bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Virgin Olive Oils Differing in Their Bioactive Compound Contents on Metabolic Syndrome and Endothelial Functional Risk Biomarkers in Healthy Adults: A Randomized Double-Blind Controlled Trial
Authors: Sanchez-Rodriguez Estefania , Lima-Cabello Elena , Biel-Glesson Sara , Fernandez-Navarro Jose R. , Calleja Miguel A. , Roca Maria , Espejo-Calvo Juan A. , Gil-Extremera Blas , Soria-Florido Maria , De la Torre Rafael , Fito Montserrat , Covas Maria-Isabel , Alche Juan de Dios , Martinez de Victoria Emilio , Gil Angel , Mesa Maria D.
Published: May 2018

The aim of this study was to evaluate the effect of virgin olive oils (VOOs) enriched with phenolic compounds and triterpenes on metabolic syndrome and endothelial function biomarkers in healthy adults. The trial was a three-week randomized, crossover, controlled, double-blind, intervention study involving 58 subjects supplemented with a daily dose (30 mL) of three oils: (1) a VOO (124 ppm of phenolic compounds and 86 ppm of triterpenes); (2) an optimized VOO (OVOO) (490 ppm of phenolic compounds and 86 ppm of triterpenes); and (3) a functional olive oil (FOO) high in phenolic compounds (487 ppm) and enriched with triterpenes (389 ppm). Metabolic syndrome and endothelial function biomarkers were determined in vivo and ex vivo. Plasma high density lipoprotein cholesterol (HDLc) increased after the OVOO intake. Plasma endothelin-1 levels decreased after the intake of the three olive oils, and in blood cell cultures challenged. Daily intake of VOO enriched in phenolic compounds improved plasma HDLc, although no differences were found at the end of the three interventions, while VOO with at least 124 ppm of phenolic compounds, regardless of the triterpenes content improved the systemic endothelin-1 levels in vivo and ex vivo. No effect of triterpenes was observed after three weeks of interventions. Results need to be confirmed in subjects with metabolic syndrome and impaired endothelial function (Clinical Trials number NCT02520739).
The Association of Dietary Fiber Intake with Cardiometabolic Risk in Four Countries across the Epidemiologic Transition
Authors: Lie Louise , Brown Laquita , Forrester Terrence E. , Plange-Rhule Jacob , Bovet Pascal , Lambert Estelle V. , Layden Brian T. , Luke Amy , Dugas Lara R.
Published: May 2018

The greatest burden of cardiovascular disease is now carried by developing countries with cardiometabolic conditions such as metabolic syndrome, obesity and inflammation believed to be the driving force behind this epidemic. Dietary fiber is known to have protective effects against obesity, type 2 diabetes, cardiovascular disease and the metabolic syndrome. Considering the emerging prevalence of these cardiometabolic disease states across the epidemiologic transition, the objective of this study is to explore these associations of dietary fiber with cardiometabolic risk factors in four countries across the epidemiologic transition. We examined population-based samples of men and women, aged 25–45 of African origin from Ghana, Jamaica, the Seychelles and the USA. Ghanaians had the lowest prevalence of obesity (10%), while Jamaicans had the lowest prevalence of metabolic syndrome (5%) across all the sites. Participants from the US presented with the highest prevalence of obesity (52%), and metabolic syndrome (22%). Overall, the Ghanaians consumed the highest dietary fiber (24.9 ± 9.7 g), followed by Jamaica (16.0 ± 8.3 g), the Seychelles (13.6 ± 7.2 g) and the lowest in the USA (14.2 ± 7.1 g). Consequently, 43% of Ghanaians met the fiber dietary guidelines (14 g/1000 kcal/day), 9% of Jamaicans, 6% of Seychellois, and only 3% of US adults. Across all sites, cardiometabolic risk (metabolic syndrome, inflammation and obesity) was inversely associated with dietary fiber intake, such that the prevalence of metabolic syndrome was 13% for those in the lowest quartile of fiber intake, compared to 9% those in the highest quartile of fiber intake. Notably, twice as many of participants (38%) in the lowest quartile were obese compared to those in the highest quartile of fiber intake (18%). These findings further support the need to incorporate strategies and policies to promote increased dietary fiber intake as one component for the prevention of cardiometabolic risk in all countries spanning the epidemiologic transition.
Capsaicin in Metabolic Syndrome
Authors: Panchal Sunil K. , Bliss Edward , Brown Lindsay
Published: May 2018

