bluuebunny/crossref_metadata_2025 huggingface dataset:
Polymerization Shrinkage Evaluation of Restorative Resin-Based Composites Using Fiber Bragg Grating Sensors
Authors: Lins Rodrigo , Vinagre Alexandra , Alberto Nélia , Domingues Maria F. , Messias Ana , Martins Luís R. , Nogueira Rogério , Ramos João C.
Published: May 2019

The purpose of this study was to compare the linear polymerization shrinkage of different restorative resin-based composites (RBCs) using fiber Bragg grating (FBG) sensors. Five RBCs were evaluated: Zirconfill® (ZFL); Aura Bulk-Fill (ABF); Tetric® N-Ceram Bulk-Fill (TBF); FiltekTM Bulk-Fill (FBF); and Admira Fusion-Ormocer® (ADF). Ten samples per resin were produced in standardized custom-made half-gutter silicone molds. Two optical FBG sensors were used to assess temperature and polymerization shrinkage. Light curing was performed for 40 s and polymerization shrinkage was evaluated at 5, 10, 40, 60, 150, and 300 s. Statistical analysis was accomplished for normal distribution (Shapiro-Wilk, p > 0.05). Two-way repeated measures ANOVA with Greenhouse-Geisser correction followed by Bonferroni′s post-hoc test was used to analyze the linear shrinkage data (p < 0.05). ZFL showed the highest linear shrinkage and ADF the lowest. Shrinkage increased for all RBCs until 300 s, where significant differences were found between ADF and all other resins (p < 0.05). Among bulk-fill RBCs, TBF showed the lowest shrinkage value, but not statistically different from FBF. The ADF presented lower linear shrinkage than all other RBCs, and restorative bulk-fill composites exhibited an intermediate behavior.
Holographic Formation of Non-uniform Diffraction Structures by Arbitrary Polarized Recording Beams in Liquid Crystal-photopolymer Compositions
Authors: Semkin Artem , Sharangovich Sergey
Published: May 2019

In this work, the theoretical model of non-uniform diffraction structures’ holographic formation in liquid crystal-photopolymer (LC-PPM) composite materials with a dye-sensitizer is developed. The model takes into account the arbitrary character of amplitude and phase spatial distributions of recording light field, its arbitrary polarization state and also a non-linearity of the recording process. Two the most common types of liquid crystal-photopolymer composite are investigated: Holographic polymer-dispersed liquid crystals (H-PDLC) and polymer-stabilized liquid crystals (PSLC). Numerical simulations for the most common cases of holographic formation schemes are made. It is shown that due to the photo-induced Freedericksz transition, in the case of arbitrary polarization states of recording light beams, the non-uniform polarization diffraction grating (PDG) is formed in LC-PPM. Numerical simulations’ results show that PDG’s contribution to the change of the dielectric tensor of the media is comparable with the contribution of the photopolymerization-diffusion process.
A Crosslinked Soybean Protein Isolate Gel Polymer Electrolyte Based on Neutral Aqueous Electrolyte for a High-Energy-Density Supercapacitor
Authors: Huo Pengfei , Ni Shoupeng , Hou Pu , Xun Zhiyu , Liu Yang , Gu Jiyou
Published: May 2019

A crosslinked membrane based on renewable, degradable and environmentally friendly soybean protein isolate was formed by solution casting method. A series of gel polymer electrolytes were prepared with the crosslinked membranes saturated with 1 mol·L−1 Li2SO4. The solid-state electric double-layer capacitors were fabricated with the prepared gel polymer electrolytes and activated carbon electrodes. The optimized solid-state supercapacitor delivered a single electrode specific capacitance of 115.17 F·g−1 at a current density of 1.0 A·g−1, which was higher than the supercapacitor assembled with the commercial separator in 1 mol·L−1 Li2SO4. The solid-state supercapacitor exhibited an outstanding cycling stability, indicating that the gel polymer electrolyte based on the crosslinked soybean protein isolate membrane could be a promising separator for a solid-state supercapacitor.
Wet-Spinning Assembly of Continuous, Highly Stable Hyaluronic/Multiwalled Carbon Nanotube Hybrid Microfibers
Authors: Zheng Ting , Xu Nuo , Kan Qi , Li Hongbin , Lu Chunrui , Zhang Peng , Li Xiaodan , Zhang Dongxing , Wang Xiaodong
Published: May 2019

