Alloy-type anodes offer high capacity, but deep lithiation usually causes severe volume expansion and structural instability. Identifying a thermodynamically stable intermediate phase with a moderate lithiation potential may offer a viable route to mitigating this dilemma. Here, we propose a hierarchical computational workflow that combines global structure search with a machine-learning interatomic potential to systematically explore phase evolution in the Li-rich region of the Li-As binary system. Fine-tuning a pretrained potential on a dataset of approximately 3500 configurations labeled by density functional theory (DFT) yields an energy root-mean-square error (RMSE) of 25.3 meV per atom on an independent test set. Structure screening with the fine-tuned potential, followed by DFT validation, reveals a thermodynamically stable intermediate phase, C2/c-Li3As2, lying on the convex hull between LiAs and Li3As. Phonon and electronic-structure calculations show that this phase is dynamically stable at 0 K and metallic. Electrochemical thermodynamic analysis indicates an equilibrium potential of about 0.95 V (vs. Li/Li+) for the LiAs ⇌ Li3As2 two-phase reaction. When lithiation is limited to Li3As2, the theoretical capacity reaches 536.6 mAh g-1 with a volume expansion of about 68.6%. Further climbing-image nudged elastic band (CI-NEB) calculations and molecular dynamics (MD) simulations show a low Li+ migration barrier. These results identify C2/c-Li3As2 as a promising intermediate phase for shallow-lithiation strategies and highlight its potential for fast-charging anodes.

Concurrent and complex aerial missions with multiple targets exceed the capabilities of a single cooperative formation of high-speed flight vehicles (HSFVs). To address this challenge, this article decomposes a fleet of HSFVs (subject to multiple compounding factors, including unknown aerodynamic disturbances, unmodeled or parametric uncertainties, actuator faults, and potential intervehicle collisions) into several subgroups and develops a recurrent neural network (RNN) online learning-based prescribed-time safe and robust cooperative group formation control protocol under dynamic event-triggered communication. A distributed prescribed-time event-triggered estimator (DP-TE-TE) is first developed to drive all HSFVs to acquire the convex hull information (i.e., input, velocity, and position) spanned by multiple virtual leader vehicles (VLVs) before grouping or the input, velocity, and position information of their respective single VLV within the group after grouping. Then, based on the constraint-following theory, the safety distance inequality between any potentially colliding pair of HSFVs, along with the first-order differential equation involving the formation position tracking error, is converted into collision motion constraints and prescribed-time trajectory tracking constraints, respectively. To enhance the flight control performance of the swarm, an RNN is constructed for each HSFV to learn the unknown nonlinear function induced by multiple compounding factors, thereby providing online compensation for the subsequent control design. Finally, by integrating the constraint-following errors derived from collision motion constraints and prescribed-time trajectory tracking constraints, the RNN compensation term, and the estimated information, the prescribed-time safe and robust cooperative group formation control scheme (P-TSRCGFCS) is proposed. In the simulation examples, the effectiveness of the proposed algorithms is verified by dividing 12 HSFVs and three VLVs into three subgroups to perform the desired cooperative group formation task.

Tracking small-bodied animals in estuarine environments entails significant technological and analytical challenges. Diamond-backed terrapins are small (max 1.4kg) turtles that inhabit salt marshes of the eastern U.S. and the Gulf of Mexico. Terrapin movements have been studied with VHF radio telemetry, acoustic telemetry, and mark and recapture methods, which have indicated maximum straight-line movement distances < 10km and mean home ranges < 1 km2. We deployed 21 Argos satellite tags on adult female terrapins at two sites on Long Island, New York to better understand the spatial ecology of this imperiled species, and to test newly available tracking technology. We processed the location data three ways: (1) we used a location data filter to remove unlikely terrestrial and oceanic locations and applied a state-space model to account for Argos location errors, (2) we applied the state-space model to unfiltered data to determine the effects of not removing unlikely locations, and (3) we used only the highest quality location class 3 (LC 3) locations. We used the data resulting from each of these approaches to calculate four different movement metrics: summer home range size (95% minimum convex polygons (MCPs) and kernel density estimates (50% and 95% KDE, with both reference [href] and least squares cross validation [LSCV] bandwidths)), the total distance traveled from June to August, maximum distance traveled in one day, and daily movement rates. Home ranges estimated from the three processing techniques were similar in size and covered the same spatial areas. Estimates for total distance traveled, daily movement rates, and maximum distance traveled were similar between the state-space modeling techniques, but LC 3 estimated distances were twice as long. Movement metrics and home ranges were similar between the two study sites, despite differences in urbanization and bay size. These results suggest that most movement metrics and home range estimates are fairly insensitive to these different analytical techniques, even at relatively smaller spatial scales. Additionally, our study indicates substantially larger home ranges and longer straight-line movements than VHF telemetry or sonic tag studies, highlighting the utility of satellite tags to improve our understanding of terrapin ecology and conservation.

