bluuebunny/crossref_metadata_2025 huggingface dataset:
Complete Chloroplast Genomes from Sanguisorba: Identity and Variation Among Four Species
Authors: Meng Xiang-Xiao , Xian Yan-Fang , Xiang Li , Zhang Dong , Shi Yu-Hua , Wu Ming-Li , Dong Gang-Qiang , Ip Siu-Po , Lin Zhi-Xiu , Wu Lan , Sun Wei
Published: August 2018

The genus Sanguisorba, which contains about 30 species around the world and seven species in China, is the source of the medicinal plant Sanguisorba officinalis, which is commonly used as a hemostatic agent as well as to treat burns and scalds. Here we report the complete chloroplast (cp) genome sequences of four Sanguisorba species (S. officinalis, S. filiformis, S. stipulata, and S. tenuifolia var. alba). These four Sanguisorba cp genomes exhibit typical quadripartite and circular structures, and are 154,282 to 155,479 bp in length, consisting of large single-copy regions (LSC; 84,405–85,557 bp), small single-copy regions (SSC; 18,550–18,768 bp), and a pair of inverted repeats (IRs; 25,576–25,615 bp). The average GC content was ~37.24%. The four Sanguisorba cp genomes harbored 112 different genes arranged in the same order; these identical sections include 78 protein-coding genes, 30 tRNA genes, and four rRNA genes, if duplicated genes in IR regions are counted only once. A total of 39–53 long repeats and 79–91 simple sequence repeats (SSRs) were identified in the four Sanguisorba cp genomes, which provides opportunities for future studies of the population genetics of Sanguisorba medicinal plants. A phylogenetic analysis using the maximum parsimony (MP) method strongly supports a close relationship between S. officinalis and S. tenuifolia var. alba, followed by S. stipulata, and finally S. filiformis. The availability of these cp genomes provides valuable genetic information for future studies of Sanguisorba identification and provides insights into the evolution of the genus Sanguisorba.
Validation of Total Mercury in Marine Sediment and Biological Samples, Using Cold Vapour Atomic Absorption Spectrometry
Authors: Elezz Ahmed Abou , Mustafa Hassan Hassan , Abdulla Alsaadi Hamood , Easa Ahmed , Al-Meer Saeed , Elsaid Khaled , Ghouri Zafar Khan , Abdala Ahmed
Published: August 2018

A method for the measurement of total mercury (T-Hg) in environmental samples using cold vapour atomic absorption spectrometry (CV AAS) has been validated yielding a dynamic range (0.04–10.00 μg/kg) and high certified reference material (CRM) recovery (>90%). The validation was carried out according to International Union of Pure and Applied Chemistry (IUPAC) validation and Eurachem Guides. A freeze-dried and homogenised sample was weighed and then digested using Suprapur acids (HNO3, H2SO4, and HF) with potassium dichromate solution in a hot block digestion system. A calibration curve was constructed (R2 > 0.999). Two CRMs (Marine Sediment Reference Material (PACS-3) and Trace Elements in Muscle Tissue (Trace Elements and Methylmercury in Mussel Tissue (NIST2976)) were utilised for quality assurance and control. The limit of quantification (LOQ) calculated as 0.04 µg/kg, and uncertainty (U) calculated as 2%. The obtained results showed the suitability of this method for direct mercury measurement in environmental samples. Additionally, the proficiency of this method was recognised by accreditation under the standard of International Organization for Standardization (ISO/IEC 17025:2017) for competence of testing and calibration laboratories.
Enhancing the Stability of Aqueous Dispersions and Foams Comprising Cellulose Nanofibrils (CNF) with CaCO3 Particles
Authors: Tenhunen Tiia-Maria , Pöhler Tiina , Kokko Annaleena , Orelma Hannes , Schenker Michel , Gane Patrick , Tammelin Tekla
Published: August 2018

