bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Assembled Supramolecular Nanoparticles Improve the Cytotoxic Efficacy of CK2 Inhibitor THN7
Authors: Nacereddine Abdelhamid , Bollacke Andre , Róka Eszter , Marminon Christelle , Bouaziz Zouhair , Fenyvesi Ferenc , Bácskay Ildikó , Jose Joachim , Perret Florent , Le Borgne Marc
Published: January 2018

Since the approval of imatinib in 2001, kinase inhibitors have revolutionized cancer therapies. Inside this family of phosphotransferases, casein kinase 2 (CK2) is of great interest and numerous scaffolds have been investigated to design CK2 inhibitors. Recently, functionalized indeno[1,2-b]indoles have been revealed to have high potency against human cancer cell lines such as MCF-7 breast carcinoma and A-427 lung carcinoma. 4-Methoxy-5-isopropyl-5,6,7,8-tetrahydroindeno[1,2-b]indole-9,10-dione (THN7), identified as a potent inhibitor of CK2 (IC50 = 71 nM), was selected for an encapsulation study in order to evaluate its antiproliferative activity as THN7-loaded cyclodextrin nanoparticles. Four α-cyclodextrins (α-CDs) were selected to encapsulate THN7 and all experiments indicated that the nanoencapsulation of this CK2 inhibitor in α-CDs was successful. No additional surface-active agent was used during the nanoformulation process. Nanoparticles formed between THN7 and α-C6H13 amphiphilic derivative gave the best results in terms of encapsulation rate (% of associated drug = 35%), with a stability constant (K11) of 298 mol·L−1 and a size of 132 nm. Hemolytic activity of the four α-CDs was determined before the in cellulo evaluation and the α-C6H13 derivative gave the lowest value of hemolytic potency (HC50 = 1.93 mol·L−1). Only the THN7-loaded cyclodextrin nanoparticles showing less toxicity on human erythrocytes (α-C6H13, α-C8H17 and α-C4H9) were tested against A-427 cells. All drug-loaded nanoparticles caused more cytotoxicity against A-427 cells than THN7 alone. Based on these results, the use of amphiphilic CD nanoparticles could be considered as a drug delivery system for indeno[1,2-b]indoles, allowing an optimized bioavailability and offering perspectives for the in vivo development of CK2 inhibitors.
The Implication of the Brain Insulin Receptor in Late Onset Alzheimer’s Disease Dementia
Authors: Folch Jaume , Ettcheto Miren , Busquets Oriol , Sánchez-López Elena , Castro-Torres Rubén , Verdaguer Ester , Manzine Patricia , Poor Saghar , García María , Olloquequi Jordi , Beas-Zarate Carlos , Auladell Carme , Camins Antoni
Published: January 2018

Alzheimer’s disease (AD) is progressive neurodegenerative disorder characterized by brain accumulation of the amyloid β peptide (Aβ), which form senile plaques, neurofibrillary tangles (NFT) and, eventually, neurodegeneration and cognitive impairment. Interestingly, epidemiological studies have described a relationship between type 2 diabetes mellitus (T2DM) and this pathology, being one of the risk factors for the development of AD pathogenesis. Information as it is, it would point out that, impairment in insulin signalling and glucose metabolism, in central as well as peripheral systems, would be one of the reasons for the cognitive decline. Brain insulin resistance, also known as Type 3 diabetes, leads to the increase of Aβ production and TAU phosphorylation, mitochondrial dysfunction, oxidative stress, protein misfolding, and cognitive impairment, which are all hallmarks of AD. Moreover, given the complexity of interlocking mechanisms found in late onset AD (LOAD) pathogenesis, more data is being obtained. Recent evidence showed that Aβ42 generated in the brain would impact negatively on the hypothalamus, accelerating the “peripheral” symptomatology of AD. In this situation, Aβ42 production would induce hypothalamic dysfunction that would favour peripheral hyperglycaemia due to down regulation of the liver insulin receptor. The objective of this review is to discuss the existing evidence supporting the concept that brain insulin resistance and altered glucose metabolism play an important role in pathogenesis of LOAD. Furthermore, we discuss AD treatment approaches targeting insulin signalling using anti-diabetic drugs and mTOR inhibitors.
Alpha-Secretase ADAM10 Regulation: Insights into Alzheimer’s Disease Treatment
Authors: Peron Rafaela , Vatanabe Izabela , Manzine Patricia , Camins Antoni , Cominetti Márcia
Published: January 2018

