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In this paper, we present a novel terrain classifica- tion framework for large-scale remote sensing images. A well- performing multi-scale superpixel tessellation based segmentation approach is employed to generate homogeneous and irregularly shaped regions, and a transfer learning technique is sequentially deployed to derive representative deep features by utilizing suc- cessful pre-trained convolutional neural network (CNN) models. This design is aimed to overcome the big problem of lacking available ground-truth data and to increase the generalization power of the multi-pixel descriptor. In the subsequent classification step, we train a fast and robust support vector machine (SVM) to assign the pixel-level labels. Its maximum-margin property can be easily combined with a graph Laplacian propagation approach. Moreover, we analyze the advantages of applying a feature selection technique to the deep CNN features which are extracted by transfer learning. In the experiments, we evaluate the whole framework based on different geographical types. Compared with other region-based classification methods, the results show that our framework can obtain state-of-the-art performance w.r.t. both classification accuracy and computational efficiency. 

Building an efficient and accurate pixel-level labeling framework for large-scale and high-resolution satellite imagery is an important machine learning application in the remote sensing area. Due to the very limited amount of the ground-truth data, we employ a well-performing superpixel tessellation approach to segment the image into homogeneous regions and then use these irregular-shaped regions as the foundation for the dense labeling work. A deep model based on generative adversarial networks is trained to learn the discriminating features from the image data without requiring any additional labeled information. In the subsequent classification step, we adopt the discriminator of this unsupervised model as a feature extractor and train a fast and robust support vector machine to assign the pixel-level labels. In the experiments, we evaluate our framework in terms of the pixel-level classification accuracy on satellite imagery with different geographical types. The results show that our dense-labeling framework is very competitive compared to the state-of-the-art methods that heavily rely on prior knowledge or other large-scale annotated datasets. 

Sorption of myo-inositol hexakisphosphate (IHP), a common type of organic phosphorus in soils, largely controls its mobility and bioavailability. Research on the interaction between IHP and phyllosilicate minerals such as kaolinite, which is commonly present in highly weathered soils, has often been neglected, probably due to the common assumption that negatively charged phyllosilicate minerals have low sorption capacity and binding affinity to IHP and thus do not play any significant role in its fate. Here, the interaction between IHP and poorly crystallized kaolinite (KGa-2) was investigated in batch experiments using Zeta (ζ) potential measurement and 31P nuclear magnetic resonance (NMR) spectroscopy. The results showed that dissolved Al(III) concentration at the adsorption initiation stage increased with increasing IHP concentration at pH 4.0. From pH 2.5 to 9.0, IHP presented a maximum sorption capacity (50 μmol g−1) at pH 4.0 at 24 hr. With IHP sorption, the ζ potential of kaolinite first decreased sharply to a negative value, then gradually increased with resorption of Al(III) released from kaolinite dissolution at acidic pH, and finally approached the original value of the pure kaolinite. 31P NMR spectroscopy and ζ potential analyses revealed that IHP formed inner-sphere surface complexes and aluminum phytate precipitated on kaolinite at low pH (2.5 and 4.0), whereas the formation of inner-sphere surface complexes were the dominant sorption mechanism at pH ≥ 5.5. This study implies that various mechanisms, depending on ambient pH condition can dominate the IHP sorption onto kaolinite, which impacts the mobilityand bioavailability of phosphorus in highly weathered soils. 

Neutrino oscillations, a quantum effect manifesting at macroscopic scales, are governed by lepton flavor mixing angles and neutrino mass-squared differences that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-flavor framework, determining the mass ordering of neutrinos, and probing possible new physics. The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton liquid-scintillator detector located 52.5 km from multiple reactor cores, designed to resolve the interference pattern of reactor neutrinos with sub-percent precision. Here we report, using the first 59.1 days of data collected since detector completion in August 2025, the first simultaneous high-precision determination of two neutrino oscillation parameters, sin²θ₁₂ = 0.3092 ± 0.0087 and Δm²₂₁ = (7.50 ± 0.12) × 10⁻⁵ eV² for the normal mass ordering scenario, improving the precision by a factor of 1.6 relative to the combination of all previous measurements. These results advance the basic understanding of neutrinos, validate the detector's design, and confirm JUNO's readiness for its primary goal of resolving the neutrino mass ordering with a larger dataset. The rapid achievement with a short exposure highlights JUNO's potential to push the frontiers of precision neutrino physics and paves the way for its broad scientific program. 

Abstract The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m²of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper presents the performance results of the detector, extensively studied during the commissioning of the water phase, the subsequent liquid scintillator filling phase, and the first physics runs. The liquid scintillator achieved an attenuation length of 20.6 m at 430 nm, while the high coverage PMT system and scintillator together yielded about 1785 photoelectrons per MeV of energy deposit at the detector centre, measured using the 2.223 MeVγfrom neutron captures on hydrogen with an Am-C calibration source. The reconstructed energy resolution is 3.4% for two 0.511 MeVγat the detector centre and 2.9% for the 0.93 MeV quenched²¹⁴Po alpha decays from natural radioactive sources. The energy non-linearity is calibrated to better than 1%. Intrinsic contaminations of²³⁸U and²³²Th in the liquid scintillator are below 10^-16g/g, assuming secular equilibrium. The water Cherenkov detector achieves a muon detection efficiency better than 99.9% for muons traversing the liquid scintillator volume. During the initial science runs, the data acquisition duty cycle exceeded 97.8%, demonstrating the excellent stability and readiness of JUNO for high-precision neutrino physics. 

Abstract Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by ($$\alpha ,\,n$$ $\alpha ,n$ ) reactions. In organic liquid scintillator detectors,$$\alpha $$ $\alpha$ particles emitted from intrinsic contaminants such as$$^{238}$$ $_{}^{238}$ U,$$^{232}$$ $_{}^{232}$ Th, and$$^{210}$$ $_{}^{210}$ Pb/$$^{210}$$ $_{}^{210}$ Po, can be captured on$$^{13}$$ $_{}^{13}$ C nuclei, followed by the emission of a MeV-scale neutron. Three distinct interaction mechanisms can produce prompt energy depositions preceding the delayed neutron capture, leading to a pair of events correlated in space and time within the detector. Thus, ($$\alpha ,\,n$$ $\alpha ,n$ ) reactions represent an indistinguishable background in liquid scintillator-based antineutrino detectors, where their expected rate and energy spectrum are typically evaluated via Monte Carlo simulations. This work presents results from the open-source SaG4n software, used to calculate the expected energy depositions from the neutron and any associated de-excitation products. Also simulated is a detailed detector response to these interactions, using a dedicated Geant4-based simulation software from the JUNO experiment. An expected measurable$$^{13}$$ $_{}^{13}$ C$$(\alpha ,\,n)^{16}$$ ${(\alpha ,n)}^{16}$ O event rate and reconstructed prompt energy spectrum with associated uncertainties, are presented in the context of JUNO, however, the methods and results are applicable and relevant to other organic liquid scintillator neutrino detectors.