bluuebunny/crossref_metadata_2025 huggingface dataset:
Deoxyschizandrin, Isolated from Schisandra Berries, Induces Cell Cycle Arrest in Ovarian Cancer Cells and Inhibits the Protumoural Activation of Tumour-Associated Macrophages
Authors: Lee Kijun , Ahn Ji-Hye , Lee Kyung-Tae , Jang Dae , Choi Jung-Hye
Published: January 2018

Deoxyschizandrin, a major lignan of Schisandra berries, has been demonstrated to have various biological activities such as antioxidant, hepatoprotective, and antidiabetic effects. However, the anti-cancer effects of deoxyschizandrin are poorly characterized. In the present study, we investigated the anti-cancer effect of deoxyschizandrin on human ovarian cancer cell lines and tumour-associated macrophages (TAMs). Deoxyschizandrin induced G0/G1 phase cell cycle arrest and inhibited cyclin E expression in human ovarian cancer cells. Overexpression of cyclin E significantly reversed the deoxyschizandrin-induced cell growth inhibition. Interestingly, increased production of reactive oxygen species and decreased activation of Akt were observed in A2780 cells treated with deoxyschizandrin, and the antioxidant compromised the deoxyschizandrin-induced cell growth inhibition and Akt inactivation. Moreover, deoxyschizandrin-induced cell growth inhibition was markedly suppressed by Akt overexpression. In addition, deoxyschizandrin was found to inhibit the expression of the M2 phenotype markers CD163 and CD209 in TAMs, macrophages stimulated by the ovarian cancer cells. Moreover, expression and production of the tumour-promoting factors MMP-9, RANTES, and VEGF, which are highly enhanced in TAMs, was significantly suppressed by deoxyschizandrin treatment. Taken together, these data suggest that deoxyschizandrin exerts anti-cancer effects by inducing G0/G1 cell cycle arrest in ovarian cancer cells and reducing the protumoural phenotype of TAMs.
Dietary Intake of Flavonoids and Ventilatory Function in European Adults: A GA2LEN Study
Authors: Garcia-Larsen Vanessa , Thawer Narjis , Charles David , Cassidy Aedin , Van Zele Thibaut , Thilsing Trine , Ahlström Matti , Haahtela Tari , Keil Thomas , Matricardi Paolo , Brożek Grzegorz , Kowalski Marek , Makowska Joanna , Niżankowska-Mogilnicka Ewa , Rymarczyk Barbara , Loureiro Carlos , Todo Bom Ana , Bachert Claus , Forsberg Bertil , Janson Christer , Torén Kjell , Potts James , Burney Peter
Published: January 2018

Background: Flavonoids exert anti-inflammatory properties and modulate oxidative stress in vitro, suggesting a protective effect on lung function, but epidemiological studies examining this association are scarce. Methods: A stratified random sample was drawn from the GA2LEN screening survey, in which 55,000 adults aged 15 to 75 answered a questionnaire on respiratory symptoms. Post-bronchodilator spirometry was obtained from 2850 subjects. Forced vital capacity (FVC), the ratio between the forced exhaled volume in 1 second (FEV1) and FVC (FEV1/FVC), FVC below lower limit of normal (FVC < LLN), and FEV1/FVC < LLN were calculated. Intake of the six main subclasses of flavonoids was estimated using the GA2LEN Food Frequency Questionnaire. Adjusted associations between outcomes and each subclass of flavonoids were examined with multivariate regressions. Simes’ procedure was used to test for multiple comparisons. Results: A total of 2599 subjects had valid lung function and dietary data. A lower prevalence of FVC < LLN (airway restriction) was observed in those with higher total flavonoid (adjusted odds ratio (aOR), higher vs. lowest quintile intake 0.58; 95% Confidence Interval (CI) 0.36, 0.94), and pro-anthocyanidin intakes (aOR 0.47; 95% CI 0.27, 0.81). A higher FEV1/FVC was associated with higher intakes of total flavonoids and pro-anthocyanidins (adjusted correlation coefficient (a β-coeff 0.33; 0.10, 0.57 and a β-coeff 0.44; 95% CI 0.19, 0.69, respectively). After Simes’ procedure, the statistical significance of each of these associations was attenuated but remained below 0.05, with the exception of total flavonoids and airway restriction. Conclusions: This population-based study in European adults provides cross-sectional evidence of a positive association of total flavonoid intake and pro-anthocyanidins and ventilatory function, and a negative association with spirometric restriction in European adults.
Acknowledgement to Reviewers of Pathogens in 2017
Authors:   Pathogens Editorial Office
Published: January 2018

