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Invasive species management demands predictive models that balance accuracy with ecological interpretability, yet traditional approaches often fail to capture complex environmental interactions. We evaluated hybrid frameworks integrating biological and machine learning models for rainbow trout ( Oncorhynchus mykiss ) growth in the Lower Colorado River using ten years of tag–recapture data and environmental covariates, comparing traditional and Bayesian von Bertalanffy (VBGM) and Gompertz models with Random Forests, XGBoost, LightGBM, Support Vector Regression, Neural Networks, and ensemble methods through probabilistic performance analysis. Incorporating environmental context and advanced modeling produced substantial gains, with top methods achieving 70–80 percent error reductions relative to baseline models, equivalent to 45–70 mm or 20–32 percent of mean fish length. A stacked ensemble of XGBoost and the VBGM achieved the best performance (RMSE = 15.96 mm, R 2 = 0.966 ) and exhibited stochastic dominance across the posterior, while gradient boosting models formed a strong second tier, led by LightGBM and XGBoost. Bayesian Model Averaging reached comparable accuracy while explicitly quantifying uncertainty. Even traditional mechanistic models improved by up to 80 percent when enhanced with covariates and Bayesian estimation, preserving biological interpretability through parameters such as asymptotic size and growth rate. Feature importance analysis identified initial length, time at large, and weight at release as dominant predictors, and the stacked ensemble outperformed baseline models in over 99 percent of posterior samples. These results establish hybrid ensemble frameworks as powerful tools for ecological forecasting that unite predictive performance with mechanistic insight, providing a generalizable template for systems where both accuracy and interpretability are required.

The ability to precisely engineer the doping of sub-nanometer bimetallic clusters offers exciting opportunities for tailoring their catalytic performance with atomic accuracy. However, the fabrication of singly dispersed bimetallic cluster catalysts with atomic-level control of dopants has been a long-standing challenge. Herein, we report a strategy for the controllable synthesis of a precisely doped single cluster catalyst consisting of partially ligand-enveloped Au 4 Pt 2 clusters supported on defective graphene. This creates a bimetal single cluster catalyst (Au 4 Pt 2 /G) with exceptional activity for electrochemical nitrogen (N 2 ) reduction. Our mechanistic study reveals that each N 2 molecule is activated in the confined region between cluster and graphene. The heteroatom dopant plays an indispensable role in the activation of N 2 via an enhanced back donation of electrons to the N 2 LUMO. Moreover, besides the heteroatom Pt, the catalytic performance of single cluster catalyst can be further tuned by using Pd in place of Pt as the dopant. The fabrication of singly dispersed metal cluster catalysts with atomic-level control of dopants is a long-standing challenge. Here, the authors report a strategy for the synthesis of a precisely doped single cluster catalyst which shows exceptional activity for electrochemical dinitrogen reduction.

The discovery of two-dimensional (2D) magnetism combined with van der Waals (vdW) heterostructure engineering offers unprecedented opportunities for creating artificial magnetic structures with non-trivial magnetic textures. Further progress hinges on deep understanding of electronic and magnetic properties of 2D magnets at the atomic scale. Although local electronic properties can be probed by scanning tunneling microscopy/spectroscopy (STM/STS), its application to investigate 2D magnetic insulators remains elusive due to absence of a conducting path and their extreme air sensitivity. Here we demonstrate that few-layer CrI 3 (FL-CrI 3 ) covered by graphene can be characterized electronically and magnetically via STM by exploiting the transparency of graphene to tunneling electrons. STS reveals electronic structures of FL-CrI 3 including flat bands responsible for its magnetic state. AFM-to-FM transition of FL-CrI 3 can be visualized through the magnetic field dependent moiré contrast in the d I /d V maps due to a change of the electronic hybridization between graphene and spin-polarised CrI 3 bands with different interlayer magnetic coupling. Our findings provide a general route to probe atomic-scale electronic and magnetic properties of 2D magnetic insulators for future spintronics and quantum technology applications. In this work Qiu et al. demonstrate that the application of van der Waals technology to STM/STS will dramatically expand the capabilities of the latter, allowing STM/STS to investigate the structure, electronic properties and magnetism of 2D magnetic insulators at the atomic scale.

Heterogeneous single-atom spin catalysts combined with magnetic fields provide a powerful means for accelerating chemical reactions with enhanced metal utilization and reaction efficiency. However, designing these catalysts remains challenging due to the need for a high density of atomically dispersed active sites with a short-range quantum spin exchange interaction and long-range ferromagnetic ordering. Here, we devised a scalable hydrothermal approach involving an operando acidic environment for synthesizing various single-atom spin catalysts with widely tunable substitutional magnetic atoms (M 1 ) in a MoS 2 host. Among all the M 1 /MoS 2 species, Ni 1 /MoS 2 adopts a distorted tetragonal structure that prompts both ferromagnetic coupling to nearby S atoms as well as adjacent Ni 1 sites, resulting in global room-temperature ferromagnetism. Such coupling benefits spin-selective charge transfer in oxygen evolution reactions to produce triplet O 2 . Furthermore, a mild magnetic field of ~0.5 T enhances the oxygen evolution reaction magnetocurrent by ~2,880% over Ni 1 /MoS 2 , leading to excellent activity and stability in both seawater and pure water splitting cells. As supported by operando characterizations and theoretical calculations, a great magnetic-field-enhanced oxygen evolution reaction performance over Ni 1 /MoS 2 is attributed to a field-induced spin alignment and spin density optimization over S active sites arising from field-regulated S( p )–Ni( d) hybridization, which in turn optimizes the adsorption energies for radical intermediates to reduce overall reaction barriers. A versatile hydrothermal approach in an operando acidic environment created ferromagnetic single-atom spin catalysts (SASCs). Ni-based SASC exhibits a giant magnetic field enhancement of OER activity, boosting both water and saline water electrolysis.

