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Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three‐dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K‐sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k‐coefficients Lipschitzian mappings for sparsity(K‐LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

The Daliuhang gold deposit in the Qipengfu (Qixia–Penglai–Fushan) ore concentration area is a typical gold deposit of medium-low temperature hydrothermal veins. Uncertainties regarding the primary sources of ore-forming fluids, as well as whether host rocks contribute materials to the mineralization of the gold deposits in the Jiaodong Peninsula, are still subject to intense debate. Hydrogen–oxygen isotope results show that atmospheric water is involved in ore-forming fluids. According to the results of the helium–argon isotopes of pyrite, it is hypothesized that the initial fluid source was located in the oceanic crust or upper mantle lithosphere above the Early Cretaceous Paleo-Pacific Plate, as it was subducted into the eastern part of the eastern North China Craton. In situ sulfur isotope results show that high δ34S values characterize the pyrite in the main mineralization period. It is inferred that during the thinning and melting process of the lithospheric mantle, the volatile components enriched in pyrite contributed to the release of δ34S. At the same time, when the fluids ascended to the weak zones, such as fissures of ore-endowed peripheral rocks, the δ34S in the peripheral rocks were extracted, and the two processes acted together to cause high δ34S values to occur. Similarly, the lead and strontium isotopic compositions indicate a crust–mantle mixing attribute of the mineralized material source. The zircon U–Pb age of the ore-hosting granodiorite was 130.35 ± 0.55 Ma, and the Rb–Sr isochron age of the pyrite from the main mineralization period was 117.60 ± 0.10 Ma, which represents the timing of felsic magmatism and gold mineralization, respectively, with at least 10 Ma between the magmatism and mineralization. The magma gradually cooled over time after its formation, and when the granodiorite cooled down to 300 ± 50 °C, the temperature and pressure conditions were most conducive to the precipitation of gold. It is inferred that gold-rich initial mantle fluids with volatile components, rising along tectonically weak zones, such as fractures, underwent fluid phase separation in the fractured position of the granite and extracted the gold from the granodiorite, forming gold deposits. [ABSTRACT FROM AUTHOR]