Capsaicin, the major active constituent of chilli, is an agonist on transient receptor potential vanilloid channel 1 (TRPV1). TRPV1 is present on many metabolically active tissues, making it a potentially relevant target for metabolic interventions. Insulin resistance and obesity, being the major components of metabolic syndrome, increase the risk for the development of cardiovascular disease, type 2 diabetes, and non-alcoholic fatty liver disease. In vitro and pre-clinical studies have established the effectiveness of low-dose dietary capsaicin in attenuating metabolic disorders. These responses of capsaicin are mediated through activation of TRPV1, which can then modulate processes such as browning of adipocytes, and activation of metabolic modulators including AMP-activated protein kinase (AMPK), peroxisome proliferator-activated receptor α (PPARα), uncoupling protein 1 (UCP1), and glucagon-like peptide 1 (GLP-1). Modulation of these pathways by capsaicin can increase fat oxidation, improve insulin sensitivity, decrease body fat, and improve heart and liver function. Identifying suitable ways of administering capsaicin at an effective dose would warrant its clinical use through the activation of TRPV1. This review highlights the mechanistic options to improve metabolic syndrome with capsaicin.
Physicochemical, Nutritional, and Organoleptic Characterization of a Skimmed Goat Milk Fermented with the Probiotic Strain Lactobacillus plantarum C4
Authors: Moreno-Montoro Miriam , Navarro-Alarcón Miguel , Bergillos-Meca Triana , Giménez-Martínez Rafael , Sánchez-Hernández Silvia , Olalla-Herrera Manuel
Published: May 2018

The benefits of goat milk, fermented milks, and probiotics for the humans are well documented. In this study, a novel fermented goat milk was manufactured with the putative probiotic strain Lactobacillus plantarum C4 together with L. bulgaricus and Streptococcus thermophilus. Ultrafiltration was chosen as the skimmed milk concentration method because it produced the best viscosity and syneresis and a high casein content. The viability rate of all bacterial strains was >107 cfu/mL, even after 5 weeks of storage or after in vitro gastrointestinal digestion, which is especially important for exertion of the probiotic strain functionalities. This fermented milk is also a good source of nutrients, having a low lactose and fat content, high protein proportion, and good mineral concentration. According to these data and the overall acceptability described by panelists, this fermented milk is a healthy dairy product comparable with commercially available fermented milks.
The Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled Trial
Authors: Reid Jessica , Makrides Maria , McPhee Andrew J. , Stark Michael J. , Miller Jacqueline , Collins Carmel T.
Published: May 2018

The aim of this study was to assess the effect of feeding high protein human milk fortifier (HMF) on growth in preterm infants. In this single-centre randomised trial, 60 infants born 28–32 weeks’ gestation were randomised to receive a higher protein HMF providing 1.8 g protein (n = 31) or standard HMF providing 1 g protein per 100 mL expressed breast milk (EBM) (n = 29). The primary outcome was rate of weight gain. Baseline characteristics were similar between groups. There was no difference between high and standard HMF groups for weight gain (mean difference (MD) −14 g/week; 95% CI −32, 4; p = 0.12), length gain (MD −0.01 cm/week; 95% CI −0.06, 0.03; p = 0.45) or head circumference gain (MD 0.007 cm/week; 95% CI −0.05, 0.06; p = 0.79), despite achieving a 0.7 g/kg/day increase in protein intake in the high protein group. Infants in the high protein group had a higher proportion of lean body mass at trial entry; however, there was no group by time effect on lean mass gains over the study. Increasing HMF protein content to 1.8 g per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.
The Role of TRP Channels in the Metastatic Cascade
Authors: Fels Benedikt , Bulk Etmar , Pethő Zoltán , Schwab Albrecht
Published: May 2018