ADAM (a disintegrin and metalloproteinase) is a family of widely expressed, transmembrane and secreted proteins of approximately 750 amino acids in length with functions in cell adhesion and proteolytic processing of the ectodomains of diverse cell-surface receptors and signaling molecules. ADAM10 is the main α-secretase that cleaves APP (amyloid precursor protein) in the non-amyloidogenic pathway inhibiting the formation of β-amyloid peptide, whose accumulation and aggregation leads to neuronal degeneration in Alzheimer’s disease (AD). ADAM10 is a membrane-anchored metalloprotease that sheds, besides APP, the ectodomain of a large variety of cell-surface proteins including cytokines, adhesion molecules and notch. APP cleavage by ADAM10 results in the production of an APP-derived fragment, sAPPα, which is neuroprotective. As increased ADAM10 activity protects the brain from β-amyloid deposition in AD, this strategy has been proved to be effective in treating neurodegenerative diseases, including AD. Here, we describe the physiological mechanisms regulating ADAM10 expression at different levels, aiming to propose strategies for AD treatment. We report in this review on the physiological regulation of ADAM10 at the transcriptional level, by epigenetic factors, miRNAs and/or translational and post-translational levels. In addition, we describe the conditions that can change ADAM10 expression in vitro and in vivo, and discuss how this knowledge may help in AD treatment. Regulation of ADAM10 is achieved by multiple mechanisms that include transcriptional, translational and post-translational strategies, which we will summarize in this review.
Recent Advances of Plasmonic Organic Solar Cells: Photophysical Investigations
Authors: Feng Lin , Niu Mengsi , Wen Zhenchuan , Hao Xiaotao
Published: January 2018

The surface plasmon resonance (SPR) of metallic nanomaterials, such as gold (Au) and silver (Ag), has been extensively exploited to improve the optical absorption, the charge carrier transport, and the ultimate device performances in organic photovoltaic cells (OPV). With the incorporation of diverse metallic nanostructures in active layers, buffer layers, electrodes, or between adjacent layers of OPVs, multiple plasmonic mechanisms may occur and need to be distinguished to better understand plasmonic enhancement. Steady-state photophysics is a powerful tool for unraveling the plasmonic nature and revealing plasmonic mechanisms such as the localized surface plasmon resonance (LSPR), the propagating plasmon-polariton (SPP), and the plasmon-gap mode. Furthermore, the charge transfer dynamics in the organic semiconductor materials can be elucidated from the transient photophysical investigations. In this review article, the basics of the plasmonic mechanisms and the related metallic nanostructures are briefly introduced. We then outline the recent advances of the plasmonic applications in OPVs emphasizing the linkage between the photophysical properties, the nanometallic geometries, and the photovoltaic performance of the OPV devices.
Ionic Liquid-Based Thermoplastic Solid Electrolytes Processed by Solvent-Free Procedures
Authors: González Francisco , Gregorio Víctor , Rubio Aitor , Garrido Leoncio , García Nuria , Tiemblo Pilar
Published: January 2018