Effective multiwalled carbon nanotube (MWCNT) fiber manufacturing methods have received a substantial amount of attention due to the low cost and excellent properties of MWCNTs. Here, we fabricated hybrid microfibers composed of hyaluronic acid (HA) and multiwalled carbon nanotubes (MWCNTs) by a wet-spinning method. HA acts as a biosurfactant and an ionic crosslinker, which improves the dispersion of MWCNTs and helps MWCNT to assemble into microfibers. The effects of HA concentration, dispersion time, injection speed, and MWCNT concentration on the formation, mechanical behavior, and conductivity of the HA/MWCNT hybrid microfibers were comprehensively investigated through SEM, UV-Vis spectroscopy, tensile testing, and conductivity testing. The obtained HA/MWCNT hybrid microfibers presented excellent tensile properties in regard to Young’s modulus (9.04 ± 1.13 GPa) and tensile strength (130.25 ± 10.78 MPa), and excellent flexibility and stability due to the superior mechanical and electrical properties of MWCNTs. This work presents an effective and easy-to-handle preparation method for high-performance MWCNT hybrid microfibers assembly, and the obtained HA/MWCNT hybrid microfibers have promising applications in the fields of energy storage, sensors, micro devices, intelligent materials, and high-performance fiber-reinforced composites.
Fabrication of Water-Compatible Molecularly Imprinted Resin in a Hydrophilic Deep Eutectic Solvent for the Determination and Purification of Quinolones in Wastewaters
Authors: Tang Weiyang , Row Kyung Ho
Published: May 2019

A novel water-compatible molecularly imprinted resin was prepared in a green solvent deep eutectic solvent (DES). Resorcinol and melamine, as functional monomers with an abundant hydrophilic group, such as –OH, –NH2 and –NH–, were introduced into the molecularly imprinted resin (MIR). Three DESs (choline chloride-ethylene glycol, tetramethylammonium bromide-ethylene glycol and tetramethylammonium chloride-ethylene glycol) were used to synthesize the molecularly imprinted resin and the resulting deep eutectic solvent-based molecularly imprinted resins were characterized by particle size analysis, elemental analysis, scanning electron microscopy, Fourier transform infrared spectroscopy and thermogravimetric analysis. The resulting deep eutectic solvent-based molecularly imprinted resins were then applied to the adsorption of quinolones (ofloxacin) in water. The adsorption process of deep eutectic solvent-based molecularly imprinted resin followed the static adsorption model, Langmuir isotherm (R2 ≥ 0.9618) and kinetic model pseudo-second-order (R2 > 0.9814). The highest theory adsorption ability of the three kinds of deep eutectic solvent-based molecularly imprinted resins was more than 23.79 mg/g. The choline chloride-ethylene glycol-based MIR was applied to solid-phase extraction for the determination and purification of quinolones (e.g., ciprofloxacin and ofloxacin). The detection limit of deep eutectic solvent-based molecularly imprinted resin-solid-phase extraction method was less than 0.018 mg/L. The recoveries of the deep eutectic solvent-based molecularly imprinted resin-solid-phase extraction method at three spiked levels were 88.7–94.5%, with a relative standard deviation of ≤4.8%. The novel deep eutectic solvent-based molecularly imprinted resin-solid-phase extraction method is a simple, selective and accurate pre-treatment method and can be used to determine the quinolones in environmental water.
Twisting of Fibers Balancing the Gel–Sol Transition in Cellulose Aqueous Suspensions
Authors: Zlenko Dmitry , Nikolsky Sergey , Vedenkin Alexander , Politenkova Galina , Skoblin Aleksey , Melnikov Valery , Mikhaleva Maria , Stovbun Sergey
Published: May 2019

