.

The marine controlled source electromagnetic method (MCSEM) is widely used in marine oil, gas exploration and deep structures investigation because of its low cost and high efficiency. In this paper, a high-precision forward algorithm for 3D modeling for MCSEM is proposed. Unstructured meshes are used to discretize the model domain, and local mesh refinement techniques are applied, which is conducive to simulating complex terrains and targets. The application of electric dipole discrete technology can load wire excitation sources with arbitrarily complex spatial shapes, which can simulate more realistic electromagnetic field distribution of marine controllable sources electromagnetic in actual exploration. Absorption boundary conditions based on real number and exponential stretching techniques are introduced to improve the numerical solution accuracy. To improve the efficiency of solving, the open-source massively parallel solver (MUMPS) based on the multifrontal algorithm is used to solve the finite element equations. Finally, some typical geoelectric models are designed to verify the correctness and effectiveness of the proposed algorithm. The calculation results show that higher order finite elements bring about higher accuracy. In addition, the loading of absorption boundary conditions is simple and effective than conventional Dirichlet boundary conditions.

The conventional, L2-norm-based, regularization term in electromagnetic (EM) inversions implements smooth constraints on model complexity in the space domain, which can smoothen the boundaries of complex underground structures. To improve the resolution of 3-D frequency-domain airborne EM (AEM) inversions, we propose a new algorithm for sparse-regularized inversion based on the shearlet transform. Unlike traditional methods that invert the model parameters in the space domain, we first transform the 3-D resistivity model into the frequency domain and then invert the sparse coefficients using an L1-norm measure to ensure the sparseness of the solution. Finally, we transform the shearlet coefficients back to the space domain to update the model. The shearlet transform has inherent multiscale and multidirectional properties, making it capable of effectively extracting complex geometries such as curved boundaries. We adopt the finite-difference method and the iteratively reweighted least-squares scheme for our 3-D AEM modeling and inversions and apply the “moving footprint” technique to speed up the inversion. Tests using synthetic data show that sparse-regularized inversion based on the shearlet transform can obtain more-focused inversion results than conventional smoothness-constrained inversions based on the L2-norm. Tests using field survey data also reveal that the new method can achieve more realistic underground structures.

In this article, to solve the frequency-domain airborne electromagnetic (EM) modeling problem, we express the electric and magnetic field by the vector magnetic potential and the scalar electric potential and derive the governing equation using the Galerkin weighted residual method under the Coulomb gauge. To avoid the strong singularity of the solution near the transmitting source, we directly solve the relatively slow-changing secondary potential. By introducing the spectral element method (SEM) based on unstructured tetrahedral grids, in which the EM field in each element is characterized by high-order Proriol–Koornwinder–Dubiner (PKD) orthogonal polynomials, we can obtain stable and accurate numerical solutions. By establishing the mapping relationship between the physical domain, the right-angled tetrahedral reference domain, and the orthogonal hexahedral domain, we can easily accomplish the element matrix analysis and calculation. The numerical examples show that, when the SEM is used to simulate the airborne EM response, a high accuracy can be obtained even if a very coarse grid is used. Furthermore, since the tetrahedral grids can easily model the complex boundaries, the SEM based on unstructured grids has significant advantages for simulating complex underground structures.

Flow-based network measurement enables operators to perform a wide range of network management tasks in a scalable manner. Recently, various algorithms have been proposed for flow record collection at very high speed. However, they all focus on processing traffic in a short time window, but overlook the fact that flow measurements are typically needed continuously for unlimited time. To this end, we propose a new algorithm named SuperFlow to support continuous and accurate flow record collection at very high speed by monitoring the flow activeness and exporting the inactive records from the data plane automatically. Our data structures and the corresponding algorithms are carefully designed and analyzed, so the above goal is achieved with limited memory and bandwidth consumption. We implement SuperFlow on both x86 CPU and state-of-the-art PISA target. Comprehensive experiments show that SuperFlow consistently outperforms its competitors significantly. Especially, compared with the best competitor, it records around 136.7% more flows, reduces the error in flow size estimation by 51.5%, and reduces the memory or bandwidth consumption by up to 71.0%, while bringing only negligible throughput degradation.

Small target detection in infrared (IR) images has been widely applied for both military and civilian purposes. In this study, because IR images contain sparse and low-rank features in most scenarios, we propose an optimal IR patch-image (OIPI) model-based detection method to detect small targets in heavily cluttered IR images. First, the OIPI model was generated based on a conventional IR image model using a novel optimal patch size and sliding step adaptive selection algorithm. Secondly, the sparse and low-rank features of IR images were extracted and fused to generate an adaptive weighted parameter. Thirdly, the adaptive inexact augmented Lagrange multiplier (AIALM) algorithm was applied in the OIPI model to solve the robust principal component analysis (RPCA) optimization problem. Finally, an adaptive threshold method is proposed to segment and calibrate targets. Experimental results indicate that the proposed algorithm is capable of detecting small targets more stably and accurately, compared with state-of-the-art methods.

To overcome the drawbacks of conventional methods to detect infrared (IR) small targets, including high false alarm probability and low detection rate, a novel approach to detect IR small targets based on local gradient field feature contrast measure (LGFFCM) is presented in this letter. First, the distinctive characters of the target, background, and clutter in the infrared gradient vector field (IGVF) are investigated. Next, a local contrast measure based on IGVF features was proposed. Then, an adaptive threshold method to obtain coarse detection results is proposed. Finally, a flux across the plane curve in the IGVF, which can eliminate clutter and enhance target, is defined. The experimental results indicate that the proposed approach shows superiority to other advanced approaches and can effectively and stably reduce false alarm probability and enhance detection rate.