A series of thermoplastic polymer electrolytes have been prepared employing poly(ethylene oxide) (PEO) as a polymer matrix, bis(trifluoromethane sulfonimide) (LiTFSI), and different room-temperature ionic liquids (RTIL) with bis(fluorosulfonyl)imide (FSI) or TFSI anions. This formulation makes them safe and non-flammable. The electrolytes have been processed in the absence of solvents by melt compounding at 120 °C, using sepiolite modified with d-α-tocoferol-polyethyleneglycol 1000 succinate (TPGS-S) as a physical cross-linker of PEO. Several concentrations of RTILs, lithium salt, and TPGS-S have been tested in order to obtain the highest ionic conductivity (σ) without losing electrolytes’ mechanical stability. The materials’ rheology and ionic conductivity have been extensively characterized. The excellent crosslinking ability of TPGS-S makes the electrolytes behave as thermoplastic materials, even those with the highest liquid concentration. The electrolytes with the highest concentrations of FSI anion present a σ over 10−3 S·cm−1 at 25 °C and close to 10−2 S·cm−1 at 70 °C, and notably behave as solids at temperatures up to 90 °C despite over 65 wt % of their formulation being liquid. The electrolytes thus obtained are safe solid thermoplastics prepared by industrially scalable procedures and are suitable for energy storage devices, proving the adequacy of polymer-based materials as solid electrolytes for batteries or supercapacitors.
Preparation, Characterization, and Performance Evaluation of Polysulfone Hollow Fiber Membrane with PEBAX or PDMS Coating for Oxygen Enhancement Process
Authors: Chong Kok , Lai Soon , Lau Woei , Thiam Hui , Ismail Ahmad , Roslan Rosyiela
Published: January 2018

Air pollution is a widely discussed topic amongst the academic and industrial spheres as it can bring adverse effects to human health and economic loss. As humans spend most of their time at the office and at home, good indoor air quality with enriched oxygen concentration is particularly important. In this study, polysulfone (PSF) hollow fiber membranes fabricated by dry-jet wet phase inversion method were coated by a layer of polydimethylsiloxane (PDMS) or poly(ether block amide) (PEBAX) at different concentrations and used to evaluate their performance in gas separation for oxygen enrichment. The surface-coated membranes were characterized using SEM and EDX to determine the coating layer thickness and surface chemical properties, respectively. Results from the gas permeation study revealed that the PSF membrane coated with PDMS offered higher permeance and selectivity compared to the membrane coated with PEBAX. The best performing PDMS-coated membrane demonstrated oxygen and nitrogen gas permeance of 18.31 and 4.01 GPU, respectively with oxygen/nitrogen selectivity of 4.56. Meanwhile, the PEBAX-coated membrane only showed 12.23 and 3.11 GPU for oxygen and nitrogen gas, respectively with a selectivity of 3.94. It can be concluded the PDMS coating is more promising for PSF hollow fiber membrane compared to the PEBAX coating for the oxygen enrichment process.
Permeability and Selectivity of PPO/Graphene Composites as Mixed Matrix Membranes for CO2 Capture and Gas Separation
Authors: Rea Riccardo , Ligi Simone , Christian Meganne , Morandi Vittorio , Giacinti Baschetti Marco , De Angelis Maria
Published: January 2018

We fabricated novel composite (mixed matrix) membranes based on a permeable glassy polymer, Poly(2,6-dimethyl-1,4-phenylene oxide) (PPO), and variable loadings of few-layer graphene, to test their potential in gas separation and CO2 capture applications. The permeability, selectivity and diffusivity of different gases as a function of graphene loading, from 0.3 to 15 wt %, was measured at 35 and 65 °C. Samples with small loadings of graphene show a higher permeability and He/CO2 selectivity than pure PPO, due to a favorable effect of the nanofillers on the polymer morphology. Higher amounts of graphene lower the permeability of the polymer, due to the prevailing effect of increased tortuosity of the gas molecules in the membrane. Graphene also allows dramatically reducing the increase of permeability with temperature, acting as a “stabilizer” for the polymer matrix. Such effect reduces the temperature-induced loss of size-selectivity for He/N2 and CO2/N2, and enhances the temperature-induced increase of selectivity for He/CO2. The study confirms that, as observed in the case of other graphene-based mixed matrix glassy membranes, the optimal concentration of graphene in the polymer is below 1 wt %. Below such threshold, the morphology of the nanoscopic filler added in solution affects positively the glassy chains packing, enhancing permeability and selectivity, and improving the selectivity of the membrane at increasing temperatures. These results suggest that small additions of graphene to polymers can enhance their permselectivity and stabilize their properties.
Elements of a Comparative Methodology in the Study of Religion
Authors: Freiberger Oliver
Published: January 2018

