bluuebunny/crossref_metadata_2025 huggingface dataset:
Isocaloric Substitution of Dietary Carbohydrate Intake with Fat Intake and MRI-Determined Total Volumes of Visceral, Subcutaneous and Hepatic Fat Content in Middle-Aged Adults
Authors: Meisinger Christa , Rospleszcz Susanne , Wintermeyer Elke , Lorbeer Roberto , Thorand Barbara , Bamberg Fabian , Peters Annette , Schlett Christopher L. , Linseisen Jakob
Published: May 2019

The present study investigated the association of carbohydrate intake and isocaloric substitution with different types of fat with visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and hepatic fat content as determined by magnetic resonance imaging (MRI). Data from 283 participants (mean age 56.1 ± 9.0 years) from the MRI sub study of the KORA FF4 study were included. VAT, SAT and total body fat were quantified by a volume-interpolated VIBE-T1w-Dixon MR sequence. Hepatic fat content was determined as the proton density fat-fraction (PDFF) derived from multiecho-T1w MR sequence. Dietary intake was estimated using information provided by two different instruments, that is, repeated 24-h food lists and a food frequency questionnaire. Replacing total carbohydrates with an isoenergetic amount of total fat was significantly positively associated with VAT and hepatic fat, while there was no significant association with SAT. The multivariable adjusted β-coefficient for replacing 5% of total energy (5E%) carbohydrates with total fat was 0.42 L (95% CI: 0.04, 0.79) for VAT. A substitution in total fat intake by 5E% was associated with a significant increase in liver fat content by 23% (p-value 0.004). If reproduced in prospective studies, such findings would strongly argue for limiting dietary fat intake.
Prolonged Collagen Peptide Supplementation and Resistance Exercise Training Affects Body Composition in Recreationally Active Men
Authors: Kirmse Marius , Oertzen-Hagemann Vanessa , de Marées Markus , Bloch Wilhelm , Platen Petra
Published: May 2019

We aimed to determine the effects of long-term collagen peptide (CP) supplementation and resistance exercise training (RET) on body composition, strength, and muscle fiber cross-sectional area (fCSA) in recreationally active men. Fifty-seven young men were randomly and double-blinded divided into a group receiving either collagen peptides (COL, 15 g/day) or a placebo (PLA). Strength testing, bioimpedance analysis, and muscle biopsies were used prior to and after an RET intervention. Food record protocols were performed during the RET intervention. The groups trained three times a week for 12 weeks. Baseline parameters showed no differences between groups, and the external training load and dietary food intake were also similar. COL showed a significant increase in fat-free mass (FFM) compared with the placebo group (p < 0.05). Body fat mass (BFM) was unchanged in COL, whereas a significant increase in BFM was observed in PLA. Both groups showed significant increases in all strength tests, with a trend for a slightly more pronounced effect in COL. The fCSA of type II muscle fibers increased significantly in both groups without differences between the two groups. We firstly demonstrated improved body composition in healthy, recreationally active men subsequent to prolonged CP supplementation in combination with RET. As the observed increase in FFM was not reflected in differences in fCSA hypertrophy between groups, we assume enhanced passive connective tissue adaptations in COL due to CP intake.
Dietary Fiber, Atherosclerosis, and Cardiovascular Disease
Authors: Soliman Ghada A.
Published: May 2019

Observational studies have shown that dietary fiber intake is associated with decreased risk of cardiovascular disease. Dietary fiber is a non-digestible form of carbohydrates, due to the lack of the digestive enzyme in humans required to digest fiber. Dietary fibers and lignin are intrinsic to plants and are classified according to their water solubility properties as either soluble or insoluble fibers. Water-soluble fibers include pectin, gums, mucilage, fructans, and some resistant starches. They are present in some fruits, vegetables, oats, and barley. Soluble fibers have been shown to lower blood cholesterol by several mechanisms. On the other hand, water-insoluble fibers mainly include lignin, cellulose, and hemicellulose; whole-grain foods, bran, nuts, and seeds are rich in these fibers. Water-insoluble fibers have rapid gastric emptying, and as such may decrease the intestinal transit time and increase fecal bulk, thus promoting digestive regularity. In addition to dietary fiber, isolated and extracted fibers are known as functional fiber and have been shown to induce beneficial health effects when added to food during processing. The recommended daily allowances (RDAs) for total fiber intake for men and women aged 19–50 are 38 gram/day and 25 gram/day, respectively. It is worth noting that the RDA recommendations are for healthy people and do not apply to individuals with some chronic diseases. Studies have shown that most Americans do not consume the recommended intake of fiber. This review will summarize the current knowledge regarding dietary fiber, sources of food containing fiber, atherosclerosis, and heart disease risk reduction.
Consumers’ Views on the Importance of Specific Front-of-Pack Nutrition Information: A Latent Profile Analysis
Authors: Dana Liyuwork Mitiku , Chapman Kathy , Talati Zenobia , Kelly Bridget , Dixon Helen , Miller Caroline , Pettigrew Simone
Published: May 2019