Peer review is an essential part in the publication process, ensuring that Pathogens maintains high quality standards for its published papers [...]
Is the Skin Absorption of Hydrocortisone Modified by the Variability in Dosing Topical Products?
Authors: Paterson Daniel , Hallier Jacqueline , Jenkins Elizabeth , Cordery Sarah , Delgado-Charro M.
Published: January 2018

Fingertip units have been proposed as a tool to standardize topical therapy with semisolid formulations. However, no studies to date have characterized the variability in dosing by patients using this concept and whether this variability ultimately affects the topical absorption of drugs. This work aimed to answer these two questions. A first study determined the dose measured, the area of spread and the area-normalized dose for a 1% hydrocortisone cream and ointment applied by members of the public using this dosing approach before and after brief counselling. Then, in vivo tape-stripping and in vitro permeation studies investigated whether the variability in the area-normalized dose altered the skin absorption of hydrocortisone. Participants applied greater doses and spread them over larger areas after a short counselling intervention leading to smaller area-normalized doses. In vivo hydrocortisone uptake by the stratum corneum was significantly greater for the higher normalized dose and the differences were further supported by the in vitro permeation studies. However, these differences were relatively small and not proportional to the increase in normalized dose. This work shows that, following brief advice, patients and carers can apply consistent and sufficient doses of corticosteroids whilst minimizing risks and variability in hydrocortisone absorption.
Ophthalmic Drug Delivery Systems for Antibiotherapy—A Review
Authors: Dubald Marion , Bourgeois Sandrine , Andrieu Véronique , Fessi Hatem
Published: January 2018

The last fifty years, ophthalmic drug delivery research has made much progress, challenging scientists about the advantages and limitations of this drug delivery approach. Topical eye drops are the most commonly used formulation in ocular drug delivery. Despite the good tolerance for patients, this topical administration is only focus on the anterior ocular diseases and had a high precorneal loss of drugs due to the tears production and ocular barriers. Antibiotics are popularly used in solution or in ointment for the ophthalmic route. However, their local bioavailability needs to be improved in order to decrease the frequency of administrations and the side effects and to increase their therapeutic efficiency. For this purpose, sustained release forms for ophthalmic delivery of antibiotics were developed. This review briefly describes the ocular administration with the ocular barriers and the currently topical forms. It focuses on experimental results to bypass the limitations of ocular antibiotic delivery with new ocular technology as colloidal and in situ gelling systems or with the improvement of existing forms as implants and contact lenses. Nanotechnology is presently a promising drug delivery way to provide protection of antibiotics and improve pathway through ocular barriers and deliver drugs to specific target sites.
Assessing the Perceptions and Practice of Self-Medication among Bangladeshi Undergraduate Pharmacy Students
Authors: Seam Md. , Bhatta Rita , Saha Bijoy , Das Abhijit , Hossain Md. , Uddin S. , Karmakar Palash , Choudhuri M. , Sattar Mohammad
Published: January 2018

