In digital healthcare, ensuring the privacy and security of sensitive mental health data remains a critical challenge. This paper introduces SymECCipher, a novel hybrid encryption framework that integrates Elliptic Curve Cryptography (ECC) for key ex...
Artificial intelligence (AI) technologies, especially large language models (LLMs), have permeated human work around the globe, but how effective is workers' usage and application of AI-generated content across language settings? This research examin...
Intensive education systems are believed to contribute to high rates of myopia. This study examined whether near-viewing behaviors in college students differ based on their pre-college educational systems and whether these behaviors can be used to cl...
RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA...
Influenza A viruses (IAVs) have historically posed significant public health threats, causing severe pandemics. Viral host specificity is typically constrained by host barriers, limiting the range of species that can be infected. However, these barri...
BACKGROUND: Existing biomarkers for epithelial ovarian cancer (EOC) have demonstrated limited sensitivity and specificity. This study aimed to investigate plasma protein and metabolite characteristics of EOC and identify novel biomarker candidates fo...
We present the TRIAGE benchmark, a novel machine ethics benchmark designed to evaluate the ethical decision-making abilities of large language models (LLMs) in mass casualty scenarios. TRIAGE uses medical dilemmas created by healthcare professionals ...
As the global population ages, enhancing community outdoor public spaces to accommodate the needs of senior citizens has emerged as a critical challenge. This research delves into the intricate relationship between community outdoor public spaces and...
Cardiotoxicity is the loss of the heart muscle's ability to contract effectively, often due to chemotherapy or radiation therapy. This study uses interpretable machine learning to predict post-chemotherapy cardiotoxicity using radiomics features extr...
Electroencephalography (EEG) recordings with visual stimuli require detailed coding to determine the periods of participant's attention. Here we propose to use a supervised machine learning model and off-the-shelf video cameras only. We extract compu...
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