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Geometry and quantum brachistochrone analysis of multiple entangled spin-1/2 particles under all-range Ising interaction
Active Polymer-Templated Porous Metal Oxide Nanospheres with Tailored Single-Atom Modification for Olfactory Intelligence
Lentinula edodes cultured extract intake alleviates long-term immune deregulation induced by early-life gut microbiota dysbiosis
Electrosynthesis of High-Valent Iridium-Oxo Species for Efficient Oxygen Transfer Defluorination
A multi-source behavioral data framework for interpretable urban tourism forecasting
Abstract Urban tourism demand prediction remains challenging due to its volatility and complex behavioral patterns. To address these issues, a hybrid forecasting framework integrating Long Short-Term Memory (LSTM) networks with Graph Neural Networks (GNNs) was developed. The framework utilizes multi-source behavioral data to improve forecasting accuracy and robustness. The dataset includes social media sentiment, online travel agency (OTA) activity, meteorological information, and mobile signaling records from eight representative Chinese cities collected between 2022 and 2024. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 6.31% and a trend accuracy of 83.7%, both outperforming single-model benchmarks. Feature interpretability and structural equation modeling (SEM) analyses indicate that sentiment indices, user engagement, and holiday effects are the main determinants of forecasting accuracy. The study offers a scalable and interpretable intelligent forecasting paradigm for smart tourism management, clarifying how data heterogeneity and model adaptability jointly enhance predictive performance in urban tourism contexts.
Enzyme-Activatable CXCL13 Chemokine Probes Enable Direct Fluorescence Detection of Hypoxic Subpopulations of Human B Cells
Performance-based criteria for safe and circular digestate use in agriculture
Abstract Anaerobic digestion converts organic waste into renewable energy (biogas) and recyclable nutrients (digestate), generating over one billion tons of digestate annually. While this represents a major resource, its safe reuse remains a bottleneck for nutrient circularity, particularly for closing global nitrogen loops. We analyzed digestates from 23 full-scale digesters in Sweden, Norway, and Denmark across whole, liquid, and solid fractions using germination index (GI) assays and chemical profiling. Three parameters predicted phytotoxicity: total ammonia nitrogen (TAN ≥ 1,122 mg N L − 1 ), potassium (K ≥ 39.6 × 10 3 mg kg − 1 ), and boron (B ≥ 22.5 mg kg − 1 ). When all thresholds were exceeded, germination indices dropped below 50% in every case. Based on these findings, we propose a decision-ready framework linking TAN-K-B thresholds to germination outcomes, guiding mitigation through acidification, stripping, blending, or source control. This outcome-based screening reduces monitoring complexity while maintaining compliance with EU and US pollutant ceilings. Its implementation strengthens nitrogen use efficiency, curbs NH 3 and N 2 O emissions, and secures crop establishment. By shifting from origin-based restrictions to performance-based thresholds, our framework provides transparent certification, builds farmer confidence, and positions digestate reuse as a global lever for climate mitigation, nutrient circularity, and food system resilience.
Synthesis and Excited-State Dynamics in Molecular Nanographene: Herzberg–Teller Vibronic Coupling and Energy Transfer to Porphyrins
Image super-resolution method using a generative adversarial network incorporating attention and residual density
Synergizing Structure-Guided Photocaged Linear Diubiquitin-Dha with Biotinylated Linear-Fab for Time-Resolved Profiling of Interactors in Living Cells
Visual spatial relationship sensitive transformer for image captioning
Switching and Quantifying the Single-Molecule Mechanochemical Reactivity of Four-Membered Carbocycle Mechanophores within a Single, Photoswitchable Polymer Strand
A randomized controlled trial investigating digital resilience training for healthcare professionals
Abstract The unpredictable coronavirus disease and complexity of healthcare settings have caused emotional exhaustion and burnout among healthcare professionals globally. Building resilience at work training can facilitate a change in healthcare professionals’ overall ability to bounce back from adversity. The study aimed to develop, validate, and evaluate a digital resilience training (BRAW) for healthcare professionals in Singapore. Considering the multifactorial nature of resilience at work, the content of the 6-session resilience training was developed based on systematic reviews and psychological theories. The overall rating was 85% of the total points across five experts using the Conduct and Health-Related Website Evaluation Form, indicating valid content. A two-armed randomized controlled trial was used among 410 healthcare professionals. Primary outcome was resilience, and secondary outcomes included work engagement, intention to leave, and counterproductive work behavior. Three study time points were assessed. A generalized estimating equations model showed that the healthcare professionals in the BRAW had significant improvement in resilience, at the post-intervention and/or 3-month follow-up. Findings suggested that BRAW can be considered as supplementary training for healthcare professionals to equip themselves for handling unpredictable pandemics in the future. Further research is needed to examine the long-term effects and generalizability of digital resilience training. Clinical trial numbers : (ClinicalTrials.gov Identifier: NCT05130879, first registration date: 23/11/2021)