Objectives: To evaluate the perceptions and extent of practicing self-medication among undergraduate pharmacy students. Methods: This cross-sectional, questionnaire-based study was conducted over a six month period (January to June 2016) among undergraduate pharmacy students in five reputable public universities of Bangladesh. It involved face-to-face interviews regarding self-medication of 250 respondents selected by simple random sampling. Results: Self-medication was reported by 88.0% of students. Antipyretics (58.40%) were mostly preferred for the treatment of fever and headaches. The major cause for self-medication was minor illness (59.60%, p = 0.73) while previous prescriptions were the main source of knowledge as well as the major factor (52.80%, p = 0.94) dominating the self-medication practice. The results also demonstrated 88.80% of students had previous knowledge on self-medication and 83.60% of students always checked the information on the label; mainly the expiry date before use (85.60%). A significant (p < 0.05) portion of the students (51% male and 43% female) perceived it was an acceptable practice as they considered self-medication to be a segment of self-care. Furthermore, students demonstrated differences in their response level towards the adverse effect of drugs, the health hazard by a higher dose of drug, a physician’s help in case of side effects, taking medicine without proper knowledge, and stopping selling medicine without prescription. Conclusions: Self-medication was commonly used among pharmacy students primarily for minor illnesses using over-the-counter medications. Although it is an inevitable practice for them it should be considered an important public health problem as this practice may increase the misuse or irrational use of medicines.
Photoinduced Cu(II)-Mediated RDRP to P(VDF-co-CTFE)-g-PAN
Authors: Hu Xin , Cui Guopeng , Zhu Ning , Zhai Jinglin , Guo Kai
Published: January 2018

Photoinduced Cu(II)-mediated reversible deactivation radical polymerization (RDRP) was employed to synthesize poly(vinylidene fluoride-co-chlorotrifluoroethylene)-graft-polyacrylonitrile (P(VDF-co-CTFE)-g-PAN). The concentration of copper catalyst (CuCl2) loading was as low as 1/64 equivalent to chlorine atom in the presence of Me6-Tren under UV irradiation. The light-responsive nature of graft polymerization was confirmed by “off-on” impulsive irradiation experiments. Temporal control of the polymerization process and varied graft contents were achieved via this photoinduced Cu(II)-mediated RDRP.
Effect of Alkane Chain Length on Crystallization in Emulsions during Supercooling in Quiescent Systems and under Mechanical Stress
Authors: Abramov Serghei , Shah Kinza , Weißenstein Lydia , Karbstein Heike
Published: January 2018

Crystallization behavior of hexadecane (C16H34), octadecane (C18H38), eicosane (C20H42), and docosane (C22H46) dispersions of similar mean droplet diameter (x50.2 ≈ 15 µm) was investigated in quiescent systems and compared to crystallization under mechanical stress. In quiescent systems, the required supercooling decreased with increasing chain length of the alkanes to initiate crystallization. Crystallization of alkane dispersions under mechanical stress resulted in similar onset crystallization supercooling, as during quiescent crystallization. Increase of mechanical stress did not affect the onset crystallization supercooling within alkane dispersions.
In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi
Authors: Wilken St. , Saxena Mohan , Petzold Linda , O’Malley Michelle
Published: January 2018

Lignocellulose is an abundant and renewable resource that holds great promise for sustainable bioprocessing. However, unpretreated lignocellulose is recalcitrant to direct utilization by most microbes. Current methods to overcome this barrier include expensive pretreatment steps to liberate cellulose and hemicellulose from lignin. Anaerobic gut fungi possess complex cellulolytic machinery specifically evolved to decompose crude lignocellulose, but they are not yet genetically tractable and have not been employed in industrial bioprocesses. Here, we aim to exploit the biomass-degrading abilities of anaerobic fungi by pairing them with another organism that can convert the fermentable sugars generated from hydrolysis into bioproducts. By combining experiments measuring the amount of excess fermentable sugars released by the fungal enzymes acting on crude lignocellulose, and a novel dynamic flux balance analysis algorithm, we screened potential consortia partners by qualitative suitability. Microbial growth simulations reveal that the fungus Anaeromyces robustus is most suited to pair with either the bacterium Clostridia ljungdahlii or the methanogen Methanosarcina barkeri—both organisms also found in the rumen microbiome. By capitalizing on simulations to screen six alternative organisms, valuable experimental time is saved towards identifying stable consortium members. This approach is also readily generalizable to larger systems and allows one to rationally select partner microbes for formation of stable consortia with non-model microbes like anaerobic fungi.
Rescue US: Birth, Django, and the Violence of Racial Redemption
Authors: Winters Joseph
Published: January 2018