A dysregulated cellular Ca2+ homeostasis is involved in multiple pathologies including cancer. Changes in Ca2+ signaling caused by altered fluxes through ion channels and transporters (the transportome) are involved in all steps of the metastatic cascade. Cancer cells thereby “re-program” and “misuse” the cellular transportome to regulate proliferation, apoptosis, metabolism, growth factor signaling, migration and invasion. Cancer cells use their transportome to cope with diverse environmental challenges during the metastatic cascade, like hypoxic, acidic and mechanical cues. Hence, ion channels and transporters are key modulators of cancer progression. This review focuses on the role of transient receptor potential (TRP) channels in the metastatic cascade. After briefly introducing the role of the transportome in cancer, we discuss TRP channel functions in cancer cell migration. We highlight the role of TRP channels in sensing and transmitting cues from the tumor microenvironment and discuss their role in cancer cell invasion. We identify open questions concerning the role of TRP channels in circulating tumor cells and in the processes of intra- and extravasation of tumor cells. We emphasize the importance of TRP channels in different steps of cancer metastasis and propose cancer-specific TRP channel blockade as a therapeutic option in cancer treatment.
The Relationship between Bulk Silicone and Benzophenone-Initiated Hydrogel Coating Properties
Authors: Keskin Damla , Mokabbar Taraneh , Pei Yutao , Van Rijn Patrick
Published: May 2018

Polydimethylsiloxane (PDMS) is a silicone elastomer-based material that is used in various applications, including coatings, tubing, microfluidics, and medical implants. PDMS has been modified with hydrogel coatings to prevent fouling, which can be done through UV-mediated free radical polymerization using benzophenone. However, to the best of our knowledge, the properties of hydrogel coatings and their influence on the bulk properties of PDMS under various preparation conditions, such as the type and concentration of monomers, and UV treatment time, have never been investigated. Acrylate-based monomers were used to perform free radical polymerization on PDMS surfaces under various reaction conditions. This approach provides insights into the relationship between the hydrogel coating and bulk properties of PDMS. Altering the UV polymerization time and the monomer concentration resulted in different morphologies with different roughness and thickness of the hydrogel coating, as well as differences in the bulk material stiffness. The surface morphology of the coated PDMS was characterized by AFM. The cross section and thickness of the coatings were examined using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy. The dependence of coating development on the monomer type and concentration used was evaluated by surface hydrophilicity, as measured by water contact angle. Elongation-until-break analysis revealed that specific reaction conditions affected the bulk properties and made the coated PDMS brittle. Therefore, boundary conditions have been identified to enable high quality hydrogel coating formation without affecting the bulk properties of the material.
Effects of Thermal Cross-Linking on the Structure and Property of Asymmetric Membrane Prepared from the Polyacrylonitrile
Authors: Jin Xin , Li Lin , Xu Ruisong , Liu Qiao , Ding Linghua , Pan Yanqiu , Wang Chunlei , Hung Weisong , Lee Kueirrarn , Wang Tonghua
Published: May 2018

Improving the thermal and chemical stabilities of classical polymer membranes will be beneficial to extend their applications in the high temperature or aggressive environment. In this work, the asymmetric ultrafiltration membranes prepared from the polyacrylonitrile (PAN) were used to fabricate the cross-linking asymmetric (CLA) PAN membranes via thermal cross-linking in air to improve their thermal and chemical stabilities. The effects of thermal cross-linking parameters such as temperature and holding time on the structure, gas separation performance, thermal and chemical stabilities of PAN membranes were investigated by Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), positron annihilation lifetime spectroscopy (PALS), scanning electron microscopy (SEM), thermogravimetic analysis (TGA) and gas permeation test. The thermal cross-linking significantly influences the chemical structure, microstructure and pore structure of PAN membrane. During the thermal cross-linking, the shrinkage of membrane and coalescence or collapse of pore and microstructure make large pores diminish, small pores disappear and pore volumes reduce. The gas permeances of CLA-PAN membranes increase as the increasing of cross-linking temperature and holding time due to the volatilization of small molecules. The CLA-PAN membranes demonstrate excellent thermal and chemical stabilities and present good prospects for application in ultrafiltration for water treatment and for use as a substrate for nanofiltration or gas separation with an aggressive and demanding environment.
A Facile Synthesis of Hexagonal Spinel λ-MnO2 Ion-Sieves for Highly Selective Li+ Adsorption
Authors: Yang Fan , Chen Sichong , Shi Chentao , Xue Feng , Zhang Xiaoxian , Ju Shengui , Xing Weihong
Published: May 2018

