959 papers
This study demonstrates that in Bangkok, Thailand, selecting specific foods based on nutrient-to-price ratios within the framework of existing dietary guidelines can significantly reduce the cost of a healthy diet—by up to 69.8%—making it more affordable for low-income populations without altering recommended serving structures.
This paper proposes a position-weighted sequence-kernel Bayesian optimization framework that efficiently designs composite laminate stacking sequences under manufacturing constraints by leveraging a specialized distance metric for Gaussian process surrogates to maximize buckling performance while minimizing candidate redundancy.
This paper proposes a scalable hybrid encryption framework that combines a password-derived Logistic Map for chaotic key generation, a dynamic Unimodular Hill Cipher for constructing large invertible matrices, and AES-CBC to provide robust, format-independent security for universal binary files with near-ideal entropy and strong resistance to cryptanalytic attacks.
This cross-sectional study of dental students in India reveals that while they possess moderate knowledge and favorable attitudes toward geropsychology in denture care, a significant gap exists between these attitudes and clinical practice due to a lack of formal training, highlighting an urgent need to integrate psychosocial education into prosthodontic curricula.
This paper proposes FSMSN, a lightweight multi-scale network that integrates physical fault frequency priors into channel attention mechanisms to achieve robust, high-accuracy bearing fault diagnosis under severe noise interference with significantly fewer parameters than existing deep learning baselines.
This study presents the discovery and experimental validation of EGFPAN_EMBS, a novel, potent, and selective EGFR inhibitor for pancreatic cancer, identified through an integrated generative AI framework that optimized a cannabigerol-inspired scaffold and confirmed its efficacy via comprehensive computational modeling and biological assays.
This study demonstrates that increasing carbon dioxide concentrations in mice elicit distinct, non-linear patterns of brain-wide neuronal activation and physiological responses, differentiating anxiety-like states at 10% CO₂ from panic-like states at 20% CO₂ through unique behavioral, autonomic, and network-level neural signatures.
This study demonstrates that enzymatic tailoring of potato proteins, through mechanisms such as crosslinking or controlled hydrolysis, enables precise multiscale control over gel network formation to rationally engineer the texture of plant-based meat analogues.
This study demonstrates that a Wav2Vec2.0-based deep learning model analyzing voice recordings can effectively distinguish patients with acromegaly from controls with high diagnostic accuracy, suggesting voice analysis as a promising non-invasive digital biomarker for the condition.
Pivot-WFSM introduces a memory-scalable approach to weighted frequent subgraph mining that replaces traditional embedding storage with on-demand re-matching, drastically reducing peak memory usage and enabling the analysis of large multigraph databases that previously caused out-of-memory failures.