Nutrition labelling can influence consumers’ assessments of food healthiness and their food choices. However, there is a lack of consensus about the optimal type and amount of nutrition information to provide on food packages. This study analysed consumers’ preferences for front-of-pack information relating to energy and various nutrients (sugar, saturated fat, sodium, fibre, carbohydrate, and protein). The aim was to identify discrete preference segments within the Australian market where the current Health Star Rating front-of-pack labelling system can be displayed with different levels of nutrition information. Adults (n = 1558) completed a survey assessing socio-demographics, self-reported nutrition knowledge, diet healthiness, special dietary requirements, and perceived importance of the provision of energy and nutrient information on the front of food packs. Latent profile analysis identified five consumer segments within the sample that ranged from groups exhibiting high levels of interest in various forms of nutrition information to one with very low interest and one with divergent scores according to whether nutrients were perceived as positive or negative for health. The results indicate that different forms of front-of-pack labelling featuring varying degrees of information about energy and specific nutrients are likely to be of interest and use to different market segments.
Dietary Intakes of Professional and Semi-Professional Team Sport Athletes Do Not Meet Sport Nutrition Recommendations—A Systematic Literature Review
Authors: Jenner Sarah L. , Buckley Georgina L. , Belski Regina , Devlin Brooke L. , Forsyth Adrienne K.
Published: May 2019

Background: to develop sport-specific and effective dietary advice, it is important to understand the dietary intakes of team sport athletes. This systematic literature review aims to (1) assess the dietary intakes of professional and semi-professional team sport athletes and (2) to identify priority areas for dietetic intervention. Methods: an extensive search of MEDLINE, Sports DISCUS, CINAHL, Web of Science, and Scopus databases in April–May 2018 was conducted and identified 646 studies. Included studies recruited team sport, competitive (i.e., professional or semi-professional) athletes over the age of 18 years. An assessment of dietary intake in studies was required and due to the variability of data (i.e., nutrient and food group data) a meta-analysis was not undertaken. Two independent authors extracted data using a standardised process. Results: 21 (n = 511) studies that assessed dietary intake of team sport athletes met the inclusion criteria. Most reported that professional and semi-professional athletes’ dietary intakes met or exceeded recommendations during training and competition for protein and/or fat, but not energy and carbohydrate. Limitations in articles include small sample sizes, heterogeneity of data and existence of underreporting. Conclusions: this review highlights the need for sport-specific dietary recommendations that focus on energy and carbohydrate intake. Further exploration of factors influencing athletes’ dietary intakes including why athletes’ dietary intakes do not meet energy and/or carbohydrate recommendations is required.
Associations of AD Biomarkers and Cognitive Performance with Nutritional Status: The NUDAD Project
Authors: Doorduijn Astrid S. , Visser Marjolein , van de Rest Ondine , Kester Maartje I. , de Leeuw Francisca A. , Boesveldt Sanne , Fieldhouse Jay L. P. , van den Heuvel Ellen G. H. M. , Teunissen Charlotte E. , Scheltens Philip , van der Flier Wiesje M. , de van der Schueren Marian A. E.
Published: May 2019