In this article, I show how the relationship between race, violence, and redemption is articulated and visualized through film. By juxtaposing DW Griffith’s The Birth of a Nation and Quentin Tarantino’s Django Unchained, I contend that the latter inverts the logic of the former. While Birth sacrifices black bodies and explains away anti-black violence for the sake of restoring white sovereignty (or rescuing the nation from threatening forms of blackness), Django adopts a rescue narrative in order to show the excessive violence that structured slavery and the emergence of the nation-state. As an immanent break within the rescue narrative, Tarantino’s film works to “rescue” images and sounds of anguish from forgetful versions of history.
One Philosopher’s Bug Can Be Another’s Feature: Reply to Almeida’s “Multiverse and Divine Creation”
Authors: Kraay Klaas
Published: January 2018

Michael Almeida once told me that he thought we were just a couple of hours of conversation away from reaching deep agreement about some important topics in the philosophy of religion pertaining to God, multiverses, and modality. This paper represents my attempt to move this conversation forward and to seek this common ground. Specifically, I respond to Almeida’s paper entitled “The Multiverse and Divine Creation”. In the first four sections, I record my disagreement with him concerning some smaller matters. In Section 5, I try to persuade him that what he considers a ‘bug’ in the theistic multiverse is actually a feature—and a desirable one at that. In Section 6, I close by identifying some points at which our views seem to converge.
Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data
Authors: Bulgin Claire , Mittaz Jonathan , Embury Owen , Eastwood Steinar , Merchant Christopher
Published: January 2018

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ~10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ~5–10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals.
Assessing Global Ocean Wind Energy Resources Using Multiple Satellite Data
Authors: Guo Qiaoying , Xu Xiazhen , Zhang Kangyu , Li Zhengquan , Huang Weijiao , Mansaray Lamin , Liu Weiwei , Wang Xiuzhen , Gao Jian , Huang Jingfeng
Published: January 2018

Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies.
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows
Authors: Zhao Liaoying , Lin Weijun , Wang Yulei , Li Xiaorun
Published: January 2018

Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the

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identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.
Instantaneous 3D Continental-Shelf Scale Imaging of Oceanic Fish by Multi-Spectral Resonance Sensing Reveals Group Behavior during Spawning Migration
Authors: Yi Dong , Gong Zheng , Jech J. , Ratilal Purnima , Makris Nicholas
Published: January 2018

The migration of extensive social groups towards specific spawning grounds in vast and diverse ocean environments is an integral part of the regular spawning process of many oceanic fish species. Oceanic fish in such migrations typically seek locations with environmental parameters that maximize the probability of successful spawning and egg/larval survival. The 3D spatio-temporal dynamics of these behavioral processes are largely unknown due to technical difficulties in sensing the ocean environment over wide areas. Here, we use ocean acoustic waveguide remote sensing (OAWRS) to instantaneously image immense herring groups over continental-shelf-scale areas at the Georges Bank spawning ground. Via multi-spectral OAWRS measurements, we capture a shift in swimbladder resonance peak correlated with the herring groups’ up-slope spawning migration, enabling 3D spatial behavioral dynamics to be instantaneously inferred over thousands of square kilometers. We show that herring groups maintain near-bottom vertical distributions with negative buoyancy throughout the migration. We find a spatial correlation greater than 0.9 between the average herring group depth and corresponding seafloor depth for migratory paths along the bathymetric gradient. This is consistent with herring groups maintaining near-seafloor paths to both search for optimal spawning conditions and reduce the risk of predator attacks during the migration to shallower waters where near-surface predators are more dangerous. This analysis shows that multi-spectral resonance sensing with OAWRS can be used as an effective tool to instantaneously image and continuously monitor the behavioral dynamics of swimbladder-bearing fish group behavior in three spatial dimensions over continental-shelf scales.
Urban Area Tomography Using a Sparse Representation Based Two-Dimensional Spectral Analysis Technique
Authors: Liang Lei , Li Xinwu , Ferro-Famil Laurent , Guo Huadong , Zhang Lu , Wu Wenjin
Published: January 2018

