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Giant Orbital Rashba–Edelstein Effect in Crystalline Cu <sub>2</sub> O/Cu Heterostructures
ABSTRACT Orbital current‐induced torque (OT) has emerged as a promising alternative to spin–orbit torque owing to the typically larger orbital Hall conductivity. A substantial OT can be generated at metal/oxide interfaces via the orbital Rashba–Edelstein effect (OREE), which has been predominantly reported in surface‐oxidized Cu structures. However, the lack of well‐defined crystalline and interfacial structures in oxidized Cu has hindered a clear understanding of the underlying mechanism and limited further enhancement of OT. Here, we demonstrate a significantly enhanced OREE in crystalline CuO x /Cu heterostructures with well‐defined interfaces. By employing controlled oxidation of single‐crystalline Cu, we fabricate crystalline CuO x layers with distinct chemical phases and quantify the resulting OT using harmonic Hall measurements. We find that the crystalline Cu 2 O/Cu heterostructure exhibits a damping‐like OT efficiency approximately seven times larger than that of naturally oxidized Cu, highlighting the crucial role of structural ordering and interface sharpness. Notably, this large OT efficiency, combined with the high electrical conductivity of Cu, yields a spin torque conductivity of 1.9 × 10 6 (ℏ/2e)Ω −1 m −1 , exceeding that of Pt. These results establish crystalline Cu 2 O/Cu heterostructures as a promising platform for ultralow‐power spin‐orbitronic devices.
Evaluation of cytotoxic and cellular effects of zinc nanoparticles from Saccharum officinarum (Jaggery) extract on breast cancer cells
An integrated approach to mining layout design for compound-hazards mines with coordinated rockburst and water inrush control
The current status and future directions of short video dissemination on urolithiasis via the TikTok platform: a cross-sectional study
Metal-organic frameworks with linear and branched polyol backbones for dye removal
Abstract In this study, novel metal-organic frameworks (MOFs) were synthesized using functionalized polyols as organic ligands. Polyvinyl alcohol (PVA) and hyperbranched polyglycerol (hPG) were first mesylated and subsequently modified with 5-aminoisophthalic acid, followed by coordination with iron (III) chloride hexahydrate to form PVA-MOF and hPG-MOF structures. Comprehensive characterization, including FTIR, NMR, XRD, SEM/EDX, BET, zeta potential, and UV–Vis DRS analyses, confirmed successful polymer functionalization, preserved MOF crystallinity, porous morphology, high surface area, elemental composition, optical properties, and surface charge relevant to dye adsorption. The resulting MOFs exhibited high adsorption capacities toward both cationic (Rhodamine B and Methylene Blue) and anionic (Fluorescein) dyes from aqueous solutions. Isotherm analysis using Langmuir and Freundlich models revealed that dye uptake was best described by the Langmuir isotherm, indicating monolayer adsorption on a homogeneous surface, with maximum adsorption capacities ranging from 128.17 to 135.34 mg g −1 depending on the dye and MOF type. Thermodynamic studies demonstrated that the adsorption process was endothermic, with increased entropy, and spontaneous for the anionic dye, while non-spontaneous for the cationic dyes. The materials also showed excellent structural stability and reusability potential over three adsorption-desorption cycles. The adsorption performance was further validated using real water samples, confirming that PVA-MOF and hPG-MOF are promising and reusable adsorbents for efficient dye removal in wastewater treatment.