As malnutrition is common in patients with Alzheimer’s disease (AD), we evaluated nutritional status and body composition of patients with AD, mild cognitive impairment (MCI) and controls, and studied associations of AD biomarkers and cognitive performance with nutritional status and body composition. We included 552 participants, of which 198 patients had AD, 135 patients had MCI and 219 controls. We assessed nutritional status (mini nutritional assessment (MNA)) and body composition (body mass index (BMI), fat-free mass (FFM) and waist circumference). Linear regression analyses (adjusted for age, gender and education where appropriate) were applied to test associations of AD biomarkers and cognitive performance on five domains with nutritional parameters (dependent). Patients with MCI and AD had a lower BMI and MNA score than controls. Worse performance in all cognitive domains was associated with lower MNA score, but not with body composition. AD biomarkers were associated with MNA score, BMI and waist circumference, and associations with MNA score remained after adjustment for cognitive performance. Both AD biomarkers and cognitive performance were associated with nutritional status, associations with AD biomarkers remained after adjustment for cognition. Our data suggest that malnutrition is not only related to impaired cognition but also to AD pathology.
Impact of Cocoa Products Intake on Plasma and Urine Metabolites: A Review of Targeted and Non-Targeted Studies in Humans
Authors: Mayorga-Gross Ana Lucía , Esquivel Patricia
Published: May 2019

Cocoa is continuously drawing attention due to growing scientific evidence suggesting its effects on health. Flavanols and methylxanthines are some of the most important bioactive compounds present in cocoa. Other important bioactives, such as phenolic acids and lactones, are derived from microbial metabolism. The identification of the metabolites produced after cocoa intake is a first step to understand the overall effect on human health. In general, after cocoa intake, methylxanthines show high absorption and elimination efficiencies. Catechins are transformed mainly into sulfate and glucuronide conjugates. Metabolism of procyanidins is highly influenced by the polymerization degree, which hinders their absorption. The polymerization degree over three units leads to biotransformation by the colonic microbiota, resulting in valerolactones and phenolic acids, with higher excretion times. Long term intervention studies, as well as untargeted metabolomic approaches, are scarce. Contradictory results have been reported concerning matrix effects and health impact, and there are still scientific gaps that have to be addresed to understand the influence of cocoa intake on health. This review addresses different cocoa clinical studies, summarizes the different methodologies employed as well as the metabolites that have been identified in plasma and urine after cocoa intake.
PLA Electrospun Scaffolds for Three-Dimensional Triple-Negative Breast Cancer Cell Culture
Authors: Polonio-Alcalá Emma , Rabionet Marc , Gallardo Xavier , Angelats David , Ciurana Joaquim , Ruiz-Martínez Santiago , Puig Teresa
Published: May 2019

Three-dimensional (3D) systems provide a suitable environment for cells cultured in vitro since they reproduce the physiological conditions that traditional cell culture supports lack. Electrospinning is a cost-effective technology useful to manufacture scaffolds with nanofibers that resemble the extracellular matrix that surround cells in the organism. Poly(lactic acid) (PLA) is a synthetic polymer suitable for biomedical applications. The main objective of this study is to evaluate electrospun (ES)-PLA scaffolds to be used for culturing cancer cells. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with no validated targeted therapy and a high relapse rate. MDA-MB-231 TNBC cells were grown in scaffolds from two different PLA concentrations (12% and 15% w/v). The appropriateness of ES-PLA scaffolds was evaluated using a cell proliferation assay. EGFR and STAT3 gene expression and protein levels were compared in cells grown in 2D versus in 3D cultures. An increase in STAT3 activation was shown, which is related to self-renewal of cancer stem cells (CSCs). Therefore, the enrichment of the breast CSC (BCSC) population was tested using a mammosphere-forming assay and gene expression of BCSC-related stemness and epithelial-to-mesenchymal transition markers. Based on the results obtained, ES-PLA scaffolds are useful for 3D cultures in short culture periods with no BCSC-enrichment.
Poly (2-hydroxyethylmethacrylate –co–methylmethacrylate)/Lignocaine Contact Lens Preparation, Characterization, and in vitro Release Dynamic
Authors: Aouak Taieb , Saeed Wassem Sharaf , Al-Hafi Nawaf M. , Al-Odayni Abdel-Basit , Alghamdi Abdulaziz Ali , Bedja Idriss
Published: May 2019