In this work, stability of dispersions and foams containing CaCO3-based pigments and cellulose nanofibrils (CNF) was evaluated with the aim to reveal the mechanisms contributing to the overall stability of the selected systems. The utmost interest lies in the recently developed hydrocolloid hybrid CaCO3 pigments and their potential to form bionanocomposite structures when incorporated with CNF. These pigments possess a polyelectrolyte layer deposited on the surface of the particle which is expected to enhance the compatibility between inorganic and organic components. Stability assessment of both dispersions and foams was conducted using turbidity profile scanning. In dispersions, CNF provides stability due to its ability to form a firm percolation network. If surface-modified pigments are introduced, the favourable surface interactions between the pigments and CNF positively influence the stability behaviour and even large macro-size pigments do not interfere with the stability of either dispersions or foams. In foams, the stability can be enhanced due to the synergistic actions brought by CNF and particles with suitable size, shape and wetting characteristics resulting in a condition where the stability mechanism is defined by the formation of a continuous plateau border incorporating a CNF network which is able to trap the inorganic particles uniformly.
Ligands-Coordinated Zr-Based MOF for Wastewater Treatment
Authors: Zhan Xue-Qing , Tsai Fang-Chang , Xie Lei , Zhang Ke-Deng , Liu Huan-Li , Ma Ning , Shi Dean , Jiang Tao
Published: August 2018

Isostructural zirconium-based metal–organic frameworks (Zr-MOFs) have attracted the attention of researchers because of their remarkable stability at high temperatures and high pressures and their chemical stabilities against acids and bases. Due to this stability, Zr-MOFs can be utilized in adsorption research, and the adsorption performance of a Zr-MOF depends on the pore size and the surroundings of the MOF. In this study, as the dimensions changed and the adsorption was carried out, the Zr-MOF material remained stable, and the adsorption of the best state was achieved at 235 mg/g. Through the simulation of theoretical kinetic models of Zr-MOFs, we initially postulated that the adsorption capacity is proportional to the pore size and that acid orange 7 (AO7) was adsorbed by the MOFs. Afterwards, we verified our hypotheses through a series of Brunauer–Emmett–Teller (BET) data analysis; non-local density function theory (NLDFT) was mainly used to analyze the data. Moreover, we determined that physical adsorption occurs on the surface of the MOFs during the adsorption process, while chemisorption occurs in the form of dye molecules combining with active sites. Ultimately, we concluded that the larger the pore size, the stronger the adsorption capacity, and this contribution casts a new light on the issue of wastewater treatment.
Worldwide Variation in Human Milk Metabolome: Indicators of Breast Physiology and Maternal Lifestyle?
Authors: Gay Melvin , Koleva Petya , Slupsky Carolyn , Toit Elloise , Eggesbo Merete , Johnson Christine , Wegienka Ganesa , Shimojo Naoki , Campbell Dianne , Prescott Susan , Munblit Daniel , Geddes Donna , Kozyrskyj Anita ,   InVIVO LactoActive Study Investigators
Published: August 2018

Human milk provides essential substrates for the optimal growth and development of a breastfed infant. Besides providing nutrients to the infant, human milk also contains metabolites which form an intricate system between maternal lifestyle, such as the mother’s diet and the gut microbiome, and infant outcomes. This study investigates the variation of these human milk metabolites from five different countries. Human milk samples (n = 109) were collected one month postpartum from Australia, Japan, the USA, Norway, and South Africa and were analyzed by nuclear magnetic resonance. The partial least squares discriminant analysis (PLS-DA) showed separation between either maternal countries of origin or ethnicities. Variation between countries in concentration of metabolites, such as 2-oxoglutarate, creatine, and glutamine, in human milk, between countries, could provide insights into problems, such as mastitis and/or impaired functions of the mammary glands. Several important markers of milk production, such as lactose, betaine, creatine, glutamate, and glutamine, showed good correlation between each metabolite. This work highlights the importance of milk metabolites with respect to maternal lifestyle and the environment, and also provides the framework for future breastfeeding and microbiome studies in a global context.
Dietary Patterns and Poor Semen Quality Risk in Men: A Cross-Sectional Study
Authors: Danielewicz Anna , Przybyłowicz Katarzyna Eufemia , Przybyłowicz Mariusz
Published: August 2018

