In this paper, we establish the existence and multiplicity of normalized solutions for a class of Hamiltonian elliptic systems. Our work complements and extends the previous results on normalized solutions for strongly indefinite problems, especially for the Hamiltonian elliptic system with general nonlinearity and the potential to be nonvanishing at infinity.
Medication recommendation constitutes a significant yet challenging frontier in personalized healthcare, modeling optimal and safe medication combinations for individualized patient. However, prediction bias still exists in existing methods as: (1) Insufficient Causal Inference Capabilities; (2) Deficiencies in Patient Embedding; (3) Limitations in Bias Correction. We propose a Large Language Model-Driven Causal Reasoning with Synergistic Experts for Medication Recommendation (LLM-CSER) method, a medication recommendation method that LLM-driven causal reasoning with synergistic experts. Specifically, a large language model-driven causal reasoning module firstly constructs an interpretable disease-procedure-medication causal graph from medical semantics. Furthermore, an expert-collaborative patient embedding module simulates multidisciplinary consultations: the intrinsic expert extracts drug molecular substructure features while the extrinsic expert integrates cross-type medical entity relations. Additionally, a hierarchical temporal encoder captures multi-level temporal patterns and cross-modal dependencies across disease, procedure, and medication sequences. Moreover, an adaptive bias correction mechanism dynamically learns dataset-specific thresholds and correction magnitudes. Extensive experiments on MIMIC-III and MIMIC-IV demonstrate that LLM-CSER outperforms existing methods in Jaccard, F1, and PRAUC metrics while maintaining lower DDI rates, showcasing advantages in accuracy, safety, and interpretability.
A network's reliability is closely tied to its fault tolerance. When a connected network is fragmented by vertex failures, an increase in the number of small components aggravates network fragmentation and reduces reachability, thereby degrading fault tolerance. Thus, analyzing the abundance and structure of small components provides a useful measure of network reliability. This paper focuses on all possible structures of small components in an arbitrary regular interconnection network G after removing at most kappa(r)(G) - 1 vertices, where kappa(r)(G) is the r-extra connectivity of G. Applying the results, we study the component reliability of (n, k)-bubble-sort network B-n,B-k and (n, k)-star network S-n,S-k. Besides, we propose an algorithm for calculating the minimum size of neighborhood of all small components by using B-n,B-k and S-n,S-k as application cases. Moreover, we verify the availability and effectiveness of the algorithm by conducting simulation experiments and analyzing their performance.
Introduction Climate conditions increasingly influence outdoor recreation, including when people travel, which destinations they choose, how far they move, and how long they remain outdoors. This issue is relevant for high-density, heat-prone cities, where urban nature areas support leisure, social interaction, restoration, and climate adaptation. Yet prolonged recreational mobility in green and blue spaces remains underexamined in transport and health research. Methods This study investigated long-duration recreation (LDR) use of urban nature areas in Guangzhou, China, a humid-subtropical metropolis. LDR was defined as stay events lasting more than 1 h within publicly accessible parks, forests, wetlands, waterfronts, and other nature-based open spaces. Large-scale mobile phone signaling data were integrated with urban nature area boundaries, spatially interpolated meteorological data, and the Universal Thermal Climate Index (UTCI). A selection–travel–stay framework was used to describe destination use, travel characteristics, and stay behavior. Associations with background UTCI were examined for daily destination use and stay-duration patterns. Results LDR activity was higher on weekends than on weekdays, with morning and late afternoon peaks in summer and an afternoon peak in winter. Destination use varied by gender and age group and was concentrated in public sites and waterside, woods, and meadow settings. In summer, UTCI associations varied across districts and destination types (r = −0.387 to 0.481), and higher-UTCI days showed lower shares of longer stays. In winter, UTCI was positively correlated with daily stay-record counts across duration categories (r = 0.148–0.433), with r = 0.374 for total LDR records. Conclusions The findings highlight seasonal patterns of prolonged urban nature use associated with background thermal conditions and their relevance to heat-responsive transport, public-space, and green-blue infrastructure planning.
Nitroxyl (HNO), the one-electron reduced form of nitric oxide, is a highly reactive nitrogen species, and its transient nature presents substantial challenges for reliable detection in biological systems. HNO can modulate cellular redox balance and has been linked to various oxidative stress-related diseases. In this study, we monitor endogenous HNO levels in cancer cells and compare them with those of normal cells. A 3-pyridyl coumarin-based fluorescent probe, Cyto-HNO, was developed to detect both exogenous and endogenous HNO in live cells. Upon excitation at 410 nm, the probe displayed a significant fluorescence enhancement at 468 nm following incubation with Angeli's salt (AS, an HNO donor). Cyto-HNO is highly selective for HNO and produces significant emission signals within the physiological pH range. The probe is primarily localized in the cytosol of cancer cells, whereas it is found in the lysosomes of normal cells. The level of endogenous HNO formation in cancer cells, such as HepG2 and A549, was found to be approximately twice that of normal Beas-2B cells. For the first time, this study suggests that endogenous HNO production is higher in cancer cells than in normal cells, potentially offering insights into the mechanisms of HNO formation in cancer biology.