While comparison has been the subject of much theoretical debate in the study of religion, it has rarely been discussed in methodological terms. A large number of comparative studies have been produced in the course of the discipline’s history, but the question of how comparison works as a method has rarely been addressed. This essay proposes, in the form of an outline, a methodological frame of comparison that addresses both the general configuration of a comparative study—its goal, mode, scale, and scope—and the comparative process, distinguishing operations of selection, description, juxtaposition, redescription, as well as rectification and theory formation. It argues that identifying and analyzing such elements of a comparative methodology helps, on the one hand, in evaluating existing comparative studies and, on the other, in producing new ones. While the article attempts to present the methodological frame in a concise form and thus offers limited illustrative material, the authors of the other essays in this collection discuss rich historical-empirical cases as they test the frame on their own comparative studies.
Exploring Multispectral ALS Data for Tree Species Classification
Authors: Axelsson Arvid , Lindberg Eva , Olsson Håkan
Published: January 2018

Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58°27′18.35″N, 13°39′8.03″E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user’s and producer’s accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification.
Validation of Sensing Ocean Surface Currents Using Multi-Frequency HF Radar Based on a Circular Receiving Array
Authors: Zhao Chen , Chen Zezong , He Chao , Xie Fei , Chen Xi , Mou Changqing
Published: January 2018

To reduce the floor space of receiving antenna arrays, the Radio Ocean Remote SEnsing (RORSE) laboratory of Wuhan University developed a circular receiving array for a multi-frequency high frequency (MHF) radar system in 2014, consisting of seven uniformly spaced antenna elements positioned on a circle with a diameter of 5 m. The new system, which is abbreviated MHF-C radar, adopts frequency modulated interrupted continuous wave (FMICW) chirps and is capable of simultaneously operating at a maximum of four frequencies in the band of 7.5–25 MHz, and providing current, wave and wind maps every ten minutes. The phase direction-finding method is utilized to estimate the directions of the current signals, and array phase uncertainties are also taken into consideration in the signal model. This paper introduces the system in detail and investigates the performance of current measurements using MHF-C radars installed at Shengshan and Zhujiajian along the coast of the East China Sea. Radial current measurements derived from 8.27 MHz and 19.20 MHz at the same range are compared. Observations and comparisons between MHF-C radars and acoustic Doppler current profilers (ADCPs) are also presented in this paper. The results preliminarily demonstrate that the MHF-C radar system is capable of maintaining the same performance for current measurements whenever it steers to any other azimuth in the coverage and has a good ability to measure currents.
Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method
Authors: Yang Lu , Zhang Xiaotong , Liang Shunlin , Yao Yunjun , Jia Kun , Jia Aolin
Published: January 2018

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m−2 (38.38%). These values are 0.64 and 42.97 W·m−2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R2 value of 0.92 and an RMSE of 15.40 W·m−2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.
Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning
Authors: Jayathunga Sadeepa , Owari Toshiaki , Tsuyuki Satoshi
Published: January 2018

Unmanned aerial vehicles (UAVs) and digital photogrammetric techniques are two recent advances in remote sensing (RS) technology that are emerging as alternatives to high-cost airborne laser scanning (ALS) data sources. Despite the potential of UAVs in forestry applications, very few studies have included detailed analyses of UAV photogrammetric products at larger scales or over a range of forest types, including mixed conifer–broadleaf forests. In this study, we assessed the performance of fixed-wing UAV photogrammetric products of a mixed conifer–broadleaf forest with varying levels of canopy structural complexity. We demonstrate that fixed-wing UAVs are capable of efficiently collecting image data at local scales and that UAV imagery can be effectively utilized with digital photogrammetric techniques to provide detailed automated reconstruction of the three-dimensional (3D) canopy surface of mixed conifer–broadleaf forests. When combined with an accurate digital terrain model (DTM), UAV photogrammetric products are promising for producing reliable structural measurements of the forest canopy. However, the performance of UAV photogrammetric products is likely to be influenced by the structural complexity of the forest canopy. Furthermore, we highlight the potential of fixed-wing UAVs in operational forest management at the forest management compartment level, for acquiring high-resolution imagery at low cost. A future direction of this research would be to address the issue of how well the photogrammetric products can predict the actual structure of mixed conifer–broadleaf forests.
Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression
Authors: Kang Jian , Jin Rui , Li Xin , Zhang Yang , Zhu Zhongli
Published: January 2018

