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Phase engineering of atomically thin magnetic chromium tellurides via molecular beam epitaxy
Stabilization of two dimensional switched systems with unstable modes based on the weighted average dwell time strategy
Dynamic acoustic-to-categorical representations of phonemes and prosody along ventral and dorsal speech streams
Abstract Phonemes and prosodic contours are fundamental elements of speech used to convey complementary meanings. Perceiving these elements requires mapping variable acoustic cues onto discrete categories along ventral and dorsal speech streams. While traditional models make clear predictions, exactly where and when this acoustic-to-categorical mapping occurs remains unclear. Using magnetoencephalography and behavioural psychophysics, combined with time-resolved representational similarity and multivariate transfer entropy analyses, we show how phonemes and prosody propagate along the dual streams and how their categorical representations are gradually formed. Contrary to theoretical predictions, acoustic and categorical representations occur in parallel, rather than serially, across time and space for both elements. Moreover, prosody categories extend further along both streams than phoneme categories, with differently weighted contributions of posterior temporal areas. These results highlight a shared principle of parallel acoustic and categorical processing, yet partially distinct abstraction mechanisms for phonemes and prosody, key to access the multilayered meaning of speech.
Performance evaluation of TCP congestion control variants across application workloads in cloud based networks
Arginine methyltransferase PRMT1 equipoises trophoblast development to prevent early pregnancy loss
Correction: Hybrid intelligent RSM–ANN modeling and optimization of precision turning of CK45 steel for calibration devices
A unifying equation for fermentation sustainability across the titer-rate-yield landscape
Abstract Industrial fermentation is central to the sustainable production of fuels and chemicals, yet commercial viability of emerging technologies hinges on improving fermentation titer, rate, and yield (TRY). How these metrics shape system cost remains difficult to generalize due to complex interactions among feedstocks, fermentation, separations, catalytic upgrading, waste management, and facility design. Here, we systematically map theoretical fermentation performance spaces (formed by all potential TRY combinations) for 32 representative biomanufacturing facilities—spanning distinct choices for feedstocks, fermentation regimes and products, separations, and catalytic upgrading—by simulating and evaluating them (via techno-economic analysis, TEA) under uncertainty (600,000 Monte Carlo simulations) and across TRY combinations (7500 TRY combinations for each of 32 configurations). Across this wide design and thermodynamic simulation space, we find the relationship between fermentation TRY and system cost is captured by a simple, generalizable mathematical equation (R 2 of 0.992 − 1.000 across our simulations; 0.954 − 1.000 when validated against prior studies that used different tools). We use this equation to elucidate key drivers that shape cost sensitivity to fermentation performance, generating widely applicable insights. By demonstrating a unifying relationship governs the impact of fermentation on biomanufacturing economics, this work establishes a foundation for agile, holistically predictive, resource-efficient strategies to prioritize fermentation research and development needs and accelerate commercialization of emerging biomanufacturing technologies.
Closed-loop constraint model for emergency care in county medical alliances: grounded theory study in western China’s underdeveloped multiethnic region
Abstract Increasing emergency medical service (EMS) capacity within integrated delivery systems is essential for universal health coverage, particularly in low- and middle-income countries. However, in underdeveloped ethnic regions of western China, developing EMS capacity within county-level medical alliances (CLMAs) remains a critical challenge. Using classic grounded theory, we conducted semistructured interviews with 47 health care professionals from 11 CLMAs across Guangxi, a representative underdeveloped, multiethnic, mountainous region. Systematic three-level coding of 575 statements revealed 57 initial concepts, 15 categories, and four core dimensions: institutional deficiencies (root cause), inefficient coordination (key bottleneck), resource shortcomings (direct manifestation), and service efficacy constraints (final outcome). These dimensions form a closed-loop constraint model reinforced by reverse feedback. Crucially, service efficacy constraints—specifically, poor emergency care quality and collective public cognitive bias—do not merely represent outcomes but actively reinforce the institutional deficiencies that generated them, trapping the system in a low-level equilibrium. This finding explains why piecemeal interventions fail. Effective strengthening requires simultaneously targeting all four dimensions to disrupt the negative cycle, a transferable strategy for integrated health systems facing analogous constraints worldwide.