The etiology of diminished sperm quality in about 30% of male infertility cases generally remains unexplained. Some studies have suggested that specific nutritional factors can affect semen quality. The aim of this study was to evaluate an association between dietary patterns (DPs) and the risk of abnormal semen quality parameters in men. This cross-sectional study was carried out in 114 men aged 20–55 years from Poland. Semen parameters were assessed via computer-aided semen. Diet was assessed by a food frequency questionnaire (FFQ). DPs were derived using Principal Component Analysis (PCA). Two DPs were derived: Pro-healthy and Western. After adjusting for potential confounders, the risk of abnormal progressive motility was significantly higher in the middle (OR: 2.89, 95% CI: 1.03–8.09) and upper (OR: 7.78, 95% CI: 1.52–15.06) tertiles of the Western DP. A trend for increased risk of the abnormal total count, progressive motility, and morphology (P-trend < 0.050) was found in Western DP. To conclude, the Western DP may increase the risk of abnormal semen parameters, whereas no association was found in the case of Pro-healthy DP. These findings stand in contrast to an increasing number of research findings indicating a positive relation between intake of healthy foods or diet and semen quality parameters. The results highlight the need to study whether modifications in diet and lifestyle factors improve semen quality.
Nucleic Acid Aptamers Targeting Epigenetic Regulators: An Innovative Therapeutic Option
Authors: Catuogno Silvia , Esposito Carla Lucia , Ungaro Paola , De Franciscis Vittorio
Published: August 2018

Epigenetic mechanisms include DNA methylation, posttranslational modifications of histones, chromatin remodeling factors, and post transcriptional gene regulation by noncoding RNAs. All together, these processes regulate gene expression by changing chromatin organization and DNA accessibility. Targeting enzymatic regulators responsible for DNA and chromatin modifications hold promise for modulating the transcriptional regulation of genes that are involved in cancer, as well as in chronic noncommunicable metabolic diseases like obesity, diabetes, and cardiovascular diseases. Increasingly studies are emerging, leading to the identification of specific and effective molecules targeting epigenetic pathways involved in disease onset. In this regard, RNA interference, which uses small RNAs to reduce gene expression and nucleic acid aptamers are arising as very promising candidates in therapeutic approach. Common to all these strategies is the imperative challenge of specificity. In this regard, nucleic acid aptamers have emerged as an attractive class of carrier molecules due to their ability to bind with high affinity to specific ligands, their high chemical flexibility as well as tissue penetration capability. In this review, we will focus on the recent progress in the field of aptamers used as targeting moieties able to recognize and revert epigenetics marks involved in diseases onset.
Structure and Stability of C:H:O Plasma Polymer Films Co-Polymerized Using Dimethyl Carbonate
Authors: Drabik Martin , Lohmann Dominik , Hanus Jan , Shelemin Artem , Rupper Patrick , Biederman Hynek , Hegemann Dirk
Published: August 2018

C:H:O plasma polymer films (PPFs) were deposited by means of plasma-enhanced chemical vapour deposition using the non-toxic, biodegradable organic compound dimethyl carbonate (DMC) at various plasma powers and pressures in order to control the degradation properties related to the carbonate ester group. Coating properties using pure DMC monomer vapours were compared to co-polymerized films from gaseous mixtures of DMC with either ethylene (C2H4) or carbon dioxide (CO2) affecting deposition rate and chemical composition. C:H:O film properties were found to depend primarily on the amount of oxygen in the plasma. To investigate the PPF stability during aging, changes in the composition and properties were studied during their storage both in air and in distilled water over extended periods up to 5 months. It was shown that aging of the films is mostly due to oxidation of the plasma polymer matrix yielding slow degradation and decomposition. The aging processes and their rate are dependent on the intrinsic amount of oxygen in the as-prepared C:H:O films which in turn depends on the experimental conditions and the working gas mixture. Adjustable film properties were mainly attained using a pure DMC plasma considering both gas phase and surface processes. It is thus possible to prepare C:H:O PPFs with controllable degradability both in air and in water.
A Systems and Treatment Perspective of Models of Influenza Virus-Induced Host Responses
Authors: Mochan Ericka , Ackerman Emily , Shoemaker Jason
Published: August 2018

Severe influenza infections are often characterized as having unique host responses (e.g., early, severe hypercytokinemia). Neuraminidase inhibitors can be effective in controlling the severe symptoms of influenza but are often not administered until late in the infection. Several studies suggest that immune modulation may offer protection to high risk groups. Here, we review the current state of mathematical models of influenza-induced host responses. Selecting three models with conserved immune response components, we determine if the immune system components which most affect virus replication when perturbed are conserved across the models. We also test each model’s response to a pre-induction of interferon before the virus is administered. We find that each model emphasizes the importance of controlling the infected cell population to control viral replication. Moreover, our work shows that the structure of current models does not allow for significant responses to increased interferon concentrations. These results suggest that the current library of available published models of influenza infection does not adequately represent the complex interactions of the virus, interferon, and other aspects of the immune response. Specifically, the method used to model virus-resistant cells may need to be adapted in future work to more realistically represent the immune response to viral infection.
Facile Fabrication of Recyclable, Superhydrophobic, and Oleophilic Sorbent from Waste Cigarette Filters for the Sequestration of Oil Pollutants from an Aqueous Environment
Authors: Ifelebuegu Augustine O. , Lale Egetadobobari E. , Mbanaso Fredrick U. , Theophilus Stephen C.
Published: August 2018