Cellulose hydrogels and films are advantageous materials that are applied in modern industry and medicine. Cellulose hydrogels have a stable scaffold and never form films upon drying, while viscous cellulose hydrosols are liquids that could be used for film production. So, stabilizing either a gel or sol state in cellulose suspensions is a worthwhile challenge, significant for the practical applications. However, there is no theory describing the cellulose fibers’ behavior and processes underlying cellulose-gel-scaffold stabilizing. In this work, we provide a phenomenological mechanism explaining the transition between the stable-gel and shapeless-sol states in a cellulose suspension. We suppose that cellulose macromolecules and nanofibrils under strong dispersing treatment (such as sonication) partially untwist and dissociate, and then reassemble in a 3D scaffold having the individual elements twisted in the nodes. The latter leads to an exponential increase in friction forces between the fibers and to the corresponding fastening of the scaffold. We confirm our theory by the data on the circular dichroism of the cellulose suspensions, as well as by the direct scanning electron microscope (SEM) observations and theoretical assessments.
Experience of Intimate Partner Violence and Help-Seeking Behaviour among Women in Uganda
Authors: Ghose Bishwajit , Yaya Sanni
Published: May 2019

Intimate partner violence (IPV) is recognised as a fundamental violation of women’s human rights and a widespread phenomenon in Africa. Women’s low socioeconomic empowerment, cultural acceptability, and lack of social support exacerbate the health and psychosocial outcomes of IPV among African women. To date, there is no systematic research on IPV and its association with healthcare use among adult women in Uganda. Therefore, we conducted the present study on IPV among Ugandan women of childbearing age (15–49 years). Cross-sectional data on 7536 women were collected from the Uganda Demographic and Health Survey (UDHS—Uganda Demographic and Health Survey 2016). The objectives were to assess the predictors of IPV as well as help-seeking behaviour for victims of IPV. IPV was assessed by women’s experience of physical, emotional and sexual violence and healthcare use was assessed by self-reported medical visits during the last 12 months. Logistic regression methods were used to analyse the data. According to descriptive findings, which showed that more than half of the women reported experiencing any IPV (55.3%, 95%CI = 53.6, 57.0), emotional IPV (41.2%, 95%CI = 39.6, 42.8) was the most prevalent of all three categories, followed by physical (39.3%, 95%CI = 37.7, 40.9) and sexual IPV (22.0%, 95%CI = 20.7, 23.3). In the multivariate analysis, higher age, rural residence, religious background (non-Christian), ethnicity (Banyankore and Itseo), secondary/higher education and husband’s alcohol drinking habit were positively associated with women’s experience of IPV. Husband’s alcohol drinking was found to be a significant barrier to seeking help among those who experienced IPV. In conclusion, our findings suggest a noticeably high prevalence of IPV among Ugandan women. There are important sociodemographic and cultural patterns in the occurrence of IPV that need to be taken into account when designing intervention policies. Special attention should be given to women living with husbands/partners who drink alcohol, as this might increase their odds of experiencing IPV, as well as reduce the likelihood of seeking help.
The LCA Methodology for Ceramic Tiles Production by Addition of MSWI BA
Authors: Sappa Giuseppe , Iacurto Silvia , Ponzi Adelaide , Tatti Fabio , Torretta Vincenzo , Viotti Paolo
Published: May 2019

Integrated waste management and sustainable use of natural resources are the basis of the Green Economy. In this context, the management of the Municipal Solid Waste Incineration Bottom Ashes (MSWI BA) is one of the current issues worldwide. This paper presents an application of the Life Cycle Assessment (LCA) procedure to the industrial production of ceramic tiles using bottom ashes in the mixture together with feldspathic sands and clays. The comparison between ashes and traditional mixture showed a similar mineralogical and rheological composition. In the reported procedure the MSWI BA, after storage, were treated to separate and recover metals. The residual ashes were added to the mixture and then they followed the traditional industrial production cycle. Samples of the different materials were taken during the experimental industrial activity and leaching tests were carried out to verify the environmental compatibility of MSWI BA use to produce ceramic tiles. The results of the LCA show large environmental and energy benefits related to the proposed reuse of BA. Metal recovery and lower use of clay in traditional mixture avoids emission of substances with a negative potential impact for environment. This study provides a sustainable alternative to the MSWI BA final disposal in landfill as MSWI BA are hazardous wastes that present complicated management and high disposal costs.
Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification
Authors: Hu Sixiu , Peng Jiangtao , Fu Yingxiong , Li Luoqing
Published: May 2019

By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.
Pointing Accuracy of an Operational Polarimetric Weather Radar
Authors: Frech Michael , Mammen Theodor , Lange Bertram
Published: May 2019