Synthetic aperture radar (SAR) tomography (TomoSAR) estimates scene reflectivity along elevation coordinates, based on multi-baseline measurements. Common TomoSAR approaches are based on every single range-azimuth cell or the cell’s neighborhood. By using an additional synthetic aperture for elevation, these techniques have higher resolution power for elevation, to discriminate scatterers with differences in location in the same range-azimuth cell. However, they cannot provide sufficient range resolution power to discriminate these scatterers by joining different spectra to a wider range spectrum, which limits the resolution power of TomoSAR. Therefore, in this paper, we proposed using a compressive sensing (CS) technique to reconstruct range and elevation signals from multi-baseline SAR images. This TomoSAR method not only retrieves the vertical signals but also improves the resolution in the range, which helps improve the resolution power of the TomoSAR technique. We present the theory of the CS-based two-dimensional TomoSAR technique and compare it to the CS-based one-dimensional TomoSAR technique. The excellent resolution power and scatterer localization accuracy of this novel technique are demonstrated using simulations and real data obtained by RADARSAT-2 in Lanzhou, China.
Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach
Authors: Chen Wenshuai , Gou Shuiping , Wang Xinlin , Li Xiaofeng , Jiao Licheng
Published: January 2018

In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image classification method based on multilayer autoencoders and self-paced learning (SPL) is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.
Impact of the Initial State on BDS Real-Time Orbit Determination Filter Convergence
Authors: Qing Yun , Lou Yidong , Liu Yang , Dai Xiaolei , Cai Yi
Published: January 2018

High precision real-time orbit of navigation satellites are usually predicted based on batch estimation solutions, which is highly dependent on the accuracy of the dynamic model. However, for the BDS satellites, the accuracy and reliability of the predicted orbit usually decrease due to the inaccurate dynamic model or orbit maneuvers. To improve this situation, the sequential estimation Square Root Information Filtering (SRIF) was applied to determine the real-time BDS orbits. In the filter algorithm, usually a long period is required for the orbit to converge to the final accuracy, due to lake of accurate initial state. This paper focuses on the impact of the initial state with different a priori Standard Deviation (STD) on the BDS orbit convergence performance in both normal and abnormal periods. For the normal period, the Ultra-Rapid (UR) orbit products and the Broadcast Ephemerides (BRDC) used as initial orbits are discussed respectively. For the abnormal period, orbit maneuver is analyzed. Experimental results show that a proper a priori STD of initial state can significantly accelerate the orbit convergence, while a loose a priori STD takes more than 10 h to converge in the radial direction for the BDS GEO/IGSO/MEO satellites. When the UR orbit product is used as the initial orbit, the orbit of the IGSO/MEO satellites can converge to decimeter-level immediately. When the BRDC product is used, the accuracy of meter-level can be obtained for the IGSO/MEO immediately, and converge to decimeter-level in about 6 h. For the period after the orbit maneuver, the real-time orbit accuracy can reach meter-level in about 6 h after the first group of broadcast ephemerides is received.
Comparative Analysis of Responses of Land Surface Temperature to Long-Term Land Use/Cover Changes between a Coastal and Inland City: A Case of Freetown and Bo Town in Sierra Leone
Authors: Tarawally Musa , Xu Wenbo , Hou Weiming , Mushore Terence
Published: January 2018