The oil industry is plagued with regular incidences of spills into the environment, causing environmental damage to flora and fauna, especially in marine environments where spills easily travel long distances from their sources. This study was carried out to investigate a simple two-step process for the conversion of waste cigarette filters into a superhydrophobic and oleophilic sorbent for application in oil/water separation and spill clean-up. Ultrasonically cleaned filters were surface modified by chemical vapour deposition using methyltrichlorosilane. The results show that the functionalised waste filters achieved superhydrophobic properties with a water contact angle of 154 ± 3.5°, adsorbing 16 to 26 times their weights in various oils, which is a better oil sorption performance than those of commercially available non-woven polypropylene adsorbents. Also, the sorption capacity did not significantly deteriorate after 20 cycles of reuse, with up to 75% sorption capacity retained. The surface modified filters demonstrated excellent water repellency, oil sorption, and recyclability showing their potential application for full scale oil spill clean-up.
Moments of Compound Renewal Sums with Dependent Risks Using Mixing Exponential Models
Authors: Marri Fouad , Adékambi Franck , Moutanabbir Khouzeima
Published: August 2018

In this paper, we study the discounted renewal aggregate claims with a full dependence structure. Based on a mixing exponential model, the dependence among the inter-claim times, the claim sizes, as well as the dependence between the inter-claim times and the claim sizes are included. The main contribution of this paper is the derivation of the closed-form expressions for the higher moments of the discounted aggregate renewal claims. Then, explicit expressions of these moments are provided for specific copulas families and some numerical illustrations are given to analyze the impact of dependency on the moments of the discounted aggregate amount of claims.
Evaluating the Potential of ALS Data to Increase the Efficiency of Aboveground Biomass Estimates in Tropical Peat–Swamp Forests
Authors: Magdon Paul , González-Ferreiro Eduardo , Pérez-Cruzado César , Purnama Edwine Setia , Sarodja Damayanti , Kleinn Christoph
Published: August 2018

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha − 1 with a standard error of 11.17 Mgha − 1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha − 1 with a slightly smaller standard error of 10.57 Mgha − 1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha − 1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision.
Visualizing the Spatiotemporal Trends of Thermal Characteristics in a Peatland Plantation Forest in Indonesia: Pilot Test Using Unmanned Aerial Systems (UASs)
Authors: Iizuka Kotaro , Watanabe Kazuo , Kato Tsuyoshi , Putri Niken Andika , Silsigia Sisva , Kameoka Taishin , Kozan Osamu
Published: August 2018

The high demand for unmanned aerial systems (UASs) reflects the notable impact that these systems have had on the remote sensing field in recent years. Such systems can be used to discover new findings and develop strategic plans in related scientific fields. In this work, a case study is performed to describe a novel approach that uses a UAS with two different sensors and assesses the possibility of monitoring peatland in a small area of a plantation forest in West Kalimantan, Indonesia. First, a multicopter drone with an onboard camera was used to collect aerial images of the study area. The structure from motion (SfM) method was implemented to generate a mosaic image. A digital surface model (DSM) and digital terrain model (DTM) were used to compute a canopy height model (CHM) and explore the vegetation height. Second, a multicopter drone combined with a thermal infrared camera (Zenmuse-XT) was utilized to collect both spatial and temporal thermal data from the study area. The temperature is an important factor that controls the oxidation of tropical peats by microorganisms, root respiration, the soil water content, and so forth. In turn, these processes can alter the greenhouse gas (GHG) flux in the area. Using principal component analysis (PCA), the thermal data were processed to visualize the thermal characteristics of the study site, and the PCA successfully extracted different feature areas. The trends in the thermal information clearly show the differences among land cover types, and the heating and cooling of the peat varies throughout the study area. This study shows the potential for using UAS thermal remote sensing to interpret the characteristics of thermal trends in peatland environments, and the proposed method can be used to guide strategical approaches for monitoring the peatlands in Indonesia.
Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data
Authors: Joiner Joanna , Yoshida Yasuko , Zhang Yao , Duveiller Gregory , Jung Martin , Lyapustin Alexei , Wang Yujie , Tucker Compton J.
Published: August 2018