Exact navigation of detected radar signals is crucial for usage of radar data in meteorological applications. The antenna pointing accuracy in azimuth and elevation of a polarimetric weather research radar depending on position of the sun is assessed using dedicated solar boxscans in a sequence of 10 min. The research radar of the German Meteorological Service (Deutscher Wetterdienst, DWD) is located at the meteorological observatory Hohenpeissenberg. It is identical to the 17 weather radars of the German weather radar network. A non-linear azimuthal variation of azimuthal pointing bias of up to 0.1 ∘ is found, which is significant as this is commonly viewed as the target pointing accuracy. This azimuthal variation can be attributed to the mechanical design of the drive train with the angle encoder. This includes the inherent backlash of the gear-drive assembly. The pointing bias estimates based on over 1000 boxscans from 26 days show a small case by case variability, which indicates that dedicated solar boxscans from one day are sufficient to characterize the pointing performance of a particular system. An azimuth and elevation range that is covered with this approach is limited and dependent on the time of the year. At Hohenpeißenberg, an azimuth range up to 50–300 ∘ was covered around summer solstice and about 90 boxscans were acquired. It is shown that the pointing bias based on solar boxscan data are consistent with results from the operational assessment of pointing bias using solar hits from operational scanning if we take into account the fact that the DWD operational scan definition has only a maximum elevation of 25 ∘ . The analysis of a full diurnal cycle of boxscans from four operational radar system shows that the azimuthal dependence of azimuth bias needs to be evaluated individually for each system. For one of the systems, the azimuthal variation of the pointing bias of about 0.2 ∘ seems related to the bull gear. A difference of the pointing bias for the horizontal and vertical polarization is an indication of beam squint and, eventually, that of a feed misalignment. Beam squint and, as such, the quality of the antenna assembly can easily be monitored with this method during the life-time of a weather radar.
Present-Day Deformation of the Gyaring Co Fault Zone, Central Qinghai–Tibet Plateau, Determined Using Synthetic Aperture Radar Interferometry
Authors: Zhang Yong , Liu Chuanjin , Zhang Wenting , Jiang Fengyun
Published: May 2019

Because of the constant northward movement of the Indian plate and blockage of the Eurasian continent, the Qinghai–Tibet Plateau has been extruded by north–south compressive stresses since its formation. This has caused the plateau to escape eastward to form a large-scale east–west strike-slip fault and a north–south extensional tectonic system. The Karakorum–Jiali fault, a boundary fault between the Qiangtang and Lhasa terranes, plays an important role in the regional tectonic evolution of the Qinghai–Tibet Plateau. The Gyaring Co fault, in the middle of the Karakoram–Jiali fault zone, is a prominent tectonic component. There have been cases of strong earthquakes of magnitude 7 or greater in this fault, providing a strong earthquake occurrence background. However, current seismic activity is weak. Regional geodetic observation stations are sparsely distributed; thus, the slip rate of the Gyaring Co fault remains unknown. Based on interferometric synthetic aperture radar (InSAR) technology, we acquired current high-spatial resolution crustal deformation characteristics of the Gyaring Co fault zone. The InSAR-derived deformation features were highly consistent with Global Positioning System observational results, and the accuracy of the InSAR deformation fields was within 2 mm/y. According to InSAR results, the Gyaring Co fault controlled the regional crustal deformation pattern, and the difference in far-field deformation on both sides of the fault was 3–5 mm/y (parallel to the fault). The inversion results of the back-slip dislocation model indicated that the slip rate of the Gyaring Co fault was 3–6 mm/y, and the locking depth was ~20 km. A number of v-shaped conjugate strike-slip faults, formed along the Bangong–Nujiang suture zone in the central and southern parts of the -Tibet Plateau, played an important role in regional tectonic evolution. V-shaped conjugate shear fault systems include the Gyaring Co and Doma–Nima faults, and the future seismic risk cannot be ignored.
Satellite Cross-Talk Impact Analysis in Airborne Interferometric Global Navigation Satellite System-Reflectometry with the Microwave Interferometric Reflectometer
Authors: Onrubia Raul , Pascual Daniel , Park Hyuk , Camps Adriano , Rüdiger Christoph , Walker Jeffrey , Monerris Alessandra
Published: May 2019