2-hydroxyethyl methacrylate, methylmethacrylate, ethylene glycol dimethyl methacrylate, and lignocaine (drug) were mixed together and the monomers were copolymerized at 60 °C through a free radical polymerization in the presence of α,α′-Azoisobutyronitrile in tetrahydrofuran. A series of copolymer/drug composites with different monoacrylate monomer compositions were prepared by solvent evaporation and characterized by different methods such as nuclear magnetic resonance, differential scanning calorimetry, Fourier transform infrared, X-ray diffraction, and mechanical and optical testing. The water content in the copolymers and the cell viability test on the samples were also examined in this investigation. The results of the analyses of the properties of this drug-carrier system are promising, indicating that this material may be a potential candidate for contact lens applications. The release dynamic of this medication from the prepared drug-carrier systems was investigated in neutral pH media. The results obtained revealed that the diffusion of lignocaine through the copolymer matrix obeys the Fick model and the dynamic release can be easily controlled by the methyl methacrylate content in the copolymer.
Complex Permittivity and Microwave Absorption Properties of OPEFB Fiber–Polycaprolactone Composites Filled with Recycled Hematite (α-Fe2O3) Nanoparticles
Authors: Mensah Ebenezer Ekow , Abbas Zulkifly , Azis Raba’ah Syahidah , Ibrahim Nor Azowa , Khamis Ahmad Mamoun
Published: May 2019

Recycled hematite (α-Fe2O3) nanoparticles with enhanced complex permittivity properties have been incorporated as a filler in a polycaprolactone (PCL) matrix reinforced with oil palm empty fruit bunch (OPEFB) fiber for microwave absorption applications. The complex permittivity values were improved by reducing the particle sizes to the nano scale via high-energy ball milling for 12 h. A total of 5–20 wt.% recycled α-Fe2O3/OPEFB/PCL nanocomposites were examined for their complex permittivity and microwave absorption properties via the open ended coaxial (OEC) technique and the transmission/reflection line measurement using a microstrip connected to a two-port vector network analyzer. The microstructural analysis of the samples included X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM) and Fourier transform infrared spectroscopy (FTIR). At 1 GHz, the real (ε′) and imaginary (ε″) parts of complex permittivity of recycled α-Fe2O3 particles, respectively, increased from 7.88 to 12.75 and 0.14 to 0.40 when the particle size was reduced from 1.73 μm to 16.2 nm. A minimum reflection loss of −24.2 dB was achieved by the 20 wt.% nanocomposite at 2.4 GHz. Recycled α-Fe2O3 nanoparticles are effective fillers for microwave absorbing polymer-based composites in 1–4 GHz range applications.
Babe Ruth: Religious Icon
Authors: Alpert Rebecca
Published: May 2019

Babe Ruth is a mythic figure in American baseball history. His extraordinary skills and legendary exploits are central to the idea of baseball as America’s national pastime and are woven into the fabric of American history and iconography. Much has been written about Ruth’s life, his extraordinary physical powers, and the legends that grew up around him that made him a mythic figure. The story of Babe Ruth as it has been told, however, has not included its meaning from the perspective of the study of religion and sport. This paper explores the life and legends of Babe Ruth to illustrate the significance of Ruth’s identity as a Catholic in early twentieth-century America and the fundamental connections between Ruth’s story and the Christian myth and ritual that is foundational to American civil religion.
Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
Authors: Sousa Daniel , Small Christopher , Spalton Andrew , Kwarteng Andy
Published: May 2019

