A molecular dynamics simulation has been carried out to investigate the dynamics heterogeneity SiO2 liquid at 2600 K and ambient pressure. We indicate that the diffusion in the liquid is realized by the rate of effective reaction, SiOx®SiOx’ and OSiy®OSiy’. Moreover, the reactions are non-uniform happened, but they are spatially clustered. In addition, we found the clustering from different sets of atoms specified by the mobility of atom or frequency of reactions. We found that the clustering become more pronounced at ambient pressure. This evidences the dynamics heterogeneity in the SiO2 liquid.

Background Adolescents who are willing to perform first aid can help prevent injuries and ultimately death among themselves and others involved in accidents or injuries. This study aims to estimate the prevalence of students’ willingness to perform first aid procedures and additionally examine associated factors among high school students in Hue, Vietnam. Methods A cross-sectional study utilizing multi-stage stratified random sampling was conducted between April to July 2020 by investigating 798 high school students in Hue, Vietnam. Participants were invited to complete a self-reported questionnaire pertaining to individual demographic characteristics, personal perception of self-efficacy, and willingness to perform first aid. To better interpret these findings, both multivariable linear and Poisson regression models were fitted to evaluate the association between individual student characteristics and the willingness to perform first aid. Results The prevalence of having willingness to perform first aid (defined as ≥4 points out of 5 to all three questions) was 49.9% (95%CI:28.6–71.2%). The major reported barriers in performing first aid were fear of making mistakes and hurting victims (34.4%, 95%CI:31.9–37.0%), no prior first aid training (29.8%, 95%CI:25.9–33.9%), and forgetting first aid steps (23.0%, 95%CI:15.8–32.2%). By employing the multivariable linear regression model, it was identified that students with high (β = 0.614, 95%CI:0.009–1.219) or very high (β = 1.64, 95%CI:0.857–2.422) levels of self-efficacy appeared to be more willing to perform first aid. Similarly, in the Poisson regression models, compared to neutral students, students who reported high (PR = 1.214, 95%CI:1.048–1.407) or very high (PR = 1.871, 95%CI:1.049–3.337) levels of self-efficacy were more willing to perform first aid. Conclusions The level of willingness to perform first aid among high school students in this study population was found to be moderate. Therefore, integrating activities to promote self-efficacy in first aid training could be considered a progressive step towards improving a student’s willingness to provide such life-saving procedures.

Compressing the ECG signal is considered a feasible solution for supporting a system to manipulate the package size, a major factor leading to congestion in an ECG wireless network. Hence, this paper proposes a compression algorithm, called the advanced two-state algorithm, which achieves three necessary characteristics: a) flexibility towards all ECG signal conditions, b) the ability to adapt to each requirement of the package size and c) be simple enough. In this algorithm, the ECG pattern is divided into two categories: "complex" durations such as QRS complexes, are labeled as low-state durations, and "plain" durations such P or T waves, are labeled as high-state durations. Each duration type can be compressed at different compression ratios, and Piecewise Cubic Spline can be used for reconstructing the signal. For evaluation, the algorithm was applied to 48 records of the MIT-BIH arrhythmia database (clear PQRST complexes) and 9 records of the CU ventricular tachyarrhythmia database (unclear PQRST complexes). Parameters including Compression Ratio (CR), Percentage Root mean square Difference (PRD), Percentage Root mean square Difference, Normalized (PRDN), root mean square (RMS), Signal-to-noise Ratio (SNR) and a new proposed index called Peak Maximum Absolute Error (PMAE) were used to comprehensively evaluate the performance of the algorithm. Eventually, the results obtained were positive with low PRD, PRDN and PMAE at different compression ratios compared to many other loss-type compressing methods, proving the high efficiency of the proposed algorithm. All in all, with its extremely low-cost computation, versatility and good-quality reconstruction, this algorithm could be applied to a number of wireless applications to control package size and overcome congested situations.

Clinical outcome analysis using patient medical data facilitates clinical decision-making and increases prognostic accuracy. Recently, deep learning (DL) with learning big data features has shown expert-level accuracy in predicting clinical outcomes. Many of these sophisticated machine learning models, however, lack interpretability, creating significant trust-related healthcare issues. This necessarily requires the need for interpretable AI systems capable of explaining their decisions. In this respect, the paper proposes an interpretable classifier of the adaptive neuro-fuzzy inference method (iANFIS), which combines the fuzzy inference system with critical rule selection by attention mechanism. The rule-based processing of ANFIS helps the user to understand the behavior of the proposed model. The essential activated rule and the most important input features that predict the outcome are identified by the attention-based rule selector. We conduct two experiments with two cancer diagnostic datasets for verifying the performance of the proposed iANFIS. By using recursive rule elimination (RRE) to prune fuzzy rules, the model’s complexity is significantly reduced while preserving system efficiency that makes it more interpretable.

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