This work analyzes the satellite cross-talk observed by the microwave interferometric reflectometer (MIR), a new global navigation satellite system (GNSS) reflectometer, during an airborne field campaign in Victoria and New South Wales, Australia. MIR is a GNSS reflectometer with two 19-element, dual-band arrays, each of them having four steerable beams. The data collected during the experiment, the characterization of the arrays, and the global positioning system (GPS) and Galileo ephemeris were used to compute the expected delays and power levels of all incoming signals, and the probability of cross-talk was then evaluated. Despite the MIR highly directive arrays, the largest ever for a GNSS-R instrument, one of the flights was found to be contaminated by cross-talk almost half of the time at the L1/E1 frequency band, and all four flights were contaminated ∼5–10% of the time at the L5/E5a frequency band. The cross-talk introduces an error of up to 40 cm of standard deviation for altimetric applications and about 0.24 dB for scatterometric applications.
Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics
Authors: Zribi Mehrez , Muddu Sekhar , Bousbih Safa , Al Bitar Ahmad , Tomer Sat Kumar , Baghdadi Nicolas , Bandyopadhyay Soumya
Published: May 2019

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.
Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami
Authors: Sublime Jérémie , Kalinicheva Ekaterina
Published: May 2019

Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and assess the damages. Presently, the increasing number of remote-sensing satellites taking pictures of Earth on a regular basis with programs such as Sentinel, ASTER, or Landsat makes it easy to acquire almost in real time images from areas struck by a disaster before and after it hits. While the manual study of such images is also a tedious task, progress in artificial intelligence and in particular deep-learning techniques makes it possible to analyze such images to quickly detect areas that have been flooded or destroyed. From there, it is possible to evaluate both the extent and the severity of the damages. In this paper, we present a state-of-the-art deep-learning approach for change detection applied to satellite images taken before and after the Tohoku tsunami of 2011. We compare our approach with other machine-learning methods and show that our approach is superior to existing techniques due to its unsupervised nature, good performance, and relative speed of analysis.
A Rapid and Automated Urban Boundary Extraction Method Based on Nighttime Light Data in China
Authors: Liu Xiaojiang , Ning Xiaogang , Wang Hao , Wang Chenggang , Zhang Hanchao , Meng Jing
Published: May 2019

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method.
Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model
Authors: Yang Fei , Guo Jiming , Meng Xiaolin , Shi Junbo , Zhou Lv
Published: May 2019

With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days.
Aquarius Sea Surface Salinity Gridding Method Based on Dual Quality–Distance Weighting
Authors: Li Yanyan , Dong Qing , Ren Yongzheng
Published: May 2019

A new method for improving the accuracy of gridded sea surface salinity (SSS) fields is proposed in this paper. The method mainly focuses on dual quality–distance weighting of the Aquarius level 2 along-track SSS data according to quality flags, which represent nonnominal data conditions for measurements. In the weighting progress, 14 data conditions were considered, and their geospatial distributions and influences on the SSS were also visualized and evaluated. Three interpolation methods were employed, and weekly gridded SSS maps were produced for the period from September 2011 to May 2015. These maps were evaluated via comparisons with concurrent Argo buoy measurements. The results show that the proposed method improved the accuracy of the SSS fields by approximately 36% compared to the officially released weekly level 3 products and yielded root mean squared difference (RMSD), correlation and bias values of 0.19 psu, 0.98 and 0.01 psu, respectively. These findings indicate a significant improvement in the accuracy of the SSS fields and provide a better understanding of the influences of different conditions on salinity.
Studying the Influence of Nitrogen Deposition, Precipitation, Temperature, and Sunshine in Remotely Sensed Gross Primary Production Response in Switzerland
Authors: Gómez Giménez Marta , de Jong Rogier , Keller Armin , Rihm Beat , Schaepman Michael E.
Published: May 2019

Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn’t been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: Grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics.
Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images
Authors: Ahmad Muhammad , Khan Asad , Khan Adil Mehmood , Mazzara Manuel , Distefano Salvatore , Sohaib Ahmed , Nibouche Omar
Published: May 2019

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.
Validation of Preliminary Results of Thermal Tropopause Derived from FY-3C GNOS Data
Authors: Liu Ziyan , Sun Yueqiang , Bai Weihua , Xia Junming , Tan Guangyuan , Cheng Cheng , Du Qifei , Wang Xianyi , Zhao Danyang , Tian Yusen , Meng Xiangguang , Liu Congliang , Cai Yuerong , Wang Dongwei
Published: May 2019