In silico materials design has become a widely accepted supplement to the experimental trial-and-error approach. Computation-ready, experimental (CoRE) crystal structures are commonly the foundation for creating high-throughput screening workflows to identify optimal compounds, with metal-organic frameworks and covalent organic frameworks being prominent examples. At the same time, data-driven studies devoted to two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs)─emerging photovoltaic materials─are hindered by the lack of consistently curated data sets. Here, we present the CoRE 2D-HOIP database, a collection of 2D HOIP crystal structures that are readily available for atomistic simulations and machine learning. In addition, density functional theory calculations were carried out to obtain thermodynamic and electronic properties, including formation energy, energy above the convex hull, band gap, and electron effective mass. We also implemented a series of graph neural networks to approximate computational and experimental quantities, whereas machine learning interatomic potential for HOIP modeling was developed by finetuning an equivariant neural network originally trained on inorganic compounds. The publicly shared data and models constituting the CoRE 2D-HOIP database are meant to advance the rational design of 2D HOIPs via establishing structure-property relationships and benchmarking machine learning algorithms.

ABSTRACT As critical nodes in the global shipping network, port anchorage areas directly reflect operational efficiency and resource allocation. However, due to limited access to occupied anchor position data, most studies remain at the ship behavior level, lacking systematic analysis of individual anchor positions. To address this gap, this study proposes a Centroid-Expansion Anchor Position Extraction (CEAPE) algorithm based on Automatic Identification System (AIS) data and defines six types of occupied anchor position changes, enabling identification and analysis of port occupied anchor positions data. The algorithm constructs convex hulls of anchoring behaviors, identifies the center areas of occupied anchor positions, and performs spatial expansion, effectively avoiding fusion and mismatch issues found in traditional convex hull overlap methods. Using AIS data from the Port of Los Angeles – Long Beach (2019–2023), experiments validate the algorithm’s accuracy and robustness. Within a three-dimensional framework analyzing changes in number, range, and location, this study systematically characterizes the dynamic evolution of occupied anchor positions and reveals the correlations between these changes and port congestion processes. Overall, the proposed approach uncovers the spatiotemporal evolution patterns of occupied anchor positions and providing a quantitative perspective for understanding port congestion and anchorage spatial planning.

To solve the problem of poor planting quality caused by soil adhesion on the surface of planter when working in wet yellow clay environment, inspired by the morphological structure of dung beetle head surface, a bionic convex hull planter (BCHP) was designed, and its anti-adhesion and desorption characteristics were studied to further explore the mechanism of the bionic convex hull anti-adhesion and desorption. Firstly, the dung beetle in Xishuangbanna Dai Autonomous Prefecture, Yunnan, China Province was taken as the bionic object, and the characteristic parameters of microscopic convex structure were extracted using a Bruker white light interferometer and Vision64 software, and the bionic parameter design of planter was completed. Secondly, three-dimensional models of 13 bionic planters were established by Solidworks2023, and Hertz-Mindlin with JKR model was selected as the contact model of yellow clay. Based on EDEM2019, the simulation of soil adhesion during planting was carried out. Aiming at the minimum amount of soil adhesion, the optimal values of height, diameter and area ratio of bionic convex hull were 0.67mm, 2.26mm and 43.45% respectively through Box-Behnken test and regression analysis. The anti-adhesion mechanism of the bionic convex hull was explored, and the anti-adhesion theory of the non-smooth surface was further improved based on the DEM simulation. Finally, three kinds of BCHP were processed and installed on a self-made small transplanting platform for soil tank test. The verification and comparison test results showed that the anti-adhesion and desorption effects were BCHP5, BCHP13 and BCHP4 in descending order. The test results were highly consistent with the simulation results of EDEM, which verified the reliability of the simulation model. Among them, BCHP5 had the best anti-adhesion and desorption effect, and when the moisture content was 22.72%, 27.13% and 31.94%, the average soil adhesion was reduced by 34.62%, 14.10% and 7.58% respectively compared with the prototype planter. The research results provided a feasible research method for the study of anti-adhesion and desorption mechanism of bionic convex hull and the optimal design of structural improvement of soil-engaging components.