Two-dimensional superconductor (2DSC) monolayers with non-centrosymmetry exhibit unconventional Ising pair superconductivity and an enhanced upper critical field beyond the Pauli paramagnetic limit, driving intense research interest. However, they are often susceptible to structural disorder and environmental oxidation, which destroy electronic coherence and provide technical challenges in the creation of artificial van der Waals heterostructures (vdWHs) for devices. Herein, we report a general and scalable synthesis of highly crystalline 2DSC monolayers via a mild electrochemical exfoliation method using flexible organic ammonium cations solvated with neutral solvent molecules as co-intercalants. Using NbSe 2 as a model system, we achieved a high yield (>75%) of large-sized single-crystal monolayers up to 300 µm. The as-fabricated, twisted NbSe 2 vdWHs demonstrate high stability, good interfacial properties and a critical current that is modulated by magnetic field when one flux quantum fits to an integer number of moiré cells. Additionally, formulated 2DSC inks can be exploited to fabricate wafer-scale 2D superconducting wire arrays and three-dimensional superconducting composites with desirable morphologies. A mild electrochemical exfoliation method has been developed to obtain large-size two-dimensional superconductor monolayers with high crystallinity and production yield, which enables the easy fabrication of twisted van der Waals heterostructures and printed films.

The discovery of designer nucleases has made genome editing much more efficient than before. The designer nucleases have been widely used for mechanistic studies, animal model generation and gene therapy development. However, potential off-targets and host immune responses are issues still need to be addressed for in vivo uses, especially clinical applications. Short term expression of the designer nucleases is necessary to reduce both risks. Currently, various delivery methods are being developed for transient expression of designer nucleases including Zinc Finger Nuclease (ZNF), Transcription Activator-Like Effector Nuclease (TALEN) and Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated (CRISPR/Cas). Recently, virus-like particles are being used for gene editing. In this review, we will talk through commonly used genome editing nucleases, discuss gene editing delivery tools and review the latest literature using virus-like particles to deliver gene editing effectors.

Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal–Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P  < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT ( P  < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P  < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P  < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.

We consider difference schemes for nonlinear time fractional Klein-Gordon type equations in this paper. A linearized scheme is proposed to solve the problem. As a result, iterative method need not be employed. One of the main difficulties for the analysis is that certain weight averages of the approximated solutions are considered in the discretization and standard energy estimates cannot be applied directly. By introducing a new grid function, which further approximates the solution, and using ideas in some recent studies, we show that the method converges with second-order accuracy in time.

In order to ensure that dual-axis rotational inertial navigation systems (RINSs) maintain a high level of accuracy over the long term, there is a demand for periodic calibration during their service life. Traditional calibration methods for inertial measurement units (IMUs) involve removing the IMU from the equipment, which is a laborious and time-consuming process. Reinstalling the IMU after calibration may introduce new installation errors. This paper focuses on dual-axis rotational inertial navigation systems and presents a system-level self-calibration method based on invariant errors, enabling high-precision automated calibration without the need for equipment disassembly. First, navigation parameter errors in the inertial frame are expressed as invariant errors. This allows the corresponding error models to estimate initial attitude even more rapidly and accurately in cases of extreme misalignment, eliminating the need for coarse alignment. Next, by utilizing the output of a gimbal mechanism, angular velocity constraint equations are established, and the backtracking navigation is introduced to reuse sensor data, thereby reducing the calibration time. Finally, a rotation scheme for the IMU is designed to ensure that all errors are observable. The observability of the system is analyzed based on a piecewise constant system method and singular value decomposition (SVD) observability analysis. The simulation and experimental results demonstrate that this method can effectively estimate IMU errors and installation errors related to the rotation axis within 12 min, and the estimated error is less than 4%. After using this method to compensate for the calibration error, the velocity and position accuracies of a RINS are significantly improved.

Creating atomically precise quantum architectures with high digital fidelity and desired quantum states is an important goal in a new era of quantum technology. The strategy of creating these quantum nanostructures mainly relies on atom-by-atom, molecule-by-molecule manipulation or molecular assembly through non-covalent interactions, which thus lack sufficient chemical robustness required for on-chip quantum device operation at elevated temperature. Here, we report a bottom-up synthesis of covalently linked organic quantum corrals (OQCs) with atomic precision to induce the formation of topology-controlled quantum resonance states, arising from a collective interference of scattered electron waves inside the quantum nanocavities. Individual OQCs host a series of atomic orbital-like resonance states whose orbital hybridization into artificial homo-diatomic and hetero-diatomic molecular-like resonance states can be constructed in Cassini oval-shaped OQCs with desired topologies corroborated by joint ab initio and analytic calculations. Our studies open up a new avenue to fabricate covalently linked large-sized OQCs with atomic precision to engineer desired quantum states with high chemical robustness and digital fidelity for future practical applications. Creating atomically-precise quantum architectures with high digital fidelity and desired quantum states is an important goal for quantum technology applications. Here the authors devise an on-surface synthetic protocol to construct atomically-precise covalently linked organic quantum corrals with the formation of a series of new quantum resonance states.