Available ground-based observation networks for the validation of soil moisture remote sensing products are commonly sparse; thus, ground truth determinations are difficult at the validated remote sensing pixel scale. Based on the consistency of temporal trends between ground truth and in situ measurements, it is feasible to estimate ground truth by building a linear relationship between temporal sparse ground observations and truth samples. Herein, auxiliary remote sensing data with a moderate spatial resolution can be transformed into truth samples depending on the stronger representation of remote sensing data to spatial heterogeneity in the validated pixel relative to limited sites. When solving weighting coefficients for the relationship model, the underlying correlations among the in situ measurements cause the multicollinearity problem, leading to failed predictions. An upscaling algorithm called ridge regression (RR) addresses this by introducing a regularization parameter. With sparse sites, the RR method is tested in two cases employing six and nine sites, and compared with the ordinary least squares and the arithmetic mean. The upscaling results of the RR method show higher prediction accuracies compared to the other two methods. When the RR method is used, the six-site case has the same estimation accuracy as the nine-site case due to maintaining the diversity of in situ measurements through the analysis of the ridge trace and variance inflation factor (VIF). Thus, the ridge trace and VIF analysis is considered as the optimal selection method for the existing observation networks if the RR method will be used in future validation work. With a different number of sites, the RR method always displays the best estimation accuracy and is not sensitive to the number of sites, which indicates that the RR method can potentially upscale sparse sites. However, if the sites are too few, e.g., one to four, it is difficult to perform the upscaling method.
Monitoring Population Evolution in China Using Time-Series DMSP/OLS Nightlight Imagery
Authors: Yu Sisi , Zhang Zengxiang , Liu Fang
Published: January 2018

Accurate and detailed monitoring of population distribution and evolution is of great significance in formulating a population planning strategy in China. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime lights time-series (NLT) image products offer a good opportunity for detecting the population distribution owing to its high correlation to human activities. However, their detection capability is greatly limited owing to a lack of in-flight calibration. At present, the synergistic use of systematically-corrected NLT products and population spatialization is rarely applied. This work proposed a methodology to improve the application precision and versatility of NLT products, explored a feasible approach to quantitatively spatialize the population to grid units of 1 km × 1 km , and revealed the spatio-temporal characteristics of population distribution from 2000 to 2010. Results indicated that, (1) after inter-calibration, geometric, incompatibility and discontinuity corrections, and adjustment based on vegetation information, the incompatibility and discontinuity of NTL products were successfully solved. Accordingly, detailed actual residential areas and luminance differences between the urban core and the peripheral regions could be obtained. (2) The population spatialization method could effectively acquire population information at per km 2 with high accuracy and exhibit more details in the evolution of population distribution. (3) Obvious differences in spatio-temporal characteristics existed in four economic regions, from the aspects of population distribution and dynamics, as well as population-weighted centroids. The eastern region was the most populous with the largest increased magnitude, followed by the central, northeastern, and western regions. The population-weighted centroids of the eastern, western, and northeastern regions moved along the southwest direction, while the population-weighted centroid of the central region moved along the southeast direction. (4) The population distribution and dynamics in nine-level population density types were significantly different. In the period of 2000–2010, the population in the basic no-man and high concentration types presented a net decrease. The population in seven other regions all increased with a net increase ranging from 25 km 2 (the moderate concentration type) to 245,668 km 2 (the general transition type). Except those in the core concentration and extremely sparse types, the population-weighted centroids in all other population density types moved along the southwest direction.
Learning a Dilated Residual Network for SAR Image Despeckling
Authors: Zhang Qiang , Yuan Qiangqiang , Li Jie , Yang Zhen , Ma Xiaoshuang
Published: January 2018

