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Enzyme-inspired single-molecule photocatalyst enables singlet-oxygen-driven asymmetric epoxidation and sulfoximination
A combined ex vivo fundus imaging–histology protocol for clinicopathological validation of human donor eyes with limited medical history
Strain-coordination strategy enabling long-cycling all-solid-state lithium-sulfur batteries
Long-memory modeling and forecasting of monthly mean sunspot numbers for cycles 25 & 26 using ARFIMA model
Abstract The number of sunspots is a key indicator of solar magnetic activity and strongly influences space weather, affecting technological systems and Earth’s environment. This study develops a long-memory statistical framework based on the Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model to forecast monthly mean sunspot numbers ( $$\hbox {SN}_m$$ ) for Solar Cycles 25 and 26 using historical data from January 1749 to October 2025. The model parameters are selected using the Bayesian Information Criterion (BIC), and the fractional integration parameter d is estimated via maximum likelihood ( $$\hat{d} \approx 0.27599$$ ), indicating significant long-memory behavior in the series. The selected ARFIMA (3,d,2) model captures the persistent dynamics of solar activity and provides accurate in-sample fitting, with a high correlation coefficient (0.989) between observed and fitted values. Forecast results predict a maximum $$\hbox {SN}_m$$ of 224.7 for Solar Cycle 25 (observed peak: approximately 216 in August 2024) and 179.3 for Solar Cycle 26 around March 2035, suggesting a slightly weaker upcoming cycle. Model performance is evaluated using standard accuracy measures, including RMSE, MAE, and relative error metrics, computed against observed data within a validation framework. The proposed model achieves an RMSE of 3.37 and a SMAPE of 9.25%, indicating improved forecasting accuracy.
Aromaticity and structure switching of cyclopropametallaindole to metallaquinolinium
Establishment and validation of a predictive scoring system for titanium clip retention at six months after endoscopic submucosal dissection for gastric lesions
Solving the Hubbard model with neural quantum states
Liquid crystal-like self-organization of glioblastoma is associated with consistent migration for high cell densities
Abstract Glioblastoma is the most lethal and frequent type of primary brain tumors, characterized by a high proliferative and infiltrative capacity. Here, we used live cell imaging to analyze the effect of cell density variations on the migratory capacity of established and primary glioblastoma cell lines. We found that proliferation events promoted local velocity of glioblastoma cells, up to three cell-length away from the proliferation event. Furthermore, two phenotypes were found when subjecting glioblastoma cells to a cell density gradient. While one phenotype was characterized by the active migration of cells, independent of proliferation, the other was mostly driven by cell proliferation. Lastly, the analysis of the effects of an overall increasing cell density demonstrated that cells showing signs of self-organization, forming liquid crystal-like structures are able to maintain a high migratory potential even for high cell densities. Notably, the emergence of small-scale liquid crystal-like order was associated with a better maintenance of cellular migration, even in cell populations that were largely in a state of migratory arrest. Thus, structure formation might help glioblastoma cells to move efficiently in states of high confinement, thereby maintaining infiltrative properties.
Automated in situ microfluidic Random-seq for robust single-nucleus and spatial total RNA profiling of diverse FFPE specimens
IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization
Negative-curvature interfaces enable highly synergistic strength-ductility-toughness at 77 K.
AI self-efficacy and anxiety among university teachers
Abstract With the rapid integration of AI into higher education, teachers’ psychological responses are critical for technology adoption. This study examines AI self-efficacy and AI anxiety among university teachers in a Chinese university. It investigates the relationship between these two constructs and explores differences based on gender, age and academic major. A quantitative survey was administered to 350 teachers selected through stratified random sampling based on major. Results showed that both AI self-efficacy and AI anxiety were significantly above the neutral midpoint (M = 4.48, SD = 0.76; M = 4.35, SD = 0.85). AI self-efficacy was strongly and negatively associated with AI anxiety ( r = − 0.59, p <0.01) and remained a significant negative predictor after controlling for gender, age, and major (β = − 0.112, p = .026). Female teachers reported higher anxiety and lower self-efficacy than male teachers, whereas computer science teachers reported the highest self-efficacy and the lowest anxiety. These findings suggest that university teachers may feel simultaneously capable of using AI and apprehensive about its broader implications. The study provides evidence from Chinese higher education and highlights the value of differentiated institutional support that addresses both teachers’ confidence in using AI and their professional concerns.
Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder
Microstructural enhancement of concrete paver blocks using steel sludge as a sustainable fine aggregate replacement
Molecule-well in platinum-zeolite engineers molecular adsorption for highly selective hydrogenation
Structural investigation, theoretical and biological studies of 8-hydroxyquinoline azo ligand and its metal chelates
Abstract In the current work, three novel bimetallic Ni(II), Cd(II), and Pt(II) complexes of the azo dye ligand with the name 5-(2,6-dimethyl-pyrimidin-4-ylazo)-quinolin-8-ol were planned as our target for synthesis and application in the medicinal field. The free ligand and the synthesized metal complexes were subjected to all the available analytical and spectral tools to get a clear and correct insight into their structures and geometries. Such tools supported the formation of bimetallic complexes with 4-coordination geometry, as assured from the results of elemental analysis, mass, infrared (IR) spectroscopy, and thermal analysis. Magnetic moment of 2.87 B.M for Ni(II) complex (per one Ni centre) along with UV-Vis spectra assured its tetrahedral geometry. Cd(II) complex was found to be tetrahedral, and Pt(II) complex is square planar. Biological evaluations demonstrated significant anticancer and antibacterial efficacy of most of the tested compounds. The highest activity as antimicrobial agent was afforded by the ligand, showing an inhibition zone diameter of 51 and 52 mm against Aspergillus fumigatus and Bacillus subtilis, respectively, showing higher activity than the metal complexes. The Cd(II) exhibited the highest efficacy, among all the tested compounds, toward both screened tumor cell lines (yielding IC 50 of 3.44 ± 0.12 µg/ml for HepG-2 and 4.91 ± 0.26 µg/ml for the MCF-7 line). Computational analysis via Density Functional Theory (DFT) was conducted to elucidate the electronic and structural properties of the synthesized complexes. Geometric optimizations validated a square planar geometry for the Pt-complex, while the Ni and Cd-complexes exhibited distorted tetrahedral geometries. Analysis of the energy profiles, dipole moments, and the energy separation between HOMO and LUMO levels provided insight into the chemical stability and reactivity of the compounds. Furthermore, molecular electrostatic potential (MEP) mapping identified key sites for nucleophilic and electrophilic interactions. To evaluate their pharmacological viability, SwissADME analysis was employed to assess essential pharmacokinetic profiles, including lipophilicity, solubility, and drug-likeness, positioning these complexes as potential therapeutic candidates.