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Existing studies on gait phase estimation generally involve walking experiments using inertial measurement units under limited walking conditions (WCs). In this study, a gait phase estimation algorithm is proposed that uses data from force sensing resistors (FSRs) and a Bi-LSTM model. The proposed algorithm estimates gait phases in real time under various WCs, e.g., walking on paved/unpaved roads, ascending and descending stairs, and ascending or descending on ramps. The performance of the proposed algorithm is evaluated by performing walking experiments on ten healthy adult participants. An average gait estimation accuracy exceeding 90% is observed with a small error (root mean square error = 0.794, R2 score = 0.906) across various WCs. These results demonstrate the wide applicability of the proposed gait phase estimation algorithm using various insole devices, e.g., in walking aid control, gait disturbance diagnosis in daily life, and motor ability analysis. [ABSTRACT FROM AUTHOR]

Author(s): Jinbong Hwang [sup.1], Ji-Su Park [sup.1], So Young Kim [sup.1], Nho-Eul Song [sup.1], Miyoung Yoo [sup.1], Youngmin Choi [sup.2], Jin-Sik Nam [sup.3], Dongwon Seo [sup.1] Author Affiliations: (1) grid.418974.7, [...], In this study, the determination of iodine in 123 different types of foods was conducted. Iodine-selective electrode, microwave digestion/inductively coupled plasma mass spectrometry and tetramethylammonium hydroxide extraction/ inductively coupled plasma mass spectrometry (TMAH/ICP-MS) methods were tested to achieve the highest recoveries for the determination of iodine. Among them, TMAH/ICP-MS method was the most effective with respect to analytical recoveries (90-99%), limits of detection (0.013 [micro]g/kg) and quantification (0.04 [micro]g/kg) values. The intra- and inter-day relative standard deviations for iodine SRM were

Background: Since severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was first reported in Wuhan, China, in December 2019, it has spread rapidly, and many coronavirus disease (COVID‐19) cases have occurred in Gwangju, South Korea. Viral mutations following the COVID‐19 epidemic have increased interest in the characteristics of epidemics in this region, and pathogen genetic analysis is required for infection control and prevention. Methods: In this study, SARS‐CoV‐2 whole‐genome analysis was performed on samples from patients with COVID‐19 in Gwangju from 2020 to 2022 to identify the trends in COVID‐19 prevalence and to analyze the phylogenetic relationships of dominant variants. B.41 and B.1.497 prevailed in 2020, the early stage of the COVID‐19 outbreak; then, B.1.619.1 mainly occurred until June 2021. B.1.617.2, classified as sublineages AY.69 and AY.122, occurred continuously from July to December 2021. Since strict measures to strengthen national quarantine management had been implemented in South Korea until this time, the analysis of mutations was also able to infer the epidemiological relationship between infection transmission routes. Since the first identification of the Omicron variant in late December 2021, the spread of infection has been very rapid, and weekly whole‐genome analysis of specimens has enabled us to monitor new Omicron sublineages occurring in Gwangju. Conclusions: Our study suggests that conducting regional surveillance in addition to nation‐level genomic surveillance will enable more rapid and detailed variant surveillance, which will be helpful in the overall prevention and management of infectious diseases. [ABSTRACT FROM AUTHOR]

As the scale of online news and social media expands, attempts to analyze the latest social issues and consumer trends are increasing. Research on detecting spatio-temporal event sentences in text data is being actively conducted. However, a document contains important spatio-temporal events necessary for event analysis, as well as non-critical events for event analysis. It is important to increase the accuracy of event analysis by extracting only the key events necessary for event analysis from among a large number of events. In this study, we define important 'representative spatio-temporal event documents' for the core subject of documents and propose a BiLSTM-based document classification model to classify representative spatio-temporal event documents. We build 10,000 gold-standard training datasets to train the proposed BiLSTM model. The experimental results show that our BiLSTM model improves the F1 score by 2.6% and the accuracy by 4.5% compared to the baseline CNN model. [ABSTRACT FROM AUTHOR]