Efficient abnormal behavior detection in information-centric internet of things using SVM
Hydrodynamic investigation of acoustic bubble–surface interactions on nanoarray-coated fins for microelectronic cooling with machine learning-based analysis
Abstract This study presents a novel phase-change cooling strategy that synergistically integrates acoustofluidic bubble dynamics with nanoarray-coated micropin fins to enhance thermal performance. A multidisciplinary framework combines experimental measurements–heat transfer coefficient (HTC), heat flux, velocity fields, and pressure drop–across SS, S30–120, and S-nanosheet architectures with machine-learning models, including LASSO, Random Forest, and Deep Neural Networks. Statistical correlation analyses (Spearman rank and Kendall tau) and interpretability techniques (SHAP, Partial Dependence Plots, Symbolic Metamodeling, Double Machine Learning, and TCAV) provide both predictive accuracy and physical insight. Nanosheet-coated surfaces deliver substantial performance gains, achieving a 71.2% increase in critical heat flux and a 160.9% improvement in HTC compared with smooth surfaces, validated by a highly accurate DNN model (R² = 0.99, MAE = 0.01). SHAP analysis identifies heat flux as the dominant factor governing bubble dynamics, while nanoarrays enhance nucleation and optimize pressure drop to improve efficiency. Symbolic Metamodeling and Double Machine Learning confirm heat flux as the primary driver of heat transfer, with secondary contributions from velocity, which influences bubble residence time, and nanoarray density. Feature-importance results further show that S-nanorod structures and pressure drop strongly regulate nucleation and flow behavior, whereas microreactor parameters have minimal influence.
Benchmarking explainable offensive language detection in Somali with human-annotated rationales
Optimization of water-saving irrigation decision using dynamic spatiotemporal graph neural networks
Abstract Water-saving irrigation is critical for improving water use efficiency and ensuring sustainable food production. However, accurate prediction of farmland water demand remains difficult because soil, crop, and meteorological processes interact across space, depth, and time. To address this problem, we develop an AI-STGNN-based irrigation decision framework in which AI-STGNN serves as the core predictive model. The main methodological contribution is a dynamic three-dimensional (3D) graph that represents vertical soil heterogeneity and time-varying hydrological connectivity, while channel-wise meteorological attention and multi-scale temporal memory are incorporated as agro-hydrology-specific enhancements. On the held-out 2021–2022 test set, AI-STGNN achieved RMSE, MAE, and R² values of 3.63 vol%, 2.88 vol%, and 0.89, respectively, reducing RMSE by 7.2% relative to STGCN and by 16.7% relative to LSTM. Under heavy rainfall, its RMSE increased by 45%, compared with 59–70% for the baseline models. In the two-season field comparison across three representative farms, the AI-STGNN-guided strategy reduced irrigation water use by 12.3–15.7%, increased crop yield by 3.5–4.2%, and improved water use efficiency by 13.7–17.8% relative to conventional empirical irrigation. Because the field comparison covers only two growing seasons and three sites, these practical gains should be interpreted as preliminary rather than definitive. Overall, the proposed framework provides a physically informed, data-driven basis for dynamic water-demand prediction and irrigation scheduling in the studied Midwest cropping system.
Cry1Ab toxicity against four species of the Diatraea sugarcane borer complex in Colombia
Increase in wild animal consumption across Central Africa
Abstract While human activities are driving widespread declines in wildlife populations 1,2 , in Central Africa, the meat of wild animals, or wild meat, represents a major component of the diets of millions of people 3 . To halt faunal degradation while ensuring sustainable use of wildlife, it is crucial to understand the scale and drivers of wild meat consumption. Here, using data from over 12,000 households from 252 locations in Central Africa, we show that wild meat is a fundamental component of the diets of rural populations, accounting for 20% of the recommended daily protein intake, compared with 13% and 6% for those living in towns and cities. We estimate that the total annual biomass of wild meat consumed in Central Africa increased from 0.73 million to 1.10 million tonnes between 2000 and 2022, with increasing demand from towns and cities. To ensure that wild meat is available to rural communities, in accordance with the Sustainable Development Goals 4 and the Kunming–Montreal Global Biodiversity Framework 5 , reducing wild meat consumption in urban metropolises is key. While our results are based on the most comprehensive dataset available, the geographical coverage is incomplete and the dataset represents a minimal fraction of the entire population of Central Africa. Targeted studies are needed to validate our model and assess critical areas of intervention.