Coherent transformation of metal halide perovskites
Market-adaptive techno-economic and business management optimization of a renewable poly-generation hub for power, water, and green hydrogen
PregMedNet: Multifaceted maternal medication impacts on neonatal complications
Abstract While medication use is common among pregnant women, medication safety remains insufficiently characterized because studies in pregnant women are challenging due to safety concerns. The recent digitization of healthcare databases and advances in computational methods have created new opportunities for large-scale, retrospective drug safety evaluations. Here, we present PregMedNet, a platform that characterizes multifaceted maternal medication associations on neonatal outcomes during pregnancy, covering more than 27,000 drug-disease pairs across 1,152 medications and 24 outcomes. These results encompass known and additional odds ratios (ORs), adjusted ORs, and drug-drug interactions, systematically analyzed using nationwide claims data and an advanced machine learning pipeline. Notably, one of the associations identified in this study is supported by in vivo experiments, increasing confidence in PregMedNet’s findings and highlighting the utility of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms underlying the associations are explored using a graph learning method, providing candidate pathways for future mechanistic investigations. We expect that PregMedNet will contribute to advancing maternal medication safety and improving neonatal outcomes by providing extensive, multifaceted drug safety information on this previously underrepresented population.
Effect of coil geometry on sensitivity distribution in field-free line magnetic particle imaging
Alignment switching in 3D-printed smectic liquid crystal elastomers
Abstract Extrusion-based additive manufacturing has emerged as a powerful platform for designing shape-morphing materials through controlled orientation. However, existing approaches primarily rely on a single mode of flow-induced alignment, limiting orientation programmability. Herein, we present a direct-ink-writing approach for smectic liquid crystal elastics that exploits two distinct alignment modes within a single ink. The smectic ink exhibits shear- and temperature-dependent orientation switching, enabling molecular alignment either perpendicular or parallel to the print direction. Combined rheological, X-ray, and molecular dynamics analyses reveal that this alignment inversion arises from the preservation or collapse of smectic layers under flow. This reversible switching encodes both contractile and elongational actuation within individual filaments, greatly expanding the design freedom of printed liquid crystal elastomers. We demonstrate 2D and 3D structures with diverse programmed shape transformations, highlighting the potential of this platform for adaptive soft actuators and architected functional materials.
A generalizable ensemble meta-learning framework for assessing 15-minute cities
Abstract Driven by the global challenges of climate change in cities, the 15-Minute City concept has gained prominence as a transformative strategy for fostering resilient and sustainable city environments. However, current approaches remain fragmented, lacking a comprehensive, multi-dimensional, and data-driven framework for systematically assessing such cities. To address this limitation, this study proposes an assessment model based on an ensemble meta-learning technique, aiming to enable a more robust, scalable, and comparable evaluation of city performance within the 15-Minute City framework. The ensemble meta-learning model was developed using eight different machine learning techniques and involves 21 cities across five continents for model training and evaluation. This ensemble meta-learning framework achieves an overall accuracy of 78.5%, with F1 scores of 79.1% for compliant areas and 77.8% for non-compliant areas. Performance levels that remain robust across diverse geographic and morphological contexts, with lower performance observed primarily in regions with limited open data coverage. The ensemble meta-learning framework was further validated and applied to three districts in Istanbul, showing consistent and competitive performance compared to recent literature. By combining cross-continental transferability with interpretable spatial outputs, the proposed framework provides a scalable, reproducible, and methodologically transparent approach to 15-Minute City assessment, offering a practical tool for evidence-based urban planning and policy development.
Development & assessment of polyherbal extracts for treating oral cancer by integrating phytochemistry, bioactivities, and network pharmacology
Abstract Oral carcinoma is a major global health concern and a leading cause of carcinoma-related mortality. This study evaluated a methanolic polyherbal extract for its phytochemical composition and biological efficacy, along with mechanistic insights via in silico network pharmacology. GC-MS and UPLC profiling confirmed diverse bioactive compounds, particularly polyphenols, with UV peaks at 272 nm. The extract showed strong antioxidant activity (IC₅₀ = 108 ± 0.17 µg/mL) and significant antimicrobial effects (inhibition zones: 6.00–21.33 mm). Cytotoxicity assays revealed anticancer activity against OECM-1 oral carcinoma cells (IC₅₀ = 18.86 µg/mL). The polyphenolic fraction suppressed pro-inflammatory cytokines (IL-6, IL-10, TNF-α, IL-1β, TGF-β) with IC₅₀ values of 3.02–6.15 µg/mL. Network pharmacology and molecular docking indicated multi-target interactions of key phytochemicals in pathways relevant to oral carcinoma progression. These findings suggest the polyherbal extract may be a complementary candidate in oral carcinoma management via antioxidant, anti-inflammatory, and antiproliferative effects.