Urban growth and its associated expansion of built-up areas are expected to continue through to the twenty second century and at a faster pace in developing countries. This has the potential to increase thermal discomfort and heat-related distress. There is thus a need to monitor growth patterns, especially in resource constrained countries such as Africa, where few studies have so far been conducted. In view of this, this study compares urban growth and temperature response patterns in Freetown and Bo town in Sierra Leone. Multispectral Landsat images obtained in 1998, 2000, 2007, and 2015 are used to quantify growth and land surface temperature responses. The contribution index (CI) is used to explain how changes per land use and land cover class (LULC) contributed to average city surface temperatures. The population size of Freetown was about eight times greater than in Bo town. Landsat data mapped urban growth patterns with a high accuracy (Overall Accuracy > 80%) for both cities. Significant changes in LULC were noted in Freetown, characterized by a 114 km2 decrease in agriculture area, 23 km2 increase in dense vegetation, and 77 km2 increase in built-up area. Between 1998 and 2015, built-up area increased by 16 km2, while dense vegetation area decreased by 14 km2 in Bo town. Average surface temperature increased from 23.7 to 25.5 °C in Freetown and from 24.9 to 28.2 °C in Bo town during the same period. Despite the larger population size and greater built-up extent, as well as expansion rate, Freetown was 2 °C cooler than Bo town in all periods. The low temperatures are attributed to proximity to sea and the very large proportion of vegetation surrounding the city. Even close to the sea and abundant vegetation, the built-up area had an elevated temperature compared to the surroundings. The findings are important for formulating heat mitigation strategies for both inland and coastal cities in developing countries.
Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision
Authors: Tu Junchao , Zhang Liyan
Published: January 2018

A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.
Characteristics of BeiDou Navigation Satellite System Multipath and Its Mitigation Method Based on Kalman Filter and Rauch-Tung-Striebel Smoother
Authors: Zhang Qiuzhao , Yang Wei , Zhang Shubi , Liu Xin
Published: January 2018

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.
Optical Fiber Demodulation System with High Performance for Assessing Fretting Damage of Steam Generator Tubes
Authors: Huang Peijian , Wang Ning , Li Junying , Zhu Yong , Zhang Jie , Xi Zhide
Published: January 2018

In order to access the fretting damage of the steam generator tube (SGT), a fast fiber Fabry-Perot (F-P) non-scanning correlation demodulation system based on a super luminescent light emitting diode (SLED) was performed. By demodulating the light signal coming out from the F-P force sensor, the radial collision force between the SGT and the tube support plate (TSP) was interrogated. For higher demodulation accuracy, the effects of the center wavelength, bandwidth, and spectrum noise of SLED were discussed in detail. Specially, a piezoelectric ceramic transducer (PZT) modulation method was developed to get rid of the interference of mode coupling induced by different types of fiber optics in the demodulation system. The reflectivity of optical wedge and F-P sensor was optimized. Finally, the demodulation system worked well in a 1:1 steam generator test loop and successfully demodulated a force signal of 32 N with a collision time of 2 ms.
High-Temperature Sensor Based on Fabry-Perot Interferometer in Microfiber Tip
Authors: Chen Zhenshi , Xiong Songsong , Gao Shecheng , Zhang Hui , Wan Lei , Huang Xincheng , Huang Bingsen , Feng Yuanhua , Liu Weiping , Li Zhaohui
Published: January 2018

