The performance of the masticatory muscle is frequently affected and presents high heterogeneity poststroke. Surface electromyography (EMG) is widely used to quantify muscle movement patterns. However, only a few studies applied EMG analysis on the research of masticatory muscle activities poststroke, and most of which used single parameter—root mean squares (RMS). The aim of this study was to fully investigate the performance of masticatory muscle at different head positions in healthy subjects and brainstem stroke patients with multiparameter EMG analysis. In this study, 15 healthy subjects and six brainstem stroke patients were recruited to conduct maximum voluntary clenching at five different head positions: upright position, left rotation, right rotation, dorsal flexion, and ventral flexion. The EMG signals of bilateral temporalis anterior and masseter muscles were recorded, and parameters including RMS, median frequency, and fuzzy approximate entropy of the EMG signals were calculated. Two-way analysis of variance (ANOVA) with repeated measures and Bonferroni post hoc test were used to evaluate the effects of muscle and head position on EMG parameters in the healthy group, and the non-parametric Wilcoxon signed rank test was conducted in the patient group. The Welch–Satterthwaite t-test was used to compare the between-subject difference. We found a significant effect of subject and muscles but no significant effect of head positions, and the masticatory muscles of patients after brainstem stroke performed significantly different from healthy subjects. Multiparameter EMG analysis might be an informative tool to investigate the neural activity related movement patterns of the deficient masticatory muscles poststroke.

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people's daily activities. Such development together with the lifelong learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners' fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

We identified Gram-negative bacteria as leading pathogens of CLABSIs in a Taiwan medical center, and good compliance to care bundle is associated with reduced CLABSI incidence rate. Malignancy, infection by MDROs or fungi, inadequate empirical or definite antimicrobial therapy are significant factors for 14-day mortality.

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Regulating the coordination environment of single‐atom sites is of high necessity to promote the catalytic performances of the photocatalysts. Herein, the preparation of atomically dispersed Co‐Ag dual‐metal sites anchored on P‐doped carbon nitride (Co1Ag1‐PCN) via supramolecular and solvothermal approaches is reported, which demonstrates desirable performance for photocatalytic H2 evolution from water splitting. The optimal Co1Ag1‐PCN catalyst achieves a remarkable hydrogen production rate of 1190 µmol g−1 h−1 with an apparent quantum yield (AQY) of 1.49% at 365 nm, superior to most of the newly reported metal‐N‐coordinated photocatalysts. Systematic experimental characterizations and density functional theoretic studies attribute the enhanced photocatalytic activity to the synergistic effect of Co‐Ag dual sites with exclusive coordination configuration of Co‐N6 and Ag‐N2C2, which enhances the charge density and promotes oriented electrons transport to the metal centers with reduced free energy barriers by facilitating the formation of H* intermediates as the key step in hydrogen evolution. This study reveals a versatile strategy to tailor the electronic structures of dual‐metal sites with synergies by engineering the neighboring coordination environment.

Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.

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