In monsoonal ecosystems, vegetation phenology is generally modulated by the timing and intensity of seasonal precipitation. Seasonal precipitation is often characterized by substantial interannual variability in both space and time. A rigorous quantitative understanding of the ecology of the landscape requires spatially explicit information regarding the strength of the relationship between seasonal precipitation and vegetation phenology, as well as the interannual variability of the system. For this information to be accurately estimated, it must be based on spatially and temporally consistent measurements. The optical satellite image archive can provide these measurements. Satellite imagery offers observations of both a) atmospheric parameters such as the timing and spatial extent of monsoon cloud cover; and, b) phenological parameters, such as the timing and spatial extent of vegetation green-up and senescence. This work presents a method to capture both atmospheric and phenological parameters from an optical image time series. The method uses Empirical Orthogonal Function (EOF) analysis of a single spectral index for unified characterization of the spatiotemporal dynamics of both monsoon cloud cover and vegetation phenology. This is made possible by leveraging well-understood differences in the visible and near infrared reflectance of green vegetation, soil, and clouds. Image time series are transformed into a temporal feature space (TFS) that is comprised of low-order Principal Components. The structure of the temporal feature space reveals spatiotemporally distinct annual cycles of both cloud cover and vegetation phenology. In order to illustrate this technique, we apply it to the retrospective analysis of a seasonal cloud forest in the Dhofar Mountains of the southern Arabian Peninsula. Our results quantify known (but previously unmapped) local gradients in monsoon duration and vegetation community response. Individual ecological subsystems are also clearly distinguishable from each other, and consistent elevation gradients emerge within each subsystem. Novel observations also emerge, such as regreening/early greening events and spatial patterns in cloud duration. The method is conceptually straightforward and could be applied to characterize other monsoon environments anywhere on Earth.
Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya
Authors: Mbaabu Purity Rima , Ng Wai-Tim , Schaffner Urs , Gichaba Maina , Olago Daniel , Choge Simon , Oriaso Silas , Eckert Sandra
Published: May 2019

Woody alien plant species have been deliberately introduced globally in many arid and semi-arid regions, as they can provide services and goods to the rural poor. However, some of these alien trees and shrubs have become invasive over time, with important impacts on biodiversity, ecosystem services, and human well-being. Prosopis was introduced in Baringo County, Kenya, in the 1980s, but since then, it has spread rapidly from the original plantations to new areas. To assess land-use and land-cover (LULC) changes and dynamics in Baringo, we used a combination of dry and wet season Landsat satellite data acquired over a seven-year time interval between 1988–2016, and performed a supervised Random Forest classification. For each time interval, we calculated the extent of Prosopis invasion, rates of spread, gains and losses of specific LULC classes, and the relative importance of Prosopis invasion on LULC changes. The overall accuracy and kappa coefficients of the LULC classifications ranged between 98.1–98.5% and 0.93–0.96, respectively. We found that Prosopis coverage increased from 882 ha in 1988 to 18,792 ha in 2016. The highest negative changes in LULC classes were found for grasslands (−6252 ha; −86%), irrigated cropland (−849 ha; −57%), Vachellia tortilis-dominated vegetation (−3602 ha; −42%), and rainfed cropland (−1432 ha; −37%). Prosopis invasion alone directly accounted for over 30% of these negative changes, suggesting that Prosopis invasion is a key driver of the observed LULC changes in Baringo County. Although the management of Prosopis by utilization has been promoted in Baringo for 10–15 years, the spread of Prosopis has not stopped or slowed down. This suggests that Prosopis management in Baringo and other invaded areas in East Africa needs to be based on a more integrated approach.
Heart ID: Human Identification Based on Radar Micro-Doppler Signatures of the Heart Using Deep Learning
Authors: Cao Peibei , Xia Weijie , Li Yi
Published: May 2019

Human identification based on radar signatures of individual heartbeats is crucial in various applications, including user authentication in mobile devices, identification of escaped criminals, etc. Usually, optical systems employed to recognize humans are sensitive to ambient light environments, while radar does not have such a drawback, since it has high penetration and all-weather capability. Meanwhile, since micro-Doppler characteristics from the heart of different people are distinct and not easy to fake, it can be used for identification. In this paper, we employed a deep convolutional neural network (DCNN) and conventional supervised learning methods to realize heartbeat-based identification. First, the heartbeat signals were acquired by a Doppler radar and processed by short-time Fourier transform. Then, predefined features were extracted for the conventional supervised learning algorithms, while time–frequency graphs were directly inputted to the DCNN since the network had its own feature extraction part. It is shown that the DCNN could achieve average accuracy of 98.5% for identifying four people, and higher than 80% when the number of people was less than ten. For conventional supervised learning algorithms when identifying four people, the accuracy of the support vector machine (SVM) was 88.75%, and the accuracy of SVM–Bayes was 91.25%, while naive Bayes had the lowest accuracy of 80.75%.
Accuracy of Deformation Rates from Campaign GPS Surveys Considering Extended Observation Session and Antenna Set-Up Errors
Authors: Turen Yener , Sanli D. Ugur
Published: May 2019