Dual-attention residual U-Net with Huber loss for robust and efficient porosity prediction from well logs
A stability-enhanced preconditioned Haar wavelet scheme for the solution of Poisson and Helmholtz equations
Abstract This study presents a modified numerical framework based on scale-3 Haar wavelets for the solution of elliptic partial differential equations, namely the Poisson and the Helmholtz equations. The spatial derivatives are discretized using Haar wavelet expansions and extended to two-dimensions through the Kronecker tensor product, with boundary conditions enforced via integration constants. A theoretical convergence analysis is conducted, and complemented by a rigorous stability investigation based on both classical and effective condition numbers to address ill-conditioning in the resulting linear systems at higher resolution levels. For the Poisson equation, the effectiveness of wavelet-based and multigrid preconditioners is examined, which shows that the multigrid preconditioner yields superior performance compared to the Haar wavelet preconditioner for scale-2 Haar discretization, while the Haar wavelet preconditioner performs better for scale-3 discretization when combined with the conjugate gradient method. For the Helmholtz equation, a shifted Laplacian preconditioner coupled with the GMRES solver is employed. Numerical simulations and spectral analyses, implemented in MATLAB R2025a, confirm the enhanced stability and convergence of the proposed preconditioned wavelet framework.
Spatial atlas of diabetic kidney disease reveals a B cell-rich subgroup
Monitoring Cell Membrane Hydration Using a Fluorescent Probe Sensitive to Trace Water
ABSTRACT The interaction between the cell membrane and water plays an important role in maintaining membrane structure and function. However, direct observation of cell membrane hydration has remained challenging due to the lack of specific and sensitive fluorescent probes for trace water in the cell membrane. Here, we present RMDR (rhodamine‐derivatized membrane deep red), a membrane‐localized fluorescent probe that reports the membrane hydration through polarity‐dependent changes in fluorescence lifetime. RMDR displays clear lifetime decreases with increasing local water content in the range of 0%–1%. Using RMDR, we visualized the increase of cell membrane hydration upon hypotonic stimulation and identified a spatial‐temporal gradient change from the periphery to the center in cell clusters. In addition, the increased membrane hydration was also observed during cuproptosis and other lipid peroxidation processes. These findings demonstrate that RMDR enables the examination of membrane hydration dynamics in living cells and may facilitate investigations of water‐associated processes in physiological and stress‐related contexts.
Roadway roof shear slip movement mechanism induced by the strong dynamic pressure while ultra-thick coal seam mining
Abstract To clarify the roof shear slip movement mechanism of roadways subjected to strong dynamic pressure during ultrathick coal seam mining, this study investigates the return airways of the 5107 fully mechanized top-coal caving (FMTC) panel at Yushupo Mine as a case study. The deformation characteristics and structural instability of surrounding rock in high-stress roadways were systematically analyzed through field observations, theoretical modeling, numerical simulation, and engineering verification. Based on the limit equilibrium theory, a dynamic pressure thick coal seam roadway roof shear slip mechanics model was established, and the spatial distribution of the shear slip zone and the minimum effective anchorage thickness were quantitatively determined. A new integrated collaborative control strategy of “bending resistance shear control displacement” was proposed for ultra thick coal seam roadway, and on-site practice was carried out. The results indicate that the roof shear slip surface initiates approximately 0.53 m from the roadway rib, propagates toward the centerline, and reaches its maximum development at a height of 4.58 m, located only 0.06 m from the centerline. The pronounced reduction in safety factors near the centerline reveals a critical zone highly susceptible to roof instability induced by strong mining-induced dynamic pressure. Discrete element method (DEM) simulations were employed to quantify the effects of key controlling parameters, including roof coal cohesion, parting layer position, and support configurations, on the evolution of roof shear slip movement. On this basis, a coordinated control strategy integrating bending resistance, shear strengthening, and displacement control was proposed. A full-length anchored cable reinforcement scheme tailored for ultrathick coal seams was developed and implemented in the field. Monitoring results demonstrate that the proposed support system effectively controls surrounding rock deformation, optimizes bolt–cable load transfer, suppresses bed separation, and significantly enhances roof stability. Field application confirms its effectiveness in mitigating large deformation in surrounding rock and ensuring the safe and efficient mining of ultrathick coal seams.