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Contextual classification of network traffic through rough set theory and convolutional neural networks
Menin-MLL inhibitors as a new therapeutic target for middle ear cholesteatoma
Identification and characterization of fibroblast-related biomarkers and pro-inflammatory subpopulations in periodontitis by integrated transcriptomic and single-cell analysis
Big data-driven establishment and bias comparison of serum ferritin reference intervals in Zhejiang Chinese adults using six algorithms
Presentation of numerical uncertainty modulates duration judgments
Correction: Immunohistochemical analysis of Filamin A expression in acromegaly and its correlation with tumor characteristics and treatment response
Correction: Psychiatrists effect on positive symptom severity and daily functioning during pharmacotherapy for first-episode psychosis patients
SULT1E1 exerts a protective role in COPD by inhibiting the AKT pathway: an in vivo and in vitro study
Socioeconomic, demographic and geographic disparities in accessibility to food pantries in the united States
Abstract Improving access to food pantries (FPs) may help address food insecurity, yet national-level assessments of geographic and socioeconomic disparities remain limited. We created a dataset of 34,475 FPs across all 50 states and the District of Columbia and analyzed their accessibility to the nearest Census block group (BG). We defined accessibility as high, medium, or low based on travel time or distance. Analyses of 239,780 BGs showed that 23.4% had low FP access. We identified geographic disparities across regions and states. For example, states in the Northeast generally have higher FP accessibility, while those in the South show lower access. Although rural BGs are more socioeconomically disadvantaged than urban ones (mean ADI: 71.3 vs. 45.8), they generally have better FP access (only 8.5% with low access vs. 26.5% in urban areas). However, some rural BGs with low FP accessibility tend to have high ADI values. In urban areas, FP placement shows relatively good alignment withneighborhood needs based on analyses of socioeconomic characteristics. In contrast, rural access patterns are more mixed. For example, less-educated rural populations face greater barriers. Findings from this study can inform policy and implementation strategies to improve FP access in underserved communities.
Smart IoT applications of multi attack detection using cluster F1MI approach
Multi-scale boundary-aware network for remote sensing image semantic segmentation
Serum extracellular vesicle RNA profiles in long COVID: insights from exercise-induced gene modulation
The vasoprotective role of Myeloid-derived suppressor cells in pathogenesis of aortic dissection
CNS-active medication use and adverse health outcomes among Thai older adults: a population-based retrospective study
Land circulation impacts the physical and mental health of the elderly in rural Chinese households
A new type of aircraft icing detection system
The element of surprise distinguishes beauty from pleasure and interest in visuo-tactile perception of art
Secure aggregation for heterogeneous enterprise data based on federated meta-learning
Abstract This paper proposes a secure and efficient data aggregation approach for heterogeneous enterprise data, leveraging federated meta-learning (FML) and data consolidation. The proposed approach addresses critical challenges in enterprise data, including data privacy, heterogeneous data distributions, and communication constraints. Specifically, the proposed approach enables decentralized devices to collaboratively train local models, which are aggregated by the server into a global model using meta-learning. Then, a consortium blockchain is used to ensure secure, immutable storage of aggregated data through a dual-chain structure: lightweight fluffy chains for temporary storage and heavyweight bulky chains for permanent records. In further, the proposed approach incorporates robust security mechanisms, such as XF authentication, timestamp-based counters to thwart replay attacks, asymmetric encryption for secure key exchange, and a Hampel filter to detect and mitigate model poisoning. Simulations results under Secure Water Treatment (SWaT) dataset are finally provided to demonstrate the superiority of the proposed approach. Specifically, the proposed approach achieves a lower validation delay scaling efficiently with training history size over the competing ones. Additionally, the proposed approach can enhance the system security by increasing the attack failure probability up to 15% over the competing ones.