Description: The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people’s lives.

Description: The main object of the present study was to explore the effect on thyroidal proteins following mild iodine deficiency (ID)-induced maternal hypothyroxinemia during pregnancy and lactation. In the present study, we established a maternal hypothyroxinemia model in female Wistar rats by using a mild ID diet. Maternal thyroid iodine content and thyroid weight were measured. Expressions of thyroid-associated proteins were analyzed. The results showed that the mild ID diet increased thyroid weight, decreased thyroid iodine content and increased expressions of thyroid transcription factor 1, paired box gene 8 and Na+/I− symporter on gestational day (GD) 19 and postpartum days (PN) 21 in the maternal thyroid. Moreover, the up-regulated expressions of type 1 iodothyronine deiodinase (DIO1) and type 2 iodothyronine deiodinase (DIO2) were detected in the mild ID group on GD19 and PN21. Taken together, our data indicates that during pregnancy and lactation, a maternal mild ID could induce hypothyroxinemia and increase the thyroidal DIO1 and DIO2 levels.

Description: In this paper, the stability of solutions of optimal control for the distributed parameter system governed by a semilinear evolution equation with compact control set in the space L 1 ( 0 , T ; E ) is discussed. The stability results for optimal control problems with respect to the right-hand side functions are obtained by the theory of set-valued mapping and the definition of essential solutions for optimal control problems.

Description: The division process used in a DFB diode laser-based optical gas sensor was studied to improve the immunity to laser power variation. Residual amplitude modulation (RAM) in wavelength modulation spectroscopy (WMS) detection was eliminated by intensity normalization using a division process. As a result the detected harmonic signals showed a significant improvement in line shape. For the first harmonic (1f) signal, Bias was improved from 38.7% to 1.2%; Baseline Difference was improved from 2.7% to 0.69% and Asymmetry was improved from 15.4% to 0.22%. For the second harmonic (2f) signal, the Asymmetry Coefficient was improved from 103% to 5.1%. Moreover the division process can further suppress the influence of unstable laser power. As a result, for the 1f signal, stable detection with a variation coefficient of 0.59% was obtained over a wide dynamic range (0.38–8.1 mW). For the 2f signal, stable detection with a variation coefficient of 0.53% was obtained from 0.64 mW to 8.27 mW. The test results showed a good agreement with the theoretical analysis and the proposed method has considerable potential application in gas sensing.

Description: Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.

Description: PurposeTo evaluate the reliability of the Attitudes to Randomized Trial Questionnaire (ARTQ) in measuring perceptions of cancer clinical trials in a predominantly African American (AA) sample in South Carolina (SC). Methods: Principal Component Analysis (PCA) and Cronbach’s alpha estimates were used to assess the reliability of the ARTQ in a convenience sample of 315 participants (81.4 % AA) who were recruited from 2008 to 2013, and who live in eleven different counties in South Carolina with high rates of racial disparities in cancer mortality rates. Results: Slightly more than half of the 315 participants had at least a college education (77.9 %), 84.8 % were female, and 53.1 % had an annual income of $40,000 or more. In this study, PCA confirmed that the ARTQ is unidimensional. Cronbach’s alpha for the ARTQ was 0.86. Conclusion: The ARTQ displayed strong evidence of high statistical reliability. This analysis has great implications for future research because it represents the first test of reliability of the ARTQ in a predominantly African American sample and lays the groundwork for use of the ARTQ in future studies in diverse populations.

