Anisotropic in-plane heat transport of Kitaev magnet Na2_2Co2_2TeO6_6

We report a study on low-temperature heat transport of Kitaev magnet Na$_2$Co$_2$TeO$_6$, with the heat current and magnetic fields along the honeycomb spin layer (the $ab$ plane). The zero-field thermal conductivity of $\kappa^a_{xx}$ and $\kappa^{a*}_{xx}$ display similar temperature dependence and small difference in their magnitudes; whereas, their magnetic field (parallel to the heat current) dependence are quite different and are related to the field-induced magnetic transitions. The $\kappa^a_{xx}(B)$ data for $B \parallel a$ at very low temperatures have an anomaly at 10.25--10.5 T, which reveals an unexplored magnetic transition. The planar thermal Hall conductivity $\kappa^a_{xy}$ and $\kappa^{a*}_{xy}$ show very weak signals at low fields and rather large values with sign change at high fields. This may point to a possible magnetic structure transition or the change of the magnon band topology that induces a radical change of magnon Berry curvature distribution before entering the spin polarized state. These results put clear constraints on the high-field phase and the theoretical models for Na$_2$Co$_2$TeO$_6$.Comment: 7 pages, 4 figure

Research on the Application of Cross-Specialty Education and Situational Simulation Teaching in Operation Nursing Practice Teaching

Objective To examine the practical effect of inter-professional education and situational simulation teaching implemented in surgical nursing practice teaching. Methods On the whole, 100 undergraduate nursing students in the operating room of the hospital of the authors from May 2019 to August 2020 were selected. These students fell to two groups with the random number table method. The control received the regular teaching, and the research group were given the interprofessional education and context. The Simulation teaching was conducted to compare the theoretical knowledge, skill level, various abilities of the two groups of students, as well as the satisfaction of the operating room doctors to the nursing cooperation of the interns. Results The research group achieved higher theoretical knowledge and a higher skill level than the control (p < 0.05); the various abilities of the research group were higher than those of the control (p < 0.05); the operating room doctors of the research group were more satisfied with the nursing cooperation of interns, as compared with those of the control (p < 0.05). Conclusion In the surgical nursing practice teaching, the inter-professional education and the situational simulation teaching have significant effects and are worth clinical applications

Changes of Adult Population Health Status in China from 2003 to 2008

Objectives: The purpose of this study was to examine the change in health status of China’s adult population between the years of 2003 and 2008 due to rapid economic growth and medical system improvement. Methods: Data from the third and fourth Chinese national health services surveys covering 141,927 residents in 2003 and 136,371 residents in 2008 who were aged.18 years were analyzed. Results: Chinese respondents in 2008 were more likely to report disease than in 2003. Smoking slightly decreased among men and women, and regular exercise showed much improvement. Stratified analyses revealed significant subpopulation disparities in rate ratios for 2008/2003 in the presence of chronic disease, with greater increases among women, elderly, the Han nationality, unmarried and widow, illiterate, rural, and regions east of China than other groups. Conclusions: Chinese adults in 2008 had worse health status than in 2003 in terms of presence of chronic disease. China’s reform of health care will face more complex challenges in coming years from the deteriorating health status in Chinese adults

From lost space to third place: the visitor's perspective

Although studies on place attachment in tourism have expanded greatly during the past decade, most have focused on nature-based settings, thereby neglecting the social dimension of place. The triadic relationship of activity involvement, place attachment, and visitor loyalty has received limited attention. In response, we investigated visitors' attachment to activities and settings within cultural creative districts (CCDs) in a manufacturing hub of China, with the aim to advance the theory of place attachment and elucidate geographic and psychological factors that can affect visitor experience. Results of an onsite questionnaire (n = 252) indicated that: 1) activity involvement positively affected place attachment; 2) attraction and social bonding were strong predictors of visitor loyalty. We identified a more effective way to implement CCDs as part of urban-regeneration strategies - namely, to become visitors' favourite third places, CCDs need to offer high-quality social encounters with a suitable mix of physical, cultural, and entertainment amenities

Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system

Enhanced active dynamic balancing of the planar robots using a three-rotating-bar balancer

The concept of full compensation against the resultant shaking forces and moments for arbitrary robots using a single active dynamic balancing mechanism is first addressed. And the application principle and general balancing conditions of the active dynamic balancing mechanism are presented. With the purpose of providing detailed description of these problems, a compact planar 3-degree-of-freedom active dynamic balancing mechanism is proposed. The active balancer is composed of three independent rotating bars with their respective actuators. The rotations of the three bars could change their center of gravity positions and then generate balancing forces for the unbalanced robots. Moreover, the changing of the angular acceleration of the bars can also generate a dynamic torque to balance the shaking moment. In order to present more detail of the balancing theory, the structure and kinematic and dynamic analysis of the proposed balancing mechanism are given. Finally, numerical examples illustrate the effectiveness of the proposed three-rotating-bar balancer

A Multi-Stage Method for Connecting Participatory Sensing and Noise Simulations

Most simulation-based noise maps are important for official noise assessment but lack local noise characteristics. The main reasons for this lack of information are that official noise simulations only provide information about expected noise levels, which is limited by the use of large-scale monitoring of noise sources, and are updated infrequently. With the emergence of smart cities and ubiquitous sensing, the possible improvements enabled by sensing technologies provide the possibility to resolve this problem. This study proposed an integrated methodology to propel participatory sensing from its current random and distributed sampling origins to professional noise simulation. The aims of this study were to effectively organize the participatory noise data, to dynamically refine the granularity of the noise features on road segments (e.g., different portions of a road segment), and then to provide a reasonable spatio-temporal data foundation to support noise simulations, which can be of help to researchers in understanding how participatory sensing can play a role in smart cities. This study first discusses the potential limitations of the current participatory sensing and simulation-based official noise maps. Next, we explain how participatory noise data can contribute to a simulation-based noise map by providing (1) spatial matching of the participatory noise data to the virtual partitions at a more microscopic level of road networks; (2) multi-temporal scale noise estimations at the spatial level of virtual partitions; and (3) dynamic aggregation of virtual partitions by comparing the noise values at the relevant temporal scale to form a dynamic segmentation of each road segment to support multiple spatio-temporal noise simulations. In this case study, we demonstrate how this method could play a significant role in a simulation-based noise map. Together, these results demonstrate the potential benefits of participatory noise data as dynamic input sources for noise simulations on multiple spatio-temporal scales

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process