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.
A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos
Authors: Chen Ting , Pennisi Andrea , Li Zhi , Zhang Yanning , Sahli Hichem
Published: August 2018

Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems, this work proposes a four-stage hierarchical association framework for multiple object tracking in airborne video. The proposed framework combines Data Association-based Tracking (DAT) methods and target tracking using a compressive tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne videos show significant tracking improvement compared to existing state-of-the-art methods.
Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV
Authors: Wierzbicki Damian , Kedzierski Michal , Fryskowska Anna , Jasinski Janusz
Published: August 2018

Imaging from low altitudes is nowadays commonly used in remote sensing and photogrammetry. More and more often, in addition to acquiring images in the visible range, images in other spectral ranges, e.g., near infrared (NIR), are also recorded. During low-altitude photogrammetric studies, small-format images of large coverage along and across the flight route are acquired that provide information about the imaged objects. The novelty presented in this research is the use of the modified method of the dark-object subtraction technique correction with a modified Walthall’s model for correction of images obtained from a low altitude. The basic versions of these models have often been used to radiometric correction of satellite imagery and classic aerial images. However, with the increasing popularity of imaging from low altitude (in particular in the NIR range), it has also become necessary to perform radiometric correction for this type of images. The radiometric correction of images acquired from low altitudes is important from the point of view of eliminating disturbances which might reduce the capabilities of image interpretation. The radiometric correction of images acquired from low altitudes should take into account the influence of the atmosphere but also the geometry of illumination, which is described by the bidirectional reflectance distribution function (BRDF). This paper presents a method of radiometric correction for unmanned aerial vehicle (UAV) NIR images. The study presents a method of low-altitude image acquisition and a fusion of the method of the dark-object subtraction technique correction with a modified Walthall’s model. The proposed solution performs the radiometric correction of images acquired in the NIR range with the root mean square error (RMSE) value not exceeding 10% with respect to the original images. The obtained results confirm that the proposed method will provide effective compensation of radiometric disturbances in UAV images.
An Improved Method for Impervious Surface Mapping Incorporating LiDAR Data and High-Resolution Imagery at Different Acquisition Times
Authors: Luo Hui , Wang Le , Wu Chen , Zhang Lei
Published: August 2018

Impervious surface mapping incorporating high-resolution remote sensing imagery has continued to attract increasing interest, as it can provide detailed information about urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping yields better performance than the use of high-resolution imagery alone. However, due to LiDAR data’s high cost of acquisition, it is difficult to obtain LiDAR data that was acquired at the same time as the high-resolution imagery in order to conduct impervious surface mapping by multi-sensor remote sensing data. Consequently, the occurrence of real landscape changes between multi-sensor remote sensing data sets with different acquisition times results in misclassification errors in impervious surface mapping. This issue has generally been neglected in previous works. Furthermore, observation differences that were generated from multi-sensor data—including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images—also present obstacles to achieving the final mapping result in the fusion of LiDAR data and high-resolution images. In order to resolve these issues, we propose an improved impervious surface-mapping method incorporating both LiDAR data and high-resolution imagery with different acquisition times that consider real landscape changes and observation differences. In the proposed method, multi-sensor change detection by supervised multivariate alteration detection (MAD) is employed to identify the changed areas and mis-registered areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution image are extracted via independent classification based on the corresponding single-sensor data. Finally, an object-based post-classification fusion is proposed that takes advantage of both independent classification results while using single-sensor data and the joint classification result using stacked multi-sensor data. The impervious surface map is subsequently obtained by combining the landscape classes in the accurate classification map. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and unambiguously improve the performance of impervious surface mapping.
A Multiple-Feature Reuse Network to Extract Buildings from Remote Sensing Imagery
Authors: Li Lin , Liang Jian , Weng Min , Zhu Haihong
Published: August 2018