The state-of-art global navigation satellite system (GNSS) occultation sounder (GNOS) onboard the FengYun 3 series C satellite (FY-3C) has been in operation for more than five years. The accumulation of FY-3C GNOS atmospheric data makes it ready to be used in atmosphere and climate research fields. This work first introduces FY-3C GNOS into tropopause research and gives the error evaluation results of long-term FY-3C atmosphere profiles. We compare FY-3C results with Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and radiosonde results and also present the FY-3C global seasonal tropopause patterns. The mean temperature deviation between FY-3C GNOS temperature profiles and COSMIC temperature profiles from January 2014 to December 2017 is globally less than 0.2 K, and the bias of tropopause height (TPH) and tropopause temperature (TPT) annual cycle derived from both collocated pairs are about 80–100 m and 1–2 K, respectively. Also, the correlation coefficients between FY-3C GNOS tropopause parameters and each radiosonde counterpart are generally larger than 0.9 and the corresponding regression coefficients are close to 1. Multiple climate phenomena shown in seasonal patterns coincide with results of other relevant studies. Our results demonstrate the long-term stability of FY-3C GNOS atmosphere profiles and utility of FY-3C GNOS data in the climate research field.
Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition
Authors: Wang Qu , Ye Langlang , Luo Haiyong , Men Aidong , Zhao Fang , Ou Changhai
Published: May 2019

Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.
Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement
Authors: Zhan Shuyue , Wang Chao , Liu Shuchang , Xia Kaibo , Huang Hui , Li Xiaorun , Liu Caicai , Xu Ren
Published: May 2019

Automatic colorless floating hazardous and noxious substances (HNS) spill segmentation is an emerging research topic. Xylene is one of the priority HNSs since it poses a high risk of being involved in an HNS incident. This paper presents a novel algorithm for the target enhancement of xylene spills and their segmentation in ultraviolet (UV) images. To improve the contrast between targets and backgrounds (waves, sun reflections, and shadows), we developed a global background suppression (GBS) method to remove the irrelevant objects from the background, which is followed by an adaptive target enhancement (ATE) method to enhance the target. Based on the histogram information of the processed image, we designed an automatic algorithm to calculate the optimal number of clusters, which is usually manually determined in traditional cluster segmentation methods. In addition, necessary pre-segmentation processing and post-segmentation processing were adopted in order to improve the performance. Experimental results on our UV image datasets demonstrated that the proposed method can achieve good segmentation results for chemical spills from different backgrounds, especially for images with strong waves, uneven intensities, and low contrast.
Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans
Authors: Ker Justin , Singh Satya P. , Bai Yeqi , Rao Jai , Lim Tchoyoson , Wang Lipo
Published: May 2019

Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radiological scans. In this paper, we apply 3-dimensional convolutional neural networks (3D CNN) to classify computed tomography (CT) brain scans into normal scans (N) and abnormal scans containing subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), acute subdural hemorrhage (ASDH) and brain polytrauma hemorrhage (BPH). The dataset used consists of 399 volumetric CT brain images representing approximately 12,000 images from the National Neuroscience Institute, Singapore. We used a 3D CNN to perform both 2-class (normal versus a specific abnormal class) and 4-class classification (between normal, SAH, IPH, ASDH). We apply image thresholding at the image pre-processing step, that improves 3D CNN classification accuracy and performance by accentuating the pixel intensities that contribute most to feature discrimination. For 2-class classification, the F1 scores for various pairs of medical diagnoses ranged from 0.706 to 0.902 without thresholding. With thresholding implemented, the F1 scores improved and ranged from 0.919 to 0.952. Our results are comparable to, and in some cases, exceed the results published in other work applying 3D CNN to CT or magnetic resonance imaging (MRI) brain scan classification. This work represents a direct application of a 3D CNN to a real hospital scenario involving a medically emergent CT brain diagnosis.
A Power Supply Technology for a Low-Power Online Monitoring Sensor Based on Electric Field Induction
Authors: Li Zong , Mei Hongwei , Wang Liming
Published: May 2019