A miniaturized tip Fabry-Perot interferometer (tip-FPI) is proposed for high-temperature sensing. It is simply fabricated for the first time by splicing a short length of microfiber (MF) to the cleaved end of a standard single mode fiber (SMF) with precise control of the relative cross section position. Such a MF acts as a Fabry-Perot (FP) cavity and serves as a tip sensor. A change in temperature modifies the length and refractive index of the FP cavity, and then a corresponding change in the reflected interference spectrum can be observed. High temperatures of up to 1000 °C are measured in the experiments, and a high sensitivity of 13.6 pm/°C is achieved. This compact sensor, with tip diameter and length both of tens of microns, is suitable for localized detection, especially in harsh environments.
An Over 90 dB Intra-Scene Single-Exposure Dynamic Range CMOS Image Sensor Using a 3.0 μm Triple-Gain Pixel Fabricated in a Standard BSI Process
Authors: Takayanagi Isao , Yoshimura Norio , Mori Kazuya , Matsuo Shinichiro , Tanaka Shunsuke , Abe Hirofumi , Yasuda Naoto , Ishikawa Kenichiro , Okura Shunsuke , Ohsawa Shinji , Otaka Toshinori
Published: January 2018

To respond to the high demand for high dynamic range imaging suitable for moving objects with few artifacts, we have developed a single-exposure dynamic range image sensor by introducing a triple-gain pixel and a low noise dual-gain readout circuit. The developed 3 μm pixel is capable of having three conversion gains. Introducing a new split-pinned photodiode structure, linear full well reaches 40 ke−. Readout noise under the highest pixel gain condition is 1 e− with a low noise readout circuit. Merging two signals, one with high pixel gain and high analog gain, and the other with low pixel gain and low analog gain, a single exposure dynamic rage (SEHDR) signal is obtained. Using this technology, a 1/2.7”, 2M-pixel CMOS image sensor has been developed and characterized. The image sensor also employs an on-chip linearization function, yielding a 16-bit linear signal at 60 fps, and an intra-scene dynamic range of higher than 90 dB was successfully demonstrated. This SEHDR approach inherently mitigates the artifacts from moving objects or time-varying light sources that can appear in the multiple exposure high dynamic range (MEHDR) approach.
Plils: A Practical Indoor Localization System through Less Expensive Wireless Chips via Subregion Clustering
Authors: Li Xiaolong , Yang Yifu , Cai Jun , Deng Yun , Yang Junfeng , Zhou Xinmin , Tan Lina
Published: January 2018

Reducing costs is a pragmatic method for promoting the widespread usage of indoor localization technology. Conventional indoor localization systems (ILSs) exploit relatively expensive wireless chips to measure received signal strength for positioning. Our work is based on a cheap and widely-used commercial off-the-shelf (COTS) wireless chip, i.e., the Nordic Semiconductor nRF24LE1, which has only several output power levels, and proposes a new power level based-ILS, called Plils. The localization procedure incorporates two phases: an offline training phase and an online localization phase. In the offline training phase, a self-organizing map (SOM) is utilized for dividing a target area into k subregions, wherein their grids in the same subregion have similar fingerprints. In the online localization phase, the support vector machine (SVM) and back propagation (BP) neural network methods are adopted to identify which subregion a tagged object is located in, and calculate its exact location, respectively. The reasonable value for k has been discussed as well. Our experiments show that Plils achieves 75 cm accuracy on average, and is robust to indoor obstacles.
Current Technologies of Electrochemical Immunosensors: Perspective on Signal Amplification
Authors: Cho Il-Hoon , Lee Jongsung , Kim Jiyeon , Kang Min-soo , Paik Jean , Ku Seockmo , Cho Hyun-Mo , Irudayaraj Joseph , Kim Dong-Hyung
Published: January 2018