Ion-sieves are a class of green adsorbent for extraction Li+ from salt lakes. Here, we propose a facile synthesis of hexagonal spinel LiMn2O4 (LMO) precursor under mild condition which was first prepared via a modified one-pot reduction hydrothermal method using KMnO4 and ethanol. Subsequently, the stable spinel structured λ-MnO2 (HMO) were prepared by acidification of LMO. The as-prepared HMO shows a unique hexagonal shape and can be used for rapid adsorption-desorption process for Li+ adsorption. It was found that Li+ adsorption capacity of HMO was 24.7 mg·g−1 in Li+ solution and the HMO also has a stable structure with manganese dissolution loss ratio of 3.9% during desorption process. Moreover, the lithium selectivity ( α Mg Li ) reaches to 1.35 × 103 in brine and the distribution coefficients ( K d ) of Li+ is much greater than that of Mg2+. The results implied that HMO can be used in extract lithium from brine or seawater containing high ratio of magnesium and lithium.
Enhancing Institutional Publication Data Using Emergent Open Science Services
Authors: Walters David , Daley Christopher
Published: May 2018

The UK open access (OA) policy landscape simultaneously preferences Gold publishing models (Finch Report, RCUK, COAF) and Green OA through repository usage (HEFCE), creating the possibility of confusion and duplication of effort for academics and support staff. Alongside these policy developments, there has been an increase in open science services that aim to provide global data on OA. These services often exist separately to locally managed institutional systems for recording OA engagement and policy compliance. The aim of this study is to enhance Brunel University London’s local publication data using software which retrieves and processes information from the global open science services of Sherpa REF, CORE, and Unpaywall. The study draws on two classification schemes; a ‘best location’ hierarchy, which enables us to measure publishing trends and whether open access dissemination has taken place, and a relational ‘all locations’ dataset to examine whether individual publications appear across multiple OA dissemination models. Sherpa REF data is also used to indicate possible OA locations from serial policies. Our results find that there is an average of 4.767 permissible open access options available to the authors in our sample each time they publish and that Gold OA publications are replicated, on average, in 3 separate locations. A total of 40% of OA works in the sample are available in both Gold and Green locations. The study considers whether this tendency for duplication is a result of localised manual workflows which are necessarily focused on institutional compliance to meet the Research Excellence Framework 2021 requirements, and suggests that greater interoperability between OA systems and services would facilitate a more efficient transformation to open scholarship.
New Material and Revision of the Carnivora, Mammalia from the Lower Pleistocene Locality Apollonia 1, Greece
Authors: Koufos George D.
Published: May 2018

During the last field campaigns in the mammal fossiliferous site Apollonia 1 (Macedonia, Greece), new carnivoran material has been discovered. The new collection added two new carnivoran taxa, Homotherium latidens and Panthera gombaszögensis. The new canid material and the revision of the old one (a) suggest the presence of two Canis species, C. etruscus and C. apolloniensis; (b) confirm the presence of the hypercarnivore Lycaon lycaonoides, and (c) allow for re-classifying the vulpine material to Vulpes praeglacialis. The taxonomic status of the species C. apolloniensis and Meles dimitrius is discussed. The composition and diversity of the Apollonia carnivoran assemblage are estimated and compared to those of various Greek and European Villafranchian ones. The results suggest close similarity to the Venta Micena (Spain) and Dmanisi (Georgia) carnivoran assemblages. The biochronological evidence indicates that Apollonia 1 is younger than Venta Micena and older than Untermassfeld (Germany), suggesting an age of 1.3–1.0 Ma. The study of the carnivoran guild structure of Apollonia 1 in comparison to the modern ones from known environments, as well as their functional morphology, suggest an open habitat, agreeing with previous interpretations.
Improving the Energy Concentration in Waste Printed Circuit Boards Using Gravity Separation
Authors: Kumar Amit , Kuppusamy Vinoth Kumar , Holuszko Maria E. , Janke Travis
Published: May 2018

