Description of three new species of Callyntrura (Japonphysa) (Collembola, Entomobryidae) from China with the aid of DNA barcoding

Callyntrura(s.l.) Börner, 1906 is the largest genus of the subfamily Salininae and contains 11 subgenera and 98 species from all over the world (mainly Asia), with eight species recorded from China. In the present paper, three new species of Callyntrura(s.l.) are described from China: C. (Japonphysa) xinjianensis sp. nov.; C. (J.) tongguensis sp. nov. and C. (J.) raoi sp. nov. Their differences in colour pattern, chaetotaxy and other characters are slight, however distances of COI mtDNA support their validation as three new distinct species. A key to the Chinese Callyntrura(s.l.) is provided

Crowdsourced Indoor Positioning with Scalable WiFi Augmentation

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark

Soil Moisture-Boundary Layer Feedbacks on the Loess Plateau in China Using Radiosonde Data with 1-D Atmospheric Boundary Layer Model

The Loess Plateau is one land-atmosphere coupling hotspot. Soil moisture has an influence on atmospheric boundary layer development under specific early-morning atmospheric thermodynamic structures. This paper investigates the sensitivity of atmospheric convection to soil moisture conditions over the Loess Plateau in China by using the convective triggering potential (CTP)—humidity index (HIlow) framework. The CTP indicates atmospheric stability and the HIlow indicates atmospheric humidity in the low-level atmosphere. By comparing the model outcomes with the observations, the one-dimensional model achieves realistic daily behavior of the radiation and surface heat fluxes and the mixed layer properties with appropriate modifications. New CTP-HIlow thresholds for soil moisture-atmosphere feedbacks are found in the Loess Plateau area. By applying the new thresholds with long-time scales sounding data, we conclude that negative feedback is dominant in the north and west portion of the Loess Plateau; positive feedback is predominant in the south and east portion. In general, this framework has predictive significance for the impact of soil moisture on precipitation. By using this new CTP-HIlow framework, we can determine under what atmospheric conditions soil moisture can affect the triggering of precipitation and under what atmospheric conditions soil moisture has no influence on the triggering of precipitation

Soil Moisture-Boundary Layer Feedbacks on the Loess Plateau in China Using Radiosonde Data with 1-D Atmospheric Boundary Layer Model

The Loess Plateau is one land-atmosphere coupling hotspot. Soil moisture has an influence on atmospheric boundary layer development under specific early-morning atmospheric thermodynamic structures. This paper investigates the sensitivity of atmospheric convection to soil moisture conditions over the Loess Plateau in China by using the convective triggering potential (CTP)—humidity index (HIlow) framework. The CTP indicates atmospheric stability and the HIlow indicates atmospheric humidity in the low-level atmosphere. By comparing the model outcomes with the observations, the one-dimensional model achieves realistic daily behavior of the radiation and surface heat fluxes and the mixed layer properties with appropriate modifications. New CTP-HIlow thresholds for soil moisture-atmosphere feedbacks are found in the Loess Plateau area. By applying the new thresholds with long-time scales sounding data, we conclude that negative feedback is dominant in the north and west portion of the Loess Plateau; positive feedback is predominant in the south and east portion. In general, this framework has predictive significance for the impact of soil moisture on precipitation. By using this new CTP-HIlow framework, we can determine under what atmospheric conditions soil moisture can affect the triggering of precipitation and under what atmospheric conditions soil moisture has no influence on the triggering of precipitation

What Are the Risk Factors of Negative Patient Experience? A Cross-Sectional Study in Chinese Public Hospitals

Hospitals are struggling to involve patients and learn from their experience. The risk factor of patient experience is increasingly recognized as a critical component in improving patient experience. Our study explored risk factors of negative patient experience in order to improve the health-service quality of public hospitals. We conducted a cross-sectional study in Hubei province, China. A total of 583 respondents were surveyed by the Outpatient Experience Questionnaire with good validity and reliability in July 2015. T -tests were conducted to compare the experience scores among different outpatient groups. Multiple linear regression was performed to determine the significant factors that influenced the outpatient experience. Outpatients between 18 and 44 years old had the lowest experience scores (65.89 ± 0.79), whereas outpatients completely paying out-of-pocket had the lowest experience scores (64.68 ± 0.81) among all participants. Outpatients with poor self-rated health status had the lowest experience scores (66.14 ± 1.61) among different self-rated health status groups. While age, type of payment, and self-rated health status were significantly risk factors that influenced outpatient experience in the multiple linear regression. Thus, health-care providers should pay more attention to outpatients who are young (age <45), completely out-of-pocket and poor health status, and provide precision health care to improve outpatient experience

CAS(ME)³: A Third Generation Facial Spontaneous Micro-Expression Database with Depth Information and High Ecological Validity.

Micro-expression (ME) is a significant non-verbal communication clue that reveals one person&#39;s genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME)3. The contribution of this article is summarized as follows: (1) CAS(ME)3 offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME)3 provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME)3 elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME)3 provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.</p