Description: Background: The clinical efficacy of nucleos(t)ide analogues (NAs) combined with interferon (IFN) therapy vs. NAs monotherapy for chronic hepatitis B (CHB) remains inconclusive. The aim of this meta-analysis was to determine whether the NAs plus IFN regimen offers synergistic efficacy that justifies the cost and burden of such a combination therapy in CHB patients. Methods: Related publications covering the period of 1966 to July 2014 were identified through searching MEDLINE, EMBASE, Cochrane library, Chinese Biomedical Literature Database, WANFANG, and CNKI database. A total of 17 studies were enrolled, including 6 in English and 11 in Chinese. Then, we established a final list of studies for the meta-analysis by systematically grading the quality and eligibility of the identified individual studies. We used hepatitis B antigen (HBeAg) loss, HBV-DNA undetectable rate, HBeAg seroconversion, hepatitis B surface antigen (HBsAg) loss, HBsAg seroconversion, and histological score at the end of treatment for efficacy evaluation. A quantitative meta-analysis (Review Manager, Version 5.1.0) was performed to assess the differences between NAs and IFN combination therapy and NAs monotherapy. Results: Our analysis demonstrated that HBeAg loss (RR = 1.73, 95% CI = 1.32–2.26, p 〈 0.001), HBV-DNA undetectable rate (RR = 1.58, 95% CI = 1.22–2.04, p 〈 0.001), HBeAg seroconversion (RR = 1.68, 95% CI = 1.36–2.07, p 〈 0.001), and HBsAg loss (RR = 2.51, 95% CI = 1.32–4.75, p 〈 0.001) in the combination therapy group were significantly higher than those in the monotherapy group. However, there were no significant differences in HBsAg seroconversion (RR = 4.25, 95% CI = 0.62–29.13, p = 0.14), sustained virological response rates, and biochemical response rates observed between the two groups. The results showed that the combination therapy group had more improved HBV histology than the NAs monotherapy group (RR = 1.14, 95% CI = 0.93–1.39, p = 0.22). Conclusions: NAs and IFN or Peg-IFN combination therapy had a better efficacy in terms of HBeAg loss, HBV-DNA undetectable rate, HBeAg seroconversion, and HBsAg loss, compared to the NA monotherapy group at the end of treatment; however, there was no significant difference in HBsAg seroconversion between the two regimens.

Description: The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems—toed-in camera configuration and parallel camera configuration—are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.

Description: This study adopts rainwater harvesting systems (RWHS) into a stormwater runoff management model (SWMM) for the spatial design of capacities and quantities of rain barrel for urban flood mitigation. A simulation-optimization model is proposed for effectively identifying the optimal design. First of all, we particularly classified the characteristic zonal subregions for spatial design by using fuzzy C-means clustering with the investigated data of urban roof, land use and drainage system. In the simulation method, a series of regular spatial arrangements specification are designed by using statistical quartiles analysis for rooftop area and rainfall frequency analysis; accordingly, the corresponding reduced flooding circumstances can be simulated by SWMM. Moreover, the most effective solution for the simulation method is identified from the calculated net benefit, which is equivalent to the subtraction of the facility cost from the decreased inundation loss. It serves as the initially identified solution for the optimization model. In the optimization method, backpropagation neural network (BPNN) are first applied for developing a water level simulation model of urban drainage systems to substitute for SWMM to conform to newly considered interdisciplinary multi-objective optimization model, and a tabu search-based algorithm is used with the embedded BPNN-based SWMM to optimize the planning solution. The developed method is applied to the Zhong-He District, Taiwan. Results demonstrate that the application of tabu search and the BPNN-based simulation model into the optimization model can effectively, accurately and fast search optimal design considering economic net benefit. Furthermore, the optimized spatial rain barrel design could reduce 72% of inundation losses according to the simulated flood events.

Description: Evidence indicates that breakfast consumption is associated with a cluster of health-related behaviors, yet studies in mainland China are scarce. This study is conducted to describe the frequency of breakfast consumption among Chinese adolescents and examine its associations with other dietary, physical activity, sedentary, sleep, cigarette-smoking, and alcohol-drinking behaviors. Breakfast consumption and other health-related behaviors data was collected via a self-administered questionnaire in a cross-sectional study in Zhejiang Province, China. A total of 19,542 school-aged adolescents were recruited in this survey. The associations between breakfast consumption and other health-related behaviors were examined using logistic regression models. A significantly higher prevalence of daily breakfast consumption was found among students who were younger (p for trend 〈0.001), from urban schools (p 〈 0.001), and academic high schools (p 〈 0.001). More frequent vegetable and milk consumption, greater physical activity, and longer sleep duration were positively associated with daily breakfast consumption, while soft drinks and fast food consumption, computer use, cigarette-smoking and alcohol-drinking behaviors were inversely associated. The prevalence of irregular breakfast consumption was relatively high among Chinese adolescents in Zhejiang Province. Daily breakfast consumption was associated with a constellation of health-related behaviors.