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise.
Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China
Authors: Tian Xinpeng , Liu Sihai , Sun Lin , Liu Qiang
Published: January 2018

Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of ~0.928, root mean square error (RMSE) of ~0.042, mean absolute error (MAE) of ~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product.
Wind Direction Signatures in GNSS-R Observables from Space
Authors: Guan Dongliang , Park Hyuk , Camps Adriano , Wang Yong , Onrubia Raul , Querol Jorge , Pascual Daniel
Published: January 2018

Wind speed and direction are important essential climate variables (ECVs). GNSS-R is an emerging remote sensing technique that can be potentially used to retrieve wind speed from space. However, few studies have addressed the wind direction retrieval from spaceborne GNSS-R observables, namely the Delay Doppler map (DDM). In this study, the feasibility of retrieving wind direction from the synthetic DDMs is analyzed. First, the simulation tool P2EPS is used to generate the DDMs under different geometry configurations, wind speed, and wind direction. Then, DDM changes caused by the wind direction are investigated, and two metrics are proposed to retrieve the wind direction from the DDM shape changes. The influence on wind direction retrieval of the wind speed, receiver’s elevation, and azimuth is further discussed. Finally, the sensitivity of DDM changes to noise is investigated, as well as the impact of noise on these two metrics.
Strapdown Airborne Gravimetry Quality Assessment Method Based on Single Survey Line Data: A Study by SGA-WZ02 Gravimeter
Authors: Wang Minghao , Wu Meiping , Cao Juliang , Zhang Kaidong , Cai Shaokun , Yu Ruihang
Published: January 2018

Quality assessment is an important part in the strapdown airborne gravimetry. Root mean square error (RMSE) evaluation method is a classical way to evaluate the gravimetry quality, but classical evaluation methods are preconditioned by extra flight or reference data. Thus, a method, which is able to largely conquer the premises of classical quality assessment methods and can be used in single survey line, has been developed in this paper. According to theoretical analysis, the method chooses the stability of two horizontal attitude angles, horizontal specific force and vertical specific force as the determinants of quality assessment method. The actual data, collected by SGA-WZ02 from 13 flights 21 lines in certain survey, was used to build the model and elaborate the method. To substantiate the performance of the quality assessment model, the model is applied in extra repeat line flights from two surveys. Compared with internal RMSE, standard deviation of assessment residuals are 0.23 mGal and 0.16 mGal in two surveys, which shows that the quality assessment method is reliable and stricter. The extra flights are not necessary by specially arranging the route of flights. The method, summarized from SGA-WZ02, is a feasible approach to assess gravimetry quality using single line data and is also suitable for other strapdown gravimeters.
Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach
Authors: Huo Hongyuan , Guo Jifa , Li Zhao-Liang
Published: January 2018

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty.
A Low-Cost Data Acquisition System for Automobile Dynamics Applications
Authors: González Alejandro , Olazagoitia José , Vinolas Jordi
Published: January 2018

This project addresses the need for the implementation of low-cost acquisition technology in the field of vehicle engineering: the design, development, manufacture, and verification of a low-cost Arduino-based data acquisition platform to be used in <80 Hz data acquisition in vehicle dynamics, using low-cost accelerometers. In addition to this, a comparative study is carried out of professional vibration acquisition technologies and low-cost systems, obtaining optimum results for low- and medium-frequency operations with an error of 2.19% on road tests. It is therefore concluded that these technologies are applicable to the automobile industry, thereby allowing the project costs to be reduced and thus facilitating access to this kind of research that requires limited resources.
Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing
Authors: Rajan Krishna , Garofalo Erik , Chiolerio Alessandro
Published: January 2018

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.
Fuzzy Traffic Control with Vehicle-to-Everything Communication
Authors: Salman Muntaser , Ozdemir Suat , Celebi Fatih
Published: January 2018

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR.
Comparing Two Independent Satellite-Based Algorithms for Detecting and Tracking Ash Clouds by Using SEVIRI Sensor
Authors: Falconieri Alfredo , Cooke Michael , Filizzola Carolina , Marchese Francesco , Pergola Nicola , Tramutoli Valerio
Published: January 2018