Automatic building extraction from remote sensing imagery is important in many applications. The success of convolutional neural networks (CNNs) has also led to advances in using CNNs to extract man-made objects from high-resolution imagery. However, the large appearance and size variations of buildings make it difficult to extract both crowded small buildings and large buildings. High-resolution imagery must be segmented into patches for CNN models due to GPU memory limitations, and buildings are typically only partially contained in a single patch with little context information. To overcome the problems involved when using different levels of image features with common CNN models, this paper proposes a novel CNN architecture called a multiple-feature reuse network (MFRN) in which each layer is connected to all the subsequent layers of the same size, enabling the direct use of the hierarchical features in each layer. In addition, the model includes a smart decoder that enables precise localization with less GPU load. We tested our model on a large real-world remote sensing dataset and obtained an overall accuracy of 94.5% and an 85% F1 score, which outperformed the compared CNN models, including a 56-layer fully convolutional DenseNet with 93.8% overall accuracy and an F1 score of 83.5%. The experimental results indicate that the MFRN approach to connecting convolutional layers improves the performance of common CNN models for extracting buildings of different sizes and can achieve high accuracy with a consumer-level GPU.
Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.
Authors: Xu Hongzhang , Yuan Qiangqiang , Li Tongwen , Shen Huanfeng , Zhang Liangpei , Jiang Hongtao
Published: August 2018

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm3 cm−3. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM.
Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination
Authors: Tamouridou Afroditi Alexandra , Pantazi Xanthoula Eirini , Alexandridis Thomas , Lagopodi Anastasia , Kontouris Giorgos , Moshou Dimitrios
Published: August 2018

Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage.
Towards Low-Cost Yet High-Performance Sensor Networks by Deploying a Few Ultra-fast Charging Battery Powered Sensors
Authors: Guo Qing , Xu Wenzheng , Liu Tang , Li Hongyou , Li Zheng , Peng Jian
Published: August 2018

The employment of mobile vehicles to charge sensors via wireless energy transfer is a promising technology to maintain the perpetual operation of wireless sensor networks (WSNs). Most existing studies assumed that sensors are powered with off-the-shelf batteries, e.g., Lithium batteries, which are cheap, but it takes some non-trivial time to fully charge such a battery (e.g., 30–80 min). The long charging time may incur long sensor dead durations, especially when there are many lifetime-critical sensors to be charged. On the other hand, other studies assumed that every sensor is powered with an ultra-fast charging battery, where it only takes some trivial time to replenish such a battery, e.g., 1 min, but the adoption of many ultra-fast sensors will bring about high purchasing cost. In this paper, we propose a novel heterogeneous sensor network model, in which there are only a few ultra-fast sensors and many low-cost off-the-shelf sensors. The deployment cost of the network in the model is low, as the number of ultra-fast sensors is limited. We also have an important observation that we can significantly shorten sensor dead durations by enabling the ultra-fast sensors to relay more data for lifetime-critical off-the-shelf sensors. We then propose a joint charging scheduling and routing allocation algorithm, such that the longest sensor dead duration is minimized. We finally evaluate the performance of the proposed algorithm through extensive simulation experiments. Experimental results show that the proposed algorithm is very promising and the longest sensor dead duration by it is only about 10% of those by existing algorithms.
Polar Organic Gate Dielectrics for Graphene Field-Effect Transistor-Based Sensor Technology
Authors: Kam Kevin A. , Tengan Brianne I. C. , Hayashi Cody K. , Ordonez Richard C. , Garmire David G.
Published: August 2018

We have pioneered the use of liquid polar organic molecules as alternatives to rigid gate-dielectrics for the fabrication of graphene field-effect transistors. The unique high net dipole moment of various polar organic molecules allows for easy manipulation of graphene’s conductivity due to the formation of an electrical double layer with a high-capacitance at the liquid and graphene interface. Here, we compare the performances of dimethyl sulfoxide (DMSO), acetonitrile, propionamide, and valeramide as polar organic liquid dielectrics in graphene field-effect transistors (GFETs). We demonstrate improved performance for a GFET with a liquid dielectric comprised of DMSO with high electron and hole mobilities of 154.0 cm2/Vs and 154.6 cm2/Vs, respectively, and a Dirac voltage <5 V.
Comparison of L1 and L5 Bands GNSS Signals Acquisition
Authors: Leclère Jérôme , Landry René , Botteron Cyril
Published: August 2018