Machine learning assessment of pathologic response in lung cancer resections after neoadjuvant therapy - IASLC MPR Project.

Magnesium-ion batteries (MBs) have the potential to revolutionize next-generation energy storage due to the earth-abundant and dendrite-free nature of magnesium, improved safety characteristics, and reduced environmental impact compared with lithium-ion batteries. However, the development of stable solid-state electrolytes remains challenging because thermodynamic phase instability, such as phase separation or miscibility gaps under operating conditions, can lead to structural degradation that compromises both ionic conductivity and mechanical integrity. In this work, the thermodynamic phase stability and configurational energetics of the selenium-doped MgSc 2 S 1-x Se x spinel system, a promising structural framework for potential magnesium solid-state electrolyte materials, are investigated using the semi-empirical Universal Cluster Expansion (UNCLE) approach combined with Monte Carlo simulations. The cluster expansion model predicted 97 candidate configurations across the S-Se compositional range. Analysis of the calculated ground-state convex hull reveals that most intermediate configurations exhibit positive heats of formation, indicating the presence of miscibility gaps across much of the compositional range. However, several selenium-rich compositions are thermodynamically stable at 0 K, with MgSc 2 S 0.25 Se 3.75 identified as the lowest-energy configuration. Finite-temperature behaviour was further examined using Monte Carlo simulations to estimate critical phase transition temperatures for various S-Se compositions. The simulations show that the system tends toward phase separation at 0 K but transitions to a mixed phase at elevated temperatures, with phase transitions occurring within the approximate temperature range of 250 – 400 K depending on composition. These results provide fundamental thermodynamic insights that may guide the design of structurally stable Mg-ion conducting frameworks and help mitigate thermodynamic phase instability issues in solid-state electrolytes. While crucial for overall electrolyte performance, a detailed investigation of ionic conductivity, which typically requires atomistic simulations such as molecular dynamics, is beyond the scope of the present study and will be addressed in future work.

Machine learning driven exploration of hydride superconductors at ambient pressure

Reactive astrocytes are a hallmark of several neurological diseases in multiple sclerosis and experimental demyelination models. Their morphological alterations are commonly assessed by qualitative histopathology, yet quantitative tools are required to better capture astrocytic heterogeneity and to allow correlations with imaging-derived biomarkers. Here, we present a workflow for the quantitative analysis of Glial Fibrillary Acidic Protein (GFAP) network remodeling in astrocytes in the cuprizone model of demyelination. C57BL/6 mice were intoxicated with cuprizone for 3 or 5 weeks to induce progressive demyelination, microglial activation, and reactive astrogliosis. Brain sections were processed for anti-GFAP immunohistochemistry, and individual astrocytes from the stratum oriens of the hippocampus were digitally reconstructed. Diverse parameters of GFAP topology, including soma size, process length, branching order, convex hull area, and ramification index, were extracted using either the commercial Neurolucida® 360 software or the open-source Simple Neurite Tracer (SNT) plugin in ImageJ. Principal component analysis revealed clear differences between control astrocytes and astrocytes in cuprizone-intoxicated animals, with reactive astrocytes displaying increased numbers of primary processes, enhanced bifurcation, and process complexity. Comparative evaluation of Neurolucida® 360 and SNT demonstrated that both tools are suitable for astrocyte reconstruction, although Neurolucida® 360 enabled faster and more detailed tracing. This protocol provides a reproducible pipeline for the quantitative assessment of astrocyte morphology under control and pathological conditions, thereby supporting future efforts to link cellular remodeling to functional outcomes in neuroinflammatory disease models.