Electronic waste is one the fastest growing waste streams in the world, and printed circuit boards (PCBs) are the most valuable fraction of this stream due to the presence of gold, silver, copper, and palladium. Printed circuit boards consist of approximately 30% metals and 70% non-metals. The non-metal fraction (NMF) is composed of 60–65% fiberglass and 35–40% organics, in the form of surface-mount plastics and epoxy resins in the printed circuit board laminates. The organics in the NMF provide a potential alternative source of energy, but hazardous flame retardants contained in epoxy resins and the presence of residual metals create challenges for utilizing this material for energy recovery. This research provides an evaluation of the energy content of printed circuit boards. Density-based separation was used to separate various components of the NMF to increase the energy content in specific density fractions while reducing the metal content. The result showed that the energy content before and after the removal of the metallic fraction from PCBs was 9 and 15 GJ/t, respectively. After the density-based separation of the NMF, the energy content in the lightest fraction increased to 21 GJ/t, while reducing the concentration of the hazardous flame retardants. The contents of the hazardous flame retardants and residual metal were analyzed, to evaluate the harmful effect of emissions produced from utilizing the NMF as an alternative feedstock in waste-to-energy applications.
On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio
Authors: Miljkovic Tatjana , Fernández Daniel
Published: May 2018

We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.
A Credit-Risk Valuation under the Variance-Gamma Asset Return
Authors: Ivanov Roman V.
Published: May 2018

This paper considers risks of the investment portfolio, which consist of distributed mortgages and sold European call options. It is assumed that the stream of the credit payments could fall by a jump. The time of the jump is modeled by the exponential distribution. We suggest that the returns on stock are variance-gamma distributed. The value at risk, the expected shortfall and the entropic risk measure for this portfolio are calculated in closed forms. The obtained formulas exploit the values of generalized hypergeometric functions.
Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation
Authors: Avanzi Francesco , Bianchi Alberto , Cina Alberto , De Michele Carlo , Maschio Paolo , Pagliari Diana , Passoni Daniele , Pinto Livio , Piras Marco , Rossi Lorenzo
Published: May 2018

Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20–0.30 m, but decreases to 0.06–0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.
DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image
Authors: Huang Min , Chen Nengcheng , Du Wenying , Chen Zeqiang , Gong Jianya
Published: May 2018

Implying the prosperity and development of the city, impervious surface area (ISA) is playing an increasingly important role in ecological processes, microclimate, material and energy flows, and urban flood. The free sub-meter resolution Google Earth image, which is integrated by several high spatial resolution data, appears to have potential for high-resolution ISA extraction, where present study is rare and performances remain to be improved. Due to the high spatial and spectral variation of the urban environment as well as confusion between ISA and soil, the accurate delineating of ISA with traditional (direct) methods can be costly and time-consuming, which is in a word resource-intensive. However, this paper presents a novel indirect ISA extraction conceptual model and a new detecting and masking background land cover (DMBLC) approach that: uses a freely available, high-resolution dataset; requires a reduced set of training samples; and consists of relatively simple, common, and feasible image processing steps. The key characteristic of DMBLC is to detect the background of ISA (vegetation, soil, and water) accurately and obtain the ISA by masking the background. The approach relies on background detection to avoid the predicaments of direct ISA extraction. Water can be directly gained by water body vector data, in DMBLC; mixture tuned matched filtering (MTMF) is exploited to detect vegetation and soil, image segmentation is used to mitigate the spectral variation problem within the same land cover, and segment rectangularity reduces the confusion between ISA and soil. From experiments in a core area of Fuzhou, China, the DMBLC approach reached high performance and outperformed the powerful traditional support vector machines (SVM) method (overall accuracy of 94.45% and Kappa coefficient of 0.8885, compared to 86.44% and 0.7329, respectively). From the comparison of different levels of complexity within the inner processing steps, it is confirmed that the DMBLC approach is a powerful and flexible changed framework for indirect ISA extraction, which can be improved by using more advanced inner methods.
Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China
Authors: Wu Lin , Wang Le , Min Lin , Hou Wei , Guo Zhengwei , Zhao Jianhui , Li Ning
Published: May 2018

Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as “dark regions” on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future.
Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer
Authors: Melchiorri Michele , Florczyk Aneta , Freire Sergio , Schiavina Marcello , Pesaresi Martino , Kemper Thomas
Published: May 2018

