Live Ambience and Homestead Away From Home: Social Media Use and Dependency by Visiting Chinese Students in the United States

This study investigates social media dependency relations among Chinese college students during their three-month study abroad sojourn in the United States. Data were collected using a multimethod approach of ethnography, field observation, and in-depth interviews. Inspired by the lens of media system dependency (MSD) theory, the analysis focuses on the diverse goals and motivations that drive student behavior in social media engagement, as well as various contextual factors leading students to adapt and transition to the U.S. social networking sites (SNS), and the subsequent outcomes. The findings indicate that task-driven and assignment-centered goals dominate social media use, and that multidimensional aspects of interaction pervade student engagement with different social networking applications. Theoretical and practical implications are discussed in light of the overall findings

The Complexity of Standing Postural Control in Older Adults: A Modified Detrended Fluctuation Analysis Based upon the Empirical Mode Decomposition Algorithm

Human aging into senescence diminishes the capacity of the postural control system to adapt to the stressors of everyday life. Diminished adaptive capacity may be reflected by a loss of the fractal-like, multiscale complexity within the dynamics of standing postural sway (i.e., center-of-pressure, COP). We therefore studied the relationship between COP complexity and adaptive capacity in 22 older and 22 younger healthy adults. COP magnitude dynamics were assessed from raw data during quiet standing with eyes open and closed, and complexity was quantified with a new technique termed empirical mode decomposition embedded detrended fluctuation analysis (EMD-DFA). Adaptive capacity of the postural control system was assessed with the sharpened Romberg test. As compared to traditional DFA, EMD-DFA more accurately identified trends in COP data with intrinsic scales and produced short and long-term scaling exponents (i.e., αShort, αLong) with greater reliability. The fractal-like properties of COP fluctuations were time-scale dependent and highly complex (i.e., αShort values were close to one) over relatively short time scales. As compared to younger adults, older adults demonstrated lower short-term COP complexity (i.e., greater αShort values) in both visual conditions (p>0.001). Closing the eyes decreased short-term COP complexity, yet this decrease was greater in older compared to younger adults (p<0.001). In older adults, those with higher short-term COP complexity exhibited better adaptive capacity as quantified by Romberg test performance (r2 = 0.38, p<0.001). These results indicate that an age-related loss of COP complexity of magnitude series may reflect a clinically important reduction in postural control system functionality as a new biomarker

Centralized Fusion of Unscented Kalman Filter Based on Huber Robust Method for Nonlinear Moving Target Tracking

We propose a robust method for tracking nonlinear target with the fusion unscented Kalman filter (FUKF). We noticed that when some outliers exist in the measurements of the sensors, they cannot track the target accurately by using the standard Kalman filters. The robust statistics theory is used in this paper to solve this problem. The measurement noise variance which is at the time of the outlier is restructured through minimizing the designed cost function. Then, the standard fusion unscented Kalman filter is used to track the target in order to avoid the bias brought by the linear approximation. Compared to the traditional tracking method and Huber robust method (HFUKF), this method has a more accurate performance and can track the target efficiently while the outliers exist. Last, simulation examples in three different conditions are given and the simulation results show the advantages of the proposed method over the fusion unscented Kalman filter (FUKF) and the Huber robust method (HFUKF)

Sensitivity and specificity of the ankle–brachial index to diagnose peripheral artery disease: a structured review

The ankle—brachial index (ABI) is a simple, inexpensive diagnostic test for peripheral artery disease (PAD). However, it has shown variable accuracy for identification of significant stenosis. The authors performed a structured review of the sensitivity and specificity of ABI ≤ 0.90 for the diagnosis of PAD. MEDLINE, EMBASE, Cochrane databases, Science Citation Index database, and Biological Abstracts database were searched for studies of the sensitivity and specificity of using ABI ≤ 0.90 for the diagnosis of PAD. Eight studies comprising 2043 patients (or limbs) met the inclusion criteria. The result indicated that, although strict inclusion criteria on studies were formulated, different reference standards were found in these studies, and methods of ABI determination and characteristics of populations varied greatly. A high level of specificity (83.3—99.0%) and accuracy (72.1—89.2%) was reported for an ABI ≤ 0.90 in detecting ≥ 50% stenosis, but there were different levels of sensitivity (15—79%). Sensitivity was low, especially in elderly individuals and patients with diabetes. In conclusion, the test of ABI ≤ 0.90 can be a simple and useful tool to identify PAD with serious stenosis, and may be substituted for other non-invasive tests in clinical practice

Detecting and removing visual distractors for video aesthetic enhancement

Personal videos often contain visual distractors, which are objects that are accidentally captured that can distract viewers from focusing on the main subjects. We propose a method to automatically detect and localize these distractors through learning from a manually labeled dataset. To achieve spatially and temporally coherent detection, we propose extracting features at the Temporal-Superpixel (TSP) level using a traditional SVM-based learning framework. We also experiment with end-to-end learning using Convolutional Neural Networks (CNNs), which achieves slightly higher performance than other methods. The classification result is further refined in a post-processing step based on graph-cut optimization. Experimental results show that our method achieves an accuracy of 81% and a recall of 86%. We demonstrate several ways of removing the detected distractors to improve the video quality, including video hole filling; video frame replacement; and camera path re-planning. The user study results show that our method can significantly improve the aesthetic quality of videos