Nowadays, civil Global Navigation Satellite System (GNSS) signals are available in both L1 and L5 bands. A receiver does not need to acquire independently the signals in both bands coming from a same satellite, since their carrier Doppler and code delay are closely related. Therefore, the question of which one to acquire first rises naturally. Although the common thought would tell the L1 band signals which are narrowband, an accurate comparison has never been done, and the decision is not as easy as it seems. Indeed, L5 band signals have several advantages such as stronger power, lower carrier Doppler, or a pilot channel, unlike the Global Positioning System (GPS) L1 C/A signal. The goal of this paper is therefore to compare the acquisition of L1 and L5 bands signals (GPS L1 C/A and L5, Galileo E1 and E5a/b) to determine which one is more complex and by which factor, in terms of processing time and memory, considering hardware receivers and the parallel code search. The results show that overall the L5 band signals are more complex to acquire, but it depends strongly on the conditions. The E5 signal is always more complex to acquire than E1, while the L5 signal can have a complexity close to the L1 C/A in some cases. Moreover, precise assistance providing accurate Doppler could significantly reduce the L5 complexity below the L1 complexity.
Spatial Retrieval of Broadband Dielectric Spectra
Authors: Bumberger Jan , Mai Juliane , Schmidt Felix , Lünenschloß Peter , Wagner Norman , Töpfer Hannes
Published: August 2018

A broadband soil dielectric spectra retrieval approach ( 1 MHz– 2 GHz) has been implemented for a layered half space. The inversion kernel consists of a two-port transmission line forward model in the frequency domain and a constitutive material equation based on a power law soil mixture rule (Complex Refractive Index Model - CRIM). The spatially-distributed retrieval of broadband dielectric spectra was achieved with a global optimization approach based on a Shuffled Complex Evolution (SCE) algorithm using the full set of the scattering parameters. For each layer, the broadband dielectric spectra were retrieved with the corresponding parameters thickness, porosity, water saturation and electrical conductivity of the aqueous pore solution. For the validation of the approach, a coaxial transmission line cell measured with a network analyzer was used. The possibilities and limitations of the inverse parameter estimation were numerically analyzed in four scenarios. Expected and retrieved layer thicknesses, soil properties and broadband dielectric spectra in each scenario were in reasonable agreement. Hence, the model is suitable for an estimation of in-homogeneous material parameter distributions. Moreover, the proposed frequency domain approach allows an automatic adaptation of layer number and thickness or regular grids in time and/or space.
On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification
Authors: Blasco Jorge , Peris-Lopez Pedro
Published: August 2018

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.
Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar
Authors: Guo Yuehao , Wang Xianpeng , Wang Wensi , Huang Mengxing , Shen Chong , Cao Chunjie , Bi Guoan
Published: August 2018

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.
Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter
Authors: Di Antonio Andrea , Popoola Olalekan A. M. , Ouyang Bin , Saffell John , Jones Roderic L.
Published: August 2018

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.
Quantification of Axial Abnormality Due to Cerebellar Ataxia with Inertial Measurements
Authors: Nguyen Nhan , Phan Dung , Pathirana Pubudu N. , Horne Malcolm , Power Laura , Szmulewicz David
Published: August 2018

Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates the use of wearable inertial sensors strategically positioned on the front-chest and upper-back locations during the Romberg and Trunk tests for objective assessment of human postural balance due to CA. The primary aim of this paper is to quantify the performance of postural stability of 34 patients diagnosed with CA and 22 healthy subjects as controls. Several forms of entropy descriptions were considered to uncover characteristics of movements intrinsic to CA. Indeed, correlation with clinical observation is vital in ascertaining the validity of the inertial measurements in addition to capturing unique features of movements not typically observed by the practicing clinician. Both of these aspects form an integral part of the underlying objective assessment scheme. Uncertainty in the velocity contained a significant level of information with respect to truncal instability and, based on an extensive clustering and discrimination analysis, fuzzy entropy was identified as an effective measure in characterising the underlying disability. Front-chest measurements demonstrated a strong correlation with clinical assessments while the upper-back measurements performed better in classifying the two cohorts, inferring that the standard clinical assessments are relatively influenced by the frontal observations. The Romberg test was confirmed to be an effective test of neurological diagnosis as well as a potential candidate for objective assessment resulting in a significant correlation with the clinical assessments. In contrast, the Trunk test is observed to be relatively less informative.