In order to provide a safe and stable power supply for low-power online monitoring sensors of transmission lines, a method of harvesting space electric field energy by using the impedance conversion characteristics of the transformer and the reactive compensation characteristics of the capacitor is proposed. The method effectively solves the key problem that the power of energy harvesting based on electric field induction is limited by the transformer excitation reactance and is difficult to upgrade. In this paper, the principle of power supply technology based on electric field induction is described in detail, and the influencing factors such as the wire erection mode, polar plate installation position, and capacitance compensation characteristics are simulated and tested. The test results show that when the power supply voltage is 50 kV, the stray capacitance is 14 pF, the compensation capacitance is 0.86 nF, and the load resistance is 1 kΩ, the energy-harvesting power is 340 mW. Finally, the power supply circuit of a drive-away-birds apparatus is designed, which shows that it can provide a stable and reliable power supply for online monitoring sensors.
A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors
Authors: He Aixiang , Wei Guangfen , Yu Jun , Li Meihua , Li Zhongzhou , Tang Zhenan
Published: May 2019

A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.
Brain Inspired Dynamic System for the Quality of Service Control over the Long-Haul Nonlinear Fiber-Optic Link
Authors: Naghshvarianjahromi Mahdi , Kumar Shiva , Deen M. Jamal
Published: May 2019

Brain-inspired intelligence using the cognitive dynamic system (CDS) concept is proposed to control the quality-of-service (QoS) over a long-haul fiber-optic link that is nonlinear and with non-Gaussian channel noise. Digital techniques such as digital-back-propagation (DBP) assume that the fiber optic link parameters, such as loss, dispersion, and nonlinear coefficients, are known at the receiver. However, the proposed CDS does not need to know about the fiber optic link physical parameters, and it can improve the bit error rate (BER) or enhance the data rate based on information extracted from the fiber optic link. The information extraction (Bayesian statistical modeling) using intelligent perception processing on the received data, or using the previously extracted models in the model library, is carried out to estimate the transmitted data in the receiver. Then, the BER is sent to the executive through the main feedback channel and the executive produces actions on the physical system/signal to ensure that the BER is continuously under the forward-error-correction (FEC) threshold. Therefore, the proposed CDS is an intelligent and adaptive system that can mitigate disturbance in the fiber optic link (especially in an optical network) using prediction in the perceptor and/or doing proper actions in the executive based on BER and the internal reward. A simplified CDS was implemented for nonlinear fiber optic systems based on orthogonal frequency division multiplexing (OFDM) to show how the proposed CDS can bring noticeable improvement in the system’s performance. As a result, enhancement of the data rate by 12.5% and the Q-factor improvement of 2.74 dB were achieved in comparison to the conventional system (i.e., the system without smart brain).
Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science during the LAPSE-RATE Campaign
Authors: Barbieri Lindsay , Kral Stephan , Bailey Sean , Frazier Amy , Jacob Jamey , Reuder Joachim , Brus David , Chilson Phillip , Crick Christopher , Detweiler Carrick , Doddi Abhiram , Elston Jack , Foroutan Hosein , González-Rocha Javier , Greene Brian , Guzman Marcelo , Houston Adam , Islam Ashraful , Kemppinen Osku , Lawrence Dale , Pillar-Little Elizabeth , Ross Shane , Sama Michael , Schmale David , Schuyler Travis , Shankar Ajay , Smith Suzanne , Waugh Sean , Dixon Cory , Borenstein Steve , de Boer Gijs
Published: May 2019

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2.6 ∘ C and 0.22 ± 0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.
An Innovative Ultrasonic Apparatus and Technology for Diagnosis of Freeze-Drying Process
Authors: Cheng Chin-Chi , Tseng Yen-Hsiang , Huang Shih-Chang
Published: May 2019

The freeze-drying process removes water from a product through freezing, sublimation and desorption procedures. However, the extreme conditions of the freeze-drying environment, such as the limited space, vacuum and freezing temperatures of as much as −50 °C, may block the ability to use certain diagnostic sensors. In this paper, an ultrasonic transducer (UT) is integrated onto the bottom of a specially designed frozen bottle for the purpose of observing the freeze-drying process of water at varying amounts. The temperatures and visual observations made with a camera are then compared with the corresponding ultrasonic signatures. Among all of the diagnostic tools and technologies available, only ultrasonic and visual records are able to analyze the entire progression of the freeze-drying process of water. Compared with typical experiment settings, the indication of drying point for water by the amplitude variations of ultrasonic L3 echo could reduce the process period and energy consumption. This study demonstrates how an innovative frozen bottle, an integrated ultrasonic sensor and diagnostic methods used to measure and optimize the freeze-drying process of water can save energy.