GPS campaign measurements are still in use in the monitoring of ground deformation. Campaign measurements are frequently referred to because installing permanent stations are costly, and they cannot be installed at the desired density. Using the data from the International Global Navigation Satellite Systems (GNSS) Service (IGS) permanent GPS stations, the duration, sampling interval, etc. of the campaign measurements can be simulated. Thus, the contribution of the campaign data to the monitoring of the ground deformation can be evaluated. In this study, we carried out an experiment with the aim of determining the deformation of tectonic plates at the selected IGS stations more accurately considering by extending the observation duration to a full 24 h length. We also made an attempt to take into consideration the antenna set up errors developing a scenario referring to the information available in the literature. We have decimated the continuous data of 40 globally scattered IGS stations into monthly intervals between 2012 and 2016 and estimated the deformation rates at the IGS stations from a continuous time series of four years. The continuous time series solutions for those stations were produced by the Jet Propulsion Laboratory (JPL), NASA. We compare velocities (i.e., the deformation rates) determined from GPS campaigns (in which the sampling was performed monthly and four-monthly) with those of the continuous data. The major conclusion of this study is that the vertical velocity estimation accuracy of the GPS campaign measurements had been improved by about 85% by extending the session duration to 24 h. The repetition interval of GPS campaign measurements as per one observation every four months produced only slightly coarser accuracy (i.e., on the average 8% poorer) than those of the measurements repeated once every month.
Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm
Authors: Zhao Jianqing , Zhang Xiaohu , Gao Chenxi , Qiu Xiaolei , Tian Yongchao , Zhu Yan , Cao Weixing
Published: May 2019

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.
Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land
Authors: Wu Ying , Qian Bo , Bao Yansong , Li Meixin , Petropoulos George P. , Liu Xulin , Li Lin
Published: May 2019

A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals.
Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
Authors: De Luca Giandomenico , N. Silva João M. , Cerasoli Sofia , Araújo João , Campos José , Di Fazio Salvatore , Modica Giuseppe
Published: May 2019

This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands.
Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter
Authors: Song Yang , Wang Jinfei
Published: May 2019

Plant height can be used as an indicator to estimate crop phenology and biomass. The Unmanned Aerial Vehicle (UAV)-based point cloud data derived from photogrammetry methods contains the structural information of crops which could be used to retrieve crop height. However, removing noise and outliers from the UAV-based crop point cloud data for height extraction is challenging. The objective of this paper is to develop an alternative method for canopy height determination from UAV-based 3D point cloud datasets using a statistical analysis method and a moving cuboid filter to remove outliers. In this method, first, the point cloud data is divided into many 3D columns. Secondly, a moving cuboid filter is applied in each column and moved downward to eliminate noise points. The threshold of point numbers in the filter is calculated based on the distribution of points in the column. After applying the moving cuboid filter, the crop height is calculated from the highest and lowest points in each 3D column. The proposed method achieved high accuracy for height extraction with low Root Mean Square Error (RMSE) of 6.37 cm and Mean Absolute Error (MAE) of 5.07 cm. The canopy height monitoring window for winter wheat using this method starts from the beginning of the stem extension stage to the end of the heading stage (BBCH 31 to 65). Since the height of wheat has limited change after the heading stage, this method could be used to retrieve the crop height of winter wheat. In addition, this method only requires one operation of UAV in the field. It could be an effective method that can be widely used to help end-user to monitor their crops and support real-time decision making for farm management.
A Multi-Primitive-Based Hierarchical Optimal Approach for Semantic Labeling of ALS Point Clouds
Authors: Ge Xuming , Wu Bo , Li Yuan , Hu Han
Published: May 2019

There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and the third is to introduce a classifier to label the point clouds. This paper investigates multiple primitives to effectively represent scenes and exploit their geometric relationships. Relationships are graded according to the properties of related primitives. Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. The proposed approach was tested using two sets of representative ALS point clouds, namely the Vaihingen datasets and Hong Kong’s Central District dataset. The results were compared with those generated by other typical methods in previous work. Quantitative assessments for the two experimental datasets showed that the performance of the proposed approach was superior to reference methods in both datasets. The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. The results reveal that our approach of labeling different classes in terms of ALS point clouds is robust and bears significance for future applications, such as 3D modeling and change detection from point clouds.
LDAF: Low-Bandwidth Distributed Applications Framework in a Use Case of Blockchain-Enabled IoT Devices
Authors: Pustišek Matevž , Dolenc Dejan , Kos Andrej
Published: May 2019