An electrochemical immunosensor employs antibodies as capture and detection means to produce electrical charges for the quantitative analysis of target molecules. This sensor type can be utilized as a miniaturized device for the detection of point-of-care testing (POCT). Achieving high-performance analysis regarding sensitivity has been one of the key issues with developing this type of biosensor system. Many modern nanotechnology efforts allowed for the development of innovative electrochemical biosensors with high sensitivity by employing various nanomaterials that facilitate the electron transfer and carrying capacity of signal tracers in combination with surface modification and bioconjugation techniques. In this review, we introduce novel nanomaterials (e.g., carbon nanotube, graphene, indium tin oxide, nanowire and metallic nanoparticles) in order to construct a high-performance electrode. Also, we describe how to increase the number of signal tracers by employing nanomaterials as carriers and making the polymeric enzyme complex associated with redox cycling for signal amplification. The pros and cons of each method are considered throughout this review. We expect that these reviewed strategies for signal enhancement will be applied to the next versions of lateral-flow paper chromatography and microfluidic immunosensor, which are considered the most practical POCT biosensor platforms.
Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
Authors: Roh Jongryun , Park Hyeong-jun , Lee Kwang , Hyeong Joonho , Kim Sayup , Lee Boreom
Published: January 2018

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced.
Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
Authors: Napoletano Paolo , Piccoli Flavio , Schettini Raimondo
Published: January 2018

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.
An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction
Authors: Li Yiyang , Jin Weiqi , Zhu Jin , Zhang Xu , Li Shuo
Published: January 2018

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods.
Surface Fitting for Quasi Scattered Data from Coordinate Measuring Systems
Authors: Mao Qing , Liu Shugui , Wang Sen , Ma Xinhui
Published: January 2018

Non-uniform rational B-spline (NURBS) surface fitting from data points is wildly used in the fields of computer aided design (CAD), medical imaging, cultural relic representation and object-shape detection. Usually, the measured data acquired from coordinate measuring systems is neither gridded nor completely scattered. The distribution of this kind of data is scattered in physical space, but the data points are stored in a way consistent with the order of measurement, so it is named quasi scattered data in this paper. Therefore they can be organized into rows easily but the number of points in each row is random. In order to overcome the difficulty of surface fitting from this kind of data, a new method based on resampling is proposed. It consists of three major steps: (1) NURBS curve fitting for each row, (2) resampling on the fitted curve and (3) surface fitting from the resampled data. Iterative projection optimization scheme is applied in the first and third step to yield advisable parameterization and reduce the time cost of projection. A resampling approach based on parameters, local peaks and contour curvature is proposed to overcome the problems of nodes redundancy and high time consumption in the fitting of this kind of scattered data. Numerical experiments are conducted with both simulation and practical data, and the results show that the proposed method is fast, effective and robust. What’s more, by analyzing the fitting results acquired form data with different degrees of scatterness it can be demonstrated that the error introduced by resampling is negligible and therefore it is feasible.
A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
Authors: González-Calabuig Andreu , Cetó Xavier , Del Valle Manel
Published: January 2018

This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L−1, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948.
Introduction to State Estimation of High-Rate System Dynamics
Authors: Hong Jonathan , Laflamme Simon , Dodson Jacob , Joyce Bryan
Published: January 2018

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer’s convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.
Next Generation Wireless Technologies for Internet of Things
Authors: Pau Giovanni , Chaudet Claude , Zhao Dixian , Collotta Mario
Published: January 2018

In the fast-growing Internet of Things (IoT)[...]
Replication Strategy for Spatiotemporal Data Based on Distributed Caching System
Authors: Xiong Lian , Yang Liu , Tao Yang , Xu Juan , Zhao Lun
Published: January 2018

The replica strategy in distributed cache can effectively reduce user access delay and improve system performance. However, developing a replica strategy suitable for varied application scenarios is still quite challenging, owing to differences in user access behavior and preferences. In this paper, a replication strategy for spatiotemporal data (RSSD) based on a distributed caching system is proposed. By taking advantage of the spatiotemporal locality and correlation of user access, RSSD mines high popularity and associated files from historical user access information, and then generates replicas and selects appropriate cache node for placement. Experimental results show that the RSSD algorithm is simple and efficient, and succeeds in significantly reducing user access delay.