Publications by authors named "X J Song"

A high security physical layer encryption scheme for dual-mode orthogonal frequency division multiplexing with index modulation (DM-OFDM-IM) in magnetic induction communication is proposed. The scheme utilizes DM-OFDM-IM, where subcarriers within each subblock are divided into two groups, each modulated by distinct signal constellations. DM-OFDM-IM leverages the sequential information from the modulated constellation to transmit extra information, leading to a substantial enhancement in spectral efficiency.

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Background: Infertility is a widespread problem for couples worldwide, and lifestyle factors are the cornerstone of infertility prevention. This research seeks to explore the association between combined healthy lifestyles and infertility risk among women of reproductive age.

Methods: This study analyzed data from the National Health and Nutrition Examination Survey (NHANES, 2013-2018), concentrating on 2,154 women aged 18 to 44.

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At present, the parameters of the controllers in hot rolling roughing microtension control systems are not adaptively adjustable to variations in working conditions, which compromises both width accuracy and production stability. To address this issue, this paper introduces an ATKB-PID adaptive micro tension control method. This method incorporates a linear attention layer and utilizes a K-Nearest Neighbors (KNN) algorithm to predict the optimal learning rate and inertia coefficient under actual operating conditions.

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Red mud (RM), an iron oxide-rich solid waste, shows potential as a catalyst for selective catalytic reduction in denitrification processes. This study investigates the catalytic performance and mechanism of metal-modified RM in reducing NO from diesel vehicle exhaust. Acid-washed RM catalysts were impregnated with varying ratios of cerium (Ce) and zirconium (Zr).

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Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.

Design: Retrospective observational study.

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