Abstract In this paper, we investigate several subsets of n -copulas and n -quasi-copulas from the perspective of convex lineability and the recently introduced concept of convex spaceability. Our purpose is to determine when such families contain extremely large algebraic structures, namely linearly independent sets of cardinality of the continuum whose convex hull, and in some cases a closed convex linearly independent subset, remain entirely inside the class under study. These include the families of asymmetric copulas, copulas with maximal asymmetric measure, and proper n -quasi-copulas, among others. In contrast, for several other natural classes of copulas, we show that (maximal) convex lineability holds, while convex spaceability remains an open problem.

Precipitation is the primary driver of phosphorus loss from farmland. However, the multitemporal and spatial characteristics, as well as the mechanisms of phosphorus loss across different farmland types at the watershed scale, remain poorly understood. This study examines the multiscale impacts of different farmland types on phosphorus loss processes driven by long-term precipitation, focusing on two adjacent small watersheds, the Yulin River and the Dahong River, in the Sichuan Basin. First, based on remote sensing data from 2021 to 2022 (precipitation, temperature, land cover) and river water quality monitoring data (total phosphorus (TP), dissolved oxygen (DO), and permanganate index (COD)), the spatiotemporal variations in river water quality are analyzed. The results indicate that under similar climate conditions and pollution source distributions, the downstream TP concentration in the Dahong River (dominated by paddy fields) is significantly lower than that in the upstream, whereas in the Yulin River (dominated by dryland farming), the downstream TP concentration is higher than that in the upstream. Secondly, the study develops a multisource coupling analysis framework that integrates detrended cross-correlation analysis (DCCA), multifractal detrended cross-correlation analysis (MFDCCA), and remote sensing data to evaluate the impact of different farmland types on phosphorus loss behavior. The DCCA analysis results show that precipitation and TP exhibit a clear long-term correlation, with scaling exponents all exceeding 0.5. In the paddy field control area, precipitation and TP exhibit a positive correlation over long time scales. In contrast, in the dryland control area, a positive correlation is observed only over short time scales (< 60days), while at longer time scales, the correlation turns negative and shows significant fluctuations. MFDCCA further reveals that the coupling relationship between TP and precipitation generally exhibits multifractal characteristics. The multifractal intensity is higher in the dryland-controlled fields (Δh = 1.47), indicating that TP is more sensitive to precipitation perturbations and less stable. The multifractal intensity is lower in the paddy fields (Δh = 1.20), indicating a more stable coupling relationship. Finally, by combining sliding window analysis with 3D convex hull volume calculations, the study quantitatively assesses the distribution differences of multifractal parameters. The results show that areas dominated by paddy fields exhibited stronger aggregation of multifractal parameters (convex hull volume difference of 0.69). In contrast, dryland areas showed more dispersed patterns (convex hull volume difference of -0.32). This study innovatively integrates fractal theory, remote sensing, and watershed observation data to establish a coupling analysis framework for identifying nonpoint source pollution. It elucidates the multiscale response patterns of phosphorus loss under different farmland types. It provides a new technical approach for quantitatively assessing the effects of farmland type on the spatial distribution and migration pathways of TP.

A bstract We re-examine positivity bounds on the 2 → 2 scattering of identical massless real scalars with a novel perspective on how these bounds can be used to constrain the spectrum of UV theories. We propose that the entire space of consistent weakly-coupled (and generically non-supersymmetric) UV amplitudes is determined as a convex hull of the massive scalar amplitude and a one-parameter family of scalarless “extremal amplitudes” parameterized by the ratio of the masses of the two lightest massive states. Further, we propose that the extremal amplitudes can be constructed from a similar one-parameter set of maximally supersymmetric amplitudes, leading to the surprising possibility that the S-matrix bootstrap with maximal supersymmetry may be sufficient to determine the entire allowed space of four-point amplitudes! Finally, we show that minimal spectrum input reduces the allowed space of Wilson coefficients to small islands around the open string Dirac-Born-Infeld tree amplitude and the closed string Virasoro-Shapiro amplitude.