In this paper, we present Low-Bandwidth Distributed Applications Framework (LDAF)—an application-aware gateway for communication-constrained Internet of things (IoT) devices. A modular approach facilitates connecting to existing cloud backend servers and managing message formats and APIs’ native application logic to meet the communication constraints of resource-limited end devices. We investigated options for positioning the LDAF server in fog computing architectures. We demonstrated the approach in three use cases: (i) a simple domain name system (DNS) query from the device to a DNS server, (ii) a complex interaction of a blockchain—based IoT device with a blockchain network, and (iii) difference based patching of binary (system) files at the IoT end devices. In a blockchain smart meter use case we effectively enabled decentralized applications (DApp) for devices that without our solution could not participate in a blockchain network. Employing the more efficient binary content encoding, we reduced the periodic traffic from 16 kB/s to ~1.1 kB/s, i.e., 7% of the initial traffic. With additional optimization of the application protocol in the gateway and message filtering, the periodic traffic was reduced to ~1% of the initial traffic, without any tradeoffs in the application’s functionality or security. Using a function of binary difference we managed to reduce the size of the communication traffic to the end device, at least when the binary patch was smaller than the patching file.
Using Video Processing for the Full-Field Identification of Backbone Curves in Case of Large Vibrations
Authors: Civera Marco , Zanotti Fragonara Luca , Surace Cecilia
Published: May 2019

Nonlinear modal analysis is a demanding yet imperative task to rigorously address real-life situations where the dynamics involved clearly exceed the limits of linear approximation. The specific case of geometric nonlinearities, where the effects induced by the second and higher-order terms in the strain–displacement relationship cannot be neglected, is of great significance for structural engineering in most of its fields of application—aerospace, civil construction, mechanical systems, and so on. However, this nonlinear behaviour is strongly affected by even small changes in stiffness or mass, e.g., by applying physically-attached sensors to the structure of interest. Indeed, the sensors placement introduces a certain amount of geometric hardening and mass variation, which becomes relevant for very flexible structures. The effects of mass loading, while highly recognised to be much larger in the nonlinear domain than in its linear counterpart, have seldom been explored experimentally. In this context, the aim of this paper is to perform a noncontact, full-field nonlinear investigation of the very light and very flexible XB-1 air wing prototype aluminum spar, applying the well-known resonance decay method. Video processing in general, and a high-speed, optical target tracking technique in particular, are proposed for this purpose; the methodology can be easily extended to any slender beam-like or plate-like element. Obtained results have been used to describe the first nonlinear normal mode of the spar in both unloaded and sensors-loaded conditions by means of their respective backbone curves. Noticeable changes were encountered between the two conditions when the structure undergoes large-amplitude flexural vibrations.
A Virtual Sensor for Collision Detection and Distinction with Conventional Industrial Robots
Authors: Li Zhijing , Ye Jinhua , Wu Haibin
Published: May 2019

Physical contact inevitably occurs during robot interaction with outside environments. A robot should have the ability to detect and distinguish whether a physical interaction between a human and the robot is contact or collision, so as to ensure human safety and improve interaction performance. In this paper, a virtual sensor that can detect and distinguish contact and collision between humans and industrial robots is proposed. Based on the generalized momentum of the robot, two observers with low-pass and band-pass filter characteristics were designed in this virtual sensor to realize the robot collision detection. Using the different frequency distribution ranges of the lighter contact force signal and the heavier collision force signal, the filter parameters in the two observers were appropriately selected to distinguish between collisions and contacts in close interaction between humans and robots. The virtual sensor does not require acceleration information or inverse dynamics calculations. It only needs to sample the motor driving current and position information of the robot joint, and can easily be applied to conventional industrial robots. The experimental results show that the low-pass and band-pass torque observers can detect different force signals in real-time, and the proposed virtual sensor can be used for collision detection and distinction in human–robot interactions.
The Advent of Salivary Breast Cancer Biomarker Detection Using Affinity Sensors
Authors: Abrao Nemeir Imad , Saab Joseph , Hleihel Walid , Errachid Abdelhamid , Jafferzic-Renault Nicole , Zine Nadia
Published: May 2019

