Whole-body vibration training effect on physical performance and obesity in mice

The purpose of this study was to verify the beneficial effects of whole-body vibration (WBV) training on exercise performance, physical fatigue and obesity in mice with obesity induced by a high-fat diet (HFD). Male C57BL/6 mice were randomly divided into two groups: normal group (n=6), fed standard diet (control), and experimental group (n=18), fed a HFD. After 4-week induction, followed by 6-week WBV of 5 days per week, the 18 obese mice were divided into 3 groups (n=6 per group): HFD with sedentary control (HFD), HFD with WBV at relatively low-intensity (5.6 Hz, 0.13 g) (HFD+VL) or high-intensity (13 Hz, 0.68 g) (HFD+VH). A trend analysis revealed that WBV increased the grip strength in mice. WBV also dose-dependently decreased serum lactate, ammonia and CK levels and increased glucose level after the swimming test. WBV slightly decreased final body weight and dose-dependently decreased weights of epididymal, retroperitoneal and perirenal fat pads and fasting serum levels of alanine aminotransferase, CK, glucose, total cholesterol and triacylglycerol. Therefore, WBV could improve exercise performance and fatigue and prevent fat accumulation and obesity-associated biochemical alterations in obese mice. It may be an effective intervention for health promotion and prevention of HFD-induced obesity

Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset

Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decision-making collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants’ cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants’ trust calibration of the robot also varies the resultant brain activities

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

BackgroundPhysical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models.Materials and methodsQuestionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors.ResultsThe XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models.ConclusionThe XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty

Earthquake Probability Assessment for the Active Faults in Central Taiwan: A Case Study

Frequent high seismic activities occur in Taiwan due to fast plate motions. According to the historical records the most destructive earthquakes in Taiwan were caused mainly by inland active faults. The Central Geological Survey (CGS) of Taiwan has published active fault maps in Taiwan since 1998. There are 33 active faults noted in the 2012 active fault map. After the Chi-Chi earthquake, CGS launched a series of projects to investigate the details to better understand each active fault in Taiwan. This article collected this data to develop active fault parameters and referred to certain experiences from Japan and the United States to establish a methodology for earthquake probability assessment via active faults. We consider the active faults in Central Taiwan as a good example to present the earthquake probability assessment process and results. The appropriate “probability model” was used to estimate the conditional probability where M ≥ 6.5 and M ≥ 7.0 earthquakes. Our result shows that the highest earthquake probability for M ≥ 6.5 earthquake occurring in 30, 50, and 100 years in Central Taiwan is the Tachia-Changhua fault system. Conversely, the lowest earthquake probability is the Chelungpu fault. The goal of our research is to calculate the earthquake probability of the 33 active faults in Taiwan. The active fault parameters are important information that can be applied in the following seismic hazard analysis and seismic simulation