In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting.
Dynamic Monitoring and Vibration Analysis of Ancient Bridges by Ground-Based Microwave Interferometry and the ESMD Method
Authors: Liu Xianglei , Lu Zhao , Yang Wanxin , Huang Ming , Tong Xiaohua
Published: May 2018

In this paper, we propose to conduct a dynamic monitoring and vibration analysis of ancient bridges by means of ground-based microwave interferometry and the extreme-point symmetric mode decomposition (ESMD) method. Ground-based microwave interferometry, a novel non-contact technology with a high accuracy, is used to acquire dynamic time series displacements with environmental excitation factors and a transient load with a car, respectively. The ESMD method, a new alternative to the Hilbert-Huang transform (HHT), is adopted to conduct the instantaneous vibration analysis of Zhaozhou Bridge. Firstly, a series of intrinsic mode functions (IMFs) are obtained together with an optimal adaptive global mean (AGM) curve by using a mode symmetric about the maxima and minima points. Secondly, the instantaneous frequency of each IMF is obtained by the use of a direct interpolation algorithm, which can reconcile the conflict between the period and the frequency for the traditional time-frequency analysis methods. As a representative case, Zhaozhou Bridge, a well-known Chinese ancient bridge constructed more than 1400 years ago, is studied in detail. Four kinds of dynamic time series displacements—two of them acquired by considering only environmental excitation factors for the mid-span and 1/4-span points and the others obtained with the transient load of a car for the mid-span and 1/4-span points—are selected to pursue a comparison of the decomposed IMFs and the instantaneous frequencies to perform the instantaneous vibration analysis of Zhaozhou Bridge. By comparing the results obtained with HHT for the decomposed IMFs and the instantaneous frequencies, the results show that the proposed method has a powerful ability to evaluate the instantaneous dynamic response of ancient bridges.
Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis
Authors: Sukhova Ekaterina , Sukhov Vladimir
Published: May 2018

The development of spectral methods of remote sensing, including measurement of a photochemical reflectance index (PRI), is a prospective trend in precision agriculture. There are many works which have investigated the connection between photosynthetic parameters and PRI; however, their results varied and were sometimes contradictory. For this paper, we performed a meta-analysis of works in this field. Here, only linear correlations of PRI with photosynthetic parameters—including quantum yield of photosystem II (ΔF/Fm’), nonphotochemical quenching of chlorophyll fluorescence (NPQ), and light use efficiency (LUE)—were investigated. First, it was shown that the correlations were dependent on conditions of PRI measurements (leaf or canopy; artificial light or sunlight). Second, it was shown that a minimal level of the photosynthetic stress, and the variation of this level among investigated plants, can influence the linear correlation of PRI with ΔF/Fm’ and NPQ; the effect was dependent on conditions of measurements. In contrast, the distribution of LUE among plants did not influence its correlation with PRI. Thus, the meta-analysis shows that the distribution of photosynthetic parameters among investigated plants can be an important factor that influences the efficiency of remote sensing on the basis of the PRI measurement.
Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval
Authors: Blix Katalin , Eltoft Torbjørn
Published: May 2018

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
Authors: Teimouri Nima , Dyrmann Mads , Nielsen Per Rydahl , Mathiassen Solvejg Kopp , Somerville Gayle J. , Jørgensen Rasmus Nyholm
Published: May 2018

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.
An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment
Authors: Park Youngmin , Kang Sangwoo , Seo Jungyun
Published: May 2018

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system’s process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system.
Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
Authors: Bermúdez Ordoñez Juan , Arnaldo Valdés Rosa , Gómez Comendador Fernando
Published: May 2018

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.
Tactile Estimation of Molded Plastic Plates Based on the Estimated Impulse Responses of Mechanoreceptive Units
Authors: Nobuyama Lisako , Kurashina Yuta , Kawauchi Kei , Matsui Koji , Takemura Kenjiro
Published: May 2018