Breast Cancer is one of the world’s most notorious diseases affecting two million women in 2018 worldwide. It is a highly heterogeneous disease, making it difficult to treat. However, its linear progression makes it a candidate for early screening programs, and the earlier its detection the higher the chance of recovery. However, one key hurdle for breast cancer screening is the fact that most screening techniques are expensive, time-consuming, and cumbersome, making them impractical for use in several parts of the world. One current trend in breast cancer detection has pointed to a possible solution, the use of salivary breast cancer biomarkers. Saliva is an attractive medium for diagnosis because it is readily available in large quantities, easy to obtain at low cost, and contains all the biomarkers present in blood, albeit in lower quantities. Affinity sensors are devices that detect molecules through their interactions with biological recognition molecules. Their low cost, high sensitivity, and selectivity, as well as rapid detection time make them an attractive alternative to traditional means of detection. In this review article, we discuss the current status of breast cancer diagnosis, its salivary biomarkers, as well as the current trends in the development of affinity sensors for their detection.
Alternating Electric Field-Based Static Gesture-Recognition Technology
Authors: Wei Haoyu , Li Pengfei , Tang Kai , Wang Wei , Chen Xi
Published: May 2019

Currently, gesture recognition based on electric-field detection technology has received extensive attention, which is mostly used to recognize the position and the movement of the hand, and rarely used for identification of specific gestures. A non-contact gesture-recognition technology based on the alternating electric-field detection scheme is proposed, which can recognize static gestures in different states and dynamic gestures. The influence of the hand on the detection system is analyzed from the principle of electric-field detection. A simulation model of the system is established to investigate the charge density on the hand surface and the potential change of the sensing electrodes. According to the simulation results, the system structure is improved, and the signal-processing circuit is designed to collect the signal of sensing electrodes. By collecting a large amount of data from different operators, the tree-model recognition algorithm is designed and a gesture-recognition experiment is implemented. The results show that the gesture-recognition correct rate is over 90%. With advantages of high response speed, low cost, small volume, and immunity to the surrounding environment, the system could be assembled on a robot that communicates with operators.
A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task
Authors: Król Michał , Król Magdalena Ewa
Published: May 2019

Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.g., those registered with vs. without an experimental manipulation in place, rather than individual scanpaths. Here, we propose that the problem might be solved by projecting a scanpath similarity matrix, obtained via a pairwise comparison algorithm, to a lower-dimensional space (the comparison and dimensionality-reduction techniques we use are ScanMatch and t-SNE). The resulting distributions of low-dimensional vectors representing individual scanpaths can be statistically compared. To assess if the differences result from temporal scanpath features, we propose to statistically compare the cross-validated accuracies of two classifiers predicting group membership: (1) based exclusively on spatial metrics; (2) based additionally on the obtained scanpath representation vectors. To illustrate, we compare autistic vs. typically-developing individuals looking at human faces during a lab experiment and find significant differences in temporal scanpath features.
Wearable Enzymatic Alcohol Biosensor
Authors: Lansdorp Bob , Ramsay William , Hamid Rashad , Strenk Evan
Published: May 2019

Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, previous biosensors have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we report a wearable alcohol sensor platform that solves the problem of sensor fouling by enabling drift-free signals in vivo for up to 24 h and an interchangeable cartridge connection that enables consecutive days of measurement. We demonstrate how alcohol oxidase enzyme and Prussian Blue can be combined to prevent baseline drift above 25 nA, enabling sensitive detection of transdermal alcohol. Laboratory characterization of the enzymatic alcohol sensor demonstrates that the sensor is mass-transfer-limited by a diffusion-limiting membrane of lower permeability than human skin and a linear sensor range between 0 mM and 50 mM. Further, we show continuous transdermal alcohol data recorded with a human subject for two consecutive days. The non-invasive sensor presented here is an objective alternative to the self-reports used commonly to quantify alcohol consumption in research studies.
Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain
Authors: Cui Yue , Wang Junfeng , Qi Jie , Zhang Zhanying , Zhu Jinqi
Published: May 2019

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.