The Eyjafjallajökull (Iceland) volcanic eruption of April–May 2010 caused unprecedented air-traffic disruption in Northern Europe, revealing some important weaknesses of current operational ash-monitoring and forecasting systems and encouraging the improvement of methods and procedures for supporting the activities of Volcanic Ash Advisory Centers (VAACs) better. In this work, we compare two established satellite-based algorithms for ash detection, namely RSTASH and the operational London VAAC method, both exploiting sensor data of the spinning enhanced visible and infrared imager (SEVIRI). We analyze similarities and differences in the identification of ash clouds during the different phases of the Eyjafjallajökull eruption. The work reveals, in some cases, a certain complementary behavior of the two techniques, whose combination might improve the identification of ash-affected areas in specific conditions. This is indicated by the quantitative comparison of the merged SEVIRI ash product, achieved integrating outputs of the RSTASH and London VAAC methods, with independent atmospheric infrared sounder (AIRS) DDA (dust-detection algorithm) observations.
An Interdigital Electrode Probe for Detection, Localization and Evaluation of Surface Notch-Type Damage in Metals
Authors: Li Lanshuo , Yang Xiaoqing , Yin Yang , Yuan Jianping , Li Xu , Li Lixin , Huang Kama
Published: January 2018

Available microwave notch-type damage detection sensors are typically based on monitoring frequency shift or magnitude changes. However, frequency shift testing needs sweep-frequency data that make scanning detection becomes difficult and time-consuming. This work presents a microwave near-field nondestructive testing sensor for detecting sub-millimeter notch-type damage detection in metallic surfaces. The sensor is loaded with an interdigital electrode element in an open-ended coaxial. It is simple to fabricate and inexpensive, as it is etched on the RC4003 patch by using printed circuit board technology. The detection is achieved by monitoring changes in reflection amplitude, which is caused by perturbing the electromagnetic field around the interdigital structure. The proposed sensor was tested on a metallic plate with different defects, and the experimental results indicated that the interdigital electrode probe can determine the orientation, localization and dimension of surface notch-type damage.
Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification
Authors: Zhou Tao , Li Zhaofu , Pan Jianjun
Published: January 2018

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.
Self-Learning Power Control in Wireless Sensor Networks
Authors: Chincoli Michele , Liotta Antonio
Published: January 2018

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.
Estimation of the Land Surface Temperature over the Tibetan Plateau by Using Chinese FY-2C Geostationary Satellite Data
Authors: Hu Yuanyuan , Zhong Lei , Ma Yaoming , Zou Mijun , Xu Kepiao , Huang Ziyu , Feng Lu
Published: January 2018

During the process of land–atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia–Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li’s SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is the smallest at the water (5.12 K), second at the snow and ice (5.45 K), third at the grasslands (19.82 K) and largest at the barren or sparsely vegetated (22.83 K).
Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review
Authors: Kucha Christopher , Liu Li , Ngadi Michael
Published: January 2018

Fat is one of the most important traits determining the quality of pork. The composition of the fat greatly influences the quality of pork and its processed products, and contribute to defining the overall carcass value. However, establishing an efficient method for assessing fat quality parameters such as fatty acid composition, solid fat content, oxidative stability, iodine value, and fat color, remains a challenge that must be addressed. Conventional methods such as visual inspection, mechanical methods, and chemical methods are used off the production line, which often results in an inaccurate representation of the process because the dynamics are lost due to the time required to perform the analysis. Consequently, rapid, and non-destructive alternative methods are needed. In this paper, the traditional fat quality assessment techniques are discussed with emphasis on spectroscopic techniques as an alternative. Potential spectroscopic techniques include infrared spectroscopy, nuclear magnetic resonance and Raman spectroscopy. Hyperspectral imaging as an emerging advanced spectroscopy-based technology is introduced and discussed for the recent development of assessment for fat quality attributes. All techniques are described in terms of their operating principles and the research advances involving their application for pork fat quality parameters. Future trends for the non-destructive spectroscopic techniques are also discussed.