This study proposes a tactile estimation method of molded plastic plates based on human tactile perception characteristics. Plastic plates are often used in consumer products. The tactile evaluation plays an important role in product development. However, physical quantities not taking into account human tactile perception have been employed in previous tactile estimation procedures. Hence, in this study, we adopted the vibrational thresholds of the mechanoreceptive units—FA I, FA II, SA I and SA II—for stimuli detection and developed a tactile estimation method for plastic plates that clarified the mechanoreceptive units related to tactile sensation. The developed tactile sensor consists of a base and a silicone rubber pad that contains strain gauges in it. We detected vibration during touch by the sensor and calculated the estimation of the firing values of the cutaneous mechanoreceptors, which are the essential data obtained by humans during tactile perception, in comparison to the amplitude spectrum of the vibration with the threshold amplitude of each mechanoreceptive unit. Simultaneously, we calculated the relationship between the normal and tangential forces recorded while the sensor ran over the samples. As a result of stepwise linear regression analysis using these values as explanatory variables, the evaluation scores for Soft were successfully estimated using the firing value of FA II and the relationship between normal/tangential forces, and the evaluation scores for Rough were estimated using the SA I firing value.
Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error
Authors: Monica Stefania , Ferrari Gianluigi
Published: May 2018

Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%.
SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
Authors: Anjomshoa Fazel , Kantarci Burak
Published: May 2018

The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration into the IoT ecosystem with their built-in sensors, particularly in Mobile Crowd-Sensing (MCS) campaigns. The MCS systems achieve the objectives of the large-scale non-dedicated sensing concept in the IoT if a sufficient number of participants are engaged to the collaborative data acquisition process. Therefore, user recruitment is a key challenge in MCS, which requires effective incentivization of cooperative, truthful and trustworthy users. A grand concern for the participants is the battery drain on the mobile devices. It is a known fact that battery drain in a smartphone is a function of the user activity, which can be modeled under various contexts. With this in mind, we propose a new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). SOBER-MCS uses sociability and the residual power of the participant smartphones as two primary criteria in the selection of participating devices. The former is an indicator of the participant willingness toward sensing campaigns, whereas the latter is used to prioritize personal use over crowd-sensing under critical battery levels. We use sociability profiles that were obtained in our previous work and use those values to simulate the sociability behavior of a large pool of participants in an MCS environment. Through simulations, we show that SOBER-MCS is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively.
Visual and Colorimetric Sensing of Metsulfuron-Methyl by Exploiting Hydrogen Bond-Induced Anti-Aggregation of Gold Nanoparticles in the Presence of Melamine
Authors: Liu Guangyang , Zhang Ruonan , Huang Xiaodong , Li Lingyun , Liu Naixin , Wang Jing , Xu Donghui
Published: May 2018

Various highly sensitive and selective analytical methods have been used to monitor metsulfuron-methyl residue in the environment. However, these methods involve costly instruments and complex, time-consuming operations performed in laboratories. Here, a rapid, convenient, and sensitive colorimetric sensor based on anti-aggregation of gold nanoparticles (AuNPs) is demonstrated for the rapid detection of metsulfuron-methyl in agricultural irrigation water. The AuNPs could be induced to aggregate in the presence of melamine and exhibited a distinct color change from wine-red to blue. The aggregation was suppressed by a strong hydrogen-bonding interaction between metsulfuron-methyl and melamine. The differences of the absorbance at 523 nm (ΔA523) and the color change was linearly related to metsulfuron-methyl concentration over the range 0.1–100 mg/L, as observed visually and by UV-vis (Ultraviolet-visible) spectrometry. The detection limit of the sensor was as low as 0.05 mg/L (signal/noise = 3), and was used to determine metsulfuron-methyl in spiked water and in agricultural irrigation water samples. Recoveries were in the range of 71.2–100.4%, suggesting that the colorimetric sensor was suitable for the determination of metsulfuron-methyl in agricultural water samples.
Overlap Spectrum Fiber Bragg Grating Sensor Based on Light Power Demodulation
Authors: Zhang Hao , Jiang Junzhen , Liu Shuang , Chen Huaixi , Zheng Xiaoqian , Qiu Yishen
Published: May 2018

Demodulation is a bottleneck for applications involving fiber Bragg gratings (FBGs). An overlap spectrum FBG sensor based on a light power demodulation method is presented in this paper. The demodulation method uses two chirp FBGs (cFBGs) of which the reflection spectra partially overlap each other. The light power variation of the overlap spectrum can be linked to changes in the measurand, and the sensor function can be realized via this relationship. A temperature experiment showed that the relationship between the overlap power spectrum of the FBG sensor and temperature had good linearity and agreed with the theoretical analysis.