Let A and B be two positive bounded linear operators acting on two complex Hilbert spaces H and K , respectively. In this paper, we study the ( A ⊗ B ) - maximal numerical range W max A ⊗ B ( T ⊗ S ) of the tensor product T ⊗ S for two bounded linear operators T and S on H and K , respectively. In the context of this work, we show under some hyponormality conditions, the following equality W max A ⊗ B ( T ⊗ S ) = co ( W max A ( T ) ⋅ W max B ( S ) ) holds, where W max A ( T ) , W max B ( S ) and co ( ⋅ ) denote respectively the A-maximal numerical range of T, the B-maximal numerical range of S and convex hull. Furthermore, we extend Fong's result to the class of operators defined on the semi-Hilbertian space.

Macrobenthos trophic responses to mangrove restoration in Yanpu Bay: Insights from trophic niches and food source dynamics.

Containment control for stochastic multiagent systems with multiple dynamic leaders and compound noises.

The vast combinatorial space of metal–organicframeworks(MOFs) has led to their widespread consideration across diverse applicationareas. That said, much remains unknown about what factors govern theirthermodynamic stability. Herein, we use density functional theoryto compute the formation energy and construct convex hull phase diagramsfor 20,000+ MOFs and coordination polymers. Using the energy abovehull as a measure of stability with respect to decomposition and phasetransitions, we validate and expand upon previous hypotheses thatall MOFs are thermodynamically metastable and that there is an inherentenergetic penalty associated with permanent porosity. We also describehow MOF composition and metal/linker identity influence the degreeof metastability, in addition to demonstrating how the energy abovehull can be used as a synthesizability metric for newly proposed MOFs.To democratize the knowledge gained from our study, we have releasedthe QMOF-Thermo Database, which is the first database of energy abovehull values for MOFs and coordination polymers. We conclude by usingthe QMOF-Thermo Database to benchmark the ability of pretrained machinelearning interatomic potentials to predict the energy above hull ofMOFs, and we identify opportunities to correct their performance.

The movements of five juvenile Booted Eagles (Hieraaetus pennatus) during the post-fledging dependence period (PFDP) were studied using GPS/GSM tracking. The Booted Eagles were tagged at two breeding territories in the Altai-Sayan region of Russia between 2014 and 2021; 2024 locations from the birds were obtained during the PFDP. The PFDP, from leaving the nest until the start of migration, lasted from 40 to 55 days. The area of home ranges during the PFDP, defined as the minimum convex polygon (MCP 100%), varied from 0.05 to 191.23 km², averaging (n=5) 52.25±81.33 km², while the core area (KDE 50%) varied from 0.0008 to 9.97 km², averaging (n=5) 2.16±4.37 km² (Median – 0.15 km²).

Convex hull pricing (CHP) is a pivotal approach to enhance market transparency by minimizing uplift costs. This pa per revisits the mathematical foundation of CHP and provides an explicit formulation of the Lagrangian dual formulation for network-constrained unit commitment (NCUC), further defining the CHP. Here, a convex hull model for single-unit commitment (1UC) problems is established with ramping constraints and minimum on/off time, making this explicit formulation implementable and further delivering the optimal Lagrangian dual solution via two linear programming (LP) models. The first LP reformulates the NCUC by replacing mixed-integer constraints with convex hull relaxations, while the second, obtained by fixing the inner variables in the Lagrangian dual problem of the NCUC to their optimal val ues from the first LP, generates the optimal Lagrangian dual solution. Numerical experiments on the IEEE-118 and Polish-2383 sys tems validate the superiority of CHP in reducing uplift costs and of this proposed pricing method in computational efficiency.

The traditional prediction methods primarily emphasize accuracy. This letter presents an end-to-end training framework that integrates a convex hull support function with the Mean Squared Error (MSE) for cost-focused learning. Specifically, by introducing a convex hull loss, decision cost information is incorporated into the model's learning process, while the MSE term ensures that the predicted results align with the actual distribution. Experimental results on the IEEE-33 bus system demonstrate that the proposed method achieves a 12.4% improvement in cost reduction compared with traditional forecasting methods. Furthermore, the proposed approach significantly improves training efficiency compared with other decision-focused baselines. The proposed framework provides a generalizable paradigm for cost-aware forecasting under renewable uncertainty.